Minimum description length model selection in associative learning

C. R. Gallistel

doi:10.1016/j.cobeha.2016.02.025

Minimum description length model selection in associative learning

2016-10-01

19

PRE

AI

DOI：10.1016/j.cobeha.2016.02.025

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Abstract

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Two principles of information theory - maximum entropy and minimum description length - motivate a computational model of associative learning that explains assignment of credit, response timing, and the parametric invariances. The maximum entropy principle gives rise to two distributions the exponential and the evitable Gaussian - which naturally lend themselves to inference involving each of the two fundamental classes of predictors - enduring states and point events. These distributions are the 'atoms' from which more complex representations are built. The representation that is 'learned' is determined by the principle of minimum-description-length. In this theory, learning is a synonym for data compression: The representation of its experience that the animal learns is the representation that best allows the data of experience to be compressed.

Keywords:

IMMEDIATE SHOCK DEFICIT

PURKINJE-CELLS

INTERVAL

EXTINCTION

STIMULI

REINFORCEMENT

INFORMATION

ATTENTION

RECOVERY

BLOCKING

Minimum description length model selection in associative learning

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