Dissociable Trait Correlates of P300 Variability and Amplitude: Conscientiousness and Disinhibition

Dissociable Trait Correlates of P300 Variability and Amplitude: Conscientiousness and Disinhibition | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 29 September 2025 V1 Latest version Share on Dissociable Trait Correlates of P300 Variability and Amplitude: Conscientiousness and Disinhibition Authors : Danielle Jones 0000-0002-0689-9796 [email protected] , Colin B. Bowyer , Chris B. Martin , Christopher Patrick , and keanan joyner Authors Info & Affiliations https://doi.org/10.22541/au.175911494.49366794/v1 184 views 137 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Event-related potentials (ERPs) have been extensively used to link neural processes with personality traits, but most work has focused on mean amplitudes, leaving intraindividual variability in brain response comparatively unexplored. The present study examined whether trial-to-trial variability in the P300 reflects a common latent factor across cognitive tasks, and whether it relates to personality traits in ways that differ from mean amplitude. A mixed student and community sample ( N = 206) completed three ERP tasks (oddball, flanker, and doors) yielding nine P300 variants. Confirmatory factor analyses revealed that mean amplitude and intraindividual variability each loaded onto cohesive but distinct higher-order factors, which were moderately correlated. Structural equation modeling showed that mean P300 amplitude was uniquely and negatively related to Disinhibition, consistent with prior research, whereas P300 variability showed a unique negative association with Conscientiousness, a novel finding. This pattern of results aligns with the idea that P300 variability could indicate the consistency of attentional engagement, whereas amplitude reflects a capacity to engage top-down control. INTRODUCTION There has been longstanding interest in using event-related potentials (ERPs) to elucidate the cognitive processes underlying psychological traits and clinical conditions. The extant literature has implicated various ERP components as biomarkers of psychiatric disorders and symptoms (Bauer & Hesselbrock, 2003; Bertoletti et al., 2014; Singh et al., 2009; Hamidovic & Wang, 2019) and both normal and abnormal personality traits (Bauer et al., 1994; Gao & Raine, 2009; Venables et al., 2018). This work has yielded significant insights into the neural processes that may maintain these conditions. In turn, linking ERP components to well-characterized psychological constructs has provided reciprocal benefits by refining theoretical accounts of the cognitive and neural processes those components reflect. However, most work linking ERPs to psychological traits has focused on the average amplitude parameter of ERP response, with other parameters such as intraindividual variability – the extent to which evoked-potential signal amplitude varies across trials –receiving considerably less attention. However, recent studies indicate that intraindividual variability may also relate to individual differences in psychological traits (Uemura & Hoshiyama, 2007; Kim et al., 2018; Bauer, 2022), though this line of research remains in its early stages. The present study extends this emerging literature by examining intraindividual variability in a common ERP component across diverse task contexts. As a starting point, it useful to consider why ERP research has traditionally focused on the average amplitude of a component across trials. This approach, termed the “signal-averaging” approach, operates to increase the signal-to-noise ratio and reliability of ERP scores (Luck et al., 2000). However, the engagement of certain neurocognitive processes can be expected to vary on a moment-to-moment basis, and the signal-averaging approach inherently obscures this dynamic variability. Examining intraindividual variability offers a means to quantify these fluctuations rather than discard them as noise. When considered alongside mean amplitude, intraindividual variability provides complementary insight. Whereas mean amplitude of the time-locked EEG signal may reflect the overall strength of a stimulus-elicited neurocognitive process, signal variability captures the consistency with which that process is distinctively engaged. Common Variants of the P300 Response Among ERP components, the P300 stands out as one of the most extensively studied, with a well-characterized neural origin and well documented elicitation across a range of cognitive tasks. The P300 is characterized by a large positive-going brain response following attended stimuli and is thought to represent various cognitive-attentional processes, with its precise interpretation dependent on task demands (Polich, 2007). Its extensive use across different task paradigms, strong theoretical foundation, and connection to a dynamic, time-varying process (i.e., working memory updating; Donchin, 1981) make it an ideal candidate for examining both mean-based and variability-based neural metrics. While multiple variants of the P300 have been identified, available evidence indicates overlap in terms of their neural bases despite some differences in latency and scalp location as a function of task contexts in which they are measured (Polich, 2007). The oddball paradigm has been most widely used to elicit the P300 and involves presenting participants with at least two types of stimuli: frequent non-target stimuli that do not require a response and infrequent targets that do. The brain response to target stimuli (a.k.a., the target P300 or P3b) is the “prototypical” P300 and is theorized to reflect the updating of working memory (Donchin, 1981) in relation to a stimulus event. Infrequent non-target distractors, when added to the oddball task as a third stimulus type, elicit an earlier P300 response dubbed the “P3a”, which is thought to reflect in part the process of attentional orienting towards novel stimuli (Polich, 2007). Furthermore, other paradigms besides the oddball task have been shown to elicit P300 responses. For example, in the Eriksen flanker-discrimination task (Eriksen & Eriksen, 1974), P300 responses occur to character-string stimuli that require a behavioral response on every trial (Klawohn et al., 2020; Santopetro et al., 2020) as well as following incorrect responses to those stimuli (Klawohn et al., 2020), and in the “doors” choice-feedback task (Proudfit, 2015), P300 responses occur both to initial choice stimuli (Santopetro et al., 2021) and to subsequent monetary feedback stimuli (Santopetro et al., 2025). Evidence for the convergence of these P300 variants is provided by work utilizing confirmatory factor analysis (CFA) to demonstrate that they load onto a common latent factor (Bowyer et al., 2020; Venables et al., 2018). The implication is that, while there is some uniqueness to each variant of the P300, the neural generators of these variants overlap considerably. Theoretical accounts suggest that the main process underlying the overlap in response amplitude among these variants is proactive attention (Patrick & Bernat, 2009), a process entailing attentional readiness for upcoming task stimuli that facilitates the application of task instructions in the service of effective performance. Building upon this, the current study examined whether variability in P300 responding similarly overlaps across different cognitive tasks, and evaluated whether this overlap reflects