Control 1 showed an optimal pattern of responding: she successfully acquired knowledge about the typical features MS-275 nmr in all

three dimensions (this can be seen clearly by comparing her pattern of responses with the set of category members in Fig. 1A; for example, she correctly classified most of the circle exemplars as B’s and the squares as A’s). This control participant performed at over 90% accuracy during the final phase of learning. Control 2 achieved much poorer learning overall (60% accuracy) but showed a similar qualitative pattern. She also learned about all three dimensions equally, albeit to a much lesser extent. The pattern in the patients was rather different and I BET 762 indicates that they were unable to form coherent representations that combined all three dimensions.

Four patients (M.T., M.B., P.L. and P.W.) learned about only one of the three critical dimensions, as indicated by strong differentiation and one dimension and a lack of discrimination on the other two dimensions. For example, P.W. classified all stimuli based on their shape, ignoring their number and background colour. 1 The remaining three patients showed a more ambiguous pattern of performance, with weak learning on two stimulus dimensions. To investigate these profiles in more detail, we calculated d′ scores for each participant. D′ is a signal detection measure that reflects a participant’s tendency to give a particular response when presented with a particular type of stimulus weighed against their propensity to make the same response to other stimuli. We computed d′ scores that expressed a participant’s sensitivity to the feature–category associations in each of the three dimensions. According to our predictions, SD patients should show strong learning (i.e., high d′ values) in one dimension but much weaker learning across the remaining dimensions. Controls were expected Reverse transcriptase to display a more even pattern of learning across the three dimensions. Once d′ scores had been computed,

an additional step was necessary to compare the results in the two groups. Since different participants learned about different aspects of the stimuli (e.g., compare patient M.T. with P.W.), a simple averaging of the d′ scores in each dimension would mask the true effects. Instead, we labelled the dimensions for each participant according to their d′ scores, with the dimension in which the greatest learning had occurred labelled as their strongest dimension (so M.T.’s strongest dimension was number, her second dimension was shape and her weakest dimension was background colour). We were then able to average d′ scores within each group based on the strongest, second and weakest dimensions of each individual. D′ scores are shown for each patient in Fig. 4A.