The hidden cost of a smartphone: The effects of smartphone notifications on cognitive control from a behavioral and electrophysiological perspective.

Since their release in 2007, smartphones and their use have seemingly become a fundamental aspect of life in western society. Prior literature has suggested a link between mobile technology use and lower levels of cognitive control when people engage in a cognitively demanding task. This effect is more evident for people who report higher levels of smartphone use. The current study examined the effects of smartphones notifications on cognitive control and attention. Participants completed the Navon Letter paradigm which paired visual (frequent and rare target letters) and auditory (smartphone and control sounds) stimuli. We found that overall, participants responded slower on trials paired with smartphone notification (vs. control) sounds. They also demonstrated larger overall N2 ERP and a larger N2 oddball effect on trials paired with smartphone (vs. control) sounds, suggesting that people generally exhibited greater levels of cognitive control on the smartphone trials. In addition, people with higher smartphone addiction proneness showed lower P2 ERP on trials with the smartphone (vs. control) sounds, suggesting lower attentional engagement. These results add to the debate on the effects of smartphones on cognition. Limitations and future directions are discussed.

Parametric Cortical Representations of Complexity and Preference for Artistic and Computer-Generated Fractal Patterns Revealed by Single-Trial EEG Power Spectral Analysis.

Fractals are self-similar patterns that repeat at different scales, the complexity of which are expressed as a fractional Euclidean dimension D between 0 (a point) and 2 (a filled plane). The drip paintings of American painter Jackson Pollock (JP) are fractal in nature, and Pollock's most illustrious works are of the high-D (~1.7) category. This would imply that people prefer more complex fractal patterns, but some research has instead suggested people prefer lower-D fractals. Furthermore, research has suggested that parietal and frontal brain activity tracks the complexity of fractal patterns, but previous research has artificially binned fractals depending on fractal dimension, rather than treating fractal dimension as a parametrically varying value. We used white layers extracted from JP artwork as stimuli, and constructed statistically matched 2-dimensional random Cantor sets as control stimuli. We recorded the electroencephalogram (EEG) while participants viewed the JP and matched random Cantor fractal patterns. Participants then rated their subjective preference for each pattern. We used a single-trial analysis to construct within-subject models relating subjective preference to fractal dimension D, as well as relating D and subjective preference to single-trial EEG power spectra. Results indicated that participants preferred higher-D images for both JP and Cantor stimuli. Power spectral analysis showed that, for artistic fractal images, parietal alpha and beta power parametrically tracked complexity of fractal patterns, while for matched mathematical fractals, parietal power tracked complexity of patterns over a range of frequencies, but most prominently in alpha band. Furthermore, parietal alpha power parametrically tracked aesthetic preference for both artistic and matched Cantor patterns. Overall, our results suggest that perception of complexity for artistic and computer-generated fractal images is reflected in parietal-occipital alpha and beta activity, and neural substrates of preference for complex stimuli are reflected in parietal alpha band activity.

Effect of transcranial direct current stimulation (tDCS) on food craving and eating when using a control method that minimizes guessing of the real vs. control condition.

PURPOSE: Validation of transcranial direct current stimulation (tDCS) to treat obesity is hampered by evidence that participants can distinguish real from the traditional-control condition. Correctly guessing the real condition precludes knowing if it is neuromodulation or expectation that suppresses food craving and eating. Therefore, this study tested the putative efficacy of tDCS to the dorsolateral prefrontal cortex (DLPFC) to reduce food craving and eating when an alternative control condition was used that would be difficult to distinguish from the real condition. METHODS: N = 28 adults with a 26-50 BMI range received a typical 20-min 2 mA current session of tDCS targeting the DLPFC as the real condition and a same duration/current tDCS session targeting the sensorimotor cortex (SMC), a region not expected to affect appetite, as the control. Food image craving ratings, in-lab food consumption, and momentary ratings of physical sensations were measured. RESULTS: DLPFC failed to reduce food craving and consumption compared to SMC stimulation. When interviewed, 71% of participants were unable to guess real from control conditions. Those who guessed DLPFC tDCS as real attributed their guess to increased number and frequency of sensations. However, their sensation ratings during tDCS did not differ between conditions. CONCLUSIONS: The results question if tDCS suppresses craving and eating at all, or if the DLPFC is the best target to do so. The results also indicate that alternate-site constant stimulation as the control method may strengthen the scientific evaluation of tDCS to treat obesity. LEVEL OF EVIDENCE: Level I, experimental study.

Classifying creativity: Applying machine learning techniques to divergent thinking EEG data.

Prior research has shown that greater EEG alpha power (8-13 â€‹Hz) is characteristic of more creative individuals, and more creative task conditions. The present study investigated the potential for machine learning to classify more and less creative brain states. Participants completed an Alternate Uses Task, in which they thought of Normal or Uncommon (more creative) uses for everyday objects (e.g., brick). We hypothesized that alpha power would be greater for Uncommon (vs. Common) uses, and that a machine learning (ML) approach would enable the reliable classification data from the two conditions. Further, we expected that ML would be successful at classifying more (vs. less) creative individuals. As expected, alpha power was significantly greater for the Uncommon than for the Normal condition. Using spectrally weighted common spatial patterns to extract EEG features, and quadratic discriminant analysis, we found that classification accuracy for the two conditions varied widely among individuals, with a mean of 63.9%. For more vs. less creative individuals, 82.3% classification accuracy was attained. These findings indicate the potential for broader adoption of machine learning in creativity research.

Attribution of emotions to body postures: an independent component analysis study of functional connectivity in autism.

The ability to interpret others' body language is a vital skill that helps us infer their thoughts and emotions. However, individuals with autism spectrum disorder (ASD) have been found to have difficulty in understanding the meaning of people's body language, perhaps leading to an overarching deficit in processing emotions. The current fMRI study investigates the functional connectivity underlying emotion and action judgment in the context of processing body language in high-functioning adolescents and young adults with autism, using an independent components analysis (ICA) of the fMRI time series. While there were no reliable group differences in brain activity, the ICA revealed significant involvement of occipital and parietal regions in processing body actions; and inferior frontal gyrus, superior medial prefrontal cortex, and occipital cortex in body expressions of emotions. In a between-group analysis, participants with autism, relative to typical controls, demonstrated significantly reduced temporal coherence in left ventral premotor cortex and right superior parietal lobule while processing emotions. Participants with ASD, on the other hand, showed increased temporal coherence in left fusiform gyrus while inferring emotions from body postures. Finally, a positive predictive relationship was found between empathizing ability and the brain areas underlying emotion processing in ASD participants. These results underscore the differential role of frontal and parietal brain regions in processing emotional body language in autism.