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What is the Scientific Revolution?

How is the scientific revolution connected to the enlightenment, what did the scientific revolution lead to.

• Why is Nicolaus Copernicus famous?
• What did Nicolaus Copernicus do for a living?

Scientific Revolution

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Scientific Revolution is the name given to a period of drastic change in scientific thought that took place during the 16th and 17th centuries. It replaced the Greek view of nature that had dominated science for almost 2,000 years. The Scientific Revolution was characterized by an emphasis on abstract reasoning, quantitative thought, an understanding of how nature works, the view of nature as a machine , and the development of an experimental scientific method .

The Enlightenment , like the Scientific Revolution, began in Europe . Taking place during the 17th and 18th centuries, this intellectual movement synthesized ideas concerning God, reason, nature, and humanity into a worldview that celebrated reason. This emphasis on reason grew out of discoveries made by prominent thinkers—including the astronomy of Nicolaus Copernicus and Galileo , the philosophy of René Descartes , and the physics and cosmology of Isaac Newton —many of whom preceded the Enlightenment.

The sudden emergence of new information during the Scientific Revolution called into question religious beliefs, moral principles, and the traditional scheme of nature. It also strained old institutions and practices, necessitating new ways of communicating and disseminating information. Prominent innovations included scientific societies (which were created to discuss and validate new discoveries) and scientific papers (which were developed as tools to communicate new information comprehensibly and test the discoveries and hypotheses made by their authors).

Scientific Revolution , drastic change in scientific thought that took place during the 16th and 17th centuries. A new view of nature emerged during the Scientific Revolution, replacing the Greek view that had dominated science for almost 2,000 years. Science became an autonomous discipline , distinct from both philosophy and technology , and it came to be regarded as having utilitarian goals. By the end of this period, it may not be too much to say that science had replaced Christianity as the focal point of European civilization. Out of the ferment of the Renaissance and Reformation there arose a new view of science, bringing about the following transformations: the reeducation of common sense in favour of abstract reasoning; the substitution of a quantitative for a qualitative view of nature; the view of nature as a machine rather than as an organism; the development of an experimental, scientific method that sought definite answers to certain limited questions couched in the framework of specific theories; and the acceptance of new criteria for explanation, stressing the “how” rather than the “why” that had characterized the Aristotelian search for final causes.

The growing flood of information that resulted from the Scientific Revolution put heavy strains upon old institutions and practices. It was no longer sufficient to publish scientific results in an expensive book that few could buy; information had to be spread widely and rapidly. Natural philosophers had to be sure of their data, and to that end they required independent and critical confirmation of their discoveries. New means were created to accomplish these ends. Scientific societies sprang up, beginning in Italy in the early years of the 17th century and culminating in the two great national scientific societies that mark the zenith of the Scientific Revolution: the Royal Society of London for Improving Natural Knowledge , created by royal charter in 1662, and the Académie des Sciences of Paris, formed in 1666. In these societies and others like them all over the world, natural philosophers could gather to examine, discuss, and criticize new discoveries and old theories. To provide a firm basis for these discussions, societies began to publish scientific papers. The old practice of hiding new discoveries in private jargon, obscure language, or even anagrams gradually gave way to the ideal of universal comprehensibility. New canons of reporting were devised so that experiments and discoveries could be reproduced by others. This required new precision in language and a willingness to share experimental or observational methods. The failure of others to reproduce results cast serious doubts upon the original reports. Thus were created the tools for a massive assault on nature’s secrets.

The Scientific Revolution began in astronomy. Although there had been earlier discussions of the possibility of Earth’s motion, the Polish astronomer Nicolaus Copernicus was the first to propound a comprehensive heliocentric theory equal in scope and predictive capability to Ptolemy’s geocentric system . Motivated by the desire to satisfy Plato’s dictum, Copernicus was led to overthrow traditional astronomy because of its alleged violation of the principle of uniform circular motion and its lack of unity and harmony as a system of the world. Relying on virtually the same data as Ptolemy had possessed, Copernicus turned the world inside out, putting the Sun at the centre and setting Earth into motion around it. Copernicus’s theory , published in 1543, possessed a qualitative simplicity that Ptolemaic astronomy appeared to lack. To achieve comparable levels of quantitative precision, however, the new system became just as complex as the old. Perhaps the most revolutionary aspect of Copernican astronomy lay in Copernicus’s attitude toward the reality of his theory. In contrast to Platonic instrumentalism , Copernicus asserted that to be satisfactory astronomy must describe the real, physical system of the world.

The reception of Copernican astronomy amounted to victory by infiltration. By the time large-scale opposition to the theory had developed in the church and elsewhere, most of the best professional astronomers had found some aspect or other of the new system indispensable. Copernicus’s book De revolutionibus orbium coelestium libri VI (“Six Books Concerning the Revolutions of the Heavenly Orbs”), published in 1543, became a standard reference for advanced problems in astronomical research, particularly for its mathematical techniques. Thus, it was widely read by mathematical astronomers, in spite of its central cosmological hypothesis , which was widely ignored. In 1551 the German astronomer Erasmus Reinhold published the Tabulae prutenicae (“Prutenic Tables”), computed by Copernican methods. The tables were more accurate and more up-to-date than their 13th-century predecessor and became indispensable to both astronomers and astrologers.

During the 16th century the Danish astronomer Tycho Brahe , rejecting both the Ptolemaic and Copernican systems, was responsible for major changes in observation, unwittingly providing the data that ultimately decided the argument in favour of the new astronomy. Using larger, stabler, and better calibrated instruments, he observed regularly over extended periods, thereby obtaining a continuity of observations that were accurate for planets to within about one minute of arc—several times better than any previous observation. Several of Tycho’s observations contradicted Aristotle’s system: a nova that appeared in 1572 exhibited no parallax (meaning that it lay at a very great distance) and was thus not of the sublunary sphere and therefore contrary to the Aristotelian assertion of the immutability of the heavens; similarly, a succession of comets appeared to be moving freely through a region that was supposed to be filled with solid, crystalline spheres. Tycho devised his own world system —a modification of Heracleides’ —to avoid various undesirable implications of the Ptolemaic and Copernican systems.

At the beginning of the 17th century, the German astronomer Johannes Kepler placed the Copernican hypothesis on firm astronomical footing. Converted to the new astronomy as a student and deeply motivated by a neo- Pythagorean desire for finding the mathematical principles of order and harmony according to which God had constructed the world, Kepler spent his life looking for simple mathematical relationships that described planetary motions. His painstaking search for the real order of the universe forced him finally to abandon the Platonic ideal of uniform circular motion in his search for a physical basis for the motions of the heavens.

In 1609 Kepler announced two new planetary laws derived from Tycho’s data: (1) the planets travel around the Sun in elliptical orbits , one focus of the ellipse being occupied by the Sun; and (2) a planet moves in its orbit in such a manner that a line drawn from the planet to the Sun always sweeps out equal areas in equal times. With these two laws, Kepler abandoned uniform circular motion of the planets on their spheres, thus raising the fundamental physical question of what holds the planets in their orbits. He attempted to provide a physical basis for the planetary motions by means of a force analogous to the magnetic force , the qualitative properties of which had been recently described in England by William Gilbert in his influential treatise , De Magnete, Magneticisque Corporibus et de Magno Magnete Tellure (1600; “On the Magnet, Magnetic Bodies, and the Great Magnet of the Earth”). The impending marriage of astronomy and physics had been announced. In 1618 Kepler stated his third law, which was one of many laws concerned with the harmonies of the planetary motions: (3) the square of the period in which a planet orbits the Sun is proportional to the cube of its mean distance from the Sun.

A powerful blow was dealt to traditional cosmology by Galileo Galilei , who early in the 17th century used the telescope , a recent invention of Dutch lens grinders, to look toward the heavens. In 1610 Galileo announced observations that contradicted many traditional cosmological assumptions. He observed that the Moon is not a smooth, polished surface, as Aristotle had claimed, but that it is jagged and mountainous. Earthshine on the Moon revealed that Earth, like the other planets, shines by reflected light. Like Earth, Jupiter was observed to have satellites; hence, Earth had been demoted from its unique position. The phases of Venus proved that that planet orbits the Sun, not Earth.

Why Has Science Been of Crucial Importance for Human Development in the Last Centuries?

• First Online: 30 March 2024

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In this chapter we deal with science. Why is science so influential and what is the reason for its rapid development in recent centuries? A crucial characteristic demonstrated by its history is that science has always aspired toward openness. Science has developed a system of scientific publishing that requires scientific research to be reproducible, which is only possible by transparently stating all the procedures and tools that led to the results. Universities have played a key role, greatly helped by their autonomy backed by legislation. Universities and other research institutions have taken publicly recognised scientific achievements into account when promoting new PhDs and habilitating professors. Bibliographic databases thus play an important role in the orderly and transparent evaluation of results, and research agencies, the funders of science, are an additional pebble in the mosaic, selecting research projects in a similar way to scientific journals when deciding on the publication of scientific articles, i.e. on the basis of transparency in three areas: clearly proposed results, the verifiability of its procedures, and the transparency of its funding. The chapter concludes with an analysis of the Slovenian Research Agency that has put the principles of transparency at the heart of its work.

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Demšar, F. (2024). Why Has Science Been of Crucial Importance for Human Development in the Last Centuries?. In: Transparency in Science and the Effects on Public Policy. Library of Public Policy and Public Administration, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-031-55645-6_2

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10 Scientific Discoveries That Changed The World

Dna, gravity, and germ theory are a few of the key findings in history that forever shifted the course of human civilization. learn how these scientific discoveries changed the world..

The only constant is change. At least, that’s what the Greek philosopher Heraclitus is credited to have said. And while science and philosophy don’t always go hand in hand, there is some truth to Heraclitus’ notion. Change is inevitable and, in some cases, necessary for our species to evolve . While some change happens automatically, like the tides going in and out, some changes bloomed from scientific discoveries. 

Using fire to cook food and keep warm propelled our ancestors toward the foundations of early settlements and continued the growth of civilization. Using fire to shape metals for weapons and building materials led to more and more discoveries and more and more advancements. While many advances shaped humanity, we’ve focused on ten significant scientific discoveries that changed the world.

The discovery of DNA didn’t so much change the world as it did our understanding of it — more so, our understanding of life. DNA is a term we’ve only started using in the 20th century, though its initial discovery dates back decades into the 19th century.

What Is DNA?

DNA is the molecule that encodes genetic information for all living things. It plays a key role in passing traits from parents to offspring and is the primary component of chromosomes in the cell nuclei of complex organisms.

Who Discovered DNA?

Many people think scientists James Watson and Francis Crick discovered DNA in the 1950s. Nope, not so fast. DNA was actually first discovered in 1869 by Swiss physician Friedrich Miescher . He identified what he referred to as “nuclein” in blood cells. Several other researchers have worked on projects around identifying DNA up until Watson and Crick. 

What Does DNA Stand For?

The term nuclein eventually evolved into what we know as DNA, the shorthand for deoxyribonucleic acid. German biochemist Albrecht Kossel , who would later go on to win the Nobel Prize, is often credited with the name.

Levene’s Polynucleotide Model

Other scientists, such as Phoebus Levene , built on Miescher’s work over the years. Levene didn't know how DNA's nucleotide components were arranged. He proposed the polynucleotide model, correctly suggesting that nucleic acids are chains of nucleotides, each with a base, a sugar, and a phosphate group. 

Watson and Crick's Double-Stranded Helix

Watson and Crick and “their” groundbreaking discovery in the field of genetics accurately identified DNA’s double-stranded helix structure, connected by hydrogen bonds. For their discovery, Watson and Crick won a Nobel Prize in 1962 and worldwide acclaim. 

Though Watson and Crick won a Nobel Prize, years later, we’ve learned that the duo likely took research without permission from chemist Rosalind Franklin . Thanks to her research, the double helix structure was realized, though her Nobel Prize was not. 

In 2014, Watson auctioned off his Nobel Prize medal for over $4 million. The buyer was a Russian billionaire who returned it to Watson a year later. In 2019, Watson was stripped of his honorary titles because of racist comments.

Read More: DNA in Unlikely Places Helps Piece Together Ancient Humans' Family Trees

2. Earth in Motion

While it may be common knowledge that Earth spins on an axis and revolves around the sun, at one point, this idea was extremely outlandish. How could the planet move and we not feel it? Thanks to a few clever scientists, the Earth in Motion theory became more than a wild idea.

What Is Earth in Motion?

Earth in motion refers to the understanding that Earth is not stationary but moves in different ways. Earth rotates on an axis and revolves around a star. 

Earth’s Rotation

Earth rotates on its axis , which is an imaginary line running from the North Pole to the South Pole. This rotation is responsible for the day-night cycle, with one complete rotation taking about 24 hours.

Earth’s Revolution

Earth revolves around the Sun, completing one orbit approximately every 365 days. This revolution, combined with the tilt of the Earth's axis, leads to the changing seasons.

Who Discovered Earth's Motion?

The discovery and acceptance of Earth's motion was a gradual process involving several key figures in the history of science.

Aristarchus Hypothesis of Earth’s Motion

An ancient Greek astronomer, Aristarchus of Samos, was one of the first to suggest that Earth orbits the Sun . This view was not widely accepted in his time as it was believed Earth was the center of the Universe, and stars, planets, and the sun all revolved around our planet.

