1
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de Gee JW, Mridha Z, Hudson M, Shi Y, Ramsaywak H, Smith S, Karediya N, Thompson M, Jaspe K, Jiang H, Zhang W, McGinley MJ. Strategic stabilization of arousal boosts sustained attention. Curr Biol 2024; 34:4114-4128.e6. [PMID: 39151432 DOI: 10.1016/j.cub.2024.07.070] [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: 01/18/2023] [Revised: 07/17/2024] [Accepted: 07/18/2024] [Indexed: 08/19/2024]
Abstract
Arousal and motivation interact to profoundly influence behavior. For example, experience tells us that we have some capacity to control our arousal when appropriately motivated, such as staying awake while driving a motor vehicle. However, little is known about how arousal and motivation jointly influence decision computations, including if and how animals, such as rodents, adapt their arousal state to their needs. Here, we developed and show results from an auditory, feature-based, sustained-attention task with intermittently shifting task utility. We use pupil size to estimate arousal across a wide range of states and apply tailored signal-detection theoretic, hazard function, and accumulation-to-bound modeling approaches in a large cohort of mice. We find that pupil-linked arousal and task utility both have major impacts on multiple aspects of task performance. Although substantial arousal fluctuations persist across utility conditions, mice partially stabilize their arousal near an intermediate and optimal level when task utility is high. Behavioral analyses show that multiple elements of behavior improve during high task utility and that arousal influences some, but not all, of them. Specifically, arousal influences the likelihood and timescale of sensory evidence accumulation but not the quantity of evidence accumulated per time step while attending. In sum, the results establish specific decision-computational signatures of arousal, motivation, and their interaction in attention. So doing, we provide an experimental and analysis framework for studying arousal self-regulation in neurotypical brains and in diseases such as attention-deficit/hyperactivity disorder.
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Affiliation(s)
- Jan Willem de Gee
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA; Cognitive and Systems Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, the Netherlands; Research Priority Area Brain and Cognition, University of Amsterdam, Science Park 904, Amsterdam 1098 XH, the Netherlands.
| | - Zakir Mridha
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA
| | - Marisa Hudson
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA
| | - Yanchen Shi
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA
| | - Hannah Ramsaywak
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA
| | - Spencer Smith
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA
| | - Nishad Karediya
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA
| | - Matthew Thompson
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA
| | - Kit Jaspe
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA
| | - Hong Jiang
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA
| | - Wenhao Zhang
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA
| | - Matthew J McGinley
- Department of Neuroscience, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, 1250 Moursund Street, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, 6100 Main Street, Houston, TX 77005, USA.
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2
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Carandini M. Sensory choices as logistic classification. Neuron 2024; 112:2854-2868.e1. [PMID: 39013468 PMCID: PMC11377159 DOI: 10.1016/j.neuron.2024.06.016] [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: 02/20/2024] [Revised: 05/13/2024] [Accepted: 06/19/2024] [Indexed: 07/18/2024]
Abstract
Logistic classification is a simple way to make choices based on a set of factors: give each factor a weight, sum the results, and use the sum to set the log odds of a random draw. This operation is known to describe human and animal choices based on value (economic decisions). There is increasing evidence that it also describes choices based on sensory inputs (perceptual decisions), presented across sensory modalities (multisensory integration) and combined with non-sensory factors such as prior probability, expected value, overall motivation, and recent actions. Logistic classification can also capture the effects of brain manipulations such as local inactivations. The brain may implement it by thresholding stochastic inputs (as in signal detection theory) acquired over time (as in the drift diffusion model). It is the optimal strategy under certain conditions, and the brain appears to use it as a heuristic in a wider set of conditions.
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Affiliation(s)
- Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London WC1 6BT, UK.
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3
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Rasanan AHH, Evans NJ, Fontanesi L, Manning C, Huang-Pollock C, Matzke D, Heathcote A, Rieskamp J, Speekenbrink M, Frank MJ, Palminteri S, Lucas CG, Busemeyer JR, Ratcliff R, Rad JA. Beyond discrete-choice options. Trends Cogn Sci 2024; 28:857-870. [PMID: 39138030 DOI: 10.1016/j.tics.2024.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 07/12/2024] [Accepted: 07/14/2024] [Indexed: 08/15/2024]
Abstract
While decision theories have evolved over the past five decades, their focus has largely been on choices among a limited number of discrete options, even though many real-world situations have a continuous-option space. Recently, theories have attempted to address decisions with continuous-option spaces, and several computational models have been proposed within the sequential sampling framework to explain how we make a decision in continuous-option space. This article aims to review the main attempts to understand decisions on continuous-option spaces, give an overview of applications of these types of decisions, and present puzzles to be addressed by future developments.
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Affiliation(s)
| | - Nathan J Evans
- School of Psychology, The University of Queensland, St Lucia, QLD 4072, Australia; Department of Psychology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Laura Fontanesi
- Department of Psychology, University of Basel, Missionsstrasse 62A, 4055, Basel, Switzerland
| | | | | | - Dora Matzke
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Andrew Heathcote
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands; School of Psychological Sciences, University of Newcastle, Newcastle, Australia
| | - Jörg Rieskamp
- Department of Psychology, University of Basel, Missionsstrasse 62A, 4055, Basel, Switzerland
| | | | - Michael J Frank
- Department of Cognitive, Linguistic, and Psychological Sciences and Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Stefano Palminteri
- Laboratoire de Neurosciences Cognitives Computationnelles, Institut National de la Santé et Recherche Médicale, Paris, France; Département d'Etudes Cognitives, Ecole Normale Supérieure, PSL Research University, Paris, France
| | | | - Jerome R Busemeyer
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Roger Ratcliff
- The Ohio State University, 1835 Neil Avenue, Columbus, OH, 43210, USA
| | - Jamal Amani Rad
- Choice Modelling Centre and Institute for Transport Studies, University of Leeds, Leeds LS2 9JT, UK.
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4
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Barbosa A, Ruarte G, Ries AJ, Kamienkowski JE, Ison MJ. Investigating the effects of context, visual working memory, and inhibitory control in hybrid visual search. Front Hum Neurosci 2024; 18:1436564. [PMID: 39257697 PMCID: PMC11384996 DOI: 10.3389/fnhum.2024.1436564] [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: 05/22/2024] [Accepted: 08/06/2024] [Indexed: 09/12/2024] Open
Abstract
Introduction In real-life scenarios, individuals frequently engage in tasks that involve searching for one of the distinct items stored in memory. This combined process of visual search and memory search is known as hybrid search. To date, most hybrid search studies have been restricted to average observers looking for previously well-memorized targets in blank backgrounds. Methods We investigated the effects of context and the role of memory in hybrid search by modifying the task's memorization phase to occur in all-new single trials. In addition, we aimed to assess how individual differences in visual working memory capacity and inhibitory control influence performance during hybrid search. In an online experiment, 110 participants searched for potential targets in images with and without context. A change detection and go/no-go task were also performed to measure working memory capacity and inhibitory control, respectively. Results We show that, in target present trials, the main hallmarks of hybrid search remain present, with a linear relationship between reaction time and visual set size and a logarithmic relationship between reaction time and memory set size. These behavioral results can be reproduced by using a simple drift-diffusion model. Finally, working memory capacity did not predict most search performance measures. Inhibitory control, when relationships were significant, could account for only a small portion of the variability in the data. Discussion This study provides insights into the effects of context and individual differences on search efficiency and termination.
