Linking neurobiology to relatively stable individual differences in cognition, emotion, motivation, and behavior can require large sample sizes to yield replicable results. Given the nature of between-person research, sample sizes at least in the hundreds are likely to be necessary in most neuroimaging studies of individual differences, regardless of whether they are investigating the whole brain or more focal hypotheses. However, the appropriate sample size depends on the expected effect size. Therefore, we propose four strategies to increase effect sizes in neuroimaging research, which may help to enable the detection of replicable between-person effects in samples in the hundreds rather than the thousands: (1) theoretical matching between neuroimaging tasks and behavioral constructs of interest; (2) increasing the reliability of both neural and psychological measurement; (3) individualization of measures for each participant; and (4) using multivariate approaches with cross-validation instead of univariate approaches. We discuss challenges associated with these methods and highlight strategies for improvements that will help the field to move toward a more robust and accessible neuroscience of individual differences.

Creative cognition is the driving force behind all cultural and scientific progress. In recent years, the field of neurocognitive creativity research (NCR) has made considerable progress in revealing the neural and psychological correlates of creative cognition. However, a detailed understanding of how cognitive processes produce creative ideas, and how these processes interact differently across tasks and individuals, remains elusive. In this article, we argue that the increased adoption of computational modeling can help greatly in achieving this goal. While the verbal theories guiding NCR have evolved from broader accounts into more specific descriptions of neurocognitive processes, they remain more open to interpretation and harder to falsify than formal models. Translating theories into computational models can make them more concrete, accessible, and easier to compare, and helps researchers to develop causal hypotheses for how variation in cognitive factors leads to variation in creative outcomes. Currently, however, computational modeling of creativity is conducted almost entirely separately from NCR, and few attempts have been made to embody the cognitive theories of NCR in models that can simulate performance on common lab-based tasks. In this article, we discuss theories of creative cognition and how they might benefit from the wider adoption of formal modeling. We also examine recent computational models of creativity and how these might be improved and better integrated with NCR. Finally, we describe a pathway toward a mechanistic understanding of creative cognition through the integration of computational modeling, psychological theory, and empirical research, outlining an example model based on dual-process accounts.

The comprehension and appreciation of poetry are inherently subjective, involving both creativity and aesthetic appeal. However, do these assessments of aesthetics and creativity rely on identical criteria, or do they vary depending on underlying factors? We addressed this question in this study. Participants (N = 96) evaluated 25 English poems across nine subjective characteristics: reading fluency, vivid imagery, perceived emotions (valence and arousal), surprise, originality, usefulness, aesthetic appeal, and creativity. Linear mixed-model analysis revealed that a poem's creativity was primarily predicted by its originality, followed by its usefulness and vivid imagery. Conversely, the evaluation of a poem's aesthetic appeal followed a different route; it was mainly predicted by a poem's reading fluency, followed by arousal, valence, and vivid imagery. Additionally, the association between creativity and originality was significantly moderated by participants' personality traits, specifically, openness, vividness of visual imagery, and curiosity. The relationship between aesthetic appeal and reading fluency was moderated by the vividness of auditory imagery trait. These findings suggest that a poem's creativity evaluation closely aligns with the standard definition of creativity, relying on its originality and usefulness. The study provides novel insights into the implicit internal models in the evaluation of poetry.

Trait social anxiety may predict differences in the cognitive control of emotional distraction when emotional face discrimination is required. This effect can be investigated using an emotional face flanker task. This study addresses an important research gap, as previous studies did not separate the effects of trait social interaction anxiety from the effects of trait social phobia upon emotional face flanker task performance. In this laboratory based behavioural experiment, the 87 participants (mean age 24.3) were university students or staff recruited via departmental adverts. We used an emotional (happy versus fearful) face flanker task, and assessed sub-clinical social anxiety with the SIAS/SPS. Elevated trait social phobia was related to an increased reaction time (RT) congruency effect, whereas trait social interaction anxiety was not. Elevated trait social interaction anxiety was related to a decreased happy face RT advantage for central target faces, but the effect of trait social phobia was very weak. Trait social interaction anxiety and trait social phobia may predict subtle differences when the cognitive control of reactions to emotional facial expressions is required.

