supplementary syntaxes and data - pone.0070245 - Cognitive Reactivity, Implicit Associations, and the Incidence of Depression: A Two-Year Prospective Study

<p>Supplementary material related to article:</p> <p>Kruijt A-W, Antypa N, Booij L, de Jong PJ, Glashouwer K, et al. (2013) Cognitive Reactivity, Implicit Associations, and the Incidence of Depression: A Two-Year Prospective Study. PLoS ONE 8(7): e70245. Doi:10.1371/journal.pone.0070245</p> <p> </p> <p>In this set:</p> <p>- dataset used for analyses in the paper</p> <p>- SPSS syntax for compiling dataset (from NESDA source datasets that are not provided).</p> <p>- SPSS syntax for all analyses reported in the paper</p> <p>- R syntax used to create 'predictor probability plots' (see file S1, on the PLoS site - the supplementary materials mentioned in the paper are hosted also on figshare, but uploaded by 'PLoS' - I 'll try to merge that set with the materials in this set).</p> <p><br>Questions or comments? -> [email protected]</p> <p> </p> <p> </p

Capturing Dynamics of Biased Attention

<p> New indices, calculated on data from the widely used Dot Probe Task, were recently proposed to capture variability in biased attention allocation. We observed that it remains unclear which data pattern is meant to be indicative of dynamic bias and thus to be captured by these indices. Moreover, we hypothesized that the new indices are sensitive to <i>SD</i> differences at the response time (RT) level in the absence of bias. </p

Mechanics of contingency-based Cognitive Bias Modification: pre-existing bias affects potency of active training but not placebo conditions.

Cognitive Bias Modification (CBM) is an overarching term for various computerized training protocols developed to change automatic information processing patterns (cognitive biases). CBM tasks are designed to reward response tendencies associated with more desired information processing patterns trough repeated practice. Target cognitive biases include those believed to be involved in anxiety, depression, addiction, and eating disorders, and CBM protocols are commonly regarded as potential new treatments. Most CBM forms rely on a (hidden) contingency between stimulus valence and response rewards. In CBM studies, active training conditions are typically contrasted with control conditions lacking the contingency, often called 50/50 placebo. This report focusses on the wide-spread, and intuitive, notion that pre-existing bias may affect the contingency experienced by an individual engaging in a 50/50 placebo control condition, and that this may inadvertently render the intended placebo condition more potent. Employing probabilistic reasoning, we conclude that, contrary to the often-forwarded notion, pre-existing bias cannot increase the potency of a 50/50 placebo condition. In contrast, we arrived at the unforeseen conclusion that lack of pre-existing bias may render an active training condition functionally similar to a placebo condition. In this paper we develop these arguments, review literature with respect to our assumptions, and discuss implications

Mechanics of contingency-based Cognitive Bias Modification: pre-existing bias affects potency of active training but not placebo conditions.

Cognitive Bias Modification (CBM) refers to various computerized training protocols aimed at modifying individuals’ automatic information processing patterns (cognitive biases). CBM protocols are commonly regarded as potential new treatments, targeting cognitive biases believed to be involved in, amongst others, anxiety, depression, substance abuse, disordered eating, pain perception, and insomnia. Designed to reward response tendencies associated with more desired information processing patterns trough repeated practice, CBM tasks tend to rely on a (hidden) contingency between stimulus valence and response rewards. In CBM studies, active training conditions are typically contrasted with control conditions lacking the contingency, often called 50/50 placebo. This report focusses on the wide-spread, and intuitive, notion that pre-existing bias may affect the contingency experienced by an individual engaging in a 50/50 placebo control condition thereby inadvertently rendering the intended placebo condition more potent. Employing probabilistic reasoning we conclude that, contrary to the often-forwarded notion, pre-existing bias cannot increase the potency of a 50/50 placebo condition. In contrast, we arrived at the unforeseen conclusion that lack of pre-existing bias may render an active training condition functionally similar to a placebo condition. In this paper we develop these arguments, review literature with respect to our assumptions, and discuss implications

A meta-analysis of bias at baseline in RCTs of attention bias modification: no evidence for dot-probe bias towards threat in clinical anxiety and PTSD.

