No abstract

to recognize a target object, brain implements strategies which involved a combination of externally sensory-driven and internally task-driven mechanisms. While previous studies have suggested the role of frontal brain areas in sending task-related information to visual cortices, especially the lateral-occipital cortex, they failed to provide quantitative evidence supporting the transaction of taskrelated information between those areas. However, recently developed representational Granger causality analysis, could allow us to track the movement of any desired information in the brain. Therefore, we designed an EEG object detection experiment and evaluated the spatiotemporal dynamics of category-and target-related information transaction across the brain using the mentioned method. Results showed that prefrontal area was the first region to process the target-related information, even before the appearance of the stimulus. This information was transferred to posterior brain areas during the stimulus presentation probably to facilitate object recognition and to direct the decision-making procedure. We also observed that, as compared to category-related information, the target-related information could predict the behavioral detection outcomes more precisely, suggesting the dominant representation of the internal compared to external information. These results provided new insights into the role of prefrontal cortex and the representation of task in the brain during object detection.

How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly uses the mean neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability that is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional mean activity. Here we compare the information content of 31 variability-sensitive features against the mean of activity, using three independent highly varied data sets. In whole-trial decoding, the classical event-related potential (ERP) components of P2a and P2b provided information comparable to those provided by original magnitude data (OMD) and wavelet coefficients (WC), the two most informative variability-sensitive features. In time-resolved decoding, the OMD and WC outperformed all the other features (including the mean), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in the theta frequency band, previously suggested to support feedforward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously such as phase, amplitude, and frequency rather than mean per se. In our active categorization data set, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.

In order to develop object recognition algorithms, which can approach human-level recognition performance, researchers have been studying how the human brain performs recognition in the past five decades. This has already in-spired AI-based object recognition algorithms, such as convolutional neural networks, which are among the most successful object recognition platforms today and can approach human performance in specific tasks. However, it is not yet clearly known how recorded brain activations convey information about object category processing. One main obstacle has been the lack of large feature sets, to evaluate the information contents of multiple aspects of neural activations. Here, we compared the information contents of a large set of 25 features, extracted from time series of electroencephalography (EEG) recorded from human participants doing an object recognition task. We could characterize the most informative aspects of brain activations about object categories. Among the evaluated features, event-related potential (ERP) components of N1 and P2a were among the most informative features with the highest information in the Theta frequency bands. Upon limiting the analysis time window, we observed more information for features detecting temporally informative patterns in the signals. The results of this study can constrain previous theories about how the brain codes object category information.