Copernicus Creates the First Model of Earth’s Motion

Mathematician and astronomer Nicolaus Copernicus is often credited with proposing the first heliocentric model of the universe. In 1543, he published his great work, On the Revolutions of the Heavenly Spheres , which explained his theories. 

Among them was that day and night was created by the Earth spinning on its axis. Copernican heliocentrism replaced the conventionally accepted Ptolemaic theory , which asserted that the Earth was stationary. Copernicus’ work was largely unknown during his lifetime but later gained support.

Galileo Galilei’s Telescopic Observations

Galileo Galilei agreed with Copernicus’ theory and proved it through his telescopic observations. In 1610, he observed phases of Venus and the moons of Jupiter, which were strong evidence against the Earth-centered model of the universe.

Galileo agreed with Copernicus’ theory and proved it by using a telescope to confirm that the different phases Venus went through resulted from orbiting around the sun.

Johannes Kepler’s Planetary Laws

German mathematician Johannes Kepler formulated a series of laws detailing the orbits of planets around the Sun. These laws, which remain relevant today, provided mathematical equations for accurately predicting planetary movements in line with the Copernican theory.

Why Don’t We Feel Earth Spinning? 

According to researchers at the California Institute of Technology (CalTech), Earth spins smoothly and at a consistent speed. If Earth were to change speeds at any time, we’d feel it. 

Read More: Earth's Rotation Has Slowed Down Over Billions of Years

3. Electricity

Did benjamin franklin discover electricity.

It’s a common misconception that Ben Franklin discovered electricity with his famous kite experiment. But his 1752 experiment, which used a key and kite, instead demonstrated that lightning is a form of electricity . Another myth is that Franklin was struck by lightning. He wasn’t, but the storm did charge the kite. 

Who First Observed Electricity?

Back in 600 B.C.E., it was the ancient Greek philosopher Thales of Miletus who first observed static electricity when fur was rubbed against fossilized tree resin, known as amber. 

Who Invented Electricity?

British scientist and doctor William Gilbert coined the word “electric,” derived from the Greek word for amber. Regarded as the “father of electricity,” Gilbert was also the first person to use the terms magnetic pole, electric force, and electric attraction. In 1600, his six-volume book set, De Magnete , was published. Among other ideas, it included the hypothesis that Earth itself is a magnet.

Read More: Ben Franklin: Founding Father, Citizen Scientist

4. Germ Theory of Disease

What is the germ theory of disease.

Germ theory is a scientific principle in medicine that attributes the cause of many diseases to microorganisms, such as bacteria and viruses, that invade and multiply within the human body. This theory was a significant shift from previous beliefs about disease causation.

Who Invented the Germ Theory?

Louis Pasteur discovered germ theory when he demonstrated that living microorganisms caused fermentation , which could make milk and wine turn sour. From there, his experiments revealed that these microbes could be destroyed by heating them — a process we now know as pasteurization. 

This advance was a game changer, saving people from getting sick from the bacteria in unpasteurized foods , such as eggs, milk, and cheeses. Before Pasteur, everyday people and scientists alike believed that disease came from inside the body. 

Pasteur’s work proved that germ theory was true and that disease was the result of microorganisms attacking the body. Because of Pasteur, attitudes changed, and became more accepting of germ theory.

How Did Koch’s Postulates Contribute to Germ Theory?

The German physician and microbiologist Robert Koch played a crucial role in establishing a systematic methodology for proving the causal relationship between microbes and diseases .

He formulated Koch's postulates and applied these principles to identify the bacteria responsible for tuberculosis and cholera, among other diseases.

Together, Pasteur and Koch laid the foundation for bacteriology as a science and dramatically shifted the medical community's understanding of infectious diseases. Their work led to improved hygiene, the development of vaccines, and the advancement of public health measures.

Read More: Why Do Some People Get Sick All the Time, While Others Stay in Freakishly Good Health?

Who Discovered Gravity?

Isaac Newton didn’t really get hit on the head with an apple, as far as we know. But seeing an apple fall from a tree did spark an idea that would lead the mathematician and physicist to discover gravity at the age of just 23. 

He pondered about how the force pulls objects straight to the ground, as opposed to following a curved path, like a fired cannonball. Gravity was the answer — a force that pulls objects toward each other. 

How Does Gravity Work?

The greater the mass an object has, the greater the force or gravitational pull. When objects are farther apart, the weaker the force. Newton’s work and his understanding of gravity are used to explain everything from the trajectory of a baseball to the Earth’s orbit around the sun. But Newton’s discoveries didn’t stop there. 

Newton’s Laws of Motion

In 1687, Newton published his book Principia , which expanded on his laws of universal gravitation and his three laws of motion. His work laid the foundation for modern physics. 

Building on the discovery, advancements in the field of electricity continued. 

In 1800, Italian physicist Alessandro Volta created the first voltaic pile , an early form of an electric battery.

Einstein’s Theory of General Relativity

In 1915, Einstein proposed the theory of general relativity . This theory redefined gravity not as a force but as a curvature of spacetime caused by the presence of mass and energy.

According to Einstein, massive objects cause a distortion in the fabric of space and time, similar to how a heavy ball placed on a trampoline causes it to warp. Other objects move along the curves in spacetime created by this distortion.

Both Newton and Einstein significantly advanced our understanding of gravity. Their theories marked critical milestones in the field of physics and have had far-reaching implications in science and technology.

Read More: 5 Eccentric Facts About Isaac Newton

6. Antibiotics

Much like Germ Theory revolutionized modern medicine, so too did the invention of antibiotics. This discovery would go on to save countless lives.  

When Were Antibiotics Invented?

According to the Microbiology Society , humans have been using some form of antibiotics for millennia. It was only in recent history that humans realized that bacteria caused certain infections and that we could now provide readily available treatment. 

In 1909, German physician Paul Ehrlich noticed that certain chemical dyes did not color certain bacteria cells as it did for others. Because of this, he believed that it would be possible to kill certain bacteria without killing the other cells around it. Ehrlich went on to discover the cure for syphilis, which many in the scientific community refer to as the first antibiotic. However, Ehrlich referred to his discovery as chemotherapy because it used chemicals to treat a disease. Ehrlich is referred to as the “Father of Immunology” for his discoveries. 

Ukrainian-American microbiologist Selman Waksman coined the term “antibiotic” about 30 years later, according to the Microbiology Society.

Who Discovered Penicillin? 

One of the most recognizable antibiotics known today is penicillin. Health professionals prescribe millions of patients with this antibiotic each year. However, one of the most well-known antibiotics was discovered by accident. 

In 1928, after some time away from the lab, Alexander Fleming — a Scottish microbiologist — discovered that a fungus Penicillium notatum had contaminated a culture plate with Staph bacteria. Fleming noticed that the fungus had created bacteria-free areas on the plate. After multiple trials, Fleming was able to successfully prove that P. notatum prevented the growth of Staph. Soon the antibiotic was ready for mass production and helped save many lives during World War Two. 

What Is Penicillin Used For? 

Penicillin is used to treat infections caused by bacteria. The medication works by stopping and preventing the growth of bacteria. 

Read More: Antibiotic-Resistant Bacteria: What They Are and How Scientists Are Combating Them

7. The Big Bang Theory

The Big Bang Theory is one of the most widely accepted theories on the beginning of the universe. The theory claims that about 13.7 billion years ago, all matter of the universe was condensed into one small point. After a massive explosion, the contents of the universe burst forth and expanded and continue to expand today. 

Who Came Up With the Big Bang Theory?

This first mention of the Big Bang came from Georges Lemaître, a Belgian cosmologist and Catholic priest. Initially, in 1927, Lemaître published a paper about General Relativity and solutions to the equations around it. Though it mostly went unnoticed. 

Though many scientists didn’t believe that the universe was expanding, a group of cosmologists was beginning to go against the grain. After Edwin Hubble noticed that galaxies further away from our own seemed to be pulling away faster than those closer to us, the idea of the universe expanding seemed to make more sense. Lemaître’s 1927 paper was recognized, and the term Big Bang appeared in Lemaître’s 1931 paper on the subject. 

What Is the Hubble Space Telescope?

Edwin Hubble’s discovery that galaxies are moving away from our own, dubbed Hubble’s Law, is on a long list of his many discoveries. Though this discovery helped add evidence to the Big Bang Theory, this discovery was hindered by the same thing that had been distributing telescopes since their inception: Earth’s atmosphere. According to NASA , Earth’s atmosphere distorts light, limiting how far a telescope can see, even on a clear night. 

Because of this, researchers, specifically Lyman Spitzer , suggested putting a telescope in space, just beyond Earth’s atmosphere and into its orbit. After a few attempts in the 1960s and 70s, NASA, along with contributions from the European Space Agency (ESA), launched a space telescope on April 24, 1990 . The Hubble Space Telescope, named for the pioneering cosmologist, became the strongest telescope known to humankind until the 2021 launch of the James Webb Space Telescope . 

What Is The Cosmic Microwave Background?

The Big Bang emitted large amounts of primeval light , according to the ESA. Over time, this light “cooled” and was no longer visible. However, researchers are able to detect what is known as Cosmic Microwave Background (CMB), which is, according to the ESA, the cooled remnant of the first light to travel through the universe. Some researchers even refer to CMB as an echo of the Big Bang. 

Read More: Did the Big Bang Happen More Than Once?

8. Vaccines

“An ounce of prevention is worth a pound of cure,” Benjamin Franklin once said. A statement that, at the time, applied to making towns safer against fires. However, the same statement can  be true for health and wellness. The advent of vaccines has helped prevent several serious diseases and keep people safe. Thanks to vaccines, people rarely get diseases like polio, and smallpox has been eradicated . 

What Is a Vaccine?

According to the Centers for Disease Control (CDC), a vaccine is a method of protection that introduces a small amount of disease to the human body so that the body can form an immune response should that disease try to enter the body again. 

Basically, through a vaccine, the human body is exposed to a small out of a disease so that the immune system can build a defense against it. 

When Was the First Vaccine Created?

According to the World Health Organization (WHO), Dr. Edward Jenner created the first vaccine in 1796 by using infected material from a cowpox sore — a disease similar to smallpox. He inoculated an 8-year-old boy named James Phipps with the matter and found that the boy, though he didn’t feel well at first, recovered from the illness. 

A few months later, Jenner tested Phipps with material from a smallpox sore and found that Phipps did not get ill at all. From there, the smallpox vaccine prevented countless deaths in the centuries to come. 

When Was the Polio Vaccine Invented?

From 1796 to 1945, doctors and scientists worked hard to create vaccines for other serious illnesses like the Spanish Flu, yellow fever, and influenza. One of these doctors was Jonas Salk. After Salk helped develop an influenza vaccine in 1945, he began working on the Polio vaccine. Between 1952 and 1955, Salk finished the vaccine, and clinical trials began. Salk’s vacation method required a needle and syringe, though, by 1960, Albert Sabin had created a different delivery method for the polio vaccine. Sabin’s version could be administered by drops or on a sugar cube.

Read More: The History of the Polio Vaccine

9. Evolution

What is evolution .

Evolution is a theory that suggests that organisms change and adapt to their environment on a genetic level from one generation to the next. This can take millions of years through methods such as natural selection. An animal’s color or beak may alter over time depending on the changes in their environment, helping them hide from predators or better capture prey. 

Who Is the Father of Evolution? 

After studying animals in the Galapagos , particularly the finches, a naturalist named Charles Darwin determined that the birds — who all resided on different Galapagos islands — were the same or similar species but had distinct characteristics. Darwin noted that the finches from each island had different beaks. These beaks helped the finches forage for their main food source on their specific island. Some had larger beaks for cracking open nuts and seeds, while others had smaller and more narrow beaks for finding insects. 

These observations earned Charles Darwin the title of the Father of Evolution. Though the theory of evolution has changed since Darwin published On the Origin of Species in 1859, he helped lay the framework for modern scientists. 

Is Evolution a Theory or Fact? 

The long-held belief for thousands of years was that the world and all of its organisms were created by one power. But, as science has advanced, there is clear evidence to argue against that. 

The answer to this question is complicated because evolution is both fact and theory. According to the National Center for Science Education , scientific understanding needs both theories and facts. There is proof that organisms have changed or evolved over time, and scientists now have the means to study and identify how those changes happen. 

Read More : 7 Things You May Not Know About Charles Darwin

What Does CRISPR Stand For? 

According to the National Human Genome Research Institute, CRISPR stands for Clustered Regularly Interspaced Short Palindromic Repeats. Researchers use this technology to modify the DNA of a living organism. 

Who Discovered CRISPR? 

There are several people involved and decades of research into the discovery of CRISPR . These researchers include Yoshizumi Ishino, Francisco Mojica, and the duo who recently won the Nobel Prize in Chemistry for CRISPR, Jennifer Doudna and Emmanuelle Charpentier. 

What Is CRISPR?

CRISPR is a technology that can edit genes or even turn a gene “on” or “off.” Researchers have described CRISPR as a molecular scissors that clips apart DNA, then replaces, deletes, or modifies genes. According to a 2018 study, scientists can use this technology to help replace certain genes that may cause diseases such as cancer or heritable diseases like Duchenne muscular dystrophy — a degenerative disorder that can cause premature death.   

