Cognitive Science

Relationship of AI to Psychology and Neuroscience

AITopics > Cognitive Science

AI Magazine cover: Herbert Simon
Herbert Simon

"AI can have two purposes. One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. Robotics and expert systems are major branches of that. The other is to use a computer's artificial intelligence to understand how humans think. In a humanoid way. If you test your programs not merely by what they can accomplish, but how they accomplish it, then you're really doing cognitive science; you're using AI to understand the human mind."
- Herbert Simon: from Doug Stewart's Interview with Herbert Simon

"From its inception, the cognitive revolution was guided by a metaphor: the mind is like a computer. We are a set of software programs running on 3 pounds of neural hardware. And cognitive psychologists were interested in the software. The computer metaphor helped stimulate some crucial scientific breakthroughs. It led to the birth of artificial intelligence and helped make our inner life a subject suitable for science. ... For the first time, cognitive psychologists were able to simulate aspects of human thought. At the seminal MIT symposium, held on Sept. 11, 1956, Herbert Simon and Allen Newell announced that they had invented a 'thinking machine' -- basically a room full of vacuum tubes -- capable of solving difficult logical problems. "
- Jonah Lehrer

Definition of the Area

What is Cognitive Science. From Cognitive Science Major at UC Berkeley. "Cognitive Science is an interdisciplinary field that has arisen during the past decade at the intersection of a number of existing disciplines, including psychology, linguistics, computer science, philosophy, and physiology. The shared interest that has produced this coalition is understanding the nature of the mind. This quest is an old one, dating back to antiquity in the case of philosophy, but new ideas are emerging from the fresh approach of Cognitive Science."

Cognitive Science entry in The Stanford Encyclopedia of Philosophy. By Paul Thagard. A solid overview plus links for further study: "Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology. Its intellectual origins are in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures."

Good Starting Places

Cognitive Science. By William J. Rapaport. Draft of the article in Encyclopedia of Computer Science, 4th edition; Anthony Ralston, Edwin D. Reilly, and David Hemmindinger, editors (New York: Grove's Dictionaries, 2000): 227 - 233. (PDF file) "The notion that mental states and processes intervene between stimuli and responses sometimes takes the form of a 'computational' metaphor or analogy, which is often used as the identifying mark of contemporary cognitive science: The mind is to the brain as software is to hardware; mental states and processes are (like) computer programs implemented (in the case of humans) in brain states and processes. ... Insofar as the methods of investigation are taken to be computational in nature, computer science in general and artificial intelligence in particular have come to play a central role in cognitive science."

A Brief History of Decision Making - Humans have perpetually sought new tools and insights to help them make decisions. From entrails to artificial intelligence, what a long, strange trip it's been. By Leigh Buchanan and Andrew O'Connell. Harvard Business Review (January 2006). "Future Nobel laureate Herbert Simon, Allen Newell, Harold Guetzkow, Richard M. Cyert, and James March were among the [Carnegie Institute of Technology] scholars who shared a fascination with organizational behavior and the workings of the human brain. The philosopher's stone that alchemized their ideas was electronic computing. By the mid-1950s, transistors had been around less than a decade, and IBM would not launch its groundbreaking 360 mainframe until 1965. But already scientists were envisioning how the new tools might improve human decision making. The collaborations of these and other Carnegie scientists, together with research by Marvin Minsky at the Massachusetts Institute of Technology and John McCarthy of Stanford, produced early computer models of human cognition -- the embryo of artificial intelligence. AI was intended both to help researchers understand how the brain makes decisions and to augment the decision-making process for real people in real organizations."

General Readings

Decision Time (February 23, 2009), Jonah Lehrer, BBC News Magazine. "So how should we make a decision? The key is something called metacognition, or thinking about thinking. ... It doesn't matter if we're choosing between mutual funds or political candidates. We might be playing poker or football. The best way to make sure that you are using your brain properly is to study your brain at work. Why is thinking about thinking so important? First, it helps us avoid stupid errors. You can't avoid loss aversion unless you know that the mind treats losses differently than gains. And you'll probably think too much about buying a house unless you know that such a strategy will lead you to buy the property. The mind is full of flaws, but we can outsmart them. There is no secret recipe for decision-making. But learning about how we think can help us think better. "

Hearts & Minds - Since Plato, scholars have drawn a clear distinction between thinking and feeling. Now science suggests that our emotions are what make thought possible. By Jonah Lehrer. The Boston Globe (April 29, 2007). "Just over 50 years ago, a group of brash young scholars at an MIT symposium introduced a series of ideas that would forever alter the way we think about how we think. In three groundbreaking papers, including one on grammar by a 27-year-old linguist named Noam Chomsky, the scholars ignited what is now known as the cognitive revolution, which was built on the radical notion that it is possible to study, with scientific precision, the actual processes of thought. The movement eventually freed psychology from the grip of behaviorism, a scientific movement popular in America that studied behavior as a proxy for understanding the mind. ... 'Because we subscribed to this false ideal of rational, logical thought, we diminished the importance of everything else,' said Marvin Minsky, a professor at MIT and pioneer of artificial intelligence. 'Seeing our emotions as distinct from thinking was really quite disastrous.' ... From its inception, the cognitive revolution was guided by a metaphor: the mind is like a computer. We are a set of software programs running on 3 pounds of neural hardware. And cognitive psychologists were interested in the software. The computer metaphor helped stimulate some crucial scientific breakthroughs. It led to the birth of artificial intelligence and helped make our inner life a subject suitable for science. For the first time, cognitive psychologists were able to simulate aspects of human thought. At the seminal MIT symposium, held on Sept. 11, 1956, Herbert Simon and Allen Newell announced that they had invented a 'thinking machine' -- basically a room full of vacuum tubes -- capable of solving difficult logical problems. (In one instance, the machine even improved on the work of Bertrand Russell.) ... But the computer metaphor was misleading, at least in one crucial respect. Computers don't have feelings. Feelings didn't fit into the preferred language of thought. Because our emotions weren't reducible to bits of information or logical structures, cognitive psychologists diminished their importance. ... This new science of emotion has brought a new conception of what it means to think, and, in some sense, a rediscovery of the unconscious. ... The lasting influence of the cognitive revolution is apparent in the language used by neuroscientists when describing the mind. For example, the unconscious is often described as a massive computer, processing millions of bits of information per second. Emotions emerge from this activity."

