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Individual Differences in Attention During Category Learning Michael D. Lee UC Irvine Ruud Wetzels University of Amsterdam
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Kruschke (1993) Condensation Experiment 8 stimuli varying in their box height and interior line position Divided into 2 categories, so that both dimensions are relevant 40 participants did 40 blocks of trials with corrective feedback
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Generalized Context Model
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Results of Standard GCM Analysis Marginal posterior over the attention parameter indicates both dimensions are important Familiar story, and a strong temptation to stop there …
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Posterior Predictive “Violin plots” of posterior predictive for each stimuli, together with aggregated data (black line) and individual data (broken lines)
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Types of Individual Differences
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Allowing for Individual Differences Continuous individual differences are modeled by drawing subject parameters from an over-arching hierarchical distribution Discrete individual differences are modeled as a latent mixture, so different subjects can be drawn from different group distributions Let WinBUGS do the heavy lifting, check chains for convergence, etc, …
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Results of Individual Differences Analysis Suggests there are two groups, with different attention
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Bayes Factor Savage-Dickey method gives approximate Bayes Factor of 2.3 in favor there being two groups (rather than one) “artist’s impression”
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Posterior Predictive Distribution Posterior predictive distributions of categorization behavior are qualitatively different tracks people’s behavior at both the sub-group and individual level
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Interpretation of Groups The two groups are shown in the panels The bars show the number of “A” vs “B” category decisions made for each stimulus
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Interpretation of Groups The group on the left pays attention to position, and so makes mistakes with stimuli 4 and 5
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Interpretation of Groups The group on the left pays attention to position, and so makes mistakes with stimuli 4 and 5 The group on the right pays attention to height, and so makes mistakes with stimuli 2 and 7
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