Hostname: page-component-8448b6f56d-qsmjn Total loading time: 0 Render date: 2024-04-23T14:22:09.277Z Has data issue: false hasContentIssue false
  • English
  • Français

Computational modelling of attentional selectivity in depression reveals perceptual deficits

Published online by Cambridge University Press:  27 July 2020

James A. Grange Open the ORCID record for James A. Grange [Opens in a new window]  and
Michelle Rydon-Grange
James A. Grange*
Affiliation:
School of Psychology, Keele University, Keele, England, UK
Michelle Rydon-Grange
Affiliation:
Midlands Partnership Foundation NHS Trust, England, UK
*
Author for correspondence: James A. Grange, E-mail: grange.jim@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Depression is associated with broad deficits in cognitive control, including in visual selective attention tasks such as the flanker task. Previous computational modelling of depression and flanker task performance showed reduced pre-potent response bias and reduced executive control efficiency in depression. In the current study, we applied two computational models that account for the full dynamics of attentional selectivity.

Method

Across three large-scale online experiments (one exploratory experiment followed by two confirmatory – and pre-registered – experiments; total N = 923), we measured attentional selectivity via the flanker task and obtained measures of depression symptomology as well as anhedonia. We then fit two computational models that account for the dynamics of attentional selectivity: The dual-stage two-phase model, and the shrinking spotlight (SSP) model.

Results

No behavioural measures were related to depression symptomology or anhedonia. However, a parameter of the SSP model that indexes the strength of perceptual input was consistently negatively associated with the magnitude of depression symptomatology.

Conclusions

The findings provide evidence for deficits in perceptual representations in depression. We discuss the implications of this in relation to the hypothesis that perceptual deficits potentially exacerbate control deficits in depression.

