Pub Date : 2022-07-03DOI: 10.1080/1047840X.2022.2106758
I. Krajbich
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.
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Pub Date : 2022-07-03DOI: 10.1080/1047840X.2022.2106752
T. Schmader, Carmelle Bareket-Shavit, A. Baron
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
1969年，乔治·米勒在向美国心理协会发表的演讲中恳求心理学界放弃其科学。就他们而言，公众已经表现出对那些可能有助于解决我们一些最棘手的社会问题的想法的渴望。隐性偏见是那些超越实验室的好想法之一，部分原因是隐性项目的积极努力(https://implicit.harvard.edu)。例如，希拉里·克林顿在2016年竞选失败期间宣称，“我们都有隐性偏见”（Merica，2016）。全世界都在关注我们的领域，以更好地了解隐性偏见是一种危险而普遍的病原体，还是一种稍微令人讨厌但大多是善意的好奇心。在象牙塔里，对于如何最好地定义这个结构没有明确的共识，这并没有起到任何作用（Cornelle&H€utter，2020）。在此背景下，Gawronski、Ledgerwood和Eastwick（本期；GLE）介绍了他们的目标文章。在他们文章的标题中，GLE向该领域的学者提出了一个简单的呼吁：无论刻板印象和态度的隐含衡量标准是什么，我们都不应该给这种隐含偏见（IB）贴上标签。我们完全同意这一结论，并很乐意在请愿书上签名，以避免这种用法继续下去（就像我们同样同意任何构造的度量和构造本身都不一样）。事实上，我们在最近的一份出版物中提出了类似的论点，“‘内隐联想’（通过IAT间接测量的头脑中概念之间的联想强度）和‘内隐偏见’（一个人与社会群体的内隐联想可能导致的不同待遇）这两个术语经常被同构地使用（参见De Houwer，2019）”（见Schmader、Dennehy和Baron的Pitfall#3，2022）。在那篇文章中，我们两人主张对什么是IB有更清晰的概念，并概述了偏见产生的不同途径，以便设计减轻偏见危害的工具的干预主义者有一个有效的行动手册。在这篇评论中，我们将分析重点放在GLE的一般定义上，即IB“抓住了人们可能在没有意识到自己的行为有偏见的情况下以有偏见的方式行事的想法”（Gawronski等人，本期，第139页）。我们同意GLE关注的是作为行为的偏见，而不是用内隐测量评估的个体差异。然而，我们建议，IB的这一特定定义必须进一步澄清意识在IB描述中的确切作用。我们认为，任何对偏见的明确概念化都需要认识到，脑海中的刻板印象和态度可能导致对他人造成伤害的偏见行为。根据最近发表的偏见类型学（Schmader et al.，2022），我们将看到，意识只是区分隐性偏见和显性（或故意）偏见所需的一个组成部分，即使在这些类别中，偏见也可以采取不同的形式，人们可以意识到过程的某些部分，但不能意识到其他部分。因此，与GLE声称IB的定义非常明确相反，我们认为还有一些模糊之处需要更多的关注。
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Pub Date : 2022-07-03DOI: 10.1080/1047840X.2022.2115729
Bertram Gawronski, A. Ledgerwood, Paul W. Eastwick
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
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Pub Date : 2022-07-03DOI: 10.1080/1047840X.2022.2106754
O. Corneille, J. Béna
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.
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Pub Date : 2022-07-03DOI: 10.1080/1047840X.2022.2106759
David E. Melnikoff, Benedek Kurdi
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
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Pub Date : 2022-07-03DOI: 10.1080/1047840X.2022.2106761
M. Olson, L. Gill
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
这种态度一旦被激活，就会引发一连串的过程，最终形成一种自发的态度-行为关系:一个怀有反黑人偏见的人
{"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}
Pub Date : 2022-07-03DOI: 10.1080/1047840X.2022.2106762
Wilson Cyrus-Lai, W. Tierney, Christilene du Plessis, M. Nguyen, M. Schaerer, Elena Giulia Clemente, E. Uhlmann
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.