Artificial Intelligence Tutorial Review. From Eyal Reingold and Johnathan Nightingale at the University of Toronto. "Welcome to the PSY371 Artificial Intelligence tutorial review. These pages were developed for the use of psychology students interested in the field of Artificial Intelligence, especially as it relates to the ongoing investigations in psychology aimed at understanding the human mind....This review has been designed with the expectation that its readers are new to the area, and care is taken to explain concepts fully. The review should provide an interesting and accessible introduction for beginners, but may be somewhat redundant for readers with more background in the area. Nevertheless, more advanced readers may find interesting links and demonstrations throughout the review. Also, in hopes of keeping the tutorial accessible, many of the more technical issues in AI have been simplified or avoided, with more emphasis being put on conceptual developments and interactive examples."

CCRG, the Cognitive Computing Research Group at The University of Memphis. "An autonomous agent senses and acts upon its environment in the service of its own agenda. An autonomous agent with human-like cognitive capabilities is called a cognitive agent. By a 'conscious' software agent, we mean one designed within the constraints of Bernard Baars' Global Workspace Theory of consciousness and cognition. The CCRG’s research revolves around the design and implementation of cognitive, sometimes "conscious," software agents, their computational applications, and their use in cognitive modeling." Be sure to see their:

  • Glossary
  • Tutorials: "The Introduction provides a brief account of why one might want to devote time to this tutorial. The Brief Tutorial displays the LIDA Cognitive Cycle diagram. Clicking on the name of a module or process in the list on the left focuses the display and provides explanatory text. The Full Tutorial consists mostly of PowerPoint presentations prepared by Stan Franklin for a class on How Minds Work during Spring 2005 together with audio versions of his lectures."

Minsky talks about life, love in the age of artificial intelligence. By Carey Goldberg. The Boston Globe (December 4, 2006). "[B]ut to actually build machines like ourselves, we'll need to develop more theories about the kinds of resources that human minds use. Researchers in the field called artificial intelligence have already developed ways to make separate machines that can do various things that people can do. What's new in this book is that it suggests a new way to combine those older ideas.

However, there still is much more that we'll need to do before we can make machines that are as resourceful as we are, so this project will need some more years of support. ...

Also, if we succeed at this, we'll develop new ideas about what happens inside our own minds -- and this should show us ways to improve some of our own ancient ways to think, as well as to enhance and extend the abilities of the machines we make."

Understand the cogs, understand AI - The future of AI is here and it's cognitive. Robert Hecht-Nielson, professor at the University of California and vice president of the fair Isaac Corporation, has discovered the universal mechanism of animal cognition and is now developing automated conversational customer service systems with human-level capabilities for use in a variety of industries. Interviewed by Justin Richards, British Computer Society (October 2007). "[H-N] ... since 1968 my passion has been understanding how cognition works and this is something I got into as a mathematics student, so I've always been leaning in the direction of trying to understand these things from underlying mathematical principles as implemented by neural tissue. ... [JR] Can you explain your theory? [H-N] ... The first part of it is that we have to have someway of representing the world in the brain, so it explains how that works exactly and it explains how those representations are used to carry out cognition. It also explains how knowledge arises and what knowledge is, specifically. ... The fact is that if you have a detailed comprehensive theory of how that works then you should be able to take that theory and apply it to information outside of the brain using a computer simulation. ... [JR] What sort of experimentation have you done to assess your theory? ... [JR] How does this relate to AI? ... "

Brainy Robots Start Stepping Into Daily Life. By John Markoff. The New York Times (July 18, 2006). "Today some scientists are beginning to use the term cognitive computing, to distinguish their research from an earlier generation of artificial intelligence work. What sets the new researchers apart is a wealth of new biological data on how the human brain functions. 'There’s definitely been a palpable upswing in methods, competence and boldness,' said Eric Horvitz, a Microsoft researcher who is president-elect of the American Association for Artificial Intelligence. ... 'There is a new synthesis of four fields, including mathematics, neuroscience, computer science and psychology,' said Dharmendra S. Modha, an I.B.M. computer scientist. 'The implication of this is amazing. What you are seeing is that cognitive computing is at a cusp where it’s knocking on the door of potentially mainstream applications.'"

Reverse-Engineering the Brain - At MIT, neuroscience and artificial intelligence are beginning to intersect. By Fred Hapgood. Technology Review (July 11, 2006). "'Maggie is a very smart monkey,' says Tim Buschman, a graduate student in Professor Earl Miller's neuroscience lab. Maggie isn't visible -- she's in a biosafety enclosure meant to protect her from human germs -- but the signs of her intelligence flow over two monitors in front of Buschman. For the last seven years, Maggie has 'worked' for MIT's Department of Brain and Cognitive Sciences (BCS). Three hours a day, the macaque plays computer games that (usually) are designed to require her to generate abstract representations and then use those abstractions as tools. 'Even I have trouble with this one,' Buschman says, nodding at a game that involves classifying logical operations. But Maggie is on a roll, slamming through problems, taking about half a second for each and getting about four out of five right. Maggie's gaming lies at the intersection of artificial intelligence (AI) and neuroscience. Under the tutelage of Buschman and Michelle Machon, another graduate student, she is contributing to research on how the brain learns and constructs logical rules, and how its performance of those tasks compares with that of the artificial neural networks used in AI."

USC's Michael Arbib. By Eric Smalley. Technology Research News (October 3, 2005). "Technology Research News Editor Eric Smalley carried out an email conversation with Michael Arbib, the Fletcher Jones Professor of Computer Science and a Professor of Biological Sciences, Biomedical Engineering, Electrical Engineering, and Neuroscience and Psychology at the University of Southern California (USC) in September 2005. ... Throughout his career Arbib has encouraged an interdisciplinary environment where computer scientists and engineers can talk to neuroscientists and cognitive scientists."

