Simulated neural dynamics of decision-making in an auditory delayed match-to-sample task

2008 ◽  
Vol 99 (1) ◽  
pp. 15-27 ◽  
Author(s):  
Shihua Wen ◽  
Antonio Ulloa ◽  
Fatima Husain ◽  
Barry Horwitz ◽  
José L. Contreras-Vidal
2014 ◽  
Vol 369 (1641) ◽  
pp. 20130211 ◽  
Author(s):  
Randolph Blake ◽  
Jan Brascamp ◽  
David J. Heeger

This essay critically examines the extent to which binocular rivalry can provide important clues about the neural correlates of conscious visual perception. Our ideas are presented within the framework of four questions about the use of rivalry for this purpose: (i) what constitutes an adequate comparison condition for gauging rivalry's impact on awareness, (ii) how can one distinguish abolished awareness from inattention, (iii) when one obtains unequivocal evidence for a causal link between a fluctuating measure of neural activity and fluctuating perceptual states during rivalry, will it generalize to other stimulus conditions and perceptual phenomena and (iv) does such evidence necessarily indicate that this neural activity constitutes a neural correlate of consciousness? While arriving at sceptical answers to these four questions, the essay nonetheless offers some ideas about how a more nuanced utilization of binocular rivalry may still provide fundamental insights about neural dynamics, and glimpses of at least some of the ingredients comprising neural correlates of consciousness, including those involved in perceptual decision-making.


2019 ◽  
Vol 30 (3) ◽  
pp. 415-423 ◽  
Author(s):  
Shouhang Yin ◽  
Jie Sui ◽  
Yu-Chin Chiu ◽  
Antao Chen ◽  
Tobias Egner

People preferentially attend to external stimuli that are related to themselves compared with others. Whether a similar self-reference bias applies to internal representations, such as those maintained in working memory (WM), is presently unknown. We tested this possibility in four experiments, in which participants were first trained to associate social labels (self, friend, stranger) with arbitrary colors and then performed a delayed match-to-sample spatial WM task on color locations. Participants consistently responded fastest to WM probes at locations of self-associated colors (Experiments 1–4). This self-bias was driven not by differential exogenous attention during encoding or retrieval (Experiments 1 and 2) but by internal attentional prioritization of self-related representations during WM maintenance (Experiment 3). Moreover, self-prioritization in WM was nonstrategic, as this bias persisted even under conditions in which it hurt WM performance. These findings document an automatic prioritization of self-referential items in WM, which may form the basis of some egocentric biases in decision making.


2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Xuhai Chen ◽  
Tingting Zheng ◽  
Lingzi Han ◽  
Yingchao Chang ◽  
Yangmei Luo

2020 ◽  
Author(s):  
Sridhar R. Jagannathan ◽  
Corinne A. Bareham ◽  
Tristan A. Bekinschtein

ABSTRACTThe ability to make decisions based on external information, prior knowledge and context is a crucial aspect of cognition and it may determine the success and survival of an organism. Despite extensive and detailed work done on the decision making mechanisms, the understanding of the effects of arousal remain limited. Here we characterise behavioural and neural dynamics of decision making in awake and low alertness periods to characterise the compensatory signatures of the cognitive system when arousal decreases. We used an auditory tone-localisation task in human participants under conditions of fully awake and low arousal. Behavioural dynamics analyses using psychophysics, signal detection theory and drift-diffusion modelling showed slower responses, decreased performance and a lower rate of evidence accumulation due to alertness fluctuations. To understand the modulation in neural dynamics we used multivariate pattern analysis (decoding), identifying a shift in the temporal and spatial signatures involved. Finally, we connected the computational parameters identified in the drift diffusion modelling with neural signatures, capturing the effective lag exerted by alertness in the neurocognitive system underlying decision making. These results define the reconfiguration of the brain networks, regions and dynamics needed for the implementation of perceptual decision making, revealing mechanisms of resilience of cognition when challenged by decreases in arousal.


2019 ◽  
Vol 10 ◽  
Author(s):  
Chengkang Zhu ◽  
Jingjing Pan ◽  
Yiwen Wang ◽  
Jianbiao Li ◽  
Pengcheng Wang

2021 ◽  
pp. JN-RM-1232-21
Author(s):  
Catherine Manning ◽  
Cameron D. Hassall ◽  
Laurence T. Hunt ◽  
Anthony M. Norcia ◽  
Eric-Jan Wagenmakers ◽  
...  

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mikhail Genkin ◽  
Owen Hughes ◽  
Tatiana A. Engel

AbstractMany complex systems operating far from the equilibrium exhibit stochastic dynamics that can be described by a Langevin equation. Inferring Langevin equations from data can reveal how transient dynamics of such systems give rise to their function. However, dynamics are often inaccessible directly and can be only gleaned through a stochastic observation process, which makes the inference challenging. Here we present a non-parametric framework for inferring the Langevin equation, which explicitly models the stochastic observation process and non-stationary latent dynamics. The framework accounts for the non-equilibrium initial and final states of the observed system and for the possibility that the system’s dynamics define the duration of observations. Omitting any of these non-stationary components results in incorrect inference, in which erroneous features arise in the dynamics due to non-stationary data distribution. We illustrate the framework using models of neural dynamics underlying decision making in the brain.


2018 ◽  
Vol 115 (10) ◽  
pp. 2502-2507 ◽  
Author(s):  
Anne G. E. Collins ◽  
Michael J. Frank

Learning from rewards and punishments is essential to survival and facilitates flexible human behavior. It is widely appreciated that multiple cognitive and reinforcement learning systems contribute to decision-making, but the nature of their interactions is elusive. Here, we leverage methods for extracting trial-by-trial indices of reinforcement learning (RL) and working memory (WM) in human electro-encephalography to reveal single-trial computations beyond that afforded by behavior alone. Neural dynamics confirmed that increases in neural expectation were predictive of reduced neural surprise in the following feedback period, supporting central tenets of RL models. Within- and cross-trial dynamics revealed a cooperative interplay between systems for learning, in which WM contributes expectations to guide RL, despite competition between systems during choice. Together, these results provide a deeper understanding of how multiple neural systems interact for learning and decision-making and facilitate analysis of their disruption in clinical populations.


2021 ◽  
Author(s):  
David Hocker ◽  
Carlos D Brody ◽  
Cristina Savin ◽  
Christine M Constantinople

Studies of neural dynamics in lateral orbitofrontal cortex (lOFC) have shown that subsets of neurons that encode distinct aspects of behavior, such as value, may project to common downstream targets. However, it is unclear whether reward history, which may subserve lOFC's well-documented role in learning, is represented by functional subpopulations in lOFC. We analyzed neural recordings from rats performing a value-based decision-making task, in which we previously documented trial-by-trial learning that required lOFC. We found five distinct clusters of lOFC neurons, either based on clustering of their trial-averaged peristimulus time histograms (PSTHs), or a feature space defined by their average conditional firing rates aligned to different task variables. We observed weak encoding of reward attributes, but stronger encoding of reward history, the animal's left or right choice, and reward receipt across all clusters. Only one cluster, however, encoded the animal's reward history at the time shortly preceding the choice, suggesting a possible role in integrating previous and current trial outcomes at the time of choice.


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