scholarly journals Rule Activation and Ventromedial Prefrontal Engagement Support Accurate Stopping in Self-Paced Learning

2017 ◽  
Author(s):  
Sean R. O’Bryan ◽  
Eric Walden ◽  
Michael J. Serra ◽  
Tyler Davis
Keyword(s):  
Prefrontal Cortex ◽  
Information Search ◽  
Relevant Information ◽  
Learning Task ◽  
Neural Mechanisms ◽  
Choice Accuracy ◽  
Effective Learning ◽  
Response Profile ◽  
Dorsolateral Prefrontal ◽  
Trial Participants

ABSTRACTWhen weighing evidence for a decision, individuals are continually faced with the choice of whether to gather more information or act on what has already been learned. The present experiment employed a self-paced category learning task and fMRI to examine the neural mechanisms underlying stopping of information search and how they contribute to choice accuracy. Participants learned to classify triads of face, object, and scene cues into one of two categories using a rule based on one of the stimulus dimensions. After each trial, participants were given the option to explicitly solve the rule or continue learning. Representational similarity analysis (RSA) was used to examine activation of rule-relevant information on trials leading up to a decision to solve the rule. We found that activation of rule-relevant information increased leading up to participants’ stopping decisions. Stopping was associated with widespread activation that included medial prefrontal cortex and visual association areas. Engagement of ventromedial prefrontal cortex (vmPFC) was associated with accurate stopping, and activation in this region was functionally coupled with signal in dorsolateral prefrontal cortex (dlPFC). Results suggest that activating rule information when deciding whether to stop an information search increases choice accuracy, and that the response profile of vmPFC during such decisions may provide an index of effective learning.

scholarly journals Neural arbitration between social and individual learning systems

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Andreea Oliviana Diaconescu ◽  
Madeline Stecy ◽  
Lars Kasper ◽  
Christopher J Burke ◽  
Zoltan Nagy ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Functional Neuroimaging ◽  
Learning Task ◽  
Brain Regions ◽  
Individual Learning ◽  
Learning Systems ◽  
Learning System ◽  
Relative Precision ◽  
Decision Confidence ◽  
Dorsolateral Prefrontal

Decision making requires integrating knowledge gathered from personal experiences with advice from others. The neural underpinnings of the process of arbitrating between information sources has not been fully elucidated. In this study, we formalized arbitration as the relative precision of predictions, afforded by each learning system, using hierarchical Bayesian modeling. In a probabilistic learning task, participants predicted the outcome of a lottery using recommendations from a more informed advisor and/or self-sampled outcomes. Decision confidence, as measured by the number of points participants wagered on their predictions, varied with our definition of arbitration as a ratio of precisions. Functional neuroimaging demonstrated that arbitration signals were independent of decision confidence and involved modality-specific brain regions. Arbitrating in favor of self-gathered information activated the dorsolateral prefrontal cortex and the midbrain, whereas arbitrating in favor of social information engaged the ventromedial prefrontal cortex and the amygdala. These findings indicate that relative precision captures arbitration between social and individual learning systems at both behavioral and neural levels.

scholarly journals Dorsolateral and dorsomedial prefrontal cortex track distinct properties of dynamic social behavior

2020 ◽  
Vol 15 (4) ◽  
pp. 383-393
Author(s):  
Kelsey R McDonald ◽  
John M Pearson ◽  
Scott A Huettel
Keyword(s):  
Prefrontal Cortex ◽  
Video Game ◽  
Brain Regions ◽  
Neural Mechanisms ◽  
Strategic Action ◽  
General Role ◽  
Temporoparietal Junction ◽  
Dorsomedial Prefrontal Cortex ◽  
Decision Neuroscience ◽  
Dorsolateral Prefrontal

Abstract Understanding how humans make competitive decisions in complex environments is a key goal of decision neuroscience. Typical experimental paradigms constrain behavioral complexity (e.g. choices in discrete-play games), and thus, the underlying neural mechanisms of dynamic social interactions remain incompletely understood. Here, we collected fMRI data while humans played a competitive real-time video game against both human and computer opponents, and then, we used Bayesian non-parametric methods to link behavior to neural mechanisms. Two key cognitive processes characterized behavior in our task: (i) the coupling of one’s actions to another’s actions (i.e. opponent sensitivity) and (ii) the advantageous timing of a given strategic action. We found that the dorsolateral prefrontal cortex displayed selective activation when the subject’s actions were highly sensitive to the opponent’s actions, whereas activation in the dorsomedial prefrontal cortex increased proportionally to the advantageous timing of actions to defeat one’s opponent. Moreover, the temporoparietal junction tracked both of these behavioral quantities as well as opponent social identity, indicating a more general role in monitoring other social agents. These results suggest that brain regions that are frequently implicated in social cognition and value-based decision-making also contribute to the strategic tracking of the value of social actions in dynamic, multi-agent contexts.