The Prospects for AI. Listen to this panel discussion with Neil Jacobstein, Patrick Lincoln, Peter Norvig and Bruno Olshausen recorded on September 17, 2005 at the Accelerating Change 2005 conference and made available by IT Conversations: "If we are to make progress in building truly intelligent systems, Olshausen says we need to turn our efforts toward understanding how intelligence arises within the brain."

Cognitive Theory and System Design. Just one of the pages in Mind Models: Artificial Intelligence Discovery At Carnegie Mellon, an online exhibit from Carnegie Mellon's University Archives. "For a half century, Carnegie Mellon University has been a leader in the research and design of artificial intelligence (AI) - the creation of 'thinking machines'. Many of CMU's achievements came from pioneering work by professors Herbert A. Simon and Allen Newell."

Intelligent Systems and their Societies: an e-book. By Walter Fritz. "We can look at human beings from many points of view, as biological beings, employees, fathers, or mothers, but when we look at the decision process for selecting an action, we should view them as intelligent systems. Analyzing artificial intelligent systems gives us a new understanding of both human intelligence and other intelligences. However, it is difficult to study the mind with a similar one--namely ours. We need a better mirror. As you will see, in artificial intelligent systems we have this mirror...." - from the Foreword.

SOAR. "Soar means different things to different people, but it can basically be considered in three different ways: 1. A theory of cognition. As such it provides the principles behind the implemented Soar system. 2. A set of principles and constraints on (cognitive) processing. Thus, it provides a (cognitive) architectural framework, within which you can construct cognitive models. In this view it can be considered as an integrated architecture for knowledge-based problem solving, learning and interacting with external environments. 3. An AI programming language." FAQ (G1) What is Soar?, from the Soar Frequently Asked Question List. Maintained by Frank E. Ritter and Jong W. Kim.

The 100 Most Influential Works in Cognitive Science from the 20th Century as selected by a panel of judges who are both faculty of the University of Minnesota and members of its Center for Cognitive Sciences.

A Computational Foundation for the Study of Cognition. By David J. Chalmers, Department of Philosophy, University of Arizona. (1994). "A careful analysis of computation and its relation to cognition suggests that the ambitions of artificial intelligence and the centrality of computation in cognitive science are justified."

Want to Remember Everything You'll Ever Learn? Surrender to This Algorithm. By Gary Wolf. Wired Magazine: 16.05. April 21, 2008. " Piotr Wozniak ... [is] the inventor of a technique to turn people into geniuses. A portion of this technique, embodied in a software program called SuperMemo, has enthusiastic users around the world. ... SuperMemo is based on the insight that there is an ideal moment to practice what you've learned. Practice too soon and you waste your time. Practice too late and you've forgotten the material and have to relearn it. The right time to practice is just at the moment you're about to forget. Unfortunately, this moment is different for every person and each bit of information. ... [SuperMemo is based on findings on the spacing effect by Hermann Ebbinghaus.] Ebbinghaus showed that it's possible to dramatically improve learning by correctly spacing practice sessions. On one level, this finding is trivial; all students have been warned not to cram. But the efficiencies created by precise spacing are so large, and the improvement in performance so predictable, that from nearly the moment Ebbinghaus described the spacing effect, psychologists have been urging educators to use it to accelerate human progress. "

Cognitive Science issue of Crossroads (Winter 2003 - 10.2). Articles include:

  • At the Crossroads of Computers and the Mind. Introduction to the special issue by Ronald Laurids Boring. "First, what exactly is cognitive science? Cognitive science is the study of the mind. The problem is that mind means different things to different people. To a computer scientist, the mind might be something that can be simulated through software or hardware. So, cognitive science would be synonymous with artificial intelligence. On the other hand, to a cognitive psychologist, the mind is the key to understanding human or animal behavior. To a cognitive neuroscientist, the mind is about the brain and its neurological underpinnings. To a philosopher of mind, cognitive science is the culmination of thousands of years of philosophical tradition. To a cognitive linguist, cognitive science is about how thinking and language interact. The list goes on."
  • A Day in the Life of... Douglas Hofstadter. "Most people in cognitive science have no concept of how deep microworlds can be, because some years ago it was unfortunately very trendy to pooh-pooh them, and many people fell for the propaganda that microworlds were outmoded and couldn't provide deep insight into thinking. How wrong they were!"
  • The Humanoid Robot Cog. By Naveed Ahmad. "COG was designed and built to emulate human thought processes and experience the world as a human. Brooks and his team further assumed that people would find it easier to interact with a robot and aid the robot in its learning process when the robot could respond in a somewhat human way. Consequently, the machine should have limbs, sensory organs, and a physical resemblance to humans. Unlike other artificial intelligence systems, like medical expert systems, COG was meant to test theories of human cognition and developmental psychology."

Christopher Longuet-Higgins - Cognitive scientist with a flair for chemistry. Obituary by Chris Darwin.The Guardian (June 10, 2004). "Christopher Longuet-Higgins, who has died aged 80, was not only a brilliant scientist in two distinct areas - theoretical chemistry and cognitive science - but also a gifted amateur musician, keen to advance the scientific understanding of the art. ... In 1967, as a result of a growing interest in the brain and the new field of artificial intelligence, Christopher made a dramatic change in direction and moved to Edinburgh to co-found the department of machine intelligence and perception, together with Richard Gregory and Donald Michie. It was Christopher who, in 1973, was the first to name this field more broadly as 'cognitive science'."

What Are Intelligence? And Why? 1996 AAAI Presidential Address by Randall Davis. AI Magazine, 19(1): Spring 1998, 91-111. (Also available from the AAAI collection of Presidential Addresses.) "This article, derived from the 1996 American Association for Artificial Intelligence Presidential Address, explores the notion of intelligence from a variety of perspectives and finds that it 'are' many things. It has, for example, been interpreted in a variety of ways even within our own field, ranging from the logical view (intelligence as part of mathematical logic) to the psychological view (intelligence as an empirical phenomenon of the natural world) to a variety of others. One goal of this article is to go back to basics...."

