A Bayesian analysis of dynamics in free recall
- Richard Socher, Sam J. Gershman, Adler Perotte, Per Sederberg, Ken A. Norman, and David M. Blei
- Abstract: We develop a probabilistic model of human memory performance in free recall experiments. In these experiments, a subject first studies a list of words and then tries to recall them. To model these data, we draw on both previous psychological research and statistical topic models of text documents. We assume that memories are formed by assimilating the semantic meaning of studied words (represented as a distribution over topics) into a slowly changing latent context (represented in the same space). During recall, this context is reinstated and used as a cue for retrieving studied words. By conceptualizing memory retrieval as a dynamic latent variable model, we are able to use Bayesian inference to represent uncertainty and reason about the cognitive processes underlying memory. We present a particle filter algorithm for performing approximate posterior inference, and evaluate our model on the prediction of recalled words in experimental data. By specifying the model hierarchically, we are also able to capture inter-subject variability.
- Advances in Neural Information Processing Systems 22 (NIPS 2009)
- Paper: BayesianRecall-nips2009.pdf
- Poster: GTCM-NIPS09-Poster_small.pdf
Matlab Code and Dataset
Bibtex
@incollection{NIPS2009_0351,
title = {A Bayesian Analysis of Dynamics in Free Recall},
author = {Richard Socher and Samuel Gershman and Adler Perotte and Per Sederberg and David Blei and Kenneth Norman},
booktitle = {Advances in Neural Information Processing Systems 22},
editor = {Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams and A. Culotta},
pages = {1714--1722},
year = {2009}
}
Comments
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