Neural Structure and Organization of Mood Pathology

2016 ◽  
pp. 213-224
Author(s):  
Brianne Disabato ◽  
Isabelle E. Bauer ◽  
Jair C. Soares ◽  
Yvette Sheline
Keyword(s):  
Bipolar Disorder ◽  
Major Depressive Disorder ◽  
Prefrontal Cortex ◽  
White Matter ◽  
Depressive Disorder ◽  
Neural Mechanisms ◽  
Anterior Cingulate ◽  
Neural Structure ◽  
Major Depressive ◽  
Dorsolateral Prefrontal

Unipolar major depressive disorder (MDD) and bipolar disorder (BD) are among the world’s leading causes of disability. This chapter highlights the importance of neuroimaging in understanding their neural mechanisms. Depression affects limbic-corticostriatopallidothalamic regions. Structurally, depressed subjects showed increased volume of lesions in white matter (WMH) and decreased gray matter in prefrontal-striatum, orbitofrontal, anterior cingulate cortices, and hippocampus. Functionally, depressed subjects showed abnormal activation in amygdala and medial prefrontal cortex and dsyconnectivity in executive and emotional networks. BD was associated with frontocingulate, limbic-striatal, and hippocampus abnormalities. Specifically, BD subjects showed increased WMH in frontocortical and subcortical areas and altered microstructure in limbic-striatal, cingulate, thalamus, corpus callosum, and prefrontal regions. Functionally, abnormal activations in dorsolateral prefrontal and ventrolimbic regions, hypoconnectivity in the cinguloinsularopercular, mesoparalimbic, and cerebellar networks, and hyperconnectivity in affective and executive networks were also observed. These studies show congruence. Full integration of them would allow better understanding of mood disorders.

Dorsolateral Prefrontal Cortex Enables Updating of Established Memories

2018 ◽  
Vol 29 (10) ◽  
pp. 4154-4168 ◽  
Author(s):  
Lisa Marieke Kluen ◽  
Lisa Catherine Dandolo ◽  
Gerhard Jocham ◽  
Lars Schwabe
Keyword(s):  
Prefrontal Cortex ◽  
Dorsolateral Prefrontal Cortex ◽  
Neural Mechanisms ◽  
Related Activity ◽  
Future Behavior ◽  
New Information ◽  
Dorsolateral Prefrontal ◽  
Increased Activity ◽  
The Brain

Abstract Updating established memories in light of new information is fundamental for memory to guide future behavior. However, little is known about the brain mechanisms by which existing memories can be updated. Here, we combined functional magnetic resonance imaging and multivariate representational similarity analysis to elucidate the neural mechanisms underlying the updating of consolidated memories. To this end, participants first learned face–city name pairs. Twenty-four hours later, while lying in the MRI scanner, participants were required to update some of these associations, but not others, and to encode entirely new pairs. Updating success was tested again 24 h later. Our results showed increased activity of the dorsolateral prefrontal cortex (dlPFC) specifically during the updating of existing associations that was significantly stronger than when simple retrieval or new encoding was required. The updating-related activity of the dlPFC and its functional connectivity with the hippocampus were directly linked to updating success. Furthermore, neural similarity for updated items was markedly higher in the dlPFC and this increase in dlPFC neural similarity distinguished individuals with high updating performance from those with low updating performance. Together, these findings suggest a key role of the dlPFC, presumably in interaction with the hippocampus, in the updating of established memories.

scholarly journals Neural mechanisms of credit assignment for inferred relationships in a structured world

2021 ◽  
Author(s):  
Phillip P Witkowski ◽  
Seongmin A Park ◽  
Erie D Boorman
Keyword(s):  
Prefrontal Cortex ◽  
Bayesian Learning ◽  
Learning Task ◽  
Neural Mechanisms ◽  
Orbital Frontal Cortex ◽  
Current Position ◽  
Credit Assignment ◽  
Compact Representations ◽  
Flexible Behavior ◽  
Structured Knowledge