a process distinct from that underlying cross-task amplitude covariation by testing for differential relations with theoretically relevant trait constructs. P300 Amplitude as an Index of General Externalizing Liability The P300 is robustly associated with psychological traits and clinical conditions characterized by deficits in top-down control, which is unsurprising given its role in the strategic allocation of attention. Reduced P300 amplitude is a common feature of disorders marked by impaired impulse regulation, including child disruptive disorders (Bauer & Hesselbrock, 2003; Bertoletti et al., 2014), antisocial personality disorder (Bauer et al., 1994; Gao & Raine, 2009), and various substance use disorders (Singh et al., 2009; Hamidovic & Wang, 2019). These patterns, along with the P300’s high within-person stability (Kamp et al., 2023) and substantial heritability (van Beijsterveldt & van Baal, 2002), have established P300 as a strong candidate for indexing broad dispositional vulnerabilities. One such vulnerability is trait disinhibition (Patrick et al., 2009), a well-validated personality construct that encompasses deficient emotional and behavioral control, recklessness, the lack of forethought or planning present among many of the externalizing disorders. Importantly, the P300 has been linked to this broader liability: The variance shared across different P300 variants (i.e., the common latent factor) has been shown to index dispositional risk for impulse control problems, termed trait disinhibition (Venables et al., 2018). The association between Disinhibition and reduced P300 amplitude has been commonly interpreted as reflecting diminished engagement of top-down cognitive control (Patrick & Bernat, 2009). Theoretical accounts suggest that individuals high in disinhibition are more stimulus-driven and less reliant on internal representations or contextual expectations when processing information. That is, rather than proactively deploying attention to process upcoming stimuli, they process and respond to stimuli at the time of their occurrence. In the context of an oddball task, for example, individuals with intact inhibitory control are likely to implicitly track the sequence of non-targets and prepare for an impending target, engaging working memory to update context as needed. In contrast, highly disinhibited individuals rely less on top-down systems that ordinarily bias attention and response preparation to support efficient performance. Evidence for this interpretation comes from studies of patients who have sustained damage to the lateral prefrontal cortex (LPFC). The occurrence of LPFC damage often results in an inability to suppress reactions to objects and events, coined “environmental dependency syndrome” (Lhermitte, 1986) – the clinical extreme of a disinhibited processing style. Importantly, LPFC damage is associated with reduced P3 amplitude (Deouell & Knight, 2005), consistent with the idea that a stimulus-driven response style is reflected in a blunted P300 response. Intraindividual Variability While mean P300 amplitude reflects working memory processing, considered a key top-down cognitive control capacity (Venables et al., 2018; Friedman & Miyake, 2000), another important contributor to P300 responding is the stability of attentional engagement across a task. Individuals may show low P300 amplitudes without heightened variability, indicating a reduced capacity for working memory (and potentially other cognitive-control operations; see Venables et al., 2018) but constant engagement in the task across trials, resulting in consistency of event-related EEG activation. In this way, ERP signal variability may reflect a process that is largely independent of mean signal amplitude. Indeed, emerging evidence does suggest that intraindividual variability is particularly sensitive to fluctuations in attentional state. For example, ERP variability decreases under conditions that enhance alertness and spatial attention (Arazi et al., 2019). Additionally, neural activity is more stable when sensory stimuli are consciously perceived (Schurger et al., 2015). These within-subject effects indicate that variability may reflect moment-to-moment changes in attentional engagement. At the between-subject level, variability has also been linked to clinical conditions characterized by attentional deficits, including dementia, schizophrenia (Kim et al., 2018), and opiate abuse (Bauer, 2022). Although the mechanisms underlying these associations remain unclear, they are often interpreted as reflecting a reduced capacity to maintain consistent attentional allocation over time. If intraindividual variability captures a trait-like feature of attentional engagement, we would expect this consistency to generalize across contexts – as has been demonstrated for the amplitude parameter of P300 response, by studies revealing a common amplitude factor for variants of P300 from different tasks. It remains an open question whether a similar factor underlies intraindividual variability across different P300 variants. Furthermore, intraindividual variability should be higher in individuals prone to lapses in focus, low persistence, or inconsistent task engagement. As such, intraindividual variability should relate to traits associated with a deficient capacity for sustained attention. Potential Correlates of Intraindividual Variability in the P300 Conscientiousness is one trait linked to sustained attention (Avisar & Shalev, 2011; Kolanowski et al., 2012), suggesting it as a potential correlate of individual differences in P300 variability. As a broad personality construct, Conscientiousness encompasses propensities towards high self-control, dutifulness to rules and others, and hard work (Roberts et al., 2014). If neural variability reflects the capacity to maintain consistent cognitive engagement over time, then higher levels of Conscientiousness should be associated with more stable neural responses. While this direct association has not yet been tested, low Conscientiousness is reported in the same list of disorders as increased intraindividual variability including dementia (Kaup et al., 2019), schizophrenia (Camisa et al., 2005), and opioid dependence (Kornør & Nordvik, 2007). This convergence provides preliminary evidence that variability and Conscientiousness may be linked through shared mechanisms related to sustained attention to task stimuli. Evidence for this shared mechanism can also be seen in laboratory measures of task engagement. In a cognitive task context, increased Conscientiousness has been associated with less mind-wandering during cognitive task performance (Jackson & Balota, 2012) and increased error monitoring (Imhof & Rüsseler, 2019). Additionally, more Conscientiousness participants have fewer omission errors (missed trials) in a Go/No-Go task, are slower (or perhaps more careful) to respond, and have more brain activation in regions related to goal-directed behavior during errors (Sosic-Vasic et al., 2012). Although Conscientiousness and Disinhibition both relate to the construct of cognitive control (REFS) and are moderately correlated (Stanley et al., 2013), they represent distinct constructs. Conscientiousness is a normally distributed personality trait that reflects sustained self-regulation and goal-directed behavior (Roberts et al., 2014). It aligns closely with components of impulsivity outlined in the UPPS-P model (Whiteside & Lynam, 2001), including (lack