English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Visual translation tolerance refers to our capacity to recognize objects overa wide range of different retinal locations. Although translation is perhapsthe simplest spatial transform that the visual system needs to cope with, theextent to which the human visual system can identify objects at previouslyunseen locations is unclear, with some studies reporting near completeinvariance over 10{\deg} and other reporting zero invariance at 4{\deg} ofvisual angle. Similarly, there is confusion regarding the extent of translationtolerance in computational models of vision, as well as the degree of matchbetween human and model performance. Here we report a series of eye-trackingstudies (total N=70) demonstrating that novel objects trained at one retinallocation can be recognized at high accuracy rates following translations up to18{\deg}. We also show that standard deep convolutional networks (DCNNs)support our findings when pretrained to classify another set of stimuli acrossa range of locations, or when a Global Average Pooling (GAP) layer is added toproduce larger receptive fields. Our findings provide a strong constraint fortheories of human vision and help explain inconsistent findings previouslyreported with CNNs.

The human visual system and CNNs can both support robust online translation tolerance following extreme displacements