ORCID Profile
0000-0002-6818-5342
Current Organisation
Shahid Rajaee Teacher Training University
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Publisher: Elsevier BV
Date: 03-2017
DOI: 10.1016/J.NEUROSCIENCE.2017.01.002
Abstract: To categorize the perceived objects, brain utilizes a broad set of its resources and encoding strategies. Yet, it remains elusive how the category information is encoded in the brain. While many classical studies have sought the category information in the across-trial-averaged activity of neurons/neural populations, several recent studies have observed category information also in the within-trial correlated variability of activities between neural populations (i.e. dependent variability). Moreover, other studies have observed that independent variability of activity, which is the variability of the measured neural activity without any influence from correlated variability with other neurons opulations, could also be modulated for improved categorization. However, it was unknown how important each of the three factors (i.e. average activity, dependent and independent variability of activities) was in category encoding. Therefore, we designed an EEG experiment in which human subjects viewed a set of object exemplars from four categories. Using a computational model, we evaluated the contribution of each factor separately in category encoding. Results showed that the average activity played a significant role while the independent variability, although effective, contributed moderately to the category encoding. The inter-channel dependent variability showed an ignorable effect on the encoding. We also investigated the role of those factors in the encoding of variations which showed similar effects. These results imply that the brain, rather than variability, seems to use the average activity to convey information on the category of the perceived objects.
Publisher: Springer Science and Business Media LLC
Date: 31-10-2017
DOI: 10.1038/S41598-017-13756-8
Abstract: One key ability of human brain is invariant object recognition, which refers to rapid and accurate recognition of objects in the presence of variations such as size, rotation and position. Despite decades of research into the topic, it remains unknown how the brain constructs invariant representations of objects. Providing brain-plausible object representations and reaching human-level accuracy in recognition, hierarchical models of human vision have suggested that, human brain implements similar feed-forward operations to obtain invariant representations. However, conducting two psychophysical object recognition experiments on humans with systematically controlled variations of objects, we observed that humans relied on specific (diagnostic) object regions for accurate recognition which remained relatively consistent (invariant) across variations but feed-forward feature-extraction models selected view-specific (non-invariant) features across variations. This suggests that models can develop different strategies, but reach human-level recognition performance. Moreover, human in iduals largely disagreed on their diagnostic features and flexibly shifted their feature extraction strategy from view-invariant to view-specific when objects became more similar. This implies that, even in rapid object recognition, rather than a set of feed-forward mechanisms which extract diagnostic features from objects in a hard-wired fashion, the bottom-up visual pathways receive, through top-down connections, task-related information possibly processed in prefrontal cortex.
Publisher: Elsevier BV
Date: 05-2017
DOI: 10.1016/J.NEUROSCIENCE.2017.02.050
Abstract: Humans perform object recognition effortlessly and accurately. However, it is unknown how the visual system copes with variations in objects' appearance and the environmental conditions. Previous studies have suggested that affine variations such as size and position are compensated for in the feed-forward sweep of visual information processing while feedback signals are needed for precise recognition when encountering non-affine variations such as pose and lighting. Yet, no empirical data exist to support this suggestion. We systematically investigated the impact of the above-mentioned affine and non-affine variations on the categorization performance of the feed-forward mechanisms of the human brain. For that purpose, we designed a backward-masking behavioral categorization paradigm as well as a passive viewing EEG recording experiment. On a set of varying stimuli, we found that the feed-forward visual pathways contributed more dominantly to the compensation of variations in size and position compared to lighting and pose. This was reflected in both the litude and the latency of the category separability indices obtained from the EEG signals. Using a feed-forward computational model of the ventral visual stream, we also confirmed a more dominant role for the feed-forward visual mechanisms of the brain in the compensation of affine variations. Taken together, our experimental results support the theory that non-affine variations such as pose and lighting may need top-down feedback information from higher areas such as IT and PFC for precise object recognition.
Location: Iran (Islamic Republic of)
Location: Iran (Islamic Republic of)
No related grants have been discovered for Nasour Bagheri.