Purchase this article with an account.
Galia Avidan, Yael Holzinger, Shimon Ullman, Marlene Behrmann; Minimal Recognizable Configurations (MIRCs) elicit category selective responses in high order visual cortex. Journal of Vision 2018;18(10):407. doi: https://doi.org/10.1167/18.10.407.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Visual object recognition is performed effortlessly by humans, but in fact, it relies on a series of complex computations which are not yet fully understood. Here we tested a novel model of the representations used for biological visual recognition and their neural correlates using fMRI in human participants. The rationale is based on previous research showing that a set of representations termed Minimal Recognizable Configurations; MIRCs, which are computationally derived and have unique psychophysical characteristics, serve as the building blocks of object recognition. We examined the responses elicited by MIRC images from different categories (faces, objects, and places) throughout the visual cortex and contrasted these responses to that elicited by sub-MIRCs, which are visually similar to MIRCs, but, instead, result in poor recognition and with the response to scrambled, unrecognizable images. Stimuli were presented in blocks, and participants indicated yes/no recognition for each image. We confirmed that MIRCs elicited higher recognition performance compared to sub-MIRCs in all three categories. Whereas fMRI activation in early visual cortex for both MIRCs and sub-MIRCs did not differ from that elicited by scrambled images, high-level visual regions exhibited overall greater activation for MIRCs compared to sub-MIRCs and scrambled images. Moreover, MIRCs and sub-MIRCs from each category elicited enhanced activation in corresponding category selective regions including FFA and OFA (faces), LOC (objects), and PPA and TOS (places). These findings reveal the neural relevance of MIRCs and enable us to make progress on deriving a more complete theory of biological object recognition.
Meeting abstract presented at VSS 2018
This PDF is available to Subscribers Only