Using “Semantic Scent” to Predict Item-Specific Clustering and Switching Patterns in Memory Search


Elucidating the mechanisms that underlie clustering and switching behavior is essential to understanding semantic memory search and retrieval. Hills, Jones, and Todd (2012) proposed a model of semantic foraging based on the observation that statistical signatures in memory search resemble optimal foraging in animal behavior. However, the original model was postdictive in explaining when a switch would occur, as opposed to predictive, and was agnostic as to the cues used by humans to make a decision to switch from local to global information. In this paper, we proposed a switching mechanism, Semantic Scent, as a predictive model underlying such behavior. Semantic Scent extends optimal foraging theory, reproducing the same switch behavior observed animal foraging behavior in memory search. We evaluated Semantic Scent against competing models including Random Walk and Fixed Count to determine its effectiveness in classifying switches made in fluency tasks. A quantitative model comparison between the switch models demonstrated Semantic Scent’s superior performance in fitting human data. These results provide further evidence of the importance of optimal foraging theory to semantic memory search.

CogSci 2022
Larry Zhang
Larry Zhang
Dual PhD Student in Complex Network Systems and Intelligent Systems Engineering

My research interests include Behavioral Signal Processing, Cognitive Science, and Affective Computing applied to real world clinical applications in mental health and neuropsychiatry. My hope is to contribute to the development of intelligent interfaces to improve human wellbeing.