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Murphy E, Holmes E, Friston K. Natural language syntax complies with the free-energy principle. SYNTHESE 2024; 203:154. [PMID: 38706520 PMCID: PMC11068586 DOI: 10.1007/s11229-024-04566-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 03/15/2024] [Indexed: 05/07/2024]
Abstract
Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design-such as "minimal search" criteria from theoretical syntax-adhere to the FEP. This affords a greater degree of explanatory power to the FEP-with respect to higher language functions-and offers linguistics a grounding in first principles with respect to computability. While we mostly focus on building new principled conceptual relations between syntax and the FEP, we also show through a sample of preliminary examples how both tree-geometric depth and a Kolmogorov complexity estimate (recruiting a Lempel-Ziv compression algorithm) can be used to accurately predict legal operations on syntactic workspaces, directly in line with formulations of variational free energy minimization. This is used to motivate a general principle of language design that we term Turing-Chomsky Compression (TCC). We use TCC to align concerns of linguists with the normative account of self-organization furnished by the FEP, by marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.
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Affiliation(s)
- Elliot Murphy
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center, Houston, TX 77030 USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center, Houston, TX 77030 USA
| | - Emma Holmes
- Department of Speech Hearing and Phonetic Sciences, University College London, London, WC1N 1PF UK
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR UK
| | - Karl Friston
- The Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, WC1N 3AR UK
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2
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Wise T, Emery K, Radulescu A. Naturalistic reinforcement learning. Trends Cogn Sci 2024; 28:144-158. [PMID: 37777463 PMCID: PMC10878983 DOI: 10.1016/j.tics.2023.08.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 10/02/2023]
Abstract
Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.
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Affiliation(s)
- Toby Wise
- Department of Neuroimaging, King's College London, London, UK.
| | - Kara Emery
- Center for Data Science, New York University, New York, NY, USA
| | - Angela Radulescu
- Center for Computational Psychiatry, Icahn School of Medicine at Mt. Sinai, New York, NY, USA
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3
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Zhao B, Lucas CG, Bramley NR. A model of conceptual bootstrapping in human cognition. Nat Hum Behav 2024; 8:125-136. [PMID: 37845519 DOI: 10.1038/s41562-023-01719-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 09/08/2023] [Indexed: 10/18/2023]
Abstract
To tackle a hard problem, it is often wise to reuse and recombine existing knowledge. Such an ability to bootstrap enables us to grow rich mental concepts despite limited cognitive resources. Here we present a computational model of conceptual bootstrapping. This model uses a dynamic conceptual repertoire that can cache and later reuse elements of earlier insights in principled ways, modelling learning as a series of compositional generalizations. This model predicts systematically different learned concepts when the same evidence is processed in different orders, without any extra assumptions about previous beliefs or background knowledge. Across four behavioural experiments (total n = 570), we demonstrate strong curriculum-order and conceptual garden-pathing effects that closely resemble our model predictions and differ from those of alternative accounts. Taken together, this work offers a computational account of how past experiences shape future conceptual discoveries and showcases the importance of curriculum design in human inductive concept inferences.
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Affiliation(s)
- Bonan Zhao
- Department of Psychology, University of Edinburgh, Edinburgh, UK.
| | | | - Neil R Bramley
- Department of Psychology, University of Edinburgh, Edinburgh, UK
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4
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Haridi S, Wu CM, Dasgupta I, Schulz E. The scaling of mental computation in a sorting task. Cognition 2023; 241:105605. [PMID: 37748248 DOI: 10.1016/j.cognition.2023.105605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 08/17/2023] [Accepted: 08/24/2023] [Indexed: 09/27/2023]
Abstract
Many cognitive models provide valuable insights into human behavior. Yet the algorithmic complexity of candidate models can fail to capture how human reaction times scale with increasing input complexity. In the current work, we investigate the algorithms underlying human cognitive processes. Computer science characterizes algorithms by their time and space complexity scaling with problem size. We propose to use participants' reaction times to study how human computations scale with increasing input complexity. We tested this approach in a task where participants had to sort sequences of rectangles by their size. Our results showed that reaction times scaled close to linearly with sequence length and that participants learned and actively used latent structure whenever it was provided. This behavior was in line with a computational model that used the observed sequences to form hypotheses about the latent structures, searching through candidate hypotheses in a directed fashion. These results enrich our understanding of plausible cognitive models for efficient mental sorting and pave the way for future studies using reaction times to investigate the scaling of mental computations across psychological domains.