How Does CRISPR Work?

In short, scientists use CRISPR technology to find specific pieces of DNA inside of a cell. Scientists then alter that piece of DNA or replace it with a different DNA sequence. CRISPR technology also ensures that the changed gene passes on to the next offspring through gene drive. 

Read More: CRISPR Gene-Editing Technology Enters the Body — and Space

This article was originally published on Oct. 22, 2021 and has since been updated with new information from the Discover staff.

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Scientific Discovery

Scientific discovery is the process or product of successful scientific inquiry. Objects of discovery can be things, events, processes, causes, and properties as well as theories and hypotheses and their features (their explanatory power, for example). Most philosophical discussions of scientific discoveries focus on the generation of new hypotheses that fit or explain given data sets or allow for the derivation of testable consequences. Philosophical discussions of scientific discovery have been intricate and complex because the term “discovery” has been used in many different ways, both to refer to the outcome and to the procedure of inquiry. In the narrowest sense, the term “discovery” refers to the purported “eureka moment” of having a new insight. In the broadest sense, “discovery” is a synonym for “successful scientific endeavor” tout court. Some philosophical disputes about the nature of scientific discovery reflect these terminological variations.

Philosophical issues related to scientific discovery arise about the nature of human creativity, specifically about whether the “eureka moment” can be analyzed and about whether there are rules (algorithms, guidelines, or heuristics) according to which such a novel insight can be brought about. Philosophical issues also arise about the analysis and evaluation of heuristics, about the characteristics of hypotheses worthy of articulation and testing, and, on the meta-level, about the nature and scope of philosophical analysis itself. This essay describes the emergence and development of the philosophical problem of scientific discovery and surveys different philosophical approaches to understanding scientific discovery. In doing so, it also illuminates the meta-philosophical problems surrounding the debates, and, incidentally, the changing nature of philosophy of science.

1. Introduction

2. scientific inquiry as discovery, 3. elements of discovery, 4. pragmatic logics of discovery, 5. the distinction between the context of discovery and the context of justification, 6.1 discovery as abduction, 6.2 heuristic programming, 7. anomalies and the structure of discovery, 8.1 discoverability, 8.2 preliminary appraisal, 8.3 heuristic strategies, 9.1 kinds and features of creativity, 9.2 analogy, 9.3 mental models, 10. machine discovery, 11. social epistemology and discovery, 12. integrated approaches to knowledge generation, other internet resources, related entries.

Philosophical reflection on scientific discovery occurred in different phases. Prior to the 1930s, philosophers were mostly concerned with discoveries in the broad sense of the term, that is, with the analysis of successful scientific inquiry as a whole. Philosophical discussions focused on the question of whether there were any discernible patterns in the production of new knowledge. Because the concept of discovery did not have a specified meaning and was used in a very wide sense, almost all discussions of scientific method and practice could potentially be considered as early contributions to reflections on scientific discovery. In the course of the 18 th century, as philosophy of science and science gradually became two distinct endeavors with different audiences, the term “discovery” became a technical term in philosophical discussions. Different elements of scientific inquiry were specified. Most importantly, during the 19 th century, the generation of new knowledge came to be clearly and explicitly distinguished from its assessment, and thus the conditions for the narrower notion of discovery as the act or process of conceiving new ideas emerged. This distinction was encapsulated in the so-called “context distinction,” between the “context of discovery” and the “context of justification”.

Much of the discussion about scientific discovery in the 20 th century revolved around this distinction It was argued that conceiving a new idea is a non-rational process, a leap of insight that cannot be captured in specific instructions. Justification, by contrast, is a systematic process of applying evaluative criteria to knowledge claims. Advocates of the context distinction argued that philosophy of science is exclusively concerned with the context of justification. The assumption underlying this argument is that philosophy is a normative project; it determines norms for scientific practice. Given this assumption, only the justification of ideas, not their generation, can be the subject of philosophical (normative) analysis. Discovery, by contrast, can only be a topic for empirical study. By definition, the study of discovery is outside the scope of philosophy of science proper.

The introduction of the context distinction and the disciplinary distinction between empirical science studies and normative philosophy of science that was tied to it spawned meta-philosophical disputes. For a long time, philosophical debates about discovery were shaped by the notion that philosophical and empirical analyses are mutually exclusive. Some philosophers insisted, like their predecessors prior to the 1930s, that the philosopher’s tasks include the analysis of actual scientific practices and that scientific resources be used to address philosophical problems. They maintained that it is a legitimate task for philosophy of science to develop a theory of heuristics or problem solving. But this position was the minority view in philosophy of science until the last decades of the 20 th century. Philosophers of discovery were thus compelled to demonstrate that scientific discovery was in fact a legitimate part of philosophy of science. Philosophical reflections about the nature of scientific discovery had to be bolstered by meta-philosophical arguments about the nature and scope of philosophy of science.

Today, however, there is wide agreement that philosophy and empirical research are not mutually exclusive. Not only do empirical studies of actual scientific discoveries in past and present inform philosophical thought about the structure and cognitive mechanisms of discovery, but works in psychology, cognitive science, artificial intelligence and related fields have become integral parts of philosophical analyses of the processes and conditions of the generation of new knowledge. Social epistemology has opened up another perspective on scientific discovery, reconceptualizing knowledge generation as group process.

Prior to the 19 th century, the term “discovery” was used broadly to refer to a new finding, such as a new cure, an unknown territory, an improvement of an instrument, or a new method of measuring longitude. One strand of the discussion about discovery dating back to ancient times concerns the method of analysis as the method of discovery in mathematics and geometry, and, by extension, in philosophy and scientific inquiry. Following the analytic method, we seek to find or discover something – the “thing sought,” which could be a theorem, a solution to a geometrical problem, or a cause – by analyzing it. In the ancient Greek context, analytic methods in mathematics, geometry, and philosophy were not clearly separated; the notion of finding or discovering things by analysis was relevant in all these fields.

In the ensuing centuries, several natural and experimental philosophers, including Avicenna and Zabarella, Bacon and Boyle, the authors of the Port-Royal Logic and Newton, and many others, expounded rules of reasoning and methods for arriving at new knowledge. The ancient notion of analysis still informed these rules and methods. Newton’s famous thirty-first query in the second edition of the Opticks outlines the role of analysis in discovery as follows: “As in Mathematicks, so in Natural Philosophy, the Investigation of difficult Things by the Method of Analysis, ought ever to precede the Method of Composition. This Analysis consists in making Experiments and Observations, and in drawing general Conclusions from them by Induction, and admitting of no Objections against the Conclusions, but such as are taken from Experiments, or other certain Truths … By this way of Analysis we may proceed from Compounds to Ingredients, and from Motions to the Forces producing them; and in general, from Effects to their Causes, and from particular Causes to more general ones, till the Argument end in the most general. This is the Method of Analysis” (Newton 1718, 380, see Koertge 1980, section VI). Early modern accounts of discovery captured knowledge-seeking practices in the study of living and non-living nature, ranging from astronomy and physics to medicine, chemistry, and agriculture. These rich accounts of scientific inquiry were often expounded to bolster particular theories about the nature of matter and natural forces and were not explicitly labeled “methods of discovery ”, yet they are, in fact, accounts of knowledge generation and proper scientific reasoning, covering topics such as the role of the senses in knowledge generation, observation and experimentation, analysis and synthesis, induction and deduction, hypotheses, probability, and certainty.

Bacon’s work is a prominent example. His view of the method of science as it is presented in the Novum Organum showed how best to arrive at knowledge about “form natures” (the most general properties of matter) via a systematic investigation of phenomenal natures. Bacon described how first to collect and organize natural phenomena and experimentally produced facts in tables, how to evaluate these lists, and how to refine the initial results with the help of further trials. Through these steps, the investigator would arrive at conclusions about the “form nature” that produces particular phenomenal natures. Bacon expounded the procedures of constructing and evaluating tables of presences and absences to underpin his matter theory. In addition, in his other writings, such as his natural history Sylva Sylvarum or his comprehensive work on human learning De Augmentis Scientiarium , Bacon exemplified the “art of discovery” with practical examples and discussions of strategies of inquiry.

Like Bacon and Newton, several other early modern authors advanced ideas about how to generate and secure empirical knowledge, what difficulties may arise in scientific inquiry, and how they could be overcome. The close connection between theories about matter and force and scientific methodologies that we find in early modern works was gradually severed. 18 th - and early 19 th -century authors on scientific method and logic cited early modern approaches mostly to model proper scientific practice and reasoning, often creatively modifying them ( section 3 ). Moreover, they developed the earlier methodologies of experimentation, observation, and reasoning into practical guidelines for discovering new phenomena and devising probable hypotheses about cause-effect relations.

It was common in 20 th -century philosophy of science to draw a sharp contrast between those early theories of scientific method and modern approaches. 20 th -century philosophers of science interpreted 17 th - and 18 th -century approaches as generative theories of scientific method. They function simultaneously as guides for acquiring new knowledge and as assessments of the knowledge thus obtained, whereby knowledge that is obtained “in the right way” is considered secure (Laudan 1980; Schaffner 1993: chapter 2). On this view, scientific methods are taken to have probative force (Nickles 1985). According to modern, “consequentialist” theories, propositions must be established by comparing their consequences with observed and experimentally produced phenomena (Laudan 1980; Nickles 1985). It was further argued that, when consequentialist theories were on the rise, the two processes of generation and assessment of an idea or hypothesis became distinct, and the view that the merit of a new idea does not depend on the way in which it was arrived at became widely accepted.

More recent research in history of philosophy of science has shown, however, that there was no such sharp contrast. Consequentialist ideas were advanced throughout the 18 th century, and the early modern generative theories of scientific method and knowledge were more pragmatic than previously assumed. Early modern scholars did not assume that this procedure would lead to absolute certainty. One could only obtain moral certainty for the propositions thus secured.

During the 18 th and 19 th centuries, the different elements of discovery gradually became separated and discussed in more detail. Discussions concerned the nature of observations and experiments, the act of having an insight and the processes of articulating, developing, and testing the novel insight. Philosophical discussion focused on the question of whether and to what extent rules could be devised to guide each of these processes.

Numerous 19 th -century scholars contributed to these discussions, including Claude Bernard, Auguste Comte, George Gore, John Herschel, W. Stanley Jevons, Justus von Liebig, John Stuart Mill, and Charles Sanders Peirce, to name only a few. William Whewell’s work, especially the two volumes of Philosophy of the Inductive Sciences of 1840, is a noteworthy and, later, much discussed contribution to the philosophical debates about scientific discovery because he explicitly distinguished the creative moment or “happy thought” as he called it from other elements of scientific inquiry and because he offered a detailed analysis of the “discoverer’s induction”, i.e., the pursuit and evaluation of the new insight. Whewell’s approach is not unique, but for late 20 th -century philosophers of science, his comprehensive, historically informed philosophy of discovery became a point of orientation in the revival of interest in scientific discovery processes.

For Whewell, discovery comprised three elements: the happy thought, the articulation and development of that thought, and the testing or verification of it. His account was in part a description of the psychological makeup of the discoverer. For instance, he held that only geniuses could have those happy thoughts that are essential to discovery. In part, his account was an account of the methods by which happy thoughts are integrated into the system of knowledge. According to Whewell, the initial step in every discovery is what he called “some happy thought, of which we cannot trace the origin; some fortunate cast of intellect, rising above all rules. No maxims can be given which inevitably lead to discovery” (Whewell 1996 [1840]: 186). An “art of discovery” in the sense of a teachable and learnable skill does not exist according to Whewell. The happy thought builds on the known facts, but according to Whewell it is impossible to prescribe a method for having happy thoughts.

In this sense, happy thoughts are accidental. But in an important sense, scientific discoveries are not accidental. The happy thought is not a wild guess. Only the person whose mind is prepared to see things will actually notice them. The “previous condition of the intellect, and not the single fact, is really the main and peculiar cause of the success. The fact is merely the occasion by which the engine of discovery is brought into play sooner or later. It is, as I have elsewhere said, only the spark which discharges a gun already loaded and pointed; and there is little propriety in speaking of such an accident as the cause why the bullet hits its mark.” (Whewell 1996 [1840]: 189).

Having a happy thought is not yet a discovery, however. The second element of a scientific discovery consists in binding together—“colligating”, as Whewell called it—a set of facts by bringing them under a general conception. Not only does the colligation produce something new, but it also shows the previously known facts in a new light. Colligation involves, on the one hand, the specification of facts through systematic observation, measurements and experiment, and on the other hand, the clarification of ideas through the exposition of the definitions and axioms that are tacitly implied in those ideas. This process is extended and iterative. The scientists go back and forth between binding together the facts, clarifying the idea, rendering the facts more exact, and so forth.

The final part of the discovery is the verification of the colligation involving the happy thought. This means, first and foremost, that the outcome of the colligation must be sufficient to explain the data at hand. Verification also involves judging the predictive power, simplicity, and “consilience” of the outcome of the colligation. “Consilience” refers to a higher range of generality (broader applicability) of the theory (the articulated and clarified happy thought) that the actual colligation produced. Whewell’s account of discovery is not a deductivist system. It is essential that the outcome of the colligation be inferable from the data prior to any testing (Snyder 1997).

