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Cerebral Cortex Advance Access originally published online on March 28, 2004
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Cerebral Cortex August 2004; 14:872-880
© Oxford University Press 2004

Article

The Role of the Lateral Frontal Cortex in Causal Associative Learning: Exploring Preventative and Super-learning

Danielle C. Turner1, Michael R.F. Aitken2, David R. Shanks3, Barbara J. Sahakian1, Trevor W. Robbins2, Christian Schwarzbauer4 and Paul C. Fletcher1

1 Department of Psychiatry, University of Cambridge, School of Clinical Medicine, Addenbrooke’s Hospital, Cambridge CB2 2QQ, UK, 2 Department of Experimental Psychology, University of Cambridge, Downing Street, Cambridge CB2 3EB, UK, 3 Department of Psychology, University College London, Gower Street, London WC1E 6BT, UK, 4 MRC Cognition and Brain Sciences Unit, Cambridge CB2 2EF, UK

Prediction error — a mismatch between expected and actual outcome — is critical to associative accounts of inferential learning. However, it has proven difficult to explore the effects of prediction error using functional magnetic resonance imaging (fMRI) while excluding the confounding effects of stimulus novelty and incorrect responses. In this event-related fMRI study we used a three-stage experiment generating preventative- and super-learning conditions. In both cases, it was possible to generate prediction error within a causal associative learning experiment while subtracting the effects of novelty and error. We show that right lateral prefrontal cortex (PFC) activation is sensitive to the magnitude of prediction error. Furthermore, super-learning activation in this region of PFC correlates, across subjects, with the amount learned. We thus provide direct evidence for a brain correlate of the surprise-dependent mechanisms proposed by associative accounts of causal learning. We show that activity in right lateral PFC is sensitive to the magnitude, though not the direction, of the prediction error. Furthermore, its activity is not directly explicable in terms of novelty or response errors and appears directly related to the learning that arises out of prediction error.


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