Deciding between Accounts of the Selection Task: A Reply to Oaksford (2002)

In this paper we report on our attempts to fit the optimal data selection (ODS) model (Oaksford & Chater, 1994; Oaksford, Chater, & Larkin, 2000) to the selection task data reported in Feeney and Handley (2000) and Handley, Feeney, and Harper (2002). Although Oaksford (2002b) reports good fits to the data described in Feeney and Handley (2000), the model does not adequately capture the data described in Handley et al. (2002). Furthermore, across all six of the experiments modelled here, the ODS model does not predict participants’ behaviour at the level of selection rates for individual cards. Finally, when people's probability estimates are used in the modelling exercise, the model adequately captures only 1 out of 18 conditions described in Handley et al. We discuss the implications of these results for models of the selection task and claim that they support deductive, rather than probabilistic, accounts of the task.

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Bayesian Rationality for the Wason Selection Task? A Test of Optimal Data Selection Theory

Thinking & Reasoning ◽

10.1080/135467899394020 ◽

1999 ◽

Vol 5 (2) ◽

pp. 115-144 ◽

Cited By ~ 19

Author(s):

Klaus Oberauer ◽

Oliver Wilhelm ◽

Ricardo Rosas Diaz

Keyword(s):

Selection Task ◽

Data Selection ◽

Wason Selection Task ◽

Selection Theory ◽

Optimal Data Selection ◽

Bayesian Rationality

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A quantitative model of optimal data selection in Wason's selection task

The Quarterly Journal of Experimental Psychology Section A ◽

10.1080/02724980244000053 ◽

2002 ◽

Vol 55 (4) ◽

pp. 1241-1272 ◽

Cited By ~ 18

Author(s):

Masasi Hattori

Keyword(s):

Experimental Data ◽

Experimental Method ◽

Quantitative Model ◽

Selection Task ◽

Data Selection ◽

Subjective Probabilities ◽

Probabilistic Information ◽

Adaptive Rationality ◽

Optimal Data Selection ◽

The Relationship

The optimal data selection model proposed by Oaksford and Chater (1994) successfully formalized Wason's selection task (Wason, 1966). The model, however, involved some questionable assumptions and was also not sufficient as a model of the task because it could not provide quantitative predictions of the card selection frequencies. In this paper, the model was revised to provide quantitative fits to the data. The model can predict the selection frequencies of cards based on a selection tendency function (STF), or conversely, it enables the estimation of subjective probabilities from data. Past experimental data were first re-analysed based on the model. In Experiment 1, the superiority of the revised model was shown. However, when the relationship between antecedent and consequent was forced to deviate from the biconditional form, the model was not supported. In Experiment 2, it was shown that sufficient emphasis on probabilistic information can affect participants’ performance. A detailed experimental method to sort participants by probabilistic strategies was introduced. Here, the model was supported by a subgroup of participants who used the probabilistic strategy. Finally, the results were discussed from the viewpoint of adaptive rationality.

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