Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-11T01:16:10.224Z Has data issue: false hasContentIssue false
  • English
  • Français

A computational analysis of flanker interference in depression

Published online by Cambridge University Press:  02 March 2015

D. G. Dillon ,
T. Wiecki ,
P. Pechtel ,
C. Webb ,
F. Goer ,
L. Murray ,
M. Trivedi ,
M. Fava ,
P. J. McGrath  and
M. Weissman
...Show all authors
D. G. Dillon
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USA
T. Wiecki
Affiliation:
Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, RI, USA
P. Pechtel
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USA
C. Webb
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USA
F. Goer
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USA
L. Murray
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USA
M. Trivedi
Affiliation:
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
M. Fava
Affiliation:
Clinical Research Program, Massachusetts General Hospital, Boston, MA, USA
P. J. McGrath
Affiliation:
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
M. Weissman
Affiliation:
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
R. Parsey
Affiliation:
Department of Psychiatry and Behavioral Science, Stony Brook University, Stony Brook, NY, USA
B. Kurian
Affiliation:
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
P. Adams
Affiliation:
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
T. Carmody
Affiliation:
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
S. Weyandt
Affiliation:
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
K. Shores-Wilson
Affiliation:
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
M. Toups
Affiliation:
Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
M. McInnis
Affiliation:
Department of Psychiatry, University of Michigan Health System, Ann Arbor, MI, USA
M. A. Oquendo
Affiliation:
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
C. Cusin
Affiliation:
Clinical Research Program, Massachusetts General Hospital, Boston, MA, USA
P. Deldin
Affiliation:
Department of Psychiatry, University of Michigan Health System, Ann Arbor, MI, USA
G. Bruder
Affiliation:
New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
D. A. Pizzagalli*
Affiliation:
Center for Depression, Anxiety and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USA
*
*Address for correspondence: D. A. Pizzagalli, Ph.D., Center for Depression, Anxiety and Stress Research, McLean Hospital, 115 Mill Street, Belmont, MA 02478-9106, USA. (Email: dap@mclean.harvard.edu)
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Depression is characterized by poor executive function, but – counterintuitively – in some studies, it has been associated with highly accurate performance on certain cognitively demanding tasks. The psychological mechanisms responsible for this paradoxical finding are unclear. To address this issue, we applied a drift diffusion model (DDM) to flanker task data from depressed and healthy adults participating in the multi-site Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) study.

Method

One hundred unmedicated, depressed adults and 40 healthy controls completed a flanker task. We investigated the effect of flanker interference on accuracy and response time, and used the DDM to examine group differences in three cognitive processes: prepotent response bias (tendency to respond to the distracting flankers), response inhibition (necessary to resist prepotency), and executive control (required for execution of correct response on incongruent trials).

Results

Consistent with prior reports, depressed participants responded more slowly and accurately than controls on incongruent trials. The DDM indicated that although executive control was sluggish in depressed participants, this was more than offset by decreased prepotent response bias. Among the depressed participants, anhedonia was negatively correlated with a parameter indexing the speed of executive control (r = −0.28, p = 0.007).

Conclusions

Executive control was delayed in depression but this was counterbalanced by reduced prepotent response bias, demonstrating how participants with executive function deficits can nevertheless perform accurately in a cognitive control task. Drawing on data from neural network simulations, we speculate that these results may reflect tonically reduced striatal dopamine in depression.