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Affiliation(s)
- Susanne Haridi
- Max Planck Institute for Biological Cybernetics, Germany; Max Planck School of Cognition, Germany.
| | | | - Ishita Dasgupta
- Princeton University, Department of Computer Science, United States of America
| | - Eric Schulz
- Max Planck Institute for Biological Cybernetics, Germany
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5
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Braem S, Held L, Shenhav A, Frömer R. Learning how to reason and deciding when to decide. Behav Brain Sci 2023; 46:e115. [PMID: 37462203 PMCID: PMC10597599 DOI: 10.1017/s0140525x22003090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
Research on human reasoning has both popularized and struggled with the idea that intuitive and deliberate thoughts stem from two different systems, raising the question how people switch between them. Inspired by research on cognitive control and conflict monitoring, we argue that detecting the need for further thought relies on an intuitive, context-sensitive process that is learned in itself.
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Affiliation(s)
- Senne Braem
- Department of Experimental Psychology, Universiteit Gent, Gent, Belgium ; https://users.ugent.be/~sbraem/
| | - Leslie Held
- Department of Experimental Psychology, Universiteit Gent, Gent, Belgium ; https://users.ugent.be/~sbraem/
| | - Amitai Shenhav
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA ; https://www.shenhavlab.org
| | - Romy Frömer
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, RI, USA ; https://www.shenhavlab.org
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
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6
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Chen X, Affourtit J, Ryskin R, Regev TI, Norman-Haignere S, Jouravlev O, Malik-Moraleda S, Kean H, Varley R, Fedorenko E. The human language system, including its inferior frontal component in "Broca's area," does not support music perception. Cereb Cortex 2023; 33:7904-7929. [PMID: 37005063 PMCID: PMC10505454 DOI: 10.1093/cercor/bhad087] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 04/04/2023] Open
Abstract
Language and music are two human-unique capacities whose relationship remains debated. Some have argued for overlap in processing mechanisms, especially for structure processing. Such claims often concern the inferior frontal component of the language system located within "Broca's area." However, others have failed to find overlap. Using a robust individual-subject fMRI approach, we examined the responses of language brain regions to music stimuli, and probed the musical abilities of individuals with severe aphasia. Across 4 experiments, we obtained a clear answer: music perception does not engage the language system, and judgments about music structure are possible even in the presence of severe damage to the language network. In particular, the language regions' responses to music are generally low, often below the fixation baseline, and never exceed responses elicited by nonmusic auditory conditions, like animal sounds. Furthermore, the language regions are not sensitive to music structure: they show low responses to both intact and structure-scrambled music, and to melodies with vs. without structural violations. Finally, in line with past patient investigations, individuals with aphasia, who cannot judge sentence grammaticality, perform well on melody well-formedness judgments. Thus, the mechanisms that process structure in language do not appear to process music, including music syntax.
