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Remembering past events usually takes less time than their actual duration - events are temporally compressed in memory. A recent study found that this compression is not systematic but emerges when continuous events exceed approximately 9 s. Unexpectedly, however, remembering shorter events (3-6 s) took more time than their actual duration. Here, we aimed to investigate the mechanisms behind this increased replay duration of short events. In Experiment 1, we developed a corrected measure accounting for recall initiation time - the time needed to access the beginning of the event. With this correction, the longer replay times for short events disappeared, suggesting the effect was partly due to unmeasured recall initiation time. In Experiment 2, we examined the potential role of a central tendency bias by exposing participants to different ranges of event durations. Replay duration was influenced by the event's relative position within the duration range, consistent with a central tendency bias. However, for events longer than 9 s, temporal compression occurred consistently across all conditions. Together, these findings suggest that while central tendency influences replay duration, temporal compression systematically emerges when events exceed a few seconds, likely reflecting memory capacity limits in representing continuous experiences.
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http://dx.doi.org/10.1080/09658211.2025.2551232 | DOI Listing |
Psychol Rev
September 2025
Neural Computation Group, Max-Planck Institute for Human Cognitive and Brain Sciences.
It has been suggested that episodic memory relies on the well-studied machinery of spatial memory. This influential notion faces hurdles that become evident with dynamically changing spatial scenes and an immobile agent. Here I propose a model of episodic memory that can accommodate such episodes via temporal indexing.
View Article and Find Full Text PDFMemory
September 2025
Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège, Belgium.
Remembering past events usually takes less time than their actual duration - events are temporally compressed in memory. A recent study found that this compression is not systematic but emerges when continuous events exceed approximately 9 s. Unexpectedly, however, remembering shorter events (3-6 s) took more time than their actual duration.
View Article and Find Full Text PDFEur J Pediatr
August 2025
Department of Pediatric and Adolescent Medicine, The Juliane Marie Centre, Copenhagen University Hospital - Rigshospitalet, Blegdamsvej 9, DK-2100, Copenhagen, Denmark.
Unlabelled: The aim of this study was to evaluate the feasibility of a rehabilitation intervention consisting of structured active play designed for preschoolers diagnosed with cancer and their parents during treatment. Eighty-four consecutively enrolled children in RePlay (NCT04672681) were included. Feasibility was addressed across five domains: (a) participation acceptance, (b) attrition during the intervention period, (c) completion of outcome assessments, (d) adherence to the intervention, and (e) occurrence of adverse events during the intervention or outcome assessments.
View Article and Find Full Text PDFPsychol Rev
June 2025
Department of Psychology, Humboldt-Universitat zu Berlin.
During active visual exploration, saccadic eye movements rapidly shift the visual image across the human retina. Although these high-speed shifts occur at a high rate and introduce considerable amounts of motion smear during natural vision, our perceptual experience is oblivious to it-a phenomenon known as saccadic omission. Using tachistoscopic displays of natural scenes, we rendered saccade-induced smear highly conspicuous.
View Article and Find Full Text PDFPLoS One
June 2025
School of Traffic Engineering, Huanghe Jiaotong University, Jiaozuo, China.
With the increasing integration of Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HDVs) in urban traffic systems, along with highly variable pedestrian crossing demands, traffic management faces unprecedented challenges. This study introduces an improved adaptive signal control approach using an enhanced dual-layer deep Q-network (EXP-DDQN), specifically tailored for intelligent connected environments. The proposed model incorporates a comprehensive state representation that integrates CAV-HDV car-following dynamics and pedestrian flow variability.
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