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Pub Date : 2022-07-03DOI: 10.1080/1047840X.2022.2106756
J. Dovidio, J. Kunst
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
Gawronski, Ledgerwood和Eastwick(本期)在新兴的内隐偏见研究中提出了一个具有理论和实践重要性的及时问题。作者“强调了关于内隐偏差和内隐测量偏差的广泛等式的概念和经验问题”(第139页)。他们并不是第一个提出并解决与解读内隐偏见的概念意义及其与内隐偏见测量的关系密切相关的问题的人，但他们以对各种文献的掌握、对核心理论问题的复杂分析和原创性见解而脱颖而出。在保持对其核心问题的稳定关注的同时，作者对他们所涵盖的工作的回顾和综合使其成为各种受众的宝贵资源。它为那些对该主题感兴趣但相对不熟悉的人提供了一个详细的，但易于理解的介绍，也为那些在该领域有相当专业知识并且可能已经对所提出的问题和所提供的答案有良好形成的观点的人提供了一个发人深省和充分论证的贡献。重要的是，在一个激烈争论已经司空见惯的领域，Gawronski等人以一种不偏不倚的方式用逻辑和数据来解决复杂的问题。这是一项令人印象深刻的学术研究。一个常见的口语表达是，“如果鞋子合脚，就穿它。”这篇文章特别令人印象深刻的是，作者对学者们试图定义隐性偏见的许多方式进行了研究。他们试了很多鞋子，是为了“隐性”，而不是“显性”。Gawronski等人(本期)考虑了过程中的差异，例如“心理水平”之间的差异。例如，他们讨论了内隐过程如何被视为反映“涉及概念之间不确定的心理联系”的联想过程，而外显过程是“涉及特定关系的感知有效性”的命题过程(第141页)。还审查了其他程序上的区别。这些倾向于以工具为中心。例如，一种测量方法被认为是隐含的，因为它的反应是自动的，也就是说，是无意的，难以控制的。相比之下，明确的衡量标准是人们以深思熟虑、有意识和自我反思的方式做出反应。事实上，这篇评论的第一作者属于程序性阵营，将隐性激活描述为无意识地(Dovidio, Kawakami， & Beach, 2001)、自动地(Dovidio, Hewstone, Glick， & ess, 2010)发生的激活，并且可以在人们没有意识到“有偏见的联想或这些联想在指导他们的判断和行动中所起的作用”的情况下运行(Greenwald, Dsagupta, et al.， 2022, p. 8)。Gawronski等人(本期)巧妙地论证了这些鞋子不合脚的原因和原因。最后，我们与Gawronski等人的关键结论产生共鸣，即“尽管经过了25年的广泛研究，目前的标签惯例仍然基于概念模糊的列表，根据该列表，如果研究人员过去将其描述为隐含的，则该测量具有隐含的资格”(第142页;另见Gawronski, De Houwer， & Sherman, 2020)。虽然我们同意Gawronski, Ledgerwood和Eastwick对当前问题的分析，即内隐偏见的设想和研究方式，但我们的分歧在于提出的解决方案。我们故意使用“分歧”这个词，而不是“不同意”，因为把我们带到这里的观点是完全不同的。不同的观点有不同的假设，决定了不同的优先顺序。Gawronski等人(本期)将内隐偏见视为一种行为现象，可以与内隐测量评估的偏见区分开来。他们写道，“偏见可以定义为社会类别线索(例如，与种族、性别等有关的线索)对行为反应的影响”，“将一个人对目标的行为反应归类为IB[内隐偏见]的实例，一个人必须证明(1)行为反应受到社会类别线索的影响，(2)这个人没有意识到相关的社会类别线索对他们的行为反应的影响”(第5页)。尽管这组定义清晰而直接，但我们也不相信这是正确的。乍一看，Gawronski等人(本期)的定义似乎与Greenwald, Dsagupta等人(2022,p. 8)最近使用的定义非常相似，这是一种偏见
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Pub Date : 2022-07-03DOI: 10.1080/1047840X.2022.2106750
Bertram Gawronski, A. Ledgerwood, Paul W. Eastwick
Abstract People can behave in a biased manner without being aware that their behavior is biased, an idea commonly referred to as implicit bias. Research on implicit bias has been heavily influenced by implicit measures, in that implicit bias is often equated with bias on implicit measures. Drawing on a definition of implicit bias as an unconscious effect of social category cues on behavioral responses, the current article argues that the widespread equation of implicit bias and bias on implicit measures is problematic on conceptual and empirical grounds. A clear separation of the two constructs will: (1) resolve ambiguities arising from the multiple meanings implied by current terminological conventions; (2) stimulate new research by uncovering important questions that have been largely ignored; (3) provide a better foundation for theories of implicit bias through greater conceptual precision; and (4) highlight the broader significance of implicit bias in a manner that is not directly evident from bias on implicit measures.
