Can We Secretly Uncover Mental Health Risk?: The Brain & Mind Series (Part 3 – Cognitive Psychology)

Can We Secretly Uncover Mental Health Risk?

Part 3 of the Brain & Mind Series

implicit cog

Lay Summary

Often people are unwilling to share the mental health issues they may be experiencing for fear of being judged or wanting to appear “normal”. These issues may include not eating, self-harming and drinking excessively. Alongside this, many people may not be aware they are experiencing symptoms of mental health disorders, or may not realise how serious their symptoms are. For example, people may not realise that their thoughts are overly negative, and that they are a symptom of depression. These issues are problematic for researchers, who rely on participants providing accurate answers to questions asking them about their thoughts, feelings and experiences. To get around these issues, researchers are increasingly using implicit measures that don’t ask participants about their symptoms explicitly, but instead measure “implicit cognition”, whereby the participant is unaware of what exactly is being measured. Implicit cognition refers to the assumptions, beliefs and knowledge that influence someone’s thoughts and behaviours without their realisation.

It is argued that measuring implicit cognition has enhanced our understanding of mental health disorders, mainly because it has allowed researchers to obtain accurate responses from participants that are not affected by the wish to conceal symptoms or a lack of insight in to their own disorder. Implicit cognition has also been found to better predict future symptoms. For instance, it has been found that associating oneself with death without realising it strongly predicts future suicide risk. However, it is also argued that the contribution that research on implicit cognition can make to our understanding of mental health is limited.


Here’s my informed opinion – what’s yours?

I believe that implicit cognition will continue to play a valuable role in mental health research. It is astounding that we may be able to uncover risk of mental health problems under the individual’s awareness in ways that we couldn’t before the development of implicit measures. This is important as there continues to be a stigma attached to mental health issues and many people do not want to admit to being ill. This is causing prolonged suffering because it means we cannot identify those at risk of problems, nor fully understand mental health itself. By studying implicit cognition, we can resolve these issues. However, as with any research approach, studying implicit cognition cannot tell us everything we need to know about mental health.

The Paper:

A Critical Evaluation of the Role of Implicit Cognition in Mental Health Research


It is not always possible to ascertain an individual’s risk of experiencing mental health issues. This may be because explicit measures, such as self-reports, are often used to gauge this risk. These rely on an individual providing accurate and honest accounts of their thoughts and feelings, as well as past behaviour and experiences. Additionally, explicit measures cannot identify the automatic beliefs, attitudes and biases that can shape thoughts and behaviour under an individual’s conscious awareness. These can however be identified using measures of implicit cognition (Greenwald & Banaji, 1995). This paper provides an ongoing comparison of the value of findings obtained using measures of implicit cognition versus explicit measures in clinical research. Firstly, it is argued that implicit cognition has provided novel theoretical insights. The validity of underlying evidence is then considered. Following this, it is contended that implicit cognition provides measures that have comparatively high predictive validity for symptom onset/recurrence, relative to explicit measures. It is then discussed whether this is because measures of implicit cognition provide more accurate data. Through this critical evaluation, the theoretical and methodological significance of implicit cognition’s role in mental health research is gauged. Finally, it is argued that the contribution implicit cognition can make is limited in a number of respects.

The Theoretical Significance of Implicit Cognition’s Role

Implicit cognition has provided novel insights that have led to the development of empirically-derived models. These propose that automatic processing biases may be important components of mental health disorders (Werntz, Steinman, Glenn, Nock & Teachman, 2016). For example, interpretation biases, implicit memory biases, and emotional processing biases are theorised to be cognitive causes or vulnerability factors, and not mood-dependent correlates, of disorders such as depression and anxiety (Beck & Haigh, 2014; Mathews, & MacLeod, 2005).

