Research documents performance decrements resulting from the activation of a negative taskrelevant stereotype. We combine a number of strands of work to identify causes of stereotype threat in a way that allows us to reverse the effects and improve the performance of individuals with negative task-relevant stereotypes. We draw on prior work suggesting that negative stereotypes induce a prevention focus, and other research suggesting that people exhibit greater flexibility when their regulatory focus matches the reward structure of the task. This work suggests that stereotype threat effects emerge from a prevention focus combined with tasks that have an explicit or implicit gains reward structure. We find flexible performance can be induced in individuals who have a negative task-relevant stereotype by using a losses reward structure. We demonstrate the interaction of stereotypes and the reward structure of the task using chronic stereotypes and GRE math problems (Experiment 1), and primed stereotypes and a category learning task (Experiments 2a and 2b). We discuss implications of this research for other work on stereotype threat. KeywordsRegulatory Fit; Stereotype Threat; Motivation; Category Learning; Math Stereotype Threat Reinterpreted as a Regulatory Mismatch Stereotypes are a pervasive part of human psychological experience. Starting with Steele and Aronson (1995), research documents the performance decrements resulting from the activation of a negative taskrelevant stereotype. These decrements occur in a range of domains from the academic sector to athletic performance and are known as stereotype threat effects (Aronson, Lustina, Good, Keough, & Steele, 1999;Stone, Lynch, Sjomeling, & Darley, 1999). Not confined to laboratory settings, stereotype threat effects can be found in real-world contexts. Steele, James, and Barnett (2002) demonstrated that women in male-dominated fields, such as math and engineering, are more likely than those in female-dominated fields to think about changing their major. They propose that this difference suggests that women are avoiding the possibility of confirming a negative stereotype about their group by switching into fields like the social sciences that do not have negative stereotypes for women.Because stereotypes are ubiquitous, it is imperative that researchers determine how to mitigate their negative effects on performance. We present data in support of one such method. Using Regulatory Focus Theory (Higgins, 1987(Higgins, , 1997, we suggest that stereotype threat effects are the result of a regulatory mismatch between the motivational state of the individual and the reward structure of the task. This explanation allows us to suggest a straightforward method to reverse stereotype threat effects. Simply, we demonstrate that negative stereotypes can produce better performance than positive ones given a "matching" task reward structure. We call the beneficial pairing of stereotype and task reward structure a stereotype fit. This result

Motivation affects the degree to which people engage in tasks as well as the processes that they bring to bear. We explore the proposal that a fit between a person's situationally-induced self-regulatory focus and the reward structure of the task that they are pursuing supports greater flexibility in processing than does a mismatch between regulatory focus and reward structure. In two experiments, we prime regulatory focus and manipulate task reward structure. Our participants perform a rulebased learning task whose solution requires flexible strategy testing as well as an informationintegration task for which flexible strategy use hinders learning. Across two experiments, we predict and obtain a three-way interaction between regulatory focus, reward structure, and task. Relative to a mismatch, a match leads to better rule-based task performance, but worse performance on the information-integration task. We relate these findings to other work on motivation and choking under pressure.

Psychology researchers often avoid running participants from subject pools at the end of the semester because they are “unmotivated”. We suggest that the end of the semester induces a situational prevention focus (i.e., sensitive to losses) unlike the beginning of the semester, which may induce a situational promotion focus (i.e., sensitive to gains). In two experiments, we presented participants with math problems at the beginning or end of an academic semester. End-of-semester participants performed better minimizing losses as compared to maximizing gains, while the opposite was true for beginning-of-semester participants.

Causal induction provides a nice test domain for examining the influence of individual-difference factors on cognition. The phenomena of both conditionalization and discounting reflect attention to multiple potential causes when people infer what caused an effect. We explored the hypothesis that individuals with an independent self-construal are relatively less sensitive to context (other causes) than are individuals with an interdependent self-construal in this domain. We found greater levels of conditionalization and data consistent with discounting for participants in whom we primed an interdependent self-construal than for participants in whom we primed an independent self-construal.