Animals have been proposed to abstract compact representations of a task's structure that could, in principle, support accelerated learning and flexible behavior. Whether and how such abstracted representations may be used to assign credit for inferred, but unobserved, relationships in structured environments are unknown. Here, we develop a novel hierarchical reversal-learning task and Bayesian learning model to assess the computational and neural mechanisms underlying how humans infer specific choice-outcome associations via structured knowledge. We find that the medial prefrontal cortex (mPFC) efficiently represents hierarchically related choice-outcome associations governed by the same latent cause, using a generalized code to assign credit for both experienced and inferred outcomes. Furthermore, mPFC and lateral orbital frontal cortex track the inferred current "position" within a latent association space that generalizes over stimuli. Collectively, these findings demonstrate the importance both of tracking the current position in an abstracted task space and efficient, generalizable representations in prefrontal cortex for supporting flexible learning and inference in structured environments.

scholarly journals Neurodevelopmental shifts in learned value transfer on cognitive control during adolescence

2019 ◽  
Author(s):  
Catherine Insel ◽  
Mia Charifson ◽  
Leah H. Somerville
Keyword(s):  
Prefrontal Cortex ◽  
Cognitive Control ◽  
Dorsal Striatum ◽  
Learning Task ◽  
High Gain ◽  
Control Performance ◽  
High Loss ◽  
Beneficial Effects ◽  
Older Adolescents ◽  
Dorsolateral Prefrontal

AbstractValue-associated cues in the environment often enhance subsequent goal-directed behaviors in adults, a phenomenon supported by integration of motivational and cognitive neural systems. Given the interactions among these systems change throughout adolescence, we tested when beneficial effects of value associations on subsequent cognitive control performance emerge during adolescence. Participants (N=81) aged 13-20 completed a reinforcement learning task with four cue-incentive pairings that could yield high gain, low gain, high loss, or low loss outcomes. Next, participants completed a Go/NoGo task during fMRI where the NoGo targets comprised the previously learned cues, which tested how prior value associations influence cognitive control performance. Improved accuracy for previously learned high gain relative to low gain cues emerged with age. Older adolescents exhibited enhanced recruitment of the dorsal striatum and ventrolateral prefrontal cortex during cognitive control execution to previously learned high gain relative to low gain cues. Older adolescents also expressed increased coupling between the dorsal striatum and dorsolateral prefrontal cortex for high gain cues, whereas younger adolescents expressed increased coupling between the striatum and ventromedial prefrontal cortex. These findings reveal that learned high value cue-incentive associations enhance cognitive control in late adolescence in parallel with value-selective recruitment of corticostriatal systems.

scholarly journals Triple dissociation of attention and decision computations across prefrontal cortex

2017 ◽  
Author(s):  
Laurence T. Hunt ◽  
W. M. Nishantha Malalasekera ◽  
Archy O. de Berker ◽  
Bruno Miranda ◽  
Simon F. Farmer ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Information Search ◽  
Action Plan ◽  
Neuronal Population ◽  
Anterior Cingulate ◽  
Information Encoding ◽  
Population Responses ◽  
Dorsolateral Prefrontal ◽  
New Evidence ◽  
Evidence Integration

Anatomical, neuroimaging and lesion studies indicate that prefrontal cortex (PFC) can be subdivided into different subregions supporting distinct aspects of decision making. However, explanations of neuronal computations within these subregions varies widely across studies. An integrated and mechanistic account of PFC function therefore remains elusive. Resolving these debates demands a rich dataset that directly contrasts neuronal activity across multiple PFC subregions within a single paradigm, whilst experimentally controlling factors such as the order, duration and frequency in which choice options are attended and compared. Here, we contrast neuronal population responses between macaque orbitofrontal (OFC), anterior cingulate (ACC) and dorsolateral prefrontal cortices (DLPFC) during sequential value-guided information search and choice. From the first fixation of choice-related stimuli, a strong triple dissociation of information encoding emerges in parallel across these PFC subregions. As further information is gathered, population responses in OFC reflect an attention-guided value comparison process. Meanwhile, parallel signals in ACC reflect belief updating in light of new evidence, integration of that evidence to a decision bound, and an emerging action plan for which option should be chosen. Our findings demonstrate the co-existence of multiple, distributed decision-related computations across PFC subregions during value-guided choice. They provide a synthesis of several competing accounts of PFC function.

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