The effects of open vs. specific goals on flow and clutch states in a cognitive task

ObjectivesRecent qualitative evidence suggests that two optimal psychological states (flow and clutch) underlie excellent performance in sport. That research further suggests that the type of goal pursued influences which state is experienced. This study aimed to examine the effects of goal types (i.e., open, specific, and do-your-best goals) on flow and clutch states during performance in a cognitive task, which was developed and has been used in sport. Secondary aims were to investigate the effects of goal types on objective and subjective performance, confidence, and perceived challenge.DesignThis study employed a repeated measure design (mixed model).MethodParticipants (N = 95; M_age = 24.89, SD = 9.27) were randomised to one of three goal conditions and asked to perform a Letter and Number Identification Task, which was repeated for six attempts.ResultsParticipants prescribed open or do-your-best goals experienced significantly higher levels of flow than those prescribed specific goals, who conversely experienced significantly higher levels of clutch states. Participants assigned specific goals performed significantly better than those prescribed open or do-your-best goals. Those assigned open or do-your-best-goals reported greater perceived performance, higher confidence, and feeling more optimally challenged.ConclusionsThese findings provide experimental support for the role of goal types in determining flow and clutch states, and further understanding of the psychological effects of these goal types. Implications are discussed, and recommendations are made for future research into the role of goals in experiencing flow and clutch states in sport and exercise.     

Computer simulation through an error-statistical lens

After showing how Deborah Mayo’s error-statistical philosophy of science might be applied to address important questions about the evidential status of computer simulation results, I argue that an error-statistical perspective offers an interesting new way of thinking about computer simulation models and has the potential to significantly improve the practice of simulation model evaluation. Though intended primarily as a contribution to the epistemology of simulation, the analysis also serves to fill in details of Mayo’s epistemology of experiment.     

Simulating biological and non-biological motion

It is widely accepted that the brain processes biological and non-biological movements in distinct neural circuits. Biological motion, in contrast to non-biological motion, refers to active movements of living beings. Aim of our experiment was to investigate the mechanisms underlying mental simulation of these two movement types. Subjects had to either simulate mentally or to overtly reproduce previously executed or observed movements. Healthy subjects showed a very high timing precision when simulating biological and a strong distortion when simulating non-biological movements. Schizophrenic subjects, however, showed the opposite. Since overt reproduction was precise in any case, this double dissociation shows that processes underlying mental simulation of biological and non-biological movements are separate from each other and from perceptual and motor-control processes.     

Bootstrapping cognition from behavior-a computerized thought experiment

We show that simple perceptual competences can emerge from an internal simulation of action effects and are thus grounded in behavior. A simulated agent learns to distinguish between dead ends and corridors without the necessity to represent these concepts in the sensory domain. Initially, the agent is only endowed with a simple value system and the means to extract low-level features from an image. In the interaction with the environment, it acquires a visuo-tactile forward model that allows the agent to predict how the visual input is changing under its movements, and whether movements will lead to a collision. From short-term predictions based on the forward model, the agent learns an inverse model. The inverse model in turn produces suggestions about which actions should be simulated in long-term predictions, and long-term predictions eventually give rise to the perceptual ability.     

The Effect of Mental Simulation (Process‐ Versus Outcome‐Focus) on Extension Evaluation:A Bipolarized Fit Perception

The present research investigates perceived fit effects according to the focus that consumers place when evaluating brand extensions. Process‐focus encourages consumers to focus their thoughts on the process of reaching an outcome, thereby affecting extension evaluations on the basis of the process similarity between the original brand and its extensions. Conversely, outcome‐focus encourages consumers to focus their thoughts on the final outcome, thereby affecting extension evaluations on the basis of the final outcome similarity between the original brand and its extensions. With this conceptual framework, the present research demonstrated that for the process‐focus condition, the extension was evaluated more favorably when the similarity between the process of the original brand and the process of its extension was present (Study 1). By contrast, for the outcome‐focus condition, the extension was evaluated more favorably when the similarity between the final outcome of the original brand and the final outcome of its extension was present (Study 2).     

含题组的测验等值

题组越来越多地出现在各类考试中, 采用标准的IRT模型对有题组的测验等值, 可能因忽略题组的局部相依性导致等值结果的失真。为解决此问题, 我们采用基于题组的2PTM模型及IRT特征曲线法等值, 以等值系数估计值的误差大小作为衡量标准, 以Wilcoxon符号秩检验为依据, 在几种不同情况下进行了大量的Monte Carlo模拟实验。实验结果表明, 考虑了局部相依性的题组模型2PTM绝大部分情况下都比2PLM等值的误差小且有显著性差异。另外, 用6种不同等值准则对2PTM等值并评价了不同条件下等值准则之间的优劣。     

Generalized canonical correlation analysis of matrices with missing rows: a simulation study

A method is presented for generalized canonical correlation analysis of two or more matrices with missing rows. The method is a combination of Carroll’s (1968) method and the missing data approach of the OVERALS technique (Van der Burg, 1988). In a simulation study we assess the performance of the method and compare it to an existing procedure called GENCOM, proposed by Green and Carroll (1988). We find that the proposed method outperforms the GENCOM algorithm both with respect to model fit and recovery of the true structure. The research of Michel van de Velden was partly funded through EU Grant HPMF-CT-2000-00664. The authors would like to thank the associate editor and three anonymous referees for their constructive comments and suggestions that led to a considerable improvement of the paper.     

Learning the form of causal relationships using hierarchical bayesian models

People learn quickly when reasoning about causal relationships, making inferences from limited data and avoiding spurious inferences. Efficient learning depends on abstract knowledge, which is often domain or context specific, and much of it must be learned. While such knowledge effects are well documented, little is known about exactly how we acquire knowledge that constrains learning. This work focuses on knowledge of the functional form of causal relationships; there are many kinds of relationships that can apply between causes and their effects, and knowledge of the form such a relationship takes is important in order to quickly identify the real causes of an observed effect. We developed a hierarchical Bayesian model of the acquisition of knowledge of the functional form of causal relationships and tested it in five experimental studies, considering disjunctive and conjunctive relationships, failure rates, and cross-domain effects. The Bayesian model accurately predicted human judgments and outperformed several alternative models.     

How the intentions of the draftsman shape perception of a drawing

The interaction between the recovery of the artist’s intentions and the perception of an artwork is a classic topic for philosophy and history of art. It also frequently, albeit sometimes implicitly, comes up in everyday thought and conversation about art and artworks. Since recent work in cognitive science can help us understand how we perceive and understand the intentions of others, this discipline could fruitfully participate in a multidisciplinary investigation of the role of intention recovery in art perception. The method I propose is to look for cases where recovery of the artist’s intentions interacts with perception of a work of art, and this cannot be explain by a simple top-down influence of conscious propositional knowledge on perception. I will focus on drawing and show that recovery of the draftsman’s intentional actions is handled by a psychological process shaped by the motor system of the observer.