scholarly journals Optimal data selection: Revision, review, and reevaluation

2003 ◽  
Vol 10 (2) ◽  
pp. 289-318 ◽  
Author(s):  
Mike Oaksford ◽  
Nick Chater
Keyword(s):  
Data Selection ◽  
Optimal Data Selection

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

2002 ◽  
Vol 55 (4) ◽  
pp. 1241-1272 ◽  
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.

scholarly journals Optimal data selection in the reduced array selection task (RAST).

1997 ◽  
Vol 23 (2) ◽  
pp. 441-458 ◽  
Author(s):  
Mike Oaksford ◽  
Nick Chater ◽  
Becki Grainger ◽  
Joanne Larkin
Keyword(s):  
Selection Task ◽  
Data Selection ◽  
Optimal Data Selection

scholarly journals Optimal Data Selection Rule Mining for Transformer Condition Assessment

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Peng Zhang ◽  
Bo Qi ◽  
Mengyu Shao ◽  
Chengrong Li ◽  
Zhihai Rong ◽  
...  
Keyword(s):  
Selection Rule ◽  
Condition Assessment ◽  
Data Selection ◽  
Rule Mining ◽  
Optimal Data Selection

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

2003 ◽  
Vol 56 (6) ◽  
pp. 1079-1088
Author(s):  
Aidan Feeney ◽  
Simon Handley ◽  
Robert W. Kentridge
Keyword(s):  
Selection Task ◽  
Data Selection ◽  
Probability Estimates ◽  
Optimal Data Selection ◽  
Level Of Selection

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.

scholarly journals Optimal Data Selection for Piezoelectric Material Characterization

PAMM ◽  
2006 ◽  
Vol 6 (1) ◽  
pp. 789-790
Author(s):  
Tom Lahmer ◽  
Barbara Kaltenbacher ◽  
Volker Schulz
Keyword(s):  
Piezoelectric Material ◽  
Material Characterization ◽  
Data Selection ◽  
Selection For ◽  
Optimal Data Selection
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