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Wang S, Falcone R, Richmond B, Averbeck BB. Attractor dynamics reflect decision confidence in macaque prefrontal cortex. Nat Neurosci 2023; 26:1970-1980. [PMID: 37798412 DOI: 10.1038/s41593-023-01445-x] [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: 12/11/2022] [Accepted: 08/31/2023] [Indexed: 10/07/2023]
Abstract
Decisions are made with different degrees of consistency, and this consistency can be linked to the confidence that the best choice has been made. Theoretical work suggests that attractor dynamics in networks can account for choice consistency, but how this is implemented in the brain remains unclear. Here we provide evidence that the energy landscape around attractor basins in population neural activity in the prefrontal cortex reflects choice consistency. We trained two rhesus monkeys to make accept/reject decisions based on pretrained visual cues that signaled reward offers with different magnitudes and delays to reward. Monkeys made consistent decisions for very good and very bad offers, but decisions were less consistent for intermediate offers. Analysis of neural data showed that the attractor basins around patterns of activity reflecting decisions had steeper landscapes for offers that led to consistent decisions. Therefore, we provide neural evidence that energy landscapes predict decision consistency, which reflects decision confidence.
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Affiliation(s)
- Siyu Wang
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Rossella Falcone
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Leo M. Davidoff Department of Neurological Surgery, Albert Einstein College of Medicine Montefiore Medical Center, Bronx, NY, USA
| | - Barry Richmond
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Bruno B Averbeck
- Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
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2
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Wang S, Falcone R, Richmond B, Averbeck BB. Attractor dynamics reflect decision confidence in macaque prefrontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.17.558139. [PMID: 37886489 PMCID: PMC10602028 DOI: 10.1101/2023.09.17.558139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Decisions are made with different degrees of consistency, and this consistency can be linked to the confidence that the best choice has been made. Theoretical work suggests that attractor dynamics in networks can account for choice consistency, but how this is implemented in the brain remains unclear. Here, we provide evidence that the energy landscape around attractor basins in population neural activity in prefrontal cortex reflects choice consistency. We trained two rhesus monkeys to make accept/reject decisions based on pretrained visual cues that signaled reward offers with different magnitudes and delays-to-reward. Monkeys made consistent decisions for very good and very bad offers, but decisions were less consistent for intermediate offers. Analysis of neural data showed that the attractor basins around patterns of activity reflecting decisions had steeper landscapes for offers that led to consistent decisions. Therefore, we provide neural evidence that energy landscapes predict decision consistency, which reflects decision confidence.
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3
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Hoxha I, Chevallier S, Ciarchi M, Glasauer S, Delorme A, Amorim MA. Accounting for endogenous effects in decision-making with a non-linear diffusion decision model. Sci Rep 2023; 13:6323. [PMID: 37072460 PMCID: PMC10113207 DOI: 10.1038/s41598-023-32841-9] [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] [Received: 09/20/2022] [Accepted: 04/03/2023] [Indexed: 05/03/2023] Open
Abstract
The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter-trial dynamics at the single-trial level and endogenous influences. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non-linear model performs better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl-DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across-trial variability for perceptual decisions and accounts for peri-stimulus influences.
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Affiliation(s)
- Isabelle Hoxha
- CIAMS, Université Paris-Saclay, Paris, France.
- CIAMS, Université d'Orléans, Orléans, France.
| | | | - Matteo Ciarchi
- Max-Planck Institute for the Physics of Complex Systems, Dresden, Germany
| | - Stefan Glasauer
- Computational Neuroscience, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany
| | - Arnaud Delorme
- CerCo, CNRS, Université Toulouse III - Paul Sabatier, Toulouse, France
- Swartz Center for Computational Neuroscience, INC, University of California San Diego, La Jolla, CA, 92093, USA
| | - Michel-Ange Amorim
- CIAMS, Université Paris-Saclay, Paris, France
- CIAMS, Université d'Orléans, Orléans, France
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4
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Azizi Z, Ebrahimpour R. Explaining Integration of Evidence Separated by Temporal Gaps with Frontoparietal Circuit Models. Neuroscience 2023; 509:74-95. [PMID: 36457229 DOI: 10.1016/j.neuroscience.2022.10.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 10/17/2022] [Accepted: 10/20/2022] [Indexed: 11/07/2022]
Abstract
Perceptual decisions rely on accumulating sensory evidence over time. However, the accumulation process is complicated in real life when evidence resulted from separated cues over time. Previous studies demonstrate that participants are able to integrate information from two separated cues to improve their performance invariant to an interval between the cues. However, there is no neural model that can account for accuracy and confidence in decisions when there is a time interval in evidence. We used behavioral and EEG datasets from a visual choice task -Random dot motion- with separated evidence to investigate three candid distributed neural networks. We showed that decisions based on evidence accumulation by separated cues over time are best explained by the interplay of recurrent cortical dynamics of centro-parietal and frontal brain areas while an uncertainty-monitoring module included in the model.