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Affiliation(s)
- Xuanyi Chen
- Department of Cognitive Sciences, Rice University, TX 77005, United States
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Josef Affourtit
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Rachel Ryskin
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Cognitive & Information Sciences, University of California, Merced, Merced, CA 95343, United States
| | - Tamar I Regev
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Samuel Norman-Haignere
- Department of Biostatistics & Computational Biology, University of Rochester Medical Center, Rochester, NY, United States
- Department of Neuroscience, University of Rochester Medical Center, Rochester, NY, United States
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY, United States
| | - Olessia Jouravlev
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- Department of Cognitive Science, Carleton University, Ottawa, ON, Canada
| | - Saima Malik-Moraleda
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- The Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138, United States
| | - Hope Kean
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
| | - Rosemary Varley
- Psychology & Language Sciences, UCL, London, WCN1 1PF, United Kingdom
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA 02139, United States
- McGovern Institute for Brain Research, MIT, Cambridge, MA 02139, United States
- The Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02138, United States
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7
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Franco JP, Murawski C. Harnessing Computational Complexity Theory to Model Human Decision-making and Cognition. Cogn Sci 2023; 47:e13304. [PMID: 37325976 DOI: 10.1111/cogs.13304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 05/22/2023] [Accepted: 05/27/2023] [Indexed: 06/17/2023]
Abstract
A central aim of cognitive science is to understand the fundamental mechanisms that enable humans to navigate and make sense of complex environments. In this letter, we argue that computational complexity theory, a foundational framework for evaluating computational resource requirements, holds significant potential in addressing this challenge. As humans possess limited cognitive resources for processing vast amounts of information, understanding how humans perform complex cognitive tasks requires comprehending the underlying factors that drive information processing demands. Computational complexity theory provides a comprehensive theoretical framework to achieve this goal. By adopting this framework, we can gain new insights into how cognitive systems work and develop a more nuanced understanding of the relation between task complexity and human behavior. We provide empirical evidence supporting our argument and identify several open research questions and challenges in applying computational complexity theory to human decision-making and cognitive science at large.
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8
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Cisler JM, Tamman AJF, Fonzo GA. Diminished prospective mental representations of reward mediate reward learning strategies among youth with internalizing symptoms. Psychol Med 2023; 53:1-11. [PMID: 36878892 PMCID: PMC10600826 DOI: 10.1017/s0033291723000478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/09/2023] [Accepted: 02/08/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Adolescent internalizing symptoms and trauma exposure have been linked with altered reward learning processes and decreased ventral striatal responses to rewarding cues. Recent computational work on decision-making highlights an important role for prospective representations of the imagined outcomes of different choices. This study tested whether internalizing symptoms and trauma exposure among youth impact the generation of prospective reward representations during decision-making and potentially mediate altered behavioral strategies during reward learning. METHODS Sixty-one adolescent females with varying exposure to interpersonal violence exposure (n = 31 with histories of physical or sexual assault) and severity of internalizing symptoms completed a social reward learning task during fMRI. Multivariate pattern analyses (MVPA) were used to decode neural reward representations at the time of choice. RESULTS MVPA demonstrated that rewarding outcomes could accurately be decoded within several large-scale distributed networks (e.g. frontoparietal and striatum networks), that these reward representations were reactivated prospectively at the time of choice in proportion to the expected probability of receiving reward, and that youth with behavioral strategies that favored exploiting high reward options demonstrated greater prospective generation of reward representations. Youth internalizing symptoms, but not trauma exposure characteristics, were negatively associated with both the behavioral strategy of exploiting high reward options as well as the prospective generation of reward representations in the striatum. CONCLUSIONS These data suggest diminished prospective mental simulation of reward as a mechanism of altered reward learning strategies among youth with internalizing symptoms.
