We examined two different accounts of why studying distinctive information reduces false memories within the DRM paradigm. The impoverished relational encoding account predicts that less memorial information, such as overall familiarity, is elicited by the critical lure after distinctive encoding than after nondistinctive encoding. By contrast, the distinctiveness heuristic predicts that participants use a deliberate retrieval strategy to withhold responding to the critical lures. This retrieval strategy refers to a decision rule whereby the absence of memory for expected distinctive information is taken as evidence for an event's nonoccurrence. We show that the typical false-recognition suppression effect only occurs when the recognition test is self paced. This suppression effect is abolished when participants make recognition decisions under time pressure, such as within 1 second of seeing the test item. These results are consistent with the distinctiveness heuristic that a time-consuming retrieval strategy is used to reduce false-recognition responses.
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http://dx.doi.org/10.3758/bf03196764 | DOI Listing |
J Exp Biol
January 2025
Centre de Recherches sur la Cognition Animale, CNRS, Université Paul Sabatier, Toulouse 31062 cedex 09, France.
Solitary foraging insects like desert ants rely heavily on vision for navigation. While ants can learn visual scenes, it is unclear what cues they use to decide if a scene is worth exploring at the first place. To investigate this, we recorded the motor behavior of Cataglyphis velox ants navigating in a virtual reality set-up (VR) and measured their lateral oscillations in response to various unfamiliar visual scenes under both closed-loop and open-loop conditions.
View Article and Find Full Text PDFEur J Epidemiol
January 2025
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, H3A 1G1, Canada.
The risk over a given time span can be calculated as one minus the exponentiated value of the negative of the integral of the incidence density function (or hazard rate function) over that time span. This relationship is widely used but, in the few instances where textbooks have presented it, the derivations of it tend to be purely mathematical. I first review the historical contexts, definitions, distinctions and links.
View Article and Find Full Text PDFCommun Biol
January 2025
Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Those with diabetes mellitus are at high-risk of developing psychiatric disorders, especially mood disorders, yet the link between hyperglycemia and altered motivation has not been thoroughly explored. Here, we characterized value-based decision-making behavior of a streptozocin-induced diabetic mouse model on Restaurant Row, a naturalistic neuroeconomic foraging paradigm capable of behaviorally capturing multiple decision systems known to depend on dissociable neural circuits. Mice made self-paced choices on a daily limited time-budget, accepting or rejecting reward offers based on cost (delays cued by tone pitch) and subjective value (flavors), in a closed-economy system tested across months.
View Article and Find Full Text PDFPhys Life Rev
January 2025
Institute of Intelligent Systems and Robotics, CNRS, Sorbonne University, Paris, France.
The pursuit of artificial consciousness requires conceptual clarity to navigate its theoretical and empirical challenges. This paper introduces a composite, multilevel, and multidimensional model of consciousness as a heuristic framework to guide research in this field. Consciousness is treated as a complex phenomenon, with distinct constituents and dimensions that can be operationalized for study and for evaluating their replication.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Science, Faculty of Computers and Informatics, Kafrelsheikh University, Kafrelsheikh, Egypt.
Missing pixel imputation is a critical task in image processing, where the presence of high percentages of missing pixels can significantly degrade the performance of downstream tasks such as image segmentation and object detection. This paper introduces a novel approach for missing pixel imputation based on Generative Adversarial Networks (GANs). We propose a new GAN architecture incorporating an identity module and a sperm motility-inspired heuristic during filtration to optimize the selection of pixels used in reconstructing missing data.
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