Keywords

Cognitive controlcognitive modellingdepression
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 5 , April 2022 , pp. 904 - 913
Check for updates
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anwyl-Irvine, A. L., Massonnié, J., Flitton, A., Kirkham, N., & Evershed, J. K. (2020). Gorilla in our midst: An online behavioral experiment builder. Behavior Research Methods, 52, 388–407. 10.3758/s13428-019-01237-x..CrossRefGoogle ScholarPubMed
Bubl, E., Kern, E., Ebert, D., Bach, M., & van Elst, L. T. (2010). Seeing gray when feeling blue? Depression can be measured in the eye of the diseased. Biological Psychiatry, 68, 205–208. doi:10.1016/j.biopsych.2010.02.009.CrossRefGoogle ScholarPubMed
Bubl, E., van Elst, L. T., Gondan, M., Ebert, D., & Greenle, M. W. (2009). Vision in depressive disorder. The World Journal of Biological Psychiatry, 10, 377–384. doi:10.3109/15622970701513756.CrossRefGoogle ScholarPubMed
Bürkner, P.-C. (2017). Brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software, 80(1), 1–28. doi:10.18637/jss.v080.i01CrossRefGoogle Scholar
Burt, D. B., Zembar, M. J., & Niederehe, G. (1995). Depression and memory impairment: A meta-analysis of the association, its pattern, and specificity. Psychological Bulletin, 117, 285–305. doi:10.1037/0033-2909.117.2.285.CrossRefGoogle Scholar
Crump, M. J. C., McDonnell, J. V., & Gureckis, T. M. (2013). Evaluating Amazon's Mechanical Turk as a tool for experimental behavioral research. PLoS One, 8, e57410. doi:10.1371/journal.pone.0057410.CrossRefGoogle Scholar
Dillon, D. G., Wiecki, R., Pechtel, P., Webb, C., Goer, F., Murray, L., … Pizzagalli, D. (2015). A computational analysis of flanker interference in depression. Psychological Medicine, 45, 2333–2344. doi:10.1017/S0033291715000276.CrossRefGoogle ScholarPubMed
Eriksen, B. A., & Eriksen, C. W. (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics, 16, 143–149. doi:10.3758/BF03203267.CrossRefGoogle Scholar
Farrell, S., & Lewandowsky, S. (2010). Computational models as aids to better reasoning in psychology. Current Directions in Psychological Science, 19, 329–335. doi:10.1177/0963721410386677.CrossRefGoogle Scholar
Franken, I. H. A., Rassin, E., & Muris, P. (2007). The assessment of anhedonia in clinical and non-clinical populations: Further validation of the Snaith–Hamilton Pleasure Scale (SHAPS). Journal of Affective Disorders, 99, 83–89. doi:10.1016/j.jad.2006.08.020.CrossRefGoogle Scholar
Fu, C. H. Y., Williams, S. C. R., Brammer, M. J., Suckling, J., Kim, J., Cleare, A. J., … Bullmore, E. T. (2007). Neural responses to happy facial expressions in major depression following antidepressant treatment. American Journal of Psychiatry, 164, 599–607.CrossRefGoogle ScholarPubMed
Gotlib, I. H., Krasnoperova, E., Yue, D. N., & Joormann, J. (2004). Attentional biases for negative interpersonal stimuli in clinical depression. Journal of Abnormal Psychology, 113, 127–135. doi:10.1037/0021-843X.113.1.121.CrossRefGoogle ScholarPubMed
Grange, J. A. (2016). Flankr: An R package implementing computational models of attentional selectivity. Behavior Research Methods, 48, 528–541. doi:10.3758/s13428-015-0615-y.CrossRefGoogle Scholar
Gratton, G., Coles, M. G. H., Sirevaag, E. J., & Eriksen, C. W. (1998). Pre- and poststimulus activation of response channels: A psychophysiological analysis. Journal of Experimental Psychology: Human Perception & Performance, 14, 331–344. doi:10.1037//0096-1523.14.3.331.Google Scholar
Hübner, R., Steinhauser, M., & Lehle, C. (2010). A dual-stage two-phase model of selective attention. Psychological Review, 117, 759–784. doi:10.1037/a0019471.CrossRefGoogle ScholarPubMed
Levin, R. L., Heller, W., Mohanty, A., Herrington, J. D., & Miller, G. A. (2007). Cognitive deficits in depression and functional specificity of regional brain activity. Cognitive Therapy & Research, 31, 211–233. doi:10.1007/s10608-007-9128-z.CrossRefGoogle Scholar
Lewandowsky, S., & Farrell, S. (2010). Computational modeling in cognition: Principles and practice. Sage: Thousand Oaks, CA.Google Scholar
Logan, G. D., & Gordon, R. D. (2001). Executive control of visual attention in dual-task situations. Psychological Review, 108, 393–434. doi:10.1037/0033-295X.108.2.393.CrossRefGoogle ScholarPubMed
