Given the large amount of information that we encounter, we often must prioritize what information we attempt to remember. Although critical for everyday functioning, relatively little research has focused on how people prioritize the encoding of information. Recent research has shown that people can and do selectively remember information assigned with higher, relative to lower, importance. However, the mechanisms underlying this prioritization process and the consequences of these processes are still not well understood. In the present study, we sought to better understand these prioritization processes and whether implementing these processes comes at the cost of memory accuracy, by increasing false memories. We used a modified form of the Deese/Roediger-McDermott (DRM) paradigm, in which participants studied DRM lists, with each list paired with low, medium, or high point values. In Experiment 1, encoding higher values led to more false memories than did encoding lower values, possibly because prioritizing information enhanced relational processing among high-value words. In Experiment 2, disrupting relational processing selectively reduced false memories for high-value words. Finally, in Experiment 3, facilitating relational processing selectively increased false memories for low-value words. These findings suggest that while prioritizing information can enhance true memory, this process concomitantly increases false memories. Furthermore, the mechanism underlying these prioritization processes depends on the ability to successfully engage in relational processing. Thus, how we prioritize the encoding of incoming information can come at a cost in terms of accurate memory.
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http://dx.doi.org/10.3758/s13421-013-0317-6 | DOI Listing |
Sensors (Basel)
December 2024
Department of Mechanical Engineering and Automation, Northeastern University, Wenhua Street, Shenyang 110819, China.
The early prediction of Alzheimer's disease (AD) risk in healthy individuals remains a significant challenge. This study investigates the feasibility of task-state EEG signals for improving detection accuracy. Electroencephalogram (EEG) data were collected from the Multi-Source Interference Task (MSIT) and Sternberg Memory Task (STMT).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical and Electronics Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.
The increasing prevalence of network connections is driving a continuous surge in the requirement for network security and safeguarding against cyberattacks. This has triggered the need to develop and implement intrusion detection systems (IDS), one of the key components of network perimeter aimed at thwarting and alleviating the issues presented by network invaders. Over time, intrusion detection systems have been instrumental in identifying network breaches and deviations.
View Article and Find Full Text PDFExp Psychol
January 2025
Department of Psychology, Louisiana State University, Baton Rouge, LA, USA.
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 PDFFront Endocrinol (Lausanne)
January 2025
School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China.
Objective: The role of immune cells in type 1 diabetes (T1D) is unclear. The aim of this study was to assess the causal effect of different immune cells on T1D using Mendelian randomization (MR).
Methods: A dataset of immune cell phenotypes (numbered from GCST0001391 to GCST0002121) was obtained from the European Bioinformatics Institute, while a T1D dataset (numbered finngen_R10_T1D) was obtained from FinnGen.
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