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Affiliation(s)
- Josh M. Cisler
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas at Austin, USA
- Institute for Early Life Adversity Research, Dell Medical School, University of Texas at Austin, USA
| | - Amanda J. F. Tamman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Greg A. Fonzo
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas at Austin, USA
- Institute for Early Life Adversity Research, Dell Medical School, University of Texas at Austin, USA
- Center for Psychedelic Research and Therapy, Dell Medical School, University of Texas at Austin, USA
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9
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Lebkuecher AL, Schwob N, Kabasa M, Gussow AE, MacDonald MC, Weiss DJ. Hysteresis in motor and language production. Q J Exp Psychol (Hove) 2023; 76:511-527. [PMID: 35361002 PMCID: PMC9936447 DOI: 10.1177/17470218221094568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Hysteresis in motor planning and syntactic priming in language planning refer to the influence of prior production history on current production behaviour. Computational efficiency accounts of action hysteresis and theoretical accounts of syntactic priming both argue that reusing an existing plan is less costly than generating a novel plan. Despite these similarities across motor and language frameworks, research on planning in these domains has largely been conducted independently. The current study adapted an existing language paradigm to mirror the incremental nature of a manual motor task to investigate the presence of parallel hysteresis effects across domains. We observed asymmetries in production choice for both the motor and language tasks that resulted from the influence of prior history. Furthermore, these hysteresis effects were more exaggerated for subordinate production forms implicating an inverse preference effect that spanned domain. Consistent with computational efficiency accounts, across both task participants exhibited reaction time savings on trials in which they reused a recent production choice. Together, these findings lend support to the broader notion that there are common production biases that span both motor and language domains.
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Affiliation(s)
- Amy L Lebkuecher
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
- Amy L Lebkuecher, Department of Psychology, The Pennsylvania State University, 460 Bruce V. Moore Building, University Park, PA 16802-3104, USA.
| | - Natalie Schwob
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
| | - Misty Kabasa
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, USA
| | - Arella E Gussow
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, USA
| | | | - Daniel J Weiss
- Department of Psychology, The Pennsylvania State University, University Park, PA, USA
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10
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Priming of probabilistic attentional templates. Psychon Bull Rev 2023; 30:22-39. [PMID: 35831678 DOI: 10.3758/s13423-022-02125-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/09/2022] [Indexed: 11/08/2022]
Abstract
Attentional priming has a dominating influence on vision, speeding visual search, releasing items from crowding, reducing masking effects, and during free-choice, primed targets are chosen over unprimed ones. Many accounts postulate that templates stored in working memory control what we attend to and mediate the priming. But what is the nature of these templates (or representations)? Analyses of real-world visual scenes suggest that tuning templates to exact color or luminance values would be impractical since those can vary greatly because of changes in environmental circumstances and perceptual interpretation. Tuning templates to a range of the most probable values would be more efficient. Recent evidence does indeed suggest that the visual system represents such probability, gradually encoding statistical variation in the environment through repeated exposure to input statistics. This is consistent with evidence from neurophysiology and theoretical neuroscience as well as computational evidence of probabilistic representations in visual perception. I argue that such probabilistic representations are the unit of attentional priming and that priming of, say, a repeated single-color value simply involves priming of a distribution with no variance. This "priming of probability" view can be modelled within a Bayesian framework where priming provides contextual priors. Priming can therefore be thought of as learning of the underlying probability density function of the target or distractor sets in a given continuous task.
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11
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The molecular memory code and synaptic plasticity: A synthesis. Biosystems 2023; 224:104825. [PMID: 36610586 DOI: 10.1016/j.biosystems.2022.104825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/06/2023]
Abstract
The most widely accepted view of memory in the brain holds that synapses are the storage sites of memory, and that memories are formed through associative modification of synapses. This view has been challenged on conceptual and empirical grounds. As an alternative, it has been proposed that molecules within the cell body are the storage sites of memory, and that memories are formed through biochemical operations on these molecules. This paper proposes a synthesis of these two views, grounded in a computational model of memory. Synapses are conceived as storage sites for the parameters of an approximate posterior probability distribution over latent causes. Intracellular molecules are conceived as storage sites for the parameters of a generative model. The model stipulates how these two components work together as part of an integrated algorithm for learning and inference.