Whewell’s theory of discovery clearly separates three elements: the non-analyzable happy thought or eureka moment; the process of colligation which includes the clarification and explication of facts and ideas; and the verification of the outcome of the colligation. His position that the philosophy of discovery cannot prescribe how to think happy thoughts has been a key element of 20 th -century philosophical reflection on discovery. In contrast to many 20 th -century approaches, Whewell’s philosophical conception of discovery also comprises the processes by which the happy thoughts are articulated. Similarly, the process of verification is an integral part of discovery. The procedures of articulation and test are both analyzable according to Whewell, and his conception of colligation and verification serve as guidelines for how the discoverer should proceed. To verify a hypothesis, the investigator needs to show that it accounts for the known facts, that it foretells new, previously unobserved phenomena, and that it can explain and predict phenomena which are explained and predicted by a hypothesis that was obtained through an independent happy thought-cum-colligation (Ducasse 1951).

Whewell’s conceptualization of scientific discovery offers a useful framework for mapping the philosophical debates about discovery and for identifying major issues of concern in 20 th -century philosophical debates. Until the late 20 th century, most philosophers operated with a notion of discovery that is narrower than Whewell’s. In more recent treatments of discovery, however, the scope of the term “discovery” is limited to either the first of these elements, the “happy thought”, or to the happy thought and its initial articulation. In the narrower conception, what Whewell called “verification” is not part of discovery proper. Secondly, until the late 20 th century, there was wide agreement that the eureka moment, narrowly construed, is an unanalyzable, even mysterious leap of insight. The main disagreements concerned the question of whether the process of developing a hypothesis (the “colligation” in Whewell’s terms) is, or is not, a part of discovery proper – and if it is, whether and how this process is guided by rules. Much of the controversies in the 20 th century about the possibility of a philosophy of discovery can be understood against the background of the disagreement about whether the process of discovery does or does not include the articulation and development of a novel thought. Philosophers also disagreed on the issue of whether it is a philosophical task to explicate these rules.

In early 20 th -century logical empiricism, the view that discovery is or at least crucially involves a non-analyzable creative act of a gifted genius was widespread. Alternative conceptions of discovery especially in the pragmatist tradition emphasize that discovery is an extended process, i.e., that the discovery process includes the reasoning processes through which a new insight is articulated and further developed.

In the pragmatist tradition, the term “logic” is used in the broad sense to refer to strategies of human reasoning and inquiry. While the reasoning involved does not proceed according to the principles of demonstrative logic, it is systematic enough to deserve the label “logical”. Proponents of this view argued that traditional (here: syllogistic) logic is an inadequate model of scientific discovery because it misrepresents the process of knowledge generation as grossly as the notion of an “aha moment”.

Early 20 th -century pragmatic logics of discovery can best be described as comprehensive theories of the mental and physical-practical operations involved in knowledge generation, as theories of “how we think” (Dewey 1910). Among the mental operations are classification, determination of what is relevant to an inquiry, and the conditions of communication of meaning; among the physical operations are observation and (laboratory) experiments. These features of scientific discovery are either not or only insufficiently represented by traditional syllogistic logic (Schiller 1917: 236–7).

Philosophers advocating this approach agree that the logic of discovery should be characterized as a set of heuristic principles rather than as a process of applying inductive or deductive logic to a set of propositions. These heuristic principles are not understood to show the path to secure knowledge. Heuristic principles are suggestive rather than demonstrative (Carmichael 1922, 1930). One recurrent feature in these accounts of the reasoning strategies leading to new ideas is analogical reasoning (Schiller 1917; Benjamin 1934, see also section 9.2 .). However, in academic philosophy of science, endeavors to develop more systematically the heuristics guiding discovery processes were soon eclipsed by the advance of the distinction between contexts of discovery and justification.

The distinction between “context of discovery” and “context of justification” dominated and shaped the discussions about discovery in 20 th -century philosophy of science. The context distinction marks the distinction between the generation of a new idea or hypothesis and the defense (test, verification) of it. As the previous sections have shown, the distinction among different elements of scientific inquiry has a long history but in the first half of the 20 th century, the distinction between the different features of scientific inquiry turned into a powerful demarcation criterion between “genuine” philosophy and other fields of science studies, which became potent in philosophy of science. The boundary between context of discovery (the de facto thinking processes) and context of justification (the de jure defense of the correctness of these thoughts) was now understood to determine the scope of philosophy of science, whereby philosophy of science is conceived as a normative endeavor. Advocates of the context distinction argue that the generation of a new idea is an intuitive, nonrational process; it cannot be subject to normative analysis. Therefore, the study of scientists’ actual thinking can only be the subject of psychology, sociology, and other empirical sciences. Philosophy of science, by contrast, is exclusively concerned with the context of justification.

The terms “context of discovery” and “context of justification” are often associated with Hans Reichenbach’s work. Reichenbach’s original conception of the context distinction is quite complex, however (Howard 2006; Richardson 2006). It does not map easily on to the disciplinary distinction mentioned above, because for Reichenbach, philosophy of science proper is partly descriptive. Reichenbach maintains that philosophy of science includes a description of knowledge as it really is. Descriptive philosophy of science reconstructs scientists’ thinking processes in such a way that logical analysis can be performed on them, and it thus prepares the ground for the evaluation of these thoughts (Reichenbach 1938: § 1). Discovery, by contrast, is the object of empirical—psychological, sociological—study. According to Reichenbach, the empirical study of discoveries shows that processes of discovery often correspond to the principle of induction, but this is simply a psychological fact (Reichenbach 1938: 403).

While the terms “context of discovery” and “context of justification” are widely used, there has been ample discussion about how the distinction should be drawn and what their philosophical significance is (c.f. Kordig 1978; Gutting 1980; Zahar 1983; Leplin 1987; Hoyningen-Huene 1987; Weber 2005: chapter 3; Schickore and Steinle 2006). Most commonly, the distinction is interpreted as a distinction between the process of conceiving a theory and the assessment of that theory, specifically the assessment of the theory’s epistemic support. This version of the distinction is not necessarily interpreted as a temporal distinction. In other words, it is not usually assumed that a theory is first fully developed and then assessed. Rather, generation and assessment are two different epistemic approaches to theory: the endeavor to articulate, flesh out, and develop its potential and the endeavor to assess its epistemic worth. Within the framework of the context distinction, there are two main ways of conceptualizing the process of conceiving a theory. The first option is to characterize the generation of new knowledge as an irrational act, a mysterious creative intuition, a “eureka moment”. The second option is to conceptualize the generation of new knowledge as an extended process that includes a creative act as well as some process of articulating and developing the creative idea.

Both of these accounts of knowledge generation served as starting points for arguments against the possibility of a philosophy of discovery. In line with the first option, philosophers have argued that neither is it possible to prescribe a logical method that produces new ideas nor is it possible to reconstruct logically the process of discovery. Only the process of testing is amenable to logical investigation. This objection to philosophies of discovery has been called the “discovery machine objection” (Curd 1980: 207). It is usually associated with Karl Popper’s Logic of Scientific Discovery .

The initial state, the act of conceiving or inventing a theory, seems to me neither to call for logical analysis not to be susceptible of it. The question how it happens that a new idea occurs to a man—whether it is a musical theme, a dramatic conflict, or a scientific theory—may be of great interest to empirical psychology; but it is irrelevant to the logical analysis of scientific knowledge. This latter is concerned not with questions of fact (Kant’s quid facti ?) , but only with questions of justification or validity (Kant’s quid juris ?) . Its questions are of the following kind. Can a statement be justified? And if so, how? Is it testable? Is it logically dependent on certain other statements? Or does it perhaps contradict them? […]Accordingly I shall distinguish sharply between the process of conceiving a new idea, and the methods and results of examining it logically. As to the task of the logic of knowledge—in contradistinction to the psychology of knowledge—I shall proceed on the assumption that it consists solely in investigating the methods employed in those systematic tests to which every new idea must be subjected if it is to be seriously entertained. (Popper 2002 [1934/1959]: 7–8)

With respect to the second way of conceptualizing knowledge generation, many philosophers argue in a similar fashion that because the process of discovery involves an irrational, intuitive process, which cannot be examined logically, a logic of discovery cannot be construed. Other philosophers turn against the philosophy of discovery even though they explicitly acknowledge that discovery is an extended, reasoned process. They present a meta-philosophical objection argument, arguing that a theory of articulating and developing ideas is not a philosophical but a psychological or sociological theory. In this perspective, “discovery” is understood as a retrospective label, which is attributed as a sign of accomplishment to some scientific endeavors. Sociological theories acknowledge that discovery is a collective achievement and the outcome of a process of negotiation through which “discovery stories” are constructed and certain knowledge claims are granted discovery status (Brannigan 1981; Schaffer 1986, 1994).

The impact of the context distinction on 20 th -century studies of scientific discovery and on philosophy of science more generally can hardly be overestimated. The view that the process of discovery (however construed) is outside the scope of philosophy of science proper was widely shared amongst philosophers of science for most of the 20 th century. The last section shows that there were some attempts to develop logics of discovery in the 1920s and 1930s, especially in the pragmatist tradition. But for several decades, the context distinction dictated what philosophy of science should be about and how it should proceed. The dominant view was that theories of mental operations or heuristics had no place in philosophy of science and that, therefore, discovery was not a legitimate topic for philosophy of science. Until the last decades of the 20 th century, there were few attempts to challenge the disciplinary distinction tied to the context distinction. Only during the 1970s did the interest in philosophical approaches to discovery begin to increase again. But the context distinction remained a challenge for philosophies of discovery.

There are several lines of response to the disciplinary distinction tied to the context distinction. Each of these lines of response opens a philosophical perspective on discovery. Each proceeds on the assumption that philosophy of science may legitimately include some form of analysis of actual reasoning patterns as well as information from empirical sciences such as cognitive science, psychology, and sociology. All of these responses reject the idea that discovery is nothing but a mystical event. Discovery is conceived as an analyzable reasoning process, not just as a creative leap by which novel ideas spring into being fully formed. All of these responses agree that the procedures and methods for arriving at new hypotheses and ideas are no guarantee that the hypothesis or idea that is thus formed is necessarily the best or the correct one. Nonetheless, it is the task of philosophy of science to provide rules for making this process better. All of these responses can be described as theories of problem solving, whose ultimate goal is to make the generation of new ideas and theories more efficient.

But the different approaches to scientific discovery employ different terminologies. In particular, the term “logic” of discovery is sometimes used in a narrow sense and sometimes broadly understood. In the narrow sense, “logic” of discovery is understood to refer to a set of formal, generally applicable rules by which novel ideas can be mechanically derived from existing data. In the broad sense, “logic” of discovery refers to the schematic representation of reasoning procedures. “Logical” is just another term for “rational”. Moreover, while each of these responses combines philosophical analyses of scientific discovery with empirical research on actual human cognition, different sets of resources are mobilized, ranging from AI research and cognitive science to historical studies of problem-solving procedures. Also, the responses parse the process of scientific inquiry differently. Often, scientific inquiry is regarded as having two aspects, viz. generation and assessments of new ideas. At times, however, scientific inquiry is regarded as having three aspects, namely generation, pursuit or articulation, and assessment of knowledge. In the latter framework, the label “discovery” is sometimes used to refer just to generation and sometimes to refer to both generation and pursuit.

One response to the challenge of the context distinction draws on a broad understanding of the term “logic” to argue that we cannot but admit a general, domain-neutral logic if we do not want to assume that the success of science is a miracle (Jantzen 2016) and that a logic of scientific discovery can be developed ( section 6 ). Another response, drawing on a narrow understanding of the term “logic”, is to concede that there is no logic of discovery, i.e., no algorithm for generating new knowledge, but that the process of discovery follows an identifiable, analyzable pattern ( section 7 ).

Others argue that discovery is governed by a methodology . The methodology of discovery is a legitimate topic for philosophical analysis ( section 8 ). Yet another response assumes that discovery is or at least involves a creative act. Drawing on resources from cognitive science, neuroscience, computational research, and environmental and social psychology, philosophers have sought to demystify the cognitive processes involved in the generation of new ideas. Philosophers who take this approach argue that scientific creativity is amenable to philosophical analysis ( section 9.1 ).

All these responses assume that there is more to discovery than a eureka moment. Discovery comprises processes of articulating, developing, and assessing the creative thought, as well as the scientific community’s adjudication of what does, and does not, count as “discovery” (Arabatzis 1996). These are the processes that can be examined with the tools of philosophical analysis, augmented by input from other fields of science studies such as sociology, history, or cognitive science.

6. Logics of discovery after the context distinction

One way of responding to the demarcation criterion described above is to argue that discovery is a topic for philosophy of science because it is a logical process after all. Advocates of this approach to the logic of discovery usually accept the overall distinction between the two processes of conceiving and testing a hypothesis. They also agree that it is impossible to put together a manual that provides a formal, mechanical procedure through which innovative concepts or hypotheses can be derived: There is no discovery machine. But they reject the view that the process of conceiving a theory is a creative act, a mysterious guess, a hunch, a more or less instantaneous and random process. Instead, they insist that both conceiving and testing hypotheses are processes of reasoning and systematic inference, that both of these processes can be represented schematically, and that it is possible to distinguish better and worse paths to new knowledge.

