Neural Basis of Motivational and Cognitive Control

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We investigated whether the individual slopes might serve as neural indicator of epistemic motivation by relating the slopes to need for cognition. Therefore, we administered the item need for cognition scale 9. After adding need for cognition as a continuous variable Fig. As illustrated in Fig. Contrary, individuals with low levels on need for cognition already invest considerable cognitive resources in the 0-back condition, which would be in line with the proposed preference for simple, structured tasks 9 , Figure 2c illustrates the stronger rise in theta from 0back to 2back for individuals high compared to low in need for cognition as a time-frequency difference plot.

The descriptive results suggest that the effect of a more positive slope for individuals high, compared to low in need for cognition is specific for the theta band. Each figure indicates whether the power in the respective frequency layer is modulated by condition i. As, on the one hand, epistemic motivation is moderately positively correlated with working memory capacity 26 and, on the other, working-memory capacity has also been found to relate to theta power 11 , it is important to show that our results do indeed refer to epistemic motivation and cannot be deduced to working memory capacity alone.

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Therefore, the pattern of theta oscillations under varying cognitive demands as a function of need for cognition cannot be explained by working memory. Because epistemic motivation is a valid predictor of cognitive achievements 5 , 6 , 7 and is related to individual differences in information processing 9 , we investigated whether need for cognition would also predict performance on the n-back task and, if so, whether the pattern of theta oscillations would account for the predictive validity of need for cognition.

We used a mixed model approach to predict trial-by-trial performance on the n-back task. Third, slopes of theta power significantly predicted performance, with better performance for individuals with higher, compared to lower slopes, i. These results suggest that the pattern of theta oscillations in response to varying levels of cognitive demands accounts for the predictive validity of need for cognition on performance, with this effect being specific to need for cognition and not more generally to other factors influencing performance such as working memory.

More generally, these results are further evidence that these patterns reflect a neural mechanism underlying individual differences in epistemic motivation. We identified a pattern of theta activity in response to tasks with varying cognitive demands as a neural indicator of epistemic motivation. Theta power indicated the amount of cognitive resources invested in response to situations possessing high, compared to low cognitive demands.

This pattern of theta activity distinguished between individuals with high, compared to low, epistemic motivation. Specifically, latent slopes of neural activity in response to varying cognitive demands: 1 correlated with need for cognition, a personality trait reflecting stable individual differences in epistemic motivation, but not with working memory; 2 predicted performance on a cognitive task; and 3 accounted for the shared variance between need for cognition and performance on the cognitive task. Additional analyses revealed that this pattern of results was specific to theta activity, as compared to other EEG frequency bands and indicators of peripheral physiological activation.

These results add to our knowledge about functional significance of EEG theta oscillations. While there is profound evidence that power in the theta band reflects mental effort, we show that theta activity is also sensitive to stable individual differences in epistemic motivation. With regards to theories linking theta oscillations to the need for cognitive control 16 , individual differences in epistemic motivation might moderate the amount of cognitive control in response to cognitive demands, presumably due to a more efficient way in allocating their resources or differences in the associated cognitive costs.

Additionally, individuals with high, compared to low levels of epistemic motivation have a higher tolerance for dealing with ambiguity and conflict 27 ; therefore, the lower level of theta power in the 0back condition might indicate that these individuals perceive such situations as less conflicting Additionally, from a personality perspective, our results provide direct evidence for the theory of the construct of need for cognition.

According to the theory by Cacioppo and colleagues 9 , 25 , individuals with higher need for cognition are more prone to engage in effortful cognitive activity when given a task or making sense of the world, and are more likely to enjoy or be less stressed by cognitively effortful problems, life circumstances, or tasks.

While the theory makes testable predictions regarding neural processes, research has mainly tested the behavioral consequences that can be deduced from the theory, such as information recall, responsiveness to argument quality, number of thoughts generated, correlation of thoughts with judgments, or subjective costs for cognitively challenging tasks 9 , 28 or has investigated more general aspects of information processing relating to attention allocation The present study provides more direct evidence to the core assumption of the theory by obtaining an indicator of mental effort in response to a task with varying levels of cognitive demands.

