Lisa R. Grimm

Stereotype threat reinterpreted as a regulatory mismatch.

This research documents performance decrements resulting from the activation of a negative task-relevant stereotype. The authors combine a number of strands of work to identify causes of stereotype threat in a way that allows them to reverse the effects and improve the performance of individuals with negative task-relevant stereotypes. The authors draw on prior work suggesting that negative stereotypes induce a prevention focus and on 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. The authors find flexible performance can be induced in individuals who have a negative task-relevant stereotype by use of a losses reward structure. The authors demonstrate the interaction of stereotypes and the reward structure of the task with chronic stereotypes and Graduate Record Examination math problems (Experiment 1), and with primed stereotypes and a category learning task (Experiments 2A and 2B). The authors discuss implications of this research for other work on stereotype threat.

Self-Construal and the Processing of Covariation Information in Causal Reasoning

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.

End-of-Semester Syndrome: How Situational Regulatory Fit Affects Test Performance Over an Academic Semester

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, whereas the opposite was true for beginning-of-semester participants.

The optimal level of fuzz: Case studies in a methodology for psychological research.

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 behaviour outside of the lab). Researchers are thus seeking the best possible trade-off 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.

Priming insight in groups: Facilitating and inhibiting solving an ambiguously worded insight problem

We extend research on the priming of insight by studying group problem solving. Groups of 2–4 participants tried to solve an ambiguously worded problem in the presence of a prime that reinforced the dominant but incorrect interpretation of the problem, a prime that reinforced the uncommon but correct interpretation, or no prime. The paradigm involved participants asking questions of the experimenter that could only be answered “yes” or “no.” In Experiment 1, the prime was present throughout the solving period; in Experiment 2, it was removed prior to the solving period. In both experiments, the primes had their predicted effects. Patterns in the time taken to solve the problem supported the idea that groups stuck at the impasse were more or less able to restructure the problem, depending on the environmental context. Data from the questions asked and questionnaires converged with time taken to solve the problem, consistent with the view that restructuring a problem is an automatic process that produces insight. A comparison of the group data in Experiment 1 with individually tested participants’ data revealed that the insight of the groups benefited from their being able to recognize lines of questions to follow, to listen to answers to questions asked, and to evaluate and reject errors or assumptions.

Determining transformation distance in similarity: Considerations for assessing representational changes a priori

The representational distortion (RD) approach to similarity (e.g., Hahn, Chater, & Richardson, 2003) proposes that similarity is computed using the transformation distance between two entities. We argue that researchers who adopt this approach need to be concerned with how representational transformations can be determined a priori. We discuss several roadblocks to using this approach. Specifically we demonstrate the difficulties inherent in determining what transformations are psychologically salient and the importance of considering the directionality of transformations.

Psychology of knowledge representation

UNLABELLED Every cognitive enterprise involves some form of knowledge representation. Humans represent information about the external world and internal mental states, like beliefs and desires, and use this information to meet goals (e.g., classification or problem solving). Unfortunately, researchers do not have direct access to mental representations. Instead, cognitive scientists design experiments and implement computational models to develop theories about the mental representations present during task performance. There are several main types of mental representation and corresponding processes that have been posited: spatial, feature, network, and structured. Each type has a particular structure and a set of processes that are capable of accessing and manipulating information within the representation. The structure and processes determine what information can be used during task performance and what information has not been represented at all. As such, the different types of representation are likely used to solve different kinds of tasks. For example, structured representations are more complex and computationally demanding, but are good at representing relational information. Researchers interested in human psychology would benefit from considering how knowledge is represented in their domain of inquiry. For further resources related to this article, please visit the WIREs website. CONFLICT OF INTEREST The author has declared no conflicts of interest for this article.