Indirection and symbol-like processing in the prefrontal cortex and basal ganglia
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Cognitive scientists have long hypothesized that variable binding plays a central role in analogical reasoning and abstract reasoning more broadly, potentially allowing generalization of abstract roles across different contexts.
“Cognitive scientists have long hypothesized that variable binding plays a central role in analogical reasoning, and abstract reasoning more broadly, as it potentially allows generalization of abstract roles across different contexts.”
From Emergent analogical reasoning in large language models (2023) · cites this paper as [55]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Parallel organization of functionally segregated circuits linking basal ganglia and cortex
Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.09.030186.002041 · Cited by 8,527 · see citing works
Connectionism and cognitive architecture: A critical analysis
Fodor, J. A., & Pylyshyn, Z. W. (1988) · Cognition · doi:10.1016/0010-0277(88)90031-5 · Cited by 4,022 · see citing works
Storage and Executive Processes in the Frontal Lobes
Smith, E. E., & Jonides, J. (1999) · Science · doi:10.1126/science.283.5408.1657 · Cited by 2,834 · see citing works
Understanding normal and impaired word reading: Computational principles in quasi-regular domains.
Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996) · Psychological Review · doi:10.1037/0033-295x.103.1.56 · Cited by 2,698 · see citing works
The Architecture of Cognitive Control in the Human Prefrontal Cortex
Koechlin, E., Ody, C., & Kouneiher, F. (2003) · Science · doi:10.1126/science.1088545 · Cited by 1,922 · see citing works
Haber, S. N., Fudge, J. L., & McFarland, N. R. (2000) · The Journal of Neuroscience · doi:10.1523/jneurosci.20-06-02369.2000 · Cited by 1,610 · see citing works
Lateral prefrontal cortex: architectonic and functional organization
Petrides, M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1631 · Cited by 1,212 · see citing works
O’Reilly, R. C., & Frank, M. J. (2005) · Neural Computation · doi:10.1162/089976606775093909 · Cited by 1,066 · see citing works
Self-citationSmolensky, P. (1990) · Artificial Intelligence · doi:10.1016/0004-3702(90)90007-m · Cited by 959 · see citing works
Dynamic binding in a neural network for shape recognition.
Hummel, J. E., & Biederman, I. (1992) · Psychological Review · doi:10.1037/0033-295x.99.3.480 · Cited by 923 · see citing works
Interactions between frontal cortex and basal ganglia in working memory: A computational model
FRANK, M. J., LOUGHRY, B., & O'REILLY, R. C. (2001) · Cognitive, Affective, & Behavioral Neuroscience · doi:10.3758/cabn.1.2.137 · Cited by 879 · see citing works
Self-citationPrécis of Simple heuristics that make us smart
Todd, P. M., & Gigerenzer, G. (2000) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00003447 · Cited by 718 · see citing works
Haber, S. N., Kim, K.-S., Mailly, P., & Calzavara, R. (2006) · The Journal of Neuroscience · doi:10.1523/jneurosci.0271-06.2006 · Cited by 686 · see citing works
Prefrontal neuronal activity in rhesus monkeys performing a delayed anti-saccade task
Funahashi, S., Chafee, M. V., & Goldman, P. S. (1993) · Nature · doi:10.1038/365753a0 · Cited by 662 · see citing works
Holographic reduced representations
Plate, T. A. (1995) · IEEE Transactions on Neural Networks · doi:10.1109/72.377968 · Cited by 657 · see citing works
Badre, D., & D'Esposito, M. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2007.19.12.2082 · Cited by 615 · see citing works
Learning the structure of event sequences.
Cleeremans, A., & McClelland, J. L. (1991) · Journal of Experimental Psychology General · doi:10.1037//0096-3445.120.3.235 · Cited by 584 · see citing works
Learning the structure of event sequences.
Cleeremans, A., & McClelland, J. L. (1991) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.120.3.235 · Cited by 516 · see citing works
Compositionality: A Connectionist Variation on a Classical Theme
van Gelder, T. (1990) · Cognitive Science · doi:10.1016/0364-0213(90)90017-q · Cited by 467 · see citing works
Letting structure emerge: connectionist and dynamical systems approaches to cognition
McClelland, J. L., Botvinick, M., Noelle, D. C., Plaut, D. C., Rogers, T. T., Seidenberg, M. S., & Smith, L. B. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.06.002 · Cited by 457 · see citing works
Self-citationBiologically Based Computational Models of High-Level Cognition
O’Reilly, R. C. (2006) · Science · doi:10.1126/science.1127242 · Cited by 447 · see citing works
Self-citationJohnson-Laird, P. N. (2001) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01751-4 · Cited by 411 · see citing works
Prefrontal cortex and flexible cognitive control: Rules without symbols
Rougier, N. P., Noelle, D. C., Braver, T. S., Cohen, J. D., & O’Reilly, R. C. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0502455102 · Cited by 397 · see citing works
Self-citationBanishing the homunculus: Making working memory work
Hazy, T. E., Frank, M. J., & O’Reilly, R. C. (2006) · Neuroscience · doi:10.1016/j.neuroscience.2005.04.067 · Cited by 385 · see citing works
Self-citationBotvinick, M., & Plaut, D. C. (2004) · Psychological Review · doi:10.1037/0033-295x.111.2.395 · Cited by 361 · see citing works
Stocco, A., Lebiere, C. J., & Anderson, J. R. (2010) · Psychological Review · doi:10.1037/a0019077 · Cited by 352 · see citing works
Rethinking Eliminative Connectionism
Marcus, G. (1998) · Cognitive Psychology · doi:10.1006/cogp.1998.0694 · Cited by 346 · see citing works
