Recall is often more successful for items from sparse (distinct) regions and less successful from dense (less distinct) regions in memory space. We propose that retrieval cues are also organized into crowded and less crowded regions depending on distinctiveness, and that successful retrieval depends on the distributional match between the organization of targets and cues. As a real-world test case, we used the labels from the European Union’s old (A+++, A++, A+, A, B, C, D) and new (A, B, C, D, E, F, G) energy-label scale as cues for memory retrieval of appliances’ kilowatt-hour (kWh) values. In a preregistered experiment, participants rated seven refrigerators, while studying associated kWh values drawn from a positive, a negative, or a non-skewed distribution. Ratings confirmed that the labels of the old scale were psychologically skewed in mental space whereas the new scale had a more linear organization. After a brief filled delay, labels served as retrieval cues for kWh recall. Analyses of recall patterns, complemented by analyses of recall error, showed that the linear cue scale yielded recalled values that were closer to the correct kWh values when target values were non-skewed, but recalled values were overly linear when targets were skewed. Conversely, recall was most successful with the skewed cue scale when target values were positively skewed. These findings suggest that retrieval cues constrain reconstruction by imposing a distributional structure on target representations, and that recall is most successful when this structure aligns with the organization of encoded values in mental space.
Examining whether displayed source and scale labels bias DSS use in a timed object-detection task suggested that source and scale labels shape initial selection, trial-level reliance, and post-use reliability judgments in different ways.
Caden M. Miller, Ebernoe Guzman-Bonilla, Adolfo R. Escobedo et al.· Proceedings of the Human Fac...· 0 citations
Visual memory for objects and their constituent features (e.g., size, color, and state) is highly malleable and heavily influenced by encoding contexts and specific task-based characteristics. Yet, how object-feature memory is influenced by different retrieval tasks is not well understood. In this study, we examined wh...
Katherine Archer, Kimele Persaud· Frontiers in cognition· 0 citations
This paper presents a revision of a recently proposed ACT–R-based cue retrieval model. Memory retrieval—the process of accessing information stored in memory—is often considered crucial to real-time sentence processing. For example, upon encountering “
himself
” in the sentence “
John introduced himself
”, inte...
Hiroki Fujita· Computational Brain & Be...· 0 citations
Because our environment is not random, it is beneficial to assimilate the statistics of sensory impressions and improve performance, such as visual search for a target object in a cluttered array of nontarget objects (contextual cueing [CC] effect). Computational models of CC have so far focused on predicting the targe...
Werner Seitz, A. Zinchenko, H. Müller et al.· Psychonomic Bulletin & Revie...· 0 citations
Vision-language models (VLMs) are usually chosen from leaderboards, which give one ranking for every application. On questions about objects that a scene suggests but does not contain, however, the model with the lowest error on the POPE hallucination benchmark is not the one with the lowest error on such questions. Fo...
ObjectiveComputationally defined visual salience can predict attentional deployment in complex, e-commerce displays but less is known about the effect of salience on later cognitive processes. For instance, can salience also predict later memory for e-commerce products? If so, visual salience could be leveraged to info...
Mary L. Still, Jeremiah D. Still· Human Factors· 0 citations
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