Keywords

Cognitive controlcomputational modellingdepressionexecutive functionflanker
Type
Original Articles
Information
Psychological Medicine , Volume 45 , Issue 11 , August 2015 , pp. 2333 - 2344
Copyright
Copyright © Cambridge University Press 2015 

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

Ambady, N, Gray, HM (2002). On being sad and mistaken: mood effects on the accuracy of thin-slice judgments. Journal of Personality and Social Psychology 83, 947–961.Google Scholar
APA (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th edn. American Psychiatric Publishing: Arlington, VA.Google Scholar
Andrews, PW, Aggen, SH, Miller, GF, Radi, C, Dencoff, JE, Neale, MC (2007). The functional design of depression's influence on attention: a preliminary test of alternative control-process mechanisms. Evolutionary Psychology 5, 584–604.Google Scholar
Andrews, PW, Thomson, JA Jr, (2009). The bright side of being blue: depression as an adaptation for analyzing complex problems. Psychological Review 116, 620–654.CrossRefGoogle ScholarPubMed
Au, K, Chan, F, Wang, D, Vertinsky, I (2003). Mood in foreign exchange trading: cognitive processes and performance. Organizational Behavior and Human Decision Processes 91, 322–328.Google Scholar
Bagby, RM, Ryder, AG, Schuller, DR, Marshall, MB (2004). The Hamilton depression rating scale: has the gold standard become a lead weight? American Journal of Psychiatry 161, 2163–2177.Google Scholar
Bogacz, R, Brown, E, Moehlis, J, Holmes, P, Cohen, JD (2006). The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks. Psychological Review 113, 700–765.Google Scholar
Bruder, GE, Alvarenga, JE, Alshculer, D, Abraham, K, Keilp, JG, Hellerstein, DJ, Stewart, JW, McGrath, PJ (2014). Neurocognitive predictors of antidepressant clinical response. Journal of Affective Disorders 166, 108–114.CrossRefGoogle ScholarPubMed
Chevalier, G, Deniau, JM (1990). Disinhibition as a basic process in the expression of striatal functions. Trends in Neuroscience 13, 277–280.Google Scholar
Chiu, PH, Deldin, PJ (2007). Neural evidence for enhanced error detection in major depressive disorder. American Journal of Psychiatry 164, 608–616.Google Scholar
Dillon, DG, Rosso, IM, Pechtel, P, Killgore, WDS, Rauch, SL, Pizzagalli, DA (2014). Peril and pleasure: an RDoC-inspired examination of threat responses and reward processing in anxiety and depression. Depression and Anxiety 31, 233–249.CrossRefGoogle ScholarPubMed
Donkin, C, Brown, S, Heathcote, A, Wagenmakers, E-J (2011). Diffusion versus linear ballistic accumulation: different models but the same conclusions about psychological processes? Psychonomic Bulletin & Review 18, 61–69.CrossRefGoogle ScholarPubMed
Dubal, S, Jouvent, R (2004). Time-on-task effect in trait anhedonia. European Psychiatry 19, 285–291.CrossRefGoogle ScholarPubMed
Dubal, S, Pierson, A, Jouvent, R (2000). Focused attention in anhedonia: a P3 study. Psychophysiology 37, 711–714.Google Scholar
Dutilh, G, Vandekerckhove, J, Forstmann, BU, Keuleers, E, Brysbaert, M, Wagenmakers, E-J (2012). Testing theories of post-error slowing. Attention, Perception & Psychophysics 74, 454–465.CrossRefGoogle ScholarPubMed
Epstein, J, Pan, H, Kocsis, JH, Yang, Y, Butler, T, Chusid, J, Hochberg, H, Murrough, J, Strohmayer, E, Stern, E, Silbersweig, DA (2006). Lack of ventral striatal response to positive stimuli in depressed versus normal subjects. American Journal of Psychiatry 163, 1784–1790.CrossRefGoogle ScholarPubMed
Eriksen, BA, Eriksen, CW (1974). Effects of noise letters upon the identification of a target letter in a nonsearch task. Perception & Psychophysics 16, 143–149.CrossRefGoogle Scholar
