TY - GEN
T1 - Exploring the Influence of Particle Filter Parameters on Order Effects in Causal Learning
AU - Abbott, Joshua T.
AU - Griffiths, Thomas L.
N1 - Publisher Copyright:
© CogSci 2011.
PY - 2011
Y1 - 2011
N2 - The order in which people observe data has an effect on their subsequent judgments and inferences. While Bayesian models of cognition have had some success in predicting human inferences, most of these models do not produce order effects, being unaffected by the order in which data are observed. Recent work has explored approximations to Bayesian inference that make the underlying computations tractable, and also produce order effects in a way that seems consistent with human behavior. One of the most popular approximations of this kind is a sequential Monte Carlo method known as a particle filter. However, there has not been a systematic investigation of how the parameters of a particle filter influence its predictions, or what kinds of order effects (such as primacy or recency effects) these models can produce. In this paper, we use a simple causal learning task as the basis for an investigation of these issues. Both primacy and recency effects are seen in this task, and we demonstrate that both kinds of effects can result from different settings of the parameters of a particle filter.
AB - The order in which people observe data has an effect on their subsequent judgments and inferences. While Bayesian models of cognition have had some success in predicting human inferences, most of these models do not produce order effects, being unaffected by the order in which data are observed. Recent work has explored approximations to Bayesian inference that make the underlying computations tractable, and also produce order effects in a way that seems consistent with human behavior. One of the most popular approximations of this kind is a sequential Monte Carlo method known as a particle filter. However, there has not been a systematic investigation of how the parameters of a particle filter influence its predictions, or what kinds of order effects (such as primacy or recency effects) these models can produce. In this paper, we use a simple causal learning task as the basis for an investigation of these issues. Both primacy and recency effects are seen in this task, and we demonstrate that both kinds of effects can result from different settings of the parameters of a particle filter.
KW - causal learning
KW - order effects
KW - particle filters
KW - rational process models
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M3 - Conference contribution
AN - SCOPUS:85139458781
T3 - Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011
SP - 2950
EP - 2955
BT - Expanding the Space of Cognitive Science - Proceedings of the 33rd Annual Meeting of the Cognitive Science Society, CogSci 2011
A2 - Carlson, Laura
A2 - Hoelscher, Christoph
A2 - Shipley, Thomas F.
PB - The Cognitive Science Society
T2 - 33rd Annual Meeting of the Cognitive Science Society: Expanding the Space of Cognitive Science, CogSci 2011
Y2 - 20 July 2011 through 23 July 2011
ER -