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Quentin Wohlfarth, Martin Arguin; Diagnostic Features for Visual Object Recognition in Humans. Journal of Vision 2019;19(10):30d. doi: https://doi.org/10.1167/19.10.30d.
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© ARVO (1962-2015); The Authors (2016-present)
The Bubbles (Gosselin & Schyns, 2001) classification image technique has been used frequently to determine the subset of the available visual information that is effectively used by humans in various face perception tasks. However, to the best of our knowledge, its application in object recognition has been limited to one study conducted in pigeons (Gibson et al., 2005). Here, human participants recognized objects from collections of six simple visual shapes displayed in one of four different viewpoints. The stimuli were of the same class as Biederman’s (1987) geons and participants were initially trained to associate each object with a particular keyboard key. In the recognition task (11520 trials per participant), the single object displayed was visible only through a number of circular Gaussian apertures and the participant indicated its identity by a key press. The classification images were calculated separately for each instance and each participant by subtracting the weighted sum of the bubbles masks leading to errors from that of masks leading to correct responses. An ideal observer was also assessed in the same experiments to determine the spatial location of the objectively most effective information to support the recognition task without the limitations or intrinsic biases of the human visual system. The results show major differences in the classification images obtained from human participants and the ideal observer. Such differences indicate that particular properties the human visual system prevented participants from focusing on the objectively most effective diagnostic information, forcing reliance on alternative sources of information. From the nature of the contrast in the classification images from human and ideal observers, it is proposed that human vision is biased towards the processing of edges and vertices for representing and recognizing the shapes from the class used in the present study.
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