Bringing the brain into personality assessment: Is there a place for event-related potentials?

Abstract

Advances in technology have provided opportunities to assess physiological correlates and further our understanding of a number of constructs, including personality traits. Event-related potentials (ERPs), scalp-recorded measures of brain activity with millisecond temporal resolution, show properties that make them potential candidates for integrating neurophysiological methods into personality research. Several commonly used ERPs have trait-like properties including test-retest stability approaching .8 over two weeks. Additionally, ERP methods are relatively inexpensive and tolerable compared to other neurophysiological methods (e.g., functional magnetic resonance imaging [fMRI]) making them easier to obtain sample sizes required for individual differences research. Finally, the tasks that elicit ERPs are flexible enough to allow researchers to customize the tasks to the psychological constructs of interest. These factors suggest that ERPs could potentially be useful in the study of personality and individual differences. A baseline approach to this line of inquiry is to examine the properties of ERPs as neurophysiological individual differences markers and probe their links to personality traits as assessed by self-report questionnaires. This article does this for three well-studied ERPs. Techniques commonly used in personality assessment research-but rarely in ERP research-were applied to these candidate ERPs to examine their psychometric properties and personality correlates. Overall, although ERPs show promising properties as neurophysiological indicators of individual differences, they were only marginally related with existing personality traits. Further research clarifying the ERPs measurement properties and potential links with known personality processes is needed. Finally, we list some strategies to further integrate these two areas of research. (PsycINFO Database Record

An fMRI investigation of the relations between Extraversion, internalizing psychopathology, and neural activation following reward receipt in the Human Connectome Project sample

“…For example, in a longitudinal sample of children, self-report positive affectivity was related to feedback negativity (i.e., activation in the ventral striatum and medial prefrontal cortex in monetary loss trials minus activation in gain trials) in a hierarchical regression model controlling for demographic covariates and negative affectivity, but not at the bivariate level (r = −.09; N = 381; Kujawa et al, 2015). In adult samples, there is some recent evidence that reward-processing (i.e., activation in medial frontal regions during unpredicted reward trials minus unpredicted non-reward trials) is uniquely positively related to Extraversion (r = .26; N = 100; Smillie et al, 2019), but other studies have not found support for this relation (r = .06; N = 371; Suzuki, Hill, Ait Oumeziane, Foti, & Samuel, 2019). In terms of functional neuroimaging methodology, there are numerous studies linking Extraversion to dopaminergic reward regions like the ventral striatum (Canli, Sivers, Whitfield, Gotlib, & Gabrieli, 2002;Canli et al, 2001;Cohen, Young, Baek, Kessler, & Ranganath, 2005;Mobbs, Hagan, Azim, Menon, & Reiss, 2005;Schaefer, Knuth, & Rumpel, 2011;Wu, Samanez-Larkin, Katovich, & Knutson, 2014), but virtually all of the previous work on Extraversion and fMRI reward-processing was statistically underpowered, with most studies using samples under 20 participants.…”

The hierarchical structure of error-related negativities elicited from affective and social stimuli and their relations to personality traits

“…The guideline published by Cohen (1992) of r ≥ .10 as small effect size, r ≥ .30 as medium effect sizes, and r ≥ .50 as large effect sizes were used. The personality profile similarities across tasks were computed by correlating the z-transformed correlation between task and personality traits (e.g., the five correlations between latent Arrow ERN and FFM domains were z-transformed and then were correlated with the five z-transformed correlations between latent Social ERN and FFM domains; Suzuki et al, 2018;Westen & Rosenthal, 2003).…”

“…We recently examined the relations between the ERN and the FFM domains (Suzuki, Hill, Ait Oumeziane, Foti, & Samuel, 2018), as assessed through the brief Five-Factor Model Rating Form (Mullins-Sweatt, Jamerson, Samuel, Olson, & Widiger, 2006). In a sample of approximately 160 undergraduate participants, the correlations between the ERN and personality traits were small, with the largest correlation being .16 with Openness to Experience.…”

Quantitative Personality Predictions from a Brief EEG Recording

“…In the present study, we propose a novel machine learning-based assessment of the 66 Big Five personality traits using a brief electroencephalography (EEG) recording. 67 EEG is one of the most commonly used non-invasive neuroimaging techniques and is 68 especially suitable for application-oriented personality assessment due to its relatively 69 inexpensive and tolerable nature (Suzuki, Hill, Ait Oumeziane, Foti, & Samuel, 2018). 70…”

“…For example, 72 extroverts were shown to be more likely to experience positive emotions(Lee Anna 73 Clark & Watson, 2008;John, Naumann, & Soto, 2008), while those scoring high on 74 neuroticism were more inclined to experience negative emotions(Lee Anna Clark & 75 Watson, 2008;John et al, 2008). Accordingly, studies of event-related potentials 76 (ERPs) have shown that personality affects one's neural response to emotional 77 stimuli (De Pascalis, Strippoli, Riccardi, & Vergari, 2004;Speed 78 et al, 2015;Suzuki et al, 2018), and there are recent studies reporting distinct EEG 79 profiles by people with high versus low level of personality traits when viewing video 80 clips (Subramanian et al, 2018;Zhao, Ge, Shen, Wei, & Wang, 2018). However, 81 personality inferences finer than binary levels based on brain activities have not yet 82 been achieved.…”