The current study aims to test whether faster recognition memory errors tend to result from stronger misleading retrieval, making them harder to correct in subsequent decisions than slower errors, and whether this pattern holds for both miss and false-alarm errors. We used a paradigm in which each single-item Old/New recognition decision was followed by a two-alternative forced-choice (2AFC) test between a target and a lure. Each 2AFC trial had one item that had just been tested for an Old/New judgment and one item that had not been previously tested. Across 183 participants, the RTs for single-item recognition errors were used to predict accuracy in the 2AFC test using a hierarchical logistic regression model. The results showed a relationship between error RT and subsequent 2AFC accuracy that was qualified by an interaction with error type. Slower miss responses were more likely to be corrected than faster misses, but no accuracy differences were observed between slower and faster false alarms. The implications of these findings are discussed as they relate to assumptions about memory processes underlying inaccurate retrieval, using the diffusion model and the two-high-threshold model as examples of accounts that explain errors in terms of misleading retrieval and failed retrieval, respectively.
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http://dx.doi.org/10.1080/09658211.2023.2265613 | DOI Listing |
Background: Episodic memory decline is a hallmark feature of aging and Alzheimer's disease, but the mechanisms underlying its earliest stages are unknown.
Method: Cognitively unimpaired older adults from the Berkeley Aging Cohort Study (n=49) completed an fMRI memory encoding task preceded by 15 minutes rsfMRI and standard neuropsychological testing. Participants viewed object, scene, and object-in-scene (pair) stimuli in the scanner for 40 minutes.
Alzheimers Dement
December 2024
Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
Background: Episodic memory decline is a hallmark feature of aging and Alzheimer's disease, but the mechanisms underlying its earliest stages are unknown.
Method: Cognitively unimpaired older adults from the Berkeley Aging Cohort Study (n=49) completed an fMRI memory encoding task preceded by 15 minutes rsfMRI and standard neuropsychological testing. Participants viewed object, scene, and object-in-scene (pair) stimuli in the scanner for 40 minutes.
Int J Psychophysiol
January 2025
Department of Experimental Psychology, The John Paul II Catholic University of Lublin, Al. Racławickie 14, 20-950 Lublin, Poland.
Perceptual fluency can increase familiarity of some of the items in recognition tests and enhance attributions of these items to the past. It is not clear, however, whether perceptual fluency can influence recognition under conditions promoting recollection-based memory. To this end, we performed a systematic replication of a study by Lucas and Paller (2013) using a letter-segregated method.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510630, China.
Real-time online monitoring of track deformation during railway construction is crucial for ensuring the safe operation of trains. However, existing monitoring technologies struggle to effectively monitor both static and dynamic events, often resulting in high false alarm rates. This paper presents a monitoring technology for track deformation during railway construction based on dynamic Brillouin optical time-domain reflectometry (Dy-BOTDR), which effectively meets requirements in the monitoring of both static and dynamic events of track deformation.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Department of Industrial Engineering, Chosun University, Gwangju 61452, Republic of Korea.
In human activity recognition, accurate and timely fall detection is essential in healthcare, particularly for monitoring the elderly, where quick responses can prevent severe consequences. This study presents a new fall detection model built on a transformer architecture, which focuses on the movement speeds of key body points tracked using the MediaPipe library. By continuously monitoring these key points in video data, the model calculates real-time speed changes that signal potential falls.
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