Research has identified multiple category-learning systems with each being “tuned” for learning categories with different task demands and each governed by different neurobiological systems. Rule-based (RB) classification involves testing verbalizable rules for category membership while the information-integration (II) classification requires the implicit learning of stimulus-response mappings. In the first study to directly test rule priming with RB and II category learning, we investigated the influence of the availability of information presented at the beginning of the task. Participants viewed lines that varied in length, orientation, and position on the screen, and were primed to focus on stimulus dimensions that were relevant or irrelevant to the correct classification rule. In Experiment 1, we used an RB category structure, and in Experiment 2, we used an II category structure. Accuracy and model-based analyses suggested that a focus on relevant dimensions improves RB task performance later in learning while a focus on an irrelevant dimension improves II task performance early in learning.

Cognitive Science research is hard to conduct, because researchers must take phenomena from the world and turn them into laboratory tasks for which a reasonable level of experimental control can be achieved. Consequently, research necessarily makes tradeoffs between internal validity (experimental control) and external validity (the degree to which a task represents behavior outside of the lab). Researchers are thus seeking the best possible tradeoff between these constraints, which we refer to as the optimal level of fuzz. We present two principles for finding the optimal level of fuzz, in research, and then illustrate these principles using research from motivation, individual differences, and cognitive neuroscience.A hallmark of cognitive science involves the interplay of methods from different disciplines. Despite the importance of this interplay, methodological discussions in Psychology under the banner of cognitive science tend to focus on statistical issues such as the possibility that null hypothesis testing may lead research astray (e.g., Killeen, 2006). Much less discussion has centered on how to use the power of multidisciplinary cognitive science to construct research questions in ways that are likely to provide insight into the difficult questions that the field must address. In this paper, we present a principle that we call the optimal level of fuzz that we believe can guide good research. We start by defining the concept of fuzz and then discuss a set of principles that can guide researchers toward finding the optimal level of fuzz for their research. Next, we present three case studies of the optimal level of fuzz in action. Finally, we discuss the implications of this principle for research. What is Fuzz?Within cognitive science, experimental research in psychology provides data that can be used to constrain theories in neuroscience and psychology and to inspire new computational methods in artificial intelligence and reinforcement learning. Experimental research in psychology must typically trade off between internal and external validity. Internal validity is the basic idea that our experiments should be free from confounds and alternative explanations so that the results of our experiments can be unambiguously attributed to the variables we manipulated in our studies. External validity is the degree to which our studies reflect behavior that might actually occur outside the laboratory. laboratory to bring about desired behaviors and has constructed systems for illuminating internal mental processes. For example, many studies use lexical decision tasks in which a subject is shown strings of letters that may or may not form a word and is asked to judge as quickly as possible whether the string forms a word. This task is quite useful for measuring the activity of concepts during a cognitive process. Tasks like this have been used in a variety of studies ranging from work on language comprehension to studies of goal activation (e.g., Fishbach, Friedman, & Kruglanski, 2003;McNamara, 2005)...

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Considering only 20.8% of American adults meet current physical activity recommendations, it is important to examine the psychological processes that affect exercise motivation and behavior. Drawing from regulatory fit theory, this study examined how manipulating regulatory focus and reward structures would affect exercise performance, with a specific interest in investigating whether exercise experience would moderate regulatory fit effects. We predicted that regulatory fit effects would appear only for participants with low exercise experience. One hundred and sixty-five young adults completed strength training exercise tasks (i.e., sit-ups, squats, plank, and wall-sit) in regulatory match or mismatch conditions. Consistent with predictions, only participants low in experience in regulatory match conditions exercised more compared with those in regulatory mismatch conditions. Although this is the first study manipulating regulatory fit in a controlled setting to examine exercise behavior, findings suggest that generating regulatory fit could positively influence those low in exercise experience.