| Title: |
Identifying Objects and Remembering Images: Insights From Deep Neural Networks |
| Authors: |
Rust, Nicole C.; Jannuzi, Barnes G. L. |
| Contributors: |
national science foundation; Simons Foundation; National Eye Institute |
| Source: |
Current Directions in Psychological Science ; volume 31, issue 4, page 316-323 ; ISSN 0963-7214 1467-8721 |
| Publisher Information: |
SAGE Publications |
| Publication Year: |
2022 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1177/09637214221083663 |
| Availability: |
https://doi.org/10.1177/09637214221083663; https://journals.sagepub.com/doi/pdf/10.1177/09637214221083663; https://journals.sagepub.com/doi/full-xml/10.1177/09637214221083663 |
| Rights: |
http://www.sagepub.com/licence-information-for-chorus |
| Accession Number: |
edsbas.88002D1D |
| Database: |
BASE |