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Affiliation(s)
- Alessandra Barbosa
- School of Psychology, University of Nottingham, Nottingham, United Kingdom
| | - Gonzalo Ruarte
- Laboratorio de Inteligencia Artificial Aplicada, Instituto de Ciencias de la Computación (Universidad de Buenos Aires - Consejo Nacional de Investigaciones Científicas y Técnicas), Buenos Aires, Argentina
| | - Anthony J Ries
- DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, United States
| | - Juan E Kamienkowski
- Laboratorio de Inteligencia Artificial Aplicada, Instituto de Ciencias de la Computación (Universidad de Buenos Aires - Consejo Nacional de Investigaciones Científicas y Técnicas), Buenos Aires, Argentina
- Departamento de Computación (Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires), Buenos Aires, Argentina
| | - Matias J Ison
- School of Psychology, University of Nottingham, Nottingham, United Kingdom
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5
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Carandini M. Sensory choices as logistic classification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.17.576029. [PMID: 38979189 PMCID: PMC11230223 DOI: 10.1101/2024.01.17.576029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Logistic classification is a simple way to make choices based on a set of factors: give each factor a weight, sum the results, and use the sum to set the log odds of a random draw. This operation is known to describe human and animal choices based on value (economic decisions). There is increasing evidence that it also describes choices based on sensory inputs (perceptual decisions), presented across sensory modalities (multisensory integration) and combined with non-sensory factors such as prior probability, expected value, overall motivation, and recent actions. Logistic classification can also capture the effects of brain manipulations such as local inactivations. The brain may implement by thresholding stochastic inputs (as in signal detection theory) acquired over time (as in the drift diffusion model). It is the optimal strategy under certain conditions, and the brain appears to use it as a heuristic in a wider set of conditions.
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Affiliation(s)
- Matteo Carandini
- UCL Institute of Ophthalmology, University College London, London WC1 6BT, UK
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6
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Tardiff N, Kang J, Gold JI. Normative evidence weighting and accumulation in correlated environments. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596489. [PMID: 38854097 PMCID: PMC11160761 DOI: 10.1101/2024.05.29.596489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
The brain forms certain deliberative decisions following normative principles related to how sensory observations are weighed and accumulated over time. Previously we showed that these principles can account for how people adapt their decisions to the temporal dynamics of the observations (Glaze et al., 2015). Here we show that this adaptability extends to accounting for correlations in the observations, which can have a dramatic impact on the weight of evidence provided by those observations. We tested online human participants on a novel visual-discrimination task with pairwise-correlated observations. With minimal training, the participants adapted to uncued, trial-by-trial changes in the correlations and produced decisions based on an approximately normative weighting and accumulation of evidence. The results highlight the robustness of our brain's ability to process sensory observations with respect to not just their physical features but also the weight of evidence they provide for a given decision.
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Affiliation(s)
- Nathan Tardiff
- Department of Otorhinolaryngology, Perelman School of Medicine, University of Pennsylvania, United States
- Department of Psychology, New York University, United States
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, United States
| | - Jiwon Kang
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, United States
| | - Joshua I Gold
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, United States
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7
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Vázquez D, Maulhardt SR, Stalnaker TA, Solway A, Charpentier CJ, Roesch MR. Optogenetic Inhibition of Rat Anterior Cingulate Cortex Impairs the Ability to Initiate and Stay on Task. J Neurosci 2024; 44:e1850232024. [PMID: 38569923 PMCID: PMC11097287 DOI: 10.1523/jneurosci.1850-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/16/2024] [Accepted: 01/20/2024] [Indexed: 04/05/2024] Open
Abstract
Our prior research has identified neural correlates of cognitive control in the anterior cingulate cortex (ACC), leading us to hypothesize that the ACC is necessary for increasing attention as rats flexibly learn new contingencies during a complex reward-guided decision-making task. Here, we tested this hypothesis by using optogenetics to transiently inhibit the ACC, while rats of either sex performed the same two-choice task. ACC inhibition had a profound impact on behavior that extended beyond deficits in attention during learning when expected outcomes were uncertain. We found that ACC inactivation slowed and reduced the number of trials rats initiated and impaired both their accuracy and their ability to complete sessions. Furthermore, drift-diffusion model analysis suggested that free-choice performance and evidence accumulation (i.e., reduced drift rates) were degraded during initial learning-leading to weaker associations that were more easily overridden in later trial blocks (i.e., stronger bias). Together, these results suggest that in addition to attention-related functions, the ACC contributes to the ability to initiate trials and generally stay on task.
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Affiliation(s)
- Daniela Vázquez
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742
| | - Sean R Maulhardt
- Department of Psychology, University of Maryland, College Park, Maryland 20742
| | - Thomas A Stalnaker
- Intramural Research Program, National Institute on Drug Abuse, Baltimore, Maryland 21224
| | - Alec Solway
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742
| | - Caroline J Charpentier
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742
| | - Matthew R Roesch
- Department of Psychology, University of Maryland, College Park, Maryland 20742
- Program in Neuroscience and Cognitive Science, University of Maryland, College Park, Maryland 20742
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8
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Wang JS, Donkin C. The neural implausibility of the diffusion decision model doesn't matter for cognitive psychometrics, but the Ornstein-Uhlenbeck model is better. Psychon Bull Rev 2024:10.3758/s13423-024-02520-5. [PMID: 38743214 DOI: 10.3758/s13423-024-02520-5] [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: 04/25/2024] [Indexed: 05/16/2024]
Abstract
In cognitive psychometrics, the parameters of cognitive models are used as measurements of the processes underlying observed behavior. In decision making, the diffusion decision model (DDM) is by far the most commonly used cognitive psychometric tool. One concern when using this model is that more recent theoretical accounts of decision-making place more emphasis on neural plausibility, and thus incorporate many assumptions not found in the DDM. One such model is the Ising Decision Maker (IDM), which builds from the assumption that two pools of neurons with self-excitation and mutual inhibition receive perceptual input from external excitatory fields. In this study, we investigate whether the lack of such mechanisms in the DDM compromises its ability to measure the processes it does purport to measure. We cross-fit the DDM and IDM, and find that the conclusions of DDM would be mostly consistent with those from an analysis using a more neurally plausible model. We also show that the Ornstein-Uhlenbeck Model (OUM) model, a variant of the DDM that includes the potential for leakage (or self-excitation), reaches similar conclusions to the DDM regarding the assumptions they share, while also sharing an interpretation with the IDM in terms of self-excitation (but not leakage). Since the OUM is relatively easy to fit to data, while being able to capture more neurally plausible mechanisms, we propose that it be considered an alternative cognitive psychometric tool to the DDM.
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Affiliation(s)
- Jia-Shun Wang
- Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany.
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, Munich, Germany.
| | - Christopher Donkin
- Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany
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9
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Zgonnikov A, Abbink D. Should I Stay or Should I Go? Cognitive Modeling of Left-Turn Gap Acceptance Decisions in Human Drivers. HUMAN FACTORS 2024; 66:1399-1413. [PMID: 36534014 PMCID: PMC10958748 DOI: 10.1177/00187208221144561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 11/16/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVE We aim to bridge the gap between naturalistic studies of driver behavior and modern cognitive and neuroscientific accounts of decision making by modeling the cognitive processes underlying left-turn gap acceptance by human drivers. BACKGROUND Understanding decisions of human drivers is essential for the development of safe and efficient transportation systems. Current models of decision making in drivers provide little insight into the underlying cognitive processes. On the other hand, laboratory studies of abstract, highly controlled tasks point towards noisy evidence accumulation as a key mechanism governing decision making. However, it is unclear whether the cognitive processes implicated in these tasks are as paramount to decisions that are ingrained in more complex behaviors, such as driving. RESULTS The drivers' probability of accepting the available gap increased with the size of the gap; importantly, response time increased with time gap but not distance gap. The generalized drift-diffusion model explained the observed decision outcomes and response time distributions, as well as substantial individual differences in those. Through cross-validation, we demonstrate that the model not only explains the data, but also generalizes to out-of-sample conditions. CONCLUSION Our results suggest that dynamic evidence accumulation is an essential mechanism underlying left-turn gap acceptance decisions in human drivers, and exemplify how simple cognitive process models can help to understand human behavior in complex real-world tasks. APPLICATION Potential applications of our results include real-time prediction of human behavior by automated vehicles and simulating realistic human-like behaviors in virtual environments for automated vehicles.
