The organizational principles of the object space represented in the human ventral visual cortex are debated. Here we contrast two prominent proposals that, in addition to an organization in terms of animacy, propose either a representation related to aspect ratio (stubby-spiky) or to the distinction between faces and bodies. We designed a critical test that dissociates the latter two categories from aspect ratio and investigated responses from human fMRI (of either sex) and deep neural networks (BigBiGAN). Representational similarity and decoding analyses showed that the object space in the occipitotemporal cortex (OTC) and BigBiGAN was partially explained by animacy but not by aspect ratio. Data-driven approaches showed clusters for face and body stimuli and animate-inanimate separation in the representational space of OTC and BigBiGAN, but no arrangement related to aspect ratio. In sum, the findings go in favor of a model in terms of an animacy representation combined with strong selectivity for faces and bodies.Significance Statement:We contrasted animacy, aspect ratio and face-body as principal dimensions characterizing object space in the occipitotemporal cortex. This is difficult to test, as typically faces and bodies differ in aspect ratio (faces are mostly stubby and bodies are mostly spiky). To dissociate the face-body distinction from the difference in aspect ratio, we created a new stimulus set in which faces and bodies have a similar and very wide distribution of values along the shape dimension of the aspect ratio. Brain imaging (fMRI) with this new stimulus set showed that, in addition to animacy, the object space is mainly organized by the face-body distinction and selectivity for aspect ratio is minor (despite its wide distribution).
A recent study, recurrence quantification analysis of EEG signals during standard tasks of Waterloo-Stanford Group Scale of hypnotic susceptibility investigated recurrence quantifiers (RQs) of hypnotic electroencephalograph (EEG) signals recorded after hypnotic induction while subjects were doing standard tasks of Waterloo-Stanford Group Scale (WSGS) of hypnotic susceptibility to distinguish subjects of different hypnotizability levels. Following the same analysis, the current study determines the capability of different RQs to distinguish subjects of low, medium and high hypnotizability level and studies the influence of hypnotizability level on underlying dynamic of tasks. Besides, EEG channels were sorted according to the number of their RQs, which differed significantly among subjects of different hypnotizability levels. Another valuable result was determination of major brain regions in observing significant differences in various task types (ideomotors, hallucination, challenge and memory).
We are frequently exposed to hand written digits 0–9 in today's modern life. Success in decoding-classification of hand written digits helps us understand the corresponding brain mechanisms and processes and assists seriously in designing more efficient brain–computer interfaces. However, all digits belong to the same semantic category and similarity in appearance of hand written digits makes this decoding-classification a challenging problem. In present study, for the first time, augmented naïve Bayes classifier is used for classification of functional Magnetic Resonance Imaging (fMRI) measurements to decode the hand written digits which took advantage of brain connectivity information in decoding-classification. fMRI was recorded from three healthy participants, with an age range of 25–30. Results in different brain lobes (frontal, occipital, parietal, and temporal) show that utilizing connectivity information significantly improves decoding-classification and capability of different brain lobes in decoding-classification of hand written digits were compared to each other. In addition, in each lobe the most contributing areas and brain connectivities were determined and connectivities with short distances between their endpoints were recognized to be more efficient. Moreover, data driven method was applied to investigate the similarity of brain areas in responding to stimuli and this revealed both similarly active areas and active mechanisms during this experiment. Interesting finding was that during the experiment of watching hand written digits, there were some active networks (visual, working memory, motor, and language processing), but the most relevant one to the task was language processing network according to the voxel selection.
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