Opening Overview Video of:
This should get you to the this year's introductory video (which seems to be just audio):
and this should get you the PDF of the PPT
Saturday, January 6, 2018
PSYC 538 Syllabus
Psychology PSYC 538, Winter 2018:
Categorization, Communication and Consciousness
Time: Tuesdays 2:35-5:25
Place:
Instructor: Stevan Harnad
Office: TBA
Skype: sharnad
Google+hangout: amsciforum@gmail.com
Google+hangout: amsciforum@gmail.com
E-mail: harnad@uqam.ca (please don’t use my mcgill email address because I don’t check it regularly)
Optional 2% Psychology Department Participant Pool
You are welcome to participate in the participant pool or to do the non-participatory alternate assignments for an extra 2% on your final grade. Participating is entirely voluntary and is between you and the Participant Pool Teaching Assistant (Sara Quinn) who will indicate to me at the end of the semester who participated and for how much credit. You are permitted to participate in any study for which you are eligible. The pool TA will visit our class to describe the process. All questions about the participant pool should be sent to the pool TA at:
Open to students interested in Cognitive Science from the Departments of Linguistics, Philosophy, Psychology, Computer Science, or Neuroscience.
Overview: What is cognition? Cognition is whatever is going on inside our heads when we think, whatever enables us to do all the things we know how to do -- to learn and to act adaptively, so we can survive and reproduce. Cognitive science tries to explain the internal mechanism that generates that know-how.
The brain is the natural place to look for the explanation of the mechanism, but that’s not enough. Unlike the mechanisms that generate the capacities of other bodily organs such as the heart or the lungs, the brain’s capacities are too vast, complex and opaque to be read off by direct observation or manipulation.
The brain can do everything that we can do. Computational modeling and robotics try, alongside behavioral neuroscience, to design and test mechanisms that can also do everything we can do. Explaining how any mechanism can do what our brains can do might also help explain how our brains do it.
What is computation? Can computation do everything that the brain can do?
The challenge of the celebrated "Turing test" is to design a model that can do everything we can do, to the point where we can no longer tell apart the model’s performance from our own. The model not only has to generate our sensorimotor capacities – the ability to do everything with the objects and organisms in the world that we are able do with them -- but it must also be able to produce and understand language, just as we do.
What is language, and what was its adaptive value for our species, so that we are the only species on the planet that has it?
Is there any truth to the Whorf Hypothesis that language shapes the way the world looks to us?
How do we learn to categorize all the things we can name with words? How do words get their meaning?
And what is consciousness? We are not the only conscious organisms, but what is consciousness for? What is its function, its adaptive value? And can consciousness be wider than out heads? Is the Web conscious?
Objectives: This course will outline the main challenges that cognitive science, still very incomplete, faces today, focusing on computation, the capacity to learn sensorimotor categories, to name and describe them verbally, and to transmit them to others through language, concluding with cognition distributed on the Web.
What is cognition? How and why did introspection fail? How and why did behaviourism fail? What is cognitive science trying to explain, and how?
What is (and is not) computation? What is the power and scope of computation? What does it mean to say (or deny) that “cognition is computation”?
Readings:
1a. What is a Turing Machine? + What is Computation? + What is a Physical Symbol System?
1b. Harnad, S. (2009) Cohabitation: Computation at 70, Cognition at 20, in Dedrick, D., Eds. Cognition, Computation, and Pylyshyn. MIT Press http://eprints.ecs.soton.ac.uk/12092/
2. The Turing test
What’s wrong and right about Turing’s proposal for explaining cognition?
Readings:
2a. Turing, A.M. (1950) Computing Machinery and Intelligence. Mind 49 433-460 http://cogprints.org/499/
2b. Harnad, S. (2008) The Annotation Game: On Turing (1950) on Computing,Machinery and Intelligence. In: Epstein, Robert & Peters, Grace (Eds.) Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Springer http://eprints.ecs.soton.ac.uk/12954/
3. Searle's Chinese room argument (against the computational theory of cognition)
What’s wrong and right about Searle’s Chinese room argument that cognition is not computation?
Readings:
3a. Searle, John. R. (1980) Minds, brains, and programs. Behavioral and Brain Sciences 3 (3): 417-457
3b. Harnad, S. (2001) What's Wrong and Right About Searle's Chinese RoomArgument? In: M. Bishop & J. Preston (eds.) Essays on Searle's Chinese Room Argument. Oxford University Press. http://cogprints.org/1622/
4. What about the brain?
Why is there controversy over whether neuroscience is relevent to explaining cognition?
Readings:
4a. Cook, R., Bird, G., Catmur, C., Press, C., & Heyes, C. (2014). Mirror neurons: from origin to function. Behavioral and Brain Sciences, 37(02), 177-192.
4b. Fodor, J. (1999) "Why, why, does everyone go on so about the brain?" London Review of Books 21(19) 68-69. http://www.lrb.co.uk/v21/n19/jerry-fodor/diary
What is the “symbol grounding problem,” and how can it be solved? (The meaning of words must be grounded in sensorimotor categories.)
Readings:
5. Harnad, S. (2003) The Symbol Grounding Problem. Encylopedia of Cognitive Science. Nature Publishing Group. Macmillan. http://eprints.ecs.soton.ac.uk/7720
[Google also for other online sources for “The Symbol Grounding Problem” in Google Scholar]
6. Categorization and cognition
That categorization is cognition makes sense, but “cognition is categorization”? (On the power and generality of categorization.)
Readings:
6a. Harnad, S. (2005) To Cognize is to Categorize: Cognition is Categorization, in Lefebvre, C. and Cohen, H., Eds. Handbook of Categorization. Elsevier. http://eprints.ecs.soton.ac.uk/11725/
6b. Harnad, S. (2003) Categorical Perception. Encyclopedia of Cognitive Science. Nature Publishing Group. Macmillan. http://eprints.ecs.soton.ac.uk/7719/
7. Evolution and cognition
Why is it that some evolutionary explanations sound plausible and make sense, whereas others seem far-fetched or even absurd?
