Information Sampling in Depression

Project Lead Category Project status
Nastasia Griffioen Depression Preparation

In this project, we aim to take a closer look at the way in which information is being sampled and integrated in individuals suffering from or at risk for depression. Since human beings are only able to perceive and process a limited amount of information, we have evolved to sample parts of information instead and attempt to draw accurate and workable conclusions based on the sample available to us. We have reason, however, to think that this process may be affected in depression, and aim to find out how exactly using methods such as behavioural computational modeling.

Project team


Above: a basic starter model of information sampling in depression developed by Nastasia which will be tested and developed over the coming years.


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.


Disability-Adjusted Life Years attributed to mental illness


Project team

Nastasia Griffioen title=
Nastasia Griffioen

Nerd, loves the brain even more than your average zombie, into etymology and reading, wants to explore information sampling in depression using neuro and computational methods.




E-mail Nastasia

Isabela Granic title=
Isabela Granic

Professor and Chair of the Developmental Psychopathology department in the Behavioural Science Institute; writer; voracious podcast consumer; mother of two upstanding little gamers


Director of GEMH Lab


E-mail Isabela

Marieke van Rooij title=
Marieke van Rooij

Assistant prof. and data geek at the GEMH lab, dynamical modelling, personalisation, wants to put the I back into AI, news junkie, cat lover.


Assistant Professor


E-mail Marieke

Ken Koontz title=
Ken Koontz

In house game designer, artist, producer and lover of games. I bring diversity, design experience and the NOISE!!!!


Creative Director


E-mail Ken

Wil Cunningham title=
Wil Cunningham

William Cunningham is a Professor in the Department of Psychology at the University of Toronto and holds a cross appointment in the Marketing Area at the Rotman School. Research and Teaching interests in: Affective Science, Social Cognition, Neuroscience, Attitude and Evaluation


Associate Professor, University of Toronto


E-mail Wil


All sources
  • Beck, A.T. (1967). Depression: Clinical, Experimental, and Theoretical Aspects. New York: Harper & Row.
  • Beckes, L., & Coan, J.A. (2011). Social baseline theory: The role of social proximity in emotion and economy of action. Social and Personality Psychology Compass, 5, 976-988.
  • Bloomberg, L., Meyers, J., & Braverman, M.T. (1994). The importance of social interaction: A new perspective on epidemiology, social risk factors, and health. Health Education and Behavior, 4, 447-463.
  • Brewin, C.R., Reynolds, M., & Tata, P. (1999). Autobiographical memory processes and the course of depression. Journal of Abnormal Psychology, 108, 511-517.
  • Downey, G., & Feldman, S. I. (1996). Implications of rejection sensitivity for intimate relationships. Journal of Personality and Social Psychology, 6, 1327-1343.
  • Slavich, G.M., & Irwin, M.R. (2014). From stress to inflammation and major depressive disorder: a social signal transduction theory of depression. Psychological Bulletin, 140, 774-815.
  • Smoski, M.J., Lynch, T.R., Rosenthal, M.Z., Cheavens, J.S., Chapman, A.L., Krishnan, R.R. (2008). Decision-making and risk aversion among depressive adults. Journal of Behavior Therapy and Experimental Psychiatry, 4, 567-576.
  • Young, J.J., Bruno, D., & Pomara, N. (2014). A review of the relationship between proinflammatory cytokines and major depressive disorder. Journal of Affective Disorders, 169, 15-20.

Want to keep up-to-date with our research?