Beyond the mere correlation between patterns of theta activation in response to situational demands and epistemic motivation, one may speculate about how these relations evolved. One possibility would be that individuals with high compared to low levels of epistemic motivation more often seek cognitively challenging tasks.

As a consequence, higher levels of exposure and practice will lead to enhanced levels of cognitive ability, a mechanism known as environmental enrichment, and might, on a neural level, lead to a more efficient resource allocation 30 , Conversely, higher levels of exposure, practice and training might positively influence the development of epistemic motivation, as individuals who repeatedly seek exposure to cognitively challenging tasks might learn to allocate their resources in a more efficient way Finally, higher levels of cognitive ability might, on one hand, positively influence efficient resource allocation and, on the other or as a result , increase the likelihood of successfully managing cognitively challenging situations which, as a consequence, positively influences preferences for seeking new and challenging situations, a mechanism known as environmental success 30 , 31 , Longitudinal studies with broad measures of cognitive ability are needed to clarify these mechanisms.

On a more general level, our results illustrate how personality traits can be linked to brain function. As is well known from interactionism 34 , 35 , 36 , situations vary in their relevance to any given trait and, therefore, trait differences matter to different extents in different situations.

If a situation is characterized by demands that are relevant for a particular trait, they have the potential to activate this trait Individual differences on this particular trait will manifest in the type and strength of a corresponding behavioral reaction to such stimuli. We hypothesize that the neural mechanism reflecting the concept of trait activation may consist of a network functionally related to the trait domain.

Situational cues that are relevant for a particular trait will thus elicit a response in this neural network. Inter-individual variability in the neural response indicates that the situational cues are processed differentially across individuals and, thus, give rise to different subsequent processing and different behavioral responses. When situational cues, corresponding neural responses to these cues, and subsequent processing and behavioral responses co-vary systematically, the activity of the particular network as a response to the situational cue provides a foundation for a particular set of responses that are manifestations of the personality trait.

The neural response to the situational cue thus constitutes what we would like to refer to as a functional neural trait , i. The neural response to the eliciting situational cues serves as a neural indicator reflecting individual differences on this particular trait. In line with this perspective are findings in the literature detailing neural correlates of personality that have found a moderating influence of personality on neural activity, specifically after inducing a situation that corresponds to the personality trait. For example, the neural response to wins and losses, as obtained in the feedback-related negativity 38 , has been found to be attenuated for individuals high compared to low in dispositional greed after the concept of greed was activated by situational cues Also, the relation between frontal brain asymmetry and individual differences in approach and avoidance have been found to be stronger and more robust when these traits had previously been activated 40 , 41 , Compared to cue-invariant or static neural traits, such as resting-state electroencephalography and structural magnetic resonance imaging 43 , 44 , functional neural traits are characterized by imposing varying situational demands that are derived from a theory of the proposed construct while assessing brain-related activity that captures the proposed differences in processing.

As functional traits investigate personality dispositions as processing dynamics in response to situational cues they might thus be viewed as a biological approach to the cognitive-affective personality system model 45 and are related to and in line with the capability model of individual differences 40 , From a diagnostic perspective functional neural traits may serve as brain-based indicators of personality that have the propensity to predict and explain behavior.

Such indicators may even provide a nonreactive alterative to self-report assessments that are currently the most common method for the assessment of personality, in the research context as well as in applied settings.


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Forty-two participants were recruited from the student population of a German university. Participants were between 19 and 29 years old on average Written informed consent was obtained for all participants. The n-back task consisted of three blocks with 62 trials each. On each trial, participants saw a fixation-cross, followed by a capital letter taken from a pool of eleven letters. Each trial took 2. Participants were instructed to press a button with their right index finger when the letter was a target, or a second button with their left index finger if the letter was a non-target.

In the second block 1-back , the letter from the last trial served as target. Therefore, participants had to constantly update and memorize the target. In the third and final block 2-back , the letter presented two trials ago served as target. In addition to the 1-back condition, participants had to memorize und constantly update a second target along with the information which of the two letters was the target.

