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Improving Memory Search through Model-Based Cue Selection

Charlotte Cornell
Rutgers University, New Brunswick ~ Psychology
Kenneth A. Norman
Princeton University, United States of America
Thomas L. Griffiths
Princeton University, United States of America
Qiong Zhang
Rutgers University, New Brunswick ~ Psychology

We often use cues from our environment when we get stuck searching our memories, but prior research in memory search has not observed a facilitative effect when providing cues after recall ended. What accounts for this discrepancy? We propose that the content of the cues critically determines their effectiveness and sought to select the right cues by building a computational model of how memory search is affected by cue presentation (in a process we refer to as cued memory search). We hypothesize that cued memory search consists of (1) a basic memory search process, identical to memory search without external cues as captured by the existing Context Maintenance and Retrieval model (CMR), and (2) an additional process in which a cue's context influences one’s internal mental context. Formulated this way, our model (with parameters pre-determined from a group of participants) was able to predict in real-time (over a new group of participants) which cues would improve memory search performance. Participants (N = 195 young adults) recalled significantly more items on trials where our model's best (vs. worst) cue was presented. Our formal model of cued memory search provides an account of why some cues are better at aiding recall: Effective cues are those most similar to the remaining items, as they facilitate recall by tapping into and reactivating an unsearched area of memory. We discuss our contributions in relation to prominent theories about the effect of external cues.



memory search
free recall
computational modeling

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Cite this as:

Cornell, C. A., Norman, K., Griffiths, T., & Zhang, Q. (2023, July). Improving Memory Search through Model-Based Cue Selection. Abstract published at MathPsych/ICCM/EMPG 2023. Via