期刊
CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE
卷 31, 期 4, 页码 316-323出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/09637214221083663
关键词
object recognition; visual memory; recognition memory; neural network; visual cortex
资金
- National Eye Institute of the National Institutes of Health [R01EY020851]
- National Science Foundation [1265480]
- Simons Foundation (Simons Collaboration on the Global Brain Award) [543033]
- Division Of Behavioral and Cognitive Sci
- Direct For Social, Behav & Economic Scie [1265480] Funding Source: National Science Foundation
Deep artificial neural networks have provided important insights into the contributions of high-level visual cortex to object identification and image memory behavior.
People have a remarkable ability to identify the objects that they are looking at, as well as remember the images that they have seen. Researchers know that high-level visual cortex contributes in important ways to supporting both of these functions, but developing models that describe how processing in high-level visual cortex supports these behaviors has been challenging. Recent breakthroughs in this modeling effort have arrived by way of the illustration that deep artificial neural networks trained to categorize objects, developed for computer vision purposes, reflect brainlike patterns of activity. Here we summarize how deep artificial neural networks have been used to gain important insights into the contributions of high-level visual cortex to object identification, as well as one characteristic of visual memory behavior: image memorability, the systematic variation with which some images are remembered better than others.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据