Exploring the neural basis and modulating factors of implicit altercentric spatial perspective-taking with fNIRS.

Humans spontaneously take the perspective of others when encoding spatial information in a scene, especially with agentive action cues present. This functional near-infrared spectroscopy (fNIRS) study explored how action observation influences implicit spatial perspective-taking (SPT) by adapting a left-right spatial judgment task to investigate whether transformation strategies underlying altercentric SPT can be predicted on the basis of cortical activation. Strategies associated with two opposing neurocognitive accounts (embodied versus disembodied) and their proposed neural correlates (human mirror neuron system; hMNS versus cognitive control network; CCN) are hypothesized. Exploratory analyses with 117 subjects uncover an interplay between perspective-taking and post-hoc factor, consistency of selection, in regions alluding to involvement of the CCN. Descriptively, inconsistent altercentric SPT elicited greater activation than consistent altercentric SPT and/or inconsistent egocentric SPT in the left inferior frontal gyrus (IFG), left dorsolateral prefrontal cortex (DLPFC) and left motor cortex (MC), but not the inferior parietal lobules (IPL). Despite the presence of grasping cues, spontaneous embodied strategies were not evident during implicit altercentric SPT. Instead, neural trends in the inconsistent subgroups (22 subjects; 13 altercentric; 9 egocentric) suggest that inconsistency in selection modulates the decision-making process and plausibly taps on deliberate and effortful disembodied strategies driven by the CCN. Implications for future research are discussed.

Effect of Subliminal Lexical Priming on the Subjective Perception of Images: A Machine Learning Approach.

The purpose of the study is to examine the effect of subliminal priming in terms of the perception of images influenced by words with positive, negative, and neutral emotional content, through electroencephalograms (EEGs). Participants were instructed to rate how much they like the stimuli images, on a 7-point Likert scale, after being subliminally exposed to masked lexical prime words that exhibit positive, negative, and neutral connotations with respect to the images. Simultaneously, the EEGs were recorded. Statistical tests such as repeated measures ANOVAs and two-tailed paired-samples t-tests were performed to measure significant differences in the likability ratings among the three prime affect types; the results showed a strong shift in the likeness judgment for the images in the positively primed condition compared to the other two. The acquired EEGs were examined to assess the difference in brain activity associated with the three different conditions. The consistent results obtained confirmed the overall priming effect on participants' explicit ratings. In addition, machine learning algorithms such as support vector machines (SVMs), and AdaBoost classifiers were applied to infer the prime affect type from the ERPs. The highest classification rates of 95.0% and 70.0% obtained respectively for average-trial binary classifier and average-trial multi-class further emphasize that the ERPs encode information about the different kinds of primes.