Parallel visual search and rapid animal detection in natural scenes
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Updated: Jun 4, 2026

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)
Published on: April 8, 2019
Jan Drewes1, Julia Trommershäuser, Karl R Gegenfurtner
1Centre de Recherche Cerveau et Cognition, UMR, CNRS, Université Paul Sabatier, Toulouse, France. mail@jandrewes.de
Abstract:
Human observers are capable of detecting animals within novel natural scenes with remarkable speed and accuracy. Recent studies found human response times to be as fast as 120 ms in a dual-presentation (2-AFC) setup (H. Kirchner & S. J. Thorpe, 2005). In most previous experiments, pairs of randomly chosen images were presented, frequently from very different contexts (e.g., a zebra in Africa vs. the New York Skyline). Here, we tested the effect of background size and contiguity on human performance by using a new, contiguous background image set. Individual images contained a single animal surrounded by a large, animal-free image area. The image could be positioned and cropped in such a manner that the animal could occur in one of eight evenly spaced positions on an imaginary circle (radius 10-deg visual angle). In the first (8-Choice) experiment, all eight positions were used, whereas in the second (2-Choice) and third (2-Image) experiments, the animals were only presented on the two positions to the left and right of the screen center. In the third experiment, additional rectangular frames were used to mimic the conditions of earlier studies. Average latencies on successful trials differed only slightly between conditions, indicating that the number of possible animal locations within the display does not affect decision latency. Detailed analysis of saccade targets revealed a preference toward both the head and the center of gravity of the target animal, affecting hit ratio, latency, and the number of saccades required to reach the target. These results illustrate that rapid animal detection operates scene-wide and is fast and efficient even when the animals are embedded in their natural backgrounds.

