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The last two decades have been marked by excitement for measuring implicit attitudes and implicit biases, as well as optimism that new technologies have made this possible. Despite considerable attention, this movement is marked by weak measures. Current implicit measures do not have the psychometric properties needed to meet the standards required for psychological assessment or necessary for reliable criterion prediction. Some of the creativity that defines this approach has also introduced measures with unusual properties that constrain their applications and limit interpretations. We illustrate these problems by summarizing our research using the Implicit Association Test (IAT) as a case study to reveal the challenges these measures face. We consider such issues as reliability, validity, model misspecification, sources of both random and systematic method variance, as well as unusual and arbitrary properties of the IAT’s metric and scoring algorithm. We then review and critique four new interpretations of the IAT that have been advanced to defend the measure and its properties. We conclude that the IAT is not a viable measure of individual differences in biases or attitudes. Efforts to prove otherwise have diverted resources and attention, limiting progress in the scientific study of racism and bias.
An urgent need exists to identify neural correlates associated with differing levels of suicide risk and develop novel, rapid-acting therapeutics to modulate activity within these neural networks.
Methods
Electrophysiological correlates of suicide were evaluated using magnetoencephalography (MEG) in 75 adults with differing levels of suicide risk. During MEG scanning, participants completed a modified Life-Death Implicit Association Task. MEG data were source-localized in the gamma (30–58 Hz) frequency, a proxy measure of excitation-inhibition balance. Dynamic causal modeling was used to evaluate differences in connectivity estimates between risk groups. A proof-of-concept, open-label, pilot study of five high risk participants examined changes in gamma power after administration of ketamine (0.5 mg/kg), an NMDAR antagonist with rapid anti-suicide ideation effects.
Results
Implicit self-associations with death were stronger in the highest suicide risk group relative to all other groups, which did not differ from each other. Higher gamma power for self-death compared to self-life associations was found in the orbitofrontal cortex for the highest risk group and the insula and posterior cingulate cortex for the lowest risk group. Connectivity estimates between these regions differentiated the highest risk group from the full sample. Implicit associations with death were not affected by ketamine, but enhanced gamma power was found for self-death associations in the left insula post-ketamine compared to baseline.
Conclusions
Differential implicit cognitive processing of life and death appears to be linked to suicide risk, highlighting the need for objective measures of suicidal states. Pharmacotherapies that modulate gamma activity, particularly in the insula, may help mitigate risk.
Cannabis use is common in patients with recent-onset schizophrenia and this is associated with poor disease outcome. More insight in the cognitive-motivational processes related to cannabis use in schizophrenia may inform treatment strategies. The present study is the first known to compare implicit and explicit cannabis associations in individuals with and without psychotic disorder.
Method
Participants consisted of 70 patients with recent-onset psychotic disorder and 61 healthy controls with various levels of cannabis use. Three Single-Category Implicit Association Tests (SC-IAT) were used to assess ‘relaxed’, ‘active’ and ‘negative’ implicit associations towards cannabis use. Explicit expectancies of cannabis use were assessed with a questionnaire using the same words as the SC-IAT.
Results
There were no differences in implicit associations between patients and controls; however, patients scored significantly higher on explicit negative affect expectancies than controls. Both groups demonstrated strong negative implicit associations towards cannabis use. Explicit relaxed expectancies were the strongest predictors of cannabis use and craving. There was a trend for implicit active associations to predict craving.
Conclusions
The findings indicate that patients suffering from schizophrenia have associations towards cannabis similar to controls, but they have stronger negative explicit cannabis associations. The strong negative implicit associations towards cannabis could imply that users of cannabis engage in a behaviour they do not implicitly like. Explicit relaxing expectancies of cannabis might be an important mediator in the continuation of cannabis use in patients and controls.
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