Disentangling periodic and aperiodic resting EEG correlates of personality.

Previous studies of resting electroencephalography (EEG) correlates of personality traits have conflated periodic and aperiodic sources of EEG signals. Because these are associated with different underlying neural dynamics, disentangling them can avoid measurement confounds and clarify findings. In a large sample (n = 300), we investigated how disentangling these activities impacts findings related to two research programs within personality neuroscience. In Study 1 we examined associations between Extraversion and two putative markers of reward sensitivity-Left Frontal Alpha asymmetry (LFA) and Frontal-Posterior Theta (FPT). In Study 2 we used machine learning to predict personality trait scores from resting EEG. In both studies, power within each EEG frequency bin was quantified as both total power and separate contributions of periodic and aperiodic activity. In Study 1, total power LFA and FPT correlated negatively with Extraversion (r ∼ -0.14), but there was no relation when LFA and FPT were derived only from periodic activity. In Study 2, all Big Five traits could be decoded from periodic power (r ∼ 0.20), and Agreeableness could also be decoded from total power and from aperiodic indices. Taken together, these results show how separation of periodic and aperiodic activity in resting EEG may clarify findings in personality neuroscience. Disentangling these signals allows for more reliable findings relating to periodic EEG markers of personality, and highlights novel aperiodic markers to be explored in future research.

Why do people seek information? The role of personality traits and situation perception.

The opportunity to learn new knowledge is ever present. How do people decide if information has sufficient value to counteract the cost of obtaining it? We proposed a conceptual model of information seeking that emphasizes how personality traits and perceptions of situations may influence motivations to seek information to explore (related to trait curiosity and openness/intellect, and situations evoking more positive emotions and opportunities for intellectual engagement) or feel safe (related to trait uncertainty intolerance and neuroticism, and situations that evoke more negative emotions). Across two studies (N = 436; N = 316), information seeking was assessed with two widely used paradigms (advance knowledge of a reward outcome and answers to trivia questions), as well as two variations of the trivia paradigm in Study 1. In all contexts, the available information was noninstrumental, having no practical utility within the context of the task. Consistent with our proposed exploration pathway, curiosity and openness/intellect predicted the choice to seek information for trivia and related stimuli, but not reward-outcome stimuli, and trivia stimuli were generally rated as more intellectually engaging, more positive, and less negative than reward-outcome stimuli. However, evidence for the safety pathway was only partially in line with predictions, with uncertainty intolerance predicting reward-outcome information seeking in Study 2 only. We consider possible modifications to our initial model and implications for information-seeking research. These studies provide a proof of concept that people display both trait- and context-dependent preferences for noninstrumental information, both of which are commonly overlooked in studies of information seeking. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Decoding personality trait measures from resting EEG: An exploratory report.

Can personality be predicted from oscillatory patterns produced by the brain at rest? To date, relatively few studies using electroencephalography (EEG) have yielded consistent relations between personality trait measures and spectral power. Thus, new exploratory research may help develop targeted hypotheses about how neural processes associated with EEG activity may relate to personality differences. We used multivariate pattern analysis to decode personality scores (i.e., Big Five traits) from resting EEG frequency power spectra. Up to 8 minutes of EEG data was recorded per participant prior to completing an unrelated task (N = 168, Mage = 23.51, 57% female) and, in a subset of participants, after task completion (N = 96, Mage = 23.22, 52% female). In each recording, participants alternated between open and closed eyes. Linear support vector regression with 10-fold cross validation was performed using the power from 62 scalp electrodes within 1 Hz frequency bins from 1 to 30 Hz. One Big Five trait, agreeableness, could be decoded from EEG power ranging from 8 to 19 Hz, and this was consistent across all four recording periods. Neuroticism was decodable using data within the 3-6 Hz range, albeit less consistently. Posterior alpha power negatively correlated with agreeableness, whereas parietal beta power positively correlated with agreeableness. We suggest methods to draw from our results and develop targeted future hypotheses, such as linking to individual alpha frequency and incorporating self-reported emotional states. Our open dataset can be harnessed to reproduce results or investigate new research questions concerning the biological basis of personality.

Extraversion and reward-processing: Consolidating evidence from an electroencephalographic index of reward-prediction-error.

Trait extraversion has been theorized to emerge from functioning of the dopaminergic reward system. Recent evidence for this view shows that extraversion modulates the scalp-recorded Reward Positivity, a putative marker of dopaminergic signaling of reward-prediction-error. We attempt to replicate this association amid several improvements on previous studies in this area, including an adequately-powered sample (N = 100) and thorough examination of convergent-divergent validity. Participants completed a passive associative learning task presenting rewards and non-rewards that were either predictable or unexpected. Frequentist and Bayesian analyses confirmed that the scalp recorded Reward Positivity (i.e., the Feedback-Related-Negativity contrasting unpredicted rewards and unpredicted non-rewards) was significantly associated with three measures of extraversion and unrelated to other basic traits from the Big Five personality model. Narrower sub-traits of extraversion showed similar, though weaker associations with the Reward Positivity. These findings consolidate previous evidence linking extraversion with a putative marker of dopaminergic reward-processing.

Combining error-driven models of associative learning with evidence accumulation models of decision-making.

As people learn a new skill, performance changes along two fundamental dimensions: Responses become progressively faster and more accurate. In cognitive psychology, these facets of improvement have typically been addressed by separate classes of theories. Reductions in response time (RT) have usually been addressed by theories of skill acquisition, whereas increases in accuracy have been explained by associative learning theories. To date, relatively little work has examined how changes in RT relate to changes in response accuracy, and whether these changes can be accounted for quantitatively within a single theoretical framework. The current work examines joint changes in accuracy and RT in a probabilistic category learning task. We report a model-based analysis of changes in the shapes of RT distributions for different category responses at the level of individual stimuli over the course of learning. We show that changes in performance are determined solely by changes in the quality of information entering the decision process. We then develop a new model that combines an associative learning front end with a sequential sampling model of the decision process, showing that the model provides a good account of all aspects of the learning data. We conclude by discussing potential extensions of the model and future directions for theoretical development that are opened up by our findings.

Contemplative interventions and employee distress: A meta-analysis.

Mindfulness, meditation, and other practices that form contemplative interventions are increasingly offered in workplaces to support employee mental health. Studies have reported benefits across various populations, yet researchers have expressed concerns that adoption of such interventions has outpaced scientific evidence. We reappraise the extant literature by meta-analytically testing the efficacy of contemplative interventions in reducing psychological distress in employees (meta-analysed set: k = 119; N = 6,044). Complementing other reviews, we also examine a range of moderators and the impact of biases that could artificially inflate effect sizes. Results suggested interventions were generally effective in reducing employee distress, yielding small to moderate effects that were sustained at last follow-up. Effects were moderated by the type of contemplative intervention offered and the type of control group utilized. We also found evidence of publication bias, which is likely inflating estimated effects. Uncontrolled single-sample studies were more affected by bias than were large or randomized controlled trial studies. Adjustments for publication bias lowered overall effects. Overall, our review supports the effectiveness of contemplative interventions in reducing employee distress, but there is a need for proactive strategies to mitigate artificially inflated effect sizes to avoid the misapplication of contemplative interventions in work settings.