Psychological Inquiry最新文献

{"title":"Too Much Flexibility in a Dynamical Model of Repetitive Negative Thinking?","authors":"Marieke K. van Vugt, H. Jamalabadi","doi":"10.1080/1047840X.2022.2149195","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2149195","url":null,"abstract":"Abstract Iftach and Bernstein propose a dynamical system model of task-unrelated thought that is designed to explain how repetitive negative thinking (RNT) and maladaptive internally-directed cognition more generally arises from attentional biases, working memory, and negative affect. They show that specifically during a period of low task demands, it is easier for negative affect to grab resources and take over with RNT. They also postulate that for individuals with high cognitive reactivity, this tendency for RNT to take over is increased. We argue this paper is an important move forward toward understanding in what circumstances RNT takes over, but also that the model is not yet sufficiently “formalized.” Specifically, we notice excessive levels of flexibility and redundancy that could undermine the explainability of the model. Moreover, the likelihood of negative thinking, as implemented in the proposed model, relies heavily on working memory capacity. In response to this observation, we give suggestions for how the parametrization of this model could be done in a more principled manner. We think such an analysis paves the way for more principled computational modeling of RNT which can be applied to describing empirical data and eventually, to inform decision-making in clinical settings.","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"276 - 279"},"PeriodicalIF":9.3,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43560869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implicit Bias as Automatic Behavior","authors":"Kate A. Ratliff, C. Smith","doi":"10.1080/1047840X.2022.2106764","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2106764","url":null,"abstract":"Researchers interested in implicit bias agree that no one agrees what implicit bias is. Gawronski, Ledgerwood, and Eastwick (this issue) join a spate of scholars calling for better conceptual clarity around what it means for a construct or a measure to be implicit (Corneille & H€ utter, 2020; Fazio, Granados Samatoa, Boggs, & Ladanyi, 2022; Schmader, Dennehy, & Baron, 2022; Van Dessel et al., 2020). Some argue we should do away with the term entirely (Corneille & H€ utter, 2020), and others argue that authors simply need to do a better job defining how they are idiosyncratically using the term each time they use it (Greenwald & Lai, 2020). In their target article, Gawronski et al. argue for a fundamental redefinition of what it means for bias to be implicit. More specifically, they argue that implicit bias (IB) and bias on implicit measures (BIM) are conceptually and empirically distinct, and that BIM (defined as “effects of social category membership on behavioral responses captured by measurement instruments conventionally describe as implicit”) should not be treated as an instance of IB (defined as “behavioral responses influenced by social category cues when respondents are unaware of the effect of social category cues on their behavioral responses”). We agree that the time has come for our definition of implicit to be revamped in light of new findings. In fact, it is past time; we co-chaired a symposium titled “What is implicit about implicit attitudes?” at the Society for Personality and Social Psychology’s annual meeting in 2009, more than a decade ago. And we applaud the authors of the target article for taking a bold step toward making a change. Further, we agree with them that bias is best defined as a behavioral phenomenon rather than a latent mental construct. This is not a statement we make lightly; it has required some serious scholarly contemplation of the current state of the literature and some serious non-scholarly contemplation of our own egos to reach this conclusion. For some time now we, like most others, have described implicit bias as something that people have–e.g., participants have an implicit bias favoring one novel individual over another (Ratliff & Nosek, 2011), have an implicit preference favoring White over Black Americans (Chen & Ratliff, 2018), or have an implicit positive or negative attitude toward feminists (Redford, Howell, Meijs, & Ratliff, 2018). Many of us are quite invested in this way of thinking. And change is hard! But we recognize that we gain a lot by taking this more functional approach to bias. Most notably, a functional approach allows researchers to circumvent the perplexing situation of using the same name for construct and measure. Further, many of us working in this area are doing so because we hope to provide insights through which people can change their behavior in order to reduce inequality on real life issues that matter. Given that the problem of bias is a behavioral problem (De Houwer, ","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"213 - 218"},"PeriodicalIF":9.3,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45531411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decomposing Implicit Bias","authors":"I. Krajbich","doi":"10.1080/1047840X.2022.2106758","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2106758","url":null,"abstract":"In their article, “Implicit Bias 61⁄4 Bias on Implicit Measures,” Gawronski, Ledgerwood, & Eastwick (this issue) characterize implicit bias and then discuss how the implicit association test (IAT) (Greenwald, McGhee, & Schwartz, 1998) fails to meet the requirements of a test for implicit bias. One of the central arguments is that people can predict their