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10
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White SR, Preston MW, Swanson K, Laubach M. Learning to Choose: Behavioral Dynamics Underlying the Initial Acquisition of Decision-Making. eNeuro 2024; 11:ENEURO.0142-24.2024. [PMID: 38724267 PMCID: PMC11103646 DOI: 10.1523/eneuro.0142-24.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 04/26/2024] [Accepted: 04/30/2024] [Indexed: 05/19/2024] Open
Abstract
Current theories of decision-making propose that decisions arise through competition between choice options. Computational models of the decision process estimate how quickly information about choice options is integrated and how much information is needed to trigger a choice. Experiments using this approach typically report data from well-trained participants. As such, we do not know how the decision process evolves as a decision-making task is learned for the first time. To address this gap, we used a behavioral design separating learning the value of choice options from learning to make choices. We trained male rats to respond to single visual stimuli with different reward values. Then, we trained them to make choices between pairs of stimuli. Initially, the rats responded more slowly when presented with choices. However, as they gained experience in making choices, this slowing reduced. Response slowing on choice trials persisted throughout the testing period. We found that it was specifically associated with increased exponential variability when the rats chose the higher value stimulus. Additionally, our analysis using drift diffusion modeling revealed that the rats required less information to make choices over time. These reductions in the decision threshold occurred after just a single session of choice learning. These findings provide new insights into the learning process of decision-making tasks. They suggest that the value of choice options and the ability to make choices are learned separately and that experience plays a crucial role in improving decision-making performance.
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Affiliation(s)
- Samantha R White
- Department of Neuroscience, American University, Washington, DC 20016
| | - Michael W Preston
- Department of Neuroscience, American University, Washington, DC 20016
| | - Kyra Swanson
- Department of Neuroscience, American University, Washington, DC 20016
| | - Mark Laubach
- Department of Neuroscience, American University, Washington, DC 20016
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11
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Qarehdaghi H, Rad JA. EZ-CDM: Fast, simple, robust, and accurate estimation of circular diffusion model parameters. Psychon Bull Rev 2024:10.3758/s13423-024-02483-7. [PMID: 38587755 DOI: 10.3758/s13423-024-02483-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/17/2024] [Indexed: 04/09/2024]
Abstract
The investigation of cognitive processes that form the basis of decision-making in paradigms involving continuous outcomes has gained the interest of modeling researchers who aim to develop a dynamic decision theory that accounts for both speed and accuracy. One of the most important of these continuous models is the circular diffusion model (CDM, Smith. Psychological Review, 123(4), 425. 2016), which posits a noisy accumulation process mathematically described as a stochastic two-dimensional Wiener process inside a disk. Despite the considerable benefits of this model, its mathematical intricacy has limited its utilization among scholars. Here, we propose a straightforward and user-friendly method for estimating the CDM parameters and fitting the model to continuous-scale data using simple formulas that can be readily computed and do not require theoretical knowledge of model fitting or extensive programming. Notwithstanding its simplicity, we demonstrate that the aforementioned method performs with a level of accuracy that is comparable to that of the maximum likelihood estimation method. Furthermore, a robust version of the method is presented, which maintains its simplicity while exhibiting a high degree of resistance to contaminant responses. Additionally, we show that the approach is capable of reliably measuring the key parameters of the CDM, even when these values are subject to across-trial variability. Finally, we demonstrate the practical application of the method on experimental data. Specifically, an illustrative example is presented wherein the method is employed along with estimating the probability of guessing. It is hoped that the straightforward methodology presented here will, on the one hand, help narrow the divide between theoretical constructs and empirical observations on continuous response tasks and, on the other hand, inspire cognitive psychology researchers to shift their laboratory investigations towards continuous response paradigms.
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Affiliation(s)
- Hasan Qarehdaghi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Jamal Amani Rad
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran.
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12
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White SR, Preston MW, Swanson K, Laubach M. Learning to Choose: Behavioral Dynamics Underlying the Initial Acquisition of Decision Making. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.28.582581. [PMID: 38464283 PMCID: PMC10925347 DOI: 10.1101/2024.02.28.582581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Current theories of decision making propose that decisions arise through competition between choice options. Computational models of the decision process estimate how quickly information about choice options is integrated and how much information is needed to trigger a choice. Experiments using this approach typically report data from well-trained participants. As such, we do not know how the decision process evolves as a decision-making task is learned for the first time. To address this gap, we used a behavioral design separating learning the value of choice options from learning to make choices. We trained male rats to respond to single visual stimuli with different reward values. Then, we trained them to make choices between pairs of stimuli. Initially, the rats responded more slowly when presented with choices. However, as they gained experience in making choices, this slowing reduced. Response slowing on choice trials persisted throughout the testing period. We found that it was specifically associated with increased exponential variability when the rats chose the higher value stimulus. Additionally, our analysis using drift diffusion modeling revealed that the rats required less information to make choices over time. Surprisingly, we observed reductions in the decision threshold after just a single session of choice learning. These findings provide new insights into the learning process of decision-making tasks. They suggest that the value of choice options and the ability to make choices are learned separately, and that experience plays a crucial role in improving decision-making performance.
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Affiliation(s)
- Samantha R White
- Department of Neuroscience, American University, Washington, DC, USA
| | - Michael W Preston
- Department of Neuroscience, American University, Washington, DC, USA
| | - Kyra Swanson
- Department of Neuroscience, American University, Washington, DC, USA
| | - Mark Laubach
- Department of Neuroscience, American University, Washington, DC, USA
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13
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Murrow M, Holmes WR. PyBEAM: A Bayesian approach to parameter inference for a wide class of binary evidence accumulation models. Behav Res Methods 2024; 56:2636-2656. [PMID: 37550470 DOI: 10.3758/s13428-023-02162-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2023] [Indexed: 08/09/2023]
Abstract
Many decision-making theories are encoded in a class of processes known as evidence accumulation models (EAM). These assume that noisy evidence stochastically accumulates until a set threshold is reached, triggering a decision. One of the most successful and widely used of this class is the Diffusion Decision Model (DDM). The DDM however is limited in scope and does not account for processes such as evidence leakage, changes of evidence, or time varying caution. More complex EAMs can encode a wider array of hypotheses, but are currently limited by computational challenges. In this work, we develop the Python package PyBEAM (Bayesian Evidence Accumulation Models) to fill this gap. Toward this end, we develop a general probabilistic framework for predicting the choice and response time distributions for a general class of binary decision models. In addition, we have heavily computationally optimized this modeling process and integrated it with PyMC, a widely used Python package for Bayesian parameter estimation. This 1) substantially expands the class of EAM models to which Bayesian methods can be applied, 2) reduces the computational time to do so, and 3) lowers the entry fee for working with these models. Here we demonstrate the concepts behind this methodology, its application to parameter recovery for a variety of models, and apply it to a recently published data set to demonstrate its practical use.
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Affiliation(s)
- Matthew Murrow
- Department of Physics and Astronomy, Vanderbilt University, 6301 Stevenson Science Center, Nashville, 37212, TN, USA
| | - William R Holmes
- Cognitive Science Program and Department of Mathematics, Indiana University, 1001 E. 10th St., Bloomington, 47405, IN, USA.
