Isolated Configuration: Holistic Processing Beyond the Realm of Faces
Zeng, Y.; Lu, Z.; Hentz, R. E.; Osher, D. E.
Show abstract
Holistic processing of visual stimuli has long been regarded as unique to faces, or otherwise extended to other object categories given sufficient expertise. We designed novel abstract stimuli that are recognizable strictly by configural information, and matched control stimuli that require the use of only featural information. We then tested four classic markers of holistic perception: inversion, misalignment, part-whole, and composite effects. We found that second-order configuration stimuli elicited robust holistic effects, while featural stimuli do not, showing that such effects emerge specifically when recognition depends on configural information, but not when it relies on featural cues. We further observed that first-order configuration stimuli were also sufficient to induce holistic effects, indicating that holistic processing can emerge from multiple levels of configural information. We also found a significant correlation between individual differences in face recognition ability and holistic processing effects with the second-order configuration stimuli, but not with the first order configuration stimuli, nor the featural stimuli. Lastly, convolutional neural networks trained on the same stimuli reproduced these patterns, strengthening the interpretation that holistic processing arises when configural information must be used to recognize a stimulus. Together, these findings demonstrate that holistic processing is fundamentally rooted in the representation of spatial configuration, with second-order relations providing the critical link to face recognition ability.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.