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For decades, researchers have tried to understand how to guide students to both pursue and excel in a career in the fields science, technology, engineering, and mathematics (STEM). Recently, creativity has been recognized as being important in STEM-based fields and some researchers have even suggested a shift from STEM to STEAM to account for the artistic side of science ( Boy, 2013 ; Henriksen, 2014 ). Creative capacities augment scientific endeavors given the role these capacities play in problem-solving and innovation. Our work focuses on identifying a method to improve both creativity and spatial abilities, as spatial ability predicts the likelihood to succeed in a STEM field ( Wai et al., 2009 ). Some scientists found that making computer games provides students with an introduction to computer science and technology through an engaging, self-driven platform. While this technique has been successful in sparking interest in STEM careers ( Javidi & Sheybani, 2010 ; Robertson & Howells, 2008 ), little research has been conducted on how it impacts STEM success. We examined if game-making could lead to cognitive improvement in creativity and spatial abilities in addition to its attitudinal effects. Using a longitudinal, pretest/post-test design, we found that making levels in Portal 2 improved creative and spatial abilities over a control. Participants who made levels also reported being more interested and confident in a variety of STEM-related activities. We conclude that game-making can be used as an engaging way to not only encourage students to pursue, but prepare them to succeed in STEM careers.

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.

Research has connected stereotype threat and regulatory fit by showing improved performance for individuals with negative stereotypes when they focused on minimizing potential losses. In the current study, non-Black participants, who were non-experts at golf putting, were told that a golf-putting task was diagnostic of natural athletic ability (i.e., negative stereotype) or sports intelligence (i.e., positive stereotype). Participants tried to maximize earned points or minimize lost points assigned after every putt, which was calculated based on the distance to a target. We demonstrate better performance for participants experiencing a fit between their global task stereotype and the task goal, and argue that regulatory fit allows for increased attention on the strategies beneficial for task performance. Interestingly, we find that performance of individuals high in working memory capacity suffers greatly when those individuals experience a regulatory mismatch.

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.

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 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.

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.

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.

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.The author has declared no conflicts of interest for this article.

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.

Cultural mindset is related to performance on a variety of cognitive tasks. In particular, studies of both chronic and situationally-primed mindsets show that individuals with a relatively interdependent mindset (i.e., an emphasis on relationships and connections among individuals) are more sensitive to background contextual information than individuals with a more independent mindset. Two experiments tested whether priming cultural mindset would affect sensitivity to background causes in a contingency learning and causal inference task. Participants were primed (either independent or interdependent), and then saw complete contingency information on each of 12 trials for two cover stories in Experiment 1 (hiking causing skin rashes, severed brakes causing wrecked cars) and two additional cover stories in Experiment 2 (school deadlines causing stress, fertilizers causing plant growth). We expected that relative to independent-primed participants, those interdependent-primed would give more weight to the explicitly-presented data indicative of hidden alternative background causes, but they did not do so. In Experiment 1, interdependents gave less weight to the data indicative of hidden background causes for the car accident cover story and showed a decreased sensitivity to the contingencies for that story. In Experiment 2, interdependents placed less weight on the observable data for cover stories that supported more extra-experimental causes, while independents' sensitivity did not vary with these extra-experimental causes. Thus, interdependents were more sensitive to background causes not explicitly presented in the experiment, but this sensitivity hurt rather than improved their acquisition of the explicitly-presented contingency information.

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.

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.