of) premeditation, (lack of) perseverance, sensation seeking, and both positive and negative urgency (Lynam et al., 2006). In contrast, Disinhibition reflects a more extreme, clinically oriented disposition marked by poor behavioral control and elevated risk for externalizing psychopathology (Joyner et al., 2021). Given this distinction, we hypothesize that Disinhibition will be more closely associated with mean P300 amplitude (reflecting diminished overall strength of cognitive control processes relevant to task performance; Venables et al., 2018), whereas Conscientiousness will relate more to the inter-trial variability parameter of P300 responding, reflecting the consistency of task engagement. The Present Study We utilized data from three distinct task contexts to test three hypotheses. First, we hypothesized that variability scores for variants of P300 from different task procedures, like amplitude scores (Nelson et al., 2011; Venables et al., 2018), would load onto a shared latent factor. Next, we hypothesized that Conscientiousness would relate to P300 variability, but not mean amplitude, reflecting the idea that variability in the P300 across task contexts indexes sustained cognitive engagement rather than the strength of the underlying attentional process. In contrast, we expected Disinhibition to relate to mean P300 amplitude, but not variability, consistent with prior work linking disinhibition to reduced P300 amplitude. Participants A sample of student and community adults collected at a large U.S. southeastern university were analyzed for this study. The sample contained 206 participants with a mean age of 20.9 years old ( SD = 4.3) and was 50% female. The racial composition of the sample was largely White (81%), with some representation of minority populations (11% Black and 5% Asian). Participants completed self-report measures at the beginning of the experimental session while EEG and EMG sensors were being applied. Following this, participants completed all experimental task procedures in one session. The study protocol was reviewed and approved by the university’s Institutional Review Board and written informed consent was obtained from all participants. Measures Questionnaires . Conscientiousness was measured within the NEO Five Factor Inventory (NEO-FFI; Costa, 1992), which asks people to rate 60 personality items on a 5-point Likert scale (Strongly Disagree to Strongly Agree). Internal consistency reliability was calculated using Cronbach’s alpha (α), which was .85 in this sample. Disinhibition was measured using a 30-item scale developed to index the general disinhibition factor of the Externalizing Spectrum Inventory (ESI; Krueger et al., 2007; Patrick et al., 2013). Responses to items on this scale were rated on a 4-point Likert scale (True, Somewhat True, Somewhat False, False). Internal consistency reliability was high, α = .89. Flanker Task. All participants completed an Eriksen flanker task (Eriksen & Eriksen, 1974) as part of a larger battery of tasks while continuous electroencephalography (EEG) data were recorded. The flanker task contained 336 trials divided into three blocks. Participants were instructed to respond to the direction of the center arrow in a series of arrows on the screen using the left and right keys on a button box. Congruent trials (118 trials) contained arrows pointing in the same direction (i.e., >>>>> or <<<<<), while incongruent trials (118 trials) had arrows pointing in the opposite direction flanking the target arrow (i.e., <<><><>>). On each trial, congruent or incongruent arrow stimuli were presented for 200 ms, followed by a variable inter-trial interval (ITI) ranging from ranging from 2,100 to 2,600 ms ( M = 2,350 ms) consisting of a white fixation dot against a black background. Participants had 1,150 ms to make a response following the stimulus presentation (see Panel A. in Supplement 1 for task schematic). Between each block, participants received digital feedback based on their performance before progressing to the next block to optimize the number of errors. Participants who got less than 70% of trials in a block correct were told to respond more carefully, whereas participants who got more than 90% correct were told to respond more quickly. Participants whose accuracy was between 70% and 90% were told that they were doing a good job and to keep going. Before completing the task, participants were given verbal instructions and completed a brief set of practice trials before completing the full task. ERPs of interest were the P300 response to congruent and incongruent stimuli, as well as to incorrect responses. Doors Task. Participants completed a forced-choice gambling task (i.e., the doors task; Proudfit, 2015) commonly used to elicit a reward response. The task contained 40 trials that began with an image of two doors, where participants were asked to make either a left or right button press to select either the left or right door, indicating which one they thought the reward was behind. After selecting a door, there was a 1,000 ms inter-stimulus interval (ISI) consisting of a white fixation cross on a black background, followed by performance feedback consisting of either a red arrow pointing down (indicating monetary loss of $0.25) or a green arrow pointing up (indicating monetary gain of $0.50) for 2000 ms (see Panel B. in Supplement 1 for task schematic). Trials were block randomized unbeknownst to the participant to produce wins on 50% of the 40 trials. ERPs of interest were the P300 responses to the feedback (i.e., gain and loss) and to the initial doors stimuli. Novelty Oddball Task. Participants completed a rotated-heads oddball paradigm (Begleiter et al., 1984) that measured their orienting response to novel and target stimuli. 240 stimuli across three categories were presented on the screen for 100 ms: 70% were standards (168 trials), 15% were target (36 trials), and 15% were novel (36 trials). Standard stimuli were simple circles that required no response. Target stimuli were circles with additional features to represent a head being viewed from the top with an “ear” and a “nose”; participants were instructed to use a left or right button press to determine the side of the head that the ear was on using the nose to determine the head’s orientation. Participants had 3,000 ms following stimulus presentation to make a response. Novel trials consisted of pleasant, unpleasant, and neutral images from the International Affective Picture Set (IAPS; Lang et al., 2005), and they required no response. Stimuli were followed by a variable ITI ranging from 3,900 to 4,900 ms ( M = 4,400 ms) consisting of a fixation dot (see Panel C. in Supplement 1 for task schematic). Trial order was pseudo-randomized to ensure that neither two novels nor two targets appeared back-to-back. ERPs of interest were P300 responses to target, standard, and novel (averaged across pleasant, unpleasant, and neutral IAPS pictures) stimuli. EEG Data. Online EEG data were collected via 128-channel Neuroscan Quik-Caps containing sintered Ag-AgCl scalp electrodes placed in accordance with Neuroscan’s nonstandard layout (NSL) system. EEG data were online referenced to an electrode placed at the vertex of the scalp and bandpass filtered from 0.05 - 200 Hz prior to digitization. Four electromyogram (EMG) electrodes were placed to collect electrooculographic data for ocular correction; two were placed at the outer canthi of the left and right