Pub Date : 2022-10-02DOI: 10.1080/1047840X.2022.2149183
E. Koster, Igor Marchetti, Ivan Grahek
Amir and Bernstein (this issue) propose a dynamical model of internally-directed cognition aimed at explaining the complex interactions between current goals, negative affect, and attentional selection in working memory. They connect the literature on internal attention, working memory, affect, rumination, and mind wandering to propose a formal mathematical model of internally-directed cognition. In this paper, they do not just provide a window on how people become stuck in loops of negative thinking, but they also provide a nice example of how clinical psychological science can move toward more formal theoretical models. In taking such an exciting step, we believe that this work also encounters some of the challenges faced by formal models of maladaptive cognition. Below we discuss some of these issues, not in order to criticize the current work, but to open a discussion, which we feel is paramount as the field of clinical psychology moves in the direction of developing formal theoretical models. In brief, the three main issues are: (1) the proposed model does not build on the existing cognitive models; (2) the model increases rather than decreases the complexity of the phenomenon; (3) there are no standard/alternative frameworks to compare the A2T model to, and it is not clear which kind of data or experiments could corroborate or falsify the model. New models should build on the existing formal models of cognitive processes. The reproducibility crisis in psychology (Open Science Collaboration, 2015; Simmons, Nelson, & Simonsohn, 2011) has led to significant changes in the way we conduct research, which include preregistration and better statistical methodology (Benjamin et al., 2018; Nosek, Ebersole, DeHaven, & Mellor, 2018). In the slipstream of this movement, a reinvigorated discussion has been opened on the role and current status of theory in psychology (e.g., Fried, 2020; Grahek, Schaller, & Tackett, 2021; Haslbeck, Ryan, Robinaugh, Waldorp, & Borsboom, 2021). Clearly, clinical psychological science has no shortage of rather vague, descriptive theories that are difficult to test and disprove. Many areas of psychology are moving in the direction of developing stronger theories, which could guide experimentation and increase the overall rigor of psychological science. In this context, clinical psychology is faced with the task of creating formal mathematical models of important phenomena, including the ones which the A2T model tackles. This effort, often referred to as computational psychiatry (Huys, Maia, & Frank, 2016; Montague, Dolan, Friston, & Dayan, 2012), is showing a lot of promise. The crucial part of this effort is to develop computational models that are relevant for understanding psychopathology, but also have direct links with the existing formal models from cognitive science. In this way, clinical psychology can build on the existing models, and extend them in order to better understand psychopathology. Such efforts are alread
{"title":"Focusing Inward: A Timely Yet Daunting Challenge for Clinical Psychological Science","authors":"E. Koster, Igor Marchetti, Ivan Grahek","doi":"10.1080/1047840X.2022.2149183","DOIUrl":"https://doi.org/10.1080/1047840X.2022.2149183","url":null,"abstract":"Amir and Bernstein (this issue) propose a dynamical model of internally-directed cognition aimed at explaining the complex interactions between current goals, negative affect, and attentional selection in working memory. They connect the literature on internal attention, working memory, affect, rumination, and mind wandering to propose a formal mathematical model of internally-directed cognition. In this paper, they do not just provide a window on how people become stuck in loops of negative thinking, but they also provide a nice example of how clinical psychological science can move toward more formal theoretical models. In taking such an exciting step, we believe that this work also encounters some of the challenges faced by formal models of maladaptive cognition. Below we discuss some of these issues, not in order to criticize the current work, but to open a discussion, which we feel is paramount as the field of clinical psychology moves in the direction of developing formal theoretical models. In brief, the three main issues are: (1) the proposed model does not build on the existing cognitive models; (2) the model increases rather than decreases the complexity of the phenomenon; (3) there are no standard/alternative frameworks to compare the A2T model to, and it is not clear which kind of data or experiments could corroborate or falsify the model. New models should build on the existing formal models of cognitive processes. The reproducibility crisis in psychology (Open Science Collaboration, 2015; Simmons, Nelson, & Simonsohn, 2011) has led to significant changes in the way we conduct research, which include preregistration and better statistical methodology (Benjamin et al., 2018; Nosek, Ebersole, DeHaven, & Mellor, 2018). In the slipstream of this movement, a reinvigorated discussion has been opened on the role and current status of theory in psychology (e.g., Fried, 2020; Grahek, Schaller, & Tackett, 2021; Haslbeck, Ryan, Robinaugh, Waldorp, & Borsboom, 2021). Clearly, clinical psychological science has no shortage of rather vague, descriptive theories that are difficult to test and disprove. Many areas of psychology are moving in the direction of developing stronger theories, which could guide experimentation and increase the overall rigor of psychological science. In this context, clinical psychology is faced with the task of creating formal mathematical models of important phenomena, including the ones which the A2T model tackles. This effort, often referred to as computational psychiatry (Huys, Maia, & Frank, 2016; Montague, Dolan, Friston, & Dayan, 2012), is showing a lot of promise. The crucial part of this effort is to develop computational models that are relevant for understanding psychopathology, but also have direct links with the existing formal models from cognitive science. In this way, clinical psychology can build on the existing models, and extend them in order to better understand psychopathology. Such efforts are alread","PeriodicalId":48327,"journal":{"name":"Psychological Inquiry","volume":"33 1","pages":"273 - 275"},"PeriodicalIF":9.3,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43088307","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 : 2022-10-02DOI: 10.1080/1047840X.2022.2141005
David R. Vago, N. Farb, R. N. Spreng
David R. Vago , Norman Farb , and R. Nathan Spreng Department of Psychology, Vanderbilt University, Nashville, Tennessee; Contemplative Sciences Center, University of Virginia, Charlottesville, Virginia; Department of Psychology, University of Toronto Mississauga, Mississauga, Canada; Laboratory of Brain and Cognition, Montreal Neurological Institute, Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, Canada; Departments of Psychiatry and Psychology, McGill University, Montreal, Canada; Douglas Mental Health University Institute, Verdun, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
David R. Vago, Norman Farb和R. Nathan spring，田纳西州纳什维尔范德比尔特大学心理学系;维吉尼亚州夏洛茨维尔市维吉尼亚大学冥想科学中心;加拿大密西沙加多伦多大学心理学系;加拿大蒙特利尔麦吉尔大学医学院神经病学与神经外科蒙特利尔神经研究所脑与认知实验室;加拿大蒙特利尔麦吉尔大学精神病学与心理学系;加拿大凡尔登道格拉斯心理健康大学研究所;加拿大蒙特利尔麦吉尔大学蒙特利尔神经学研究所麦康奈尔脑成像中心
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Pub Date : 2022-10-02DOI: 10.1080/1047840X.2022.2149195
Marieke K. van Vugt, H. Jamalabadi
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.
{"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}
Pub Date : 2022-07-03DOI: 10.1080/1047840X.2022.2106764
Kate A. Ratliff, C. Smith
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,
{"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}
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.
{"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}
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。
{"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}
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
{"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}
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.
{"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}
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
{"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}
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
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