Predicting Fluid Intelligence by Components of Reaction Time Distributions from Simple Choice Reaction Time Tasks

Joint Modelling of Latent Cognitive Mechanisms Shared Across Decision-Making Domains

Decision-making behavior is often understood using the framework of evidence accumulation models (EAMs). Nowadays, EAMs are applied to various domains of decision-making with the underlying assumption that the latent cognitive constructs proposed by EAMs are consistent across these domains. In this study, we investigate both the extent to which the parameters of EAMs are related between four different decision-making domains and across different time points. To that end, we make use of the novel joint modelling approach, that explicitly includes relationships between parameters, such as covariances or underlying factors, in one combined joint model. Consequently, this joint model also accounts for measurement error and uncertainty within the estimation of these relations. We found that EAM parameters were consistent between time points on three of the four decision-making tasks. For our between-task analysis, we constructed a joint model with a factor analysis on the parameters of the different tasks. Our two-factor joint model indicated that information processing ability was related between the different decision-making domains. However, other cognitive constructs such as the degree of response caution and urgency were only comparable on some domains.

Dissecting the chain of information processing and its interplay with neurochemicals and fluid intelligence across development

Previous research has highlighted the role of glutamate and gamma-aminobutyric acid (GABA) in perceptual, cognitive, and motor tasks. However, the exact involvement of these neurochemical mechanisms in the chain of information processing, and across human development, is unclear. In a cross-sectional longitudinal design, we used a computational approach to dissociate cognitive, decision, and visuomotor processing in 293 individuals spanning early childhood to adulthood. We found that glutamate and GABA within the intraparietal sulcus (IPS) explained unique variance in visuomotor processing, with higher glutamate predicting poorer visuomotor processing in younger participants but better visuomotor processing in mature participants, while GABA showed the opposite pattern. These findings, which were neurochemically, neuroanatomically and functionally specific, were replicated ~21 mo later and were generalized in two further different behavioral tasks. Using resting functional MRI, we revealed that the relationship between IPS neurochemicals and visuomotor processing is mediated by functional connectivity in the visuomotor network. We then extended our findings to high-level cognitive behavior by predicting fluid intelligence performance. We present evidence that fluid intelligence performance is explained by IPS GABA and glutamate and is mediated by visuomotor processing. However, this evidence was obtained using an uncorrected alpha and needs to be replicated in future studies. These results provide an integrative biological and psychological mechanistic explanation that links cognitive processes and neurotransmitters across human development and establishes their potential involvement in intelligent behavior.

Testing Replicability and Generalizability of the Time on Task Effect

The time on task (ToT) effect describes the relationship of the time spent on a cognitive task and the probability of successful task completion. The effect has been shown to vary in size and direction across tests and even within tests, depending on the test taker and item characteristics. Specifically, investing more time has a positive effect on response accuracy for difficult items and low ability test-takers, but a negative effect for easy items and high ability test-takers. The present study sought to test the replicability of this result pattern of the ToT effect across samples independently drawn from the same populations of persons and items. Furthermore, its generalizability was tested in terms of differential correlations across ability tests. To this end, ToT effects were estimated for three different reasoning tests and one test measuring natural sciences knowledge in 10 comparable subsamples with a total N = 2640. Results for the subsamples were highly similar, demonstrating that ToT effects are estimated with sufficient reliability. Generally, faster answers tended to be more accurate, suggesting a relatively effortless processing style. However, with increasing item difficulty and decreasing person ability, the effect flipped to the opposite direction, i.e., higher accuracy with longer processing times. The within-task moderation of the ToT effect can be reconciled with an account on effortful processing or cognitive load. By contrast, the generalizability of the ToT effect across different tests was only moderate. Cross-test relations were stronger in relative terms if performance in the respective tasks was more strongly related. This suggests that individual differences in the ToT effect depend on test characteristics such as their reliabilities but also similarities and differences of their processing requirements.

Task Switching: On the Relation of Cognitive Flexibility with Cognitive Capacity

The task-switching paradigm is deemed a measure of cognitive flexibility. Previous research has demonstrated that individual differences in task-switch costs are moderately inversely related to cognitive ability. However, current theories emphasize multiple component processes of task switching, such as task-set preparation and task-set inertia. The relations of task-switching processes with cognitive ability were investigated in the current study. Participants completed a task-switching paradigm with geometric forms and a visuospatial working memory capacity (WMC) task. The task-switch effect was decomposed with the diffusion model. Effects of task-switching and response congruency were estimated as latent differences using structural equation modeling. Their magnitudes and relations with visuospatial WMC were investigated. Effects in the means of parameter estimates replicated previous findings, namely increased non-decision time in task-switch trials. Further, task switches and response incongruency had independent effects on drift rates, reflecting their differential effects on task readiness. Findings obtained with the figural tasks employed in this study revealed that WMC was inversely related to the task-switch effect in non-decision time. Relations with drift rates were inconsistent. Finally, WMC was moderately inversely related to response caution. These findings suggest that more able participants either needed less time for task-set preparation or that they invested less time for task-set preparation.