How Can Psychology Help Artificial Intelligence? By Alvaro del Val. Interfaces da Psicologia, University of Evora, Portugal (1999). "In particular, I'll suggest that cognitive psychology, in order to be useful to AI, needs to study common-sense knowledge and reasoning in realistic settings; and to focus less in errors in performance in favour of studying how people do well the things they do well. " [Other formats can be accessed from this paper's entry in CiteSeer.]

The robot that thinks like you... Scientists built a robot that thinks like we do and set it loose to explore the world. New Scientist discovers what happened next By Douglas Fox. New Scientist (November 5, 2005; subscription req'd.; Issue 2524). "The infant I am watching wander around its rather spartan playpen in the Neurosciences Institute (NSI) in La Jolla, California, is a more limited creature. It is a trashcan-shaped robot called Darwin VII, and it has just 20,000 brain cells. Despite this, it has managed to master the abilities of a 18-month-old baby -- a pretty impressive feat for a machine. ... Darwin VII is the fourth in a series of robots that Jeff Krichmar and his colleagues at NSI have created in a quest to better understand how our own brains work. ... The idea of an artificial neural network that could perform computations was proposed as long ago as 1943, by Warren McCullough and Walter Pitts at the University of Illinois. ... [I]n the past few years, neuroscientists and AI researchers have started collaborating more closely, and their labours are beginning to bear fruit. Their conclusions challenge two decades of research into artificial neural networks."

Safe and Sound: Artificial Intelligence in Hazardous Applications. By John Fox and Subrata Das. AAAI Press. The following excerpt is from the Preface: "This book is about the nature of cognition, both natural and artificial. It has grown out of a program of research into intelligent functions like reasoning, problem solving and decisionmaking. These are well-established research topics, but our program is unusual in its focus on the integration of these and related cognitive processes. Many cognitive scientists seek a unified theory of their subject matter but, as in many other fields of scientific enquiry, the discipline tends to fragment into more and more specialist areas and unification eludes us. Our long-term aim is to develop intellectual and methodological tools that will foster a unified cognitive science."

Mechanical Mind. Gilbert Harman reviews "Mind as Machine: A History of Cognitive Science," by Margaret A. Boden (Oxford University Press, 2006). American Scientist Online [January / Ferbruary 2008]. "The term cognitive science, which gained currency in the last half of the 20th century, is used to refer to the study of cognition -- cognitive structures and processes in the mind or brain, mostly in people rather than, say, rats or insects. Cognitive science in this sense has reflected a growing rejection of behaviorism in favor of the study of mind and 'human information processing.' The field includes the study of thinking, perception, emotion, creativity, language, consciousness and learning. Sometimes it has involved writing (or at least thinking about) computer programs that attempt to model mental processes or that provide tools such as spreadsheets, theorem provers, mathematical-equation solvers and engines for searching the Web. The programs might involve rules of inference or 'productions,' 'mental models,' connectionist 'neural' networks or other sorts of parallel 'constraint satisfaction' approaches. Cognitive science so understood includes cognitive neuroscience, artificial intelligence (AI), robotics and artificial life; conceptual, linguistic and moral development; and learning in humans, other animals and machines. ... Boden's goal, she says, is to show how cognitive scientists have tried to find computational or informational answers to frequently asked questions about the mind -- 'what it is, what it does, how it works, how it evolved, and how it's even possible.' How do our brains generate consciousness? Are animals or newborn babies conscious? Can machines be conscious? If not, why not? How is free will possible, or creativity? How are the brain and mind different? What counts as a language? ... The first five chapters present the historical background of the field, delving into such topics as cybernetics and feedback, and discussing important figures such as René Descartes, Immanuel Kant, Charles Babbage, Alan Turing and John von Neumann, as well as Warren McCulloch and Walter Pitts, who in 1943 cowrote a paper on propositional calculus, Turing machines and neuronal synapses. ... Chapter 6 introduces the founding personalities of cognitive science from the 1950s. ... Herbert Simon and Allen Newell developed a computer program for proving logic theorems.

Talking Heads ... A Review of Speaking Minds: Interviews with Twenty Eminent Cognitive Scientists. By Patrick J. Hayes and Kenneth M. Ford. AI Magazine 18(2): Summer 1997, 123-125.

The Real Transformers - Researchers are programming robots to learn in humanlike ways and show humanlike traits. Could this be the beginning of robot consciousness -- and of a better understanding of ourselves? By Robin Marantz Henig. The New York Times Sunday Magazine (July 29, 2007 cover story). "'We’re all machines,' [Rodney Brooks] told me when we talked in his office at M.I.T. 'Robots are made of different sorts of components than we are -- we are made of biomaterials; they are silicon and steel -- but in principle, even human emotions are mechanistic.' A robot’s level of a feeling like sadness could be set as a number in computer code, he said. But isn’t a human’s level of sadness basically a number, too, just a number of the amounts of various neurochemicals circulating in the brain? Why should a robot’s numbers be any less authentic than a human’s? ... 'I want to understand what it is that makes living things living,' Rodney Brooks told me. At their core, robots are not so very different from living things. 'It’s all mechanistic,' Brooks said."

Brain and Cognitive Sciences courses available from MIT OpenCourseWare, "a free and open educational resource for faculty, students, and self-learners around the world. OCW supports MIT's mission to advance knowledge and education, and serve the world in the 21st century." include:

Humans That Think: A Future Trialogue. By Pamela McCorduck. AI Magazine 4(3): Fall 1983, 35. " We can expect, then, a conference such as this in fifty years ( a hundred years, no need to frame it precisely) to feature as its centerpiece a panel discussion among computers on the fascinating topic of whether humans can really be said to think. Picture three computers, named for no particular reason, Edward, Marvin and Seymour, debating before a learned group such as yourselves."

