KFU. Card publication. Integration of cognitive model and predictive coding models

KAZAN
FEDERAL UNIVERSITY

- RUS
- 中文
- ES

INTEGRATION OF COGNITIVE MODEL AND PREDICTIVE CODING MODELS

Form of presentation

Conference proceedings in Russian journals and collections

Year of publication

2024

Язык

английский

Authors, employees of KFU

Granica Aleksandr Stanislavovich, author

Other authors

Murtazin Albert Inzirovich, author

Bibliographic description in the original language

Murtazin A., Granitsa A. Integration of cognitive model and predictive coding models/EABCT 2024 ~ New Age of CBT – Challenges and Perspectives. Book of abstracts.-Belgrade,Serbia-September 4-7, 2024,p.401

Annotation

ABCT 2024 ~ New Age of CBT – Challenges and Perspectives. Book of abstracts.-Belgrade,Serbia-September 4-7, 2024

Keywords

cognitive model, predictive model, Bayesian brain hypothesis, generative models, integration

The name of the journal

ABCT 2024 ~ New Age of CBT – Challenges and Perspectives. Book of abstracts.-Belgrade,Serbia-September 4-7, 2024

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=303775&p_lang=2

Resource files

File name	Size (MB)	Format
Book_of_Abstracts_EABCT2024.pdf	12,77	pdf	show / download

Full metadata record

Field DC	Value	Language
dc.contributor.author	Granica Aleksandr Stanislavovich	ru_RU
dc.contributor.author	Murtazin Albert Inzirovich	ru_RU
dc.date.accessioned	2024-01-01T00:00:00Z	ru_RU
dc.date.available	2024-01-01T00:00:00Z	ru_RU
dc.date.issued	2024	ru_RU
dc.identifier.citation	Murtazin A., Granitsa A. Integration of cognitive model and predictive coding models/EABCT 2024 ~ New Age of CBT – Challenges and Perspectives. Book of abstracts.-Belgrade,Serbia-September 4-7, 2024,p.401	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=303775&p_lang=2	ru_RU
dc.description.abstract	ABCT 2024 ~ New Age of CBT – Challenges and Perspectives. Book of abstracts.-Belgrade,Serbia-September 4-7, 2024	ru_RU
dc.description.abstract	Cognitive-behavioural therapy (CBT) is a practical, goal-focused approach that helps understand the relationship be tween thoughts, feelings and behaviours. The aim is to identify the dysfunctional and distorted cognitions associated with their psychological problems and to create more functional and balanced cognitive patterns that create less emotional distress and more helpful behaviours. Beck's cognitive model has provided an evidence-based way to conceptualize and treat psychological disorders. CBT theoretical models and mechanisms of change have been the most researched and are in line with the current main stream paradigms of human mind and behavior (e.g., information processing). When information processing provides faulty information, other systems (e.g., affective, motivational, behavioral) no longer function in an adaptive way. Er rors can result in other cognitive biases (e.g., interpretation, attention, memory), excessive or inappropriate affect, and maladaptive behavior. A negative bias will assure reactions to true danger; however, at the cost of many false alarms. Consequently, individuals are likely to experience unwarranted anxiety in many seemingly dangerous but innocuous situations. Similarly, a positive bias exaggerates the probability or degree of positive outcomes and consequently increases or maintains motivation to engage in a task (Beck, Hugh, 2015). An erroneous or exaggerated interpretation of threat, for example, will result in inappropriate or excessive anxiety and avoidance (Clark & Beck, 2011). A biased information-processing system reflects predictive coding models. Predictive coding models of brain process ing propose that top-down cortical signals promote efficient neural signaling by carrying predictions about incoming sensory information (Gilbert et al, 2022). The framework is rooted in Bayesian probability theory and the so-called Bayesian brain hypothesis [Knill and Pouget, 2004] that conceptualizes perception as a constructive process that uses internal or generative models to encode prior beliefs about sensory inputs and their causes. Generative models help an individual formulate predictions about incoming sensory information that are tested against incoming sensory inputs and produce prediction errors. Prediction errors, in turn, are used by the brain to revise its model of the world by updating predictions in order to minimize prediction errors [Friston, 2010] These ideas are actually used in cognitive conceptualizations of clinical cases. Distant antecedents as childhood events can be viewed as incoming perceptual signals that may run counter to the predictive model of well-being. And this discrepancy, accompanied by frustration of needs, forms a generalized conclusion about the causes of these discrepancies as a basic belief. Thus, basic beliefs and cognitive schemas are part of a universal predictive model and relate to a fairly wide range of particular situations. Although numerou	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	cognitive model	ru_RU
dc.subject	predictive model	ru_RU
dc.subject	Bayesian brain hypothesis	ru_RU
dc.subject	generative models	ru_RU
dc.subject	integration	ru_RU
dc.title	Integration of cognitive model and predictive coding models	ru_RU
dc.type	Conference proceedings in Russian journals and collections	ru_RU