Intra-Subject Variability, Intelligence, and ADHD Traits in a Community-Based Sample

OBJECTIVE

The present study investigates the predictive validity of intra-subject variability (ISV) for ADHD traits in a community-based sample and the stability of the relationship between ISV and fluid intelligence (gf) across the continuum of ADHD traits.

METHOD

Age-residualized data from 426 participants (8-18 years, 6% ADHD) was used to investigate whether ex-Gaussian and DDM parameters derived from simple choice-reaction-time tasks can predict continuously assessed ADHD traits. Multiple-Group-Analyses and Latent-Moderated-Structural-Equations were used to test whether ADHD traits moderate the relationship between ISV and gf.

RESULTS

σ and μ of the ex-Gaussian model as well as DDM parameters drift rate (v) and boundary separation (a) significantly predicted general ADHD traits, while τ predicted attention difficulties specifically. Across the ADHD continuum, σ and v were significant predictors of gf.

CONCLUSION

The results confirm the link between ISV and ADHD. The relationship between ISV and gf appears stable across the ADHD continuum.

Cognitive efficiency beats top-down control as a reliable individual difference dimension relevant to self-control

Top-down control of responses is a key construct in cognitive science that is thought to be critical for self-control. It is typically measured by subtracting performance in experimental conditions in which top-down control is theoretically present against performance in matched conditions in which it is assumed to be absent. Recently, however, subtraction-based metrics of top-down control have been criticized for having low test-retest reliability, weak intercorrelations, and little relation to self-report measures of self-control. Concurrently, there is growing evidence that task-general cognitive efficiency, indexed by the drift rate parameter of the diffusion model (Ratcliff, 1978), constitutes a cohesive, reliable individual difference dimension relevant to self-control. However, no previous studies have directly compared latent factors for top-down control (derived from subtraction metrics) with factors for task-general efficiency "head-to-head" in the same sample in terms of their cohesiveness, temporal stability, and relation to self-control. In this re-analysis of a large open data set (Eisenberg et al., 2019; N = 522), we find that top-down control metrics fail to form cohesive latent factors, that the resulting factors have poor temporal stability, and that they exhibit tenuous connections to questionnaire measures of self-control. In contrast, cognitive efficiency measures-drawn from conditions of the same tasks that both are, and are not, assumed to demand top-down control-form a robust, temporally stable factor that correlates with questionnaire measures of self-control. These findings suggest that task-general efficiency is a central individual difference dimension relevant to self-control. Moreover, they go beyond recent measurement-based critiques of top-down control metrics, and instead suggest problems with key theoretical assumptions that have long guided this research paradigm.

Widespread theta coherence during spatial cognitive control

Cognitive control allows humans to process relevant sensory information while minimizing distractions from irrelevant stimuli. The neural basis of cognitive control is known to involve frontal regions of the brain such as the medial prefrontal cortex (mPFC) and anterior cingulate cortex (ACC), but the temporal dynamics of larger scale networks is unclear. Here we used EEG with source localization to identify how the neural oscillations localized to the mPFC and ACC coordinate with parietal, sensory, and motor areas during spatial cognitive control. Theta coherence (3-8 Hz) between the mPFC and ACC increased with task difficulty and predicted individual differences in reaction time. Individual differences in accuracy were predicted by earlier activation of ACC-motor coherence, highlighting the relationship between processing speed and task performance. Our results provide evidence that successful cognitive control requires dynamic coordination between a widespread network of brain regions. Long range theta coherence may be a key mechanism for efficient cognitive control across the neocortex.

Manipulations of the Response-Stimulus Intervals as a Factor Inducing Controlled Amount of Reaction Time Intra-Individual Variability

Aggrandized fluctuations in the series of reaction times (RTs) are a very sensitive marker of neurocognitive disorders present in neuropsychiatric populations, pathological ageing and in patients with acquired brain injury. Even though it was documented that processing inconsistency founds a background of higher-order cognitive functions disturbances, there is a vast heterogeneity regarding types of task used to compute RT-related variability, which impedes determining the relationship between elementary and more complex cognitive processes. Considering the above, our goal was to develop a relatively new assessment method based on a simple reaction time paradigm, conducive to eliciting a controlled range of intra-individual variability. It was hypothesized that performance variability might be induced by manipulation of response-stimulus interval’s length and regularity. In order to verify this hypothesis, a group of 107 healthy students was tested using a series of digitalized tasks and their results were analyzed using parametric and ex-Gaussian statistics of RTs distributional markers. In general, these analyses proved that intra-individual variability might be evoked by a given type of response-stimulus interval manipulation even when it is applied to the simple reaction time task. Collected outcomes were discussed with reference to neuroscientific concepts of attentional resources and functional neural networks.

