scholarly journals A Bayesian model of context-sensitive value attribution

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Francesco Rigoli ◽  
Karl J Friston ◽  
Cristina Martinelli ◽  
Mirjana Selaković ◽  
Sukhwinder S Shergill ◽  
...  
Keyword(s):  
Contextual Information ◽  
Empirical Support ◽  
Prediction Errors ◽  
Substantial Evidence ◽  
Prior Beliefs ◽  
Context Sensitive ◽  
Incentive Value ◽  
Hierarchical Levels ◽  
Key Features ◽  
The Brain

Substantial evidence indicates that incentive value depends on an anticipation of rewards within a given context. However, the computations underlying this context sensitivity remain unknown. To address this question, we introduce a normative (Bayesian) account of how rewards map to incentive values. This assumes that the brain inverts a model of how rewards are generated. Key features of our account include (i) an influence of prior beliefs about the context in which rewards are delivered (weighted by their reliability in a Bayes-optimal fashion), (ii) the notion that incentive values correspond to precision-weighted prediction errors, (iii) and contextual information unfolding at different hierarchical levels. This formulation implies that incentive value is intrinsically context-dependent. We provide empirical support for this model by showing that incentive value is influenced by context variability and by hierarchically nested contexts. The perspective we introduce generates new empirical predictions that might help explaining psychopathologies, such as addiction.

scholarly journals Adaptive coding of pain prediction error in the anterior insula

2021 ◽  
Author(s):  
Robert Hoskin ◽  
Deborah Talmi
Keyword(s):  
Prediction Error ◽  
Contextual Information ◽  
Neural Mechanism ◽  
Axiomatic Approach ◽  
Anterior Insula ◽  
Prediction Errors ◽  
Adaptive Coding ◽  
Pain Prediction ◽  
The Brain ◽  
Behavioural Evidence

Background: To reduce the computational demands of the task of determining values, the brain is thought to engage in adaptive coding, where the sensitivity of some neurons to value is modulated by contextual information. There is good behavioural evidence that pain is coded adaptively, but controversy regarding the underlying neural mechanism. Additionally, there is evidence that reward prediction errors are coded adaptively, but no parallel evidence regarding pain prediction errors. Methods: We tested the hypothesis that pain prediction errors are coded adaptively by scanning 19 healthy adults with fMRI while they performed a cued pain task. Our analysis followed an axiomatic approach. Results: We found that the left anterior insula was the only region which was sensitive both to predicted pain magnitudes and the unexpectedness of pain delivery, but not to the magnitude of delivered pain. Conclusions: This pattern suggests that the left anterior insula is part of a neural mechanism that serves the adaptive prediction error of pain.

scholarly journals Decision-related feedback in visual cortex lacks spatial selectivity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Katrina R. Quinn ◽  
Lenka Seillier ◽  
Daniel A. Butts ◽  
Hendrikje Nienborg
Keyword(s):  
Contextual Information ◽  
Relevant Information ◽  
Common Mechanism ◽  
Spatial Selectivity ◽  
Population Responses ◽  
Stimulus Choice ◽  
Visual Areas ◽  
Computational Work ◽  
Feature Based ◽  
The Brain

AbstractFeedback in the brain is thought to convey contextual information that underlies our flexibility to perform different tasks. Empirical and computational work on the visual system suggests this is achieved by targeting task-relevant neuronal subpopulations. We combine two tasks, each resulting in selective modulation by feedback, to test whether the feedback reflected the combination of both selectivities. We used visual feature-discrimination specified at one of two possible locations and uncoupled the decision formation from motor plans to report it, while recording in macaque mid-level visual areas. Here we show that although the behavior is spatially selective, using only task-relevant information, modulation by decision-related feedback is spatially unselective. Population responses reveal similar stimulus-choice alignments irrespective of stimulus relevance. The results suggest a common mechanism across tasks, independent of the spatial selectivity these tasks demand. This may reflect biological constraints and facilitate generalization across tasks. Our findings also support a previously hypothesized link between feature-based attention and decision-related activity.

scholarly journals Recurrent Multi-Fiber Network for 3D MRI Brain Tumor Segmentation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 320
Author(s):  
Yue Zhao ◽  
Xiaoqiang Ren ◽  
Kun Hou ◽  
Wentao Li
Keyword(s):  
Brain Tumor ◽  
Network Architecture ◽  
Contextual Information ◽  
Computational Cost ◽  
Disease Diagnosis ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fiber Network ◽  
3D Network ◽  
The Brain

