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 fMRI (functional Magnetic Resonance (...) imaging) 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. (shrink)

Most cognitive psychology experiments evaluate models of human cognition using a relatively small, well-controlled set of stimuli. This approach stands in contrast to current work in neuroscience, perception, and computer vision, which have begun to focus on using large databases of natural images. We argue that natural images provide a powerful tool for characterizing the statistical environment in which people operate, for better evaluating psychological theories, and for bringing the insights of cognitive science closer to real applications. We discuss how (...) some of the challenges of using natural images as stimuli in experiments can be addressed through increased sample sizes, using representations from computer vision, and developing new experimental methods. Finally, we illustrate these points by summarizing recent work using large image databases to explore questions about human cognition in four different domains: modeling subjective randomness, defining a quantitative measure of representativeness, identifying prior knowledge used in word learning, and determining the structure of natural categories. (shrink)

Access to other minds once presupposed other individuals’ expressions and narrations. Today, several methods have been developed which can measure brain states relevant for assessments of mental states without 1st person overt external behavior or speech. Functional magnetic resonance imaging and trace conditioning are used clinically to identify patterns of activity in the brain that suggest the presence of consciousness in people suffering from severe consciousness disorders and methods to communicate cerebrally with patients who are motorically unable to communicate. The (...) techniques are also used non-clinically to access subjective awareness in adults and infants. In this article we inspect technical and theoretical limits on brain–machine interface access to other minds. We argue that these techniques hold promises of important medical breakthroughs, open up new vistas of communication, and of understanding the infant mind. Yet they also give rise to ethical concerns, notably misuse as a consequence of hypes and misinterpretations. (shrink)