Interpersonal factors in internet-based cognitive behavioral therapy for depression: Attachment style and alliance with the program and with the therapist.

Objective This open-trial study examined effects of a culturally-adapted Hebrew version of guided internet-based cognitive behavioural therapy (ICBT) for depression. We examined therapeutic alliance with the therapist and with the programme (content) as potential predictors of outcomes. Furthermore, we examined whether anxious and avoidant attachment styles improved, although relationships were not the focus of treatment. Method: We examined alliance with therapist and alliance with programme and their time-lagged (1 week), longitudinal relationship with depression outcomes, and change in anxious and avoidant attachment during treatment. Results: Depression and insomnia improved significantly (Cohen's d: depression = 1.34, insomnia = 0.86), though dropout was relatively high (49%). Alliance with programme and with the therapist predicted adherence and dropout, whereas only alliance with therapist predicted symptom improvement. Avoidant attachment decreased over treatment whereas anxious attachment did not. Conclusion: A culturally-adapted version of ICBT for depression showed that alliance with therapist and alliance with programme both can play an important role in its effectiveness: alliance with programme and the therapist drive adherence and dropout and alliance with therapist is related to symptom improvement. Although the focus of treatment is not interpersonal, avoidant attachment style can improve following ICBT.

The construction of categorization judgments: using subjective confidence and response latency to test a distributed model.

The classification of objects to natural categories exhibits cross-person consensus and within-person consistency, but also some degree of between-person variability and within-person instability. What is more, the variability in categorization is also not entirely random but discloses systematic patterns. In this study, we applied the Self-Consistency Model (SCM, Koriat, 2012) to category membership decisions, examining the possibility that confidence judgments and decision latency track the stable and variable components of categorization responses. The model assumes that category membership decisions are constructed on the fly depending on a small set of clues that are sampled from a commonly shared population of pertinent clues. The decision and confidence are based on the balance of evidence in favor of a positive or a negative response. The results confirmed several predictions derived from SCM. For each participant, consensual responses to items were more confident than non-consensual responses, and for each item, participants who made the consensual response tended to be more confident than those who made the nonconsensual response. The difference in confidence between consensual and nonconsensual responses increased with the proportion of participants who made the majority response for the item. A similar pattern was observed for response speed. The pattern of results obtained for cross-person consensus was replicated by the results for response consistency when the responses were classified in terms of within-person agreement across repeated presentations. These results accord with the sampling assumption of SCM, that confidence and response speed should be higher when the decision is consistent with what follows from the entire population of clues than when it deviates from it. Results also suggested that the context for classification can bias the sample of clues underlying the decision, and that confidence judgments mirror the effects of context on categorization decisions. The model and results offer a principled account of the stable and variable contributions to categorization behavior within a decision-making framework.