Poetry is arguably the most creative expression of language and can evoke diverse subjective experiences, such as emotions and aesthetic responses, subsequently influencing the subjective judgment of the creativity of poem. This study investigated how certain personality traits-specifically openness, intellect, awe-proneness, and epistemic curiosity-influence the relationship between these subjective experiences and the creativity judgment of 36 English language poems. One hundred and twenty-nine participants rated each poem across six dimensions: clarity, aesthetic appeal, felt valence, felt arousal, surprise, and overall creativity. Initially, we obtained a parsimonious model that suggested aesthetic appeal, felt valence, and surprise as key predictors of poetic creativity. Subsequently, using multilevel analysis, we investigated the interactions between the four personality traits and these three predictors. Among the personality traits, openness emerged as the primary moderator in predicting judgments of poetic creativity, followed by curiosity and awe-proneness. Among the predictors, aesthetic appeal was moderated by all four personality traits, while surprise was moderated by openness, awe-proneness, and curiosity. Valence, on the other hand, was moderated by openness only. These findings provide novel insights into the ways individual differences influence evaluations of poetic creativity.

Agency is the sense that one has control over one’s own actions and the consequences of those actions. Despite the critical role that agency plays in the human condition, little is known about its neural basis. A novel theory proposes that increases in agency disinhibit the dopamine system and thereby increase the number of tonically active dopamine neurons in the ventral tegmental area. The theory, called ADDS (Agency Disinhibits the Dopamine System), proposes a specific neural network that mediates these effects. ADDS accurately predicts a variety of relevant neuroscience results, and makes many novel predictions, including that increases in agency will 1) increase motivation, 2) improve executive function, 3) facilitate procedural learning, but only in the presence of immediate trial-by-trial feedback, 4) have little or no effect on learning-related effects of stimulus repetition or on standard eyeblink conditioning, 5) facilitate the development of automatic behaviors, but have little or no effect on the production of behaviors that are already automatized, 6) amplify the cognitive benefits of positive mood, and 7) reduce pain. The implications of this new theory are considered for several purely psychological theories that assign prominent roles to agency, including self-efficacy theory, hope theory, and goal-focused positive psychotherapy.

While frontal midline theta (FMθ) has been associated with threat processing, with cognitive control in the context of anxiety, and with reinforcement learning, most reinforcement learning studies on FMθ have used reward rather than threat-related stimuli as reinforcer. Accordingly, the role of FMθ in threat-related reinforcement learning is largely unknown. Here, n = 23 human participants underwent one reward-, and one punishment-, based reversal learning task, which differed only with regard to the kind of reinforcers that feedback was tied to (i.e., monetary gain vs. loud noise burst, respectively). In addition to single-trial EEG, we assessed single-trial feedback expectations based on both a reinforcement learning computational model and trial-by-trial subjective feedback expectation ratings. While participants' performance and feedback expectations were comparable between the reward and punishment tasks, FMθ was more reliably amplified to negative vs. positive feedback in the reward vs. punishment task. Regressions with feedback valence, computationally derived, and self-reported expectations as predictors and FMθ as criterion further revealed that trial-by-trial variations in FMθ specifically relate to reward-related feedback-valence and not to threat-related feedback or to violated expectations/prediction errors. These findings suggest that FMθ as measured in reinforcement learning tasks may be less sensitive to the processing of events with direct relevance for fear and anxiety.

