1a. What is Computation?

What is a Turing Machine? 
Computation is Symbol Manipulation 
What is a Physical Symbol System?

Optional Reading:
Pylyshyn, Z (1989) Computation in cognitive science. In MI Posner (Ed.) Foundations of Cognitive Science. MIT Press 

Overview:  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 reading 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 basic ideas, which are all 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

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 

http://www.youtube.com/watch?v=jUAabgb161k

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?"

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 

http://www.youtube.com/watch?v=cDxCBqSm1ys

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. 

see also:

Click here --> SEARLE VIDEO

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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.

http://www.youtube.com/watch?v=l7fsQkmcuSI

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. Rizzolatti, G., & Destro, M. F. (2008). Mirror neurons. Scholarpedia, 3(1), 2055.

Rizzolatti, G., & Destro, M. F. (2008). Mirror neurons. Scholarpedia, 3(1), 2055.

[Note that the main reading has been updated. Bonini et al (2022) was too hard to understand, Rizzolattti & Destro (2008) has been substituted for it]

What might be the functional role of the mirror neuron system? A series of hypotheses such as imitation, action understanding, intention understanding, and empathy have been put forward to explain the functional role of the mirror neurons. In addition to these, it has also been suggested that the mirror neuron system represents the basic neural mechanism from which language evolved.

Bonini, L., Rotunno, C., Arcuri, E., & Gallese, V. (2022). Mirror neurons 30 years later: implications and applications. Trends in Cognitive Sciences.

Mirror neurons (MNs) were first described in a seminal paper in 1992 as a class of premotor cells discharging during both action execution and observation. Despite their debated origin and function, recent studies in several species, from birds to humans, revealed that beyond MNs properly so called, a variety of cell types distributed among multiple motor, sensory, and emotional brain areas form a ‘mirror mechanism’ more complex and flexible than originally thought, which has an evolutionarily conserved role in social interaction. Here, we trace the current limits and envisage the future trends of this discovery, showing that it inspired translational research and the development of new neurorehabilitation approaches, and constitutes a point of no return in social and affective neuroscience.

Optional:

Bandera, J. P., Marfil, R., Molina-Tanco, L., Rodriguez, J. A., Bandera, A., & Sandoval, F. (2007). Robot learning by active imitation. INTECH Open Access Publisher.

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.

Bonini, L., Rotunno, C., Arcuri, E., & Gallese, V. (2022). Mirror neurons 30 years later: implications and applications. Trends in Cognitive Sciences.