In this chapter we identify scientific gaps research to date regarding the ability of IAT scores to explain real world racial gaps. We use the term “IAT scores” rather than “implicit bias” because, as we show: (1) Implicit bias has no consensual scientific definition; (2) A definition offered by Greenwald (2017) is shown to be logically incoherent and empirically unjustified; (3) Exactly what the IAT measures remains unclear. Nonetheless, meta-analyses have shown that IAT scores predict discrimination to a modest extent. Alternative explanations for gaps are briefly reviewed, highlighting that IAT scores offer only one of many possible such explanations. We then present a series of heuristic models that assume that IAT scores can only explain what is left over, after accounting for other explanations of gaps. This review concludes that IAT scores probably explain a modest portion of those gaps. Even if the IAT captures implicit biases, and those implicit biases were completely eliminated, the extent to which racial gaps would be reduced is minimal. We conclude by arguing that, despite its limitations, the IAT should not be abandoned, but that, even after twenty years, much more research is needed to fully understand what the IAT measures and explains.

The implicit revolution seems to have arrived with the declaration that “explicit measures are informed by and (possibly) rendered invalid by unconscious cognition.” What is the view from survey research, which has relied on explicit methodology for over a century, and whose methods have extended to the political domain in ways that have changed the landscape of politics in the United States and beyond? One survey researcher weighs in. The overwhelming evidence points to the continuing power of explicit measures to predict voting and behavior. Whether implicit measures can do the same, especially beyond what explicit measures can do, is far more ambiguous. The analysis further raises doubts, as others before have done, as to what exactly implicit measures measure, and particularly questions the co-opting among implicit researchers the word “attitude” when such measures instead represent associations. The conclusion: Keep your torches at home. There is no revolution.

The core question in suicide prevention is: why does a person in a particular situation take their own life, while another person in the same situation would react in a different way? This chapter investigates to what extent and in which way neurocognitive studies may contribute to finding an answer to this question. The term 'neurocognitive' refers to the study of the relationship between the brain and behavior by utilizing specialized tests that have been designed to evaluate a wide variety of behavioral, cognitive and emotional domains. Thus, neurocognitive studies contribute to understanding how behavioral decisions following exposure to particular environmental stimuli relate to changes in brain functions. Such studies offer a great opportunity to measure and quantify cognitive functions, emotional states and behavioral repertoires through standardized questionnaires and testing. From such neurocognitive data, inferences are made regarding brain function and the localization of brain dysfunctions based on patterns of cognitive strengths and weaknesses. As conclusions from neuropsychological assessments are necessarily inferential, findings from neuropsychological investigations are commonly combined with those from neuroimaging (see next chapter) in order to fully understand the relationship between behavioral phenomena and changes in brain functions.