behavior on the IAT, indicating awareness of their implicit bias. However, part of the definition of implicit bias is that it occurs outside of awareness. In my commentary I discuss the thorny issue of awareness and suggest a more pragmatic definition of implicit bias that may help resolve the discord. I also discuss computational-modeling and process-tracing tools that allow us to decompose decisions in ways that can identify the mechanisms underlying behavioral biases. Together, hopefully these approaches will yield better insight into the nature of implicit bias.","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"181 - 184"},"PeriodicalIF":9.3,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43197686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Awareness: The Many Forms of Implicit Bias and Its Implications","authors":"T. Schmader, Carmelle Bareket-Shavit, A. Baron","doi":"10.1080/1047840X.2022.2106752","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2106752","url":null,"abstract":"In 1969, in his address to the American Psychological Association, George Miller implored the field of psychology to give its science away. For their part, the general public has shown itself to have a thirst for those ideas that might be useful to solving some of our most intractable social problems. Implicit bias is one of those good ideas that has transcended the laboratory, in part because of the positive efforts of Project Implicit (https://implicit.harvard.edu). For example, during her failed election campaign in 2016, Hilary Clinton declared, “We all have implicit biases” (Merica, 2016). The world is looking to our field to better understand if implicit bias is a dangerous and prevalent pathogen or a mildly annoying but mostly benign curiosity. It hasn’t helped that within the ivory tower, there is no clear consensus on how to best define the construct (Corneille & H€ utter, 2020). Into this context, Gawronski, Ledgerwood, and Eastwick (this issue; GLE) introduce their target article. In the title of their article, GLE make a simple plea to scholars in the field: Whatever it is that implicit measures of stereotypes and attitudes capture, we should not label this implicit bias (IB). We completely agree with this conclusion and would like to happily sign our names to the petition to refrain from this usage moving forward (just as we would similarly agree that a measure of any construct and the construct itself are not one in the same). In fact, we made a similar argument in a recent publication stating, “too often the terms ‘implicit associations’ (the strength of the associations between concepts in the mind, measured indirectly by the IAT) and ‘implicit bias’ (disparate treatment that can result from one’s implicit associations with social groups) are used isomorphically (cf. De Houwer, 2019)” (see Pitfall #3 of Schmader, Dennehy, & Baron, 2022). In that article, two of us argued for having greater conceptual clarity over what IB is and outlined different pathways by which bias unfolds, so that interventionists designing tools to mitigate the harms from bias have an effective playbook to work from. In this commentary, we focus our analysis on GLE’s general definition that IB “captures the idea that people may behave in a biased way without being aware that their behavior is biased” (Gawronski et al., this issue, p. 139). We agree with GLE’s focus on bias as behavior, not on the individual differences assessed with implicit measures. We suggest, however, that this particular definition of IB must go further to clarify the precise role of awareness in the delineation of IB. We argue that any clear conceptualization of bias needs to recognize the process by which stereotypes and attitudes in mind can lead to biased behavior that does harm to others. Drawing from and extending a recently published bias typology (Schmader et al., 2022), we will see that awareness is only one component needed to distinguish implicit from explicit (or intentional","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"156 - 161"},"PeriodicalIF":9.3,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42675046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reflections on the Difference Between Implicit Bias and Bias on Implicit Measures","authors":"Bertram Gawronski, A. Ledgerwood, Paul W. Eastwick","doi":"10.1080/1047840X.2022.2115729","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2115729","url":null,"abstract":"We are pleased about the considerable interest in our target article and that there is overwhelming agreement with our central thesis that, if the term implicit is understood as unconscious in reference to bias, implicit bias (IB) should not be equated with bias on implicit measures (BIM) (Cesario, this issue; Corneille & B ena, this issue; Cyrus-Lai et al., this issue; De Houwer & Boddez, this issue; Dovidio & Kunst, this issue; Melnikoff & Kurdi, this issue; Norman & Chen, this issue; Olson & Gill, this issue; Schmader et al., this issue; but see Krajbich, this issue; Ratliff & Smith, this issue). We are also grateful for the insightful commentaries, which continue to advance the field’s thinking on this topic. The comments inspired us to think further about the relation between IB and BIM as well as the implications of a clear distinction between the two. In the current reply, we build on these comments, respond to some critical questions, and clarify some arguments that were insufficiently clear in our target article. Before doing so, we would like to express our appreciation for the extreme thoughtfulness of the commentaries, every single one of which deserves their own detailed response. For the purpose of this