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Pub Date : 2022-07-03DOI: 10.1080/1047840X.2022.2106755
J. de Houwer, Y. Boddez
The target paper of Gawronski, Ledgerwood, and Eastwick (this issue) provides a valuable contribution to the literature on implicit bias (IB). We find ourselves in agreement with many of the points that the authors put forward. Most importantly, we agree that it is important to realize that scores on implicit measurement tasks such as the Implicit Association Test (IAT) cannot by default be interpreted as instances of unconscious bias. We also agree that the focus on bias in implicit measures (BIM) may have slowed progress in research on IB, that the focus of bias research should be on reducing real-world instances of bias, and that societal dispar-ities can result in social discrimination in a way that is not captured by the psychological concept of bias. We are happy to see that Gawronski et al. share many aspects of our perspective on IB and implicit measures (see De Houwer, 2006, 2014, 2019; De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009, De Houwer, Gawronski, & Barnes-Holmes, 2013; De Houwer, Van Dessel, & Moran, 2021). Most importantly, (a) IB can indeed be conceived of as a behavioral phenomenon that refers to the impact of social cues on behavior, and (b) implicit measures are not the same as indirect measures, nor do they necessarily reflect associative processes. In sum, we support much of what Gawronski et al. put forward in their target paper. Nevertheless, we also disagree with Gawronski et al. (this issue) on some points. First, we continue to believe IB should not be limited to unconscious bias but should include also instances of bias that are automatic in other ways (e.g., unintentional). Second, we continue to see a potential role for BIM in research on IB, more specifically as an educational tool and as a lab model of IB in the real world.
{"title":"Bias in Implicit Measures as Instances of Biased Behavior under Suboptimal Conditions in the Laboratory","authors":"J. de Houwer, Y. Boddez","doi":"10.1080/1047840X.2022.2106755","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2106755","url":null,"abstract":"The target paper of Gawronski, Ledgerwood, and Eastwick (this issue) provides a valuable contribution to the literature on implicit bias (IB). We find ourselves in agreement with many of the points that the authors put forward. Most importantly, we agree that it is important to realize that scores on implicit measurement tasks such as the Implicit Association Test (IAT) cannot by default be interpreted as instances of unconscious bias. We also agree that the focus on bias in implicit measures (BIM) may have slowed progress in research on IB, that the focus of bias research should be on reducing real-world instances of bias, and that societal dispar-ities can result in social discrimination in a way that is not captured by the psychological concept of bias. We are happy to see that Gawronski et al. share many aspects of our perspective on IB and implicit measures (see De Houwer, 2006, 2014, 2019; De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009, De Houwer, Gawronski, & Barnes-Holmes, 2013; De Houwer, Van Dessel, & Moran, 2021). Most importantly, (a) IB can indeed be conceived of as a behavioral phenomenon that refers to the impact of social cues on behavior, and (b) implicit measures are not the same as indirect measures, nor do they necessarily reflect associative processes. In sum, we support much of what Gawronski et al. put forward in their target paper. Nevertheless, we also disagree with Gawronski et al. (this issue) on some points. First, we continue to believe IB should not be limited to unconscious bias but should include also instances of bias that are automatic in other ways (e.g., unintentional). Second, we continue to see a potential role for BIM in research on IB, more specifically as an educational tool and as a lab model of IB in the real world.","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"173 - 176"},"PeriodicalIF":9.3,"publicationDate":"2022-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47285513","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}
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