Creating a Robot Culture - An Interview with Luc Steels. The well-known researcher shares his views on the Turing test, robot evolution, and the quest to understand intelligence. By Tyrus L. Manuel. IEEE Intelligent Systems (May/June 2003). "Computers and robots are used as experimental platforms for investigating issues about intelligence. Researchers who are motivated in this way, and I am one of them, try to make contributions to biology or the cognitive sciences. ... AI has had an enormous impact on how we think today about the brain and the mechanisms underlying cognitive behavior."

Programs of the Mind. Review by Gary Marcus. Science Magazine (June 4, 2004; subscription required). "Eric Baum's What Is Thought? [MIT Press, Cambridge, MA, 2004], consciously patterned after [Erwin] Schrödinger's book [What Is Life?], represents a computer scientist's look at the mind. Baum is an unrepentant physicalist. He announces from the outset that he believes that the mind can be understood as a computer program. Much as Schrödinger aimed to ground the understanding of life in well-understood principles of physics, Baum aims to ground the understanding of thought in well-understood principles of computation. In a book that is admirable as much for its candor as its ambition, Baum lays out much of what is special about the mind by taking readers on a guided tour of the successes and failures in the two fields closest to his own research: artificial intelligence and neural networks. ... Advocates of what the philosopher John Haugeland famously characterized as GOFAI (good old-fashioned artificial intelligence) create hand-crafted intricate models that are often powerful yet too brittle to be used in the real world. ... At the opposite extreme are researchers working within the field of neural networks, most of whom eschew built-in structure almost entirely and rely instead on statistical techniques that extract regularities from the world on the basis of massive experience."

Video by Tom Mitchell from his home page.] "Thesis of This Talk: The synergy between AI and Brain Sciences will yield profound advances in our understanding of intelligence over the coming decade, fundamentally changing the nature of our field."

Whatever happened to machines that think? By Justin Mullins. New Scientist (April 23, 2005; Issue 2496: pages 32 - 37). "Where could the secret to intelligence lie? According to [Tom] Mitchell, the human brain is the place to look. He has been using functional magnetic resonance imaging (fMRI) to see which parts of the brain become active when a person thinks about a specific object. He has found that when people are asked to imagine a tool such as a hammer or a building such as a house, the same areas of the brain are activated as when they are shown a picture of these objects. He has also found that the area activated for each object - hammer or house - differs by a discernable amount depending on the object."

Do our brains work like Google? New Scientist (December 8, 2007; Issue 2633: page 27). "Google's patented and powerful search algorithm, PageRank, may mimic the way the human brain retrieves information. ... It seems it might. In tests against other word-retrieval algorithms, PageRank most clearly matched the human model (Psychological Science, vol 18, p 1069). The results suggest human memory studies could be improved by examining the tricks that search engines employ, and vice versa, says [Tom] Griffiths."

The Isaac Newton of logic - It was 150 years ago that George Boole published his classic The Laws of Thought, in which he outlined concepts that form the underpinnings of the modern high-speed computer. By Siobhan Roberts. The Globe and Mail (March 27, 2004; page F9). "It was 150 years ago that George Boole published his literary classic The Laws of Thought, wherein he devised a mathematical language for dealing with mental machinations of logic. It was a symbolic language of thought -- an algebra of logic (algebra is the branch of mathematics that uses letters and other general symbols to represent numbers and quantities in formulas and equations). ... 'Boole was the first cognitive scientist,' says Keith Devlin, executive director of the Center for the Study of Language and Information at Stanford University."

Models of Human Memory. By Suzanne Ross. Microsoft Research News & Highlights. "'Memory is a core aspect of intelligence that gives us an ability to review the past and anticipate the future. I've been pursuing methods and models that show promise for giving computers insights about what people will remember and forget,' said [Eric] Horvitz. 'Models of memory can be used in applications that help people remember-as well as to help them to search or browse through large amounts of content.' ... 'We're fascinated by cognitive psychology and all that it has revealed about our nature and limitations. Guided by insights from psychology, we're working to mesh learning and reasoning methods with application design to develop new computing experiences. I'm excited about the prototypes and the possibilities.'"

The Intersection of Cognitive Science and Robotics: From Interfaces to Intelligence Papers from the 2004 AAAI Fall Symposium, ed. Alan Schultz. Technical Report FS-04-05. American Association for Artificial Intelligence, Menlo Park, California. "Principles and methodologies from cognitive science are starting to be applied to autonomous robots. The use of cognitive science in robotics takes varied forms, from using computational cognitive models as reasoning mechanisms for robots, to the design and control of human-robot interaction. This interdisciplinary symposium brought together researchers in robotics, cognitive science, and human-machine interfaces to examine this emerging area, with the hope of establishing a new community for this emerging discipline. We need to make clear what we mean by cognitive science and by robotics. By cognitive science, we mean work that has some cognitive plausibility (i.e., can arguably be claimed that the representation, strategies, and/or actions have some basis in human cognition; in general C++ code written to do formal reasoning are not cognitively plausible) or person-in-the-loop issues. By robotics, we wish to emphasize embodied systems such as mobile robots and autonomous vehicles, and not just software agents."

Related Resources

About Intelligence. Reference point on understanding intelligence and how we can use it. Features and articles are written by professional journalists and experts who have a particular interest, or a background in this area.

AI, Cognitive Science and Robotics. Maintained by Uwe R. Zimmer, Fellow at the Australian National University.

Celebrities of Cognitive Science. Maintained by Martin Ryder. Links to homepages and papers of leading researchers.

CCRC, "The Center for Research on Concepts and Cognition (aka the Fluid Analogies Research Group, or simply FARG) is an interdisciplinary center for research in cognitive science, directed by Douglas Hofstadter. CRCC is affiliated with the Cognitive Science Program at IU, and has close ties with the Computer Science Department. CRCC research focuses mainly on emergent computational models of creative analogical thinking and its subcognitive substrate -- namely, fluid concepts."