Automated brain tumor segmentation based on 3D magnetic resonance imaging (MRI) is critical to disease diagnosis. Moreover, robust and accurate achieving automatic extraction of brain tumor is a big challenge because of the inherent heterogeneity of the tumor structure. In this paper, we present an efficient semantic segmentation 3D recurrent multi-fiber network (RMFNet), which is based on encoder–decoder architecture to segment the brain tumor accurately. 3D RMFNet is applied in our paper to solve the problem of brain tumor segmentation, including a 3D recurrent unit and 3D multi-fiber unit. First of all, we propose that recurrent units segment brain tumors by connecting recurrent units and convolutional layers. This quality enhances the model’s ability to integrate contextual information and is of great significance to enhance the contextual information. Then, a 3D multi-fiber unit is added to the overall network to solve the high computational cost caused by the use of a 3D network architecture to capture local features. 3D RMFNet combines both advantages from a 3D recurrent unit and 3D multi-fiber unit. Extensive experiments on the Brain Tumor Segmentation (BraTS) 2018 challenge dataset show that our RMFNet remarkably outperforms state-of-the-art methods, and achieves average Dice scores of 89.62%, 83.65% and 78.72% for the whole tumor, tumor core and enhancing tumor, respectively. The experimental results prove our architecture to be an efficient tool for brain tumor segmentation accurately.

scholarly journals A nonlinear updating algorithm captures suboptimal inference in the presence of signal-dependent noise

2018 ◽  
Author(s):  
Seth W. Egger ◽  
Mehrdad Jazayeri
Keyword(s):  
Probability Distributions ◽  
Nonlinear Function ◽  
Alternative View ◽  
Time Interval ◽  
Prior Beliefs ◽  
Optimal Behavior ◽  
Multiple Measurements ◽  
The Brain ◽  
Full Probability ◽  
Sequential Measurements

AbstractBayesian models of behavior have advanced the idea that humans combine prior beliefs and sensory observations to minimize uncertainty. How the brain implements Bayes-optimal inference, however, remains poorly understood. Simple behavioral tasks suggest that the brain can flexibly represent and manipulate probability distributions. An alternative view is that brain relies on simple algorithms that can implement Bayes-optimal behavior only when the computational demands are low. To distinguish between these alternatives, we devised a task in which Bayes-optimal performance could not be matched by simple algorithms. We asked subjects to estimate and reproduce a time interval by combining prior information with one or two sequential measurements. In the domain of time, measurement noise increases with duration. This property makes the integration of multiple measurements beyond the reach of simple algorithms. We found that subjects were able to update their estimates using the second measurement but their performance was suboptimal, suggesting that they were unable to update full probability distributions. Instead, subjects’ behavior was consistent with an algorithm that predicts upcoming sensory signals, and applies a nonlinear function to errors in prediction to update estimates. These results indicate that inference strategies humans deploy may deviate from Bayes-optimal integration when the computational demands are high.

scholarly journals Prefrontal solution to the bias-variance tradeoff during reinforcement learning

2020 ◽  
Author(s):  
Dongjae Kim ◽  
Jaeseung Jeong ◽  
Sang Wan Lee
Keyword(s):  
Adaptive Control ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Brain Regions ◽  
Decision Task ◽  
Prediction Errors ◽  
Model Based ◽  
Model Free ◽  
Bias Variance ◽  
The Brain

AbstractThe goal of learning is to maximize future rewards by minimizing prediction errors. Evidence have shown that the brain achieves this by combining model-based and model-free learning. However, the prediction error minimization is challenged by a bias-variance tradeoff, which imposes constraints on each strategy’s performance. We provide new theoretical insight into how this tradeoff can be resolved through the adaptive control of model-based and model-free learning. The theory predicts the baseline correction for prediction error reduces the lower bound of the bias–variance error by factoring out irreducible noise. Using a Markov decision task with context changes, we showed behavioral evidence of adaptive control. Model-based behavioral analyses show that the prediction error baseline signals context changes to improve adaptability. Critically, the neural results support this view, demonstrating multiplexed representations of prediction error baseline within the ventrolateral and ventromedial prefrontal cortex, key brain regions known to guide model-based and model-free learning.One sentence summaryA theoretical, behavioral, computational, and neural account of how the brain resolves the bias-variance tradeoff during reinforcement learning is described.

scholarly journals Learning to synchronize: Midfrontal theta dynamics during rule switching

2020 ◽  
Author(s):  
Pieter Verbeke ◽  
Kate Ergo ◽  
Esther De Loof ◽  
Tom Verguts
Keyword(s):  
Negative Feedback ◽  
Human Subjects ◽  
Learning Task ◽  
Theta Oscillations ◽  
Prediction Errors ◽  
Hierarchical Learning ◽  
Theta Power ◽  
Negative Prediction ◽  
Theta Phase ◽  
The Brain

