Single-session label training alters neural competition between objects and faces.

Abstract

The extent to which visuocortical processing is altered when observers learn to categorize novel visual stimuli via labeling is not well understood. The present investigation used steady state visual evoked potential (ssVEP) frequency tagging to test the hypothesis that learning to categorize novel objects via labeling prompts a competitive advantage over concurrently presented stimuli. In the learning (label-training) phase, participants (n = 24) categorized objects according to two different species labels and faces according to gender. A control group (n = 26) viewed the same stimuli without label learning. Before and after learning, faces and objects were superimposed and viewed concurrently while periodically turned on and off at unique temporal rates (5/s or 6/s). The spectral power of the ssVEP at each frequency was projected to an L2 (minimum) norm estimated source space, and competition between faces and objects was compared using permutation-controlled mass univariate t tests. Results showed that, only in the training group, learning to label novel objects led to a competitive advantage over faces across a network of occipito-temporal and fronto-parietal cortical regions. These changes were more pronounced in participants showing more improvement across the label learning phase. Together, the findings support the notion that learning to label novel object categories affects neural competition though recurrent neural interactions in regions commonly associated with visual perception and selective attention.

The FreqTag toolbox: A principled approach to analyzing electrophysiological time series in frequency tagging paradigms

“…Example data from one adult ( Silva et al, 2021 ) and one infant (Barry-Anwar et al, in preparation) are provided on the Open Science Framework companion site of this paper. Both data sets have undergone initial segmenting, filtering, and artifact control.…”

“…The first example data set data comes from a study with adult observers ( Silva et al, 2021 ). The full data set for this study can be found at: https://osf.io/a53s9/?view_only=1966f70fac954bac886381f908c7a275 .…”

“…The first dataset (exampledata_1.mat) was recorded with a sample rate of 500 Hz and has already been filtered by means of a 30 Hz low-pass (18th order Butterworth) and a 1-Hz high-pass (4th order Butterworth). The epochs were extracted from continuous EEG data, containing 400 ms pre- and 7400 ms post-stimulus onset (see Silva et al, 2021 for detailed description). Thus, if researchers were to transform the entire segment, including the pre-stimulus data, into the frequency domain, the frequency resolution would be 0.1282 (frequency resolution = 1/7.802, i.e., one divided by the segment’s duration in seconds).…”

“…Finally, the code provided is intended to be tailored to specific research questions and populations. Many of the intermediate results computed in this pipeline may be used in ways that suit researchers interested in concepts like connectivity, neural competition, source distribution, and inter-trial variability of visuocortical signals (e.g., Silva et al, 2021 ).…”