However, it is also possible that the participants did not individually categorize the stimuli. As their name implies,the guessing models assumed that participants randomly
chose a response on each trial, without considering the individual category membership of the stimuli. Finally, the similarity model defined similarity as an exponentially
decreasing function of the weighted Euclidean distance and assumed that participants responded ‘‘Same’’ for high similarities and ‘‘Different’’ for low similarities. The similarity model had two free parameters, one to differentially weight the stimulus dimensions and another to describe the rate of the exponential decrease. Like the guessing model,the similarity model also does not assume separate classification
of the stimuli.The results from the model-based analyses are shown in Table 1. As can be seen, most participants in the rule-based conditions appeared to be responding optimally. Furthermore,note that the responses of these participants were
more likely to be best fit by an optimal model later in training. A one-tail binomial test showed that the difference in proportion of best-fitting optimal models between
early and late performance was statistically significant for the 1D-Width condition (p.05), but not for the 1DOrientation condition. Even so, the performance of most
participants in both rule conditions was best fit by an optimal categorization model by the end of training. In the information-integration condition, only one participant
used an optimal strategy at the beginning of the experiment,and no participant used an optimal categorization strategy by the end of the experiment. A one-tail binomial
test showed that this decrease in the proportion of optimal best-fitting models was not significant. It should be noted that none of the participants in any block was best fit by a similarity model. Participants not best fit by an optimal categorization model were best fit by a guessing model.