Background: Considerable effort and funding have been spent on developing Attention Bias Modification (ABM) as a treatment for anxiety disorders, theorized to exert therapeutic effects through reduction of a tendency to orient attention towards threat. However, meta-analytical evidence that clinical anxiety is characterized by threat-related attention bias is thin. The largest meta-analysis to date included dot-probe data for n=337 clinically anxious individuals. Baseline measures of biased attention obtained in ABM RCTs form an additional body of data that has not previously been meta-analyzed. Method: This paper presents a meta-analysis of threat-related dot-probe bias measured at baseline for 1005 clinically anxious individuals enrolled in 13 ABM RCTs. Results: Random-effects meta-analysis indicated no evidence that the mean bias index (BI) differed from zero (k= 13, n= 1005, mean BI = 1.8 ms, SE = 1.26 ms, p = .144, 95% CI [-0.6 - 4.3]. Additional Bayes factor analyses also supported the point-zero hypothesis (BF10 = .23), whereas interval-based analysis indicated that mean bias in clinical anxiety is unlikely to extend beyond the 0 to 5 ms interval. Discussion: Findings are discussed with respect to strengths (relatively large samples, possible bypassing of publication bias), limitations (lack of control comparison, repurposing data, specificity to dot-probe data), and theoretical and practical context. We suggest that it should no longer be assumed that clinically anxious individuals are characterized by selective attention towards threat. Conclusion: Clinically anxious individuals enrolled in RCTs for Attention Bias Modification are not characterized by threat-related attention bias at baseline

Psychological Science needs a standard practice of reporting the reliability of cognitive behavioural measurements

Psychological science relies on behavioural measures to assess cognitive processing; however, the field has not yet developed a tradition of routinely examining the reliability of these behavioural measures. Reliable measures are essential to draw robust inferences from statistical analyses, while subpar reliability has severe implications for the measures’ validity and interpretation. Without examining and reporting the reliability of cognitive behavioural measurements, it is near impossible to ascertain whether results are robust or have arisen largely from measurement error. In this paper we propose that researchers adopt a standard practice of estimating and reporting the reliability of behavioural assessments. We illustrate this proposal using an example from experimental psychopathology, the dot-probe task; although we argue that reporting reliability is relevant across fields (e.g. social cognition and cognitive psychology). We explore several implications of low measurement reliability, and the detrimental impact that failure to assess measurement reliability has on interpretability and comparison of results and therefore research quality. We argue that the field needs to a) report measurement reliability as routine practice so that we can b) develop more reliable assessment tools. To provide some guidance on estimating and reporting reliability, we describe bootstrapped split half estimation and IntraClass Correlation Coefficient procedures to estimate internal consistency and test-retest reliability, respectively. For future researchers to build upon current results it is imperative that all researchers provide sufficient psychometric information to estimate the accuracy of inferences and inform further development of cognitive behavioural assessments

ABV time series (BI per bin) and traditional BI when increasing dynamic bias switching frequency and increasing dynamic bias magnitude.

<p>Each row depicts data for the ‘change group’ in the 1000th study of runs 1, 5, and 10 of the increasing dynamic frequency simulation (top) and the increasing dynamic magnitude simulation (bottom).</p

TL-BS time series and traditional BI for increased <i>SD</i>, mean, and bias.

<p>Panels B, C, and D show data for the 'change group' in the 1000th study of the 10th run in the increasing <i>SD</i>, mean, and BI simulations respectively. Similar to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166600#pone.0166600.g002" target="_blank">Fig 2</a> reported by Zvielli and colleauges [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0166600#pone.0166600.ref001" target="_blank">1</a>], a smoothing procedure was applied to the TL-BS data.</p

Observed average values of BI, ABV, TL-BS variability, and Average TL-BS positive for each study and each run of the first three simulation series (<i>SD</i>, mean, and bias increasing).

<p>Each data point represents the group average for a single study, lines represents the average observed for each run of 1000 studies. The shades of the data points (but not the lines) indicate the percentage significant group differences observed per run (1000 studies).</p

Observed average values of BI, ABV, TL-BS variability, and Average TL-BS positive for each study and each run of the dynamic bias simulations.

<p>Each data point represents the group average for a single study, lines represents the average observed for each run of 1000 studies. The shades of the data points (but not the lines) indicate the percentage significant group differences observed per run (1000 studies).</p