Abstract

The value of case studies in informing our understanding of dissociations and interactions in memory was recognized early on by Endel Tulving, whose comprehensive work with the amnesic case K.C. helped to confirm distinctions between episodic and semantic memory. Following in this tradition, we examined memory and the use of cognitive strategies in the developmental amnesic case H.C., a young woman with structural abnormalities in the extended hippocampal system (Rosenbaum et al., 2014). H.C. was tested on two tasks, transitivity and transverse patterning, that each required learning the relations among items, and for the former, also examined the ability to make inferences across sets of relations. H.C. was tested across multiple sessions and demonstrated two seemingly contradictory patterns of performance: evidence of gradual learning, yet an inability to flexibly switch to a cognitive strategy that may otherwise benefit performance. Specifically, on the transitivity task, H.C. showed gradual learning of novel relations that led to successful inferential performance. On transverse patterning, H.C. showed some gradual learning of the relations among the objects across sessions, and expressed knowledge that the task followed ‘rock-paper-scissors’ rules. However, H.C. did not benefit from a unitization strategy, which had shown previous success with other amnesic cases (D’Angelo et al., 2015; Ryan, Moses, Barense, & Rosenbaum, 2013). H.C.’s over-reliance on ‘rock-paper-scissors’ rules, even in the face of alternate strategies, is suggestive of an inability to enact cognitive flexibility. Poor performance thus may have resulted from interference from the experimentally presented strategy on her self-imposed strategy. The present findings echo work reported by Tulving in case K.C. (Tulving, Hayman, & Macdonald, 1991). Whereas neurologically intact individuals may rely on the functions of the hippocampal system to rapidly learn new information and resolve interference, some individuals with hippocampal amnesia may learn information gradually, but such learning is particularly prone to interference, resulting in an inability to flexibly adapt to changes in the learning conditions in order to optimize performance.

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