eye, and two were placed above and below the left eye. EEG data were recorded with SynAmps 2/RT amplifiers using Neuroscan Acquire software at a 1,000 Hz sampling rate, and all electrode impedances were kept below 10 kOhms. Task stimuli were presented via Eprime 2 (MEL Software, Inc) on a cathode-ray tube (CRT) monitor using a separate computer than the one used for data acquisition. Task timing information (i.e., triggers) were sent to the data acquisition computer via parallel port. EEG Data Processing EEG data were processed offline using MATLAB Version 2022b, the EEGLAB toolbox v2024.0 (Delorme & Makeig, 2004) and the ERPLAB toolbox v11.03 (Lopez-Calderon & Luck, 2014). Data were first downsampled to 250 Hz then re-referenced to the average of the left and right mastoids. Next, a .1 Hz – 30 Hz bandpass filter was applied to the data and abnormal channels (assessed as being below a correlational threshold of less than .7 with its robust estimate from surrounding channels) were interpolated via spherical splines. Data were then epoched from -200 ms prestimulus to 800 ms poststimulus. Next, a blink detection algorithm was run to flag trials containing blinks in the pre-stimulus baseline period (-200 to 0 ms) for removal, then the Gratton and Coles (Gratton et al., 1983) blink correction algorithm was run on the epoched data. Data were then baseline corrected to the average activity in the pre-stimulus baseline period. Finally, trials were excluded when they contained artifacts meeting the following criteria: an amplitude exceeding 200 ms window, flatline (<|1| µV sustained over 400 ms), or a sample-to-sample difference exceeding |50| µV. ERP Scoring Individual ERPs were scored as the average amplitude within a time window determined via the collapsed localizers approach (Luck & Gaspelin, 2017). Briefly, this approach helps to reduce type I error when determining ERP component windows by averaging across conditions of interest (e.g., congruent and incongruent), drawing a time window based on prior research, examining topographic maps of activation within that time window to guide site selection, and adjusting the time window at the chosen site to include the component of interest based on waveform plots at the electrode site showing the highest activation. Single trial amplitudes for the P300 were extracted from MATLAB and exported for further analysis in R. Flanker Task ERPs. The P300 to incongruent and congruent flanker stimuli were defined as 300 ms to 600 ms post-stimulus at electrode site CPPz. The P300 to incorrect responses (‘P300e’) was defined as 150 ms to 400 ms post-response at electrode site Cz. Grand average waveforms and topographical maps are shown below in Figure 1. Doors Task ERPs. The feedback P300 to gain and loss feedback were defined as 250 ms to 500 ms post-feedback at electrode site CPPz. Grand average waveforms and topographical maps are shown below in Figure 2. Novelty Oddball Task ERPs. The target P300 was defined as 350 ms to 550 ms post-stimulus at electrode site PPOz. The standard P300 was defined as 300 ms to 500 ms post-stimulus at electrode site PPOz. The P300 to the novel stimuli was defined as 300 ms to 500 ms post-stimulus at electrode site POz. Grand average waveforms and topographical maps are shown below in Figure 3. Outlier Correction and Averaging Due to the focus on both average amplitude and variability of amplitude, outliers within ERP components of interest were corrected both within-subject and between-subject. For each P300 component, single trial amplitudes were first winsorized to +/- 2.5 interquartile ranges from their respective within-subject median value. The variabilities and the means of the average amplitudes were derived from these winsorized single-trial values. When a participant had fewer than six trials for a given ERP component, their scores for that component were set to missing (Boudewyn et al., 2018). Finally, these variabilities and means were then winsorized to +/- 2.5 interquartile ranges from their respective between-subject median values across the sample. Data Analysis To ensure that all data met analytic assumptions of normality, skewness ( S ) and kurtosis ( k ) values were examined. Additionally, internal consistency reliability for the ERP measures was computed using the Spearman-Brown corrected split-half reliability coefficient (Spearman, 1910; Brown, 1910). After determining univariate normality, Pearson correlations between the ERP variables were run prior to the subsequent confirmatory factor analysis (CFA). For each set of study ERP variables (i.e., mean, variability), two CFA models were evaluated to determine which best fit the data. The first model (termed the one-factor model) had each of the nine manifest ERP indicators (flanker congruent P300, flanker incongruent P300, flanker error P300, doors gain feedback P300, doors loss feedback P300, doors stimulus P300, oddball target P300, oddball novelty P300, and oddball standard P300) loading onto a single latent P300 factor. The second model (termed the hierarchical model) had each set of manifest ERP measures load onto task-specific latent variables (i.e., doors P300 variables loaded onto a latent doors factor, flanker P300 variables loaded onto a latent flanker factor, and oddball P300 variables loaded onto a latent oddball factor) which loaded onto a single higher-order latent P300 factor. All CFAs were performed using the lavaan package (Rosseel, 2012) in R (R Core Team, 2024). Missing data were handled via full-information maximum likelihood estimation (FIML). Absolute fit was evaluated using the chi-square and root mean square error of approximation (RMSEA) indices. Incremental fit was evaluated using the comparative fit index (CFI) and the Tucker-Lewis Index (TLI; Tucker & Lewis, 1973). Generally, these measures indicate good fit when RMSEA is less than .08, CFI is greater than .95, and TLI is greater than .95 (Hu & Bentler, 1999). Next, the best-fitting model for each group of ERP variables was determined via a chi-square difference test that compared each one-factor model against the corresponding hierarchical model. Finally, to assess the degree of covariance between the two latent variables and the trait constructs of interest, the two latent factors were specified as simultaneous predictors of Disinhibition and Conscientiousness in a joint SEM. RESULTS Descriptive Statistics and Correlations Tables 1 and 2 show descriptive statistics for each of the P300 component means and variabilities. For mean values, S and k values were all below |2|, suggesting that these variables were normally distributed. Spearman-Brown corrected split-half reliability coefficients for the mean values were acceptable to good. Variabilities were also normally distributed ( S and k both below |2| for all variables), with acceptable-to-good internal consistency. The mean Conscientiousness score was .68 ( SD = 0.15; range = .10 to .98) and scores were normally distributed ( S = -0.69; k = 0.83); therefore, no outlier correction was performed. The mean Disinhibition score was .19 ( SD = 0.14; range = .00 to .73) and scores were also normally distributed ( S = 1.37; k = 1.93). Correlations among P300 mean amplitudes ranged from non-significant ( r = -.09) to large ( r = .91), with most in the modest to moderate range (median r = .32), indicating substantial shared variance among mean P300 components (see Supplemental Figure 2). Correlations among P300 amplitude