Objective: The present exploratory study uses a longitudinal design to assess the associations between electronic cigarette (e-cigarette) use and cigarette smoking, motivation to quit and trait impulsivity among adult smokers.Methods: One hundred and sixteen cigarette smokers only and ninety-one dual users (smoke cigarettes and use e-cigarettes) were recruited (mean age [standard deviation] = 25.11[6.94], male = 48.3%). Participants were recruited online and 121 were followed-up at 3 months. Participants completed online questionnaires regarding socio-demographics, smoking/e-cigarette use characteristics and trait impulsivity at baseline, and they self-reported their smoking status at 3-month follow-up.Results: The use of e-cigarettes in cigarette smokers was associated with a higher rate of stopping cigarette smoking at follow-up, relative to smokers who did not use e-cigarettes (chi(2)[1] = 11.03, p < 0.01). Multivariate logistic regressions controlling for age and gender showed a significant association between motivation to quit and stopping cigarette smoking at follow-up (odds ratio = 2.11, 99% Confidence Interval = 1.12-3.97, p < 0.01). There was no link between trait impulsivity and stopping cigarette smoking.Conclusions: The data suggest that e-cigarettes may increase rates of quitting cigarette smoking.

According to the standard definition, creative ideas must be both novel and useful. While a handful of recent studies suggest that novelty is more important than usefulness to evaluations of creativity, little is known about the contextual and interpersonal factors that affect how people weigh these two components when making an overall creativity judgment. We used individual participant regressions and mixed-effects modeling to examine how the contributions of novelty and usefulness to ratings of creativity vary according to the context of the idea (i.e., how relevant it is to the real world) and the personality of the rater. Participants (N = 121) rated the novelty, usefulness, and creativity of ideas from two contexts: responses to the alternative uses task (AUT) and genuine suggestions for urban planning projects. We also assessed three personality traits of participants: openness, intellect, and risk-taking. We found that novelty contributed more to evaluations of creativity among AUT ideas than projects, while usefulness contributed more among projects than AUT ideas. Further, participants with higher openness and higher intellect placed a greater emphasis on novelty when evaluating AUT ideas, but a greater emphasis on usefulness when evaluating projects. No significant effects were found for the risk-taking trait. Plain Language Summary Understanding how creativity is perceived and defined in different contexts and across different individuals is highly important, not just to our understanding of how to assess creativity, but to our understanding of creativity itself. However, relatively few existing studies have examined differences in how individuals evaluate creativity, and the factors they consider during their evaluations. We investigated how personality and problem context affect how individuals consider novelty and usefulness when making an overall creativity judgment. Participants rated ideas from two contexts: responses to a common lab-based measure of creative ability and genuine suggestions for urban planning projects (ideas with more real-world relevance). We also assessed three personality traits of participants: openness, intellect, and risk-taking. Data was analyzed using both individual participant regressions and linear mixed-effects models. We found that participants considered novelty more when evaluating the creativity of AUT ideas (relative to projects), and usefulness more when evaluating the creativity of projects (relative to AUT ideas). Furthermore, when evaluating the creativity of AUT ideas, participants with higher openness and higher intellect placed a greater emphasis on novelty, but when evaluating projects, these same participants placed a greater emphasis on usefulness. Overall, our findings highlight the importance of considering contextual and interpersonal factors when researchers examine how creativity is evaluated, defined, and perceived, strengthening recent calls for creativity assessments that can account for variation across raters.

This paper represents the outcome of a multidisciplinary discussion on what works, what does not, and what can be improved, in ongoing work on biobehavioral taxonomies and their biomarkers. The authors of this paper, representing a wide spectrum of biobehavioral disciplines (clinical, developmental, differential psychology, neurophysiology, endocrinology, psychiatry, neurochemistry, and neurosciences), have contributed more extensive opinions to the Theme Issue 'Neurobiology of temperament, personality and psychopathology: what's next?'. The authors identified 10 directions in international and multidisciplinary cooperation, and multiple insights for 'what is next' for each of these directions.