Readings:
7a. Confer, Jaime C., Judith A. Easton, Diana S. Fleischman, Cari D. Goetz, David M. G. Lewis, Carin Perilloux, and David M. Buss (2010) Evolutionary Psychology Controversies, Questions, Prospects, and Limitations. American Psychologist 65 (2): 110–126
7b. MacLean, E.L., Matthews, L.J., Hare, B.A., Nunn, C.L., Anderson, R.C., Aureli, F., Brannon, E.M., Call, J., Drea, C.M., Emery, N.J. and Haun, D.B. (2012) How does cognition evolve?Phylogenetic comparative psychology. Animal cognition, 15(2): 223-238.
8. The evolution of language
What’s wrong and right about Steve Pinker’s views on language evolution? And what was so special about language that the capacity to acquire it became evolutionarily encoded in the brains of our ancestors – and of no other surviving species – about 300,000 years ago? (It gave our species a unique new way to acquire categories, through symbolic instruction rather than just direct sensorimotor induction.)
Readings:
8a. Pinker, S. & Bloom, P. (1990). Natural language and natural selection. Behavioral and Brain Sciences13(4): 707-784.
8b. Blondin-Massé, Alexandre; Harnad, Stevan; Picard, Olivier; and St-Louis, Bernard (2013) Symbol Grounding and the Origin of Language: From Show to Tell. In, Lefebvre, Claire; Cohen, Henri; and Comrie, Bernard (eds.) New Perspectives on the Origins of Language. Benjamin
9. Noam Chomsky and the poverty of the stimulus
A close look at one of the most controversial issues at the heart of cognitive science: Chomsky’s view that Universal Grammar has to be inborn because it cannot be learned from the data available to the language-learning child.
Readings:
9a. Pinker, S. Language Acquisition. in L. R. Gleitman, M. Liberman, and D. N. Osherson (Eds.), An Invitation to Cognitive Science, 2nd Ed. Volume 1: Language. Cambridge, MA: MIT Press. http://users.ecs.soton.ac.uk/harnad/Papers/Py104/pinker.langacq.html
9b. Pullum, G.K. & Scholz BC (2002) Empirical assessment of stimulus poverty arguments. Linguistic Review 19: 9-50 http://www.ucd.ie/artspgs/research/pullum.pdf
10. The mind/body problem and the explanatory gap
Once we can pass the Turing test -- because we can generate and explain everything that cognizers are able to do -- will we have explained all there is to explain about the mind? Or will something still be left out?
Readings:
10a. Dennett, D. (unpublished) The fantasy of first-person science. http://ase.tufts.edu/cogstud/papers/chalmersdeb3dft.htm
10b. Harnad, S. (unpublished) On Dennett on Consciousness: The Mind/Body Problem is the Feeling/Function Problem. http://cogprints.org/2130
10c. Harnad, S. & Scherzer, P. (2008) Spielberg's AI: Another Cuddly No-Brainer. Artificial Intelligence in Medicine 44(2): 83-89 http://eprints.ecs.soton.ac.uk/14430/
10d. Harnad, S. (2012) Alan Turing and the “hard” and “easy” problem of cognition: doing and feeling. [in special issue: Turing Year 2012] Turing100: Essays in Honour of Centenary Turing Year 2012, Summer Issue
11. Distributed cognition and the World Wide Web
Can a mind be wider than a head? Collective cognition in the online era: the Cognitive Commons.
Readings:
Clark, A. & Chalmers, D. (1998) The Extended Mind. Analysis58(1) http://www.cogs.indiana.edu/andy/TheExtendedMind.pdf
Dror, I. & Harnad, S. (2009) Offloading Cognition onto CognitiveTechnology. In Dror & Harnad (Eds): Cognition Distributed: How Cognitive Technology Extends Our Minds. Amsterdam: John Benjamins http://eprints.ecs.soton.ac.uk/16602/
12. Overview
Drawing it all together.
Evaluation:
1. Blog skywriting (30 marks) -- quote/commentary on all 24 readings
2. Class discussion (20 marks) -- (do more skywritings if you are shy to speak in class)
3. Midterm (10 marks) -- 6 online questions (about 250 words for each answer)
4. Final (40 marks) -- 8 online integrative questions (about 500 words each answer)
Course website: http://catcomconm2018.blogspot.ca
Use your gmail account to register to comment, and either use your real name or send me an email to tell me what pseudonym you are using (so I can give you credit).
Every week, everyone does at least one blog comment on each of that (coming) week’s two papers. In your blog comments, quote the passage on which you are commenting (italics, indent). Comments can also be on the comments of others.
Make sure you first edit your comment in another text processor, because if you do it directly in the blogger window you may lose it and have to write it all over again. Also, check how many comments have been made, and if they are close to 50, go to the overflow comments because blogger only allows 50 in each batch. (Each paper has room for a first 50 and then an oveflow 50.)
Also do your comments early in the week or I may not be able to get to them in time to reply. (I won't be replying to all comments, just the ones where I think I have something interesting to add. You should comment on one another's comments too -- that counts -- but make sure you're basing it on having read the original skyreading too.)
For samples, see summer school: http://turingc.blogspot.ca
1. Blog skywriting (30 marks) -- quote/commentary on all 24 readings
2. Class discussion (20 marks) -- (do more skywritings if you are shy to speak in class)
3. Midterm (10 marks) -- 6 online questions (about 250 words for each answer)
4. Final (40 marks) -- 8 online integrative questions (about 500 words each answer)
Optional 2% Psychology Department Participant Pool
You are welcome to participate in the participant pool or to do the non-participatory alternate assignments for an extra 2% on your final grade. Participating is entirely voluntary and is between you and the Participant Pool Teaching Assistant (Sara Quinn) who will indicate to me at the end of the semester who participated and for how much credit. You are permitted to participate in any study for which you are eligible. The pool TA will visit our class to describe the process. All questions about the participant pool should be sent to the pool TA at:
Course website: http://catcomconm2018.blogspot.ca
Use your gmail account to register to comment, and either use your real name or send me an email to tell me what pseudonym you are using (so I can give you credit).