CNBC, The Center for the Neural Basis of Cognition, a joint project of Carnegie Mellon University and the University of Pittsburgh.

COGS, The Centre for Research in Cognitive Science: "COGS fosters interaction and collaboration among all those working in Cognitive Science at Sussex, including researchers and students in Artificial Intelligence, Psychology, Linguistics, Neuroscience and Philosophy. It is a pioneering, internationally recognised centre for interdisciplinary investigation into the nature of cognition, be it natural or artificial."

CSD, The Cognitive Science Department at Rensselaer Polytechnic Institute. "Having been launched in 2002, the Cognitive Science Department (CSD) at Rensselaer is the world's newest department of Cognitive Science. ... Our research and doctoral program is aimed at the creation of an integrated, interdisciplinary department whose research and teaching is focused on three powerful, driving ideas: * 'Next generation' artificial intelligence (AI): the design and construction of fully integrated artificial cognitive systems that reach across the full spectrum of cognition, from low-level perception/action to high-level reasoning, implemented in significant part on the basis of empirical data regarding natural cognitive systems. * 'Next generation' computational cognitive modeling: the design and implementation of cognitive architectures that extend beyond currently available architectures (e.g., ACT-R and SOAR) toward Newell’s original dream of an architecture that accurately reflects the full range of cognitive processes present in natural cognitive systems. * Cognitive engineering: engineering the interface between natural cognitive systems and task environments by, once again, exploiting empirical data concerning natural cognitive systems. These three ideas are the core of a philosophy of doctoral education that we call Teaching Integrated Cognitive Systems (TICS)."

  • Also see:
    • the department's Minds & Machines Program: "an applied cognitive science undergraduate program. Students who enter the Minds & Machines Program perform cutting-edge scientific research into the nature of reasoning, perception, memory, and learning, create intelligent artificial agents and smart enabling technology, and address philosophical questions about the fundamental nature of our mind and the ethical implications of cognitive technology."
    • RAIR: the Rensselaer Artificial Intelligence and Reasoning Laboratory
    • Building a Better Brain. By Sheila Nason. Rensselaer Research Quarterly (Winter 2004).

The Cognition and Affect Project at the University of Birmingham School of Computer Science's Cognitive Science Research Centre. "The main goal of this project is to understand the types of architectures that are capable of accounting for the whole range of human (and non-human) mental states and processes, including not only intelligent capabilities, such as the ability to learn to find your way in an unfamiliar town and the ability to think about infinite sets, but also moods, emotions, desires, and the like."

Cognitive Science at the University of Edinburgh.

Cognitive Science 2007 event (May 2-3, 2007) - a Multi-disciplinary Synthesis of Neuroscience, Computer Science, Mathematics, Cognitive Neuroscience, and Information Theory.

  • "What is Cognitive Computing? Cognitive Computing is when computer science meets neuroscience to explain and implement psychology. ... Cognitive Computing is different from Artificial Intelligence (AI) and Neural Networks (NN). ... AI and NN technologies take one or more cognitive phenomena exhibited by the brain as a starting point and then try to replicate that capability by inventing algorithms/learning rules. In contrast, CC is about learning how the brain operates, about algorithms, about diligent reverse engineering and testing plausible models. Cognitive Computing is about engineering the mind by reverse engineering the brain."

Cognitive Systems - a UK Foresight programme project designed "A) to examine recent progress in two major areas of research - computer science and neuroscience (and their related fields) - to understand whether progress in understanding cognition in living systems has new insights to offer those researching the construction of artificial cognitive systems; B) to scope likely developments in these fields over the next decade, and in particular to scope the likely rate of progress in our capability to build artificial cognitive systems; C) to articulate significant conclusions to a wider audience."

photo of a baby

Developmental Robotics. Sony Computer Laboratory Paris. "Generating plausible models for the processes underlying children's development in the first years of their life is a challenging scientific issue at the crossroads of neuroscience, learning theories and developmental psychology. Children seem to acquire new know-how in a continuous and open-ended manner. A large amount of work describes how new skills seem to build one upon another, suggesting a continuum between sensory-motor development and higher cognitive functions. But very few plausible low-level mechanisms exist to explain how such skills emerge or self-organize. Studying development is intrinsically difficult because of the complex interplay between embodiment, learning mechanisms and environmental dynamics. A relevant integrative approach can be pursued by viewing development as a complex system the dynamics of which can be studied with embodied models. In order to capture part of the open-ended nature that characterizes children's development, we design new biologically-inspired architectures to control autonomous robots. ... This approach might not only help us understand the mechanisms underlying human development, but it might also provide radically new techniques for building intelligent robots. Indeed, as opposed to the work in classical artificial intelligence in which engineers impose pre-defined anthropocentric tasks to robots, the techniques we develop endow the robots with the capacity of deciding by themselves which are the activities that are maximally fitted to their current capabilities."

"The Genesis Group [MIT] is dedicated to the proposition that the time is ripe to answer fundamental questions about the computations that account for human intelligence. Members of the group believe that if we're to understand the nature of human intelligence, we have to understand the contributions of our vision, language, motor, and faculty-connection mechanisms. We further believe we have to understand how those faculties make it possible to understand the physical world and how that understanding provides a foundation for abstract thinking and learning.

The roots of the Genesis Group lie in the thinking that led to the thoughts of Robert C. Berwick, Thomas F. Knight, Jr., Gerald Jay Sussman, Shimon Ullman, Patrick Henry Winston, and Kenneth Yip, a group that styled itself as The Human Intelligence Enterprise."

MIT Computational Cognitive Science Group: "We study the computational basis of human learning and inference. Through a combination of mathematical modeling, computer simulation, and behavioral experiments, we try to uncover the logic behind our everyday inductive leaps: constructing perceptual representations, separating 'style' and 'content' in perception, learning concepts and words, judging similarity or representativeness, inferring causal connections, noticing coincidences, predicting the future."

MIT OpenCourseWare: Brain and Cognitive Sciencescourses available online.

Scientific American Mind - "a quarterly publication focusing on the workings of the mind and brain."