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12
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Moughrabi N, Botsford C, Gruichich TS, Azar A, Heilicher M, Hiser J, Crombie KM, Dunsmoor JE, Stowe Z, Cisler JM. Large-scale neural network computations and multivariate representations during approach-avoidance conflict decision-making. Neuroimage 2022; 264:119709. [PMID: 36283543 PMCID: PMC9835092 DOI: 10.1016/j.neuroimage.2022.119709] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 10/20/2022] [Accepted: 10/21/2022] [Indexed: 11/09/2022] Open
Abstract
Many real-world situations require navigating decisions for both reward and threat. While there has been significant progress in understanding mechanisms of decision-making and mediating neurocircuitry separately for reward and threat, there is limited understanding of situations where reward and threat contingencies compete to create approach-avoidance conflict (AAC). Here, we leverage computational learning models, independent component analysis (ICA), and multivariate pattern analysis (MVPA) approaches to understand decision-making during a novel task that embeds concurrent reward and threat learning and manipulates congruency between reward and threat probabilities. Computational modeling supported a modified reinforcement learning model where participants integrated reward and threat value into a combined total value according to an individually varying policy parameter, which was highly predictive of decisions to approach reward vs avoid threat during trials where the highest reward option was also the highest threat option (i.e., approach-avoidance conflict). ICA analyses demonstrated unique roles for salience, frontoparietal, medial prefrontal, and inferior frontal networks in differential encoding of reward vs threat prediction error and value signals. The left frontoparietal network uniquely encoded degree of conflict between reward and threat value at the time of choice. MVPA demonstrated that delivery of reward and threat could accurately be decoded within salience and inferior frontal networks, respectively, and that decisions to approach reward vs avoid threat were predicted by the relative degree to which these reward vs threat representations were active at the time of choice. This latter result suggests that navigating AAC decisions involves generating mental representations for possible decision outcomes, and relative activation of these representations may bias subsequent decision-making towards approaching reward or avoiding threat accordingly.
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Affiliation(s)
- Nicole Moughrabi
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin
| | - Chloe Botsford
- Department of Psychiatry, University of Wisconsin-Madison
| | | | - Ameera Azar
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin
| | | | - Jaryd Hiser
- Department of Psychiatry, University of Wisconsin-Madison
| | - Kevin M Crombie
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin
| | - Joseph E Dunsmoor
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin; Institute for Early Life Adversity Research, University of Texas at Austin
| | - Zach Stowe
- Department of Psychiatry, University of Wisconsin-Madison
| | - Josh M Cisler
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin; Institute for Early Life Adversity Research, University of Texas at Austin.
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13
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Nalborczyk L, Debarnot U, Longcamp M, Guillot A, Alario FX. The Role of Motor Inhibition During Covert Speech Production. Front Hum Neurosci 2022; 16:804832. [PMID: 35355587 PMCID: PMC8959424 DOI: 10.3389/fnhum.2022.804832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Covert speech is accompanied by a subjective multisensory experience with auditory and kinaesthetic components. An influential hypothesis states that these sensory percepts result from a simulation of the corresponding motor action that relies on the same internal models recruited for the control of overt speech. This simulationist view raises the question of how it is possible to imagine speech without executing it. In this perspective, we discuss the possible role(s) played by motor inhibition during covert speech production. We suggest that considering covert speech as an inhibited form of overt speech maps naturally to the purported progressive internalization of overt speech during childhood. We further argue that the role of motor inhibition may differ widely across different forms of covert speech (e.g., condensed vs. expanded covert speech) and that considering this variety helps reconciling seemingly contradictory findings from the neuroimaging literature.