Every week, everyone does at least one blog comment on each of that (coming) week’s two papers. In your blog comments, quote the passage on which you are commenting (italics, indent). Comments can also be on the comments of others.
Make sure you first edit your comment in another text processor, because if you do it directly in the blogger window you may lose it and have to write it all over again. Also, check how many comments have been made, and if they are close to 50, go to the overflow comments because blogger only allows 50 in each batch. (Each paper has room for a first 50 and then an oveflow 50.)
Also do your comments early in the week or I may not be able to get to them in time to reply. (I won't be replying to all comments, just the ones where I think I have something interesting to add. You should comment on one another's comments too -- that counts -- but make sure you're basing it on having read the original skyreading too.)
For samples, see summer school: http://turingc.blogspot.ca
Opening Overview Video of Categorization, Communication and Consciousness
Opening Overview Video of:
This should get you to the this year's introductory video (which seems to be just audio):
and this should get you the PDF of the PPT
(Opening Overview Comment Overflow) (50+)
(Opening Overview Comment Overflow) (50+)
The blogger software only accepts 50 comments, so when skywriting reaches 50, please switch to the overflow comments link, otherwise your comment will not appear. (Always check if your comment appears after you have posted it.)
The blogger software only accepts 50 comments, so when skywriting reaches 50, please switch to the overflow comments link, otherwise your comment will not appear. (Always check if your comment appears after you have posted it.)
(Opening Overview Comment Overflow) (50+)
(Opening Overview Comment Overflow) (50+)
The blogger software only accepts 50 comments, so when skywriting reaches 50, please switch to the overflow comments link, otherwise your comment will not appear. (Always check if your comment appears after you have posted it.)
The blogger software only accepts 50 comments, so when skywriting reaches 50, please switch to the overflow comments link, otherwise your comment will not appear. (Always check if your comment appears after you have posted it.)
1a. What is Computation?
Pylyshyn, Z (1989) Computation in cognitive science. In MI Posner (Ed.) Foundations of Cognitive Science. MIT Press
Overfiew: Nobody doubts that computers have had a profound influence on the study of human cognition. The very existence of a discipline called Cognitive Science is a tribute to this influence. One of the principal characteristics that distinguishes Cognitive Science from more traditional studies of cognition within Psychology, is the extent to which it has been influenced by both the ideas and the techniques of computing. It may come as a surprise to the outsider, then, to discover that there is no unanimity within the discipline on either (a) the nature (and in some cases the desireabilty) of the influence and (b) what computing is --- or at least on its -- essential character, as this pertains to Cognitive Science. In this essay I will attempt to comment on both these questions.
Alternative sources for points on which you find Pylyshyn heavy going. (Remember that you do not need to master the technical details for this seminar, you just have to master the ideas, which are clear and simple.)
Milkowski, M. (2013). Computational Theory of Mind. Internet Encyclopedia of Philosophy.
Horst, S. (2009). The computational theory of mind.
Pylyshyn, Z. W. (1980). Computation and cognition: Issues in the foundations of cognitive science. Behavioral and Brain Sciences, 3(01), 111-132.
Pylyshyn, Z. W. (1984). Computation and cognition. Cambridge, MA: MIT press.
1a. What is Computation?
Pylyshyn, Z (1989) Computation in cognitive science. In MI Posner (Ed.) Foundations of Cognitive Science. MIT Press
Overfiew: Nobody doubts that computers have had a profound influence on the study of human cognition. The very existence of a discipline called Cognitive Science is a tribute to this influence. One of the principal characteristics that distinguishes Cognitive Science from more traditional studies of cognition within Psychology, is the extent to which it has been influenced by both the ideas and the techniques of computing. It may come as a surprise to the outsider, then, to discover that there is no unanimity within the discipline on either (a) the nature (and in some cases the desireabilty) of the influence and (b) what computing is --- or at least on its -- essential character, as this pertains to Cognitive Science. In this essay I will attempt to comment on both these questions.
Alternative sources for points on which you find Pylyshyn heavy going. (Remember that you do not need to master the technical details for this seminar, you just have to master the ideas, which are clear and simple.)
Milkowski, M. (2013). Computational Theory of Mind. Internet Encyclopedia of Philosophy.
Horst, S. (2009). The computational theory of mind.
Pylyshyn, Z. W. (1980). Computation and cognition: Issues in the foundations of cognitive science. Behavioral and Brain Sciences, 3(01), 111-132.
Pylyshyn, Z. W. (1984). Computation and cognition. Cambridge, MA: MIT press.
1b. Harnad, S. (2009) Cohabitation: Computation at 70, Cognition at 20
Harnad, S. (2009) Cohabitation: Computation at 70, Cognition at 20, in Dedrick, D., Eds. Cognition, Computation, and Pylyshyn. MIT Press
Zenon Pylyshyn cast cognition's lot with computation, stretching the Church/Turing Thesis to its limit: We had no idea how the mind did anything, whereas we knew computation could do just about everything. Doing it with images would be like doing it with mirrors, and little men in mirrors. So why not do it all with symbols and rules instead? Everything worthy of the name "cognition," anyway; not what was too thick for cognition to penetrate. It might even solve the mind/body problem if the soul, like software, were independent of its physical incarnation. It looked like we had the architecture of cognition virtually licked. Even neural nets could be either simulated or subsumed. But then came Searle, with his sino-spoiler thought experiment, showing that cognition cannot be all computation (though not, as Searle thought, that it cannot be computation at all). So if cognition has to be hybrid sensorimotor/symbolic, it turns out we've all just been haggling over the price, instead of delivering the goods, as Turing had originally proposed 5 decades earlier.