variabilities, ranging from modest ( r = .21) to large ( r = .88), with most in the moderate range (median r = .45), also suggesting that these components shared substantial common covariance (see Supplemental Figure 3). Table 3 shows the correlations among mean amplitudes and amplitude variabilities for each respective P300 component. Overall, correlations between metrics were small to moderate. ERP Measurement Models Two confirmatory factor analyses were compared to (1) test the hypothesis that intraindividual variability in the P300 would load onto a single latent factor and (2) to determine the best-fitting model of that latent factor’s structure. In the first model, all P300 component variabilities were specified as indicators of a single latent factor. The second model was a hierarchical model in which P300 component variabilities were specified to load onto first-order factors grouped by the task they were measured in, and those first-order factors were specified to load onto a single second-order factor. Fit statistics for both models are detailed in Supplemental Table 1. A chi-square difference test indicated that the hierarchical model provided a significantly better fit to the data than the one-factor model, Δχ²(3) = 69.98, p < .001. All other model fit indices were also better for the hierarchical model compared with the one-factor model. Thus, a hierarchical model (depicted in Figure 4) was used to model latent P300 variability. This process was repeated to determine the best way to model mean P300 amplitudes. Similarly to P300 amplitude variabilities, the hierarchical model (depicted in Figure 5) provided significantly better fit than the one-factor model Δχ²(3) = 267.93, p < .001. Fit statistics for these two models are provided in Supplemental Table 2. Although the mean Doors P300 amplitude indicator exhibited poor loading (λ = .01) and substantially impaired model fit when included (Δχ² = 19.61, p = .006), it was retained in the final model to maintain the same loading structure as the variability measurement model, which showed comparatively stronger contribution from the Doors P300 amplitude variability (λ = .48).11 Results from the structural equation model described below remained consistent regardless of the inclusion of the Doors P300 amplitude as an indicator. ERP Amplitude and Variability Factors: Relations with Trait Measures In a final step, we used structural equation modeling to test the hypothesis that Disinhibition and Conscientiousness would relate differentially to the mean and variability factors. More specifically, we hypothesized that Conscientiousness would relate to intraindividual variability in the P300 while Disinhibition would relate to the mean amplitude. Results from this model, which exhibited acceptable fit (see Supplemental Table 3) are depicted in Figure 6. This modeling analysis demonstrated that the P300 amplitude and variability factors reflect distinct underlying constructs that share only about 10% of their variance ( r = .33, p < .001). By evaluating relations of these ERP factors with the two trait dimensions via separate regressions, the analysis shows that each ERP factor contains unique trait-related variance – resulting in differential relations of each with Conscientiousness versus Disinhibition. Specifically, the model shows that amplitude variability was related inversely with higher Conscientiousness (β = −.20, p = .013), whereas mean amplitude showed no relationship (β = .01, p = .946). In contrast, lower mean amplitude predicted higher Disinhibition (β = −.22, p = .013), with no effect for amplitude variability (β = .06, p = .475). DISCUSSION The primary goal of this study was to test for cross-task convergence of the inter-trial variability parameter of P300 responding, and to clarify the psychological meaning of this response parameter. To this end, we first examined whether intraindividual variability in the P300 is organized by a common latent factor across tasks. We then evaluated how this P300 variability factor differed from the factor underlying mean amplitude scores across P300 responses from different tasks. Based on prior literature, we expected a common latent factor to emerge from a structural analysis of mean P300 amplitudes from different tasks that would relate significantly and negatively to trait Disinhibition (Venables et al., 2018). Separately, intraindividual variability in brain response has been implicated in active attention (Arazi et al., 2019; Schurger et al., 2015), leading us to hypothesize that P300 variability would organize into a common latent factor potentially reflecting consistency of task engagement. Since Conscientiousness has been link to sustained attention (Avisar & Shalev, 2011; Kolanowski et al., 2012), we expected this trait to relate specifically to the common P300 variability factor. Finally, we expected P300 variability and the mean P300 amplitude to reflect distinct and separable factors, distinguished by their differential relationship to the relevant traits. Using CFA to model nine P300 variants from three cognitive tasks, we found that amplitudes and intraindividual variabilities each loaded onto cohesive higher-order factors, which were distinct from one another. The implication is that P300 intraindividual variability and amplitude index different neurocognitive processes. While the presence of a common latent factor for mean P300 amplitude across variants and tasks has previously been demonstrated, our work is the first to show that intraindividual variability in P300 responding also overlaps across different tasks, indicating the presence of common influences contributing to this parameter of the P300. Of note, these two ERP factors were positively correlated to some extent, a relationship that is mathematically expected given certain constraints on P300 amplitude. Because the P300 is typically positive-going, its lower bound is constrained by a floor near zero, whereas the upper bound is more open-ended. This asymmetry means that individuals with larger mean amplitudes have a greater potential range for trial-to-trial fluctuations, while those with smaller amplitudes are inherently limited in how much variability they can exhibit. As a result, some portion of the observed correlation between amplitude and variability likely reflects this methodological dependency rather than a shared underlying cognitive process. This is ubiquitously observed across fields outside of psychology and neuroscience, as the positive association between mean and variability appears to be near-guaranteed methodologically (Taylor, 1961; Eisler et al., 2008). Results also demonstrated differential and meaningful relations of these two ERP factors with theoretically relevant traits. The main structural equation model revealed that, when accounting for overlap between the intraindividual variability and mean factors, intraindividual variability in P300 amplitude related selectively to Conscientiousness, whereas mean P300 amplitude related distinctively to Disinhibition. This divergence provides further evidence that intraindividual variability and mean amplitude index distinct neurocognitive processes. Implications of ERP-Trait Relations The observed relationship between a blunted mean P300 amplitude and Disinhibition replicated a consistent and large literature. Theoretical accounts attribute this association to a tendency toward stimulus-driven responding (Patrick & Bernat, 2009). Individuals