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Affiliation(s)
- Zahra Azizi
- Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran.
| | - Reza Ebrahimpour
- Institute for Convergence Science and Technology (ICST), Sharif University of Technology, Tehran, P.O.Box: 11155-8639, Iran; Faculty of Computer Engineering, Shahid Rajaee Teacher Training University, Postal Box: 16785-163, Tehran, Iran; School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Niavaran, Postal Box: 19395-5746, Tehran, Iran.
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5
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Khona M, Fiete IR. Attractor and integrator networks in the brain. Nat Rev Neurosci 2022; 23:744-766. [DOI: 10.1038/s41583-022-00642-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/22/2022] [Indexed: 11/06/2022]
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6
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Boyd-Meredith JT, Piet AT, Dennis EJ, El Hady A, Brody CD. Stable choice coding in rat frontal orienting fields across model-predicted changes of mind. Nat Commun 2022; 13:3235. [PMID: 35688813 PMCID: PMC9187710 DOI: 10.1038/s41467-022-30736-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 05/13/2022] [Indexed: 11/09/2022] Open
Abstract
During decision making in a changing environment, evidence that may guide the decision accumulates until the point of action. In the rat, provisional choice is thought to be represented in frontal orienting fields (FOF), but this has only been tested in static environments where provisional and final decisions are not easily dissociated. Here, we characterize the representation of accumulated evidence in the FOF of rats performing a recently developed dynamic evidence accumulation task, which induces changes in the provisional decision, referred to as “changes of mind”. We find that FOF encodes evidence throughout decision formation with a temporal gain modulation that rises until the period when the animal may need to act. Furthermore, reversals in FOF firing rates can be accounted for by changes of mind predicted using a model of the decision process fit only to behavioral data. Our results suggest that the FOF represents provisional decisions even in dynamic, uncertain environments, allowing for rapid motor execution when it is time to act. A leaky accumulation model can predict rats’ changes of mind during decision making in a dynamic environment explaining reversals in frontal cortical activity and demonstrating a stable choice code despite environmental uncertainty.
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Affiliation(s)
| | - Alex T Piet
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.,Allen Institute, Seattle, WA, USA
| | - Emily Jane Dennis
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Ahmed El Hady
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
| | - Carlos D Brody
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA. .,Howard Hughes Medical Institute, Princeton University, Princeton, NJ, USA.
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Pérez-Parra JE, Rojas-Líbano D. Drift-diffusion cognitive models: description, applications and perspectives ( Modelos cognitivos de deriva-difusión: descripción, aplicaciones y perspectivas). STUDIES IN PSYCHOLOGY 2022. [DOI: 10.1080/02109395.2022.2056802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
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8
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Hernández-Navarro L, Hermoso-Mendizabal A, Duque D, de la Rocha J, Hyafil A. Proactive and reactive accumulation-to-bound processes compete during perceptual decisions. Nat Commun 2021; 12:7148. [PMID: 34880219 PMCID: PMC8655090 DOI: 10.1038/s41467-021-27302-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 11/03/2021] [Indexed: 11/09/2022] Open
Abstract
Standard models of perceptual decision-making postulate that a response is triggered in reaction to stimulus presentation when the accumulated stimulus evidence reaches a decision threshold. This framework excludes however the possibility that informed responses are generated proactively at a time independent of stimulus. Here, we find that, in a free reaction time auditory task in rats, reactive and proactive responses coexist, suggesting that choice selection and motor initiation, commonly viewed as serial processes, are decoupled in general. We capture this behavior by a novel model in which proactive and reactive responses are triggered whenever either of two competing processes, respectively Action Initiation or Evidence Accumulation, reaches a bound. In both types of response, the choice is ultimately informed by the Evidence Accumulation process. The Action Initiation process readily explains premature responses, contributes to urgency effects at long reaction times and mediates the slowing of the responses as animals get satiated and tired during sessions. Moreover, it successfully predicts reaction time distributions when the stimulus was either delayed, advanced or omitted. Overall, these results fundamentally extend standard models of evidence accumulation in decision making by showing that proactive and reactive processes compete for the generation of responses.
