scholarly journals Application of RT-middleware to Computational Modelling of the Brain System

2016 ◽  
Vol 52 (5) ◽  
pp. 264-275
Author(s):  
Kazuki URABE ◽  
Daiki NAKAMURA ◽  
Shunji SATOH ◽  
Xuehua HAN
Keyword(s):  
Computational Modelling ◽  
Brain System ◽  
The Brain

Computational specificity in the human brain

2016 ◽  
Vol 39 ◽  
Author(s):  
James M. Shine ◽  
Ian Eisenberg ◽  
Russell A. Poldrack
Keyword(s):  
Human Brain ◽  
Brain Function ◽  
Specific Task ◽  
Relative Lack ◽  
Brain System ◽  
Computational Capacity ◽  
The Brain

AbstractAlthough meta-analytic neuroimaging studies demonstrate a relative lack of specificity in the brain, this evidence may be the result of limits inherent to these types of studies. From this perspective, we review recent findings that suggest that brain function is most appropriately categorized according to the computational capacity of each brain system, rather than the specific task states that elicit its activity.

Abnormal function of the brain system supporting motivated attention in medicated patients with schizophrenia: an fMRI study

2006 ◽  
Vol 36 (8) ◽  
pp. 1097-1108 ◽  
Author(s):  
PETER F. LIDDLE ◽  
KRISTIN R. LAURENS ◽  
KENT A. KIEHL ◽  
ELTON T. C. NGAN
Keyword(s):  
Basal Forebrain ◽  
Anterior Cingulate ◽  
Orbital Frontal Cortex ◽  
Brain System ◽  
Cerebral Activity ◽  
Auditory Oddball ◽  
Fmri Study ◽  
Motivated Attention ◽  
Subcortical Nuclei ◽  
The Brain

Background. Patients with schizophrenia have an impaired ability to generate activity that is appropriate to current circumstances and goals.Method. We report a study using functional magnetic resonance imaging (fMRI) to examine cerebral activity during a three-tone auditory oddball target detection task in a sample of 28 patients with schizophrenia and 28 healthy controls.Results. The patients exhibited significantly less activation in response to target stimuli relative to baseline in an extensive set of sites in association neocortex, paralimbic cortex, limbic structures and subcortical nuclei, yet demonstrated a normal level of activation in the sensorimotor cortex. Comparison of activity elicited by rare target stimuli with that elicited by equally rare novel stimuli makes it possible to distinguish cerebral activity associated with attention to behaviourally salient stimuli from activity associated with attending to other attention-capturing stimuli. This comparison revealed that the patients with schizophrenia also exhibited a deficit in activation of basal forebrain areas that mediate motivation during the processing of behaviourally salient stimuli, including the amygdala, ventral striatum, orbital frontal cortex and rostral anterior cingulate cortex.Conclusion. Patients with schizophrenia have a deficit in function of the brain system concerned with mediating motivation, in addition to a more general deficit in the cerebral response to attention-captivating stimuli.

scholarly journals Auditory and visual scene analysis: an overview

2017 ◽  
Vol 372 (1714) ◽  
pp. 20160099 ◽  
Author(s):  
Hirohito M. Kondo ◽  
Anouk M. van Loon ◽  
Jun-Ichiro Kawahara ◽  
Brian C. J. Moore
Keyword(s):  
Scene Perception ◽  
Computational Modelling ◽  
Visual Scene ◽  
Scene Analysis ◽  
Theoretical Approaches ◽  
Visual Inputs ◽  
Sensory Modalities ◽  
Stimulus Features ◽  
The Brain ◽  
Selection Of

We perceive the world as stable and composed of discrete objects even though auditory and visual inputs are often ambiguous owing to spatial and temporal occluders and changes in the conditions of observation. This raises important questions regarding where and how ‘scene analysis’ is performed in the brain. Recent advances from both auditory and visual research suggest that the brain does not simply process the incoming scene properties. Rather, top-down processes such as attention, expectations and prior knowledge facilitate scene perception. Thus, scene analysis is linked not only with the extraction of stimulus features and formation and selection of perceptual objects, but also with selective attention, perceptual binding and awareness. This special issue covers novel advances in scene-analysis research obtained using a combination of psychophysics, computational modelling, neuroimaging and neurophysiology, and presents new empirical and theoretical approaches. For integrative understanding of scene analysis beyond and across sensory modalities, we provide a collection of 15 articles that enable comparison and integration of recent findings in auditory and visual scene analysis. This article is part of the themed issue ‘Auditory and visual scene analysis’.

scholarly journals Precision-weighting of superior frontal cortex unsigned prediction error signals benefits learning, is mediated by dopamine, and is impaired in psychosis

2019 ◽  
Author(s):  
J. Haarsma ◽  
P.C. Fletcher ◽  
J.D. Griffin ◽  
H.J. Taverne ◽  
H. Ziauddeen ◽  
...  
Keyword(s):  
Prediction Error ◽  
Computational Modelling ◽  
Anterior Cingulate ◽  
Prediction Errors ◽  
Healthy Individuals ◽  
Cortical Function ◽  
Dopaminergic Modulation ◽  
Hierarchical Bayesian Inference ◽  
The Brain ◽  
Superior Frontal Cortex

AbstractRecent theories of cortical function construe the brain as performing hierarchical Bayesian inference. According to these theories, the precision of cortical unsigned prediction error (i.e., surprise) signals plays a key role in learning and decision-making, to be controlled by dopamine, and to contribute to the pathogenesis of psychosis. To test these hypotheses, we studied learning with variable outcome-precision in healthy individuals after dopaminergic modulation and in patients with early psychosis. Behavioural computational modelling indicated that precision-weighting of unsigned prediction errors benefits learning in health, and is impaired in psychosis. FMRI revealed coding of unsigned prediction errors relative to their precision in bilateral superior frontal gyri and dorsal anterior cingulate, which was perturbed by dopaminergic modulation, impaired in psychosis, and associated with task performance and schizotypy. We conclude that precision-weighting of cortical prediction error signals is a key mechanism through which dopamine modulates inference and contributes to the pathogenesis of psychosis.

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 Developmental Computational Psychiatry

2018 ◽  
Author(s):  
Tobias U. Hauser ◽  
Geert-Jan Will ◽  
Magda Dubois ◽  
Raymond J Dolan
Keyword(s):  
Cognitive Development ◽  
Psychiatric Disorders ◽  
Cognitive Processes ◽  
Computational Modelling ◽  
Brain Maturation ◽  
Childhood And Adolescence ◽  
Social Evaluation ◽  
Computational Psychiatry ◽  
Underlying Mechanisms ◽  
The Brain

Most psychiatric disorders emerge during childhood and adolescence. This is also a period when the brain undergoes substantial growth and reorganisation. However, it remains unclear how a heightened vulnerability to psychiatric disorder relates to brain maturation, and what the underlying mechanisms might be. Here, we propose ‘developmental computational psychiatry’ as a framework for linking brain maturation to cognitive development. We propose that through modelling some of the brain’s fundamental cognitive computations and relating them to brain development, we can bridge the gap between brain and cognitive development. This in turn can lead to a richer understanding of the ontogeny of psychiatric disorders. We illustrate this perspective by taking examples from reinforcement learning (RL) and dopamine function, showing how computational modelling deepens an understanding of how cognitive processes, such as reward learning, effort learning, and social evaluation might go awry in psychiatric disorders. Finally, we formulate testable hypotheses and sketch the potential and limitations of developmental computational psychiatry.

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.

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