low in Disinhibition are thought to maintain the task context in working memory and build expectations about upcoming stimuli, with larger P300 amplitudes reflecting this enhanced attentional capacity. This proactive attentional strategy is believed to facilitate task performance. In contrast, individuals high in Disinhibition are less likely to form such expectations, instead responding reactively as stimuli appear. The negative relationship between Conscientiousness and intraindividual variability in the P300 represents a novel contribution of the present study. Although Conscientiousness has been linked to sustained attention in prior work (Avisar & Shalev, 2011; Kolanowski et al., 2012), its association with neural variability has not previously been demonstrated. Notably, neural variability has been related to fluctuations in attentional state (Arazi et al., 2019; Schurger et al., 2015). This previously observed relationship aligns with an interpretation that higher Conscientiousness may reflect a greater capacity to maintain stable attentional focus, which would likely result in reduced trial-to-trial variability. This interpretation also aligns with behavioral evidence indicating that individuals high in Conscientiousness exhibit greater task engagement (Imhof & Rüsseler, 2019) and fewer lapses in attention (Jackson & Balota, 2012), in lab-experimental tasks. However, because these neurocognitive processes are not directly measured here, the specific mechanism linking Conscientiousness to reduced P300 intraindividual variability remains to be determined and should be addressed in follow up research. Limitations and Future Directions Findings from the current study should be considered in light of some limitations and opportunities for future work. First, while our study included several well-validated EEG tasks that elicit different P300 variants, the list of tasks administered was not exhaustive. Future work could include P300 responses to more varied stimulus types (e.g., auditory) or from a wider array of task contexts (e.g., learning, memory, etc.). Second, the sample size was relatively modest and composed mainly of White participants. Prior work has demonstrated that some aspects of Big Five personality domains vary across racial groups (Lui et al., 2020), calling for caution in generalizing the relationship between Conscientiousness and reduced P300 amplitude variability. Furthermore, the trait constructs of Disinhibition and Conscientiousness are both susceptible to effects of social desirability – the tendency to endorse socially desirable traits and deny unfavorable ones (Phillips & Clancy, 1972). This highlights our reliance on self-report assessment of these dispositional constructs as an additional limitation of this study, and call for future research utilizing multi-method trait assessments (see, e.g., Patrick, Iacono, & Venables, 2019; Venables et al., 2018). A critical next step, given our results linking Conscientiousness to intraindividual variability in the P300, will be to determine whether sustained, task-directed attention is actually the mechanism driving this relationship. Establishing this link would not only clarify the cognitive basis of the observed association but also help determine whether neural variability could serve as an objective marker of engagement in EEG tasks. Because intraindividual variability in the P300 may reflect moment-to-moment stability in attentional allocation, future work should incorporate direct behavioral or physiological measures of attention—such as eye-tracking, pupil dilation, or self-reported mind-wandering—alongside EEG to evaluate whether these indices covary with the P300 variability factor. Such multimodal approaches could also help separate the substantive cognitive sources of intraindividual variability from methodological noise, thereby improving both theoretical interpretation and data quality. If sustained attention is indeed a driver of the variability–Conscientiousness link, variability measures could become a valuable tool for detecting disengagement in real time and for enhancing the reliability of cognitive and clinical EEG research. Notwithstanding these points, the current study extends prior work on the latent structure of P300 amplitude by presenting evidence for a coherent common factor contributing to intraindividual variability in P300 responding across task contexts. Although amplitude and variability are moderately correlated, our work indicates that they index separable, domain-general processes with distinct personality correlates – mean P300 amplitude relating to trait disinhibition, and P300 variability relating to Conscientiousness. These findings highlight the theoretical value of distinguishing between these ERP response parameters and point to sustained attention as a promising, testable mechanism underlying the intraindividual variability–Conscientiousness association. Future work aimed at verifying this mechanism stands to advance personality neuroscience and improve EEG measurement methods. References Arazi, A., Yeshurun, Y., & Dinstein, I. (2019). Neural Variability Is Quenched by Attention. Journal of Neuroscience , 39 (30), 5975–5985. https://doi.org/10.1523/JNEUROSCI.0355-19.2019Avisar, A., & Shalev, L. (2011). Sustained Attention and Behavioral Characteristics Associated with ADHD in Adults. Applied Neuropsychology , 18 (2), 107–116. https://doi.org/10.1080/09084282.2010.547777Bauer, L. O. (2022). Inter-trial variability in postural control and brain activation: Effects of previous opiate abuse. Biological Psychology , 174 , 108424. https://doi.org/10.1016/j.biopsycho.2022.108424Bauer, L. O., & Hesselbrock, V. M. (2003). Brain Maturation and Subtypes of Conduct Disorder: Interactive Effects on P300 Amplitude and Topography in Male Adolescents. Journal of the American Academy of Child & Adolescent Psychiatry , 42 (1), 106–115. https://doi.org/10.1097/00004583-200301000-00017Bauer, L. O., O’Connor, S., & Hesselbrock, V. M. (1994). Frontal P300 Decrements in Antisocial Personality Disorder. Alcoholism: Clinical and Experimental Research , 18 (6), 1300–1305. https://doi.org/10.1111/j.1530-0277.1994.tb01427.xBegleiter, H., Porjesz, B., Bihari, B., & Kissin, B. (1984). Event-Related Brain Potentials in Boys at Risk for Alcoholism. Science , 225 (4669), 1493–1496. https://doi.org/10.1126/science.6474187Bertoletti, E., Michelini, G., Moruzzi, S., Ferrer, G., Ferini-Strambi, L., Stazi, M. A., Ogliari, A., & Battaglia, M. (2014). A General Population Twin Study of Conduct Problems and the Auditory P300 Waveform. Journal of Abnormal Child Psychology , 42 (5), 861–869. https://doi.org/10.1007/s10802-013-9836-7Boudewyn, M. A., Luck, S. J., Farrens, J. L., & Kappenman, E. S. (2018). How many trials does it take to get a significant ERP effect? It depends. Psychophysiology , 55 (6), e13049. https://doi.org/10.1111/psyp.13049Bowyer, C. B., Joyner, K. J., Latzman, R. D., Venables, N. C., Foell, J., & Patrick, C. J. (2020). A Model-Based Strategy for Interfacing Traits of the DSM-5 AMPD With Neurobiology. Journal of Personality Disorders , 34 (5), 586–608. https://doi.org/10.1521/pedi.2020.34.5.586Brown, Wm. (1910). Some experimental results in the correlation of mental abilities. British Journal of Psychology , 3 , 296–322.Camisa, K. M., Bockbrader, M. A., Lysaker, P., Rae, L. L., Brenner, C. A., & O’Donnell, B. F. (2005). Personality traits in schizophrenia and related personality disorders. Psychiatry Research , 133 (1), 23–33. https://doi.org/10.1016/j.psychres.2004.09.002Costa, P. (1992). Neo PI-R professional manual. Psychological Assessment Resources . https://cir.nii.ac.jp/crid/1370298757174462347Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods , 134 (1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009Deouell, L. Y., & Knight, R. T. (2005). CHAPTER 56—ERP Measures of Multiple Attention Deficits Following Prefrontal Damage. In L. Itti, G. Rees, & J. K. Tsotsos (Eds.), Neurobiology of Attention (pp. 339–344). Academic Press. https://doi.org/10.1016/B978-012375731-9/50060-4Donchin, E. (1981). Surprise!