Purchase this article with an account.
Mauro Manassi, Árni Kristjánsson, David Whitney; Serial dependence determines object classification in visual search. Journal of Vision 2017;17(10):221. doi: https://doi.org/10.1167/17.10.221.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
In everyday life, we continuously search and classify the environment around us: we look for keys in our messy room, for a friend in the street and so on. A very important kind of visual search is performed by radiologists, who have to search and classify tumors in X-rays. An underlying assumption of such tasks is that search and recognition are independent of our past experience. However, recent studies have shown that our percepts can be strongly biased toward previously seen stimuli (Fischer & Whitney, 2014; Liberman et al., 2014). Here, we tested whether serial dependence can influence search and classification of objects in critical tasks such as tumor detection. We created three objects with random shapes (objects A/B/C) and generated 48 morph objects in between each pair (147 objects in total). Observers were presented on each trial with a random object and were asked to classify the morph as A/B/C. In order to simulate a tumor search task, we embedded the morph in a noisy background and randomized its location. We found that subjects made consistent perceptual errors when classifying the shape on the current trial, seeing it as more similar to the shape presented on the previous trial. This perceptual attraction extended over 15 seconds back in time (up to 3 trials back). In a control experiment, we checked whether this kind of serial dependence is due to response repetition, on some trials asking subjects to press the space bar instead of classifying the object. Serial dependence still occurred from those trials, ruling out a mere response bias. Our results showed that object classification in visual search can be strongly biased by previously seen stimuli. These results are particularly important for radiologists, who search and classify tumors when viewing many consecutive X-rays.
Meeting abstract presented at VSS 2017
This PDF is available to Subscribers Only