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Brown LS, Cho JR, Bolkan SS, Nieh EH, Schottdorf M, Tank DW, Brody CD, Witten IB, Goldman MS. Neural circuit models for evidence accumulation through choice-selective sequences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.01.555612. [PMID: 38234715 PMCID: PMC10793437 DOI: 10.1101/2023.09.01.555612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Decision making is traditionally thought to be mediated by populations of neurons whose firing rates persistently accumulate evidence across time. However, recent decision-making experiments in rodents have observed neurons across the brain that fire sequentially as a function of spatial position or time, rather than persistently, with the subset of neurons in the sequence depending on the animal's choice. We develop two new candidate circuit models, in which evidence is encoded either in the relative firing rates of two competing chains of neurons or in the network location of a stereotyped pattern ("bump") of neural activity. Encoded evidence is then faithfully transferred between neuronal populations representing different positions or times. Neural recordings from four different brain regions during a decision-making task showed that, during the evidence accumulation period, different brain regions displayed tuning curves consistent with different candidate models for evidence accumulation. This work provides mechanistic models and potential neural substrates for how graded-value information may be precisely accumulated within and transferred between neural populations, a set of computations fundamental to many cognitive operations.
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Lawlor J, Zagala A, Jamali S, Boubenec Y. Pupillary dynamics reflect the impact of temporal expectation on detection strategy. iScience 2023; 26:106000. [PMID: 36798438 PMCID: PMC9926307 DOI: 10.1016/j.isci.2023.106000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 11/09/2022] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
Everyday life's perceptual decision-making is informed by experience. In particular, temporal expectation can ease the detection of relevant events in noisy sensory streams. Here, we investigated if humans can extract hidden temporal cues from the occurrences of probabilistic targets and utilize them to inform target detection in a complex acoustic stream. To understand what neural mechanisms implement temporal expectation influence on decision-making, we used pupillometry as a proxy for underlying neuromodulatory activity. We found that participants' detection strategy was influenced by the hidden temporal context and correlated with sound-evoked pupil dilation. A model of urgency fitted on false alarms predicted detection reaction time. Altogether, these findings suggest that temporal expectation informs decision-making and could be implemented through neuromodulatory-mediated urgency signals.
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Affiliation(s)
- Jennifer Lawlor
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA,Corresponding author
| | - Agnès Zagala
- International Laboratory for Brain, Music and Sound Research (BRAMS), Montreal, Canada
| | - Sara Jamali
- Institut Pasteur, INSERM, Institut de l’Audition, Paris, France
| | - Yves Boubenec
- Laboratoire des systèmes perceptifs, Département d’études cognitives, École normale supérieure, PSL University, CNRS, 75005 Paris, France
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Rational arbitration between statistics and rules in human sequence processing. Nat Hum Behav 2022; 6:1087-1103. [PMID: 35501360 DOI: 10.1038/s41562-021-01259-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 11/17/2021] [Indexed: 01/29/2023]
Abstract
Detecting and learning temporal regularities is essential to accurately predict the future. A long-standing debate in cognitive science concerns the existence in humans of a dissociation between two systems, one for handling statistical regularities governing the probabilities of individual items and their transitions, and another for handling deterministic rules. Here, to address this issue, we used finger tracking to continuously monitor the online build-up of evidence, confidence, false alarms and changes-of-mind during sequence processing. All these aspects of behaviour conformed tightly to a hierarchical Bayesian inference model with distinct hypothesis spaces for statistics and rules, yet linked by a single probabilistic currency. Alternative models based either on a single statistical mechanism or on two non-commensurable systems were rejected. Our results indicate that a hierarchical Bayesian inference mechanism, capable of operating over distinct hypothesis spaces for statistics and rules, underlies the human capability for sequence processing.
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Booras A, Stevenson T, McCormack CN, Rhoads ME, Hanks TD. Change point detection with multiple alternatives reveals parallel evaluation of the same stream of evidence along distinct timescales. Sci Rep 2021; 11:13098. [PMID: 34162943 PMCID: PMC8222317 DOI: 10.1038/s41598-021-92470-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 06/08/2021] [Indexed: 11/09/2022] Open
Abstract
In order to behave appropriately in a rapidly changing world, individuals must be able to detect when changes occur in that environment. However, at any given moment, there are a multitude of potential changes of behavioral significance that could occur. Here we investigate how knowledge about the space of possible changes affects human change point detection. We used a stochastic auditory change point detection task that allowed model-free and model-based characterization of the decision process people employ. We found that subjects can simultaneously apply distinct timescales of evidence evaluation to the same stream of evidence when there are multiple types of changes possible. Informative cues that specified the nature of the change led to improved accuracy for change point detection through mechanisms involving both the timescales of evidence evaluation and adjustments of decision bounds. These results establish three important capacities of information processing for decision making that any proposed neural mechanism of evidence evaluation must be able to support: the ability to simultaneously employ multiple timescales of evidence evaluation, the ability to rapidly adjust those timescales, and the ability to modify the amount of information required to make a decision in the context of flexible timescales.