A Distributed Connectionist Production System
Touretzky, D. S., & Hinton, G. E. (1988) · Cognitive Science · doi:10.1207/s15516709cog1203_4 · Cited by 286 · see citing works
Learning and morphological change
Hare, M. L., & Elman, J. L. (1995) · Cognition · doi:10.1016/0010-0277(94)00655-5 · Cited by 272 · see citing works
The What and How of prefrontal cortical organization
O’Reilly, R. C. (2010) · Trends in Neurosciences · doi:10.1016/j.tins.2010.05.002 · Cited by 271 · see citing works
Self-citationLevitt, J. B., Lewis, D. A., Yoshioka, T., & Lund, J. S. (1993) · The Journal of Comparative Neurology · doi:10.1002/cne.903380304 · Cited by 260 · see citing works
O'Reilly, R. C. (2002) · Cerebral Cortex · doi:10.1093/cercor/12.3.246 · Cited by 241 · see citing works
Self-citationGeneralization in Interactive Networks: The Benefits of Inhibitory Competition and Hebbian Learning
O’Reilly, R. C. (2001) · Neural Computation · doi:10.1162/08997660152002834 · Cited by 133 · see citing works
Self-citationOn the Systematicity of Language and Thought
Johnson, K. R. (2004) · The Journal of Philosophy · doi:10.5840/jphil2004101321 · Cited by 125 · see citing works
Domain specificity in the primate prefrontal cortex
Romanski, L. M. (2004) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/cabn.4.4.421 · Cited by 98 · see citing works
Microstructural organizational patterns in the human corticostriatal system
Verstynen, T., Badre, D., Jarbo, K., & Schneider, W. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00995.2011 · Cited by 88 · see citing works
Steiner, H., & Gerfen, C. R. (1996) · The Journal of Comparative Neurology · doi:10.1002/(sici)1096-9861(19961223)376:4 · Cited by 81 · see citing works
A Connectionist Implementation of the ACT-R Production System
Lebiere, C. J., & Anderson, J. R. (2018) · KiltHub Repository · doi:10.1184/r1/6613085 · Cited by 66 · see citing works
Developing PFC representations using reinforcement learning
Reynolds, J. R., & O’Reilly, R. C. (2009) · Cognition · doi:10.1016/j.cognition.2009.05.015 · Cited by 52 · see citing works
Self-citationStewart, T. C., Bekolay, T., & Eliasmith, C. (2011) · Connection Science · doi:10.1080/09540091.2011.571761 · Cited by 34 · see citing works
Classicalism and Cognitive Architecture
van Gelder, T., & Niklasson, L. (2019) · Routledge eBooks · doi:10.4324/9781315789354-157 · Cited by 18 · see citing works
Generalisation benefits of output gating in a model of prefrontal cortex
Kriete, T., & Noelle, D. C. (2011) · Connection Science · doi:10.1080/09540091.2011.569881 · Cited by 17 · see citing works
Self-citationDynamically Partitionable Autoassociative Networks as a Solution to the Neural Binding Problem
Hayworth, K. J. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2012.00073 · Cited by 13 · see citing works
Generativity and Systematicity in Neural Network Combinatorial Learning
Brousse, O. (1993) · Cited by 8 · see citing works
Methods for Learning Articulated Attractors over Internal Representations
Noelle, D. C., & Zimdars, A. L. (2020) · Psychology Press eBooks · doi:10.4324/9781410603494-89 · Cited by 3 · see citing works
Self-citationIn Search Of Articulated Attractors
Noelle, D. C., & Cottrell, G. W. (1996) · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Pucak ML, Levitt JB, Lund JS, Lewis DA (1996) Patterns of intrinsic and associational circuitry in monkey prefrontal cortex. J Comp Neurol 376(4):614-630.
Cited by
Sorted by citations
Emergent analogical reasoning in large language models
Webb, T., Holyoak, K. J., & Lu, H. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01659-w · Cited by 404 · see citing works
Multiple gates on working memory
Chatham, C. H., & Badre, D. (2015) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2014.08.001 · Cited by 268 · see citing works
Cognitive Control: Core Constructs and Current Considerations
Cohen, J. D. (2017) · The Wiley Handbook of Cognitive Control · doi:10.1002/9781118920497.ch1 · Cited by 179 · see citing works
Self-citationToward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works
Binding by random bursts: a computational model of cognitive control
Verguts, T. (2017) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_01117 · Cited by 76 · see citing works
Snyder, H. R., Banich, M. T., & Munakata, Y. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00652 · Cited by 24 · see citing works
- Authors
- Kriete, T., Noelle, D. C., Cohen, J. D., & O’Reilly, R. C.
- Affiliations
- University of Colorado Boulder (US)
- University of California, Merced (US)
- Princeton University (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-09-23
- Volume
- 110
- Issue
- 41
- Pages
- 16390-16395
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3799308
- OpenAlex
- W2107139863
- Cited by
- 181
- References
- 46
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 27 · 2023: 13 · 2022: 8 · 2021: 26 · 2020: 14 · 2019: 27 · 2018: 16 · 2017: 14 · 2016: 9 · 2015: 6 · 2014: 8 · 2013: 1
- FWCI
- 4.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Intelligence Advanced Research Projects Activity (D10PC20021)
- National Cancer Institute (D10PC20021)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- indirection, symbolic processing, prefrontal cortex, basal ganglia, working memory, variable binding, systematicity, generalization, cognitive flexibility
- MeSH terms
- Basal Ganglia, Cognition, Humans, Models, Neurological, Neurons, Symbolism, Prefrontal Cortex
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review