First, MB, Spitzer, RL, Gibbon, M, Williams, JBW (2002). Structured Clinical Interview for DSM-IV-TR Axis I Disorders, Research Version, Patient Edition. (SCID-I/P) Biometrics Research, New York State Psychiatric Institute: New York.Google Scholar
Franken, IHA, 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.CrossRefGoogle ScholarPubMed
Gratton, G, Coles, MG, Donchin, E (1992). Optimizing the use of information: strategic control of activation of responses. Journal of Experimental Psychology: General 121, 480–506.CrossRefGoogle ScholarPubMed
Frank, MJ (2005). Dynamic dopamine modulation in the basal ganglia: a neurocomputational account of cognitive deficits in medicated and non-medicated parkinsonism. Journal of Cognitive Neuroscience 17, 51–72.Google Scholar
Hamilton, M (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry 23, 56–62.CrossRefGoogle ScholarPubMed
Herrera-Guzmán, I, Gudayol-Ferré, E, Lira-Mandujano, J, Herrera-Abarca, J, Herrera-Guzmán, D, Montoya-Pérez, K, Guardia-Olmos, J (2008). Cognitive predictors of treatment response to bupropion and cognitive effects of bupropion in patients. Psychiatry Research 160, 72–82.Google Scholar
Hertel, PT (1997). On the contributions of deficient cognitive control to memory impairments in depression. Cognition and Emotion 11, 569–583.CrossRefGoogle Scholar
Holmes, AJ, Bogden, R, Pizzagalli, DA (2010). Serotonin transporter genotype and action monitoring dysfunction: a possible substrate underlying increased vulnerability to depression. Neuropsychopharmacology 35, 1186–1197.CrossRefGoogle ScholarPubMed
Holmes, AJ, Pizzagalli, DA (2008). Spatiotemporal dynamics of error processing dysfunctions in major depressive disorder. Archives of General Psychiatry 65, 179–188.CrossRefGoogle ScholarPubMed
Holmes, AJ, Pizzagalli, DA (2010). Effects of task-relevant incentives on the electrophysiological correlates of error processing in major depressive disorder. Cognitive, Affective, & Behavioral Neuroscience 10, 119–128.Google Scholar
Hübner, R, Steinhauser, M, Lehle, C (2010). A dual-stage two-phase model of selective attention. Psychological Review 117, 759–784.Google Scholar
Laming, D (1979). Autocorrelation of choice-reaction times. Acta Psychologica 43, 381–412.Google Scholar
Mink, JW (1996). The basal ganglia: focused selection and inhibition of competing motor programs. Progress in Neurobiology 50, 381–425.Google Scholar
Montague, PR, Dolan, RJ, Friston, KJ, Dayan, P (2012). Computational psychiatry. Trends in Cognitive Sciences 16, 72–80.CrossRefGoogle ScholarPubMed
Nolen-Hoeksema, S (1991). Responses to depression and their effects on the duration of depressive episodes. Journal of Abnormal Psychology 100, 569–582.Google Scholar
Noorani, I, Carpenter, RHS (2013). Antisaccades as decisions: LATER model predicts latency distributions and error responses. European Journal of Neuroscience 37, 330–338.CrossRefGoogle ScholarPubMed
Pe, ML, Vandekerckhove, J, Kuppens, P (2013). A diffusion model account of the relationship between the emotional flanker task and rumination and depression. Emotion 13, 739–747.Google Scholar
Pizzagalli, DA, Holmes, AJ, Dillon, DG, Goetz, EL, Birk, JL, Bogdan, R, Dougherty, DD, Iosifescu, DV, Rauch, SL, Fava, M (2009). Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder. American Journal of Psychiatry 166, 702–710.Google Scholar
Powell, MJD (1964). An efficient method for finding the minimum of a function of several variables without calculating derivatives. Computer Journal 7, 155–162.Google Scholar
R Core Team (2013). R: A Language and Environment for Statistical Computing (http://www.R-project.org/). R Foundation for Statistical Computing: Vienna, Austria.Google Scholar
Rabbitt, PM (1966). Errors and error correction in choice-response tasks. Journal of Experimental Psychology 71, 264–272.Google Scholar
