Optogenetics Research in Behavioral Neuroscience: Insights into the Brain Basis of Reward Learning and Goal-directed Behavior

Optogenetics ◽  
2017 ◽  
pp. 276-291
Author(s):  
Adam C. G. Crego ◽  
Stephen E. Chang ◽  
William N. Butler ◽  
Kyle S. Smith
Keyword(s):  
Behavioral Neuroscience ◽  
Reward Learning ◽  
Goal Directed Behavior ◽  
The Brain

scholarly journals Corticostriatal White Matter Integrity and Dopamine D1 Receptor Availability Predict Age Differences in Prefrontal Value Signaling during Reward Learning

2020 ◽  
Vol 30 (10) ◽  
pp. 5270-5280
Author(s):  
Lieke de Boer ◽  
Benjamín Garzón ◽  
Jan Axelsson ◽  
Katrine Riklund ◽  
Lars Nyberg ◽  
...  
Keyword(s):  
White Matter ◽  
D1 Receptor ◽  
Learning Ability ◽  
Learning Performance ◽  
Reward Learning ◽  
White Matter Integrity ◽  
Tentative Evidence ◽  
Independent Replication ◽  
The Brain ◽  
Corticostriatal Circuit

Abstract Probabilistic reward learning reflects the ability to adapt choices based on probabilistic feedback. The dopaminergically innervated corticostriatal circuit in the brain plays an important role in supporting successful probabilistic reward learning. Several components of the corticostriatal circuit deteriorate with age, as it does probabilistic reward learning. We showed previously that D1 receptor availability in NAcc predicts the strength of anticipatory value signaling in vmPFC, a neural correlate of probabilistic learning that is attenuated in older participants and predicts probabilistic reward learning performance. We investigated how white matter integrity in the pathway between nucleus accumbens (NAcc) and ventromedial prefrontal cortex (vmPFC) relates to the strength of anticipatory value signaling in vmPFC in younger and older participants. We found that in a sample of 22 old and 23 young participants, fractional anisotropy in the pathway between NAcc and vmPFC predicted the strength of value signaling in vmPFC independently from D1 receptor availability in NAcc. These findings provide tentative evidence that integrity in the dopaminergic and white matter pathways of corticostriatal circuitry supports the expression of value signaling in vmPFC which supports reward learning, however, the limited sample size calls for independent replication. These and future findings could add to the improved understanding of how corticostriatal integrity contributes to reward learning ability.

A Model of Motivation with Chaotic Neuronal Dynamics

1997 ◽  
Vol 05 (02) ◽  
pp. 301-323 ◽  
Author(s):  
Lev E. Tsitolovsky
Keyword(s):  
Living Systems ◽  
Computer Algorithms ◽  
Metabolic Disturbances ◽  
Neuronal Dynamics ◽  
Optimal State ◽  
Artificial Brain ◽  
Full Knowledge ◽  
Goal Directed Behavior ◽  
The Brain ◽  
Conventional Computer

The key problem to creating an autonomous system is: how does the brain choose its reactions, and how are motivation determined by ongoing signals, memory and heredity. In attempts to design a robot brain, several efforts have been made to design a self-contained control system that mimics biological motivation. However, it is impossible to develop an artificial brain using conventional computer algorithms, since a conventional program cannot predict all of the possible perturbations and disturbances in the environment, and hence cannot plan strategies that allow the system to overcome these perturbations and return to an optimal state. An external programmer must constantly update the system about the proper strategies needed to overcome newly-encountered perturbations. In contradistinction, living systems demonstrate excellent goal-directed behavior without the participation of an external programmer, and without full knowledge of the external environment. Biological motivation refers to actions on the part of an organism that lead to the attainment of a specific goal. When the organism attains the goal it is in an optimal state, and no further actions are generated. A deviation from the optimum will result in a change in activity that leads to a return to the optimum. Biologic motivations arise as the result of metabolic disturbances and are related to transient injury of the specific neurons. Treatments which protect neurons satisfy motivations and exert a psychotropic action relative to relief. I have developed a novel hypothesis of how living systems achieve a goal, based on data gathered on the effects of motivation on individual neurons. I claim that if the neuron affects the non-stability of its postsynaptic targets (probably by means of motivationally-relevant substances) in the end it chooses its reaction, although at each instant it acts by chance.

scholarly journals Neural mechanisms underlying expectation-dependent inhibition of distracting information