Soar Technology, Inc. "The foundation for all of Soar Technology’s projects is rooted in cognitive science. This primarily includes behavior, cognition, perception, memory, performance, learning, and emotion. Much of this scientific base is encoded within Soar, a computational cognitive architecture that enables the creation of autonomous software agents capable of sophisticated reasoning while utilizing large amounts of human-level knowledge." - from Cognitive Research & Architectures

Yale Social Robotics Lab: "Founded in 2001, the Yale University Social Robotics Lab's main project is the development of anthropomorphic robots that interact with people using natural social cues. When completed, the robot, named Nico, will serve as a test-bed for theories of social learning. Designed to resemble a 9 month-old baby, Nico will be able to take part in standard child psychology experiments, allowing its cognitive models to be tested under the same conditions as undergone by human babies."

Other References Offline

Adelson, B. 1984. When Novices Surpass Experts: The Difficulty of a Task May Increase with Expertise. Journal of Experimental Psychology: Learning, Memory & Cognition 10: 483-495. Anderson, J. R. 1983. The Architecture of Cognition. Cambridge, MA: Harvard University Press.

Arbib, Michael A., editor. 1995. Handbook of Brain Theory and Neural Networks. Cambridge, MA: MIT Press. Hundreds of experts contribute articles charting progress in the study of how the brain works and how we can build intelligent machines.

Arkes, H. R., and M. R. Freedman. 1984. A Demonstration of the Costs and Benefits of Expertise in Recognition Memory. Memory & Cognition 12: 84-89.

Boden, Margaret A. 2006. Mind as Machine - A History of Cognitive Science. Oxford, UK: Oxford University Press. As described by the publisher: "Psychology is the thematic heart of cognitive science, which aims to understand human (and animal) minds. But its core theoretical ideas are drawn from cybernetics and artificial intelligence, and many cognitive scientists try to build functioning models of how the mind works. In that sense, Margaret Boden suggests, its key insight is that mind is a (very special) machine. Because the mind has many different aspects, the field is highly interdisciplinary. It integrates psychology not only with cybernetics/AI, but also with neuroscience and clinical neurology; with the philosophy of mind, language, and logic; with linguistic work on grammar, semantics, and communication; with anthropological studies of cultures; and with biological (and A-Life) research on animal behaviour, evolution, and life itself. Each of these disciplines, in its own way, asks what the mind is, what it does, how it works, how it develops---and how it is even possible." [See review above.]

Boden, Margaret A. 1998. Creativity and Artificial Intelligence. Artificial Intelligence 103(1-2): 347-356. Boden describes how AI techniques can be used to create new ideas.

Boden, Margaret A. 1996. Artificial Genius. Discover 17: 104-107.

Chi, M. T. H., P. J. Feltovich and R. Glaser. 1981. Categorization and Representation of Physics Problems by Experts and Novices. Cognitive Science 5: 121-152.

Chi, M. T. H., and R. D. Koeske. 1983. Network Representation of a Child's Dinosaur Knowledge. Developmental Psychology 19: 29-39.

Clark, Andy. 1997. Being There: Putting Brain, Body, and World Together Again. Cambridge, MA. and London: MIT Press.

Colby, Kim. 1967. Computer Simulation of Change in Personal Belief Systems. Behavioral Science 12 (May 1967): 248-253.

De Groot, A. D. 1978. Thought and Choice in Chess, 2nd edition. Paris and The Hague: Mouton.

Doyle, Jon. 1983. What is Rational Psychology? Toward a Modern Mental Philosophy. AI Magazine 4(3): Fall 1983, 50-53. "Rational psychology is the conceptual investigation of psychology by means of the most fit mathematical concepts. Several practical benefits should accrue from its recognition."

Epstein, Robert. 1992. The Quest for the Thinking Computer. AI Magazine 13(2): Summer 1992, 81-95. "Can machines think? Alan Turing’s decades-old question still influences artificial intelligence because of the simple test he proposed in his article in Mind. In this article, AI Magazine collects presentations about the first round of the classic Turing Test of machine intelligence, held November 8, 1991 at The Computer Museum, Boston. Robert Epstein, Director Emeritus, Cambridge Center for Behavioral Studies, and an adjunct professor of psychology, Boston University, University of Massachusetts (Amherst), and University of California (San Diego) summarizes some of the difficult issues during the planning of this first real-time competition, and describes the event. He then speculates about the future of the competition and about its significance to the AI community. Presented in tandem with Dr. Epstein’s article is the actual transcript of session that won the Loebner Prize Competition--Joseph Weintraub’s computer program PC Therapist."

Feltovich, Paul J., Kenneth M. Ford, and Robert R. Hoffman, editors. 1997. Expertise in Context: Human and Machine. Menlo Park, CA. and Cambridge, MA: AAAI Press/MIT Press. Fischler, Martin, and Oscar Firschein. 1987. Intelligence: The Eye, the Brain, and the Computer. Reading, MA: Addison-Wesley. An overview intended for a general readership.

Ford, Kenneth, Clark Glymour, and Patrick Hayes, editors. 1995. Thinking about Android Epistemology. Menlo Park, CA: AAAI Press. Approaches artificial intelligence and cognitive psychology as a unified endeavor, with AI focused on possible ways of engineering intelligence and cognitive science. Sixteen essays by computer scientists and philosophers.

Gleitman, Lila R., and Mark Liberman. 1995. An Invitation to Cognitive Science. Vol. 1: Language. Cambridge, MA: MIT Press/Bradford Books.

Guterl, Frederick V. 1997. Beauty and Magnets. Discover 18 (March 1997): 38-40.

Hofstadter, Douglas R. 1979. Godel, Escher, Bach: An Eternal Golden Braid. New York: Basic Books.

Haugeland, John., editor. 1997. Mind Design II: Philosophy, Psychology, Artificial Intelligence. Cambridge, MA: MIT Press.

Holtzman, Steven R. 1995. Painting By Number. Technology Review 98: 60-68.