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14
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Asadpour A, Tan H, Lenfesty B, Wong-Lin K. Of Rodents and Primates: Time-Variant Gain in Drift-Diffusion Decision Models. COMPUTATIONAL BRAIN & BEHAVIOR 2024; 7:195-206. [PMID: 38798787 PMCID: PMC11111503 DOI: 10.1007/s42113-023-00194-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 12/10/2023] [Indexed: 05/29/2024]
Abstract
Sequential sampling models of decision-making involve evidence accumulation over time and have been successful in capturing choice behaviour. A popular model is the drift-diffusion model (DDM). To capture the finer aspects of choice reaction times (RTs), time-variant gain features representing urgency signals have been implemented in DDM that can exhibit slower error RTs than correct RTs. However, time-variant gain is often implemented on both DDM's signal and noise features, with the assumption that increasing gain on the drift rate (due to urgency) is similar to DDM with collapsing decision bounds. Hence, it is unclear whether gain effects on just the signal or noise feature can lead to a different choice behaviour. This work presents an alternative DDM variant, focusing on the implications of time-variant gain mechanisms, constrained by model parsimony. Specifically, using computational modelling of choice behaviour of rats, monkeys, and humans, we systematically showed that time-variant gain only on the DDM's noise was sufficient to produce slower error RTs, as in monkeys, while time-variant gain only on drift rate leads to faster error RTs, as in rodents. We also found minimal effects of time-variant gain in humans. By highlighting these patterns, this study underscores the utility of group-level modelling in capturing general trends and effects consistent across species. Thus, time-variant gain on DDM's different components can lead to different choice behaviours, shed light on the underlying time-variant gain mechanisms for different species, and can be used for systematic data fitting. Supplementary Information The online version contains supplementary material available at 10.1007/s42113-023-00194-1.
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Affiliation(s)
- Abdoreza Asadpour
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland UK
| | - Hui Tan
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland UK
- Département Electronique et Technologies Numériques, Polytech Nantes, Nantes Université, Nantes, France
| | - Brendan Lenfesty
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland UK
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry~Londonderry, Northern Ireland UK
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15
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Calder-Travis J, Bogacz R, Yeung N. Expressions for Bayesian confidence of drift diffusion observers in fluctuating stimuli tasks. JOURNAL OF MATHEMATICAL PSYCHOLOGY 2023; 117:102815. [PMID: 38188903 PMCID: PMC7615478 DOI: 10.1016/j.jmp.2023.102815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
We introduce a new approach to modelling decision confidence, with the aim of enabling computationally cheap predictions while taking into account, and thereby exploiting, trial-by-trial variability in stochastically fluctuating stimuli. Using the framework of the drift diffusion model of decision making, along with time-dependent thresholds and the idea of a Bayesian confidence readout, we derive expressions for the probability distribution over confidence reports. In line with current models of confidence, the derivations allow for the accumulation of "pipeline" evidence that has been received but not processed by the time of response, the effect of drift rate variability, and metacognitive noise. The expressions are valid for stimuli that change over the course of a trial with normally-distributed fluctuations in the evidence they provide. A number of approximations are made to arrive at the final expressions, and we test all approximations via simulation. The derived expressions contain only a small number of standard functions, and require evaluating only once per trial, making trial-by-trial modelling of confidence data in stochastically fluctuating stimuli tasks more feasible. We conclude by using the expressions to gain insight into the confidence of optimal observers, and empirically observed patterns.
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Affiliation(s)
| | - Rafal Bogacz
- MRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neuroscience, University of Oxford, UK
| | - Nick Yeung
- Department of Experimental Psychology, University of Oxford, UK
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16
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Mihali A, Broeker M, Ragalmuto FDM, Horga G. Introspective inference counteracts perceptual distortion. Nat Commun 2023; 14:7826. [PMID: 38030601 PMCID: PMC10687029 DOI: 10.1038/s41467-023-42813-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Introspective agents can recognize the extent to which their internal perceptual experiences deviate from the actual states of the external world. This ability, also known as insight, is critically required for reality testing and is impaired in psychosis, yet little is known about its cognitive underpinnings. We develop a Bayesian modeling framework and a psychophysics paradigm to quantitatively characterize this type of insight while people experience a motion after-effect illusion. People can incorporate knowledge about the illusion into their decisions when judging the actual direction of a motion stimulus, compensating for the illusion (and often overcompensating). Furthermore, confidence, reaction-time, and pupil-dilation data all show signatures consistent with inferential adjustments in the Bayesian insight model. Our results suggest that people can question the veracity of what they see by making insightful inferences that incorporate introspective knowledge about internal distortions.
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Affiliation(s)
- Andra Mihali
- New York State Psychiatric Institute, New York, NY, USA.
- Columbia University, Department of Psychiatry, New York, NY, USA.
| | - Marianne Broeker
- New York State Psychiatric Institute, New York, NY, USA
- Columbia University, Department of Psychiatry, New York, NY, USA
- Columbia University, Teachers College, New York, NY, USA
- University of Oxford, Department of Experimental Psychology, Oxford, UK
| | - Florian D M Ragalmuto
- New York State Psychiatric Institute, New York, NY, USA
- Columbia University, Department of Psychiatry, New York, NY, USA
- Vrije Universiteit, Faculty of Behavioral and Movement Science, Amsterdam, the Netherlands
- Berliner FortbildungsAkademie, Berlin, DE, Germany
| | - Guillermo Horga
- New York State Psychiatric Institute, New York, NY, USA.
- Columbia University, Department of Psychiatry, New York, NY, USA.
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17
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Shinn M. Phantom oscillations in principal component analysis. Proc Natl Acad Sci U S A 2023; 120:e2311420120. [PMID: 37988465 PMCID: PMC10691246 DOI: 10.1073/pnas.2311420120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/18/2023] [Indexed: 11/23/2023] Open
Abstract
Principal component analysis (PCA) is a dimensionality reduction method that is known for being simple and easy to interpret. Principal components are often interpreted as low-dimensional patterns in high-dimensional space. However, this simple interpretation fails for timeseries, spatial maps, and other continuous data. In these cases, nonoscillatory data may have oscillatory principal components. Here, we show that two common properties of data cause oscillatory principal components: smoothness and shifts in time or space. These two properties implicate almost all neuroscience data. We show how the oscillations produced by PCA, which we call "phantom oscillations," impact data analysis. We also show that traditional cross-validation does not detect phantom oscillations, so we suggest procedures that do. Our findings are supported by a collection of mathematical proofs. Collectively, our work demonstrates that patterns which emerge from high-dimensional data analysis may not faithfully represent the underlying data.
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Affiliation(s)
- Maxwell Shinn
- University College London (UCL) Queen Square Institute of Neurology, University College London, LondonWC1E 6BT, United Kingdom
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18
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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: 4] [Impact Index Per Article: 4.0] [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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19
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Mulder MJ, Prummer F, Terburg D, Kenemans JL. Drift-diffusion modeling reveals that masked faces are preconceived as unfriendly. Sci Rep 2023; 13:16982. [PMID: 37813970 PMCID: PMC10562405 DOI: 10.1038/s41598-023-44162-y] [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: 06/10/2022] [Accepted: 10/04/2023] [Indexed: 10/11/2023] Open
Abstract
During the COVID-19 pandemic, the use of face masks has become a daily routine. Studies have shown that face masks increase the ambiguity of facial expressions which not only affects (the development of) emotion recognition, but also interferes with social interaction and judgement. To disambiguate facial expressions, we rely on perceptual (stimulus-driven) as well as preconceptual (top-down) processes. However, it is unknown which of these two mechanisms accounts for the misinterpretation of masked expressions. To investigate this, we asked participants (N = 136) to decide whether ambiguous (morphed) facial expressions, with or without a mask, were perceived as friendly or unfriendly. To test for the independent effects of perceptual and preconceptual biases we fitted a drift-diffusion model (DDM) to the behavioral data of each participant. Results show that face masks induce a clear loss of information leading to a slight perceptual bias towards friendly choices, but also a clear preconceptual bias towards unfriendly choices for masked faces. These results suggest that, although face masks can increase the perceptual friendliness of faces, people have the prior preconception to interpret masked faces as unfriendly.
