PEPPR: A post-encoding pre-production reinstatement model of dual-list free recall.
Mem Cognit
; 52(1): 163-181, 2024 Jan.
Article
in En
| MEDLINE
| ID: mdl-37782445
ABSTRACT
Recent events are easy to recall, but they also interfere with the recall of more distant, non-recent events. In many computational models, non-recent memories are recalled by using the context associated with those events as a cue. Some models, however, do little to explain how people initially activate non-recent contexts in the service of accurate recall. We addressed this limitation by evaluating two candidate mechanisms within the Context-Maintenance and Retrieval model. The first is a Backward-Walk mechanism that iteratively applies a generate/recognize process to covertly retrieve progressively less recent items. The second is a Post-Encoding Pre-Production Reinstatement (PEPPR) mechanism that formally implements a metacognitive control process that reinstates non-recent contexts prior to retrieval. Models including these mechanisms make divergent predictions about the dynamics of response production and monitoring when recalling non-recent items. Before producing non-recent items, Backward-Walk cues covert retrievals of several recent items, whereas PEPPR cues few, if any, covert retrievals of that sort. We tested these predictions using archival data from a dual-list externalized free recall paradigm that required subjects to report all items that came to mind while recalling from the non-recent list. Simulations showed that only the model including PEPPR accurately predicted covert recall patterns. That same model fit the behavioral data well. These findings suggest that self-initiated context reinstatement plays an important role in recall of non-recent memories and provides a formal model that uses a parsimonious non-hierarchical context representation of how such reinstatement might occur.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Mental Recall
/
Metacognition
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Mem Cognit
Year:
2024
Type:
Article
Affiliation country:
United States