This thesis examined the role of procedural learning in human probabilistic category learning (PCL). It was proposed that there was a lack of clear behavioural evidence for learning without awareness in PCL. Eleven experiments are reported that investigated the characteristics of learning in a prototypical probabilistic category learning task (the weather prediction task). The results were interpreted as contradicting the popular interpretation of weather prediction task learning as procedurally based. Rather, it was shown that behavioural data was consistent with declarative learning. This learning was not dissociable with measures of cue knowledge. Strategy analysis converged with the behavioural data, suggesting the dominance of declarative learning in this task. It was proposed that a single system account (e.g., Lagnado et al., 2006; Newell et al., 2007) which does not posit a role for procedural learning was the most appropriate way to understand learning in the weather prediction task.
| Identifer | oai:union.ndltd.org:ADTP/273081 |
| Date | January 2010 |
| Creators | Heffernan, Megan Mary, Psychology, Faculty of Science, UNSW |
| Publisher | Awarded By:University of New South Wales. Psychology |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | http://unsworks.unsw.edu.au/copyright, http://unsworks.unsw.edu.au/copyright |
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