Zenon Pylyshyn cast cognition's lot with computation, stretching the Church/Turing Thesis to its limit: We had no idea how the mind did anything, whereas we knew computation could do just about everything. Doing it with images would be like doing it with mirrors, and little men in mirrors. So why not do it all with symbols and rules instead? Everything worthy of the name "cognition," anyway; not what was too thick for cognition to penetrate. It might even solve the mind/body problem if the soul, like software, were independent of its physical incarnation. It looked like we had the architecture of cognition virtually licked. Even neural nets could be either simulated or subsumed. But then came Searle, with his sino-spoiler thought experiment, showing that cognition cannot be all computation (though not, as Searle thought, that it cannot be computation at all). So if cognition has to be hybrid sensorimotor/symbolic, it turns out we've all just been haggling over the price, instead of delivering the goods, as Turing had originally proposed 5 decades earlier.
1b. Harnad, S. (2009) Cohabitation: Computation at 70, Cognition at 20
Harnad, S. (2009) Cohabitation: Computation at 70, Cognition at 20, in Dedrick, D., Eds. Cognition, Computation, and Pylyshyn. MIT Press
Zenon Pylyshyn cast cognition's lot with computation, stretching the Church/Turing Thesis to its limit: We had no idea how the mind did anything, whereas we knew computation could do just about everything. Doing it with images would be like doing it with mirrors, and little men in mirrors. So why not do it all with symbols and rules instead? Everything worthy of the name "cognition," anyway; not what was too thick for cognition to penetrate. It might even solve the mind/body problem if the soul, like software, were independent of its physical incarnation. It looked like we had the architecture of cognition virtually licked. Even neural nets could be either simulated or subsumed. But then came Searle, with his sino-spoiler thought experiment, showing that cognition cannot be all computation (though not, as Searle thought, that it cannot be computation at all). So if cognition has to be hybrid sensorimotor/symbolic, it turns out we've all just been haggling over the price, instead of delivering the goods, as Turing had originally proposed 5 decades earlier.
Zenon Pylyshyn cast cognition's lot with computation, stretching the Church/Turing Thesis to its limit: We had no idea how the mind did anything, whereas we knew computation could do just about everything. Doing it with images would be like doing it with mirrors, and little men in mirrors. So why not do it all with symbols and rules instead? Everything worthy of the name "cognition," anyway; not what was too thick for cognition to penetrate. It might even solve the mind/body problem if the soul, like software, were independent of its physical incarnation. It looked like we had the architecture of cognition virtually licked. Even neural nets could be either simulated or subsumed. But then came Searle, with his sino-spoiler thought experiment, showing that cognition cannot be all computation (though not, as Searle thought, that it cannot be computation at all). So if cognition has to be hybrid sensorimotor/symbolic, it turns out we've all just been haggling over the price, instead of delivering the goods, as Turing had originally proposed 5 decades earlier.
2a. Turing, A.M. (1950) Computing Machinery and Intelligence
Turing, A.M. (1950) Computing Machinery and Intelligence. Mind 49 433-460
I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?"
I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?"
1. Video about Turing's work: Alan Turing: Codebreaker and AI Pioneer
2. Two-part video about his life: The Strange Life of Alan Turing: BBC Horizon Documentary and
3. Le modèle Turing (vidéo, langue française)
2a. Turing, A.M. (1950) Computing Machinery and Intelligence
Turing, A.M. (1950) Computing Machinery and Intelligence. Mind 49 433-460
I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?"
I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the 'imitation game." It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart front the other two. The object of the game for the interrogator is to determine which of the other two is the man and which is the woman. He knows them by labels X and Y, and at the end of the game he says either "X is A and Y is B" or "X is B and Y is A." The interrogator is allowed to put questions to A and B. We now ask the question, "What will happen when a machine takes the part of A in this game?" Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman? These questions replace our original, "Can machines think?"
1. Video about Turing's work: Alan Turing: Codebreaker and AI Pioneer
2. Two-part video about his life: The Strange Life of Alan Turing: BBC Horizon Documentary and
3. Le modèle Turing (vidéo, langue française)
2b. Harnad, S. (2008) The Annotation Game: On Turing (1950) on Computing, Machinery and Intelligence
Harnad, S. (2008) The Annotation Game: On Turing (1950) on Computing,Machinery and Intelligence. In: Epstein, Robert & Peters, Grace (Eds.) Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Springer
This is Turing's classical paper with every passage quote/commented to highlight what Turing said, might have meant, or should have meant. The paper was equivocal about whether the full robotic test was intended, or only the email/penpal test, whether all candidates are eligible, or only computers, and whether the criterion for passing is really total, liefelong equavalence and indistinguishability or merely fooling enough people enough of the time. Once these uncertainties are resolved, Turing's Test remains cognitive science's rightful (and sole) empirical criterion today.
This is Turing's classical paper with every passage quote/commented to highlight what Turing said, might have meant, or should have meant. The paper was equivocal about whether the full robotic test was intended, or only the email/penpal test, whether all candidates are eligible, or only computers, and whether the criterion for passing is really total, liefelong equavalence and indistinguishability or merely fooling enough people enough of the time. Once these uncertainties are resolved, Turing's Test remains cognitive science's rightful (and sole) empirical criterion today.
2b. Harnad, S. (2008) The Annotation Game: On Turing (1950) on Computing, Machinery and Intelligence
Harnad, S. (2008) The Annotation Game: On Turing (1950) on Computing,Machinery and Intelligence. In: Epstein, Robert & Peters, Grace (Eds.) Parsing the Turing Test: Philosophical and Methodological Issues in the Quest for the Thinking Computer. Springer
This is Turing's classical paper with every passage quote/commented to highlight what Turing said, might have meant, or should have meant. The paper was equivocal about whether the full robotic test was intended, or only the email/penpal test, whether all candidates are eligible, or only computers, and whether the criterion for passing is really total, liefelong equavalence and indistinguishability or merely fooling enough people enough of the time. Once these uncertainties are resolved, Turing's Test remains cognitive science's rightful (and sole) empirical criterion today.
