SD patients also demonstrated over-generalisation of the successful learning in their preferred dimension: information from one dimension dominated category decisions, even when the other features of the stimulus pointed towards an alternative response. This over-generalisation of remaining knowledge is also common when SD patients attempt to make use of their remaining conceptual knowledge in everyday life and in clinical assessment (Lambon Ralph and Patterson, 2008 and Lambon Ralph et al., 2010). Over the

course of the disease, patients become increasingly likely to Selleck AZD1208 over-extend category boundaries on the basis of superficial characteristics (e.g., accepting a butterfly as a type of bird; Mayberry et al., 2011), to use a single, highly familiar concept label to refer to a whole class of items (e.g., all forms of fruit may be called “apples”; Hodges, Graham, & Patterson, 1995), and to imbue items with over-generalised, stereotypical attributes in delayed-copy drawing (e.g., the case of the four-legged duck; Bozeat et al., 2003 and Lambon Ralph and Howard, 2000). In the present study, we were able to unmask one of the basic mechanisms underpinning this profound deterioration in conceptual representation: cerebral atrophy in SD affects integrated conceptual Anti-diabetic Compound Library supplier representations that bind together the various sources of information that characterise a particular

set of items. Without these coherent concepts, classification and identification of objects comes to depend on superficial surface Adenosine triphosphate characteristics. Interestingly, another study indicates that SD patients can successfully make category judgements about

novel items when they are not required to form integrated representations. Koenig et al. (2006) investigated six SD patients’ ability to classify novel stimuli based on a category membership rule and on similarity to a prototype. Koenig et al.’s study differs from ours in that Koenig et al. explicitly provided patients with the appropriate rule to apply or prototype to compare during categorisation. In contrast, we required patients to learn the relevant category structure themselves through feedback. Patients in the Koenig et al. study performed similarly to controls and the authors attributed this good performance to intact attentional and executive processes. One possibility for the difference between the two studies is that the application of explicit rules to determine category membership depends heavily on executive and attentional processes, while the acquisition of multi-dimensional feature structure is a more automatic process involving implicit learning mechanisms in temporal regions. This assertion is supported by an investigation in healthy participants, on which the present learning task was based (Waldron & Ashby, 2001).