Similarity-dependent memory integration of scene images

People often encounter novel events similar to their previous experiences. An intriguing question is how the similar representations interact during learning. Previous studies suggested that feature-based similarity resulted in systematic memory distortions. The present study examined the effect of memory integration due to learning similar scenes. We used generative adversarial networks (GANs) to generate scene wheels from which the to-be-remembered scenes were selected. In an online experiment (n = 59), we evaluated the perceptual similarity of images from the scene wheels and selected scene-pairmates (A1 and A2) with varying perceptual similarities. In three main experiments (n = 27, 27, 28), A1 and A2 were paired with different images (B1 and B2) to form competitive associations. Subjects learned these associations with explicit knowledge that scenes paired with different images were always different (even though they might look similar). Importantly, learning of competitive associations was temporally separated (“A1-B1” preceded its competitor, “A2-B2”). Across three experiments, we found robust attractive memory distortion of A2 towards its highly-similar competitor (A1). In Experiment 1 and Experiment 2 (with increased training on A2-B2), the attraction effects were asymmetric: memory of A1 was not biased relative to A2. Interestingly, in Experiment 3 in which training on A1-B1 was increased, the asymmetry disappeared: memories of A1 and A2 were biased towards each other. Moreover, we examined the consequences of the distortions. As expected, attractive distortions decreased discriminability between highly-similar associative memories. We unified these findings using a Hebbian learning framework and suggested that (1) greater coactivation between B2 and A1 as compare to the coactivation between B1 and A2 caused asymmetric integration, and (2) the balanced coactivations eliminated such asymmetry. Collectively, we showed that similarity-dependent integration of complex visual experiences might cause asymmetric memory distortion. The degree of the asymmetry depends on the level of coactivation during integration.

Authors

Institutions

Publication Details

Journal
Journal of Vision
Published
2024-09-15
DOI
https://doi.org/10.1167/jov.24.10.301
Primary Topic
Image Retrieval and Classification Techniques
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Similarity-dependent memory integration of scene images

Simeng Guo, Sheng Li
Journal of Vision
Image Retrieval and Classification Techniques
article

Similarity-dependent memory integration of scene images

Simeng Guo, Sheng Li
article en

Abstract

People often encounter novel events similar to their previous experiences. An intriguing question is how the similar representations interact during learning. Previous studies suggested that feature-based similarity resulted in systematic memory distortions. The present study examined the effect of memory integration due to learning similar scenes. We used generative adversarial networks (GANs) to generate scene wheels from which the to-be-remembered scenes were selected. In an online experiment (n = 59), we evaluated the perceptual similarity of images from the scene wheels and selected scene-pairmates (A1 and A2) with varying perceptual similarities. In three main experiments (n = 27, 27, 28), A1 and A2 were paired with different images (B1 and B2) to form competitive associations. Subjects learned these associations with explicit knowledge that scenes paired with different images were always different (even though they might look similar). Importantly, learning of competitive associations was temporally separated (“A1-B1” preceded its competitor, “A2-B2”). Across three experiments, we found robust attractive memory distortion of A2 towards its highly-similar competitor (A1). In Experiment 1 and Experiment 2 (with increased training on A2-B2), the attraction effects were asymmetric: memory of A1 was not biased relative to A2. Interestingly, in Experiment 3 in which training on A1-B1 was increased, the asymmetry disappeared: memories of A1 and A2 were biased towards each other. Moreover, we examined the consequences of the distortions. As expected, attractive distortions decreased discriminability between highly-similar associative memories. We unified these findings using a Hebbian learning framework and suggested that (1) greater coactivation between B2 and A1 as compare to the coactivation between B1 and A2 caused asymmetric integration, and (2) the balanced coactivations eliminated such asymmetry. Collectively, we showed that similarity-dependent integration of complex visual experiences might cause asymmetric memory distortion. The degree of the asymmetry depends on the level of coactivation during integration.

Journal of VisionVol. 24(10)
Peking University (CN), Chinese Institute for Brain Research (CN)
Openalex Percentile: Top 84%
Image Retrieval and Classification Techniques
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.