The Neurobiological Bases for the Computational Theory of Mind

When we were young, Jerry Fodor and I, so, too, was the computational theory of mind, the central doctrine of cognitive science. It broke the theoretical fetters imposed by the mindless behaviorism that had dominated psychology, philosophy, and behavioral neuroscience for decades. Fodor was making major contributions to cognitive science by spelling out its implications. Some of the implications seemed to me, then and now, to be obvious, but only after Fodor had spelled them out. One such implication was that the mind must possess (unconscious) symbols and (equally unconscious) rules for manipulating them. That is, it must have a language of thought (Fodor, 1975), just as do computing machines. Because the symbols are, on the one hand, objects of principled manipulation— they are the stuff of computation— and because, on the other hand, some of them refer to things outside the mind, it follows that the language of thought has a syntax and a semantics. When Fodor pointed this out, it seemed to me beyond reasonable dispute, although it has in fact been disputed, even unto the present day (Aydede, 1977; Laurence & Margolis, 1997: Schneider, 2009). What I found thought provoking about Fodor’s insight was just what connectionists objected to: its neuroscientific implications. If one believed in the computational theory of mind, then the symbols and the machinery for manipulating them must have a material realization in the brain. The problem was that no one knew what it might be. If there were symbols, then they must reside in memory, because the basic function of a symbol in a computing machine is to carry information forward in time in a computationally accessible form (Gallistel & King, 2010). Most neuroscientists were and are OUP UNCORRECTED PROOF – FIRSTPROOFS, Mon Jun 19 2017, NEWGEN

[1]  M. Glickstein,et al.  Discrete lesions of the cerebellar cortex abolish the classically conditioned nictitating membrane response of the rabbit , 1984, Behavioural Brain Research.

[2]  J. Fodor,et al.  Connectionism and cognitive architecture: A critical analysis , 1988, Cognition.

[3]  Stephen Grossberg,et al.  Neural dynamics of adaptive timing and temporal discrimination during associative learning , 1989, Neural Networks.

[4]  R. Harvey,et al.  Quantitatives studies on the mammalian cerebellum , 1991, Progress in Neurobiology.

[5]  Richard F. Thompson,et al.  Localization of a memory trace in the mammalian brain. , 1993, Science.

[6]  W. Gehring,et al.  Homology of the eyeless gene of Drosophila to the Small eye gene in mice and Aniridia in humans. , 1994, Science.

[7]  P. Callaerts,et al.  Induction of ectopic eyes by targeted expression of the eyeless gene in Drosophila. , 1995, Science.

[8]  K M Gothard,et al.  Binding of hippocampal CA1 neural activity to multiple reference frames in a landmark-based navigation task , 1996, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[9]  R. Axel,et al.  The molecular biology of olfactory perception. , 1996, Cold Spring Harbor symposia on quantitative biology.

[10]  K M Gothard,et al.  Dynamics of Mismatch Correction in the Hippocampal Ensemble Code for Space: Interaction between Path Integration and Environmental Cues , 1996, The Journal of Neuroscience.

[11]  S. Laurence,et al.  Regress arguments against the language of thought , 1997 .

[12]  M. Wilson,et al.  Trajectory Encoding in the Hippocampus and Entorhinal Cortex , 2000, Neuron.

[13]  C. Gallistel,et al.  Time, rate, and conditioning. , 2000, Psychological review.

[14]  Arne D. Ekstrom,et al.  Dynamics of Hippocampal Ensemble Activity Realignment: Time versus Space , 2000, The Journal of Neuroscience.

[15]  S. Laughlin,et al.  An Energy Budget for Signaling in the Grey Matter of the Brain , 2001, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[16]  S. J. Martin,et al.  New life in an old idea: The synaptic plasticity and memory hypothesis revisited , 2002, Hippocampus.

[17]  Richard F. Thompson,et al.  Cerebellar cortical inhibition and classical eyeblink conditioning , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Richard F. Thompson,et al.  Neural substrates of eyeblink conditioning: acquisition and retention. , 2003, Learning & memory.

[19]  M. Nicolelis,et al.  Interval timing and the encoding of signal duration by ensembles of cortical and striatal neurons. , 2003, Behavioral neuroscience.

[20]  R. Muller,et al.  Representation of Objects in Space by Two Classes of Hippocampal Pyramidal Cells , 2004, The Journal of general physiology.

[21]  J. Rawson,et al.  Evidence that Climbing Fibers Control an Intrinsic Spike Generator in Cerebellar Purkinje Cells , 2004, The Journal of Neuroscience.

