Key Visual Features for Rapid Categorization of Animals in Natural Scenes

June 23, 2010
Arnaud Delorme, PhD

Delorme, A., Richard, G., Fabre-Thorpe, M. (2010). Key visual features for rapid categorization of animals in natural scenes. Frontiers in Psychology, 1:21.


In speeded categorization tasks, decisions could be based on diagnostic target features or they may need the activation of complete representations of the object. Depending on task requirements, the priming of feature detectors through top-down expectation might lower the threshold of selective units or speed up the rate of information accumulation. In the present paper, 40 subjects performed a rapid go/no-go animal/non-animal categorization task with 400 briefly flashed natural scenes to study how performance depends on physical scene characteristics, target configuration, and the presence or absence of diagnostic animal features. Performance was evaluated both in terms of accuracy and speed and d’ curves were plotted as a function of reaction time (RT). Such d’ curves give an estimation of the processing dynamics for studied features and characteristics over the entire subject population. Global image characteristics such as color and brightness do not critically influence categorization speed, although they slightly influence accuracy. Global critical factors include the presence of a canonical animal posture and animal/background size ratio suggesting the role of coarse global form. Performance was best for both accuracy and speed, when the animal was in a typical posture and when it occupied about 20-30% of the image. The presence of diagnostic animal features was another critical factor. Performance was significantly impaired both in accuracy (drop 3.3-7.5%) and speed (median RT increase 7-16 ms) when diagnostic animal parts (eyes, mouth, and limbs) were missing. Such animal features were shown to influence performance very early when only 15-25% of the response had been produced. In agreement with other experimental and modeling studies, our results support fast diagnostic recognition of animals based on key intermediate features and priming based on the subject’s expertise.

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