Johnson-Laird, P. N. 1988. The Computer and the Mind: An Introduction to Cognitive Science. Cambridge, MA: Harvard University Press.

Kahneman, D., P. Slovic, and A. Tversky. 1982. Judgements Under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press. Includes a classic paper "Causal Schemata in Judgements Under Uncertainty" by Tversky and Kahneman.

Kolodner, J. L. 1983. Towards an Understanding of the Role of Experience in the Evolution from Novice to Expert. International Journal of Man-Machine Studies 19: 497-518.

Kosslyn, Stephen M., and Daniel N. Osherson. 1995. An Invitation to Cognitive Science. Vol. 2: Visual Cognition. Cambridge, MA: MIT Press/Bradford Books.

Kurzweil, Raymond, et. al 1990. The Science of Art. In the Age of the Intelligent Machine, ed. Kurzweil, Raymond, 351-395. Cambridge, MA: MIT Press.

Luger, George F. 1994. Cognitive Science: The Science of Intelligent Systems. Academic Press.

Mayer, R. F. 1988. From Novice to Expert. In Handbook of Human-Computer Interaction, ed. Holander, M., 569-580. Amsterdam: North-Holland.

Means, M. L., and J. F. Voss 1985. Star Wars: A Developmental Study of Expert and Novice Knowledge Structures. Journal of Memory and Language 24: 746-757.

Miller, George A. 1956. The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. The Psychological Review 63 (March 1956): 81-97. A classic paper.

Myles-Worsley, M., W. A. Johnston, and M. A. Simons 1988. The Influence of Expertise on X-Ray Image Processing. Journal of Experimental Psychology: Learning, Memory & Cognition 14: 553-557.

Newell, Allen. 1990. Unified Theories of Cognition. Cambridge, MA: Harvard University Press.

Newell, Allen. 1988. Putting it All Together. In Complex Information Processing: The Impact of Herbert A. Simon, ed. D. Klahr and K. Kotovsky, Hillsdale, NJ: Erlbaum and Associates.

Newell, Allen, and Herbert Simon. 1972. Human Problem Solving. Englewood Cliffs, NJ: Prentice-Hall.

Novick, L. R. 1988. Analogical Transfer, Problem Similarity, and Expertise. Journal of Experimental Psychology: Learning, Memory & Cognition 14: 510-520.

Port, R., and T. van Gelder 1995. Mind as Motion: Explorations in the Dynamics of Cognition. Cambridge, MA: Bradford Books/MIT Press.

Scarborough, Don, and Saul Sternberg. 1998. An Invitation to Cognitive Science. Vol. 4: Methods, Models, and Conceptual Issues. Cambridge, MA: MIT Press/Bradford Books. Covers artificial intelligence, neural networks, animal cognition, signal detection theory, computational models, cognitive neuroscience and more.

Scheines, Richard. 1988. Automating Creativity. In Aspects of Artificial Intelligence, ed. Fetzer, James H., Dordrecht, Netherlands: Kluwer Academic Press.

Shanteau, J. 1987. Psychological Characteristics of Expert Decision Makers. In Expert Judgment and Expert Systems, ed. Mumpower, J. L., O. Renn, L. D. Phillips, et al., 289-304. Berlin: Springer-Verlag.

Smith, Edward E., and Daniel N. Osherson. 1995. An Invitation to Cognitive Science. Volume 3: Thinking. Cambridge, MA: MIT Press/Bradford Books.

Steinert-Threlkeld, Tom. Marvin Minsky Wants Machines To Get Emotional. ZDNet/Interactive Week. (February 25, 2001). "Because the main point of the book [The Emotion Machine] is that it's trying to make theories of how thinking works. Our traditional idea is that there is something called 'thinking' and that it is contaminated, modulated or affected by emotions. What I am saying is that emotions aren't separate."

Sternberg, Robert J., editor. 1998. The Nature of Cognition. Introduces major themes in the field by contrasting alternative approaches and synthesizing them. Covers general issues, representation and process, methodology, kinds of cognition, and group and individual differences. Cambridge, MA: MIT Press/Bradford Books.

Stewart, Doug. Interview with Herbert Simon, June 1994. Omni Magazine. See excerpt at the top of this page. [No longer available online.]

Stillings, Neil A., Steven Weisler, Christopher Chase, Mark Feinstein, Jay Garfield, and Edwina Rissland. 1995. Cognitive Science: An Introduction. 2nd edition. Cambridge, MA: MIT Press/Bradford Books. A comprehensive undergraduate text that includes ideas in psychology, philosophy, linguistics, and artificial intelligence, and covers the new connectionist approach as well as the classical symbolic approach, with a new chapter on advances in neuroscience.

Stonier, Tom. 1992. Beyond Information: the Natural History of Intelligence. London and New York: Springer-Verlag. Thagard, Paul, editor. 1998. Mind Readings: Introductory Selections on Cognitive Science. Cambridge, MA: MIT Press/Bradford Books. Recent accessible readings in the field of cognition, both from the point of view that thinking is a computational procedure on a mental representation and from challengers to that point of view.

Thagard, Paul 1996. Mind: Introduction to Cognitive Science. Cambridge, MA: Bradford Book/MIT Press.

Ullman, Ellen. 2002. Programming the Post-Human: Computer science redefines "life." Harper's, Vol. 305, No. 1829: 60-70. "Ants are not generally thought of as being particularly smart. But as a model they have one enormous advantage over human brains: an explanation of how apparent complexity can arise without an overseeing designer. A group of dumb ant produces the complexity of the ant colony - an example of organizational intelligence without recourse to the perennial difficulties of religion or philosophy. Again, the source for this key idea seems to be Herbert Simon. The third chapter of The Sciences of the Artificial opens by describing an ant...." [p. 64]

Winograd, T. and F. Flores. 1986. Understanding Computers and Cognition: A New Foundation for Design. Norwood, NJ: Ablex.

AAAI   Recent Changes   Edit   History   Print   Contact Us
Page last modified on July 07, 2012, at 10:26 PM