This line of argument has much in common with the logics of discovery described in section 4 above but it is now explicitly pitched against the disciplinary distinction tied to the context distinction. There are two main ways of developing this argument. The first is to conceive of discovery in terms of abductive reasoning ( section 6.1 ). The second is to conceive of discovery in terms of problem-solving algorithms, whereby heuristic rules aid the processing of available data and enhance the success in finding solutions to problems ( section 6.2 ). Both lines of argument rely on a broad conception of logic, whereby the “logic” of discovery amounts to a schematic account of the reasoning processes involved in knowledge generation.

One argument, elaborated prominently by Norwood R. Hanson, is that the act of discovery—here, the act of suggesting a new hypothesis—follows a distinctive logical pattern, which is different from both inductive logic and the logic of hypothetico-deductive reasoning. The special logic of discovery is the logic of abductive or “retroductive” inferences (Hanson 1958). The argument that it is through an act of abductive inferences that plausible, promising scientific hypotheses are devised goes back to C.S. Peirce. This version of the logic of discovery characterizes reasoning processes that take place before a new hypothesis is ultimately justified. The abductive mode of reasoning that leads to plausible hypotheses is conceptualized as an inference beginning with data or, more specifically, with surprising or anomalous phenomena.

In this view, discovery is primarily a process of explaining anomalies or surprising, astonishing phenomena. The scientists’ reasoning proceeds abductively from an anomaly to an explanatory hypothesis in light of which the phenomena would no longer be surprising or anomalous. The outcome of this reasoning process is not one single specific hypothesis but the delineation of a type of hypotheses that is worthy of further attention (Hanson 1965: 64). According to Hanson, the abductive argument has the following schematic form (Hanson 1960: 104):

• Some surprising, astonishing phenomena p 1 , p 2 , p 3 … are encountered.
• But p 1 , p 2 , p 3 … would not be surprising were an hypothesis of H ’s type to obtain. They would follow as a matter of course from something like H and would be explained by it.
• Therefore there is good reason for elaborating an hypothesis of type H—for proposing it as a possible hypothesis from whose assumption p 1 , p 2 , p 3 … might be explained.

Drawing on the historical record, Hanson argues that several important discoveries were made relying on abductive reasoning, such as Kepler’s discovery of the elliptic orbit of Mars (Hanson 1958). It is now widely agreed, however, that Hanson’s reconstruction of the episode is not a historically adequate account of Kepler’s discovery (Lugg 1985). More importantly, while there is general agreement that abductive inferences are frequent in both everyday and scientific reasoning, these inferences are no longer considered as logical inferences. Even if one accepts Hanson’s schematic representation of the process of identifying plausible hypotheses, this process is a “logical” process only in the widest sense whereby the term “logical” is understood as synonymous with “rational”. Notably, some philosophers have even questioned the rationality of abductive inferences (Koehler 1991; Brem and Rips 2000).

Another argument against the above schema is that it is too permissive. There will be several hypotheses that are explanations for phenomena p 1 , p 2 , p 3 …, so the fact that a particular hypothesis explains the phenomena is not a decisive criterion for developing that hypothesis (Harman 1965; see also Blackwell 1969). Additional criteria are required to evaluate the hypothesis yielded by abductive inferences.

Finally, it is worth noting that the schema of abductive reasoning does not explain the very act of conceiving a hypothesis or hypothesis-type. The processes by which a new idea is first articulated remain unanalyzed in the above schema. The schema focuses on the reasoning processes by which an exploratory hypothesis is assessed in terms of its merits and promise (Laudan 1980; Schaffner 1993).

In more recent work on abduction and discovery, two notions of abduction are sometimes distinguished: the common notion of abduction as inference to the best explanation (selective abduction) and creative abduction (Magnani 2000, 2009). Selective abduction—the inference to the best explanation—involves selecting a hypothesis from a set of known hypotheses. Medical diagnosis exemplifies this kind of abduction. Creative abduction, by contrast, involves generating a new, plausible hypothesis. This happens, for instance, in medical research, when the notion of a new disease is articulated. However, it is still an open question whether this distinction can be drawn, or whether there is a more gradual transition from selecting an explanatory hypothesis from a familiar domain (selective abduction) to selecting a hypothesis that is slightly modified from the familiar set and to identifying a more drastically modified or altered assumption.

Another recent suggestion is to broaden Peirce’s original account of abduction and to include not only verbal information but also non-verbal mental representations, such as visual, auditory, or motor representations. In Thagard’s approach, representations are characterized as patterns of activity in mental populations (see also section 9.3 below). The advantage of the neural account of human reasoning is that it covers features such as the surprise that accompanies the generation of new insights or the visual and auditory representations that contribute to it. Surprise, for instance, could be characterized as resulting from rapid changes in activation of the node in a neural network representing the “surprising” element (Thagard and Stewart 2011). If all mental representations can be characterized as patterns of firing in neural populations, abduction can be analyzed as the combination or “convolution” (Thagard) of patterns of neural activity from disjoint or overlapping patterns of activity (Thagard 2010).

The concern with the logic of discovery has also motivated research on artificial intelligence at the intersection of philosophy of science and cognitive science. In this approach, scientific discovery is treated as a form of problem-solving activity (Simon 1973; see also Newell and Simon 1971), whereby the systematic aspects of problem solving are studied within an information-processing framework. The aim is to clarify with the help of computational tools the nature of the methods used to discover scientific hypotheses. These hypotheses are regarded as solutions to problems. Philosophers working in this tradition build computer programs employing methods of heuristic selective search (e.g., Langley et al. 1987). In computational heuristics, search programs can be described as searches for solutions in a so-called “problem space” in a certain domain. The problem space comprises all possible configurations in that domain (e.g., for chess problems, all possible arrangements of pieces on a board of chess). Each configuration is a “state” of the problem space. There are two special states, namely the goal state, i.e., the state to be reached, and the initial state, i.e., the configuration at the starting point from which the search begins. There are operators, which determine the moves that generate new states from the current state. There are path constraints, which limit the permitted moves. Problem solving is the process of searching for a solution of the problem of how to generate the goal state from an initial state. In principle, all states can be generated by applying the operators to the initial state, then to the resulting state, until the goal state is reached (Langley et al. 1987: chapter 9). A problem solution is a sequence of operations leading from the initial to the goal state.

The basic idea behind computational heuristics is that rules can be identified that serve as guidelines for finding a solution to a given problem quickly and efficiently by avoiding undesired states of the problem space. These rules are best described as rules of thumb. The aim of constructing a logic of discovery thus becomes the aim of constructing a heuristics for the efficient search for solutions to problems. The term “heuristic search” indicates that in contrast to algorithms, problem-solving procedures lead to results that are merely provisional and plausible. A solution is not guaranteed, but heuristic searches are advantageous because they are more efficient than exhaustive random trial and error searches. Insofar as it is possible to evaluate whether one set of heuristics is better—more efficacious—than another, the logic of discovery turns into a normative theory of discovery.

Arguably, because it is possible to reconstruct important scientific discovery processes with sets of computational heuristics, the scientific discovery process can be considered as a special case of the general mechanism of information processing. In this context, the term “logic” is not used in the narrow sense of a set of formal, generally applicable rules to draw inferences but again in a broad sense as a label for a set of procedural rules.

The computer programs that embody the principles of heuristic searches in scientific inquiry simulate the paths that scientists followed when they searched for new theoretical hypotheses. Computer programs such as BACON (Simon et al. 1981) and KEKADA (Kulkarni and Simon 1988) utilize sets of problem-solving heuristics to detect regularities in given data sets. The program would note, for instance, that the values of a dependent term are constant or that a set of values for a term x and a set of values for a term y are linearly related. It would thus “infer” that the dependent term always has that value or that a linear relation exists between x and y . These programs can “make discoveries” in the sense that they can simulate successful discoveries such as Kepler’s third law (BACON) or the Krebs cycle (KEKADA).

Computational theories of scientific discoveries have helped identify and clarify a number of problem-solving strategies. An example of such a strategy is heuristic means-ends analysis, which involves identifying specific differences between the present and the goal situation and searches for operators (processes that will change the situation) that are associated with the differences that were detected. Another important heuristic is to divide the problem into sub-problems and to begin solving the one with the smallest number of unknowns to be determined (Simon 1977). Computational approaches have also highlighted the extent to which the generation of new knowledge draws on existing knowledge that constrains the development of new hypotheses.

As accounts of scientific discoveries, the early computational heuristics have some limitations. Compared to the problem spaces given in computational heuristics, the complex problem spaces for scientific problems are often ill defined, and the relevant search space and goal state must be delineated before heuristic assumptions could be formulated (Bechtel and Richardson 1993: chapter 1). Because a computer program requires the data from actual experiments, the simulations cover only certain aspects of scientific discoveries; in particular, it cannot determine by itself which data is relevant, which data to relate and what form of law it should look for (Gillies 1996). However, as a consequence of the rise of so-called “deep learning” methods in data-intensive science, there is renewed philosophical interest in the question of whether machines can make discoveries ( section 10 ).

Many philosophers maintain that discovery is a legitimate topic for philosophy of science while abandoning the notion that there is a logic of discovery. One very influential approach is Thomas Kuhn’s analysis of the emergence of novel facts and theories (Kuhn 1970 [1962]: chapter 6). Kuhn identifies a general pattern of discovery as part of his account of scientific change. A discovery is not a simple act, but an extended, complex process, which culminates in paradigm changes. Paradigms are the symbolic generalizations, metaphysical commitments, values, and exemplars that are shared by a community of scientists and that guide the research of that community. Paradigm-based, normal science does not aim at novelty but instead at the development, extension, and articulation of accepted paradigms. A discovery begins with an anomaly, that is, with the recognition that the expectations induced by an established paradigm are being violated. The process of discovery involves several aspects: observations of an anomalous phenomenon, attempts to conceptualize it, and changes in the paradigm so that the anomaly can be accommodated.

It is the mark of success of normal science that it does not make transformative discoveries, and yet such discoveries come about as a consequence of normal, paradigm-guided science. The more detailed and the better developed a paradigm, the more precise are its predictions. The more precisely the researchers know what to expect, the better they are able to recognize anomalous results and violations of expectations:

novelty ordinarily emerges only for the man who, knowing with precision what he should expect, is able to recognize that something has gone wrong. Anomaly appears only against the background provided by the paradigm. (Kuhn 1970 [1962]: 65)

Drawing on several historical examples, Kuhn argues that it is usually impossible to identify the very moment when something was discovered or even the individual who made the discovery. Kuhn illustrates these points with the discovery of oxygen (see Kuhn 1970 [1962]: 53–56). Oxygen had not been discovered before 1774 and had been discovered by 1777. Even before 1774, Lavoisier had noticed that something was wrong with phlogiston theory, but he was unable to move forward. Two other investigators, C. W. Scheele and Joseph Priestley, independently identified a gas obtained from heating solid substances. But Scheele’s work remained unpublished until after 1777, and Priestley did not identify his substance as a new sort of gas. In 1777, Lavoisier presented the oxygen theory of combustion, which gave rise to fundamental reconceptualization of chemistry. But according to this theory as Lavoisier first presented it, oxygen was not a chemical element. It was an atomic “principle of acidity” and oxygen gas was a combination of that principle with caloric. According to Kuhn, all of these developments are part of the discovery of oxygen, but none of them can be singled out as “the” act of discovery.

In pre-paradigmatic periods or in times of paradigm crisis, theory-induced discoveries may happen. In these periods, scientists speculate and develop tentative theories, which may lead to novel expectations and experiments and observations to test whether these expectations can be confirmed. Even though no precise predictions can be made, phenomena that are thus uncovered are often not quite what had been expected. In these situations, the simultaneous exploration of the new phenomena and articulation of the tentative hypotheses together bring about discovery.

In cases like the discovery of oxygen, by contrast, which took place while a paradigm was already in place, the unexpected becomes apparent only slowly, with difficulty, and against some resistance. Only gradually do the anomalies become visible as such. It takes time for the investigators to recognize “both that something is and what it is” (Kuhn 1970 [1962]: 55). Eventually, a new paradigm becomes established and the anomalous phenomena become the expected phenomena.

Recent studies in cognitive neuroscience of brain activity during periods of conceptual change support Kuhn’s view that conceptual change is hard to achieve. These studies examine the neural processes that are involved in the recognition of anomalies and compare them with the brain activity involved in the processing of information that is consistent with preferred theories. The studies suggest that the two types of data are processed differently (Dunbar et al. 2007).

8. Methodologies of discovery

Advocates of the view that there are methodologies of discovery use the term “logic” in the narrow sense of an algorithmic procedure to generate new ideas. But like the AI-based theories of scientific discovery described in section 6 , methodologies of scientific discovery interpret the concept “discovery” as a label for an extended process of generating and articulating new ideas and often describe the process in terms of problem solving. In these approaches, the distinction between the contexts of discovery and the context of justification is challenged because the methodology of discovery is understood to play a justificatory role. Advocates of a methodology of discovery usually rely on a distinction between different justification procedures, justification involved in the process of generating new knowledge and justification involved in testing it. Consequential or “strong” justifications are methods of testing. The justification involved in discovery, by contrast, is conceived as generative (as opposed to consequential) justification ( section 8.1 ) or as weak (as opposed to strong) justification ( section 8.2 ). Again, some terminological ambiguity exists because according to some philosophers, there are three contexts, not two: Only the initial conception of a new idea (the creative act is the context of discovery proper, and between it and justification there exists a separate context of pursuit (Laudan 1980). But many advocates of methodologies of discovery regard the context of pursuit as an integral part of the process of justification. They retain the notion of two contexts and re-draw the boundaries between the contexts of discovery and justification as they were drawn in the early 20 th century.

