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

Schulz-Zhecheva, Yoanna; Voelkle, Manuel C.; Beauducel, André; Biscaldi, Monica; Klein, Christoph

doi:10.3390/jintelligence4030008

Cited by 25 publications

(35 citation statements)

References 39 publications

(82 reference statements)

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“…Interestingly, while the EEA construct has been extensively invoked to explain task performance in hundreds of studies, the mechanistic basis of individual-differences in EEA, and how these differences relate to differences in other cognitive constructs, are much less explored (but see 29,48). Several studies using perceptual decision tasks find drift rates are related to general intelligence [54][55][56][57] , similar to the pattern we found in the present study. But, again similar to prior work, the correlations we found in this study between EEA and general intelligence were moderate in size, with individuals' general intelligence explaining less than 25% of the variance in EEA in this sample.…”

Section: Discussionsupporting

confidence: 78%

Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

Sripada¹,

Weigard²

2020

Preprint

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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.

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Section: Discussionsupporting

confidence: 78%

Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

Sripada¹,

Weigard²

2020

Preprint

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“…Higher drift values, thus, indicate that the process is faster and ends more frequently at the correct threshold. It has been shown that easier trials feature higher drift rates (e.g., Ratcliff, 2014;Voss et al, 2004), as do more intelligent individuals (Ratcliff, Thapar, & McKoon, 2010;Schmiedek, Oberauer, Wilhelm, Süß, & Wittmann, 2007;Schubert, Hagemann, Voss, Schankin, & Bergmann, 2015;Schulz-Zhecheva, Voelkle, Beauducel, Biscaldi, & Klein, 2016). The decision process is influenced not only by the drift rate but also by additive Gaussian noise (the standard deviation of this noise is termed the diffusion constant and is often described as a scaling parameter of the diffusion model).…”

Section: Introduction To Diffusion Modelingmentioning

confidence: 99%

Speed–accuracy manipulations and diffusion modeling: Lack of discriminant validity of the manipulation or of the parameter estimates?

Lerche

Voss

2018

Behav Res

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The diffusion model (Ratcliff, 1978) is a mathematical model theorized to untangle different cognitive processes involved in binary decision tasks. To test the validity of the diffusion model parameters, several experimental validation studies have been conducted. In these studies, the validity of the threshold separation parameter was tested with speed-accuracy manipulations. Typically, this manipulation not only results in the expected effect on the threshold separation parameter but it also impacts nondecision time: Nondecision time is longer in the accuracy than in the speed condition. There are two possible interpretations of the finding: On the one hand, it could indicate that speed versus accuracy instructions really have an impact on the duration of extradecisional processes. On the other hand, the effect on the measure for nondecision time could be spurious-that is, based on a problem in the parameter estimation procedures. In simulation studies-with the parameter sets based on typical values from experimental validation studies-we checked for possible biases in the parameter estimation. Our analyses strongly suggest that the observed pattern (i.e., slower nondecision processes under accuracy instructions) is attributable to a lack of discriminant validity of the manipulation rather than to trade-offs in the parameter estimations.

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“…Therefore, individuals with lower EEA would be expected to display reduced accuracy rates and more variable RTs across a range of cognitive tasks. (94)(95)(96)(97)(98)(99). Additionally, EEA measured via relatively simple choice tasks nonetheless correlates strongly with EEA measured in more complex tasks, and also predicts better working memory ability and general intelligence (95)(96)(97)(98)(99)(100)(101)(102)(103).…”

Section: Efficiency Of Evidence Accumulation As a Foundational Indivimentioning

confidence: 99%

Task-general efficiency of evidence accumulation as a computationally-defined neurocognitive trait: Implications for clinical neuroscience

Weigard¹,

Sripada²

2020

Preprint

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Quantifying individual differences in higher-order cognitive functions is a foundational area of cognitive science that also has profound implications for research on psychopathology. For the last two decades, the dominant approach in these fields has been to attempt to fractionate higher-order functions into hypothesized components (e.g., “inhibition”, “updating”) through a combination of experimental manipulation and factor analysis. However, the putative structures obtained through this paradigm have recently been met with substantial criticism on both theoretical and empirical grounds. Concurrently, an alternative approach has emerged focusing on parameters of formal computational models of cognition that have been developed in mathematical psychology. These models posit biologically plausible and experimentally validated explanations of the data-generating process for cognitive tasks, allowing them to be used to measure the latent mechanisms that underlie performance. One of the primary insights provided by recent applications of such models is that individual and clinical differences in performance on a wide variety of cognitive tasks, ranging from simple choice tasks to complex executive paradigms, are largely driven by efficiency of evidence accumulation (EEA), a computational mechanism defined by sequential sampling models. This review assembles evidence for the hypothesis that EEA is a central individual difference dimension that explains neurocognitive deficits in multiple clinical disorders and identifies ways in which in this insight can advance clinical neuroscience research. We propose that recognition of EEA as a major driver of neurocognitive differences will allow the field to make clearer inferences about cognitive abnormalities in psychopathology and their links to neurobiology.

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

Cited by 25 publications

References 39 publications

Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

Impaired Evidence Accumulation as a Transdiagnostic Vulnerability Factor in Psychopathology

Speed–accuracy manipulations and diffusion modeling: Lack of discriminant validity of the manipulation or of the parameter estimates?

Task-general efficiency of evidence accumulation as a computationally-defined neurocognitive trait: Implications for clinical neuroscience

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