Efficient visual search by category: Specifying the features that mark the difference between artifacts and animals in preattentive vision

Abstract

In this report, we explored the features that support visual search for broadly inclusive natural categories. We used a paradigm in which subjects searched for a randomly selected target from one category (e.g., one of 32 line drawings of artifacts or animals in displays ranging from three to nine items) among a mixed set of distractors from the other. We found that search was surprisingly fast. Targetpresent slopes for animal targets among artifacts ranged from 10.8 to 16.0 msec/item, and slopes for artifact targets ranged from 5.5 to 6.2 msec/item. Experiments 2-5 tested factors that affect both the speed of the search and the search asymmetry favoring detection of artifacts among animals. They converge on the conclusion that target-distractor differences in global contour shape (e.g., rectilinearity/ curvilinearity) and visual typicality of parts and form facilitate search by category. We argue that existing theories are helpful in understanding these findings but that they need to be supplemented to account for the specific features that specify categories and to account for subjects' ability to quickly locate targets representing heterogeneous and formally complex categories.

A new look at novelty effects: Guiding search away from old distractors

“…People are able to search very efficiently for categorically defined targets (e.g., , and it may be the case that the subjects in our task were treating the old distractors as a group and negatively guiding their search away from this category of objects. Although previous studies have addressed the topic of categorical guidance (e.g., Levin, Takarae, Miner, & Keil, 2001;Wolfe, Horowitz, Kenner, Hyle, & Vasan, 2004), theories for this variety of search are still in their infancy. One promising approach is to use machine-learning techniques to obtain the discriminative features for an object category, and then to use these features to guide search rather than the features specific to a given target object (Zhang, Yang, Samaras, & Zelinsky, 2006).…”

Perception of Objects in Natural Scenes: Is It Really Attention Free?

“…Behavioral tests that we have used include showing that the candidate features can mediate both parallel search and texture segregation (implying that the features are replicated across a spatial area); examining whether they migrate independently in illusory conjunctions when attention is diverted; and testing whether they can be separately attended (implying in both cases that they are independently coded). Levin et al (2001) used correlational analyses to show that features such as rectilinearity or part typicality can facilitate search for a target animal among distractor artifacts.…”

Short-term consolidation of individual identities leads to Lag-1 sparing.

“…We are inclined to attribute the discrepancy between the present results and those of Evans and Treisman (2005) to factors that are not related to counting per se but that owe instead to something peculiar in the visual structure of letters and digits that made them different from the stimuli used in other investigations involving categorization. Levin, Takarae, Miner, and Keil (2001) have shown that searching for an animal among distracting artifacts (or vice versa) was as efficient when the search target was displayed tachistoscopically in a canonical format as when the target was cut in parts and the parts were randomly scattered around fixation. Analogous examples involve the use of faces as stimuli.…”