The cognitive processing of somatic anxiety: Using functional measurement to understand and address the fear of pain

Although anxiety has both dispositional and situational determinants, little is known about how individuals' anxiety-related sensitivities and their expectations about stressful events combine to determine anxiety. This research used Information Integration Theory and Functional Measurement to assess how participants' anxiety sensitivity and event expectancy are cognitively integrated to determine their anxiety about physical pain. Two studies were conducted-one with university students and one with anxiety clinic patients-in which participants were presented with multiple scenarios of a physically painful event, each representing a different degree of event probability, from which subjective expectancies were derived. Independent variables included anxiety sensitivity (low, moderate, high) and event expectancy (low, medium, high, no probability information). Participants were asked to indicate their anxiety (dependent measure) in each expectancy condition in this 3 X 4 mixed, quasi-experimental design. The results of both studies strongly suggest that anxiety sensitivity and event expectancy are integrated additively to produce somatic anxiety. Additional results and their implications for the treatment of anxiety-related disorders are also discussed

Crowdsourcing prior information to improve study design and data analysis.

Though Bayesian methods are being used more frequently, many still struggle with the best method for setting priors with novel measures or task environments. We propose a method for setting priors by eliciting continuous probability distributions from naive participants. This allows us to include any relevant information participants have for a given effect. Even when prior means are near-zero, this method provides a principle way to estimate dispersion and produce shrinkage, reducing the occurrence of overestimated effect sizes. We demonstrate this method with a number of published studies and compare the effect of different prior estimation and aggregation methods

True parameter values (red) for each parameter compared with the HBM posterior estimates for the four top-level hyperparameters fit to one set of twenty subjects simulated from fixed parameters.

<p>True parameter values (red) for each parameter compared with the HBM posterior estimates for the four top-level hyperparameters fit to one set of twenty subjects simulated from fixed parameters.</p

Example pooling coefficients for m and s parameters in the control condition of question 4.

<p>The pooling coefficient is a measure of how much a given parameter is shrunk toward the grand means, in this case <i>μ</i> and <i>σ</i>: 0 is no pooling and 1 is infinite pooling.</p

Power (Pwr), probability of sign error (Sn), and proportion of magnitude error (Mag) for each of the eight studies.

<p>Power (Pwr), probability of sign error (Sn), and proportion of magnitude error (Mag) for each of the eight studies.</p

Cumulative normal curves generated from maximum likelihood estimates for each participant and condition, faceted by question.

<p>Participant curves for both conditions are in gray, while the curves corresponding the median mean and standard parameters in each condition are used to create consensus curves, colored by condition.</p

Cumulative normal curves generated from a Bayesian multilevel model fit independently to each question.

<p>Participant curves for both conditions are in gray, while the curves corresponding the median mean and standard parameters in each condition are used to create consensus curves, colored by condition.</p

Densities of raw best guess responses to concrete elicitation, colored by condition and faceted by study question.

<p>The horizontal axis is logarithmically scaled to minimize the visual effect of extreme estimates.</p

t-values as reported by the original studies and as recalculated using our elicitation and aggregation methods.

<p>t-values as reported by the original studies and as recalculated using our elicitation and aggregation methods.</p

Sums of squared error by participant, collapsing across conditions, for each of the eight questions.

<p>Sums of squared error by participant, collapsing across conditions, for each of the eight questions.</p