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Affiliation(s)
- Alexa Booras
- grid.27860.3b0000 0004 1936 9684Center for Neuroscience, University of California Davis, Davis, CA USA
| | - Tanner Stevenson
- grid.27860.3b0000 0004 1936 9684Center for Neuroscience, University of California Davis, Davis, CA USA
| | - Connor N. McCormack
- grid.27860.3b0000 0004 1936 9684Center for Neuroscience, University of California Davis, Davis, CA USA
| | - Marie E. Rhoads
- grid.27860.3b0000 0004 1936 9684Center for Neuroscience, University of California Davis, Davis, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Neuroscience, University of California Los Angeles, Los Angeles, CA USA
| | - Timothy D. Hanks
- grid.27860.3b0000 0004 1936 9684Center for Neuroscience, University of California Davis, Davis, CA USA ,grid.27860.3b0000 0004 1936 9684Department of Neurology, University of California Davis, Sacramento, CA USA
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Talluri BC, Urai AE, Bronfman ZZ, Brezis N, Tsetsos K, Usher M, Donner TH. Choices change the temporal weighting of decision evidence. J Neurophysiol 2021; 125:1468-1481. [PMID: 33689508 PMCID: PMC8285578 DOI: 10.1152/jn.00462.2020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 02/16/2021] [Accepted: 03/04/2021] [Indexed: 12/02/2022] Open
Abstract
Many decisions result from the accumulation of decision-relevant information (evidence) over time. Even when maximizing decision accuracy requires weighting all the evidence equally, decision-makers often give stronger weight to evidence occurring early or late in the evidence stream. Here, we show changes in such temporal biases within participants as a function of intermittent judgments about parts of the evidence stream. Human participants performed a decision task that required a continuous estimation of the mean evidence at the end of the stream. The evidence was either perceptual (noisy random dot motion) or symbolic (variable sequences of numbers). Participants also reported a categorical judgment of the preceding evidence half-way through the stream in one condition or executed an evidence-independent motor response in another condition. The relative impact of early versus late evidence on the final estimation flipped between these two conditions. In particular, participants' sensitivity to late evidence after the intermittent judgment, but not the simple motor response, was decreased. Both the intermittent response as well as the final estimation reports were accompanied by nonluminance-mediated increases of pupil diameter. These pupil dilations were bigger during intermittent judgments than simple motor responses and bigger during estimation when the late evidence was consistent than inconsistent with the initial judgment. In sum, decisions activate pupil-linked arousal systems and alter the temporal weighting of decision evidence. Our results are consistent with the idea that categorical choices in the face of uncertainty induce a change in the state of the neural circuits underlying decision-making.NEW & NOTEWORTHY The psychology and neuroscience of decision-making have extensively studied the accumulation of decision-relevant information toward a categorical choice. Much fewer studies have assessed the impact of a choice on the processing of subsequent information. Here, we show that intermittent choices during a protracted stream of input reduce the sensitivity to subsequent decision information and transiently boost arousal. Choices might trigger a state change in the neural machinery for decision-making.
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Affiliation(s)
- Bharath Chandra Talluri
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Anne E Urai
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | | | - Noam Brezis
- School of Psychology, Tel-Aviv University, Tel-Aviv, Israel
| | - Konstantinos Tsetsos
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Marius Usher
- School of Psychology, Tel-Aviv University, Tel-Aviv, Israel
- Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel
| | - Tobias H Donner
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Brain and Cognition Center, University of Amsterdam, Amsterdam, The Netherlands
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Harun R, Jun E, Park HH, Ganupuru P, Goldring AB, Hanks TD. Timescales of Evidence Evaluation for Decision Making and Associated Confidence Judgments Are Adapted to Task Demands. Front Neurosci 2020; 14:826. [PMID: 32903672 PMCID: PMC7438826 DOI: 10.3389/fnins.2020.00826] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Accepted: 07/15/2020] [Indexed: 01/29/2023] Open
Abstract
Decision making often involves choosing actions based on relevant evidence. This can benefit from focussing evidence evaluation on the timescale of greatest relevance based on the situation. Here, we use an auditory change detection task to determine how people adjust their timescale of evidence evaluation depending on task demands for detecting changes in their environment and assessing their internal confidence in those decisions. We confirm previous results that people adopt shorter timescales of evidence evaluation for detecting changes in contexts with shorter signal durations, while bolstering those results with model-free analyses not previously used and extending the results to the auditory domain. We also extend these results to show that in contexts with shorter signal durations, people also adopt correspondingly shorter timescales of evidence evaluation for assessing confidence in their decision about detecting a change. These results provide important insights into adaptability and flexible control of evidence evaluation for decision making.