AbstractIn recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning, remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model employs bursts at theta frequency to flexibly bind appropriate task modules by synchrony. 64-channel EEG signal was recorded while 27 human subjects (Female: 21, Male: 6) performed a probabilistic reversal learning task. In line with the Sync model, post-feedback theta power showed a linear relationship with negative prediction errors, but not with positive prediction errors. This relationship was especially pronounced for subjects with better behavioral fit (measured via AIC) of the Sync model. Also consistent with Sync model simulations, theta phase-coupling between midfrontal electrodes and temporo-parietal electrodes was stronger after negative feedback. Our data suggest that the brain uses theta power and synchronization for flexibly switching between task rule modules, as is useful for example when multiple stimulus-action mappings must be retained and used.Significance StatementEveryday life requires flexibility in switching between several rules. A key question in understanding this ability is how the brain mechanistically coordinates such switches. The current study tests a recent computational framework (Sync model) that proposed how midfrontal theta oscillations coordinate activity in hierarchically lower task-related areas. In line with predictions of this Sync model, midfrontal theta power was stronger when rule switches were most likely (strong negative prediction error), especially in subjects who obtained a better model fit. Additionally, also theta phase connectivity between midfrontal and task-related areas was increased after negative feedback. Thus, the data provided support for the hypothesis that the brain uses theta power and synchronization for flexibly switching between rules.

The epigenetic regulation of synaptic genes contributes to the etiology of autism

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Annamaria Srancikova ◽  
Zuzana Bacova ◽  
Jan Bakos
Keyword(s):  
Epigenetic Regulation ◽  
Neurodevelopmental Disorders ◽  
Autism Spectrum ◽  
Prader Willi Syndrome ◽  
Epigenetic Mechanisms ◽  
Substantial Evidence ◽  
Autistic Symptoms ◽  
Spectrum Disorders ◽  
The Brain

Abstract Epigenetic mechanisms greatly affect the developing brain, as well as the maturation of synapses with pervasive, long-lasting consequences on behavior in adults. Substantial evidence exists that implicates dysregulation of epigenetic mechanisms in the etiology of neurodevelopmental disorders. Therefore, this review explains the role of enzymes involved in DNA methylation and demethylation in neurodevelopment by emphasizing changes of synaptic genes and proteins. Epigenetic causes of sex-dependent differences in the brain are analyzed in conjunction with the pathophysiology of autism spectrum disorders. Special attention is devoted to the epigenetic regulation of the melanoma-associated antigen-like gene 2 (MAGEL2) found in Prader-Willi syndrome, which is known to be accompanied by autistic symptoms.

Could Emotions Be Modelled through Information Processing?

2015 ◽  
pp. 63-82
Author(s):  
Ebrahim Oshni Alvandi
Keyword(s):  
Information Processing ◽  
Cognitive Processes ◽  
Environmental Variables ◽  
Emotional Processing ◽  
Processing System ◽  
Human Beings ◽  
Context Sensitive ◽  
Emotional Agents ◽  
The Brain

One way to evaluate cognitive processes in living or nonliving systems is by using the notion of “information processing”. Emotions as cognitive processes orient human beings to recognize, express and display themselves or their wellbeing through dynamical and adaptive form of information processing. In addition, humans behave or act emotionally in an embodied environment. The brain embeds symbols, meaning and purposes for emotions as well. So any model of natural or autonomous emotional agents/systems needs to consider the embodied features of emotions that are processed in an informational channel of the brain or a processing system. This analytical and explanatory study described in this chapter uses the pragmatic notion of information to develop a theoretical model for emotions that attempts to synthesize some essential aspects of human emotional processing. The model holds context-sensitive and purpose-based features of emotional pattering in the brain. The role of memory is discussed and an idea of control parameters that have roles in processing environmental variables in emotional patterning is introduced.

The Context-Security Nexus in Ubiquitous Computing

2016 ◽  
pp. 660-679
Author(s):  
M. Fahim Ferdous Khan ◽  
Ken Sakamura
Keyword(s):  
Access Control ◽  
Ubiquitous Computing ◽  
Context Awareness ◽  
Contextual Information ◽  
Healthcare Services ◽  
Context Aware ◽  
Context Sensitive ◽  
Access Control Models ◽  
Spatio Temporal ◽  
The Right

Context-awareness is a quintessential feature of ubiquitous computing. Contextual information not only facilitates improved applications, but can also become significant security parameters – which in turn can potentially ensure service delivery not to anyone anytime anywhere, but to the right person at the right time and place. Specially, in determining access control to resources, contextual information can play an important role. Access control models, as studied in traditional computing security, however, have no notion of context-awareness; and the recent works in the nascent field of context-aware access control predominantly focus on spatio-temporal contexts, disregarding a host of other pertinent contexts. In this paper, with a view to exploring the relationship of access control and context-awareness in ubiquitous computing, the authors propose a comprehensive context-aware access control model for ubiquitous healthcare services. They explain the design, implementation and evaluation of the proposed model in detail. They chose healthcare as a representative application domain because healthcare systems pose an array of non-trivial context-sensitive access control requirements, many of which are directly or indirectly applicable to other context-aware ubiquitous computing applications.

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