We offer a set of prescriptions that are designed to facilitate low-variance (i.e. stable), low-bias, interpretive regression modeling of small, wide data sets.
One potential validity check is for data derived from interviews to be validated against researcher analysis of the recorded session, further reducing the potential for bias in interpretive analysis.
Pre and post-intervention, measures were taken not only of interpretive bias, but also of attentional bias, to determine transfer of training from interpretation to attentional bias (as previously found by Amir et al., 2010; but not yet replicated).
The major finding was that interpretive bias mediated the relationship between bias condition and hesitance time, supporting the causal role of interpretive biases for avoidance behaviors in current chronic pain models.
Interpretive bias training procedures were initially designed to train negative interpretive biases in order to test causal hypotheses about the role of such biases in anxiety (Mathews and Mackintosh, 2000).
Groups also did not differ in baseline interpretive bias, F < 1, or attentional bias, F < 1.
CBM procedures are either designed to modify an interpretive bias (CBM-I) or an attentional bias (CBM-A).
This version of the recognition task has been widely used to assess change in interpretive bias due to Cognitive Bias Modification procedures and has well established validity (e.g., Mackintosh et al., 2006; Mathews & Mackintosh, 2000; Salemink, van den Hout, & Kindt, 2007; Yiend, Mackintosh, & Mathews, 2005).
It is possible, therefore, that amongst such samples participants have relatively low, homogenous interpretive bias, and a greater readiness to acquire a positive bias than to acquire a negative bias.
Amir, Bomyea, and Beard (2010) modified interpretive bias using a Word Sentence Association Paradigm and examined its impact upon attentional bias (as assessed by a modified Posner task).