The methodology of discovery has sometimes been characterized as a form of justification that is complementary to the methodology of testing (Nickles 1984, 1985, 1989). According to the methodology of testing, empirical support for a theory results from successfully testing the predictive consequences derived from that theory (and appropriate auxiliary assumptions). In light of this methodology, justification for a theory is “consequential justification,” the notion that a hypothesis is established if successful novel predictions are derived from the theory or claim. Generative justification complements consequential justification. Advocates of generative justification hold that there exists an important form of justification in science that involves reasoning to a claim from data or previously established results more generally.

One classic example for a generative methodology is the set of Newton’s rules for the study of natural philosophy. According to these rules, general propositions are established by deducing them from the phenomena. The notion of generative justification seeks to preserve the intuition behind classic conceptions of justification by deduction. Generative justification amounts to the rational reconstruction of the discovery path in order to establish its discoverability had the researchers known what is known now, regardless of how it was first thought of (Nickles 1985, 1989). The reconstruction demonstrates in hindsight that the claim could have been discovered in this manner had the necessary information and techniques been available. In other words, generative justification—justification as “discoverability” or “potential discovery”—justifies a knowledge claim by deriving it from results that are already established. While generative justification does not retrace exactly those steps of the actual discovery path that were actually taken, it is a better representation of scientists’ actual practices than consequential justification because scientists tend to construe new claims from available knowledge. Generative justification is a weaker version of the traditional ideal of justification by deduction from the phenomena. Justification by deduction from the phenomena is complete if a theory or claim is completely determined from what we already know. The demonstration of discoverability results from the successful derivation of a claim or theory from the most basic and most solidly established empirical information.

Discoverability as described in the previous paragraphs is a mode of justification. Like the testing of novel predictions derived from a hypothesis, generative justification begins when the phase of finding and articulating a hypothesis worthy of assessing is drawing to a close. Other approaches to the methodology of discovery are directly concerned with the procedures involved in devising new hypotheses. The argument in favor of this kind of methodology is that the procedures of devising new hypotheses already include elements of appraisal. These preliminary assessments have been termed “weak” evaluation procedures (Schaffner 1993). Weak evaluations are relevant during the process of devising a new hypothesis. They provide reasons for accepting a hypothesis as promising and worthy of further attention. Strong evaluations, by contrast, provide reasons for accepting a hypothesis as (approximately) true or confirmed. Both “generative” and “consequential” testing as discussed in the previous section are strong evaluation procedures. Strong evaluation procedures are rigorous and systematically organized according to the principles of hypothesis derivation or H-D testing. A methodology of preliminary appraisal, by contrast, articulates criteria for the evaluation of a hypothesis prior to rigorous derivation or testing. It aids the decision about whether to take that hypothesis seriously enough to develop it further and test it. For advocates of this version of the methodology of discovery, it is the task of philosophy of science to characterize sets of constraints and methodological rules guiding the complex process of prior-to-test evaluation of hypotheses.

In contrast to the computational approaches discussed above, strategies of preliminary appraisal are not regarded as subject-neutral but as specific to particular fields of study. Philosophers of biology, for instance, have developed a fine-grained framework to account for the generation and preliminary evaluation of biological mechanisms (Darden 2002; Craver 2002; Bechtel and Richardson 1993; Craver and Darden 2013). Some philosophers have suggested that the phase of preliminary appraisal be further divided into two phases, the phase of appraising and the phase of revising. According to Lindley Darden, the phases of generation, appraisal and revision of descriptions of mechanisms can be characterized as reasoning processes governed by reasoning strategies. Different reasoning strategies govern the different phases (Darden 1991, 2002; Craver 2002; Darden 2009). The generation of hypotheses about mechanisms, for instance, is governed by the strategy of “schema instantiation” (see Darden 2002). The discovery of the mechanism of protein synthesis involved the instantiation of an abstract schema for chemical reactions: reactant 1 + reactant 2 = product. The actual mechanism of protein synthesis was found through specification and modification of this schema.

Neither of these strategies is deemed necessary for discovery, and they are not prescriptions for biological research. Rather, these strategies are deemed sufficient for the discovery of mechanisms. The methodology of the discovery of mechanisms is an extrapolation from past episodes of research on mechanisms and the result of a synthesis of rational reconstructions of several of these historical episodes. The methodology of discovery is weakly normative in the sense that the strategies for the discovery of mechanisms that were successful in the past may prove useful in future biological research (Darden 2002).

As philosophers of science have again become more attuned to actual scientific practices, interest in heuristic strategies has also been revived. Many analysts now agree that discovery processes can be regarded as problem solving activities, whereby a discovery is a solution to a problem. Heuristics-based methodologies of discovery are neither purely subjective and intuitive nor algorithmic or formalizable; the point is that reasons can be given for pursuing one or the other problem-solving strategy. These rules are open and do not guarantee a solution to a problem when applied (Ippoliti 2018). On this view, scientific researchers are no longer seen as Kuhnian “puzzle solvers” but as problem solvers and decision makers in complex, variable, and changing environments (Wimsatt 2007).

Philosophers of discovery working in this tradition draw on a growing body of literature in cognitive psychology, management science, operations research, and economy on human reasoning and decision making in contexts with limited information, under time constraints, and with sub-optimal means (Gigerenzer & Sturm 2012). Heuristic strategies characterized in these studies, such as Gigerenzer’s “tools to theory heuristic” are then applied to understand scientific knowledge generation (Gigerenzer 1992, Nickles 2018). Other analysts specify heuristic strategies in a range of scientific fields, including climate science, neurobiology, and clinical medicine (Gramelsberger 2011, Schaffner 2008, Gillies 2018). Finally, in analytic epistemology, formal methods are developed to identify and assess distinct heuristic strategies currently in use, such as Bayesian reverse engineering in cognitive science (Zednik and Jäkel 2016).

As the literature on heuristics continues to grow, it has become clear that the term “heuristics” is itself used in a variety of different ways. (For a valuable taxonomy of meanings of “heuristic,” see Chow 2015, see also Ippoliti 2018.) Moreover, as in the context of earlier debates about computational heuristics, debates continue about the limitations of heuristics. The use of heuristics may come at a cost if heuristics introduce systematic biases (Wimsatt 2007). Some philosophers thus call for general principles for the evaluation of heuristic strategies (Hey 2016).

9. Cognitive perspectives on discovery

The approaches to scientific discovery presented in the previous sections focus on the adoption, articulation, and preliminary evaluation of ideas or hypotheses prior to rigorous testing, not on how a novel hypothesis or idea is first thought up. For a long time, the predominant view among philosophers of discovery was that the initial step of discovery is a mysterious intuitive leap of the human mind that cannot be analyzed further. More recent accounts of discovery informed by evolutionary biology also do not explicate how new ideas are formed. The generation of new ideas is akin to random, blind variations of thought processes, which have to be inspected by the critical mind and assessed as neutral, productive, or useless (Campbell 1960; see also Hull 1988), but the key processes by which new ideas are generated are left unanalyzed.

With the recent rapprochement among philosophy of mind, cognitive science and psychology and the increased integration of empirical research into philosophy of science, these processes have been submitted to closer analysis, and philosophical studies of creativity have seen a surge of interest (e.g. Paul & Kaufman 2014a). The distinctive feature of these studies is that they integrate philosophical analyses with empirical work from cognitive science, psychology, evolutionary biology, and computational neuroscience (Thagard 2012). Analysts have distinguished different kinds and different features of creative thinking and have examined certain features in depth, and from new angles. Recent philosophical research on creativity comprises conceptual analyses and integrated approaches based on the assumption that creativity can be analyzed and that empirical research can contribute to the analysis (Paul & Kaufman 2014b). Two key elements of the cognitive processes involved in creative thinking that have been in the focus of philosophical analysis are analogies ( section 9.2 ) and mental models ( section 9.3 ).

General definitions of creativity highlight novelty or originality and significance or value as distinctive features of a creative act or product (Sternberg & Lubart 1999, Kieran 2014, Paul & Kaufman 2014b, although see Hills & Bird 2019). Different kinds of creativity can be distinguished depending on whether the act or product is novel for a particular individual or entirely novel. Psychologist Margaret Boden distinguishes between psychological creativity (P-creativity) and historical creativity (H-creativity). P-creativity is a development that is new, surprising and important to the particular person who comes up with it. H-creativity, by contrast, is radically novel, surprising, and important—it is generated for the first time (Boden 2004). Further distinctions have been proposed, such as anthropological creativity (construed as a human condition) and metaphysical creativity, a radically new thought or action in the sense that it is unaccounted for by antecedents and available knowledge, and thus constitutes a radical break with the past (Kronfeldner 2009, drawing on Hausman 1984).

Psychological studies analyze the personality traits and creative individuals’ behavioral dispositions that are conducive to creative thinking. They suggest that creative scientists share certain distinct personality traits, including confidence, openness, dominance, independence, introversion, as well as arrogance and hostility. (For overviews of recent studies on personality traits of creative scientists, see Feist 1999, 2006: chapter 5).

Recent work on creativity in philosophy of mind and cognitive science offers substantive analyses of the cognitive and neural mechanisms involved in creative thinking (Abrams 2018, Minai et al 2022) and critical scrutiny of the romantic idea of genius creativity as something deeply mysterious (Blackburn 2014). Some of this research aims to characterize features that are common to all creative processes, such as Thagard and Stewart’s account according to which creativity results from combinations of representations (Thagard & Stewart 2011, but see Pasquale and Poirier 2016). Other research aims to identify the features that are distinctive of scientific creativity as opposed to other forms of creativity, such as artistic creativity or creative technological invention (Simonton 2014).

Many philosophers of science highlight the role of analogy in the development of new knowledge, whereby analogy is understood as a process of bringing ideas that are well understood in one domain to bear on a new domain (Thagard 1984; Holyoak and Thagard 1996). An important source for philosophical thought about analogy is Mary Hesse’s conception of models and analogies in theory construction and development. In this approach, analogies are similarities between different domains. Hesse introduces the distinction between positive, negative, and neutral analogies (Hesse 1966: 8). If we consider the relation between gas molecules and a model for gas, namely a collection of billiard balls in random motion, we will find properties that are common to both domains (positive analogy) as well as properties that can only be ascribed to the model but not to the target domain (negative analogy). There is a positive analogy between gas molecules and a collection of billiard balls because both the balls and the molecules move randomly. There is a negative analogy between the domains because billiard balls are colored, hard, and shiny but gas molecules do not have these properties. The most interesting properties are those properties of the model about which we do not know whether they are positive or negative analogies. This set of properties is the neutral analogy. These properties are the significant properties because they might lead to new insights about the less familiar domain. From our knowledge about the familiar billiard balls, we may be able to derive new predictions about the behavior of gas molecules, which we could then test.

Hesse offers a more detailed analysis of the structure of analogical reasoning through the distinction between horizontal and vertical analogies between domains. Horizontal analogies between two domains concern the sameness or similarity between properties of both domains. If we consider sound and light waves, there are similarities between them: sound echoes, light reflects; sound is loud, light is bright, both sound and light are detectable by our senses. There are also relations among the properties within one domain, such as the causal relation between sound and the loud tone we hear and, analogously, between physical light and the bright light we see. These analogies are vertical analogies. For Hesse, vertical analogies hold the key for the construction of new theories.

Analogies play several roles in science. Not only do they contribute to discovery but they also play a role in the development and evaluation of scientific theories. Current discussions about analogy and discovery have expanded and refined Hesse’s approach in various ways. Some philosophers have developed criteria for evaluating analogy arguments (Bartha 2010). Other work has identified highly significant analogies that were particularly fruitful for the advancement of science (Holyoak and Thagard 1996: 186–188; Thagard 1999: chapter 9). The majority of analysts explore the features of the cognitive mechanisms through which aspects of a familiar domain or source are applied to an unknown target domain in order to understand what is unknown. According to the influential multi-constraint theory of analogical reasoning developed by Holyoak and Thagard, the transfer processes involved in analogical reasoning (scientific and otherwise) are guided or constrained in three main ways: 1) by the direct similarity between the elements involved; 2) by the structural parallels between source and target domain; as well as 3) by the purposes of the investigators, i.e., the reasons why the analogy is considered. Discovery, the formulation of a new hypothesis, is one such purpose.

“In vivo” investigations of scientists reasoning in their laboratories have not only shown that analogical reasoning is a key component of scientific practice, but also that the distance between source and target depends on the purpose for which analogies are sought. Scientists trying to fix experimental problems draw analogies between targets and sources from highly similar domains. In contrast, scientists attempting to formulate new models or concepts draw analogies between less similar domains. Analogies between radically different domains, however, are rare (Dunbar 1997, 2001).

