In two experiments, we provided support for the hypothesis that stimuli with preexisting memory representations (e.g., famous faces) are easier to associate to their encoding context than are stimuli that lack long-term memory representations (e.g., unknown faces). Subjects viewed faces superimposed on different backgrounds (e.g., the Eiffel Tower). Face recognition on a surprise memory test was better when the encoding background was reinstated than when it was swapped with a different background; however, the reinstatement advantage was modulated by how many faces had been seen with a given background, and reinstatement did not improve recognition for unknown faces. The follow-up experiment added a drug intervention that inhibited the ability to form new associations. Context reinstatement did not improve recognition for famous or unknown faces under the influence of the drug. The results suggest that it is easier to associate context to faces that have a preexisting long-term memory representation than to faces that do not.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4180115 | PMC |
| http://dx.doi.org/10.1177/0956797612457396 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Latent representation learning for classification of the Doppler ultrasound images.
Comput Biol Med
December 2024
Shandong Technology and Business University, 191 Binhai Middle Road, Yantai, Shandong, China.
The classification of Doppler ultrasound images plays an important role in the diagnosis of pregnancy. However, it is a challenging problem that suffers from a variable length of these images with a dimension gap between them. In this study, we propose a latent representation weights learning method (LRWL) for pregnancy prediction using Doppler ultrasound images.
View Article and Find Full Text PDFLearning enhances behaviorally relevant representations in apical dendrites.
Elife
December 2024
Department of Neuroscience, Columbia University, New York, United States.
Learning alters cortical representations and improves perception. Apical tuft dendrites in cortical layer 1, which are unique in their connectivity and biophysical properties, may be a key site of learning-induced plasticity. We used both two-photon and SCAPE microscopy to longitudinally track tuft-wide calcium spikes in apical dendrites of layer 5 pyramidal neurons in barrel cortex as mice learned a tactile behavior.
View Article and Find Full Text PDFWild Tibetan Macaques Use a Route-Based Mental Map to Navigate in Large-Scale Space.
Am J Primatol
January 2025
School of Resources and Environmental Engineering, Anhui University, Hefei, Anhui, China.
Many animals face significant challenges in locating and acquiring resources that are unevenly distributed in space and time. In the case of nonhuman primates, it remains unclear how individuals remember goal locations and whether they navigate using a route-based or a coordinate-based mental representation when moving between out-of-sight feeding and resting sites (i.e.
View Article and Find Full Text PDFThe Anatomy of Context.
Hippocampus
January 2025
Department of Cognitive and Psychological Sciences, Brown University, Providence, Rhode Island, USA.
For most of my career, I focused on understanding how and where spatial context, the place where things happen, is represented in the brain. My interest in this began in the early 1990's, during my postdoctoral training with David Amaral, when we defined the rodent homolog of the primate parahippocampal cortex, a region implicated in processing spatial and contextual information. We parceled out the caudal portion of the rat perirhinal cortex (PER) and called it the postrhinal cortex (POR).
View Article and Find Full Text PDFDeep learning models are used to minimize the number of polyps that goes unnoticed by the experts and to accurately segment the detected polyps during interventions. Although state-of-the-art models are proposed, it remains a challenge to define representations that are able to generalize well and that mediate between capturing low-level features and higher-level semantic details without being redundant. Another challenge with these models is that they are computation and memory intensive, which can pose a problem with real-time applications.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!