Half of the trials per block were targets. The same randomized sequence of targets and non-targets and the same letters were used for all participants. Performance on the n-back task was coded trial-by-trial. Otherwise, responses were coded as correct if the correct button was pressed for either a target or a non-target, and as non-correct if the wrong button was pressed for either a target or a non-target. For the assessment of need for cognition, the item short scale was used The construct need for cognition can be located in the Big Five personality framework, pertaining to the aspect Intellect of the domain openness to experience 32 , Items were presented in German language.

Half of the items are reversed scored and were recoded before aggregating across all items.

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For Fig. Working memory capacity was assessed by three tasks from the Wechsler Adult Intelligence Scale The tasks were manually administered by an examiner in a one-to-one situation. Examiners were blind with regard to all other study variables e. The first task demanded the repetition of an increasing number of digits 2 to 18 ; the second task was a backwards-repetition of an increasing number of digits 2 to Both tasks had two items on each difficulty level. The third task consisted of the sorting and repetition of an increasing number of digits and letters 2 to 8 with three items per difficulty level; thereby, digits had to be given first, in ascending order, followed by alphabetically ordered letters.

Within each task and each difficulty level, a termination criterion was defined; specifically, if all items within a difficulty level were answered incorrectly, the task was aborted. Within each task, performance was computed as the sum of all correctly answered items. Working memory performance was estimated as the aggregate of z-standardized values in the three tasks.

Motivational Influences on Cognitive Control: A Cognitive Neuroscience Perspective

While participants performed the n-back task, EEG analog bandpass: 0. For detection of blinks and eye-movements the vertical electrooculogram EOG was recorded. Data were processed offline, using Brain Vision Analyzer 2.

The thalamus in cognitive control and flexibility

First, data were filtered, using a 0. Afterwards, data were corrected for ocular artifacts using an Independent Component Analysis based correction method implemented in the Brain Vision Analyzer. Larger artifacts were automatically detected by a computer algorithm implemented in Brain Vision Analyzer 2. At least 32 artifact-free trials on average 56 trials were available per participant and condition.

Subsequently, data were re-referenced to an averaged reference across all electrodes excluding vertical EOG. The complex Morlet wavelets are defined as Gaussian-windowed complex sine functions: , with and , the latter resulting in total energy of 1 for all frequency levels Segments were averaged for each participant and condition of interest. The ground electrode was placed below the left collarbone, the negative electrode below the right collarbone and the positive electrode on the left side below the rib cage.

To analyze the event-related heart period response we first detected the peaks of the R-waves using QRSTool Following an automatic detection, the detected beats were checked and if necessary corrected manually. Next, the times of the R-peaks were exported and event-related inter-beat-intervals were extracted using a custom-built Matlab script. For every stimulus in the n-back task we extracted the inter-beat-interval surrounding the stimulus as well as the two following intervals.

Heart period was defined as the interval in milliseconds between sequential R-waves and was estimated for the interval following the stimulus. Based on the timing of the R-peaks exported from QRSTool we computed separated inter-beat-interval series for every condition of the n-back task per participant mean length of the series across all participants and conditions: Since those values were not normally distributed, we transformed them using the natural logarithm before running the statistical analysis.

Latent growth curve analysis 23 with maximum likelihood function was performed using AMOS Latent growth curve analysis is a special case of multilevel modeling and captures the pattern of change across multiple sampling points. For each individual, a linear function described by slope and intercept was estimated.

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As depicted in Fig. Therefore, the intercept specifies predicted theta power in the 2-back condition.

Need for cognition was modeled as latent variable indicated by three manifest variables; the latter were computed by randomly assigning each of the 18 items see above to one of three parcels, and subsequently averaging across these items. Working memory was modeled as a latent variable with the z-standardized values of each of the three working memory tasks as indicators.

Factor score weights were used to predict the estimated slope for each subject and each condition for subsequent analyses. How to cite this article : Mussel, P. Patterns of theta oscillation reflect the neural basis of individual differences in epistemic motivation.