Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
Although theories of emotion associate negative emotional symptoms with cognitive biases in information processing, they rarely specify the details. Here, we characterize cognitive biases in information processing of pleasant and unpleasant information, and how these biases covary with anxious and depressive symptoms, while controlling for general stress and cognitive ability. Forty undergraduates provided emotional symptom scores (Depression Anxiety Stress Scale-21) and performed a statistical learning task that required predicting the next sound in a long sequence of either pleasant or unpleasant naturalistic sounds (blocks). We used an information weights framework to determine if the degree of behavioral change associated with observing either confirmatory ("B" follows "A") or disconfirmatory ("B" does not follow "A") transitions differs for pleasant and unpleasant sounds. Bayesian mixed-effects models revealed that negative emotional symptom scores predicted performance as well as processing biases of pleasant and unpleasant information. Further, information weights differed between pleasant and unpleasant information, and importantly, this difference varied based on symptom scores. For example, higher depressive symptom scores predicted a bias of underutilizing disconfirmatory information in unpleasant content. These findings have implications for models of emotional disorders by offering a mechanistic explanation and formalization of the associated cognitive biases.
Dimitri Nestor Alice Van De Ville, Julian Gaviria
Michael Herzog, Simona Adele Garobbio, Maya Roinishvili
, , , ,