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Affiliation(s)
- Martijn J Mulder
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands.
| | - Franziska Prummer
- School of Computing and Communications, Lancaster University, Lancaster, UK
| | - David Terburg
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - J Leon Kenemans
- Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
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20
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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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21
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Smith R. The path forward for modeling action-oriented cognition as active inference: Comment on "An active inference model of hierarchical action understanding, learning and imitation" by Riccardo Proietti, Giovanni Pezzulo, Alessia Tessari. Phys Life Rev 2023; 46:152-154. [PMID: 37437406 DOI: 10.1016/j.plrev.2023.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 06/27/2023] [Indexed: 07/14/2023]
Affiliation(s)
- Ryan Smith
- Laureate Institute for Brain Research, United States of America.
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22
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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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23
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Letkiewicz AM, Kottler HC, Shankman SA, Cochran AL. Quantifying aberrant approach-avoidance conflict in psychopathology: A review of computational approaches. Neurosci Biobehav Rev 2023; 147:105103. [PMID: 36804398 PMCID: PMC10023482 DOI: 10.1016/j.neubiorev.2023.105103] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 02/02/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
Making effective decisions during approach-avoidance conflict is critical in daily life. Aberrant decision-making during approach-avoidance conflict is evident in a range of psychological disorders, including anxiety, depression, trauma-related disorders, substance use disorders, and alcohol use disorders. To help clarify etiological pathways and reveal novel intervention targets, clinical research into decision-making is increasingly adopting a computational psychopathology approach. This approach uses mathematical models that can identify specific decision-making related processes that are altered in mental health disorders. In our review, we highlight foundational approach-avoidance conflict research, followed by more in-depth discussion of computational approaches that have been used to model behavior in these tasks. Specifically, we describe the computational models that have been applied to approach-avoidance conflict (e.g., drift-diffusion, active inference, and reinforcement learning models), and provide resources to guide clinical researchers who may be interested in applying computational modeling. Finally, we identify notable gaps in the current literature and potential future directions for computational approaches aimed at identifying mechanisms of approach-avoidance conflict in psychopathology.
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Affiliation(s)
- Allison M Letkiewicz
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA.
| | - Haley C Kottler
- Department of Mathematics, University of Wisconsin, Madison, WI, USA
| | - Stewart A Shankman
- Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA; Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Amy L Cochran
- Department of Mathematics, University of Wisconsin, Madison, WI, USA; Department of Population Health Sciences, University of Wisconsin, Madison, WI, USA
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24
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Koob V, Mackenzie I, Ulrich R, Leuthold H, Janczyk M. The role of task-relevant and task-irrelevant information in congruency sequence effects: Applying the diffusion model for conflict tasks. Cogn Psychol 2023; 140:101528. [PMID: 36584549 DOI: 10.1016/j.cogpsych.2022.101528] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 11/15/2022] [Accepted: 11/15/2022] [Indexed: 12/29/2022]
Abstract
In conflict tasks, such as the Simon, Eriksen flanker, or Stroop task, the congruency effect is often reduced after an incongruent compared to a congruent trial: the congruency sequence effect (CSE). It was suggested that the CSE may reflect increased processing of task-relevant information and/or suppression of task-irrelevant information after experiencing an incongruent relative to a congruent trial. In the present study, we contribute to this discussion by applying the Diffusion Model for Conflict tasks (DMC) framework in the context of CSEs to flanker and Simon tasks. We argue that DMC independently models the task-relevant and task-irrelevant information and thus is a first good candidate for disentangling their unique contributions. As a first approach, we fitted DMC conjointly or separately to previously congruent or incongruent trials, using four empirical flanker and two Simon data sets. For the flanker task, we fitted the classical DMC version. For the Simon task, we fitted a generalized DMC version which allows the task-irrelevant information to undershoot when swinging back to zero. After considering the model fits, we present a second approach, where we implemented a cognitive control mechanism to simulate the influence of increased processing of task-relevant information or increased suppression of task-irrelevant information. Both approaches demonstrate that the suppression of task-irrelevant information is essential to create the typical CSE pattern. Increased processing of task-relevant information, however, could rarely describe the CSE accurately.
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Affiliation(s)
- Valentin Koob
- Department of Psychology, University of Bremen, 28359 Bremen, Germany.
| | - Ian Mackenzie
- Department of Psychology, University of Tübingen, 72076 Tübingen, Germany
| | - Rolf Ulrich
- Department of Psychology, University of Tübingen, 72076 Tübingen, Germany
| | - Hartmut Leuthold
- Department of Psychology, University of Tübingen, 72076 Tübingen, Germany
| | - Markus Janczyk
- Department of Psychology, University of Bremen, 28359 Bremen, Germany
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25
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Mafi F, Tang MF, Afarinesh MR, Ghasemian S, Sheibani V, Arabzadeh E. Temporal order judgment of multisensory stimuli in rat and human. Front Behav Neurosci 2023; 16:1070452. [PMID: 36710957 PMCID: PMC9879721 DOI: 10.3389/fnbeh.2022.1070452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 12/16/2022] [Indexed: 01/13/2023] Open
Abstract
We do not fully understand the resolution at which temporal information is processed by different species. Here we employed a temporal order judgment (TOJ) task in rats and humans to test the temporal precision with which these species can detect the order of presentation of simple stimuli across two modalities of vision and audition. Both species reported the order of audiovisual stimuli when they were presented from a central location at a range of stimulus onset asynchronies (SOA)s. While both species could reliably distinguish the temporal order of stimuli based on their sensory content (i.e., the modality label), rats outperformed humans at short SOAs (less than 100 ms) whereas humans outperformed rats at long SOAs (greater than 100 ms). Moreover, rats produced faster responses compared to humans. The reaction time data further revealed key differences in decision process across the two species: at longer SOAs, reaction times increased in rats but decreased in humans. Finally, drift-diffusion modeling allowed us to isolate the contribution of various parameters including evidence accumulation rates, lapse and bias to the sensory decision. Consistent with the psychophysical findings, the model revealed higher temporal sensitivity and a higher lapse rate in rats compared to humans. These findings suggest that these species applied different strategies for making perceptual decisions in the context of a multimodal TOJ task.