Abstract. The State-Trait Anxiety Inventory (STAI) is widely used to measure anxiety in academic, psychiatric, and medical settings. However, it has been proposed that the trait scale does not measure pure anxiety but contains subscales that measure either anxiety or depression. As this may have implications for the interpretation of research, we investigated the differential correlates of these two subscales. Participants ( N = 171; Mage = 23.6 years) completed measures of attentional control (attentional focusing; attentional shifting), behavioral approach sensitivity (BAS), trait anger, and interpersonal fear. STAI-anxiety was predicted by interpersonal fear, and trait anger when STAI-depression was held constant. By contrast, STAI-depression was predicted by BAS and attentional shifting when STAI-anxiety was held constant. To further illustrate the functional significance of these results, we reanalyzed a published study that originally showed that total STAI scores predicted affective cognitive control in the emotional face Stroop task. Results showed that STAI-anxiety and STAI-depression differentially predicted the cognitive control of distraction. The significance of our results is discussed in relation to interpreting the STAI in academic and neuropsychiatric/clinical settings.

Participating in creative activities is associated with increased positive emotions and enhanced subjective well-being in general populations. However, these relationships are less understood in the daily lives of creative individuals who regularly engage in both professional creative behaviors and everyday creative experiences. Therefore, in this study, we recruited a sample of creative adults (N= 290; creative professionals, students studying creative disciplines, and hobbyists engaged in 20+ hours of creative activities per week) who provided daily responses on their creative behaviors, emotions, and flourishing over two weeks. Creative adults were found to be the most creative on days with highly activated positive emotions and increased well-being and were significantly less creative on days with negative emotions. Individuals with higher levels of openness have stronger ties between their emotions and their overall daily creativity and everyday creativity than those with lower levels of openness. Increased openness does not appear to have the same moderating effects on professional creativity and emotion relationships. Finally, high conscientiousness and low neuroticism were also found to predict increased levels of creative activity. Overall, these findings provide novel insights into the links between the specific nature of daily creative activities and the personality and subjective well-being of creative individuals.

Marek et al. analyzed three very large magnetic resonance imaging (MRI) datasets and concluded that thousands of participants are necessary to ensure replicable results in “brain-wide associations studies,” which they defined as “studies of the associations between common inter-individual variability in human brain structure/function and cognition or psychiatric symptomatology.” This conclusion overgeneralizes the implications of their findings and is likely to have an unwarranted chilling effect on neuroimaging research focused on individual differences, preventing good research with samples in the hundreds from being funded and conducted. To fend off these negative consequences, we explain why their conclusion is not fully justified, discuss methods that can yield larger effects, and suggest practical guidelines for sample size, recognizing the potential utility of samples in the hundreds.

The State-Trait Anxiety Inventory (STAI) is widely used to measure anxiety in academic, psychiatric, and medical settings. However, it has been proposed that the trait scale does not measure pure anxiety but contains subscales that measure either anxiety or depression. As this may have implications for the interpretation of research, we investigated the differential correlates of these two subscales. Participants (N = 171; M-age = 23.6 years) completed measures of attentional control (attentional focusing; attentional shifting), behavioral approach sensitivity (BAS), trait anger, and interpersonal fear. STAI-anxiety was predicted by interpersonal fear, and trait anger when STAI-depression was held constant. By contrast, STAI-depression was predicted by BAS and attentional shifting when STAI-anxiety was held constant. To further illustrate the functional significance of these results, we reanalyzed a published study that originally showed that total STAI scores predicted affective cognitive control in the emotional face Stroop task. Results showed that STAI-anxiety and STAI-depression differentially predicted the cognitive control of distraction. The significance of our results is discussed in relation to interpreting the STAI in academic and neuropsychiatric/clinical settings.

AIMS:The present study aimed to investigate the relationship between impulsivity-related personality traits based on the UPPS-P model and e-cigarette use. The study used a sample of mainly European adults and compared e-cigarette users with non-smokers, cigarette smokers and dual users (those who currently smoke cigarettes and use e-cigarettes). Additionally, the relationship between impulsivity-related traits and frequency and intensity of e-cigarette use was examined, while the main reasons for e-cigarette use were also assessed.METHODS:Participants were 720 adults (234 non-smokers, 164 smokers, 150 e-cigarette users, 172 dual users), who completed online questionnaires regarding sociodemographics, smoking/e-cigarette use behaviour, and impulsivity (UPPS-P scale).RESULTS:Impulsivity-related traits did not significantly differentiate e-cigarette users from non-smokers. E-cigarette users showed lower levels of lack of perseverance than cigarette smokers, and they exhibited lower levels of negative and positive urgency than dual users. Negative urgency also significantly differentiated smokers and non-smokers, with smokers having higher levels of the trait. No significant results were found examining the relationship between the impulsivity-related traits and e-cigarette behaviour (number of days vaping per month, number of times vaping per day, and millilitres of e-liquid used per day). The main reason given for e-cigarette use was the perception that it is less harmful than cigarettes.CONCLUSION:The present study found that trait impulsivity differentiated e-cigarette users from cigarette smokers and dual users, but did not differentiate e-cigarette users from non-smokers. Such findings are important to not only help us identify factors associated with e-cigarette use, but also to potentially inform treatment plans and decisions.

