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Modelos Bayesianos de Cognição

2000
  • Thomas Bayes
  • Joshua Tenenbaum
  • Thomas Griffiths
Laboratório de pesquisa em psicologia cognitiva com análise de modelos Bayesianos.

(Imagem gerada apenas para fins ilustrativos)

Bayesian models of cognition frame the mind as a probabilistic inference engine. This approach posits that the brain represents knowledge as probability distributions and updates these beliefs upon receiving new evidence according to Bayes’ theorem. It models perception, learning, and reasoning as optimal or near-optimal statistical inference under uncertainty, providing a unifying mathematical estrutura for many cognitive functions.

The core of this approach is Bayes’ rule: [latex]P(H|D) = frac{P(D|H)P(H)}{P(D)}[/latex], where [latex]H[/latex] is a hypothesis and [latex]D[/latex] is observed data. The posterior probability [latex]P(H|D)[/latex] (belief in the hypothesis after seeing data) is proportional to the product of the likelihood [latex]P(D|H)[/latex] (how well the hypothesis explains the data) and the prior probability [latex]P(H)[/latex] (initial belief in the hypothesis). This framework provides a normative standard for how a rational agent should update its beliefs.

In cognitive science, this is applied by assuming that the mind implicitly performs these calculations. For example, in perception, the brain combines noisy sensory input (the data) with prior knowledge about the world to form a stable percept (the posterior). This can explain many visual illusions, where prior expectations override sensory data. In language learning, a child might use Bayesian inference to figure out the meaning of a new word by considering which potential meaning best explains the contexts in which the word was used. The approach is powerful because it provides a unifying mathematical framework for diverse cognitive phenomena and connects cognition directly to statistics and machine learning.

UNESCO Nomenclature: 6105
Psicologia experimental

Tipo

Sistema abstrato

Interrupção

Substancial

Uso

Tecnologia emergente

Precursores

  • Bayes’ theorem by Thomas Bayes and Pierre-Simon Laplace
  • teoria da probabilidade
  • Teoria da informação por Claude Shannon
  • signal detection theory in psychology

Aplicações

  • modeling visual perception and illusions
  • theories of language acquisition and word learning
  • models of causal reasoning and decision-making
  • computational neuroscience
  • machine learning algorithms like Bayesian networks and Kalman filters

Patentes:

NA

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Related to: Bayesian cognition, probabilistic models, Bayes’ theorem, computational modeling, cognitive science, statistical inference, uncertainty, prior probability, posterior probability, Joshua Tenenbaum.

Contexto histórico

Modelos Bayesianos de Cognição

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1990

(Caso a data seja desconhecida ou irrelevante, por exemplo, "mecânica dos fluidos", é fornecida uma estimativa aproximada de seu surgimento notável)

Princípios relacionados à invenção, inovação e tecnologia

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