However, much of the evidence driving these models was not obtained using prospective designs. Consequently, it is not valid to draw conclusions concerning whether cognitive vulnerability factors are risk/causal factors or merely consequences of mental health disorders (Phillips, Hine & Thorsteinsson, 2010). Although, there has been a recent influx of prospective designs that have provided supportive evidence for the notion that investigating implicit cognition can reveal underlying risk/causal factors of mental health issues. Such research is discussed subsequently. Firstly however, it is important to note that this research has either involved non-clinical samples or has not controlled for comorbidities. Therefore, it is not possible to establish whether there is a direct causal link between the cognitive factors identified, and the manifestation of specific disorders.

The Methodological Significance of Implicit Cognition’s Role

Nevertheless, evidence has revealed that implicit biases and implicit associations are strong predictors of symptom onset/recurrence across disorders. As such, measures of implicit cognition may provide a more sensitive index of risk than explicit measures can. For example, it has been found that implicit associations linking self to death predicted suicide attempts, and severity of suicide ideation, over and above known risk factors identified using explicit measures (Barnes et al., 2016; Ellis, Rufino & Green, 2016; Glenn et al., 2017; Nock et al., 2010). Likewise, it has been found that negatively biased self-referential implicit cognitions predicted past, current, and future depression (LeMoult, Kircanski, Prasad & Gotlib, 2017; Phillips et al., 2010). Similarly, evidence has consistently revealed that implicit cognitive processes (e.g. memory associations and attentional biases) predict addictive behaviours (Stacy & Wiers, 2010).

This raises the question of why measures of implicit cognition may possess stronger predictive validity for the onset/recurrence of symptoms, relative to explicit measures. One possible explanation is that they obtain more accurate data, since they allow researchers to circumvent key methodological issues associated with using explicit measures. These issues stem from the problematic assumption that self-report data are accurate. The first issue is that responses on self-report (i.e. explicit) measures can be affected by social desirability bias. This bias, coupled with the mental health stigma (Corrigan, 2000), can mean participants are less honest in their responses (Henderson, Evans-Lacko, Flach & Thornicroft, 2012). Responses to tasks measuring implicit cognition may not be as affected by these issues. This is because participants are less aware of what is being tested, and cannot exert conscious control over their responses to the same extent, if at all (Wiers, Teachman & De Houwer, 2007).

Additionally, it is widely reported that individuals with mental health disorders, such as addiction, emotional and eating disorders, tend to try to conceal symptoms (Busch, Fawcett, & Jacobs, 2003; Mental Health Foundation, 2006; Rapoport et al., 2000; Veale, 2004). This may also reduce the accuracy of self-reports. Although, this tendency appears not to have been empirically tested – presumably because it is inherently difficult to test. These tendencies could however be measured using implicit measures. For example, the implicit association test has been used to demonstrate mental health stigma itself (Teachman, Wilson & Komarovskaya, 2006).

This further highlights the methodological significance of implicit cognition’s role in mental health research. Although, responses to explicit measures may often be honest anyway, because participants are told that their responses are kept confidential, and studies are often conducted online. Therefore, social desirability, and the desire to conceal symptoms, may not always affect the accuracy of responses from explicit measures. However, accuracy could also be compromised by participants’ introspective abilities, found to vary inter-individually (Fleming, Weil, Nagy, Dolan & Rees, 2010), and by insight impairment, found to characterise common mental health conditions (Carpenter, Strauss & Bartko, 1973; David, 1990; Goldstein et al., 2009; Hartmann, Thomas, Wilson & Wilhelm, 2013; Moeller et al., 2010; Peralta & Cuesta, 1998; Reddy, 2015; Silva et al., 2013). Because responses collected from measures of implicit cognition are not influenced by these factors, this may explain why they more strongly predict the occurrence of future symptoms.

Implicit Cognition’s Contribution is Limited

However, it is difficult to determine whether cognitive factors (e.g. implicit associations and biases) identified using implicit measures represent a general risk for developing mental health issues, a risk for qualitatively-alike symptoms, or a condition-specific risk. This is because many studies investigating implicit cognition have focused on conceptually-overlapping often co-occurring mental health disorders, such as emotional, addiction and body image disorders. Another implication of this focus is that implicit cognition’s scope of relevancy, such as its relevance to other mental health disorders, remains to be determined.