This is Turing's classical paper with every passage quote/commented to highlight what Turing said, might have meant, or should have meant. The paper was equivocal about whether the full robotic test was intended, or only the email/penpal test, whether all candidates are eligible, or only computers, and whether the criterion for passing is really total, liefelong equavalence and indistinguishability or merely fooling enough people enough of the time. Once these uncertainties are resolved, Turing's Test remains cognitive science's rightful (and sole) empirical criterion today.
3a. Searle, John. R. (1980) Minds, brains, and programs
Searle, John. R. (1980) Minds, brains, and programs. Behavioral and Brain Sciences 3 (3): 417-457
This article can be viewed as an attempt to explore the consequences of two propositions. (1) Intentionality in human beings (and animals) is a product of causal features of the brain I assume this is an empirical fact about the actual causal relations between mental processes and brains It says simply that certain brain processes are sufficient for intentionality. (2) Instantiating a computer program is never by itself a sufficient condition of intentionality The main argument of this paper is directed at establishing this claim The form of the argument is to show how a human agent could instantiate the program and still not have the relevant intentionality. These two propositions have the following consequences (3) The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program. This is a strict logical consequence of 1 and 2. (4) Any mechanism capable of producing intentionality must have causal powers equal to those of the brain. This is meant to be a trivial consequence of 1. (5) Any attempt literally to create intentionality artificially (strong AI) could not succeed just by designing programs but would have to duplicate the causal powers of the human brain. This follows from 2 and 4.
This article can be viewed as an attempt to explore the consequences of two propositions. (1) Intentionality in human beings (and animals) is a product of causal features of the brain I assume this is an empirical fact about the actual causal relations between mental processes and brains It says simply that certain brain processes are sufficient for intentionality. (2) Instantiating a computer program is never by itself a sufficient condition of intentionality The main argument of this paper is directed at establishing this claim The form of the argument is to show how a human agent could instantiate the program and still not have the relevant intentionality. These two propositions have the following consequences (3) The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program. This is a strict logical consequence of 1 and 2. (4) Any mechanism capable of producing intentionality must have causal powers equal to those of the brain. This is meant to be a trivial consequence of 1. (5) Any attempt literally to create intentionality artificially (strong AI) could not succeed just by designing programs but would have to duplicate the causal powers of the human brain. This follows from 2 and 4.
3a. Searle, John. R. (1980) Minds, brains, and programs
Searle, John. R. (1980) Minds, brains, and programs. Behavioral and Brain Sciences 3 (3): 417-457
This article can be viewed as an attempt to explore the consequences of two propositions. (1) Intentionality in human beings (and animals) is a product of causal features of the brain I assume this is an empirical fact about the actual causal relations between mental processes and brains It says simply that certain brain processes are sufficient for intentionality. (2) Instantiating a computer program is never by itself a sufficient condition of intentionality The main argument of this paper is directed at establishing this claim The form of the argument is to show how a human agent could instantiate the program and still not have the relevant intentionality. These two propositions have the following consequences (3) The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program. This is a strict logical consequence of 1 and 2. (4) Any mechanism capable of producing intentionality must have causal powers equal to those of the brain. This is meant to be a trivial consequence of 1. (5) Any attempt literally to create intentionality artificially (strong AI) could not succeed just by designing programs but would have to duplicate the causal powers of the human brain. This follows from 2 and 4.
This article can be viewed as an attempt to explore the consequences of two propositions. (1) Intentionality in human beings (and animals) is a product of causal features of the brain I assume this is an empirical fact about the actual causal relations between mental processes and brains It says simply that certain brain processes are sufficient for intentionality. (2) Instantiating a computer program is never by itself a sufficient condition of intentionality The main argument of this paper is directed at establishing this claim The form of the argument is to show how a human agent could instantiate the program and still not have the relevant intentionality. These two propositions have the following consequences (3) The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program. This is a strict logical consequence of 1 and 2. (4) Any mechanism capable of producing intentionality must have causal powers equal to those of the brain. This is meant to be a trivial consequence of 1. (5) Any attempt literally to create intentionality artificially (strong AI) could not succeed just by designing programs but would have to duplicate the causal powers of the human brain. This follows from 2 and 4.
3b. Harnad, S. (2001) What's Wrong and Right About Searle's Chinese RoomArgument?
Harnad, S. (2001) What's Wrong and Right About Searle's Chinese RoomArgument? In: M. Bishop & J. Preston (eds.) Essays on Searle's Chinese Room Argument. Oxford University Press.
Searle's Chinese Room Argument showed a fatal flaw in computationalism (the idea that mental states are just computational states) and helped usher in the era of situated robotics and symbol grounding (although Searle himself thought neuroscience was the only correct way to understand the mind).
3b. Harnad, S. (2001) What's Wrong and Right About Searle's Chinese RoomArgument?
Harnad, S. (2001) What's Wrong and Right About Searle's Chinese RoomArgument? In: M. Bishop & J. Preston (eds.) Essays on Searle's Chinese Room Argument. Oxford University Press.
Searle's Chinese Room Argument showed a fatal flaw in computationalism (the idea that mental states are just computational states) and helped usher in the era of situated robotics and symbol grounding (although Searle himself thought neuroscience was the only correct way to understand the mind).
4a. Cook, R. et al (2014). Mirror neurons: from origin to function
Cook, R., Bird, G., Catmur, C., Press, C., & Heyes, C. (2014). Mirror neurons: from origin to function. Behavioral and Brain Sciences, 37(02), 177-192.
This article argues that mirror neurons originate in sensorimotor associative learning and therefore a new approach is needed to investigate their functions. Mirror neurons were discovered about 20 years ago in the monkey brain, and there is now evidence that they are also present in the human brain. The intriguing feature of many mirror neurons is that they fire not only when the animal is performing an action, such as grasping an object using a power grip, but also when the animal passively observes a similar action performed by another agent. It is widely believed that mirror neurons are a genetic adaptation for action understanding; that they were designed by evolution to fulfill a specific socio-cognitive function. In contrast, we argue that mirror neurons are forged by domain-general processes of associative learning in the course of individual development, and, although they may have psychological functions, they do not necessarily have a specific evolutionary purpose or adaptive function. The evidence supporting this view shows that (1) mirror neurons do not consistently encode action “goals”; (2) the contingency- and context-sensitive nature of associative learning explains the full range of mirror neuron properties; (3) human infants receive enough sensorimotor experience to support associative learning of mirror neurons (“wealth of the stimulus”); and (4) mirror neurons can be changed in radical ways by sensorimotor training. The associative account implies that reliable information about the function of mirror neurons can be obtained only by research based on developmental history, system-level theory, and careful experimentation.