[22]  Murat Aydede,et al.  Language of Thought: The Connectionist Contribution , 1997, Minds and Machines.

[23]  M. Glickstein,et al.  Classical conditioning of the nictitating membrane response of the rabbit , 2004, Experimental Brain Research.

[24]  S. Carroll.  Endless forms most beautiful : the new science of evo devo and the making of the animal kingdom , 2005 .

[25]  J. Wolpaw,et al.  Spinal cord plasticity in acquisition and maintenance of motor skills , 2007, Acta physiologica.

[26]  U. Windhorst.  Muscle proprioceptive feedback and spinal networks , 2007, Brain Research Bulletin.

[27]  G. Hesslow,et al.  Acquisition, Extinction, and Reacquisition of a Cerebellar Cortical Memory Trace , 2007, The Journal of Neuroscience.

[28]  G. Buzsáki,et al.  Hippocampal Network Dynamics Constrain the Time Lag between Pyramidal Cells across Modified Environments , 2008, The Journal of Neuroscience.

[29]  Warren H Meck,et al.  Cortico-striatal Representation of Time in Animals and Humans This Review Comes from a Themed Issue on Cognitive Neuroscience Edited Evidence from Patient Populations and Electrical Potentials Neuroimaging Evidence Using Fmri and Pet , 2022 .

[30]  C. Gallistel,et al.  Temporal maps and informativeness in associative learning , 2009, Trends in Neurosciences.

[31]  Charles R. Gallistel,et al.  Memory and the Computational Brain: Why Cognitive Science will Transform Neuroscience , 2009 .

[32]  Jonathan R. Whitlock,et al.  Fragmentation of grid cell maps in a multicompartment environment , 2009, Nature Neuroscience.

[33]  J. Carlson,et al.  Olfactory Perception: Receptors, Cells, and Circuits , 2009, Cell.

[34]  Shigeru Tanaka,et al.  Computational Models of Timing Mechanisms in the Cerebellar Granular Layer , 2009, The Cerebellum.

[35]  S. Carroll,et al.  Deep homology and the origins of evolutionary novelty , 2009, Nature.

[36]  Susan Schneider,et al.  LOT, CTM, and the Elephant in the Room , 2009, Synthese.

[37]  T. Sejnowski,et al.  Metabolic cost as a unifying principle governing neuronal biophysics , 2010, Proceedings of the National Academy of Sciences.

[38]  C. Gallistel,et al.  Time and Associative Learning. , 2010, Comparative cognition & behavior reviews.

[39]  H. Eichenbaum,et al.  Hippocampal “Time Cells” Bridge the Gap in Memory for Discontiguous Events , 2011, Neuron.

[40]  G. Hesslow,et al.  Learning Stimulus Intervals—Adaptive Timing of Conditioned Purkinje Cell Responses , 2011, The Cerebellum.

[41]  G. Hesslow,et al.  Time Course of Classically Conditioned Purkinje Cell Response Is Determined by Initial Part of Conditioned Stimulus , 2011, The Journal of Neuroscience.

[42]  D. McDermott.  LANGUAGE OF THOUGHT , 2012 .

[43]  H. Eichenbaum.  Memory on time , 2013, Trends in Cognitive Sciences.

[44]  Anders Rasmussen,et al.  Classical conditioning of motor responses: What is the learning mechanism? , 2013, Neural Networks.

[45]  Ewan Birney,et al.  Towards practical, high-capacity, low-maintenance information storage in synthesized DNA , 2013, Nature.

[46]  C. Gallistel,et al.  The neuroscience of learning: beyond the Hebbian synapse. , 2013, Annual review of psychology.

[47]  D. Glanzman,et al.  Reinstatement of long-term memory following erasure of its behavioral and synaptic expression in Aplysia , 2014, eLife.

[48]  George J Augustine,et al.  Precise Control of Movement Kinematics by Optogenetic Inhibition of Purkinje Cell Activity , 2014, The Journal of Neuroscience.

[49]  G. Hesslow,et al.  Memory trace and timing mechanism localized to cerebellar Purkinje cells , 2014, Proceedings of the National Academy of Sciences.

[50]  Michele Pignatelli,et al.  Engram cells retain memory under retrograde amnesia , 2015, Science.

[51]  Marina Daecher.  Master Control Genes In Development And Evolution The Homeobox Story , 2016 .

[52]  Russell A. Epstein,et al.  Environmental Geometry Aligns the Hippocampal Map during Spatial Reorientation , 2017, Current Biology.