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Affiliation(s)
- Fatemeh Mafi
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
- Cognitive Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Matthew F. Tang
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
| | - Mohammad Reza Afarinesh
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
- Cognitive Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Sadegh Ghasemian
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
- Cognitive Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Vahid Sheibani
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
- Cognitive Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
| | - Ehsan Arabzadeh
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
- Cognitive Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
- Eccles Institute of Neuroscience, John Curtin School of Medical Research, The Australian National University, Canberra, ACT, Australia
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26
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Barbosa J, Stein H, Zorowitz S, Niv Y, Summerfield C, Soto-Faraco S, Hyafil A. A practical guide for studying human behavior in the lab. Behav Res Methods 2023; 55:58-76. [PMID: 35262897 DOI: 10.3758/s13428-022-01793-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/04/2022] [Indexed: 11/08/2022]
Abstract
In the last few decades, the field of neuroscience has witnessed major technological advances that have allowed researchers to measure and control neural activity with great detail. Yet, behavioral experiments in humans remain an essential approach to investigate the mysteries of the mind. Their relatively modest technological and economic requisites make behavioral research an attractive and accessible experimental avenue for neuroscientists with very diverse backgrounds. However, like any experimental enterprise, it has its own inherent challenges that may pose practical hurdles, especially to less experienced behavioral researchers. Here, we aim at providing a practical guide for a steady walk through the workflow of a typical behavioral experiment with human subjects. This primer concerns the design of an experimental protocol, research ethics, and subject care, as well as best practices for data collection, analysis, and sharing. The goal is to provide clear instructions for both beginners and experienced researchers from diverse backgrounds in planning behavioral experiments.
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Affiliation(s)
- Joao Barbosa
- Brain Circuits & Behavior lab, IDIBAPS, Barcelona, Spain.
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Supérieure - PSL Research University, 75005, Paris, France.
| | - Heike Stein
- Brain Circuits & Behavior lab, IDIBAPS, Barcelona, Spain
- Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Supérieure - PSL Research University, 75005, Paris, France
| | - Sam Zorowitz
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Yael Niv
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
- Department of Psychology, Princeton University, Princeton, USA
| | | | - Salvador Soto-Faraco
- Multisensory Research Group, Center for Brain and Cognition, Universitat Pompeu Fabra Barcelona, Spain, and Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
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27
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Lin H, Pennycook G, Rand DG. Thinking more or thinking differently? Using drift-diffusion modeling to illuminate why accuracy prompts decrease misinformation sharing. Cognition 2023; 230:105312. [PMID: 36334467 DOI: 10.1016/j.cognition.2022.105312] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 10/17/2022] [Accepted: 10/19/2022] [Indexed: 11/27/2022]
Abstract
Recent experiments have found that prompting people to think about accuracy reduces misinformation sharing intentions. The process by which this effect operates, however, remains unclear. Do accuracy prompts cause people to "stop and think," increasing deliberation? Or do they change what people think about, drawing attention to accuracy? Since these two accounts predict the same behavioral outcomes (i.e., increased sharing discernment following a prompt), we used computational modeling of sharing decisions with response time data, as well as out-of-sample ratings of headline perceived accuracy, to test the accounts' divergent predictions across six studies (N = 5633). The results suggest that accuracy prompts do not increase the amount of deliberation people engage in. Instead, they increase the weight participants put on accuracy while deliberating. By showing that prompting people makes them think better even without thinking more, our results challenge common dual-process interpretations of the accuracy-prompt effect. Our findings also highlight the importance of understanding how social media distracts people from considering accuracy, and provide evidence for scalable interventions that redirect people's attention.
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Affiliation(s)
- Hause Lin
- Hill/Levene Schools of Business, University of Regina, Canada; Sloan School, Massachusetts Institute of Technology, USA.
| | - Gordon Pennycook
- Hill/Levene Schools of Business, University of Regina, Canada; Department of Psychology, University of Regina, Canada
| | - David G Rand
- Sloan School, Massachusetts Institute of Technology, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, USA
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28
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Barendregt NW, Gold JI, Josić K, Kilpatrick ZP. Normative decision rules in changing environments. eLife 2022; 11:e79824. [PMID: 36282065 PMCID: PMC9754630 DOI: 10.7554/elife.79824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/20/2022] [Indexed: 11/13/2022] Open
Abstract
Models based on normative principles have played a major role in our understanding of how the brain forms decisions. However, these models have typically been derived for simple, stable conditions, and their relevance to decisions formed under more naturalistic, dynamic conditions is unclear. We previously derived a normative decision model in which evidence accumulation is adapted to fluctuations in the evidence-generating process that occur during a single decision (Glaze et al., 2015), but the evolution of commitment rules (e.g. thresholds on the accumulated evidence) under dynamic conditions is not fully understood. Here, we derive a normative model for decisions based on changing contexts, which we define as changes in evidence quality or reward, over the course of a single decision. In these cases, performance (reward rate) is maximized using decision thresholds that respond to and even anticipate these changes, in contrast to the static thresholds used in many decision models. We show that these adaptive thresholds exhibit several distinct temporal motifs that depend on the specific predicted and experienced context changes and that adaptive models perform robustly even when implemented imperfectly (noisily). We further show that decision models with adaptive thresholds outperform those with constant or urgency-gated thresholds in accounting for human response times on a task with time-varying evidence quality and average reward. These results further link normative and neural decision-making while expanding our view of both as dynamic, adaptive processes that update and use expectations to govern both deliberation and commitment.
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Affiliation(s)
- Nicholas W Barendregt
- Department of Applied Mathematics, University of Colorado BoulderBoulderUnited States
| | - Joshua I Gold
- Department of Neuroscience, University of PennsylvaniaPhiladelphiaUnited States
| | - Krešimir Josić
- Department of Mathematics, University of HoustonHoustonUnited States
| | - Zachary P Kilpatrick
- Department of Applied Mathematics, University of Colorado BoulderBoulderUnited States
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29
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Persistent activity in human parietal cortex mediates perceptual choice repetition bias. Nat Commun 2022; 13:6015. [PMID: 36224207 PMCID: PMC9556658 DOI: 10.1038/s41467-022-33237-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 09/08/2022] [Indexed: 11/09/2022] Open
Abstract
Humans and other animals tend to repeat or alternate their previous choices, even when judging sensory stimuli presented in a random sequence. It is unclear if and how sensory, associative, and motor cortical circuits produce these idiosyncratic behavioral biases. Here, we combined behavioral modeling of a visual perceptual decision with magnetoencephalographic (MEG) analyses of neural dynamics, across multiple regions of the human cerebral cortex. We identified distinct history-dependent neural signals in motor and posterior parietal cortex. Gamma-band activity in parietal cortex tracked previous choices in a sustained fashion, and biased evidence accumulation toward choice repetition; sustained beta-band activity in motor cortex inversely reflected the previous motor action, and biased the accumulation starting point toward alternation. The parietal, not motor, signal mediated the impact of previous on current choice and reflected individual differences in choice repetition. In sum, parietal cortical signals seem to play a key role in shaping choice sequences.
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30
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Tardiff N, Suriya-Arunroj L, Cohen YE, Gold JI. Rule-based and stimulus-based cues bias auditory decisions via different computational and physiological mechanisms. PLoS Comput Biol 2022; 18:e1010601. [PMID: 36206302 PMCID: PMC9581427 DOI: 10.1371/journal.pcbi.1010601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/19/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022] Open
Abstract
Expectations, such as those arising from either learned rules or recent stimulus regularities, can bias subsequent auditory perception in diverse ways. However, it is not well understood if and how these diverse effects depend on the source of the expectations. Further, it is unknown whether different sources of bias use the same or different computational and physiological mechanisms. We examined how rule-based and stimulus-based expectations influenced behavior and pupil-linked arousal, a marker of certain forms of expectation-based processing, of human subjects performing an auditory frequency-discrimination task. Rule-based cues consistently biased choices and response times (RTs) toward the more-probable stimulus. In contrast, stimulus-based cues had a complex combination of effects, including choice and RT biases toward and away from the frequency of recently presented stimuli. These different behavioral patterns also had: 1) distinct computational signatures, including different modulations of key components of a novel form of a drift-diffusion decision model and 2) distinct physiological signatures, including substantial bias-dependent modulations of pupil size in response to rule-based but not stimulus-based cues. These results imply that different sources of expectations can modulate auditory processing via distinct mechanisms: one that uses arousal-linked, rule-based information and another that uses arousal-independent, stimulus-based information to bias the speed and accuracy of auditory perceptual decisions. Prior information about upcoming stimuli can bias our perception of those stimuli. Whether different sources of prior information bias perception in similar or distinct ways is not well understood. We compared the influence of two kinds of prior information on tone-frequency discrimination: rule-based cues, in the form of explicit information about the most-likely identity of the upcoming tone; and stimulus-based cues, in the form of sequences of tones presented before the to-be-discriminated tone. Although both types of prior information biased auditory decision-making, they demonstrated distinct behavioral, computational, and physiological signatures. Our results suggest that the brain processes prior information in a form-specific manner rather than utilizing a general-purpose prior. Such form-specific processing has implications for understanding decision biases real-world contexts, in which prior information comes from many different sources and modalities.