4a. Cook, R. et al (2014). Mirror neurons: from origin to function
Cook, R., Bird, G., Catmur, C., Press, C., & Heyes, C. (2014). Mirror neurons: from origin to function. Behavioral and Brain Sciences, 37(02), 177-192.
This article argues that mirror neurons originate in sensorimotor associative learning and therefore a new approach is needed to investigate their functions. Mirror neurons were discovered about 20 years ago in the monkey brain, and there is now evidence that they are also present in the human brain. The intriguing feature of many mirror neurons is that they fire not only when the animal is performing an action, such as grasping an object using a power grip, but also when the animal passively observes a similar action performed by another agent. It is widely believed that mirror neurons are a genetic adaptation for action understanding; that they were designed by evolution to fulfill a specific socio-cognitive function. In contrast, we argue that mirror neurons are forged by domain-general processes of associative learning in the course of individual development, and, although they may have psychological functions, they do not necessarily have a specific evolutionary purpose or adaptive function. The evidence supporting this view shows that (1) mirror neurons do not consistently encode action “goals”; (2) the contingency- and context-sensitive nature of associative learning explains the full range of mirror neuron properties; (3) human infants receive enough sensorimotor experience to support associative learning of mirror neurons (“wealth of the stimulus”); and (4) mirror neurons can be changed in radical ways by sensorimotor training. The associative account implies that reliable information about the function of mirror neurons can be obtained only by research based on developmental history, system-level theory, and careful experimentation.
4b. Fodor, J. (1999) "Why, why, does everyone go on so about thebrain?"
Fodor, J. (1999) "Why, why, does everyone go on so about thebrain?" London Review of Books21(19) 68-69.
I once gave a (perfectly awful) cognitive science lecture at a major centre for brain imaging research. The main project there, as best I could tell, was to provide subjects with some or other experimental tasks to do and take pictures of their brains while they did them. The lecture was followed by the usual mildly boozy dinner, over which professional inhibitions relaxed a bit. I kept asking, as politely as I could manage, how the neuroscientists decided which experimental tasks it would be interesting to make brain maps for. I kept getting the impression that they didn’t much care. Their idea was apparently that experimental data are, ipso facto, a good thing; and that experimental data about when and where the brain lights up are, ipso facto, a better thing than most. I guess I must have been unsubtle in pressing my question because, at a pause in the conversation, one of my hosts rounded on me. ‘You think we’re wasting our time, don’t you?’ he asked. I admit, I didn’t know quite what to say. I’ve been wondering about it ever since.
See also:
Grill-Spector, K., & Weiner, K. S. (2014). The functional architecture of the ventral temporal cortex and its role in categorization. Nature Reviews Neuroscience, 15(8), 536-548.
ABSTRACT: Visual categorization is thought to occur in the human ventral temporal cortex (VTC), but how this categorization is achieved is still largely unknown. In this Review, we consider the computations and representations that are necessary for categorization and examine how the microanatomical and macroanatomical layout of the VTC might optimize them to achieve rapid and flexible visual categorization. We propose that efficient categorization is achieved by organizing representations in a nested spatial hierarchy in the VTC. This spatial hierarchy serves as a neural infrastructure for the representational hierarchy of visual information in the VTC and thereby enables flexible access to category information at several levels of abstraction.
I once gave a (perfectly awful) cognitive science lecture at a major centre for brain imaging research. The main project there, as best I could tell, was to provide subjects with some or other experimental tasks to do and take pictures of their brains while they did them. The lecture was followed by the usual mildly boozy dinner, over which professional inhibitions relaxed a bit. I kept asking, as politely as I could manage, how the neuroscientists decided which experimental tasks it would be interesting to make brain maps for. I kept getting the impression that they didn’t much care. Their idea was apparently that experimental data are, ipso facto, a good thing; and that experimental data about when and where the brain lights up are, ipso facto, a better thing than most. I guess I must have been unsubtle in pressing my question because, at a pause in the conversation, one of my hosts rounded on me. ‘You think we’re wasting our time, don’t you?’ he asked. I admit, I didn’t know quite what to say. I’ve been wondering about it ever since.
See also:
Grill-Spector, K., & Weiner, K. S. (2014). The functional architecture of the ventral temporal cortex and its role in categorization. Nature Reviews Neuroscience, 15(8), 536-548.
ABSTRACT: Visual categorization is thought to occur in the human ventral temporal cortex (VTC), but how this categorization is achieved is still largely unknown. In this Review, we consider the computations and representations that are necessary for categorization and examine how the microanatomical and macroanatomical layout of the VTC might optimize them to achieve rapid and flexible visual categorization. We propose that efficient categorization is achieved by organizing representations in a nested spatial hierarchy in the VTC. This spatial hierarchy serves as a neural infrastructure for the representational hierarchy of visual information in the VTC and thereby enables flexible access to category information at several levels of abstraction.
4b. Fodor, J. (1999) "Why, why, does everyone go on so about thebrain?"
Fodor, J. (1999) "Why, why, does everyone go on so about thebrain?" London Review of Books21(19) 68-69.