A diffusion model analysis of belief bias: Different cognitive mechanisms explain how cognitive abilities and thinking styles contribute to conflict resolution in reasoning

Recent results have challenged the widespread assumption of dual process models of belief bias that sound reasoning relies on slow, careful reflection, whereas biased reasoning is based on fast intuition. Instead, parallel process models of reasoning suggest that rule- and belief-based problem features are processed in parallel and that reasoning problems that elicit a conflict between rule- and belief-based problem features may also elicit more than one Type 1 response. This has important implications for individual-differences research on reasoning, because rule-based responses by certain individuals may reflect that these individuals were either more likely to give a rule-based default response or that they successfully inhibited and overrode a belief-based default response. In two studies, we used the diffusion model to describe decision making in a transitive reasoning task. In Study 1, 41 participants were asked to evaluate conclusions based on their validity. In Study 2, 133 participants evaluated conclusions based on their validity or believability. We tested which diffusion model parameters reflected conflict resolution and related those model parameters to individual differences in cognitive abilities and thinking styles. Individual differences in need for cognition predicted successful conflict resolution under logic instruction, which suggests that a disposition to engage in reflective thinking facilitates the inhibition and override of Type 1 responses. Intelligence, however, was negatively related to successful conflict resolution under belief instruction, which suggests that individuals with high cognitive abilities quickly generated a higher-level logical response that interfered with their ability to evaluate lower-level intrinsic problem features. Taken together, this double dissociation indicates that cognitive abilities and thinking styles affect the processing of conflict information through different mechanisms and at different stages: Greater cognitive abilities facilitate the efficient creation of decoupled problem representations, whereas a greater disposition to engage in critical thinking facilitates the detection and override of Type 1 responses.

Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

There is substantial interest in identifying biobehavioral dimensions of individual variation that cut across heterogenous disorder categories, and computational models can play a major role in advancing this goal. In this report, we focused on efficiency of evidence accumulation (EEA), a computationally characterized variable derived from sequential sampling models of choice tasks. We created an EEA factor from three behavioral tasks in the UCLA Phenomics dataset (n = 272), which includes healthy participants (n = 130) as well-participants with schizophrenia (n = 50), bipolar disorder (n = 49), and attention-deficit/hyperactivity disorder (n = 43). We found that the EEA factor was significantly reduced in all three disorders, and that it correlated with an overall severity score for psychopathology as well as self-report measures of impulsivity. Although EEA was significantly correlated with general intelligence, it remained associated with psychopathology and symptom scales even after controlling for intelligence scores. Taken together, these findings suggest EEA is a promising computationally-characterized dimension of neurocognitive variation, with diminished EEA conferring transdiagnostic vulnerability to psychopathology.

Scoring Alternatives for Mental Speed Tests: Measurement Issues and Validity for Working Memory Capacity and the Attentional Blink Effect

Research suggests that the relation of mental speed with working memory capacity (WMC) depends on complexity and scoring methods of speed tasks and the type of task used to assess capacity limits in working memory. In the present study, we included conventional binding/updating measures of WMC as well as rapid serial visual presentation paradigms. The latter allowed for a computation of the attentional blink (AB) effect that was argued to measure capacity limitations at the encoding stage of working memory. Mental speed was assessed with a set of tasks and scored by diverse methods, including response time (RT) based scores, as well as ex-Gaussian and diffusion model parameterization. Relations of latent factors were investigated using structure equation modeling techniques. RT-based scores of mental speed yielded substantial correlations with WMC but only weak relations with the AB effect, while WMC and the AB magnitude were independent. The strength of the speed-WMC relation was shown to depend on task type. Additionally, the increase in predictive validity across RT quantiles changed across task types, suggesting that the worst performance rule (WPR) depends on task characteristics. In contrast to the latter, relations of speed with the AB effect did not change across RT quantiles. Relations of the model parameters were consistently found for the ex-Gaussian tau parameter and the diffusion model drift rate. However, depending on task type, other parameters showed plausible relations as well. The finding that characteristics of mental speed tasks determined the overall strength of relations with WMC, the occurrence of a WPR effect, and the specific pattern of relations of model parameters, implies that mental speed tasks are not exchangeable measurement tools. In spite of reflecting a general factor of mental speed, different speed tasks possess different requirements, supporting the notion of mental speed as a hierarchical construct.

Cognitive Models in Intelligence Research: Advantages and Recommendations for Their Application

Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive processes that may help in overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses.