Correction of a Technical Error in the Golf Swing: Error Amplification Versus Direct Instruction

Performance errors drive motor learning for many tasks. The authors' aim was to determine which of two strategies, method of amplification of error (MAE) or direct instruction (DI), would be more beneficial for error correction during a full golfing swing with a driver. Thirty-four golfers were randomly assigned to one of three training conditions (MAE, DI, and control). Participants were tested in a practice session in which each golfer performed 7 pretraining trials, 6 training-intervention trials, and 7 posttraining trials; and a retention test after 1 week. An optoeletronic motion capture system was used to measure the kinematic parameters of each golfer's performance. Results showed that MAE is an effective strategy for correcting the technical errors leading to a rapid improvement in performance. These findings could have practical implications for sport psychology and physical education because, while practice is obviously necessary for improving learning, the efficacy of the learning process is essential in enhancing learners' motivation and sport enjoyment.     

The Religious Function of Jaina Philosophy:AnekāntavādaReconsidered

This paper considers the socio-religious rationale for the Jaina theory of the non-one-sided nature of reality (anekāntavāda). In doing so it rejects the received view thatanekāntavāda's exclusive function is to promote non-violence at the intellectual level. Instead it advances a model which emphasizes the way in whichanekāntavādasustains a real connection between karmic matter and the soul (jīva) and so maintains the rationale for identity defining ascetic practice. The social and religious dangers of one-sided (ekānta) views for the Jaina tradition are demonstrated by a consideration of the Digambara teacher Kundakunda's idiosyncratic use of the two truths model of reality.     

Infants' understanding of actions performed by mechanical devices

Recent research suggests that 9-month-old infants tested in a modified version of the A-not-B search task covertly imitate actions performed by the experimenter. The current study examines whether infants also simulate actions performed by mechanical devices, and whether this varies with whether or not they have been familiarized with the devices and their function. In Experiment 1, infants observed hiding and retrieving actions performed by a pair of mechanical claws on the A-trials, and then searched for the hidden toy on the B-trial. In Experiment 2, infants were first familiarized with the experimenter and the claws but not their function. In Experiment 3, infants were familiarized with the function of the claws. The results revealed that search errors were at chance levels in Experiments 1 and 2, but a significant proportion of the infants showed the A-not-B error in Experiment 3. These results suggest that 9-month-old infants are less likely to simulate observed actions performed by mechanical devices than by human agents, unless they are familiarized with the function of the devices so that their actions are perceived as goal-directed.     

Hail the impossible: p-values, evidence, and likelihood

Significance testing based on p-values is standard in psychological research and teaching. Typically, research articles and textbooks present and use p as a measure of statistical evidence against the null hypothesis (the Fisherian interpretation), although using concepts and tools based on a completely different usage of p as a tool for controlling long-term decision errors (the Neyman-Pearson interpretation). There are four major problems with using p as a measure of evidence and these problems are often overlooked in the domain of psychology. First, p is uniformly distributed under the null hypothesis and can therefore never indicate evidence for the null. Second, p is conditioned solely on the null hypothesis and is therefore unsuited to quantify evidence, because evidence is always relative in the sense of being evidence for or against a hypothesis relative to another hypothesis. Third, p designates probability of obtaining evidence (given the null), rather than strength of evidence. Fourth, p depends on unobserved data and subjective intentions and therefore implies, given the evidential interpretation, that the evidential strength of observed data depends on things that did not happen and subjective intentions. In sum, using p in the Fisherian sense as a measure of statistical evidence is deeply problematic, both statistically and conceptually, while the Neyman-Pearson interpretation is not about evidence at all. In contrast, the likelihood ratio escapes the above problems and is recommended as a tool for psychologists to represent the statistical evidence conveyed by obtained data relative to two hypotheses.     

Behind the wheel: What drives the effects of error handling?

Existing research comparing error management (a strategy focusing on increasing the positive and decreasing the negative consequences of errors) to error prevention (a strategy focusing on working faultlessly), has identified error management as beneficial for multiple outcomes. Yet, due to various methodological limitations, it is unclear whether the effects previously found are due to error prevention, error management, or both. We examine this in an experimental study with a 2 (error prevention: yes vs. no) × 2 (error management: yes vs. no) factorial design. Error prevention had negative effects on cognition and adaptive transfer performance. Error management alleviated worry and boosted one’s perceived self-efficacy. Overall, the results show that error prevention and error management have unique outcomes on negative affect, self-efficacy, cognition, and performance.     

