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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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2
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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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Hoxha I, Chevallier S, Ciarchi M, Glasauer S, Delorme A, Amorim MA. Accounting for endogenous effects in decision-making with a non-linear diffusion decision model. Sci Rep 2023; 13:6323. [PMID: 37072460 PMCID: PMC10113207 DOI: 10.1038/s41598-023-32841-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 04/03/2023] [Indexed: 05/03/2023] Open
Abstract
The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter-trial dynamics at the single-trial level and endogenous influences. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non-linear model performs better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl-DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across-trial variability for perceptual decisions and accounts for peri-stimulus influences.
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Affiliation(s)
- Isabelle Hoxha
- CIAMS, Université Paris-Saclay, Paris, France.
- CIAMS, Université d'Orléans, Orléans, France.
| | | | - Matteo Ciarchi
- Max-Planck Institute for the Physics of Complex Systems, Dresden, Germany
| | - Stefan Glasauer
- Computational Neuroscience, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany
| | - Arnaud Delorme
- CerCo, CNRS, Université Toulouse III - Paul Sabatier, Toulouse, France
- Swartz Center for Computational Neuroscience, INC, University of California San Diego, La Jolla, CA, 92093, USA
| | - Michel-Ange Amorim
- CIAMS, Université Paris-Saclay, Paris, France
- CIAMS, Université d'Orléans, Orléans, France
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4
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Lawlor J, Zagala A, Jamali S, Boubenec Y. Pupillary dynamics reflect the impact of temporal expectation on detection strategy. iScience 2023; 26:106000. [PMID: 36798438 PMCID: PMC9926307 DOI: 10.1016/j.isci.2023.106000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 11/09/2022] [Accepted: 01/12/2023] [Indexed: 01/18/2023] Open
Abstract
Everyday life's perceptual decision-making is informed by experience. In particular, temporal expectation can ease the detection of relevant events in noisy sensory streams. Here, we investigated if humans can extract hidden temporal cues from the occurrences of probabilistic targets and utilize them to inform target detection in a complex acoustic stream. To understand what neural mechanisms implement temporal expectation influence on decision-making, we used pupillometry as a proxy for underlying neuromodulatory activity. We found that participants' detection strategy was influenced by the hidden temporal context and correlated with sound-evoked pupil dilation. A model of urgency fitted on false alarms predicted detection reaction time. Altogether, these findings suggest that temporal expectation informs decision-making and could be implemented through neuromodulatory-mediated urgency signals.
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Affiliation(s)
- Jennifer Lawlor
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA,Corresponding author
| | - Agnès Zagala
- International Laboratory for Brain, Music and Sound Research (BRAMS), Montreal, Canada
| | - Sara Jamali
- Institut Pasteur, INSERM, Institut de l’Audition, Paris, France
| | - Yves Boubenec
- Laboratoire des systèmes perceptifs, Département d’études cognitives, École normale supérieure, PSL University, CNRS, 75005 Paris, France
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5
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Corbett EA, Martinez-Rodriguez LA, Judd C, O'Connell RG, Kelly SP. Multiphasic value biases in fast-paced decisions. eLife 2023; 12:67711. [PMID: 36779966 PMCID: PMC9925050 DOI: 10.7554/elife.67711] [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: 02/19/2021] [Accepted: 01/04/2023] [Indexed: 02/11/2023] Open
Abstract
Perceptual decisions are biased toward higher-value options when overall gains can be improved. When stimuli demand immediate reactions, the neurophysiological decision process dynamically evolves through distinct phases of growing anticipation, detection, and discrimination, but how value biases are exerted through these phases remains unknown. Here, by parsing motor preparation dynamics in human electrophysiology, we uncovered a multiphasic pattern of countervailing biases operating in speeded decisions. Anticipatory preparation of higher-value actions began earlier, conferring a 'starting point' advantage at stimulus onset, but the delayed preparation of lower-value actions was steeper, conferring a value-opposed buildup-rate bias. This, in turn, was countered by a transient deflection toward the higher-value action evoked by stimulus detection. A neurally-constrained process model featuring anticipatory urgency, biased detection, and accumulation of growing stimulus-discriminating evidence, successfully captured both behavior and motor preparation dynamics. Thus, an intricate interplay of distinct biasing mechanisms serves to prioritise time-constrained perceptual decisions.
