Categorical learning revealed in activity pattern of left fusiform cortex

Abstract

The brain is organized such that it encodes and maintains category information about thousands of objects. However, how learning shapes these neural representations of object categories is unknown. The present study focuses on faces, examining whether: (1) Enhanced categorical discrimination or (2) Feature analysis enhances face/non-face categorization in the brain. Stimuli ranged from non-faces to faces with two-toned Mooney images used for testing and gray-scale images used for training. The stimulus set was specifically chosen because it has a true categorical boundary between faces and non-faces but the stimuli surrounding that boundary have very similar features, making the boundary harder to learn. Brain responses were measured using functional magnetic resonance imaging while participants categorized the stimuli before and after training. Participants were either trained with a categorization task, or with non-categorical semblance analyzation. Interestingly, when participants were categorically trained, the neural activity pattern in the left fusiform gyrus shifted from a graded representation of the stimuli to a categorical representation. This corresponded with categorical face/non-face discrimination, critically including both an increase in selectivity to faces and a decrease in false alarm response to non-faces. By contrast, while activity pattern in the right fusiform cortex correlated with face/non-face categorization prior to training, it was not affected by learning. Our results reveal the key role of the left fusiform cortex in learning face categorization. Given the known right hemisphere dominance for face-selective responses, our results suggest a rethink of the relationship between the two hemispheres in face/non-face categorization. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

Twofold advantages of face processing with or without visual awareness.

“…Though failing to reach significant level, the current results suggest a trend of LVF face advantages in suppressed conditions and RVF face advantages in dominant conditions. These results are consistent with the known functional lateralization of configural versus local feature face processing (Goold & Meng, 2017;Meng et al, 2012;Rossion et al, 2000) and further support the possibility of twofold mechanisms accounting for face advantages during visible and invisible states in binocular rivalry.…”

Visual expertise modulates baseline brain activity: a preliminary resting-state fMRI study using expertise model of radiologists

“…The FG is constantly reported in task fMRI studies using visual expertise models of other domains, such as cars [ 10 , 52 ], birds [ 53 ], chess [ 54 ] and faces [ 55 ]. It plays a vital role in visual categorization learning [ 56 , 57 ]. Specifically, FG process higher-level visual information [ 12 ] and is involved in fine-grained visual recognition independent of the categories of visual stimuli, either for real-life or lab-based objects [ 14 , 41 , 58 ].…”

Visual experience modulates whole‐brain connectivity dynamics: A resting‐state fMRI study using the model of radiologists

“…Specifically, the left FFG was found to be engaged in the processing of abstract visual information (Devlin, Jamison, Gonnerman, & Matthews, 2006), its activity in radiologists could reliably distinguish between upright and inverted X‐rays (Bilalic et al, 2016). Left FFG plays a vital role in visual categorization learning (Goold & Meng, 2017; Lech, Gunturkun, & Suchan, 2016); its activity was observed to be positively correlated with participants' perceptual performance (Bi, Chen, Zhou, He, & Fang, 2014; Liu et al, 2016; Mukai et al, 2007) and could be modulated by visual learning (Goold & Meng, 2017). The left FFG works for a different neural system involved in semantic category‐based visual processes than ACG (Wei, Zhangi, Lyu, Hu, & Li, 2017), and was observed to have diverged function in contrast to the decision‐making network through visual categorization learning for visually similar objects (Xu et al, 2013).…”