Additionally, not all studies have demonstrated implicit cognition to be a strong predictor for the onset/recurrence of symptoms. For example, Sova and Roberts (2018) found that only explicit measures, and not implicit measures, of negative cognitive content (e.g. self esteem and dysfunctional attitudes) interacted with rumination to predict changes in dysphoric affect. Also, studies have consistently found that, in contrast to that found with explicit measures, implicit self-esteem was actually positive for patients with depressive disorder, social phobia, and body dysmorphic disorder (see review by Roefs et al., 2011). This suggests that findings from implicit cognition studies are not always clear and insightful to the field of clinical psychology.

Furthermore, as highlighted in a meta-analysis by Rooke, Hine and Thorsteinsson (2008), there is a lack of consensus concerning what constitutes a measure of implicit cognition and this may be the root cause of inconsistencies. For example, while their meta-analysis did verify the aforementioned findings that implicit cognitive processes predicted addictive behaviours, effect sizes were found to vary as a function of methodological features (Rooke et al. 2008). More recently, in another meta-analysis it was found that the aforementioned relationship between interpretation biases and depression also varied as a function of methodological features (Everaert, Podina & Koster, 2017). Therefore, if clinical psychology is to benefit from the study of implicit cognition, this methodological issue needs resolving, by for instance researchers adhering to De Houwer’s (2006) criteria for implicit measures.

Moreover, a fundamental weakness of implicit cognition research is that it is un-decided whether implicit cognition should be conceptualised as a single system, a collection of highly interrelated subsystems, or an array of independently-operating modules (Rooke et al., 2008). Additionally, there is no universal definition of implicit cognition, such that its focus changes dependent upon the area of enquiry (Stacy & Wiers, 2010), perhaps as a result of this lack of conceptual understanding. Relatedly, it could be argued that implicit cognition measures are quite abstract, and do not closely relate to the symptoms experienced, putting in to question whether they are measuring something substantial. Although, findings from explicit measures are used to guide investigations of implicit cognition and there is some evidence to suggest that findings may have clinical utility. For instance, modifications of implicit cognition (e.g. cognitive biases) have been found to relieve symptoms of mood and addiction disorders (Beard, 2011; Fadardi & Cox, 2009; Hertel & Mathews, 2011; Schoenmakers et al., 2010), demonstrating that implicit cognition research offers something palpable and clinically-relevant.

Nonetheless, more research is needed to test whether such interventions have an effect on outcomes across disorders in order to demonstrate whether this is a worthwhile area of enquiry for mental health research. This is pertinent since a recent meta-analysis revealed that many positive outcomes of cognitive bias modification, for the treatment of anxiety and depression, were caused by extreme outliers (Cristea, Kok & Cuijpers, 2015), further highlighting that implicit cognition’s contribution in the clinical domain may be limited.


Overall, implicit cognition has nonetheless played a valuable role in mental health research, because it has provided us with novel theoretical insights and with measures that can more strongly predict symptom onset/recurrence. This is perhaps due to the methodological superiority of implicit measures. That is, they can bypass the issues that can compromise the accuracy of data obtained using explicit measures, such as social desirability bias, mental health stigma, insight impairment and introspective abilities. However, implicit cognitions’ contribution to the field is limited. This is because its scope of relevance is yet to be determined, it has not always provided insightful and clinically relevant findings, and fundamentally there is not a unified conceptual understanding of implicit cognition.


This video inspired me to study the cognitive processes underlying hallucinations and delusions in my undergraduate degree.

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Barnes, S. M., Bahraini, N. H., Forster, J. E., Stearns‐Yoder, K. A., Hostetter, T. A., Smith, G., … & Nock, M. K. (2016). Moving beyond self‐report: implicit associations about death/life prospectively predict suicidal behavior among veterans. Suicide and Life-Threatening Behavior47(1), 67-77.

Beard, C. (2011). Cognitive bias modification for anxiety: current evidence and future directions. Expert Review of Neurotherapeutics11(2), 299-311.

Beck, A. T., & Haigh, E. A. (2014). Advances in cognitive theory and therapy: the generic cognitive model. Annual Review of Clinical Psychology10, 1-24.

Busch, K. A., Fawcett, J., & Jacobs, D. G. (2003). Clinical correlates of inpatient suicide. The Journal of Clinical Psychiatry, 64(1), 14-19.

Carpenter, W. T., Strauss, J. S., & Bartko, J. J. (1973). Flexible system for the diagnosis of schizophrenia: Report from the WHO International Pilot Study of Schizophrenia. Science182(4118), 1275-1278.

Corrigan, P. W. (2000). Mental health stigma as social attribution: Implications for research methods and attitude change. Clinical Psychology: Science and Practice7(1), 48-67.

Cristea, I. A., Kok, R. N., & Cuijpers, P. (2015). Efficacy of cognitive bias modification interventions in anxiety and depression: meta-analysis. The British Journal of Psychiatry206(1), 7-16.

David, A. S. (1990). Insight and psychosis. The British Journal of Psychiatry156(6), 798-808.

De Houwer, J. (2006). What are implicit measures and why are we using them? In R. W. Wiers & A. W. Stacy (Eds.), Handbook of Implicit Cognition and Addiction (pp. 11−28). Thousand Oaks, CA: Sage Publications.

Ellis, T. E., Rufino, K. A., & Green, K. L. (2016). Implicit measure of life/death orientation predicts response of suicidal ideation to treatment in psychiatric inpatients. Archives of Suicide Research20(1), 59-68.

Everaert, J., Podina, I. R., & Koster, E. H. (2017). A comprehensive meta-analysis of interpretation biases in depression. Clinical Psychology Review, 58. doi: 10.1016/j.cpr.2017.09.005.

Fadardi, J. S., & Cox, W. M. (2009). Reversing the sequence: reducing alcohol consumption by overcoming alcohol attentional bias. Drug & Alcohol Dependence101(3), 137-145.

Fleming, S. M., Weil, R. S., Nagy, Z., Dolan, R. J., & Rees, G. (2010). Relating introspective accuracy to individual differences in brain structure. Science329(5998), 1541-1543.

Glenn, C. R., Kleiman, E. M., Cha, C. B., Deming, C. A., Franklin, J. C., & Nock, M. K. (2017). Understanding suicide risk within the Research Domain Criteria (RDoC) framework: A meta‐analytic review. Depression and Anxiety35(1), 65-88.

Goldstein, R. Z., Bechara, A., Garavan, H., Childress, A. R., Paulus, M. P., & Volkow, N. D. (2009). The neurocircuitry of impaired insight in drug addiction. Trends in Cognitive Sciences13(9), 372-380.

Greenwald, A. G., & Banaji, M. R. (1995). Implicit social cognition: attitudes, self-esteem, and stereotypes. Psychological Review102(1), 4-27.

Hartmann, A. S., Thomas, J. J., Wilson, A. C., & Wilhelm, S. (2013). Insight impairment in body image disorders: delusionality and overvalued ideas in anorexia nervosa versus body dysmorphic disorder. Psychiatry Research210(3), 1129-1135.

Henderson, C., Evans-Lacko, S., Flach, C., & Thornicroft, G. (2012). Responses to mental health stigma questions: the importance of social desirability and data collection method. The Canadian Journal of Psychiatry57(3), 152-160.

Hertel, P. T., & Mathews, A. (2011). Cognitive bias modification: Past perspectives, current findings, and future applications. Perspectives on Psychological Science6(6), 521-536.

LeMoult, J., Kircanski, K., Prasad, G., & Gotlib, I. H. (2017). Negative self-referential processing predicts the recurrence of major depressive episodes. Clinical Psychological Science5(1), 174-181.