Maekawa, T., Anderson, S. J., de Brecht, M., & Yamagishi, N. (2018). The effect of mood state on visual search times for detecting a target in noise: An application of smartphone technology. PLoS One, 13, e0195865. doi:10.1371/journal.pone.0195865.CrossRefGoogle ScholarPubMed
McDermott, L. M., & Ebmeier, K. P. (2009). A meta-analysis of depression severity and cognitive function. Journal of Affective Disorders, 119, 1–8. doi:10.1016/j.jad.2009.04.022.CrossRefGoogle ScholarPubMed
Miyake, A., Friedman, N. P., Emerson, M. J., Witzkia, A. H., Howertera, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49–100. doi:10.1006/cogp.1999.0734.CrossRefGoogle ScholarPubMed
Noorani, I., & Carpenter, R. H. S. (2013). Antisaccades as decisions: LATER model predicts latency distributions and error responses. European Journal of Neuroscience, 37, 330–338. doi:10.1111/ejn.12025.CrossRefGoogle ScholarPubMed
Norman, D. A., & Shallice, T. (1986). Attention to action: Willed and automatic control of behavior. In Davidson, R. J., Schwartz, G. E., & Shapiro, D. (Eds.), Consciousness and self-regulation: Advances in research and theory (pp. 1–18). New York: Plenum.Google Scholar
Normann, C., Schmitz, D., Furmaier, A., Doing, C., & Bach, M. (2007). Long-term plasticity of visually evoked potentials in humans is altered in major depression. Biological Psychiatry, 62, 373–380. doi:10.1016/j.biopsych.2006.10.006.CrossRefGoogle ScholarPubMed
R Core Team (2017). R: A language and environment for statistical computing [computer software manual]. Vienna, Austria: R Core Team. Retrieved from https://www.R-project.org/.Google Scholar
Rock, P. L., Roiser, J. P., Riedel, W. J., & Blackwell, A. D. (2014). Cognitive impairment in depression: A systematic review and meta-analysis. Psychological Medicine, 44, 2029–2040. doi:10.1017/S0033291713002535.CrossRefGoogle ScholarPubMed
Rush, A. J., Trivedi, M. H., Ibrahim, H. M., Carmody, T. J., Arnow, B., Klein, D. N., … Keller, M. B. (2003). The 16-item Quick Inventory of Depressive Symptomatology (QIDS) clinician rating (qids-c) and self-report (qids-sr): A psychometric evaluation in patients with chronic major depression. Biological Psychiatry, 54, 573–583.CrossRefGoogle ScholarPubMed
Servant, M., Montagnini, A., & Burle, B. (2014). Conflict tasks and the diffusion framework: Insight in model constraints based on psychological laws. Cognitive Psychology, 72, 162–195. doi:10.1016/j.cogpsych.2014.03.002.CrossRefGoogle ScholarPubMed
Snaith, R. P., Hamildon, M., Morley, S., Humayan, A., Hargreaves, D., & Trigwell, P. (1995). A scale for the assessment of hedonic tone: The Snaith–Hamilton pleasure scale. British Journal of Psychiatry, 167, 99–103. doi:10.1192/bjp.167.1.99.CrossRefGoogle ScholarPubMed
Snyder, H. R. (2013). Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: A meta-analysis and review. Psychological Bulletin, 139, 81–132. doi:10.1037/a0028727.CrossRefGoogle ScholarPubMed
Stuhrmann, A., Suslow, T., & Dannlowski, U. (2011). Facial emotion processing in major depression: A systematic review of neuroimaging findings. Biology of Mood and Anxiety Disorders, 1, 10. doi:10.1186/2045-5380-1-10.CrossRefGoogle ScholarPubMed
Stuss, D. T., & Knight, R. T. (Eds.), (2002). Principles of frontal lobe function. New York, NY, USA: Oxford University Press.CrossRefGoogle Scholar
Wagenmakers, E.-J., van der Maas, H. L. J., & Grassman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14, 3–22. doi:10.3758/BF03194023.CrossRefGoogle ScholarPubMed
White, C. N., Ratcliff, R., & Starns, J. J. (2011). Diffusion models of the flanker task: Discrete versus gradual attentional selection. Cognitive Psychology, 63, 210–238. doi:10.1016/j.cogpsych.2011.08.001.CrossRefGoogle ScholarPubMed
White, C. N., Ratcliff, R., Vasey, M. W., & McKoon, G. (2010). Using diffusion models to understand clinical disorders. Journal of Mathematical Psychology, 54, 39–52. doi:10.1016/j.jmp.2010.01.004.CrossRefGoogle ScholarPubMed
Wickelgren, W. A. (1977). Speed–accuracy trade-off and information processing dynamics. Acta Psychologica, 41, 67–85. doi:10.1016/0001-6918(77)90012-9.CrossRefGoogle Scholar
Supplementary material: PDF

Grange and Rydon-Grange supplementary material

Grange and Rydon-Grange supplementary material

Download Grange and Rydon-Grange supplementary material(PDF)
PDF 9.6 MB