How do humans learn models supporting decision making? Reinforcement learning (RL) is a success story both in artificial intelligence and neuroscience. Essential to these RL models are state representations. Based on what current state an animal or artificial agent is in, they learn optimal actions by maximizing future expected reward. But how are humans able to learn and create representations of states? We introduce a novel sequence prediction task with hidden structure where participants have to combine learning and memory to find the proper state representation, without the task explicitly indicating such structure. We show that humans are able to find this pattern, while a sensory prediction error version of RL cannot, unless equipped with appropriate state representations. Furthermore, in slight variations of the task, making it more difficult for humans, the RL-derived model with simple state representations sufficiently describes behaviour and suggests that humans fall back on simple state representations when a more optimal task representation cannot be found. We argue this task allows to investigate previously proposed models of state and task representations as well as supporting recent results indicating that RL describes a more general sensory prediction error function for dopamine, rather than predictions focussed solely on reward.

Background: Electronic cigarette (e-cigarette) use continues to rise, while there is conflicting evidence about the health effects of its use. As such, research is needed to better determine risks factors for e-cigarette use. Accumulating evidence suggests that attitudes toward e-cigarette use could be a potential risk factor for e-cigarette use. Objectives: This study sought to examine the psychometric properties of the Comparing E-cigarette And Cigarette questionnaire (CEAC), and to replicate a structural model of the relationship between impulsive-related personality traits and e-cigarette use mediated by positive attitudes toward e-cigarettes. Methods: Participants were 525 adults (mean age = 33.42, SD = 11.27) who completed the CEAC and UPPS-P (trait impulsivity) questionnaires online. Results: Confirmatory factor analysis of the CEAC replicated the a priori factor structure of the questionnaire reasonably well (χ2(df = 32) =172.85, CFI = 0.94, TLI = 0.91, RMSEA = 0.09 (0.08-0.11, 90% Confidence Interval, SRMR = 0.06). Structural path analysis showed that deficits in conscientiousness was significantly negatively related to e-cigarette attitudes (β = -0.20, p = .01), while urgency (β = 0.19, p = .018) showed a significant positive relationship to e-cigarette attitudes. E-cigarette users showed significantly more positive attitudes toward e-cigarettes than nonusers (β = 0.59, p < .001). No significant direct effects were found between impulsivity-related traits and e-cigarette use. Conclusions: The present study suggests that impulsivity-related traits and attitudes toward e-cigarettes are likely to be important risk factors for e-cigarette use. Future prospective and experimental studies should test if the causal model described in this study predicts risk for e-cigarette use, and whether this model could therefore be used to guide strategies for reducing risk for e-cigarette use.

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.

On our path towards artificial general intelligence, video games have become excellent tools for research. Reinforcement learning (RL) algorithms are particularly successful in this domain, with the added benefit of having fairly well established biological foundations. To improve how artificial intelligence research and the cognitive sciences can inform each other, we argue the StarCraft II Learning Environment is an ideal candidate for an environment where humans and artificial agents can be tested on the same tasks. We present an upcoming study using this environment, where the goal is to investigate how RL can be extended to enable abstract human abilities such as moments of insight. We claim this is valuable for advancing our understanding of both artificial and natural intelligence, thereby leading to improved models of player behaviour and for general video game playing.