reply, we will focus on recurring themes and individual points that we deem most important for moving forward. We start our reply with basic questions about the concept of bias, including the difference between behavioral effects and explanatory mental constructs, the role of social context, goals, and values in evaluating instances of bias, and issues pertaining to the role of social category cues in biased behavior. Expanding on the analysis of the bias construct, the next sections address questions related to the implicitness of bias, including the presumed unconsciousness of BIM, methodological difficulties of studying unconscious effects, and the implications of a broader interpretation of implicit as automatic. The next sections again build on the discussions in the preceding sections, addressing questions about the presumed significance of IB research for understanding societal disparities and the value of BIM research if IB is treated as distinct from BIM. The final section presents our general conclusions from the conversation about our target article and several suggestions on how to move forward. Reflections on Bias","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"219 - 231"},"PeriodicalIF":9.3,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46426469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The “Implicit Bias” Wording Is a Relic. Let’s Move On and Study Unconscious Social Categorization Effects","authors":"O. Corneille, J. Béna","doi":"10.1080/1047840X.2022.2106754","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2106754","url":null,"abstract":"In their article, Gawronski, Ledgerwood, and Eastwick (this issue; hereafter, GLE) explain why “implicit bias” (defined as the unconscious effect of social category cues on behavioral responses) should not be confused with “bias on implicit measures.” We see much value in their clarification and agree with their bleak assessment of research on implicit tasks when they are said to measure “implicit bias” (hereafter “implicit measures of bias”), the most prominent of which is the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998). The article opens with a puzzling statement, though. GLE celebrate the educational value of “implicit measures of bias”: “implicit measures of bias deserve enormous credit for providing a tool for the widespread dissemination of the idea that people can be biased without being aware of it” (Gawronski et al., this issue, p. 139). However, while reading their article, it becomes quickly clear (1) that “implicit measures of bias” have little conceptual consistency, and (2) that critical assumptions underlying their use and interpretation are unsubstantiated (e.g., the assumption that these tasks tap into unconscious mental contents or hold a special relation to associative learning). GLE also note that social cognition research has barely started to study the unconsciousness of category-driven biases beyond responses entered on computer keyboards. It is an open secret that we do not clearly know how to interpret outcomes from “implicit measures of bias” (see, e.g., Fiedler, Messner & Bluemke, 2006). The managers of Project Implicit, the largest educational and researchoriented platform conventionally said to study “implicit biases” feature an honest disclaimer on the website of the platform: the designers of the task, their promoters, and their associated institutions “make no claim for the validity” of their suggested interpretations of IAT scores (https:// implicit.harvard.edu/implicit/takeatest.html). If we want to be honest about it, we do not know much which and when social behaviors are driven by an unconscious influence of social categories either. If social cognition research relied on tasks and study settings that are detached from “implicit biases” (as GLE define them), then this begs the question of how accurate and profitable the education around this notion has been. As a case in point, introductory psychology textbooks generally fail to accurately portray the most prominent “implicit measure of bias” (Bartels & Schoenrade, 2022). We suspect that extraacademic education does not fare better. In the present commentary, we speculate on how we got here, we discuss how bad it can get when scientists conflate science with mere opinions, and we propose ways forward. We argue that strong research on “implicit bias” can finally see the light if drastic changes are implemented in social cognition research, starting with radical terminological changes.","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"167 - 172"},"PeriodicalIF":9.3,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42887446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What Implicit Measures of Bias Can Do","authors":"David E. Melnikoff, Benedek Kurdi","doi":"10.1080/1047840X.2022.2106759","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2106759","url":null,"abstract":"Gawronski, Ledgerwood, and Eastwick (this issue; GLE) bring much needed attention to the limitations of currently available implicit measures as tools for studying unconscious bias. We agree with the authors of the target article that the current state of the literature offers little reason to believe that commonly used implicit measures, such as sequential priming (Fazio, Sanbonmatsu, Powell, & Kardes, 1986), the Implicit Association Test (Greenwald, McGhee, & Schwartz, 1998), and the Affect Misattribution Procedure (Payne, Cheng, Govorun, & Stewart, 2005), capture unconscious influences of social category cues on behavioral responses. If anything, the evidence suggests the opposite: Participants may well be aware of how their responses are influenced by social