Visual object recognition is performed effortlessly by humans notwithstanding the fact that it requires a series of complex computations, which are, as yet, not well understood. Here, we tested a novel account of the representations used for visual recognition and their neural correlates using fMRI. 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 contrasted the BOLD responses elicited by MIRC images, derived from different categories (faces, objects, and places), sub-MIRCs, which are visually similar to MIRCs, but, instead, result in poor recognition and 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 for all three categories. Whereas fMRI activation in early visual cortex for both MIRCs and sub-MIRCs of each category did not differ from that elicited by scrambled images, high-level visual regions exhibited overall greater activation for MIRCs compared to sub-MIRCs or scrambled images. Moreover, MIRCs and sub-MIRCs from each category elicited enhanced activation in corresponding category-selective regions including fusiform face area and occipital face area (faces), lateral occipital cortex (objects), and parahippocampal place area and transverse occipital sulcus (places). These findings reveal the psychological and neural relevance of MIRCs and enable us to make progress in developing a more complete account of object recognition.

Object-related areas in the ventral visual system in humans are known from imaging studies to be preferentially activated by object images compared with noise or texture patterns. It is unknown, however, which features of the object images are extracted and represented in these areas. Here we tested the extent to which the representation of visual classes used object fragments selected by maximizing the information delivered about the class. We tested functional magnetic resonance imaging blood oxygenation level-dependent activation of highly informative object features in low- and high-level visual areas, compared with noninformative object features matched for low-level image properties. Activation in V1 was similar, but in the lateral occipital area and in the posterior fusiform gyrus, activation by “informative” fragments was significantly higher for three object classes. Behavioral studies also revealed high correlation between performance and fragments information. The results show that an objective class-information measure can predict classification performance and activation in human object-related areas.