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Affiliation(s)
- Ladislas Nalborczyk
- Aix Marseille Univ, CNRS, LPC, Marseille, France
- Aix Marseille Univ, CNRS, LNC, Marseille, France
| | - Ursula Debarnot
- Inter-University Laboratory of Human Movement Biology-EA 7424, University of Lyon, University Claude Bernard Lyon 1, Villeurbanne, France
- Institut Universitaire de France, Paris, France
| | | | - Aymeric Guillot
- Inter-University Laboratory of Human Movement Biology-EA 7424, University of Lyon, University Claude Bernard Lyon 1, Villeurbanne, France
- Institut Universitaire de France, Paris, France
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14
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Frömer R, Shenhav A. Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making. Neurosci Biobehav Rev 2022; 134:104483. [PMID: 34902441 PMCID: PMC8844247 DOI: 10.1016/j.neubiorev.2021.12.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 12/01/2021] [Accepted: 12/04/2021] [Indexed: 12/26/2022]
Abstract
While often seeming to investigate rather different problems, research into value-based decision making and cognitive control have historically offered parallel insights into how people select thoughts and actions. While the former studies how people weigh costs and benefits to make a decision, the latter studies how they adjust information processing to achieve their goals. Recent work has highlighted ways in which decision-making research can inform our understanding of cognitive control. Here, we provide the complementary perspective: how cognitive control research has informed understanding of decision-making. We highlight three particular areas of research where this critical interchange has occurred: (1) how different types of goals shape the evaluation of choice options, (2) how people use control to adjust the ways they make their decisions, and (3) how people monitor decisions to inform adjustments to control at multiple levels and timescales. We show how adopting this alternate viewpoint offers new insight into the determinants of both decisions and control; provides alternative interpretations for common neuroeconomic findings; and generates fruitful directions for future research.
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Affiliation(s)
- R Frömer
- Cognitive, Linguistic, and Psychological Sciences, Carney Institute for Brain Science, Brown University, Providence, RI, United States.
| | - A Shenhav
- Cognitive, Linguistic, and Psychological Sciences, Carney Institute for Brain Science, Brown University, Providence, RI, United States.
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15
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Fedorenko E, Shain C. Similarity of computations across domains does not imply shared implementation: The case of language comprehension. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 2021; 30:526-534. [PMID: 35295820 PMCID: PMC8923525 DOI: 10.1177/09637214211046955] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
Understanding language requires applying cognitive operations (e.g., memory retrieval, prediction, structure building)-relevant across many cognitive domains-to specialized knowledge structures (a particular language's phonology, lexicon, and syntax). Are these computations carried out by domain-general circuits or by circuits that store domain-specific representations? Recent work has characterized the roles in language comprehension of the language-selective network and the multiple demand (MD) network, which has been implicated in executive functions and linked to fluid intelligence, making it a prime candidate for implementing computations that support information processing across domains. The language network responds robustly to diverse aspects of comprehension, but the MD network shows no sensitivity to linguistic variables. We therefore argue that the MD network does not play a core role in language comprehension, and that past claims to the contrary are likely due to methodological artifacts. Although future studies may discover some aspects of language that require the MD network, evidence to date suggests that those will not be related to core linguistic processes like lexical access or composition. The finding that the circuits that store linguistic knowledge carry out computations on those representations aligns with general arguments against the separation between memory and computation in the mind and brain.
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Affiliation(s)
- Evelina Fedorenko
- Brain & Cognitive Sciences Department and McGovern Institute for Brain Research, MIT
| | - Cory Shain
- Brain & Cognitive Sciences Department and McGovern Institute for Brain Research, MIT
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16
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Abstract
Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Peter F Hitchcock
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island 02912, USA; ,
| | - Eiko I Fried
- Department of Clinical Psychology, Leiden University, 2333 AK Leiden, The Netherlands;
| | - Michael J Frank
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island 02912, USA; , .,Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02192
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17
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Abstract
The decisions we make are shaped by a lifetime of learning. Past experience guides the way that we encode information in neural systems for perception and valuation, and determines the information we retrieve when making decisions. Distinct literatures have discussed how lifelong learning and local context shape decisions made about sensory signals, propositional information, or economic prospects. Here, we build bridges between these literatures, arguing for common principles of adaptive rationality in perception, cognition, and economic choice. We discuss how a single common framework, based on normative principles of efficient coding and Bayesian inference, can help us understand a myriad of human decision biases, including sensory illusions, adaptive aftereffects, choice history biases, central tendency effects, anchoring effects, contrast effects, framing effects, congruency effects, reference-dependent valuation, nonlinear utility functions, and discretization heuristics. We describe a simple computational framework for explaining these phenomena. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Christopher Summerfield
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom;
| | - Paula Parpart
- Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, United Kingdom;
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