2020 ◽  
Author(s):  
Dirk van Moorselaar ◽  
Eline Lampers ◽  
Elisa Cordesius ◽  
Heleen A. Slagter
Keyword(s):  
Search Task ◽  
Visual Search Task ◽  
Neural Mechanisms ◽  
Distractor Interference ◽  
Sensory Representation ◽  
Task Relevance ◽  
Dependent Inhibition ◽  
Statistical Regularities ◽  
Goal Directed Behavior ◽  
The Brain

AbstractPredictions based on learned statistical regularities in the visual world have been shown to facilitate attention and goal-directed behavior by sharpening the sensory representation of goal-relevant stimuli in advance. Yet, how the brain learns to ignore predictable goal-irrelevant or distracting information is unclear. Here, we used EEG and a visual search task in which the predictability of a distractor’s location and/or spatial frequency was manipulated to determine how spatial and feature distractor expectations are neurally implemented and reduce distractor interference. We find that expected distractor features could not only be decoded pre-stimulus, but their representation differed from the representation of that same feature when part of the target. Spatial distractor expectations did not induce changes in preparatory neural activity, but a strongly reduced Pd, an ERP index of inhibition. These results demonstrate that neural effects of statistical learning critically depend on the task relevance and dimension (spatial, feature) of predictions.

scholarly journals Dissociable mechanisms of reward learning co-mature during human adolescence as predicted by macaque lesion models

2021 ◽  
Author(s):  
Marco K. Wittmann ◽  
Maximilian Scheuplein ◽  
Sophie G. Gibbons ◽  
MaryAnn P. Noonan
Keyword(s):  
Reward Learning ◽  
Learning Mechanisms ◽  
Credit Assignment ◽  
Guided Learning ◽  
First Choice ◽  
Specific Outcome ◽  
Component Processes ◽  
Adaptive Ability ◽  
The Brain ◽  
Developmental Maturation

AbstractReward-guided learning and decision-making is a fundamental adaptive ability and depends on a number of component processes. We investigate how such component processes mature during human adolescence. Our approach was guided by analyses of the effects of lateral orbitofrontal lesions in macaque monkeys, as this part of the brain shows clear developmental maturation in humans during adolescence. Using matched tasks and analyses in humans (n=388, 11-35yrs), we observe developmental changes in two key learning mechanisms as predicted from the monkey data. First, choice-reward credit assignment – the ability to link a specific outcome to a specific choice – is reduced in adolescents. Second, the effects of the global reward state – how good the environment is overall recently − exerts a distinctive pattern of influence on learning in humans compared to other primates and across adolescence this pattern becomes more pronounced. Both mechanisms were correlated across participants suggesting that associative learning of correct reward assignments and GRS based learning constitute two complementary mechanisms of reward-learning that co-mature during adolescence.

Behavioral Flexibility and Anticipatory Behavior

2017 ◽  
Author(s):  
Martin V. Butz ◽  
Esther F. Kutter
Keyword(s):  
Predictive Models ◽  
Information Seeking ◽  
Sensory Information ◽  
Behavioral Flexibility ◽  
Anticipatory Behavior ◽  
Sensorimotor Knowledge ◽  
Abstract Learning ◽  
Goal Directed Behavior ◽  
The Brain ◽  
Selection Of

While reward-oriented learning can adapt and optimize behavior, this chapter shows how behavior can become anticipatory and selectively goal-oriented. Flexibility and adaptability are necessary when living in changing environmental niches. As a consequence, different locations in the environment need to be distinguished to enable selective and optimally attuned interactions. To accomplish this, sensorimotor learning is necessary. With sufficient sensorimotor knowledge, the progressively abstract learning of environmental predictive models becomes possible. These models enable forward anticipations about action consequences and incoming sensory information. As a consequence, our own influences on the environment can be distinguished from other influences, following the re-afference principle. Moreover, inverse anticipations enable the selection of the behavior that is believed to reach current goals most effectively. Coupled with motivations, goal-directed behavior can be generated self-motivatedly. Furthermore, curious, information seeking, epistemic behavior can be generated. The remainder of the book addresses how the brain accomplishes this goal-oriented, self-motivated generation of behavior and thought, where the latter can be considered mental behavior.

scholarly journals Association between GLP-1 receptor gene polymorphisms with reward learning, anhedonia and depression diagnosis

