Above: a basic starter model of information sampling in depression developed by Nastasia which will be tested and developed over the coming years.
INFORMATION SAMPLING IN DEPRESSION
We rely on social interactions for many things that are essential to our survival, such as food, safety, and even health (Bloomberg, Meyers, & Braverman, 1994). Some theories go as far as to propose that the human brain is uniquely attuned to (and to a certain degree dependent on) social interactions in order to deal with environmental challenges in a minimally effortful way (Social Baseline Theory, seeBeckes & Coan, 2011). While we navigate this social world in our daily lives, a combination of genetically predetermined dispositions and life experiences will lead the individual to form and consolidate psychological and physiological mechanisms and tendencies. In some cases, these sets can develop to be inappropriate and maladaptive, often needlessly perpetuating old psychological or behavioural problems or even leading to altogether new ones. And when they have sufficiently profound effects on one's functioning, such sets of tendencies and mechanisms are often identified as disorders.
One such disorder is depression, which - while being a mood disorder - can be described in terms of maladaptive psychological and physiological tendencies that are related to social interactions and information. Such tendencies include increased sensitivity to imagined and real rejection (Downey & Feldman, 1996), a proneness to remember and recall unpleasant social experiences involving the self (Brewin, Reynolds, & Tata, 1999), maladaptive inflammatory activity (Slavich & Irwin, 2014; Young, Bruno, & Pomara, 2014), and even changes in decision-making, with people in depression displaying a stronger aversion to monetary risk (Smoski et al., 2008).
On a daily basis we are faced with large amounts of information, and it can prove challenging to process all of it and make the right decisions. Thankfully, humans have evolved to be able to deduce and generalise: we sample a limited amount of information from the world around us, and make inferences about this same world based on the sample we took. This way, we can avoid having to find and process every bit of information that exists in the world on a given subject, and nevertheless form and express the relevant mental representations and behaviours. Imaginably, any factors influencing the way in which we sample information from our environment are going to have a profound effect on the way in which we think about and behave towards the world around us.
Since information sampling is such a vital part of one’s everyday functioning, I will be aiming to investigate how the cognitive biases identified in depression (in restrictive settings in which information is automatically presented, exogenous and fixed) arise from, manifest in, and influence social information sampling (which is a far more realistic informational context) in depression. Computationally modelling this process in more life-like paradigms in which information is hidden and needs to be discovered/retrieved will allow us to construct a larger and more informative framework of depression. Modelling how cognitive biases may interact with information sampling and vice versa will allow us to address the issue of heterogeneity in the expression of depression, as well as bring into view ways in which depression might develop from certain starting parameters. This, in turn, will allow us to shed light on very specific opportunities for intervention and change, once certain ‘malfunctioning’ parameters in the process are identified.
Using Beck’s ‘negative cognitive triad’ model of depression (Beck, 1967) as a framework for our models, which proposes the ‘self’, ‘others’ and the ‘future’ as ‘problem areas’ in depression, I will aim to build models that describe and explain the relation of various cognitive biases to the process of information sampling in these domains and how they may interact to form a larger construct of maladaptive processes in depression.