Abstract

The last two decades have witnessed a resurgence of Bayesian statistics, which was regarded as a marginal discipline during most of the twentieth century. This phenomenon has had a profound effect on neuroscience, not only in terms of the kinds of methods used to analyze experimental data, but also in the way perception and action are conceptualized from a theoretical standpoint. This shift can be summarized in the Bayesian brain hypothesis, which holds that one of the central features of this organ is to mount Bayesian statistical inferences. In this context, the principle of free energy proposed by Karl Friston has emerged as a possible candidate for a unified theory of cognition. The purpose of this article is twofold. The first is to introduce the principle of free energy from a philosophical perspective; the second is to clarify whether this principle should be seen as a normative theory of cognition or if, on the contrary, it can be used to make empirical predictions about the sort computational processes that characterize human cognition. In conclusion, the principle of free energy, as often presented by Friston, is a descriptive theory on the type of computational algorithms the brain uses. Moreover, there is not enough empirical evidence in its favor and, in fact, a large number of findings point in the opposite direction.

Highlights

  • Resumen: las últimas dos décadas han visto un resurgimiento de la estadística bayesiana, la cual fue vista como una disciplina marginal durante la mayor parte del siglo XX

  • Sorprendentemente, el papel de este grupo de métodos no se ha limitado al análisis de datos experimentales, sino que también ha generado interés en la ciencia cognitiva (Griffiths, Kemp y Tenenbaum, 2008) e indirectamente en la filosofía de la mente (Clark 2013; Jones y Love 2011)

  • The free energy principle has been used to account for a variety of phenomena in sensory, cognitive and motor neuroscience and has provided useful insights into structure-function relationships in the brain. [...] These formulations provide an important link between information theory and general formulations of adaptive agents in terms of utility theory and optimal decision theory (Friston 2012)

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Summary

La hipótesis bayesiana del cerebro

La teoría estadística bayesiana tiene su origen en la formulación y aplicación de un sencillo teorema que se deriva de los axiomas generales de la probabilidad (por ejemplo en la axiomatización de Kolmogorov). Como mencioné, el interés hacia esta teoría ha crecido drásticamente en las últimas décadas. Parte de este resurgimiento se ha reflejado en el creciente interés en utilizar el lenguaje y los métodos de esta teoría para explicar un gran número de procesos cognitivos. Combinar la teoría estadística bayesiana y la ciencia cognitiva ha conducido a la hipótesis bayesiana del cerebro, la cual puede ser resumida bajo la tesis de que una de las funciones fundamentales del cerebro es realizar inferencias bayesianas

Inferencias bayesianas
VB kT dV
Maximización de la energía libre como un algoritmo inferencial
El algoritmo variacional de Bayes
La interpretación fuerte del principio de la energía libre
Referencias bibliográficas
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