Cognitive biases in information processing of valenced stimuli are a major
contributor to the phenomenology of mood disorders. However, current screening
tools for mood disorders rely on self-report questionnaires, which include
uncomfortably invasive questions and are confounded by socially desirable
responding. Taken together, assessing information processing biases may be
a promising proxy to screen non-invasively for mood disorders.
Here, we report data of 60 participants that performed a continuous
statistical learning task in which respondents were asked to predict the
next event in a sequence of musical chords. An underlying transitional
probability matrix governed the chord sequences. Each participant performed
both a positive- and negative-valence block of this task, where blocks
differed in the precise musical chords used. A pilot experiment established
that the sequences from both blocks evoked their intended perceived
valence. Furthermore, cognitive assessment (Raven’s advanced matrices) as
well as mood scores (DASS-21) were collected.
Bayesian mixed effects models revealed that participants were able to
extract the underlying transitional probabilities and that higher cognitive
ability predicted higher performance. Furthermore, there was strong
evidence that the depression, anxiety, and stress subscales all predicted
learning trajectories, and interacted with stimulus valence. Thus, the
present results show that information processing differences in a musical
context are consistent with the phenomenology of mood disorders. The
present study is one step towards a non-invasive musical tool to screen for
mood disorders.