The Process Dissociation Model with guessing.

<p>Branches lead to correct (+) and incorrect (-) responses.</p

Relationship of default Bayes factor and approximated Bayes factor for a two-sample t-test with support for the alternative hypothesis; left: N = 50, right: N = 200.

<p><i>Note</i>. a. = anecdotal. m. = moderate. s. = strong. v.s. = very strong evidence.</p

Gamma distribution used as the prior for the ratio of standard deviations.

<p>Parameters are k = 8 and <i>θ</i> = 0.125.</p

<i>ABrox</i>—A user-friendly Python module for approximate Bayesian computation with a focus on model comparison

<div><p>We give an overview of the basic principles of approximate Bayesian computation (ABC), a class of stochastic methods that enable flexible and likelihood-free model comparison and parameter estimation. Our new open-source software called <i>ABrox</i> is used to illustrate ABC for model comparison on two prominent statistical tests, the two-sample t-test and the Levene-Test. We further highlight the flexibility of ABC compared to classical Bayesian hypothesis testing by computing an approximate Bayes factor for two multinomial processing tree models. Last but not least, throughout the paper, we introduce <i>ABrox</i> using the accompanied graphical user interface.</p></div

Approximate Bayes factors (log-scale) expressing support for the Stroop Model.

<p>Data are simulated from the Stroop Model (left) or the Process Dissociation Model (right).</p

Cauchy distribution with a scale of <i>γ</i> = 0.707 used as the prior distribution.

<p>Cauchy distribution with a scale of <i>γ</i> = 0.707 used as the prior distribution.</p

Box plots for sdNN and LF/HF for five investigated age decades divided by gender.

<p>The box plots show two selected HRV indices (A: sdNN (TD) and B: LF/HF (FD)) for the five investigated age decades (1: 25–34, 2: 35–44, 3: 45–54, 4: 55–64 and 5: 65–74 years) and divided into males (black cross-striped boxes) and females (white boxes). The boxes show the data between the 25th and 75 Percentile, the middle line represents the median.</p

A Diffusion Model Analysis of Decision Biases Affecting Delayed Recognition of Emotional Stimuli

<div><p>Previous empirical work suggests that emotion can influence accuracy and cognitive biases underlying recognition memory, depending on the experimental conditions. The current study examines the effects of arousal and valence on delayed recognition memory using the diffusion model, which allows the separation of two decision biases thought to underlie memory: response bias and memory bias. Memory bias has not been given much attention in the literature but can provide insight into the retrieval dynamics of emotion modulated memory. Participants viewed emotional pictorial stimuli; half were given a recognition test 1-day later and the other half 7-days later. Analyses revealed that emotional valence generally evokes liberal responding, whereas high arousal evokes liberal responding only at a short retention interval. The memory bias analyses indicated that participants experienced greater familiarity with high-arousal compared to low-arousal items and this pattern became more pronounced as study-test lag increased; positive items evoke greater familiarity compared to negative and this pattern remained stable across retention interval. The findings provide insight into the separate contributions of valence and arousal to the cognitive mechanisms underlying delayed emotion modulated memory.</p></div

Exclusion criteria and the related number of excluded subjects (, with possible multiple assignments).

<p>In total 2201 subjects were excluded from the 4107 subjects leading to 1906 remaining healthy subjects.</p

The Diffusion Model [17].

<p> Illustration of the diffusion process for the classification of an “old” item as either “old” or “new”. The decision process starts at point <i>z</i> and moves toward the upper boundary or lower boundary by a drift rate ν. In this example, “old” response corresponds to the upper (and correct) boundary <i>a</i>, and is driven by a positive drift rate. Three sample paths are illustrated with responses 1 and 2 ending in a correct response at the upper boundary (“old”) but path 3 drifts toward the lower boundary 0, ending in an incorrect response “new”. RT = reaction time; <i>t</i>₀ = perceptual motor RT.</p