Abstract
Emotion enhances episodic memory, an effect thought to be an adaptation to prioritize the memories that best serve evolutionary fitness. However, viewing this effect largely in terms of prioritizing what to encode or consolidate neglects broader rational considerations about what sorts of associations should be formed at encoding, and which should be retrieved later. Although neurobiological investigations have provided many mechanistic clues about how emotional arousal modulates item memory, these effects have not been wholly integrated with the cognitive and computational neuroscience of memory more generally. Here we apply the Context Maintenance and Retrieval Model (CMR; Polyn, Norman, & Kahana, 2009) to this problem by extending it to describe the way people may represent and process emotional information. A number of ways to operationalize the effect of emotion were tested. The winning emotional CMR (eCMR) model conceptualizes emotional memory effects as arising from the modulation of a process by which memories become bound to ever-changing temporal and emotional contexts. eCMR provides a good qualitative fit for the emotional list-composition effect and the emotional oddball effect, illuminating how these effects are jointly determined by the interplay of encoding and retrieval processes. eCMR can account for the increased advantage of emotional memories in delayed memory tests by assuming a limited ability to reinstate the temporal context of encoding after a delay. By leveraging the rich tradition of temporal context models, eCMR helps integrate existing effects of emotion and provides a powerful tool to test mechanisms by which emotion affects memory in a broad range of paradigms.
Original language | English (US) |
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Pages (from-to) | 455-485 |
Number of pages | 31 |
Journal | Psychological Review |
Volume | 126 |
Issue number | 4 |
DOIs | |
State | Published - Jul 2019 |
All Science Journal Classification (ASJC) codes
- General Psychology
Keywords
- Arousal
- Emotion
- Free recall
- Memory
- Retrieved context models