… Surprise? Psychophysiology , 18 (5), 493–513. https://doi.org/10.1111/j.1469-8986.1981.tb01815.xEisler, Z., Bartos, I., & Kertész, J. (2008). Fluctuation scaling in complex systems: Taylor’s law and beyond1. Advances in Physics , 57 (1), 89–142. https://doi.org/10.1080/00018730801893043Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics , 16 (1), 143–149. https://doi.org/10.3758/BF03203267Friedman, N. P., & Miyake, A. (2000). Differential roles for visuospatial and verbal working memory in situation model construction. Journal of Experimental Psychology: General , 129 (1), 61–83. https://doi.org/10.1037/0096-3445.129.1.61Gao, Y., & Raine, A. (2009). P3 event-related potential impairments in antisocial and psychopathic individuals: A meta-analysis. Biological Psychology , 82 (3), 199–210. https://doi.org/10.1016/j.biopsycho.2009.06.006Gratton, G., Coles, M. G., & Donchin, E. (1983). A new method for off-line removal of ocular artifact. Electroencephalography and Clinical Neurophysiology , 55 (4), 468–484. https://doi.org/10.1016/0013-4694(83)90135-9Hamidovic, A., & Wang, Y. (2019). The P300 in alcohol use disorder: A meta-analysis and meta-regression. Progress in Neuro-Psychopharmacology and Biological Psychiatry , 95 , 109716. https://doi.org/10.1016/j.pnpbp.2019.109716Hogan, M. J., Carolan, L., Roche, R. A. P., Dockree, P. M., Kaiser, J., Bunting, B. P., Robertson, I. H., & Lawlor, B. A. (2006). Electrophysiological and information processing variability predicts memory decrements associated with normal age-related cognitive decline and Alzheimer’s disease (AD). Brain Research , 1119 (1), 215–226. https://doi.org/10.1016/j.brainres.2006.08.075Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling , 6 (1), 1–55. https://doi.org/10.1080/10705519909540118Imhof, M. F., & Rüsseler, J. (2019). Performance Monitoring and Correct Response Significance in Conscientious Individuals. Frontiers in Human Neuroscience , 13 . https://doi.org/10.3389/fnhum.2019.00239Jackson, J. D., & Balota, D. A. (2012). Mind-wandering in younger and older adults: Converging evidence from the sustained attention to response task and reading for comprehension. Psychology and Aging , 27 (1), 106–119. https://doi.org/10.1037/a0023933Joyner, K. J., Daurio, A. M., Perkins, E. R., Patrick, C. J., & Latzman, R. D. (2021). The difference between trait disinhibition and impulsivity—And why it matters for clinical psychological science. Psychological Assessment , 33 (1), 29–44. https://doi.org/10.1037/pas0000964Kamp, S.-M., Forester, G., & Knopf, L. (2023). Reliability and stability of oddball P300 amplitude in older adults: The role of stimulus sequence effects. Brain and Cognition , 169 , 105998. https://doi.org/10.1016/j.bandc.2023.105998Kaup, A. R., Harmell, A. L., & Yaffe, K. (2019). Conscientiousness Is Associated with Lower Risk of Dementia among Black and White Older Adults. Neuroepidemiology , 52 (1–2), 86–92. https://doi.org/10.1159/000492821Kim, M., Lee, T. H., Kim, J.-H., Hong, H., Lee, T. Y., Lee, Y., Salisbury, D. F., & Kwon, J. S. (2018). Decomposing P300 into correlates of genetic risk and current symptoms in schizophrenia: An inter-trial variability analysis. Schizophrenia Research , 192 , 232–239. https://doi.org/10.1016/j.schres.2017.04.001Klawohn, J., Santopetro, N. J., Meyer, A., & Hajcak, G. (2020). Reduced P300 in depression: Evidence from a flanker task and impact on ERN, CRN, and Pe. Psychophysiology , 57 (4), e13520. https://doi.org/10.1111/psyp.13520Kolanowski, A., Bossen, A., Hill, N., Guzman-Velez, E., & Litaker, M. (2012). Factors Associated with Sustained Attention during an Activity Intervention in Persons with Dementia. Dementia and Geriatric Cognitive Disorders , 33 (4), 233–239. https://doi.org/10.1159/000338604Kornør, H., & Nordvik, H. (2007). Five-factor model personality traits in opioid dependence. BMC Psychiatry , 7 (1), 37. https://doi.org/10.1186/1471-244X-7-37Krueger, R. F., Markon, K. E., Patrick, C. J., Benning, S. D., & Kramer, M. D. (2007). Linking antisocial behavior, substance use, and personality: An integrative quantitative model of the adult externalizing spectrum. Journal of Abnormal Psychology , 116 (4), 645–666. https://doi.org/10.1037/0021-843X.116.4.645Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (2005). International Affective Picture System . https://doi.org/10.1037/t66667-000Lhermitte, F. (1986). Human autonomy and the frontal lobes. Part II: Patient behavior in complex and social situations: The “environmental dependency syndrome.” Annals of Neurology , 19 (4), 335–343. https://doi.org/10.1002/ana.410190405Lopez-Calderon, J., & Luck, S. J. (2014). ERPLAB: An open-source toolbox for the analysis of event-related potentials. Frontiers in Human Neuroscience , 8 . https://doi.org/10.3389/fnhum.2014.00213Luck, S. J., & Gaspelin, N. (2017). How to get statistically significant effects in any ERP experiment (and why you shouldn’t). Psychophysiology , 54 (1), 146–157. https://doi.org/10.1111/psyp.12639Luck, S. J., Woodman, G. F., & Vogel, E. K. (2000). Event-related potential studies of attention. Trends in Cognitive Sciences , 4 (11), 432–440. https://doi.org/10.1016/S1364-6613(00)01545-XLui, P. P., Samuel, D. B., Rollock, D., Leong, F. T. L., & Chang, E. C. (2020). Measurement Invariance of the Five Factor Model of Personality: Facet-Level Analyses Among Euro and Asian Americans. Assessment , 27 (5), 887–902. https://doi.org/10.1177/1073191119873978Lynam, D. R., Smith, G. T., Whiteside, S. P., & Cyders, M. A. (2006). The UPPS-P: Assessing five personality pathways to impulsive behavior (10). Purdue University.Nelson, L. D., Patrick, C. J., & Bernat, E. M. (2011). Operationalizing proneness to externalizing psychopathology as a multivariate psychophysiological phenotype. Psychophysiology , 48 (1), 64–72. https://doi.org/10.1111/j.1469-8986.2010.01047.xPatrick, C. J., & Bernat, E. M. (2009). From markers to mechanisms: Using psychophysiological measures to elucidate basic processes underlying aggressive externalizing behaviour. In The neurobiological basis of violence: Science and rehabilitation (pp. 223–250). Oxford University Press.Patrick, C. J., Fowles, D. C., & Krueger, R. F. (2009). Triarchic conceptualization of psychopathy: Developmental origins of disinhibition, boldness, and meanness. Development and Psychopathology , 21 (3), 913–938. https://doi.org/10.1017/S0954579409000492Patrick, C. J., Kramer, M. D., Krueger, R. F., & Markon, K. E. (2013). Optimizing efficiency of psychopathology assessment through quantitative modeling: Development of a brief form of the Externalizing Spectrum Inventory. Psychological Assessment , 25 (4), 1332–1348. https://doi.org/10.1037/a0034864Phillips, D. L., & Clancy, K. J. (1972). Some Effects of “Social Desirability” in Survey Studies. American Journal of Sociology , 77 (5), 