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Affiliation(s)
- Elaine A Corbett
- Trinity College Institute of Neuroscience, Trinity College DublinDublinIreland,School of Psychology, Trinity College DublinDublinIreland,School of Electrical and Electronic Engineering and UCD Centre for Biomedical Engineering, University College DublinDublinIreland
| | - L Alexandra Martinez-Rodriguez
- School of Electrical and Electronic Engineering and UCD Centre for Biomedical Engineering, University College DublinDublinIreland
| | - Cian Judd
- Trinity College Institute of Neuroscience, Trinity College DublinDublinIreland
| | - Redmond G O'Connell
- Trinity College Institute of Neuroscience, Trinity College DublinDublinIreland,School of Psychology, Trinity College DublinDublinIreland
| | - Simon P Kelly
- Trinity College Institute of Neuroscience, Trinity College DublinDublinIreland,School of Electrical and Electronic Engineering and UCD Centre for Biomedical Engineering, University College DublinDublinIreland
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6
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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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Chen Y, Liu Y, Wang Z, Yang T, Fan Q. Accumulation of evidence during decision making in OCD patients. Front Psychiatry 2022; 13:980905. [PMID: 36213896 PMCID: PMC9539281 DOI: 10.3389/fpsyt.2022.980905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/30/2022] [Indexed: 11/23/2022] Open
Abstract
Decision-making often entails the accumulation of evidence. Previous studies suggested that people with obsessive-compulsive disorder (OCD) process decision-making differently from healthy controls. Both their compulsive behavior and obsessive thoughts may influence the evidence accumulation process, yet the previous studies disagreed on the reason. To address this question, we employed a probabilistic reasoning task in which subjects made two alternative forced choices by viewing a series of visual stimuli. These stimuli carried probabilistic information toward the choices. While the OCD patients achieved similar accuracy to the control, they took longer time and accumulated more evidence, especially in difficult trials in which the evidence strength was low. We further modeled the subjects' decision making as a leaky drifting diffusion process toward two collapsing bounds. The control group showed a higher drifting rate than the OCD group, indicating that the OCD group was less sensitive to evidence. Together, these results demonstrated that the OCD patients were less efficient than the control at transforming sensory information into evidence. However, their evidence accumulation was comparable to the healthy control, and they compensated for their decision-making accuracy with longer reaction times.
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Affiliation(s)
- Yilin Chen
- Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Ying Liu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhen Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tianming Yang
- Key Laboratory of Primate Neurobiology, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China.,Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China
| | - Qing Fan
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China
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Abstract
We live in a world that changes on many timescales. To learn and make decisions appropriately, the human brain has evolved to integrate various types of information, such as sensory evidence and reward feedback, on multiple timescales. This is reflected in cortical hierarchies of timescales consisting of heterogeneous neuronal activities and expression of genes related to neurotransmitters critical for learning. We review the recent findings on how timescales of sensory and reward integration are affected by the temporal properties of sensory and reward signals in the environment. Despite existing evidence linking behavioral and neuronal timescales, future studies must examine how neural computations at multiple timescales are adjusted and combined to influence behavior flexibly.
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Affiliation(s)
- Alireza Soltani
- Department of Psychological and Brain Sciences, Dartmouth College, Moore Hall, 3 Maynard St, Hanover, NH 03755
| | - John D. Murray
- Department of Psychiatry, Yale School of Medicine, 300 George Street, New Haven, CT 06511
| | - Hyojung Seo
- Department of Psychiatry, Yale School of Medicine, 300 George Street, New Haven, CT 06511
| | - Daeyeol Lee
- The Zanvyl Krieger Mind/Brain Institute, Department of Neuroscience, Department of Psychological Sciences, Kavli Neuroscience Discovery Institute, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218
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9
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Markkula G, Uludağ Z, Wilkie RM, Billington J. Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection. PLoS Comput Biol 2021; 17:e1009096. [PMID: 34264935 PMCID: PMC8282001 DOI: 10.1371/journal.pcbi.1009096] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 05/19/2021] [Indexed: 11/24/2022] Open
Abstract
Evidence accumulation models provide a dominant account of human decision-making, and have been particularly successful at explaining behavioral and neural data in laboratory paradigms using abstract, stationary stimuli. It has been proposed, but with limited in-depth investigation so far, that similar decision-making mechanisms are involved in tasks of a more embodied nature, such as movement and locomotion, by directly accumulating externally measurable sensory quantities of which the precise, typically continuously time-varying, magnitudes are important for successful behavior. Here, we leverage collision threat detection as a task which is ecologically relevant in this sense, but which can also be rigorously observed and modelled in a laboratory setting. Conventionally, it is assumed that humans are limited in this task by a perceptual threshold on the optical expansion rate-the visual looming-of the obstacle. Using concurrent recordings of EEG and behavioral responses, we disprove this conventional assumption, and instead provide strong evidence that humans detect collision threats by accumulating the continuously time-varying visual looming signal. Generalizing existing accumulator model assumptions from stationary to time-varying sensory evidence, we show that our model accounts for previously unexplained empirical observations and full distributions of detection response. We replicate a pre-response centroparietal positivity (CPP) in scalp potentials, which has previously been found to correlate with accumulated decision evidence. In contrast with these existing findings, we show that our model is capable of predicting the onset of the CPP signature rather than its buildup, suggesting that neural evidence accumulation is implemented differently, possibly in distinct brain regions, in collision detection compared to previously studied paradigms.