论《荀子》的幽默艺术特色

目前对《荀子》作为著名散文著作之一的研究,从艺术性上的分析止于对其譬喻和大量排比句法的一般肯定。本文认为《荀子》中的幽默风格是应予关注到的艺术特点之一。文中的荒谬意味比喻、错倒描写、对因果不协调的现象原因的欲擒故纵的揭示、"猜谜语"谐隐游戏形式等幽默手法都值得珍视和进一步探索。     

Goodness-of-fit and confidence intervals of approximate models

If the model for the data are strictly speaking incorrect, then how can one test whether the model fits? Standard goodness-of-fit (GOF) tests rely on strictly correct or incorrect models. But in practice the correct model is not assumed to be available. It would still be of interest to determine how good or how bad the approximation is. But how can this be achieved? If it is determined that a model is a good approximation and hence a good explanation of the data, how can reliable confidence intervals be constructed? In this paper, an attempt is made to answer the above questions. Several GOF tests and methods of constructing confidence intervals are evaluated both in a simulation and with real data from the internet-based daily news memory test.     

Prediction in infants and adults: A pupillometry study

Adults use both bottom‐up sensory inputs and top‐down signals to generate predictions about future sensory inputs. Infants have also been shown to make predictions with simple stimuli and recent work has suggested top‐down processing is available early in infancy. However, it is unknown whether this indicates that top‐down prediction is an ability that is continuous across the lifespan or whether an infant's ability to predict is different from an adult's, qualitatively or quantitatively. We employed pupillometry to provide a direct comparison of prediction abilities across these disparate age groups. Pupil dilation response (PDR) was measured in 6‐month olds and adults as they completed an identical implicit learning task designed to help learn associations between sounds and pictures. We found significantly larger PDR for visual omission trials (i.e. trials that violated participants’ predictions without the presentation of new stimuli to control for bottom‐up signals) compared to visual present trials (i.e. trials that confirmed participants’ predictions) in both age groups. Furthermore, a computational learning model that is closely linked to prediction error (Rescorla‐Wagner model) demonstrated similar learning trajectories suggesting a continuity of predictive capacity and learning across the two age groups.     

A step forward: Bayesian hierarchical modelling as a tool in assessment of individual discrimination performance

Individual assessment of infants’ speech discrimination is of great value for studies of language development that seek to relate early and later skills, as well as for clinical work. The present study explored the applicability of the hybrid visual fixation paradigm (Houston et al., 2007) and the associated statistical analysis approach to assess individual discrimination of a native vowel contrast, /aː/ - /eː/, in Dutch 6 to 10-month-old infants. Houston et al. found that 80% (8/10) of the 9-month-old infants successfully discriminated the contrast between pseudowords boodup - seepug. Using the same approach, we found that 12% (14/117) of the infants in our sample discriminated the highly salient /aː/ -/eː/ contrast. This percentage was reduced to 3% (3/117) when we corrected for multiple testing. Bayesian hierarchical modeling indicated that 50% of the infants showed evidence of discrimination. Advantages of Bayesian hierarchical modeling are that 1) there is no need for a correction for multiple testing and 2) better estimates at the individual level are obtained. Thus, individual speech discrimination can be more accurately assessed using state of the art statistical approaches.     

Acceptable timing error at ball-bat impact for different pitches and its implications for baseball skills

In baseball hitting, batters need high precision timing control to hit the ball with bat’s sweet spot. Knowing the acceptable range of timing error for hitting the ball in the aimed direction for various pitch types is helpful to understand whether the cause of the batter's mis-hit is a spatial or temporal error and highlight the motor skills required by the batter. The purpose of this study was to determine the acceptable timing error in different baseball pitches and the impact characteristics of mis-hits. Twenty-six high school baseball players hit a ball launched from a pitching machine with three types of pitches: fastballs, curveballs, and slowballs. We recorded the three-dimensional behavior of the ball, bat, and human body (pelvis) using an optical motion capture system. We then defined the optimal impact location based on timing accuracy, and determined the acceptable range of timing error by the interactive relationship between the horizontal orientation of the bat’s long axis at the time of ball impact and the horizontal direction of the batted ball. The ±30° width in the horizontal direction of the batted ball was set as the precondition for the tolerance of timing. The acceptable timing error was ±7.9 ms for fastballs, ±10.7 ms for curveballs, and ±10.7 ms for slowballs, and the optimal timing for outside pitches was approximately 10 ms later than that for inside pitches. The timing error was also explained 38.1% by variation in the impact location along the long axis of the bat (R² = 0.381, P < 0.001) and was minimized at a position close to the bat’s sweet spot. These results suggest that the optimal impact location and acceptable range of timing error depend on the pitching course and speed and that timing accuracy is essential to achieve the spatial accuracy required to hit the ball at the bat’s sweet spot.