scholarly journals Computational modelling of social cognition and behaviour—a reinforcement learning primer

2020 ◽  
Author(s):  
Patricia L Lockwood ◽  
Miriam C Klein-Flügge
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Observational Learning ◽  
Impression Formation ◽  
Computational Models ◽  
Computational Modelling ◽  
Neural Systems ◽  
Neural Basis ◽  
The Past ◽  
The Brain

Abstract Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalizing and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

scholarly journals Computational modelling of social cognition and behaviour – a reinforcement learning primer

2019 ◽  
Author(s):  
Patricia Lockwood ◽  
Miriam Klein-Flugge
Keyword(s):  
Reinforcement Learning ◽  
Social Cognition ◽  
Observational Learning ◽  
Impression Formation ◽  
Computational Models ◽  
Computational Modelling ◽  
Neural Systems ◽  
Neural Basis ◽  
The Past ◽  
The Brain

Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalising and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

scholarly journals The role of morality in social cognition

2019 ◽  
Author(s):  
Jennifer Lauren Ray ◽  
Peter Mende-Siedlecki ◽  
Ana P. Gantman ◽  
Jay Joseph Van Bavel
Keyword(s):  
Social Cognition ◽  
Social Perception ◽  
Impression Formation ◽  
Factor Models ◽  
The Other ◽  
One Dimension ◽  
Primary Role ◽  
The Past ◽  
Important Basis

Over the past few decades, two-factor models of social cognition have emerged as the dominant framework for understanding impression formation. Despite the differences in the labels, there is wide agreement that one dimension reflects sociability potential, and the other, competence. One way in which the various two-factor models do clearly differ, however, is in the way the dimensions incorporate or produce evaluations of morality. Aristotle saw morality as the most important basis on which to form positive evaluations, because competence and sociability could only be virtuous, sincere, and trustworthy if expressed through a moral character. This chapter highlights research demonstrating the unique and possibly primary role of morality in social cognition. We clarify the dynamic, interactive, and conjoint effects of morality on social perception, and argue morality, competence, and sociability are three influential and interactive dimensions of social perception.

The puzzle of chaotic neurodynamics

2001 ◽  
Vol 24 (5) ◽  
pp. 812-813
Author(s):  
Roman Borisyuk
Keyword(s):  
Information Processing ◽  
Experimental Evidence ◽  
Computational Models ◽  
Dynamical Behaviour ◽  
Neural Systems ◽  
Dynamical Regime ◽  
The Brain

Experimental evidence and mathematical/computational models show that in many cases chaotic, nonregular oscillations are adequate to describe the dynamical behaviour of neural systems. Further work is needed to understand the meaning of this dynamical regime for modelling information processing in the brain.

scholarly journals The best laid plans: Computational principles of ACC

2020 ◽  
Author(s):  
Clay B. Holroyd ◽  
Tom Verguts
Keyword(s):  
Reinforcement Learning ◽  
Anterior Cingulate Cortex ◽  
Hierarchical Model ◽  
Computational Models ◽  
Cingulate Cortex ◽  
Anterior Cingulate ◽  
Hierarchical Reinforcement Learning ◽  
The Past ◽  
Model Based ◽  
High Level

Despite continual debate for the past thirty years about the function of anterior cingulate cortex (ACC), its key contribution to neurocognition remains unknown. Here we review computational models that illustrate three core principles of ACC function (related to hierarchy, world models and cost), as well as four constraints on the neural implementation of these principles (related to modularity, binding, encoding and learning and regulation). These observations suggest a role for ACC in hierarchical model-based hierarchical reinforcement learning, which instantiates a mechanism for motivating the execution of high-level plans.

scholarly journals The Neural Basis of Shared Preference Learning

2019 ◽  
Author(s):  
Harry Farmer ◽  
Uri Hertz ◽  
Antonia Hamilton
Keyword(s):  
Social Interactions ◽  
Computational Modelling ◽  
Brain Regions ◽  
The Self ◽  
Neural Activation ◽  
Neural Basis ◽  
Daily Lives ◽  
Neural Processes ◽  
Specific Manner ◽  
The Brain

AbstractDuring our daily lives, we often learn about the similarity of the traits and preferences of others to our own and use that information during our social interactions. However, it is unclear how the brain represents similarity between the self and others. One possible mechanism is to track similarity to oneself regardless of the identity of the other (Similarity account); an alternative is to track each confederate in terms of consistency of the similarity to the self, with respect to the choices they have made before (consistency account). Our study combined fMRI and computational modelling of reinforcement learning (RL) to investigate the neural processes that underlie learning about preference similarity. Participants chose which of two pieces of artwork they preferred and saw the choices of one confederate who usually shared their preference and another who usually did not. We modelled neural activation with RL models based on the similarity and consistency accounts. Data showed more brain regions whose activity pattern fits with the consistency account, specifically, areas linked to reward and social cognition. Our findings suggest that impressions of other people can be calculated in a person-specific manner which assumes that each individual behaves consistently with their past choices.

scholarly journals Neuro-computational account of arbitration between imitation and emulation during human observational learning

2019 ◽  
Author(s):  
Caroline C. Charpentier ◽  
Kiyohito Iigaya ◽  
John P. O’Doherty
Keyword(s):  
Prefrontal Cortex ◽  
Observational Learning ◽  
Computational Modelling ◽  
Neural Correlates ◽  
Fundamental Question ◽  
Behavioral Task ◽  
Reliable Prediction ◽  
Ventrolateral Prefrontal Cortex ◽  
Temporoparietal Junction ◽  
The Brain