921–940.Polich, J. (2007). Updating P300: An integrative theory of P3a and P3b. Clinical Neurophysiology , 118 (10), 2128–2148. https://doi.org/10.1016/j.clinph.2007.04.019Proudfit, G. H. (2015). The reward positivity: From basic research on reward to a biomarker for depression. Psychophysiology , 52 (4), 449–459. https://doi.org/10.1111/psyp.12370R Core Team. (2024). R: The R Project for Statistical Computing . https://www.r-project.org/Roberts, B. W., Lejuez, C., Krueger, R. F., Richards, J. M., & Hill, P. L. (2014). What is conscientiousness and how can it be assessed? Developmental Psychology , 50 (5), 1315–1330. https://doi.org/10.1037/a0031109Rosseel, Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software , 48 , 1–36. https://doi.org/10.18637/jss.v048.i02Santopetro, N. J., Brush, C. J., Burani, K., Bruchnak, A., & Hajcak, G. (2021). Doors P300 moderates the relationship between reward positivity and current depression status in adults. Journal of Affective Disorders , 294 , 776–785. https://doi.org/10.1016/j.jad.2021.07.091Santopetro, N. J., Kallen, A. M., Threadgill, A. H., & Hajcak, G. (2020). Reduced flanker P300 prospectively predicts increases in depression in female adolescents. Biological Psychology , 156 , 107967. https://doi.org/10.1016/j.biopsycho.2020.107967Santopetro, N. J., Nelson, B. D., Hajcak, G., & Klein, D. N. (2025). Childhood P300 predicts development of depressive disorders into adolescence. Cognitive, Affective, & Behavioral Neuroscience . https://doi.org/10.3758/s13415-025-01327-8Schurger, A., Sarigiannidis, I., Naccache, L., Sitt, J. D., & Dehaene, S. (2015). Cortical activity is more stable when sensory stimuli are consciously perceived. Proceedings of the National Academy of Sciences , 112 (16), E2083–E2092. https://doi.org/10.1073/pnas.1418730112Singh, S. M., Basu, D., Kohli, A., & Prabhakar, S. (2009). Auditory P300 Event-Related Potentials and Neurocognitive Functions in Opioid Dependent Men and Their Brothers. American Journal on Addictions , 18 (3), 198–205. https://doi.org/10.1080/10550490902786975Sosic-Vasic, Z., Ulrich, M., Ruchsow, M., Vasic, N., & Grön, G. (2012). The Modulating Effect of Personality Traits on Neural Error Monitoring: Evidence from Event-Related fMRI. PLOS ONE , 7 (8), e42930. https://doi.org/10.1371/journal.pone.0042930Spearman, C. (1910). Correlation calculated from faulty data. British Journal of Psychology , 3 , 271–295.Stanley, J. H., Wygant,Dustin B., & and Sellbom, M. (2013). Elaborating on the Construct Validity of the Triarchic Psychopathy Measure in a Criminal Offender Sample. Journal of Personality Assessment , 95 (4), 343–350. https://doi.org/10.1080/00223891.2012.735302Taylor, L. R. (1961). Aggregation, Variance and the Mean. Nature , 189 (4766), 732–735. https://doi.org/10.1038/189732a0Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika , 38 (1), 1–10. https://doi.org/10.1007/BF02291170Uemura, J., & Hoshiyama, M. (2007). Variability of P300 in elderly patients with dementia during a single day. International Journal of Rehabilitation Research , 30 (2), 167. https://doi.org/10.1097/MRR.0b013e32813a2e6fvan Beijsterveldt, C. E. M., & van Baal, G. C. M. (2002). Twin and family studies of the human electroencephalogram: A review and a meta-analysis. Biological Psychology , 61 (1), 111–138. https://doi.org/10.1016/S0301-0511(02)00055-8Venables, N. C., Foell, J., Yancey, J. R., Kane, M. J., Engle, R. W., & Patrick, C. J. (2018). Quantifying inhibitory control as externalizing proneness: A cross-domain model. Clinical Psychological Science , 6 (4), 561–580. https://doi.org/10.1177/2167702618757690Whiteside, S. P., & Lynam, D. R. (2001). The Five Factor Model and impulsivity: Using a structural model of personality to understand impulsivity. Personality and Individual Differences , 30 (4), 669–689. https://doi.org/10.1016/S0191-8869(00)00064-7 FOOTNOTES Conversely, if one were to drop the Doors P300 from the variability model, there is no impact on the other Doors P3 loadings in the model with all three indicators versus a model with only two indicators (gain λ = .71 versus .71; loss λ = .75 versus .76, respectively) nor with the loading of the Doors first-order factor onto the higher-order factor (λ = .78 versus .74). As such, the Doors P300 was retained in both models. TABLES n Mean (µV) Median SD Skewness Kurtosis Split-Half* Reliability Oddball Task Target P300 199 15.49 14.62 7.03 0.36 -0.15 .92 Standard P300 203 6.88 6.64 3.44 0.32 -0.01 .93 Novel P300 199 10.15 9.90 5.93 0.26 0.09 .86 Doors Task Gain P300 191 18.69 18.64 6.78 -0.08 0.31 .89 Loss P300 188 18.84 18.17 6.43 -0.08 0.33 .88 Doors P300 199 1.99 1.93 2.44 0.36 0.25 .75 Flanker Task Incongruent P300 200 11.42 11.27 5.59 0.18 -0.35 .98 Congruent P300 199 11.19 11.33 4.95 0.01 -0.23 .98 P300e 181 9.26 9.07 6.14 0.17 0.17 .91 Spearman-Brown corrected split-half reliability n Mean (µV 2 ) Median SD Skewness Kurtosis Split-Half* Reliability Oddball Task Target P300 199 110.87 99.84 54.86 0.91 0.54 .66 Standard P300 203 104.92 94.08 45.50 0.95 0.57 .90 Novel P300 199 108.06 98.95 48.15 0.76 0.02 .64 Doors Task Gain P300 191 79.32 67.33 43.65 1.01 0.66 .55 Loss P300 188 72.06 61.94 38.21 1.07 0.68 .63 Doors P300 199 41.95 34.85 25.87 1.11 0.52 .75 Flanker Task Incongruent P300 200 81.75 76.71 32.49 0.81 0.40 .92 Congruent P300 199 84.61 78.83 34.54 1.01 1.04 .92 P300e 181 77.95 66.09 40.92 0.87 -0.03 .64 Spearman-Brown corrected split-half reliability Target P300 Standard P300 Novel P300 Gain P300 Loss P300 Doors P300 Incongruent P300 Congruent P300 P300e .20** .03 .21** -.12 -.15* .30*** .21** .20** .20** p < .05; ** p < .01; *** p < .001 FIGURE CAPTIONS Figure 1. Grand average waveforms and topographical maps for P300 components in the Flanker task Figure 2. Grand average waveforms and topographical maps for P300 components in the Doors task Figure 3. Grand average waveforms and topographical maps for P300 components from the Oddball task Figure 4. Hierarchical measurement model of P300 amplitude variabilities Figure 5. Hierarchical measurement model of P300 amplitude means Figure 6. Structural equation model of mean and variabilities FIGURES Figure 1 Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Information & Authors Information Version history V1 Version 1 29 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Danielle Jones 0000-0002-0689-9796 [email protected] Florida State University Department of Psychology View all articles by this author Colin B. Bowyer Medical University of South Carolina Department of Psychiatry and Behavioral Sciences View all articles by this author Chris B. Martin Florida State University Department of Psychology View all articles by this author Christopher Patrick Florida State University Department of Psychology View all articles by this author keanan joyner University of California Berkeley Department of Psychology View all articles by this author Metrics & Citations Metrics Article Usage 184 views 137 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Danielle Jones, Colin B. Bowyer, Chris B. Martin, et al. Dissociable Trait Correlates of P300 Variability and Amplitude: Conscientiousness and Disinhibition. Authorea . 29 September 2025. DOI: https://doi.org/10.22541/au.175911494.49366794/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175911494.49366794/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9feacdc24f428650',t:'MTc3OTI3NDQ3MA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();