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Affiliation(s)
- Nathan Tardiff
- Department of Otorhinolaryngology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- * E-mail:
| | - Lalitta Suriya-Arunroj
- Department of Otorhinolaryngology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Yale E. Cohen
- Department of Otorhinolaryngology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Joshua I. Gold
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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31
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Alonso-Díaz S, Penagos-Londoño GI. Reduced choice-confidence in negative numerals. PLoS One 2022; 17:e0272796. [PMID: 36190954 PMCID: PMC9529092 DOI: 10.1371/journal.pone.0272796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 07/26/2022] [Indexed: 11/07/2022] Open
Abstract
Negative numbers are central in math. However, they are abstract, hard to learn, and manipulated slower than positive numbers regardless of math ability. It suggests that confidence, namely the post-decision estimate of being correct, should be lower than positives. We asked participants to pick the larger single-digit numeral in a pair and collected their implicit confidence with button pressure (button pressure was validated with three empirical signatures of confidence). We also modeled their choices with a drift-diffusion decision model to compute the post-decision estimate of being correct. We found that participants had relatively low confidence with negative numerals. Given that participants compared with high accuracy the basic base-10 symbols (0–9), reduced confidence may be a general feature of manipulating abstract negative numerals as they produce more uncertainty than positive numerals per unit of time.
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Affiliation(s)
- Santiago Alonso-Díaz
- Department of Economics, Pontificia Universidad Javeriana, Bogotá, Colombia
- * E-mail:
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32
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Fengler A, Bera K, Pedersen ML, Frank MJ. Beyond Drift Diffusion Models: Fitting a Broad Class of Decision and Reinforcement Learning Models with HDDM. J Cogn Neurosci 2022; 34:1780-1805. [PMID: 35939629 DOI: 10.1162/jocn_a_01902] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Computational modeling has become a central aspect of research in the cognitive neurosciences. As the field matures, it is increasingly important to move beyond standard models to quantitatively assess models with richer dynamics that may better reflect underlying cognitive and neural processes. For example, sequential sampling models (SSMs) are a general class of models of decision-making intended to capture processes jointly giving rise to RT distributions and choice data in n-alternative choice paradigms. A number of model variations are of theoretical interest, but empirical data analysis has historically been tied to a small subset for which likelihood functions are analytically tractable. Advances in methods designed for likelihood-free inference have recently made it computationally feasible to consider a much larger spectrum of SSMs. In addition, recent work has motivated the combination of SSMs with reinforcement learning models, which had historically been considered in separate literatures. Here, we provide a significant addition to the widely used HDDM Python toolbox and include a tutorial for how users can easily fit and assess a (user-extensible) wide variety of SSMs and how they can be combined with reinforcement learning models. The extension comes batteries included, including model visualization tools, posterior predictive checks, and ability to link trial-wise neural signals with model parameters via hierarchical Bayesian regression.
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33
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Boelts J, Lueckmann JM, Gao R, Macke JH. Flexible and efficient simulation-based inference for models of decision-making. eLife 2022; 11:77220. [PMID: 35894305 PMCID: PMC9374439 DOI: 10.7554/elife.77220] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 07/26/2022] [Indexed: 11/22/2022] Open
Abstract
Inferring parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model—however, for many models of interest in cognitive neuroscience, the associated likelihoods cannot be computed efficiently. Simulation-based inference (SBI) offers a solution to this problem by only requiring access to simulations produced by the model. Previously, Fengler et al. introduced likelihood approximation networks (LANs, Fengler et al., 2021) which make it possible to apply SBI to models of decision-making but require billions of simulations for training. Here, we provide a new SBI method that is substantially more simulation efficient. Our approach, mixed neural likelihood estimation (MNLE), trains neural density estimators on model simulations to emulate the simulator and is designed to capture both the continuous (e.g., reaction times) and discrete (choices) data of decision-making models. The likelihoods of the emulator can then be used to perform Bayesian parameter inference on experimental data using standard approximate inference methods like Markov Chain Monte Carlo sampling. We demonstrate MNLE on two variants of the drift-diffusion model and show that it is substantially more efficient than LANs: MNLE achieves similar likelihood accuracy with six orders of magnitude fewer training simulations and is significantly more accurate than LANs when both are trained with the same budget. Our approach enables researchers to perform SBI on custom-tailored models of decision-making, leading to fast iteration of model design for scientific discovery.
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Affiliation(s)
- Jan Boelts
- University of Tübingen, Tübingen, Germany
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34
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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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35
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A leaky evidence accumulation process for perceptual experience. Trends Cogn Sci 2022; 26:451-461. [DOI: 10.1016/j.tics.2022.03.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 03/08/2022] [Accepted: 03/09/2022] [Indexed: 11/23/2022]
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36
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Shinn M, Lee D, Murray JD, Seo H. Transient neuronal suppression for exploitation of new sensory evidence. Nat Commun 2022; 13:23. [PMID: 35013222 PMCID: PMC8748884 DOI: 10.1038/s41467-021-27697-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 12/06/2021] [Indexed: 11/29/2022] Open
Abstract
In noisy but stationary environments, decisions should be based on the temporal integration of sequentially sampled evidence. This strategy has been supported by many behavioral studies and is qualitatively consistent with neural activity in multiple brain areas. By contrast, decision-making in the face of non-stationary sensory evidence remains poorly understood. Here, we trained monkeys to identify and respond via saccade to the dominant color of a dynamically refreshed bicolor patch that becomes informative after a variable delay. Animals’ behavioral responses were briefly suppressed after evidence changes, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to that frequently observed after stimulus onset but sensitive to stimulus strength. Generalized drift-diffusion models revealed consistency of behavior and neural activity with brief suppression of motor output, but not with pausing or resetting of evidence accumulation. These results suggest that momentary arrest of motor preparation is important for dynamic perceptual decision making. While evidence is constantly changing during real-world decisions, little is known about how the brain deals with such changes. Here, the authors show that the brain strategically suppresses motor output via the frontal eye fields in response to stimulus changes.
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Affiliation(s)
- Maxwell Shinn
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06520, USA.,Department of Psychiatry, Yale University, New Haven, CT, 06520, USA
| | - Daeyeol Lee
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, 21218, USA.,Kavli Discovery Neuroscience Institute, Johns Hopkins University, Baltimore, MD, 21218, USA.,Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, 21218, USA.,Department of Neuroscience, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - John D Murray
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06520, USA. .,Department of Psychiatry, Yale University, New Haven, CT, 06520, USA. .,Department of Physics, Yale University, New Haven, CT, 06520, USA. .,Department of Neuroscience, Yale University, New Haven, CT, 06520, USA.
| | - Hyojung Seo
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06520, USA. .,Department of Psychiatry, Yale University, New Haven, CT, 06520, USA. .,Department of Neuroscience, Yale University, New Haven, CT, 06520, USA.