In current cognitive science, human cognition is often explored in terms of model-based reasoning. The starting point of this approach is the notion that much of human reasoning, including probabilistic and causal reasoning as well as problem solving takes place through mental modeling rather than through the application of logic or methodological criteria to a set of propositions (Johnson-Laird 1983; Magnani et al. 1999; Magnani and Nersessian 2002). In model-based reasoning, the mind constructs a structural representation of a real-world or imaginary situation and manipulates this structure. In this perspective, conceptual structures are viewed as models and conceptual innovation as constructing new models through various modeling operations. Analogical reasoning—analogical modeling—is regarded as one of three main forms of model-based reasoning that appear to be relevant for conceptual innovation in science. Besides analogical modeling, visual modeling and simulative modeling or thought experiments also play key roles (Nersessian 1992, 1999, 2009). These modeling practices are constructive in that they aid the development of novel mental models. The key elements of model-based reasoning are the call on knowledge of generative principles and constraints for physical models in a source domain and the use of various forms of abstraction. Conceptual innovation results from the creation of new concepts through processes that abstract and integrate source and target domains into new models (Nersessian 2009).

Some critics have argued that despite the large amount of work on the topic, the notion of mental model is not sufficiently clear. Thagard seeks to clarify the concept by characterizing mental models in terms of neural processes (Thagard 2010). In his approach, mental models are produced through complex patterns of neural firing, whereby the neurons and the interconnections between them are dynamic and changing. A pattern of firing neurons is a representation when there is a stable causal correlation between the pattern or activation and the thing that is represented. In this research, questions about the nature of model-based reasoning are transformed into questions about the brain mechanisms that produce mental representations.

The above sections again show that the study of scientific discovery integrates different approaches, combining conceptual analysis of processes of knowledge generation with empirical work on creativity, drawing heavily and explicitly on current research in psychology and cognitive science, and on in vivo laboratory observations, as well as brain imaging techniques (Kounios & Beeman 2009, Thagard & Stewart 2011).

Earlier critics of AI-based theories of scientific discoveries argued that a computer cannot devise new concepts but is confined to the concepts included in the given computer language (Hempel 1985: 119–120). It cannot design new experiments, instruments, or methods. Subsequent computational research on scientific discovery was driven by the motivation to contribute computational tools to aid scientists in their research (Addis et al. 2016). It appears that computational methods can be used to generate new results leading to refereed scientific publications in astrophysics, cancer research, ecology, and other fields (Langley 2000). However, the philosophical discussion has continued about the question of whether these methods really generate new knowledge or whether they merely speed up data processing. It is also still an open question whether data-intensive science is fundamentally different from traditional research, for instance regarding the status of hypothesis or theory in data-intensive research (Pietsch 2015).

In the wake of recent developments in machine learning, some older discussions about automated discovery have been revived. The availability of vastly improved computational tools and software for data analysis has stimulated new discussions about computer-generated discovery (see Leonelli 2020). It is largely uncontroversial that machine learning tools can aid discovery, for instance in research on antibiotics (Stokes et al, 2020). The notion of “robot scientist” is mostly used metaphorically, and the vision that human scientists may one day be replaced by computers – by successors of the laboratory automation systems “Adam” and “Eve”, allegedly the first “robot scientists” – is evoked in writings for broader audiences (see King et al. 2009, Williams et al. 2015, for popularized descriptions of these systems), although some interesting ethical challenges do arise from “superhuman AI” (see Russell 2021). It also appears that, on the notion that products of creative acts are both novel and valuable, AI systems should be called “creative,” an implication which not all analysts will find plausible (Boden 2014)

Philosophical analyses focus on various questions arising from the processes involving human-machine complexes. One issue relevant to the problem of scientific discovery arises from the opacity of machine learning. If machine learning indeed escapes human understanding, how can we be warranted to say that knowledge or understanding is generated by deep learning tools? Might we have reason to say that humans and machines are “co-developers” of knowledge (Tamaddoni-Nezhad et al. 2021)?

New perspectives on scientific discovery have also opened up in the context of social epistemology (see Goldman & O’Connor 2021). Social epistemology investigates knowledge production as a group process, specifically the epistemic effects of group composition in terms of cognitive diversity and unity and social interactions within groups or institutions such as testimony and trust, peer disagreement and critique, and group justification, among others. On this view, discovery is a collective achievement, and the task is to explore how assorted social-epistemic activities or practices have an impact on the knowledge generated by groups in question. There are obvious implications for debates about scientific discovery of recent research in the different branches of social epistemology. Social epistemologists have examined individual cognitive agents in their roles as group members (as providers of information or as critics) and the interactions among these members (Longino 2001), groups as aggregates of diverse agents, or the entire group as epistemic agent (e.g., Koons 2021, Dragos 2019).

Standpoint theory, for instance, explores the role of outsiders in knowledge generation, considering how the sociocultural structures and practices in which individuals are embedded aid or obstruct the generation of creative ideas. According to standpoint theorists, people with standpoint are politically aware and politically engaged people outside the mainstream. Because people with standpoint have different experiences and access to different domains of expertise than most members of a culture, they can draw on rich conceptual resources for creative thinking (Solomon 2007).

Social epistemologists examining groups as aggregates of agents consider to what extent diversity among group members is conducive to knowledge production and whether and to what extent beliefs and attitudes must be shared among group members to make collective knowledge possible (Bird 2014). This is still an open question. Some formal approaches to model the influence of diversity on knowledge generation suggest that cognitive diversity is beneficial to collective knowledge generation (Weisberg and Muldoon 2009), but others have criticized the model (Alexander et al (2015), see also Thoma (2015) and Poyhönen (2017) for further discussion).

This essay has illustrated that philosophy of discovery has come full circle. Philosophy of discovery has once again become a thriving field of philosophical study, now intersecting with, and drawing on philosophical and empirical studies of creative thinking, problem solving under uncertainty, collective knowledge production, and machine learning. Recent approaches to discovery are typically explicitly interdisciplinary and integrative, cutting across previous distinctions among hypothesis generation and theory building, data collection, assessment, and selection; as well as descriptive-analytic, historical, and normative perspectives (Danks & Ippoliti 2018, Michel 2021). The goal no longer is to provide one overarching account of scientific discovery but to produce multifaceted analyses of past and present activities of knowledge generation in all their complexity and heterogeneity that are illuminating to the non-scientist and the scientific researcher alike.

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• Published: 02 August 2023

Scientific discovery in the age of artificial intelligence

• Hanchen Wang   ORCID: orcid.org/0000-0002-1691-024X 1 , 2   na1   nAff37   nAff38 ,
• Tianfan Fu 3   na1 ,
• Yuanqi Du 4   na1 ,
• Wenhao Gao 5 ,
• Kexin Huang 6 ,
• Ziming Liu 7 ,
• Payal Chandak   ORCID: orcid.org/0000-0003-1097-803X 8 ,
• Shengchao Liu   ORCID: orcid.org/0000-0003-2030-2367 9 , 10 ,
• Peter Van Katwyk   ORCID: orcid.org/0000-0002-3512-0665 11 , 12 ,
• Andreea Deac 9 , 10 ,
• Anima Anandkumar 2 , 13 ,
• Karianne Bergen 11 , 12 ,
• Carla P. Gomes   ORCID: orcid.org/0000-0002-4441-7225 4 ,
• Shirley Ho 14 , 15 , 16 , 17 ,
• Pushmeet Kohli   ORCID: orcid.org/0000-0002-7466-7997 18 ,
• Joan Lasenby 1 ,
• Jure Leskovec   ORCID: orcid.org/0000-0002-5411-923X 6 ,
• Tie-Yan Liu 19 ,
• Arjun Manrai 20 ,
• Debora Marks   ORCID: orcid.org/0000-0001-9388-2281 21 , 22 ,
• Bharath Ramsundar 23 ,
• Le Song 24 , 25 ,
• Jimeng Sun 26 ,
• Jian Tang 9 , 27 , 28 ,
• Petar Veličković 18 , 29 ,
• Max Welling 30 , 31 ,
• Linfeng Zhang 32 , 33 ,
• Connor W. Coley   ORCID: orcid.org/0000-0002-8271-8723 5 , 34 ,
• Yoshua Bengio   ORCID: orcid.org/0000-0002-9322-3515 9 , 10 &
• Marinka Zitnik   ORCID: orcid.org/0000-0001-8530-7228 20 , 22 , 35 , 36  

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A Publisher Correction to this article was published on 30 August 2023

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Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment and accelerate research, helping scientists to generate hypotheses, design experiments, collect and interpret large datasets, and gain insights that might not have been possible using traditional scientific methods alone. Here we examine breakthroughs over the past decade that include self-supervised learning, which allows models to be trained on vast amounts of unlabelled data, and geometric deep learning, which leverages knowledge about the structure of scientific data to enhance model accuracy and efficiency. Generative AI methods can create designs, such as small-molecule drugs and proteins, by analysing diverse data modalities, including images and sequences. We discuss how these methods can help scientists throughout the scientific process and the central issues that remain despite such advances. Both developers and users of AI tools need a better understanding of when such approaches need improvement, and challenges posed by poor data quality and stewardship remain. These issues cut across scientific disciplines and require developing foundational algorithmic approaches that can contribute to scientific understanding or acquire it autonomously, making them critical areas of focus for AI innovation.

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Change history, 30 august 2023.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-023-06559-7

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Acknowledgements

M.Z. gratefully acknowledges the support of the National Institutes of Health under R01HD108794, U.S. Air Force under FA8702-15-D-0001, awards from Harvard Data Science Initiative, Amazon Faculty Research, Google Research Scholar Program, Bayer Early Excellence in Science, AstraZeneca Research, Roche Alliance with Distinguished Scientists, and Kempner Institute for the Study of Natural and Artificial Intelligence. C.P.G. and Y.D. acknowledge the support from the U.S. Air Force Office of Scientific Research under Multidisciplinary University Research Initiatives Program (MURI) FA9550-18-1-0136, Defense University Research Instrumentation Program (DURIP) FA9550-21-1-0316, and awards from Scientific Autonomous Reasoning Agent (SARA), and AI for Discovery Assistant (AIDA). Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders. We thank D. Hassabis, A. Davies, S. Mohamed, Z. Li, K. Ma, Z. Qiao, E. Weinstein, A. V. Weller, Y. Zhong and A. M. Brandt for discussions on the paper.

Author information

Hanchen Wang

Present address: Department of Research and Early Development, Genentech Inc, South San Francisco, CA, USA

Present address: Department of Computer Science, Stanford University, Stanford, CA, USA

These authors contributed equally: Hanchen Wang, Tianfan Fu, Yuanqi Du

Authors and Affiliations

Department of Engineering, University of Cambridge, Cambridge, UK

Hanchen Wang & Joan Lasenby

Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

Hanchen Wang & Anima Anandkumar

Department of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

Department of Computer Science, Cornell University, Ithaca, NY, USA

Yuanqi Du & Carla P. Gomes

Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Wenhao Gao & Connor W. Coley

Department of Computer Science, Stanford University, Stanford, CA, USA

Kexin Huang & Jure Leskovec

Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA

Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, USA

Payal Chandak

Mila – Quebec AI Institute, Montreal, Quebec, Canada

Shengchao Liu, Andreea Deac, Jian Tang & Yoshua Bengio

Université de Montréal, Montreal, Quebec, Canada

Shengchao Liu, Andreea Deac & Yoshua Bengio

Department of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI, USA

Peter Van Katwyk & Karianne Bergen

Data Science Institute, Brown University, Providence, RI, USA

NVIDIA, Santa Clara, CA, USA

Anima Anandkumar

Center for Computational Astrophysics, Flatiron Institute, New York, NY, USA

Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA

Department of Physics, Carnegie Mellon University, Pittsburgh, PA, USA

Department of Physics and Center for Data Science, New York University, New York, NY, USA

Google DeepMind, London, UK

Pushmeet Kohli & Petar Veličković

Microsoft Research, Beijing, China

Tie-Yan Liu

Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

Arjun Manrai & Marinka Zitnik

Department of Systems Biology, Harvard Medical School, Boston, MA, USA

Debora Marks

Broad Institute of MIT and Harvard, Cambridge, MA, USA

Debora Marks & Marinka Zitnik

Deep Forest Sciences, Palo Alto, CA, USA

Bharath Ramsundar

BioMap, Beijing, China

Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates

University of Illinois at Urbana-Champaign, Champaign, IL, USA

HEC Montréal, Montreal, Quebec, Canada

CIFAR AI Chair, Toronto, Ontario, Canada

Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

Petar Veličković

University of Amsterdam, Amsterdam, Netherlands

Max Welling

Microsoft Research Amsterdam, Amsterdam, Netherlands

DP Technology, Beijing, China

Linfeng Zhang

AI for Science Institute, Beijing, China

Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

Connor W. Coley

Harvard Data Science Initiative, Cambridge, MA, USA

Marinka Zitnik

Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA, USA

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All authors contributed to the design and writing of the paper, helped shape the research, provided critical feedback, and commented on the paper and its revisions. H.W., T.F., Y.D. and M.Z conceived the study and were responsible for overall direction and planning. W.G., K.H. and Z.L. contributed equally to this work (equal second authorship) and are listed alphabetically.

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Essay on Science and Technology for Students and Children

500+ words essay on science and technology.