AbstractIn observational learning (OL), organisms learn from observing the behavior of others. There are at least two distinct strategies for OL. Imitation involves learning to repeat the previous actions of other agents, while in emulation, learning proceeds from inferring the goals and intentions of others. While putative neural correlates for these forms of learning have been identified, a fundamental question remains unaddressed: how does the brain decides which strategy to use in a given situation? Here we developed a novel computational model in which arbitration between the strategies is determined by the predictive reliability, such that control over behavior is adaptively weighted toward the strategy with the most reliable prediction. To test the theory, we designed a novel behavioral task in which our experimental manipulations produced dissociable effects on the reliability of the two strategies. Participants performed this task while undergoing fMRI in two independent studies (the second a pre-registered replication of the first). Behavior manifested patterns consistent with both emulation and imitation and flexibly changed between the two strategies as expected from the theory. Computational modelling revealed that behavior was best described by an arbitration model, in which the reliability of the emulation strategy determined the relative weights allocated to behavior for each strategy. Emulation reliability - the model’s arbitration signal - was encoded in the ventrolateral prefrontal cortex, temporoparietal junction and rostral cingulate cortex. Being replicated across two fMRI studies, these findings suggest a neuro-computational mechanism for allocating control between emulation and imitation during observational learning.

scholarly journals Watershed Brain Regions for Characterizing Brand Equity-Related Mental Processes

2021 ◽  
Vol 11 (12) ◽  
pp. 1619
Author(s):  
Shinya Watanuki
Keyword(s):  
Brand Equity ◽  
Meta Analysis ◽  
Brain Regions ◽  
Parahippocampal Gyrus ◽  
Neural Systems ◽  
Mental Processes ◽  
Chi Square ◽  
The Past ◽  
Lingual Gyrus ◽  
The Brain

Brand equity is an important intangible for enterprises. As one advantage, products with brand equity can increase revenue, compared with those without such equity. However, unlike tangibles, it is difficult for enterprises to manage brand equity because it exists within consumers’ minds. Although, over the past two decades, numerous consumer neuroscience studies have revealed the brain regions related to brand equity, the identification of unique brain regions related to such equity is still controversial. Therefore, this study identifies the unique brain regions related to brand equity and assesses the mental processes derived from these regions. For this purpose, three analysis methods (i.e., the quantitative meta-analysis, chi-square tests, and machine learning) were conducted. The data were collected in accordance with the general procedures of a qualitative meta-analysis. In total, 65 studies (1412 foci) investigating branded objects with brand equity and unbranded objects without brand equity were examined, whereas the neural systems involved for these two brain regions were contrasted. According to the results, the parahippocampal gyrus and the lingual gyrus were unique brand equity-related brain regions, whereas automatic mental processes based on emotional associative memories derived from these regions were characteristic mental processes that discriminate branded from unbranded objects.

scholarly journals Studies of Altered Social Cognition in Neuropsychiatric Disorders Using Functional Neuroimaging

2002 ◽  
Vol 47 (4) ◽  
pp. 327-336 ◽  
Author(s):  
Cheryl L Grady ◽  
Michelle L Keightley
Keyword(s):  
Social Cognition ◽  
Brain Function ◽  
Functional Neuroimaging ◽  
Neuropsychiatric Disorders ◽  
Brain Regions ◽  
The Past ◽  
Complex Interactions ◽  
Social Abilities ◽  
The Brain ◽  
Self Reference

In this paper, we review studies using functional neuroimaging to examine cognition in neuropsychiatric disorders. The focus is on social cognition, which is a topic that has received increasing attention over the past few years. A network of brain regions is proposed for social cognition that includes regions involved in processes relevant to social functioning (for example, self reference and emotion). We discuss the alterations of activity in these areas in patients with autism, depression, schizophrenia, and posttraumatic stress disorder in relation to deficits in social behaviour and symptoms. The evidence to date suggests that there may be some specificity of the brain regions involved in these 4 disorders, but all are associated with dysfunction in the amygdala and dorsal cingulate gyrus. Although there is much work remaining in this area, we are beginning to understand the complex interactions of brain function and behaviour that lead to disruptions of social abilities.

scholarly journals Analysis of Neuromorphic Computing Systems and its Applications in Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 5309-5312
Author(s):  
Pranava Bhat
Keyword(s):  
Machine Learning ◽  
Human Brain ◽  
Neural Systems ◽  
Neuromorphic Computing ◽  
Computing Systems ◽  
The Past ◽  
Computational Capability ◽  
Biological World ◽  
Areas Of Interest ◽  
The Brain

The domain of engineering has always taken inspiration from the biological world. Understanding the functionalities of the human brain is one of the key areas of interest over time and has caused many advancements in the field of computing systems. The computational capability per unit power per unit volume of the human brain exceeds the current best supercomputers. Mimicking the physics of computations used by the nervous system and the brain can bring a paradigm shift to the computing systems. The concept of bridging computing and neural systems can be termed as neuromorphic computing and it is bringing revolutionary changes in the computing hardware. Neuromorphic computing systems have seen swift progress in the past decades. Many organizations have introduced a variety of designs, implementation methodologies and prototype chips. This paper discusses the parameters that are considered in the advanced neuromorphic computing systems and the tradeoffs between them. There have been attempts made to make computer models of neurons. Advancements in the hardware implementation are fuelling the applications in the field of machine learning. This paper presents the applications of these modern computing systems in Machine Learning.

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