Indirection and symbol-like processing in the prefrontal cortex and basal ganglia

The ability to flexibly, rapidly, and accurately perform novel tasks is a hallmark of human behavior. In our everyday lives we are often faced with arbitrary instructions that we must understand and follow, and we are able to do so with remarkable ease. It has frequently been argued that this ability relies on symbol processing, which depends critically on the ability to represent variables and bind them to arbitrary values. Whereas symbol processing is a fundamental feature of all computer systems, it remains a mystery whether and how this ability is carried out by the brain. Here, we provide an example of how the structure and functioning of the prefrontal cortex/basal ganglia working memory system can support variable binding, through a form of indirection (akin to a pointer in computer science). We show how indirection enables the system to flexibly generalize its behavior substantially beyond its direct experience (i.e., systematicity). We argue that this provides a biologically plausible mechanism that approximates a key component of symbol processing, exhibiting both the flexibility, but also some of the limitations, that are associated with this ability in humans.

[1]  G. E. Alexander,et al.  Parallel organization of functionally segregated circuits linking basal ganglia and cortex. , 1986, Annual review of neuroscience.

[2]  Geoffrey E. Hinton,et al.  A Distributed Connectionist Production System , 1988, Cogn. Sci..

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

[4]  Geoffrey E. Hinton,et al.  A Distributed Connectionist Production System , 1988, Cogn. Sci..

[5]  Tim van Gelder,et al.  Compositionality: A Connectionist Variation on a Classical Theme , 1990, Cogn. Sci..

[6]  P. Smolensky Representation in Connectionist Networks , 1990 .

[7]  James L. McClelland,et al.  Learning the structure of event sequences. , 1991, Journal of experimental psychology. General.

[8]  O. Brousse Generativity and systematicity in neural network combinatorial learning , 1992 .

[9]  I. Biederman,et al.  Dynamic binding in a neural network for shape recognition. , 1992, Psychological review.

[10]  J. B. Levitt,et al.  Topography of pyramidal neuron intrinsic connections in macaque monkey prefrontal cortex (areas 9 and 46) , 1993, The Journal of comparative neurology.

[11]  P. Goldman-Rakic,et al.  Prefrontal neuronal activity in rhesus monkeys performing a delayed anti-saccade task , 1993, Nature.

[12]  J. Elman,et al.  Learning and morphological change , 1995, Cognition.

[13]  Tony A. Plate,et al.  Holographic reduced representations , 1995, IEEE Trans. Neural Networks.

[14]  James L. McClelland,et al.  Understanding normal and impaired word reading: computational principles in quasi-regular domains. , 1996, Psychological review.

[15]  J. B. Levitt,et al.  Patterns of intrinsic and associational circuitry in monkey prefrontal cortex , 1996, The Journal of comparative neurology.

[16]  Randall C. O'Reilly,et al.  Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm , 1996, Neural Computation.

[17]  L. Abbott,et al.  Synaptic Depression and Cortical Gain Control , 1997, Science.

[18]  G. Marcus Rethinking Eliminative Connectionism , 1998, Cognitive Psychology.

[19]  J. Jonides,et al.  Storage and executive processes in the frontal lobes. , 1999, Science.

[20]  P M Todd,et al.  Précis of Simple heuristics that make us smart , 2000, Behavioral and Brain Sciences.

[21]  Nikolaus R. McFarland,et al.  Striatonigrostriatal Pathways in Primates Form an Ascending Spiral from the Shell to the Dorsolateral Striatum , 2000, The Journal of Neuroscience.

[22]  Michael J. Frank,et al.  Interactions between frontal cortex and basal ganglia in working memory: A computational model , 2001, Cognitive, affective & behavioral neuroscience.

[23]  P. Johnson-Laird Mental models and deduction , 2001, Trends in Cognitive Sciences.

[24]  Randall C. O'Reilly,et al.  Generalization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning , 2001, Neural Computation.

[25]  Jonathan D. Cohen,et al.  Prefrontal cortex and dynamic categorization tasks: representational organization and neuromodulatory control. , 2002, Cerebral cortex.

[26]  E. Koechlin,et al.  The Architecture of Cognitive Control in the Human Prefrontal Cortex , 2003, Science.

[27]  Lizabeth M Romanski,et al.  Domain specificity in the primate prefrontal cortex , 2004, Cognitive, affective & behavioral neuroscience.

[28]  D. Plaut,et al.  Doing without schema hierarchies: a recurrent connectionist approach to normal and impaired routine sequential action. , 2004, Psychological review.

[29]  Jonathan D. Cohen,et al.  Prefrontal cortex and flexible cognitive control: rules without symbols. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[30]  M. Petrides Lateral prefrontal cortex: architectonic and functional organization , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[31]  S. Haber,et al.  Reward-Related Cortical Inputs Define a Large Striatal Region in Primates That Interface with Associative Cortical Connections, Providing a Substrate for Incentive-Based Learning , 2006, The Journal of Neuroscience.

[32]  R. O’Reilly Biologically Based Computational Models of High-Level Cognition , 2006, Science.

[33]  Thomas E. Hazy,et al.  Banishing the homunculus: Making working memory work , 2006, Neuroscience.

[34]  Michael J. Frank,et al.  Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia , 2006, Neural Computation.

[35]  David Badre,et al.  Functional Magnetic Resonance Imaging Evidence for a Hierarchical Organization of the Prefrontal Cortex , 2007, Journal of Cognitive Neuroscience.

[36]  R. Cummins,et al.  ON THE SYSTEMATICITY OF LANGUAGE AND THOUGHT , 2008 .

[37]  Brian Mingus,et al.  The Emergent neural modeling system , 2008, Neural Networks.

[38]  John R. Anderson,et al.  A Connectionist Implementation of the ACT-R Production System , 2008 .

[39]  Jeremy R. Reynolds,et al.  Developing PFC representations using reinforcement learning , 2009, Cognition.

[40]  C. Lebiere,et al.  Conditional routing of information to the cortex: a model of the basal ganglia's role in cognitive coordination. , 2010, Psychological review.

[41]  Thomas E. Hazy,et al.  Neural mechanisms of acquired phasic dopamine responses in learning , 2010, Neuroscience & Biobehavioral Reviews.

[42]  R. O’Reilly The What and How of prefrontal cortical organization , 2010, Trends in Neurosciences.

[43]  James L. McClelland,et al.  Letting structure emerge: connectionist and dynamical systems approaches to cognition , 2010, Trends in Cognitive Sciences.

[44]  Trenton E. Kriete,et al.  Generalisation benefits of output gating in a model of prefrontal cortex , 2011, Connect. Sci..

[45]  Trevor Bekolay,et al.  Neural representations of compositional structures: representing and manipulating vector spaces with spiking neurons , 2011, Connect. Sci..

[46]  Kenneth J. Hayworth,et al.  Dynamically Partitionable Autoassociative Networks as a Solution to the Neural Binding Problem , 2012, Front. Comput. Neurosci..

[47]  David Badre,et al.  Microstructural organizational patterns in the human corticostriatal system. , 2012, Journal of neurophysiology.

[48]  Lars Niklasson,et al.  Classicalism and Cognitive Architecture , 2019, Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society.

[49]  David C. Noelle,et al.  Methods for Learning Articulated Attractors over Internal Representations , 1999 .