scholarly journals The neural dynamics underlying the interpersonal effects of emotional expression on decision making

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


2008 ◽  
Vol 99 (1) ◽  
pp. 15-27 ◽  
Author(s):  
Shihua Wen ◽  
Antonio Ulloa ◽  
Fatima Husain ◽  
Barry Horwitz ◽  
José L. Contreras-Vidal

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.


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