Safe and sensible preprocessing and baseline correction of pupil-size data

Measurement of pupil size (pupillometry) has recently gained renewed interest from psychologists, but there is little agreement on how pupil-size data is best analyzed. Here we focus on one aspect of pupillometric analyses: baseline correction, i.e., analyzing changes in pupil size relative to a baseline period. Baseline correction is useful in experiments that investigate the effect of some experimental manipulation on pupil size. In such experiments, baseline correction improves statistical power by taking into account random fluctuations in pupil size over time. However, we show that baseline correction can also distort data if unrealistically small pupil sizes are recorded during the baseline period, which can easily occur due to eye blinks, data loss, or other distortions. Divisive baseline correction (corrected pupil size = pupil size/baseline) is affected more strongly by such distortions than subtractive baseline correction (corrected pupil size = pupil size ? baseline). We discuss the role of baseline correction as a part of preprocessing of pupillometric data, and make five recommendations: (1) before baseline correction, perform data preprocessing to mark missing and invalid data, but assume that some distortions will remain in the data; (2) use subtractive baseline correction; (3) visually compare your corrected and uncorrected data; (4) be wary of pupil-size effects that emerge faster than the latency of the pupillary response allows (within ±220 ms after the manipulation that induces the effect); and (5) remove trials on which baseline pupil size is unrealistically small (indicative of blinks and other distortions).     

Individually-tailored, Internet-based treatment for anxiety disorders: A randomized controlled trial

Previous studies on Internet-based treatment with minimal to moderate therapist guidance have shown promising results for a number of specific diagnoses. The aim of this study was to test a new approach to Internet treatment that involves tailoring the treatment according to the patient’s unique characteristics and comorbidities.A total of 54 participants, regardless of specific anxiety diagnosis, were included after an in-person, semi-structured diagnostic interview and randomized to a 10 week treatment program or to a control group. Treatment consisted of a number of individually-prescribed modules in conjunction with online therapist guidance. Significant results were found for all dependent measures both immediately following treatment and at 1 and 2 year intervals. Mean between-group effect size including measures of anxiety, depression and quality of life was Cohen’s d = 0.69 at post-treatment, while the mean within-group effect size was d = 1.15 at post-treatment and d = 1.13 and d = 1.04 at 1 and 2 year follow-up respectively.The tentative conclusion drawn from these results is that tailoring the Internet-based therapy can be a feasible approach in the treatment of anxiety in a homogeneous population.