Hypotheses About the Relationship of Cognition With Psychopathology Should be Tested by Embedding Them Into Empirical Priors

Moutoussis, Michael; Hopkins, Alexandra; Dolan, Raymond J.

doi:10.3389/fpsyg.2018.02504

Cited by 15 publications

(17 citation statements)

References 6 publications

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“…For instance, the relation between dogmatism and a general tendency to seek out less information can be formalized as follows:

Here,

describes the relation between dogmatism and

, while

represents individual variation in this parameter that is not explained by dogmatism. If the credible interval of

does not include zero, this indicates a significant association between dogmatism and

( 32 ). In what follows, we report 95% credible intervals.…”

Section: Resultsmentioning

confidence: 99%

“…Conventionally, parameters obtained through such an approach can then be correlated with an external measure of differences between individuals. However, this procedure is suboptimal because it assumes no variability in the mean of the population in the initial model fit, possibly distorting or minimizing potential relationships between the parameter and external factors ( 32 ). To maintain the advantages of hierarchical fitting while avoiding such pitfalls regarding individual differences, here we employ a procedure recently prescribed by Moutoussis et al ( 32 ).…”

Section: Methodsmentioning

confidence: 99%

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Dogmatism manifests in lowered information search under uncertainty

Schulz

Rollwage

Dolan

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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When knowledge is scarce, it is adaptive to seek further information to resolve uncertainty and obtain a more accurate worldview. Biases in such information-seeking behavior can contribute to the maintenance of inaccurate views. Here, we investigate whether predispositions for uncertainty-guided information seeking relate to individual differences in dogmatism, a phenomenon linked to entrenched beliefs in political, scientific, and religious discourse. We addressed this question in a perceptual decision-making task, allowing us to rule out motivational factors and isolate the role of uncertainty. In two independent general population samples (n = 370 and n = 364), we show that more dogmatic participants are less likely to seek out new information to refine an initial perceptual decision, leading to a reduction in overall belief accuracy despite similar initial decision performance. Trial-by-trial modeling revealed that dogmatic participants placed less reliance on internal signals of uncertainty (confidence) to guide information search, rendering them less likely to seek additional information to update beliefs derived from weak or uncertain initial evidence. Together, our results highlight a cognitive mechanism that may contribute to the formation of dogmatic worldviews.

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“…For instance, the relation between dogmatism and a general tendency to seek out less information can be formalized as follows:

Here,

describes the relation between dogmatism and

, while

represents individual variation in this parameter that is not explained by dogmatism. If the credible interval of

does not include zero, this indicates a significant association between dogmatism and

( 32 ). In what follows, we report 95% credible intervals.…”

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Dogmatism manifests in lowered information search under uncertainty

Schulz

Rollwage

Dolan

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

View full text Add to dashboard Cite

show abstract

“…These infer the characteristics of each individual not only from the data they provided, but also from the specific population from which they are drawn. Clinically informed model-fitting allowed (yet did not force) empirical priors over cognitive parameters like learning rates to be informed by clinical data, here BFNE scores (Moutoussis et al, 2018). It thus allowed more accurate estimation of the correlation between parameters and FNE.…”

Section: Discussionmentioning

confidence: 99%

“…Traditional hierarchical modelling reduces noise in parameter estimates, but we have found that empirical (population) priors which do not take adequately into account the possible correlations with external measures can increase the rates of Type 1 or Type 2 error, in subsequent correlation analyses with unmodelled psychometric measures (Moutoussis, Hopkins, & Dolan, 2018). Here, incorporating key psychological hypotheses in the model-fitting can give more accurate estimates of the relationship between model parameters and BFNE scores.…”

Section: Modelling the Relation To Fear Of Negative Evaluationmentioning

confidence: 98%

A Reduced Self-Positive Belief Underpins Greater Sensitivity to Negative Evaluation in Socially Anxious Individuals

Hopkins

Dolan

Button

et al. 2021

Computational Psychiatry

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Positive self-beliefs are important for well-being, and are influenced by how others evaluate us during social interactions. Mechanistic accounts of self-beliefs have mostly relied on associative learning models. These account for choice behaviour but not for the explicit beliefs that trouble socially anxious patients. Neither do they speak to selfschemas, which underpin vulnerability according to psychological research. Here, we compared belief-based and associative computational models of social-evaluation, in individuals that varied in fear of negative evaluation (FNE), a core symptom of social anxiety. We used a novel analytic approach, 'clinically informed model-fitting', to determine the influence of FNE symptom scores on model parameters. We found that high-FNE participants learn more easily from negative feedback about themselves, manifesting in greater self-negative learning rates. Crucially, we provide evidence that this bias is underpinned by an overall reduced belief about self-positive attributes. The study population could be characterized equally well by belief-based or associative models, however large individual differences in model likelihood indicated that some individuals relied more on an associative (model-free), while others more on a belief-guided strategy. Our findings have therapeutic importance, as positive belief activation may be used to specifically modulate learning.

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“…In addition, inference regarding subgroups is automatically incorporated within this framework through the regression models and therefore does not require multiple fittings to different subgroups (Ahn et al, 2017). Fitting the model separately for different groups and then performing a comparison between the estimated parameters could be problematic in that it may introduce biases and false positives (Moutoussis, Hopkins, & Dolan, 2018). Similarly, in our simulation study, fitting the model separately for each individual resulted in substantial and systematic bias for parameter estimates across individuals, confounding the interpretation of these individual differences with respect to biological or psychological covariates.…”

Section: Discussionmentioning

confidence: 99%

A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model

Zhang

Chandra

Kayser

et al. 2020

Computational Psychiatry

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Social and decision-making deficits are often the first symptoms of neuropsychiatric disorders. In recent years, economic games, together with computational models of strategic learning, have been increasingly applied to the characterization of individual differences in social behavior, as well as their changes across time due to disease progression, treatment, or other factors. At the same time, the high dimensionality of these data poses an important challenge to statistical estimation of these models, potentially limiting the adoption of such approaches in patients and special populations. We introduce a hierarchical Bayesian implementation of a class of strategic learning models, experience-weighted attraction (EWA), that is widely used in behavioral game theory. Importantly, this approach provides a unified framework for capturing between- and within-participant variation, including changes associated with disease progression, comorbidity, and treatment status. We show using simulated data that our hierarchical Bayesian approach outperforms representative agent and individual-level estimation methods that are commonly used in extant literature, with respect to parameter estimation and uncertainty quantification. Furthermore, using an empirical dataset, we demonstrate the value of our approach over competing methods with respect to balancing model fit and complexity. Consistent with the success of hierarchical Bayesian approaches in other areas of behavioral science, our hierarchical Bayesian EWA model represents a powerful and flexible tool to apply to a wide range of behavioral paradigms for studying the interplay between complex human behavior and biological factors.

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Hypotheses About the Relationship of Cognition With Psychopathology Should be Tested by Embedding Them Into Empirical Priors

Cited by 15 publications

References 6 publications

Dogmatism manifests in lowered information search under uncertainty

Dogmatism manifests in lowered information search under uncertainty

A Reduced Self-Positive Belief Underpins Greater Sensitivity to Negative Evaluation in Socially Anxious Individuals

A Hierarchical Bayesian Implementation of the Experience-Weighted Attraction Model

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