I once gave a (perfectly awful) cognitive science lecture at a major centre for brain imaging research. The main project there, as best I could tell, was to provide subjects with some or other experimental tasks to do and take pictures of their brains while they did them. The lecture was followed by the usual mildly boozy dinner, over which professional inhibitions relaxed a bit. I kept asking, as politely as I could manage, how the neuroscientists decided which experimental tasks it would be interesting to make brain maps for. I kept getting the impression that they didn’t much care. Their idea was apparently that experimental data are, ipso facto, a good thing; and that experimental data about when and where the brain lights up are, ipso facto, a better thing than most. I guess I must have been unsubtle in pressing my question because, at a pause in the conversation, one of my hosts rounded on me. ‘You think we’re wasting our time, don’t you?’ he asked. I admit, I didn’t know quite what to say. I’ve been wondering about it ever since.
See also:
Grill-Spector, K., & Weiner, K. S. (2014). The functional architecture of the ventral temporal cortex and its role in categorization. Nature Reviews Neuroscience, 15(8), 536-548.
ABSTRACT: Visual categorization is thought to occur in the human ventral temporal cortex (VTC), but how this categorization is achieved is still largely unknown. In this Review, we consider the computations and representations that are necessary for categorization and examine how the microanatomical and macroanatomical layout of the VTC might optimize them to achieve rapid and flexible visual categorization. We propose that efficient categorization is achieved by organizing representations in a nested spatial hierarchy in the VTC. This spatial hierarchy serves as a neural infrastructure for the representational hierarchy of visual information in the VTC and thereby enables flexible access to category information at several levels of abstraction.
I once gave a (perfectly awful) cognitive science lecture at a major centre for brain imaging research. The main project there, as best I could tell, was to provide subjects with some or other experimental tasks to do and take pictures of their brains while they did them. The lecture was followed by the usual mildly boozy dinner, over which professional inhibitions relaxed a bit. I kept asking, as politely as I could manage, how the neuroscientists decided which experimental tasks it would be interesting to make brain maps for. I kept getting the impression that they didn’t much care. Their idea was apparently that experimental data are, ipso facto, a good thing; and that experimental data about when and where the brain lights up are, ipso facto, a better thing than most. I guess I must have been unsubtle in pressing my question because, at a pause in the conversation, one of my hosts rounded on me. ‘You think we’re wasting our time, don’t you?’ he asked. I admit, I didn’t know quite what to say. I’ve been wondering about it ever since.
See also:
Grill-Spector, K., & Weiner, K. S. (2014). The functional architecture of the ventral temporal cortex and its role in categorization. Nature Reviews Neuroscience, 15(8), 536-548.
ABSTRACT: Visual categorization is thought to occur in the human ventral temporal cortex (VTC), but how this categorization is achieved is still largely unknown. In this Review, we consider the computations and representations that are necessary for categorization and examine how the microanatomical and macroanatomical layout of the VTC might optimize them to achieve rapid and flexible visual categorization. We propose that efficient categorization is achieved by organizing representations in a nested spatial hierarchy in the VTC. This spatial hierarchy serves as a neural infrastructure for the representational hierarchy of visual information in the VTC and thereby enables flexible access to category information at several levels of abstraction.
5. Harnad, S. (2003) The Symbol Grounding Problem
Harnad, S. (2003) The Symbol Grounding Problem. Encylopedia of Cognitive Science. Nature Publishing Group. Macmillan.
or: Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1), 335-346.
or: https://en.wikipedia.org/wiki/Symbol_grounding
The Symbol Grounding Problem is related to the problem of how words get their meanings, and of what meanings are. The problem of meaning is in turn related to the problem of consciousness, or how it is that mental states are meaningful.
Barsalou, L. W. (2010). Grounded cognition: past, present, and future. Topics in Cognitive Science, 2(4), 716-724.
Bringsjord, S. (2014) The Symbol Grounding Problem... Remains Unsolved. Journal of Experimental & Theoretical Artificial Intelligence (in press)
or: Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1), 335-346.
or: https://en.wikipedia.org/wiki/Symbol_grounding
The Symbol Grounding Problem is related to the problem of how words get their meanings, and of what meanings are. The problem of meaning is in turn related to the problem of consciousness, or how it is that mental states are meaningful.
If you can't think of anything to skywrite, this might give you some ideas:In M. de Vega (Ed.), Symbols and Embodiment: Debates on Meaning and Cognition. Oxford University Press.Taddeo, M., & Floridi, L. (2005). Solving the symbol grounding problem: a critical review of fifteen years of research. Journal of Experimental & Theoretical Artificial Intelligence, 17(4), 419-445.
Steels, L. (2008) The Symbol Grounding Problem Has Been Solved. So What's Next?
Barsalou, L. W. (2010). Grounded cognition: past, present, and future. Topics in Cognitive Science, 2(4), 716-724.
Bringsjord, S. (2014) The Symbol Grounding Problem... Remains Unsolved. Journal of Experimental & Theoretical Artificial Intelligence (in press)
5. Harnad, S. (2003) The Symbol Grounding Problem
Harnad, S. (2003) The Symbol Grounding Problem. Encylopedia of Cognitive Science. Nature Publishing Group. Macmillan.
or: Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1), 335-346.
or: https://en.wikipedia.org/wiki/Symbol_grounding
The Symbol Grounding Problem is related to the problem of how words get their meanings, and of what meanings are. The problem of meaning is in turn related to the problem of consciousness, or how it is that mental states are meaningful.
Barsalou, L. W. (2010). Grounded cognition: past, present, and future. Topics in Cognitive Science, 2(4), 716-724.
Bringsjord, S. (2014) The Symbol Grounding Problem... Remains Unsolved. Journal of Experimental & Theoretical Artificial Intelligence (in press)
or: Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1), 335-346.
or: https://en.wikipedia.org/wiki/Symbol_grounding
The Symbol Grounding Problem is related to the problem of how words get their meanings, and of what meanings are. The problem of meaning is in turn related to the problem of consciousness, or how it is that mental states are meaningful.
If you can't think of anything to skywrite, this might give you some ideas:In M. de Vega (Ed.), Symbols and Embodiment: Debates on Meaning and Cognition. Oxford University Press.
Taddeo, M., & Floridi, L. (2005). Solving the symbol grounding problem: a critical review of fifteen years of research. Journal of Experimental & Theoretical Artificial Intelligence, 17(4), 419-445.