Essay on Science and Technology: Science and technology are important parts of our day to day life. We get up in the morning from the ringing of our alarm clocks and go to bed at night after switching our lights off. All these luxuries that we are able to afford are a resultant of science and technology . Most importantly, how we can do all this in a short time are because of the advancement of science and technology only. It is hard to imagine our life now without science and technology. Indeed our existence itself depends on it now. Every day new technologies are coming up which are making human life easier and more comfortable. Thus, we live in an era of science and technology.

Essentially, Science and Technology have introduced us to the establishment of modern civilization . This development contributes greatly to almost every aspect of our daily life. Hence, people get the chance to enjoy these results, which make our lives more relaxed and pleasurable.

Benefits of Science and Technology

If we think about it, there are numerous benefits of science and technology. They range from the little things to the big ones. For instance, the morning paper which we read that delivers us reliable information is a result of scientific progress. In addition, the electrical devices without which life is hard to imagine like a refrigerator, AC, microwave and more are a result of technological advancement.

Furthermore, if we look at the transport scenario, we notice how science and technology play a major role here as well. We can quickly reach the other part of the earth within hours, all thanks to advancing technology.

In addition, science and technology have enabled man to look further than our planet. The discovery of new planets and the establishment of satellites in space is because of the very same science and technology. Similarly, science and technology have also made an impact on the medical and agricultural fields. The various cures being discovered for diseases have saved millions of lives through science. Moreover, technology has enhanced the production of different crops benefitting the farmers largely.

Get the huge list of more than 500 Essay Topics and Ideas

India and Science and Technology

Ever since British rule, India has been in talks all over the world. After gaining independence, it is science and technology which helped India advance through times. Now, it has become an essential source of creative and foundational scientific developments all over the world. In other words, all the incredible scientific and technological advancements of our country have enhanced the Indian economy.

Looking at the most recent achievement, India successfully launched Chandrayaan 2. This lunar exploration of India has earned critical acclaim from all over the world. Once again, this achievement was made possible due to science and technology.

In conclusion, we must admit that science and technology have led human civilization to achieve perfection in living. However, we must utilize everything in wise perspectives and to limited extents. Misuse of science and technology can produce harmful consequences. Therefore, we must monitor the use and be wise in our actions.

{ “@context”: “https://schema.org”, “@type”: “FAQPage”, “mainEntity”: [{ “@type”: “Question”, “name”: “List some benefits of science and technology.”, “acceptedAnswer”: { “@type”: “Answer”, “text”: “Science and Technology helps us to function daily comfortably. It has given us railway systems, TV, refrigerator, internet and more.” } }, { “@type”: “Question”, “name”: “Name the most recent achievement of India with the help of science and technology.”, “acceptedAnswer”: { “@type”: “Answer”, “text”:”India most recently launched Chandrayaan 2 successfully. This lunar exploration helped India make a distinctive place amongst the other developed countries.”} }] }

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InBrief: The Science of Early Childhood Development

This brief is part of a series that summarizes essential scientific findings from Center publications.

Content in This Guide

Step 1: why is early childhood important.

• : Brain Hero
• : The Science of ECD (Video)
• You Are Here: The Science of ECD (Text)

Step 2: How Does Early Child Development Happen?

• : 3 Core Concepts in Early Development
• : 8 Things to Remember about Child Development
• : InBrief: The Science of Resilience

Step 3: What Can We Do to Support Child Development?

• : From Best Practices to Breakthrough Impacts
• : 3 Principles to Improve Outcomes

The science of early brain development can inform investments in early childhood. These basic concepts, established over decades of neuroscience and behavioral research, help illustrate why child development—particularly from birth to five years—is a foundation for a prosperous and sustainable society.

Brains are built over time, from the bottom up.

The basic architecture of the brain is constructed through an ongoing process that begins before birth and continues into adulthood. Early experiences affect the quality of that architecture by establishing either a sturdy or a fragile foundation for all of the learning, health and behavior that follow. In the first few years of life, more than 1 million new neural connections are formed every second . After this period of rapid proliferation, connections are reduced through a process called pruning, so that brain circuits become more efficient. Sensory pathways like those for basic vision and hearing are the first to develop, followed by early language skills and higher cognitive functions. Connections proliferate and prune in a prescribed order, with later, more complex brain circuits built upon earlier, simpler circuits.

The interactive influences of genes and experience shape the developing brain.

Scientists now know a major ingredient in this developmental process is the “ serve and return ” relationship between children and their parents and other caregivers in the family or community. Young children naturally reach out for interaction through babbling, facial expressions, and gestures, and adults respond with the same kind of vocalizing and gesturing back at them. In the absence of such responses—or if the responses are unreliable or inappropriate—the brain’s architecture does not form as expected, which can lead to disparities in learning and behavior.

The brain’s capacity for change decreases with age.

The brain is most flexible, or “plastic,” early in life to accommodate a wide range of environments and interactions, but as the maturing brain becomes more specialized to assume more complex functions, it is less capable of reorganizing and adapting to new or unexpected challenges. For example, by the first year, the parts of the brain that differentiate sound are becoming specialized to the language the baby has been exposed to; at the same time, the brain is already starting to lose the ability to recognize different sounds found in other languages. Although the “windows” for language learning and other skills remain open, these brain circuits become increasingly difficult to alter over time. Early plasticity means it’s easier and more effective to influence a baby’s developing brain architecture than to rewire parts of its circuitry in the adult years.

Cognitive, emotional, and social capacities are inextricably intertwined throughout the life course.

The brain is a highly interrelated organ, and its multiple functions operate in a richly coordinated fashion. Emotional well-being and social competence provide a strong foundation for emerging cognitive abilities, and together they are the bricks and mortar that comprise the foundation of human development. The emotional and physical health, social skills, and cognitive-linguistic capacities that emerge in the early years are all important prerequisites for success in school and later in the workplace and community.

Toxic stress damages developing brain architecture, which can lead to lifelong problems in learning, behavior, and physical and mental health.

Scientists now know that chronic, unrelenting stress in early childhood, caused by extreme poverty, repeated abuse, or severe maternal depression, for example, can be toxic to the developing brain. While positive stress (moderate, short-lived physiological responses to uncomfortable experiences) is an important and necessary aspect of healthy development, toxic stress is the strong, unrelieved activation of the body’s stress management system. In the absence of the buffering protection of adult support, toxic stress becomes built into the body by processes that shape the architecture of the developing brain.

Policy Implications

• The basic principles of neuroscience indicate that early preventive intervention will be more efficient and produce more favorable outcomes than remediation later in life.
• A balanced approach to emotional, social, cognitive, and language development will best prepare all children for success in school and later in the workplace and community.
• Supportive relationships and positive learning experiences begin at home but can also be provided through a range of services with proven effectiveness factors. Babies’ brains require stable, caring, interactive relationships with adults — any way or any place they can be provided will benefit healthy brain development.
• Science clearly demonstrates that, in situations where toxic stress is likely, intervening as early as possible is critical to achieving the best outcomes. For children experiencing toxic stress, specialized early interventions are needed to target the cause of the stress and protect the child from its consequences.

Suggested citation: Center on the Developing Child (2007). The Science of Early Childhood Development (InBrief). Retrieved from www.developingchild.harvard.edu .

Related Topics: toxic stress , brain architecture , serve and return

Explore related resources.

• Reports & Working Papers
• Tools & Guides
• Presentations
• Infographics

Videos : Serve & Return Interaction Shapes Brain Circuitry

Reports & Working Papers : From Best Practices to Breakthrough Impacts

Briefs : InBrief: The Science of Neglect

Videos : InBrief: The Science of Neglect

Reports & Working Papers : The Science of Neglect: The Persistent Absence of Responsive Care Disrupts the Developing Brain

Reports & Working Papers : Young Children Develop in an Environment of Relationships

Tools & Guides , Briefs : 5 Steps for Brain-Building Serve and Return

Briefs : 8 Things to Remember about Child Development

Partner Resources : Building Babies’ Brains Through Play: Mini Parenting Master Class

Podcasts : About The Brain Architects Podcast

Videos : FIND: Using Science to Coach Caregivers

Videos : How-to: 5 Steps for Brain-Building Serve and Return

Briefs : How to Support Children (and Yourself) During the COVID-19 Outbreak

Videos : InBrief: The Science of Early Childhood Development

Partner Resources , Tools & Guides : MOOC: The Best Start in Life: Early Childhood Development for Sustainable Development

Presentations : Parenting for Brain Development and Prosperity

Videos : Play in Early Childhood: The Role of Play in Any Setting

Videos : Child Development Core Story

Videos : Science X Design: Three Principles to Improve Outcomes for Children

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Podcasts : The Brain Architects Podcast: Serve and Return: Supporting the Foundation

Videos : Three Core Concepts in Early Development

Reports & Working Papers : Three Principles to Improve Outcomes for Children and Families

Partner Resources , Tools & Guides : Training Module: “Talk With Me Baby”

Infographics : What Is COVID-19? And How Does It Relate to Child Development?

Partner Resources , Tools & Guides : Vroom

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Add a method, remove a method, edit datasets, gamedevdojo -- an educational game for teaching game development concepts.

4 Jul 2024  ·  Michael Holly , Lisa Habich , Johanna Pirker · Edit social preview

Computer Science (CS) has experienced significant growth and diversification in recent decades. However, there is a lack of diversity in CS learning approaches. Traditional teaching methods and hands-on learning dominate this field, with limited use of playful and interactive learning methods such as educational games. This gap is particularly evident in game development as a subfield of CS. To address this problem, we present a game-based learning approach to teach foundational concepts for game development. The paper aims to expand the educational landscape within CSE, offering a unique and engaging platform for learners to explore the intricacies of game creation by integrating gamified learning strategies. In this paper, we investigate the user's learning experience and motivation, and the differences between traditional learning and game-based learning methods for teaching game development concepts. The study involves 57 participants in an AB test to assess learners' motivation, user experience, and learning outcomes. The results indicate a significantly increased learning outcome for the game-based learning approach, as well as higher motivation in learning game development concepts.

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High Purity Silica Mineral Systems Study

Last updated: 22 August 2024

Home Geoscience Australia > Scientific topics > Minerals > Australian Critical Minerals Research and Development Hub > High Purity Silica Mineral Systems Study High Purity Silica Mineral Systems Study

Geoscience Australia is undertaking a detailed mineral potential study of High Purity Silica (HPS) to identify the favourable geology and regions across Australia that have the greatest potential to supply raw material suitable for the production of silicon.

Why are we researching High Purity Silica?

High purity silica is the only naturally occurring and economically viable feedstock for the production of silicon. Silicon, a critical mineral , is the key component in advanced modern technologies such as semiconductors and solar photovoltaic cells.

The global transition towards a net-zero carbon future means there is a growing demand for new discoveries of HPS to meet the needs of the ever-expanding silicon production industry.

While silicon is among the most plentiful elements in the Earth’s crust, it does not occur naturally in its elemental form and is commonly found in various silicate minerals. Quartz (SiO 2 ), the second most common mineral on Earth, is the primary form of silica and the main source of silicon production.

Despite its widespread presence, finding silica with the required purity needed to develop high-tech applications remains a challenge due to its rarity. For semiconductor and solar cell production, the silica used must exceed 98% SiO 2 purity.

We are researching high purity silica to help gain an understanding about the geological factors that control the production of high purity silica in Australia and predict where their occurrences can be found.

Geoscientists assessing quartz in the Mount Isa Cloncurry region

What are we doing?

As part of this project Geoscience Australia is:

• Undertaking a summary review of the state of the HPS industry, exploration and deposit styles in Australia.
• Delivering a mineral potential assessment to identify the regions of Australia that have the greatest potential for the discovery of suitable HPS deposits.
• Defining controls on targeted HPS mineral systems and determining relevant datasets to support the mineral potential assessments.
• Identifying required analytical techniques to deliver an ‘Explorer’s Toolbox’ that will guide explorers through the analytical processes required to discover and define high-quality mineral deposits.

To complement this work, ANSTO will be developing processing routes for High Purity Quartz (HPQ) production from Australian quartz and silica sand projects.

HPQ is used for the manufacture of the consumables (fused quartz products) required in elemental silicon production, plus a range of optical and specialty applications.

ANSTO’s project will provide access to the technologies and know-how required for HPQ production, in particular, high temperature chlorination for all future Australian projects.

What is Geoscience Australia’s goal?

Our goal is to identify mineral systems and regions that have the greatest potential to supply raw material suitable to support the production of silicon, stimulating exploration and supporting the development of a downstream silicon industry in Australia.

Find out more

Data and publications.

• Jennings, A., Senior, A., Guerin, K., Main, P., & Walsh, J. (2024). A review of high-purity quartz for silicon production in Australia. Australian Journal of Earth Sciences, 1–13. https://doi.org/10.1080/08120099.2024.2362296

Conference papers and presentations:

• 16-17 April 2024: GEMIS: Annual Geoscience Exploration Seminar (AGES) Proceedings, Alice Springs, Northern Territory
• 16-17 April 2024: Annual Geoscience Exploration Seminar (AGES). Presentations and posters
• 28 March 2024: Geoscience Australia Wednesday Seminar: Quartz - the unsuspected mineral