cues on implicit measures (Hahn, Judd, Hirsh, & Blair, 2014; Hahn & Gawronski, 2019), although to what degree and as a result of what type of process or processes remains to be investigated (Morris & Kurdi, 2022). Nonetheless, even if the extent of awareness differs depending on the specific conditions of the task, the lack of compelling evidence for the ability of currently available implicit measures to index unconscious bias is surprising. As GLE observe, the concepts of unconscious bias and bias on implicit measures have been, and continue to be, conflated, both in the empirical literature and popular discourse. This conundrum will prompt many readers to wonder: If implicit measures of bias are not useful for measuring unconscious bias, are they useful at all? They are. Whether or not they shed light on unconscious bias, implicit measures have been, and we believe will remain, essential to the study of social cognition. We suspect that the lead author of the target article, who has used implicit measures of bias to make numerous contributions to the understanding of social information processing, would agree. But what is it, exactly, that implicit measures of bias are good for, if not probing the human unconscious? This is the question we address in the current commentary. Broadly speaking, implicit measures of bias have been and continue to be critical for addressing two related questions: (i) what is the nature of unintentional bias? and (ii) what is the cognitive architecture of bias? In what follows, we show how implicit measures fuel progress on both fronts while, crucially, also advancing the translational goal of revealing the nature of, and reducing, groupbased inequality. Using Implicit Measures of Bias to Reveal the Nature of Unintentional Bias","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"185 - 192"},"PeriodicalIF":9.3,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49425156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary on Gawronski, Ledgerwood, and Eastwick, Implicit Bias ≠ Bias on Implicit Measures","authors":"M. Olson, L. Gill","doi":"10.1080/1047840X.2022.2106761","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2106761","url":null,"abstract":"The authors of the target article offer a definition of implicit bias as “unconscious effects of social category cues” (Gawronski, Ledgerwood, & Eastwick, this issue, p. 140), and, so defined, make a case for examining its causes, effects, and possible amelioration. We support this pursuit and offer some suggestions on how that might be accomplished. For years we have argued that however implicit bias might be defined, to equate it to the output of a measure that one happens to also call “implicit” or is a bad idea (e.g., Fazio & Olson, 2003; Olson & Fazio, 2009; Olson & Gill, 2022; Olson & Zabel, 2016). Indeed, we wrote nearly twenty years ago, “We would encourage researchers not to equate an implicitly measured construct with an unconscious one” (Fazio & Olson, 2003, p. 303). Since then, evidence has accumulated that bias on implicit measures is not implicit in the sense of being inaccessible to consciousness. For example, in 2007 we showed that implicitly measured self-esteem was consciously accessible and hence reportable on explicit measures when respondents were implored to be honest (Olson, Fazio, & Hermann, 2007). Similarly, implicitly-assessed antiBlack bias correlates with explicitly-assessed anti-Black bias under conditions of honesty and anonymity (Phillips & Olson, 2014; see also Hahn, Judd, Hirsh, & Blair, 2014). Nevertheless, we see the need for the authors’ treatise on the problems of conflating implicit bias with bias on an implicit measure, as prominent researchers in these domains persist in equating the two (Greenwald et al., 2022). However, and despite social scientists’ proliferation of near-synonyms, we also want to make a case that a focus on automaticity over implicitness with regard to implicit measures (and, as we will see, implicit bias) has a strong theoretical foundation and empirical support. Before the term “implicit” was popularized and applied to prejudice or attitude measurement, Fazio and colleagues (e.g., Fazio, Sanbonmatsu, Powell, & Kardes, 1986) were investigating the automatic activation of attitudes. The evaluative priming measure they developed is probably the second most-used implicit measure, after the IAT. On a given trial in a priming task, a prime (the attitude object, usually in image form) is presented briefly, followed by a clearly valenced target adjective (e.g., wonderful) participants are tasked with identifying as either good or bad by pressing one of two corresponding keys as quickly as possible. This seminal work found that for particularly strong attitudes, primes facilitate the identification of valence-congruent target adjectives: cake primes facilitated identification of positive targets, and death primes facilitated identification of negative targets (inhibition of valence incongruent prime-target pairs was also observed). This facilitation effect is automatic because the activation of participants’ attitudes occurred despite their goal to identify the valence of the targets, not the p","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"199 - 202"},"PeriodicalIF":9.3,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45581056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Avoiding Bias in the Search for Implicit Bias","authors":"Wilson Cyrus-Lai, W. Tierney, Christilene du Plessis, M. Nguyen, M. Schaerer, Elena Giulia Clemente, E. Uhlmann","doi":"10.1080/1047840X.2022.2106762","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2106762","url":null,"abstract":"To