2020 ◽  
Vol 32 (4) ◽  
pp. 218-225
Author(s):  
Hale Yapici-Eser ◽  
Vivek Appadurai ◽  
Candan Yasemin Eren ◽  
Dilek Yazici ◽  
Chia-Yen Chen ◽  
...  
Keyword(s):  
Response Bias ◽  
Receptor Gene ◽  
Objective Response ◽  
Reward Learning ◽  
Major Depressive ◽  
Depression Diagnosis ◽  
Glucagon Like Peptide ◽  
Glucagon Like Peptide 1 ◽  
Receptor Gene Polymorphisms ◽  
The Brain

AbstractBackground:Glucagon-like peptide-1 receptors (GLP-1Rs) are widely expressed in the brain. Evidence suggests that they may play a role in reward responses and neuroprotection. However, the association of GLP-1R with anhedonia and depression diagnosis has not been studied. Here, we examined the association of GLP-1R polymorphisms with objective and subjective measures of anhedonia, as well as depression diagnosis.Methods:Objective [response bias assessed by the probabilistic reward task (PRT)] and subjective [Snaith-Hamilton Pleasure Scale (SHAPS)] measures of anhedonia, clinical variables and DNA samples were collected from 100 controls and 164 patients at McLean Hospital. An independent sample genotyped as part of the Psychiatric Genomics Consortium (PGC) was used to study the effect of putative GLP-1R polymorphisms linked to response bias in PRT on depression diagnosis.Results:The C allele in rs1042044 was significantly associated with increased PRT response bias, when controlling for age, sex, case-control status and PRT discriminability. AA genotype of rs1042044 showed higher anhedonia phenotype based on SHAPS scores. However, analysis of PGC major depressive disorder data showed no association between rs1042044 and depression diagnosis.Conclusion:Findings suggest a possible association of rs1042044 with anhedonia but no association with depression diagnosis.

scholarly journals How the Brain Converts Negative Evaluation into Performance Facilitation

2017 ◽  
Vol 28 (2) ◽  
pp. 602-611 ◽  
Author(s):  
Charlotte Prévost ◽  
Hakwan Lau ◽  
Dean Mobbs
Keyword(s):  
Brain Regions ◽  
Negative Evaluation ◽  
Neural Representation ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Success Stories ◽  
Goal Directed Behavior ◽  
The Brain ◽  
Insight Into ◽  
Recurrent Theme

Abstract Surpassing negative evaluation is a recurrent theme of success stories. Yet, there is little evidence supporting the counterintuitive idea that negative evaluation might not only motivate people, but also enhance performance. To address this question, we designed a task that required participants to decide whether taking up a risky challenge after receiving positive or negative evaluations from independent judges. Participants believed that these evaluations were based on their prior performance on a related task. Results showed that negative evaluation caused a facilitation in performance. Concurrent functional magnetic resonance imaging revealed that the motivating effect of negative evaluation was represented in the insula and striatum, while the performance boost was associated with functional positive connectivity between the insula and a set of brain regions involved in goal-directed behavior and the orienting of attention. These findings provide new insight into the neural representation of negative evaluation-induced facilitation.

scholarly journals Neural mechanisms underlying expectation-dependent inhibition of distracting information

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Dirk van Moorselaar ◽  
Eline Lampers ◽  
Elisa Cordesius ◽  
Heleen A Slagter
Keyword(s):  
Search Task ◽  
Visual Search Task ◽  
Neural Mechanisms ◽  
Distractor Interference ◽  
Sensory Representation ◽  
Task Relevance ◽  
Dependent Inhibition ◽  
Statistical Regularities ◽  
Goal Directed Behavior ◽  
The Brain

Predictions based on learned statistical regularities in the visual world have been shown to facilitate attention and goal-directed behavior by sharpening the sensory representation of goal-relevant stimuli in advance. Yet, how the brain learns to ignore predictable goal-irrelevant or distracting information is unclear. Here, we used EEG and a visual search task in which the predictability of a distractor’s location and/or spatial frequency was manipulated to determine how spatial and feature distractor expectations are neurally implemented and reduce distractor interference. We find that expected distractor features could not only be decoded pre-stimulus, but their representation differed from the representation of that same feature when part of the target. Spatial distractor expectations did not induce changes in preparatory neural activity, but a strongly reduced Pd, an ERP index of inhibition. These results demonstrate that neural effects of statistical learning critically depend on the task relevance and dimension (spatial, feature) of predictions.

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