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Affiliation(s)
| | | | | | | | - Alexandre Hyafil
- Center for Brain and Cognition, Universitat Pompeu Fabra, Ramón Trias Fargas, 25, 08018, Barcelona, Spain.
- Center of Mathematical Research, Campus UAB Edifici C, 08193, Bellaterra (Barcelona), Spain.
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Lange RD, Chattoraj A, Beck JM, Yates JL, Haefner RM. A confirmation bias in perceptual decision-making due to hierarchical approximate inference. PLoS Comput Biol 2021; 17:e1009517. [PMID: 34843452 PMCID: PMC8659691 DOI: 10.1371/journal.pcbi.1009517] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 12/09/2021] [Accepted: 10/01/2021] [Indexed: 11/18/2022] Open
Abstract
Making good decisions requires updating beliefs according to new evidence. This is a dynamical process that is prone to biases: in some cases, beliefs become entrenched and resistant to new evidence (leading to primacy effects), while in other cases, beliefs fade over time and rely primarily on later evidence (leading to recency effects). How and why either type of bias dominates in a given context is an important open question. Here, we study this question in classic perceptual decision-making tasks, where, puzzlingly, previous empirical studies differ in the kinds of biases they observe, ranging from primacy to recency, despite seemingly equivalent tasks. We present a new model, based on hierarchical approximate inference and derived from normative principles, that not only explains both primacy and recency effects in existing studies, but also predicts how the type of bias should depend on the statistics of stimuli in a given task. We verify this prediction in a novel visual discrimination task with human observers, finding that each observer's temporal bias changed as the result of changing the key stimulus statistics identified by our model. The key dynamic that leads to a primacy bias in our model is an overweighting of new sensory information that agrees with the observer's existing belief-a type of 'confirmation bias'. By fitting an extended drift-diffusion model to our data we rule out an alternative explanation for primacy effects due to bounded integration. Taken together, our results resolve a major discrepancy among existing perceptual decision-making studies, and suggest that a key source of bias in human decision-making is approximate hierarchical inference.
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Affiliation(s)
- Richard D. Lange
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
- Computer Science, University of Rochester, Rochester, New York, United States of America
| | - Ankani Chattoraj
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
| | - Jeffrey M. Beck
- Department of Neurobiology, Duke University, Durham, North Carolina, United States of America
| | - Jacob L. Yates
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
| | - Ralf M. Haefner
- Brain and Cognitive Sciences, University of Rochester, Rochester, New York, United States of America
- Computer Science, University of Rochester, Rochester, New York, United States of America
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Adaptive circuit dynamics across human cortex during evidence accumulation in changing environments. Nat Neurosci 2021; 24:987-997. [PMID: 33903770 DOI: 10.1038/s41593-021-00839-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Accepted: 03/12/2021] [Indexed: 02/02/2023]
Abstract
Many decisions under uncertainty entail the temporal accumulation of evidence that informs about the state of the environment. When environments are subject to hidden changes in their state, maximizing accuracy and reward requires non-linear accumulation of evidence. How this adaptive, non-linear computation is realized in the brain is unknown. We analyzed human behavior and cortical population activity (measured with magnetoencephalography) recorded during visual evidence accumulation in a changing environment. Behavior and decision-related activity in cortical regions involved in action planning exhibited hallmarks of adaptive evidence accumulation, which could also be implemented by a recurrent cortical microcircuit. Decision dynamics in action-encoding parietal and frontal regions were mirrored in a frequency-specific modulation of the state of the visual cortex that depended on pupil-linked arousal and the expected probability of change. These findings link normative decision computations to recurrent cortical circuit dynamics and highlight the adaptive nature of decision-related feedback to the sensory cortex.
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