Bibliographic Details
| Title: |
Visual continuous recognition reveals behavioral and neural differences for short- and long-term scene memory |
| Authors: |
Timothy M. Ellmore, Chelsea Reichert Plaska, Kenneth Ng, Ning Mei |
| Source: |
Frontiers in Behavioral Neuroscience, Vol 16 (2022) |
| Publisher Information: |
Frontiers Media S.A., 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Neurosciences. Biological psychiatry. Neuropsychiatry |
| Subject Terms: |
EEG, working memory, naturalistic stimuli, ERP, signal detection, retention intervals, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571 |
| More Details: |
Humans have a remarkably high capacity and long duration memory for complex scenes. Previous research documents the neural substrates that allow for efficient categorization of scenes from other complex stimuli like objects and faces, but the spatiotemporal neural dynamics underlying scene memory at timescales relevant to working and longer-term memory are less well understood. In the present study, we used high density EEG during a visual continuous recognition task in which new, old, and scrambled scenes consisting of color outdoor photographs were presented at an average rate 0.26 Hz. Old scenes were single repeated presentations occurring within either a short-term (< 20 s) or longer-term intervals of between 30 s and 3 min or 4 and 10 min. Overall recognition was far above chance, with better performance at shorter- than longer-term intervals. Sensor-level ANOVA and post hoc pairwise comparisons of event related potentials (ERPs) revealed three main findings: (1) occipital and parietal amplitudes distinguishing new and old from scrambled scenes; (2) frontal amplitudes distinguishing old from new scenes with a central positivity highest for hits compared to misses, false alarms and correct rejections; and (3) frontal and parietal changes from ∼300 to ∼600 ms distinguishing among old scenes previously encountered at short- and longer-term retention intervals. These findings reveal how distributed spatiotemporal neural changes evolve to support short- and longer-term recognition of complex scenes. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1662-5153 |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fnbeh.2022.958609/full; https://doaj.org/toc/1662-5153 |
| DOI: |
10.3389/fnbeh.2022.958609 |
| Access URL: |
https://doaj.org/article/10c089b93fdc4b86971d3680a4ca30b2 |
| Accession Number: |
edsdoj.10c089b93fdc4b86971d3680a4ca30b2 |
| Database: |
Directory of Open Access Journals |