Steels, L. (2008) The Symbol Grounding Problem Has Been Solved. So What's Next?
Barsalou, L. W. (2010). Grounded cognition: past, present, and future. Topics in Cognitive Science, 2(4), 716-724.
Bringsjord, S. (2014) The Symbol Grounding Problem... Remains Unsolved. Journal of Experimental & Theoretical Artificial Intelligence (in press)
6a. Harnad, S. (2005) To Cognize is to Categorize: Cognition is Categorization
Harnad, S. (2005) To Cognize is to Categorize: Cognition is Categorization, in Lefebvre, C. and Cohen, H., Eds. Handbook of Categorization. Elsevier.
We organisms are sensorimotor systems. The things in the world come in contact with our sensory surfaces, and we interact with them based on what that sensorimotor contact “affords”. All of our categories consist in ways we behave differently toward different kinds of things -- things we do or don’t eat, mate-with, or flee-from, or the things that we describe, through our language, as prime numbers, affordances, absolute discriminables, or truths. That is all that cognition is for, and about.
6a. Harnad, S. (2005) To Cognize is to Categorize: Cognition is Categorization
Harnad, S. (2005) To Cognize is to Categorize: Cognition is Categorization, in Lefebvre, C. and Cohen, H., Eds. Handbook of Categorization. Elsevier.
We organisms are sensorimotor systems. The things in the world come in contact with our sensory surfaces, and we interact with them based on what that sensorimotor contact “affords”. All of our categories consist in ways we behave differently toward different kinds of things -- things we do or don’t eat, mate-with, or flee-from, or the things that we describe, through our language, as prime numbers, affordances, absolute discriminables, or truths. That is all that cognition is for, and about.
6b. Harnad, S. (2003b) Categorical Perception.
Harnad, S. (2003b) Categorical Perception. Encyclopedia of Cognitive Science. Nature Publishing Group. Macmillan.
Differences can be perceived as gradual and quantitative, as with different shades of gray, or they can be perceived as more abrupt and qualitative, as with different colors. The first is called continuous perception and the second categorical perception. Categorical perception (CP) can be inborn or can be induced by learning. Formerly thought to be peculiar to speech and color perception, CP turns out to be far more general, and may be related to how the neural networks in our brains detect the features that allow us to sort the things in the world into their proper categories, "warping" perceived similarities and differences so as to compress some things into the same category and separate others into different categories.
Pullum, Geoffrey K. (1991). The Great Eskimo Vocabulary Hoax and other Irreverent Essays on the Study of Language. University of Chicago Press.
6b. Harnad, S. (2003b) Categorical Perception.
Harnad, S. (2003b) Categorical Perception. Encyclopedia of Cognitive Science. Nature Publishing Group. Macmillan.
Differences can be perceived as gradual and quantitative, as with different shades of gray, or they can be perceived as more abrupt and qualitative, as with different colors. The first is called continuous perception and the second categorical perception. Categorical perception (CP) can be inborn or can be induced by learning. Formerly thought to be peculiar to speech and color perception, CP turns out to be far more general, and may be related to how the neural networks in our brains detect the features that allow us to sort the things in the world into their proper categories, "warping" perceived similarities and differences so as to compress some things into the same category and separate others into different categories.
Pullum, Geoffrey K. (1991). The Great Eskimo Vocabulary Hoax and other Irreverent Essays on the Study of Language. University of Chicago Press.
7a. Confer et al (2010) Evolutionary Psychology Controversies, Questions, Prospects, and Limitations
Confer, Jaime C., Judith A. Easton, Diana S. Fleischman, Cari D. Goetz, David M. G. Lewis, Carin Perilloux, and David M. Buss (2010) Evolutionary Psychology Controversies, Questions, Prospects, and Limitations. American Psychologist 65 (2): 110–126 DOI: 10.1037/a0018413
Evolutionary psychology has emerged over the past 15 years as a major theoretical perspective, generating an increasing volume of empirical studies and assuming a larger presence within psychological science. At the same time, it has generated critiques and remains controversial among some psychologists. Some of the controversy stems from hypotheses that go against traditional psychological theories; some from empirical findings that may have disturbing implications; some from misunderstandings about the logic of evolutionary psychology; and some from reasonable scientific concerns about its underlying framework. This article identifies some of the most common concerns and attempts to elucidate evolutionary psychology’s stance pertaining to them. These include issues of testability and falsifiability; the domain specificity versus domain generality of psychological mechanisms; the role of novel environments as they interact with evolved psychological circuits; the role of genes in the conceptual structure of evolutionary psychology; the roles of learning, socialization, and culture in evolutionary psychology; and the practical value of applied evolutionary psychology. The article concludes with a discussion of the limitations of current evolutionary psychology.
Evolutionary psychology has emerged over the past 15 years as a major theoretical perspective, generating an increasing volume of empirical studies and assuming a larger presence within psychological science. At the same time, it has generated critiques and remains controversial among some psychologists. Some of the controversy stems from hypotheses that go against traditional psychological theories; some from empirical findings that may have disturbing implications; some from misunderstandings about the logic of evolutionary psychology; and some from reasonable scientific concerns about its underlying framework. This article identifies some of the most common concerns and attempts to elucidate evolutionary psychology’s stance pertaining to them. These include issues of testability and falsifiability; the domain specificity versus domain generality of psychological mechanisms; the role of novel environments as they interact with evolved psychological circuits; the role of genes in the conceptual structure of evolutionary psychology; the roles of learning, socialization, and culture in evolutionary psychology; and the practical value of applied evolutionary psychology. The article concludes with a discussion of the limitations of current evolutionary psychology.
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Harnad, S. (2003b) Categorical Perception . Encyclopedia of Cognitive Science . Nature Publishing Group. Macmillan. http://www.youtube.com...
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Psychology PSYC 538, Winter 2018: Categorization, Communication and Consciousness Time : Tuesdays 2:35-5:25 Place : STBIO S3/3 ...