revitalize the study of unconscious bias, Gawronski, Ledgerwood, and Eastwick (this issue) propose a paradigm shift away from implicit measures of intergroup attitudes and beliefs. Specifically, researchers should capture discriminatory biases and demonstrate that participants are unaware of the influence of social category cues on their judgments and actions. Individual differences in scores on implicit measures will be useful to predict and better understand implicitly prejudiced behaviors, but the latter should be the collective focus of researchers interested in unconscious biases against social groups. We welcome Gawronski et al.’s (this issue) proposal and seek to build on their insights. We begin by summarizing recent empirical challenges to the implicit measurement approach, which has for the last quarter century focused heavily on capturing individual differences and examining their potential antecedents and consequences. In our view, Gawronski et al. (this issue) underestimate the problems the subfield of implicit bias research is currently facing; the need for a paradigm shift in focus and approach is truly urgent. Although we strongly agree with their basic thesis, we also stress the importance of avoiding various forms of potential bias in the search for implicit bias. First, research in this area should leverage open science innovations such as pre-registration of competing predictions to allow for intellectually and ideologically dissonant conclusions of equal treatment and “reverse” discrimination against members of historically privileged groups. Second, in assessing awareness of bias, researchers should avoid equating unconsciousness with the null hypothesis that evidence of awareness will not emerge, and instead seek positive evidence that the behavioral bias is implicit in nature. Finally, to avoid underestimating the pervasiveness of intergroup bias, scientists should continue to develop and attempt to validate implicit measures of attitudes and beliefs, which may tap latent prejudices expressed in only a small subset of overt actions.","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"203 - 212"},"PeriodicalIF":9.3,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48002446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Delight in Disorder: Inclusively Defining and Operationalizing Implicit Bias","authors":"J. Dovidio, J. Kunst","doi":"10.1080/1047840X.2022.2106756","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2106756","url":null,"abstract":"Gawronski, Ledgerwood, and Eastwick (this issue) address a timely issue of both theoretical and practical importance in the burgeoning study of implicit bias. The authors “highlight conceptual and empirical problems with the widespread equation of implicit bias and bias on implicit measures” (p. 139). They are not the first to raise and grapple with a question closely related to deciphering the conceptual meaning of implicit bias and its relationship to measures of implicit bias, but they distinguish themselves with their mastery of diverse literatures, sophisticated analyses of core theoretical issues, and original insights. While maintaining a steady focus on their core question, the authors’ review and synthesis of the work that they cover makes this a valuable resource for various audiences. It provides a detailed, yet accessible introduction for those who are interested in but relatively unfamiliar with the topic, as well as a thought-provoking and well-argued contribution for those who have considerable expertise in the area and may already have well-formed perspectives on the questions posed and answers provided. Importantly, in an area in which heated debate has been common, Gawronski et al. navigate through complex issues with logic and data in an even-handed way. This is an impressive piece of scholarship. A common colloquial expression is, “If the shoe fits, wear it.” The article is particularly impressive in the way the authors examine the many ways that scholars have attempted to define implicit bias. They try on many shoes for the term “implicit,” as compared to “explicit.” Gawronski et al. (this issue) consider distinctions in process, such as in differences between “mental levels.” For instance, they discuss how implicit has been treated as reflecting associative processes “involving unqualified mental links between concepts”, whereas explicit processes are propositional “involving the perceived validity of specific relations” (p. 141). Alternative, procedural distinctions are also reviewed. These tend to be instrument-focused. For example, a measure would qualify as implicit to the extent to which the response is automatic—that is, unintentional and difficult to control. By contrast, an explicit measure would be one in which people respond in a deliberative, intentional, and selfreflective way. Indeed, the first author of this commentary falls into this procedural camp, describing implicit as activation that occurs unintentionally (Dovidio, Kawakami, & Beach, 2001), automatically (Dovidio, Hewstone, Glick, & Esses, 2010), and which can operate without people being aware of the “biased associations or of the role those associations play in guiding their judgment and action” (Greenwald, Dsagupta, et al., 2022, p. 8). However, Gawronski et al. (this issue) skillfully argue how and why none of these shoes fit. In the end, we resonate with Gawronski et al.’s critical conclusion that “despite 25 years of extensive research, the current","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"177 - 180"},"PeriodicalIF":9.3,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44465910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}