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Affiliation(s)
- Gustav Markkula
- Institute for Transport Studies, University of Leeds, Leeds, United Kingdom
| | - Zeynep Uludağ
- School of Psychology, University of Leeds, Leeds, United Kingdom
| | | | - Jac Billington
- School of Psychology, University of Leeds, Leeds, United Kingdom
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Derosiere G, Thura D, Cisek P, Duque J. Trading accuracy for speed over the course of a decision. J Neurophysiol 2021; 126:361-372. [PMID: 34191623 DOI: 10.1152/jn.00038.2021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Humans and other animals often need to balance the desire to gather sensory information (to make the best choice) with the urgency to act, facing a speed-accuracy tradeoff (SAT). Given the ubiquity of SAT across species, extensive research has been devoted to understanding the computational mechanisms allowing its regulation at different timescales, including from one context to another, and from one decision to another. However, animals must frequently change their SAT on even shorter timescales-that is, over the course of an ongoing decision-and little is known about the mechanisms that allow such rapid adaptations. The present study aimed at addressing this issue. Human subjects performed a decision task with changing evidence. In this task, subjects received rewards for correct answers but incurred penalties for mistakes. An increase or a decrease in penalty occurring halfway through the trial promoted rapid SAT shifts, favoring speeded decisions either in the early or in the late stage of the trial. Importantly, these shifts were associated with stage-specific adjustments in the accuracy criterion exploited for committing to a choice. Those subjects who decreased the most their accuracy criterion at a given decision stage exhibited the highest gain in speed, but also the highest cost in terms of performance accuracy at that time. Altogether, the current findings offer a unique extension of previous work, by suggesting that dynamic cha*nges in accuracy criterion allow the regulation of the SAT within the timescale of a single decision.NEW & NOTEWORTHY Extensive research has been devoted to understanding the mechanisms allowing the regulation of the speed-accuracy tradeoff (SAT) from one context to another and from one decision to another. Here, we show that humans can voluntarily change their SAT on even shorter timescales-that is, over the course of a decision. These rapid SAT shifts are associated with dynamic adjustments in the accuracy criterion exploited for committing to a choice.
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Affiliation(s)
- Gerard Derosiere
- Institute of Neuroscience, Laboratory of Neurophysiology, Université catholique de Louvain, Brussels, Belgium
| | - David Thura
- Lyon Neuroscience Research Center, Lyon 1 University, Bron, France
| | - Paul Cisek
- Department of Neuroscience, Université de Montréal, Montreal, Quebec, Canada
| | - Julie Duque
- Institute of Neuroscience, Laboratory of Neurophysiology, Université catholique de Louvain, Brussels, Belgium
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Shinn M, Lam NH, Murray JD. A flexible framework for simulating and fitting generalized drift-diffusion models. eLife 2020; 9:56938. [PMID: 32749218 PMCID: PMC7462609 DOI: 10.7554/elife.56938] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 08/03/2020] [Indexed: 01/10/2023] Open
Abstract
The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are solved numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We demonstrate fitting of GDDMs within our framework to both animal and human datasets from perceptual decision-making tasks, with better accuracy and fewer parameters than several DDMs implemented using the latest methodology, to test hypothesized decision-making mechanisms. Overall, our framework will allow for decision-making model innovation and novel experimental designs.
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Affiliation(s)
- Maxwell Shinn
- Department of Psychiatry, Yale University, New Haven, United States.,Interdepartmental Neuroscience Program, Yale University, New Haven, United States
| | - Norman H Lam
- Department of Physics, Yale University, New Haven, United States
| | - John D Murray
- Department of Psychiatry, Yale University, New Haven, United States.,Interdepartmental Neuroscience Program, Yale University, New Haven, United States.,Department of Physics, Yale University, New Haven, United States
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