Ultra-rapid object categorization in real-world scenes with top-down manipulations

Bingjie Xu, Mohan S. Kankanhalli, Qi Zhao

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Humans are able to achieve visual object recognition rapidly and effortlessly. Object categorization is commonly believed to be achieved by interaction between bottom-up and top-down cognitive processing. In the ultra-rapid categorization scenario where the stimuli appear briefly and response time is limited, it is assumed that a first sweep of feedforward information is sufficient to discriminate whether or not an object is present in a scene. However, whether and how feedback/top-down processing is involved in such a brief duration remains an open question. To this end, here, we would like to examine how different top-down manipulations, such as category level, category type and real-world size, interact in ultra-rapid categorization. We have constructed a dataset comprising real-world scene images with a built-in measurement of target object display size. Based on this set of images, we have measured ultra-rapid object categorization performance by human subjects. Standard feedforward computational models representing scene features and a state-of-the-art object detection model were employed for auxiliary investigation. The results showed the influences from 1) animacy (animal, vehicle, food), 2) level of abstraction (people, sport), and 3) real-world size (four target size levels) on ultra-rapid categorization processes. This had an impact to support the involvement of top-down processing when rapidly categorizing certain objects, such as sport at a fine grained level. Our work on human vs. model comparisons also shed light on possible collaboration and integration of the two that may be of interest to both experimental and computational vision researches. All the collected images and behavioral data as well as code and models are publicly available at https://osf.io/mqwjz/.

Original languageEnglish (US)
Article numbere0214444
JournalPloS one
Volume14
Issue number4
DOIs
StatePublished - Apr 2019

Bibliographical note

Funding Information:
This research was funded in part by the NSF under Grant 1849107, in part by the University of Minnesota Department of Computer Science and Engineering Start-up Fund (QZ), and in part by the National Research Foundation, Prime Minister’s Office, Singapore under its Strategic Capability Research Centres Funding Initiative.

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