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37
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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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38
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39
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DMCfun: An R package for fitting Diffusion Model of Conflict (DMC) to reaction time and error rate data. METHODS IN PSYCHOLOGY 2021. [DOI: 10.1016/j.metip.2021.100074] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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40
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Tusche A, Bas LM. Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2021; 12:e1571. [PMID: 34340256 PMCID: PMC9286344 DOI: 10.1002/wcs.1571] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 06/23/2021] [Accepted: 07/01/2021] [Indexed: 01/09/2023]
Abstract
This article discusses insights from computational models and social neuroscience into motivations, precursors, and mechanisms of altruistic decision-making and other-regard. We introduce theoretical and methodological tools for researchers who wish to adopt a multilevel, computational approach to study behaviors that promote others' welfare. Using examples from recent studies, we outline multiple mental and neural processes relevant to altruism. To this end, we integrate evidence from neuroimaging, psychology, economics, and formalized mathematical models. We introduce basic mechanisms-pertinent to a broad range of value-based decisions-and social emotions and cognitions commonly recruited when our decisions involve other people. Regarding the latter, we discuss how decomposing distinct facets of social processes can advance altruistic models and the development of novel, targeted interventions. We propose that an accelerated synthesis of computational approaches and social neuroscience represents a critical step towards a more comprehensive understanding of altruistic decision-making. We discuss the utility of this approach to study lifespan differences in social preference in late adulthood, a crucial future direction in aging global populations. Finally, we review potential pitfalls and recommendations for researchers interested in applying a computational approach to their research. This article is categorized under: Economics > Interactive Decision-Making Psychology > Emotion and Motivation Neuroscience > Cognition Economics > Individual Decision-Making.
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Affiliation(s)
- Anita Tusche
- Department of Psychology, Queen's University, Ontario, Kingston, Canada.,Department of Economics, Queen's University, Ontario, Kingston, Canada.,Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, California, USA
| | - Lisa M Bas
- Department of Psychology, Queen's University, Ontario, Kingston, Canada
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41
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Feltgen Q, Daunizeau J. An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data. Front Artif Intell 2021; 4:531316. [PMID: 33898982 PMCID: PMC8064018 DOI: 10.3389/frai.2021.531316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 02/17/2021] [Indexed: 11/13/2022] Open
Abstract
Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible when more general DDM variants are considered (e.g., with collapsing bounds). In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a "self-consistency" equation that RT fulfill under the DDM. This effectively bypasses the computational bottleneck of standard DDM parameter estimation approaches, at the cost of estimating the trial-specific neural noise variables that perturb the underlying evidence accumulation process. For the purpose of behavioral data analysis, these act as nuisance variables and render the model "overcomplete," which is finessed using a variational Bayesian system identification scheme. However, for the purpose of neural data analysis, estimates of neural noise perturbation terms are a desirable (and unique) feature of the approach. Using numerical simulations, we show that this "overcomplete" approach matches the performance of current parameter estimation approaches for simple DDM variants, and outperforms them for more complex DDM variants. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.
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Affiliation(s)
- Q. Feltgen
- Paris Brain Institute (ICM), Sorbonne Université, Inserm, CNRS, Hôpital Pitié‐Salpêtrière, Paris, France
| | - J. Daunizeau
- Paris Brain Institute (ICM), Sorbonne Université, Inserm, CNRS, Hôpital Pitié‐Salpêtrière, Paris, France
- ETH, Zurich, Switzerland
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42
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Fengler A, Govindarajan LN, Chen T, Frank MJ. Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience. eLife 2021; 10:e65074. [PMID: 33821788 PMCID: PMC8102064 DOI: 10.7554/elife.65074] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 04/01/2021] [Indexed: 11/13/2022] Open
Abstract
In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by the set of models with known likelihood functions. For many models, the lack of a closed-form likelihood typically impedes Bayesian inference methods. As a result, standard models are evaluated for convenience, even when other models might be superior. Likelihood-free methods exist but are limited by their computational cost or their restriction to particular inference scenarios. Here, we propose neural networks that learn approximate likelihoods for arbitrary generative models, allowing fast posterior sampling with only a one-off cost for model simulations that is amortized for future inference. We show that these methods can accurately recover posterior parameter distributions for a variety of neurocognitive process models. We provide code allowing users to deploy these methods for arbitrary hierarchical model instantiations without further training.
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Affiliation(s)
- Alexander Fengler
- Department of Cognitive, Linguistic and Psychological Sciences, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Lakshmi N Govindarajan
- Department of Cognitive, Linguistic and Psychological Sciences, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
| | - Tony Chen
- Psychology and Neuroscience Department, Boston CollegeChestnut HillUnited States
| | - Michael J Frank
- Department of Cognitive, Linguistic and Psychological Sciences, Brown UniversityProvidenceUnited States
- Carney Institute for Brain Science, Brown UniversityProvidenceUnited States
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43
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Shinn M, Ehrlich DB, Lee D, Murray JD, Seo H. Confluence of Timing and Reward Biases in Perceptual Decision-Making Dynamics. J Neurosci 2020; 40:7326-7342. [PMID: 32839233 PMCID: PMC7534922 DOI: 10.1523/jneurosci.0544-20.2020] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 08/09/2020] [Accepted: 08/12/2020] [Indexed: 01/22/2023] Open
Abstract
Although the decisions of our daily lives often occur in the context of temporal and reward structures, the impact of such regularities on decision-making strategy is poorly understood. Here, to explore how temporal and reward context modulate strategy, we trained 2 male rhesus monkeys to perform a novel perceptual decision-making task with asymmetric rewards and time-varying evidence reliability. To model the choice and response time patterns, we developed a computational framework for fitting generalized drift-diffusion models, which flexibly accommodate diverse evidence accumulation strategies. We found that a dynamic urgency signal and leaky integration, in combination with two independent forms of reward biases, best capture behavior. We also tested how temporal structure influences urgency by systematically manipulating the temporal structure of sensory evidence, and found that the time course of urgency was affected by temporal context. Overall, our approach identified key components of cognitive mechanisms for incorporating temporal and reward structure into decisions.SIGNIFICANCE STATEMENT In everyday life, decisions are influenced by many factors, including reward structures and stimulus timing. While reward and timing have been characterized in isolation, ecologically valid decision-making involves a multiplicity of factors acting simultaneously. This raises questions about whether the same decision-making strategy is used when these two factors are concurrently manipulated. To address these questions, we trained rhesus monkeys to perform a novel decision-making task with both reward asymmetry and temporal uncertainty. In order to understand their strategy and hint at its neural mechanisms, we used the new generalized drift diffusion modeling framework to model both reward and timing mechanisms. We found two of each reward and timing mechanisms are necessary to explain our data.
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Affiliation(s)
- Maxwell Shinn
- Department of Psychiatry, Yale University, New Haven, Connecticut 06511
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut 06520
| | - Daniel B Ehrlich
- Department of Psychiatry, Yale University, New Haven, Connecticut 06511
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut 06520
| | - Daeyeol Lee
- Department of Neuroscience, Yale University, New Haven, Connecticut 21218
- Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, Maryland 21218
- Kavli Discovery Neuroscience Institute, Johns Hopkins University, Baltimore, Maryland 21218
- Department of Psychological and Brain Sciences, Department of Neuroscience, Johns Hopkins University, Baltimore, Maryland 21218
- Department of Neuroscience, Johns Hopkins University, Baltimore, Maryland 21218
| | - John D Murray
- Department of Psychiatry, Yale University, New Haven, Connecticut 06511
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut 06520
| | - Hyojung Seo
- Department of Psychiatry, Yale University, New Haven, Connecticut 06511
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut 06520
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