Mathews, A., & MacLeod, C. (2005). Cognitive vulnerability to emotional disorders. Annual Review Clinical Psychology1, 167-195.

Mental Health Foundation (2006). Report of the National Inquiry into Self-harm among Young People. London, UK: Mental Health Foundation.

Moeller, S. J., Maloney, T., Parvaz, M. A., Alia-Klein, N., Woicik, P. A., Telang, F., … & Goldstein, R. Z. (2010). Impaired insight in cocaine addiction: laboratory evidence and effects on cocaine-seeking behaviour. Brain133(5), 1484-1493.

Nock, M. K., Park, J. M., Finn, C. T., Deliberto, T. L., Dour, H. J., & Banaji, M. R. (2010). Measuring the suicidal mind: implicit cognition predicts suicidal behavior. Psychological Science21(4), 511-517.

Peralta, V., & Cuesta, M. J. (1998). Lack of insight in mood disorders. Journal of Affective Disorders49(1), 55-58.

Phillips, W. J., Hine, D. W., & Thorsteinsson, E. B. (2010). Implicit cognition and depression: A meta-analysis. Clinical Psychology Review30(6), 691-709.

Rapoport, J. L., Inoff-Germain, G., Weissman, M. M., Greenwald, S., Narrow, W. E., Jensen, P. S., … & Canino, G. (2000). Childhood obsessive–compulsive disorder in the NIMH MECA Study: Parent versus child identification of cases. Journal of Anxiety Disorders14(6), 535-548.

Reddy, M. S. (2015). Insight and psychosis. Indian Journal of Psychological Medicine, 37(3), 257-260.

Roefs, A., Huijding, J., Smulders, F. T., MacLeod, C. M., de Jong, P. J., Wiers, R. W., & Jansen, A. (2011). Implicit measures of association in psychopathology research. Psychological Bulletin, 137(1), 149-193.

Rooke, S. E., Hine, D. W., & Thorsteinsson, E. B. (2008). Implicit cognition and substance use: A meta-analysis. Addictive Behaviors33(10), 1314-1328.

Schoenmakers, T. M., de Bruin, M., Lux, I. F., Goertz, A. G., Van Kerkhof, D. H., & Wiers, R. W. (2010). Clinical effectiveness of attentional bias modification training in abstinent alcoholic patients. Drug & Alcohol Dependence109(1), 30-36.

Silva, R. D. A. D., Mograbi, D. C., Silveira, L. A. S., Nunes, A. L. S., Novis, F. D., Cavaco, P. A., … & Cheniaux, E. (2013). Mood self-assessment in bipolar disorder: a comparison between patients in mania, depression, and euthymia. Trends in Psychiatry and Psychotherapy35(2), 141-145.

Sova, C. C., & Roberts, J. E. (2018). Testing the cognitive catalyst model of rumination with explicit and implicit cognitive content. Journal of Behavior Therapy and Experimental Psychiatry, 59, 115-120.

Stacy, A. W., & Wiers, R. W. (2010). Implicit cognition and addiction: a tool for explaining paradoxical behavior. Annual Review of Clinical Psychology6, 551-575.

Teachman, B. A., Wilson, J. G., & Komarovskaya, I. (2006). Implicit and explicit stigma of mental illness in diagnosed and healthy samples. Journal of Social and Clinical Psychology25(1), 75-95.

Veale, D. (2004). Advances in a cognitive behavioural model of body dysmorphic disorder. Body Image1(1), 113-125.

Werntz, A.J., Steinman, S.A., Glenn, J.J., Nock, M.K., & Teachman, B.A. (2016). Characterizing implicit mental health associations across clinical domains. Journal of Behavior Therapy and Experimental Psychiatry, 52, 17–28.

Wiers, R. W., Teachman, B. A., & De Houwer, J. (2007). Implicit cognitive processes in psychopathology: An introduction. Journal of Behavior Therapy and Experimental Psychiatry, 38, 95-104.



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