Ratcliff, R, Frank, MJ (2012). Reinforcement-based decision making in corticostriatal circuits: mutual constraints by neurocomputational and diffusion models. Neural Computation 24, 1186–1229.Google Scholar
Ratcliff, R, McKoon, G (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural Computation 20, 873–922.Google Scholar
Robinson, MD, Meier, BP, Wilkowski, BM, Ode, S (2007). Introversion, inhibition, and displayed anxiety: the role of error reactivity processes. Journal of Research in Personality 41, 558–578.Google Scholar
Rush, AJ, Trivedi, MH, Ibrahim, HM, Carmody, TJ, Arnow, B, Klein, DN, Markowitz, JC, Ninan, PT, Kornstein, S, Manber, R, Thase, ME, Kocsis, JH, Keller, MB (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.Google Scholar
Siegle, GJ, Steinhauer, SR, Thase, ME (2004). Pupillary assessment and computational modeling of the Stroop task in depression. International Journal of Psychophysiology 52, 63–76.Google Scholar
Snaith, RP, Hamilton, 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.Google Scholar
Snyder, HR (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.Google Scholar
Snyder, HR, Kaiser, RH, Whisman, MA, Turner, AEJ, Guild, RM, Munakata, Y (2014). Opposite effects of anxiety and depressive symptoms on executive function: the case of selecting among competing options. Cognition and Emotion 28, 893–902.Google Scholar
Taylor, BP, Bruder, GE, Stewart, JW, McGrath, PJ, Halperin, J, Ehrlichman, H, Quitkin, FM (2006). Psychomotor slowing as a predictor of fluoxetine nonresponse in depressed outpatients. American Journal of Psychiatry 163, 73–78.Google Scholar
Treadway, MT, Zald, DH (2011). Reconsidering anhedonia in depression: lessons from translational neuroscience. Neuroscience and Biobehavioral Reviews 35, 537–555.CrossRefGoogle ScholarPubMed
Turner, BM, Sederberg, PB (2014). A generalized, likelihood-free method for posterior estimation. Psychonomic Bulletin & Review 21, 227–250.Google Scholar
Vallesi, A, Canalaz, F, Balestrieri, M, Brambilla, P (2015). Modulating speed-accuracy strategies in major depression. Journal of Psychiatric Research 60, 103–108.Google Scholar
Wagner, G, Sinsel, E, Sobanski, T, Köhler, S, Marinou, V, Mentzel, HJ, Sauer, H, Schlösser, RG (2006). Cortical inefficiency in patients with unipolar depression: an event-related fMRI study with the Stroop task. Biological Psychiatry 59, 958–965.Google Scholar
Wales, DJ, Doye, JPK (1997). Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. Journal of Physical Chemistry A 101, 5111–5116.CrossRefGoogle Scholar
White, CN, Ratcliff, R, Starns, JJ (2011). Diffusion models of the flanker task: discrete versus gradual attentional selection. Cognitive Psychology 63, 210–238.Google Scholar
Wiecki, TV, Frank, MJ (2010). Neurocomputational models of motor and cognitive deficits in Parkinson's disease. Progress in Brain Research 183, 275–297.Google Scholar
Wiecki, TV, Frank, MJ (2013). A computational model of inhibitory control in frontal cortex and basal ganglia. Psychological Review 120, 329–355.CrossRefGoogle ScholarPubMed
Wiecki, TV, Poland, JS, Frank, MJ (in press). Model-based cognitive neuroscience approaches to computational psychiatry: clustering and classification. Clinical Psychological Science. doi:10.1177/2167702614565359.Google Scholar
Wiecki, TV, Riedinger, K, Meyerhofer, A, Schmidt, W, Frank, MJ (2009). A neurocomputational account of catalepsy sensitization induced by D2-receptor-blockade in rats: context-dependency, extinction, and renewal. Psychopharmacology 204, 265–277.CrossRefGoogle Scholar
Supplementary material: File

Dillon supplementary material

Appendix

Download Dillon supplementary material(File)
File 124.9 KB