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Affiliation(s)
- Rashed Harun
- Department of Neurology and Center for Neuroscience, University of California, Davis, Davis, CA, United States
| | - Elizabeth Jun
- Department of Neurology and Center for Neuroscience, University of California, Davis, Davis, CA, United States
| | - Heui Hye Park
- Department of Neurology and Center for Neuroscience, University of California, Davis, Davis, CA, United States
| | - Preetham Ganupuru
- Department of Neurology and Center for Neuroscience, University of California, Davis, Davis, CA, United States
| | - Adam B Goldring
- Department of Neurology and Center for Neuroscience, University of California, Davis, Davis, CA, United States
| | - Timothy D Hanks
- Department of Neurology and Center for Neuroscience, University of California, Davis, Davis, CA, United States
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Kilpatrick ZP, Holmes WR, Eissa TL, Josić K. Optimal models of decision-making in dynamic environments. Curr Opin Neurobiol 2019; 58:54-60. [PMID: 31326724 PMCID: PMC6859206 DOI: 10.1016/j.conb.2019.06.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2018] [Accepted: 06/22/2019] [Indexed: 11/16/2022]
Abstract
Nature is in constant flux, so animals must account for changes in their environment when making decisions. How animals learn the timescale of such changes and adapt their decision strategies accordingly is not well understood. Recent psychophysical experiments have shown humans and other animals can achieve near-optimal performance at two alternative forced choice (2AFC) tasks in dynamically changing environments. Characterization of performance requires the derivation and analysis of computational models of optimal decision-making policies on such tasks. We review recent theoretical work in this area, and discuss how models compare with subjects' behavior in tasks where the correct choice or evidence quality changes in dynamic, but predictable, ways.
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Affiliation(s)
| | - William R Holmes
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, USA; Department of Mathematics, Vanderbilt University, Nashville, TN, USA; Quantitative Systems Biology Center, Vanderbilt University, Nashville, TN, USA
| | - Tahra L Eissa
- Department of Applied Mathematics, University of Colorado, Boulder, CO, USA
| | - Krešimir Josić
- Department of Mathematics, University of Houston, Houston, TX, USA; Department of Biology and Biochemistry, University of Houston, Houston, TX, USA; Department of BioSciences, Rice University, Houston, TX, USA.
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Ganupuru P, Goldring AB, Harun R, Hanks TD. Flexibility of Timescales of Evidence Evaluation for Decision Making. Curr Biol 2019; 29:2091-2097.e4. [PMID: 31178325 DOI: 10.1016/j.cub.2019.05.037] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Revised: 04/05/2019] [Accepted: 05/15/2019] [Indexed: 12/13/2022]
Abstract
To understand the neural mechanisms that support decision making, it is critical to characterize the timescale of evidence evaluation. Recent work has shown that subjects can adaptively adjust the timescale of evidence evaluation across blocks of trials depending on context [1]. However, it's currently unknown if adjustments to evidence evaluation occur online during deliberations based on a single stream of evidence. To examine this question, we employed a change-detection task in which subjects report their level of confidence in judging whether there has been a change in a stochastic auditory stimulus. Using a combination of psychophysical reverse correlation analyses and single-trial behavioral modeling, we compared the time period over which sensory information has leverage on detection report choices versus confidence. We demonstrate that the length of this period differs on separate sets of trials based on what's being reported. Surprisingly, confidence judgments on trials with no detection report are influenced by evidence occurring earlier than the time period of influence for detection reports. Our findings call into question models of decision formation involving static parameters that yield a singular timescale of evidence evaluation and instead suggest that the brain represents and utilizes multiple timescales of evidence evaluation during deliberation.
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Affiliation(s)
- Preetham Ganupuru
- Department of Neurology and Center for Neuroscience, University of California Davis, 1544 Newton Ct., Davis, CA 95618, USA
| | - Adam B Goldring
- Department of Neurology and Center for Neuroscience, University of California Davis, 1544 Newton Ct., Davis, CA 95618, USA
| | - Rashed Harun
- Department of Neurology and Center for Neuroscience, University of California Davis, 1544 Newton Ct., Davis, CA 95618, USA
| | - Timothy D Hanks
- Department of Neurology and Center for Neuroscience, University of California Davis, 1544 Newton Ct., Davis, CA 95618, USA.
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