Yet Another Revised DDE? A Note on David K. Chan's DDEd

David K. Chan wants to save the DDE from the considerable criticism levelled against it, by making the moral distinction it refers to rest on a difference in desire instead of in intention. I argue that the revised version, too, is counter-intuitive and confuses the blameworthiness of an actor with the wrongness of the act. It also invites abuse instead of preventing it. Besides, Chan's DDE omits three of the four criteria of the traditional DDE, and it is couched in terms of lesser objectionability, while the traditional DDE is couched in terms of permission. Therefore, Chan's DDE has so little in common with the traditional DDE that it should not even count as a revised version of it.     

Hume on the Problem of Other Minds

Hume is not often cited as a philosopher who posited a solution to the Problem of Other Minds. He instead seems to assume the belief in other minds in his moral philosophy without justification. However, Hume needs to explain how we experience and respond to others’ affections, and hence generate moral sentiments, given how central the latter are to his moral theory. Two recent interpretations of Hume’s solution to the Problem are the Wittgensteinian Interpretation, and the Simulation Theory Interpretation. Both focus on the concept of sympathy as a solution to the Problem, claiming that, for Hume, sympathy produces the belief in other minds. This paper critically examines these two interpretations and offers an alternative called ‘the Analogical Argument Interpretation’, which reconstructs Hume’s version of an analogical argument carried out not by our rational faculty of mind but by custom and imagination. On this interpretation, Hume does not think that sympathy generates the belief in other minds, but rather, sympathy presupposes that belief.     

Reproducible research in sport and exercise psychology: The role of sample sizes

ObjectivesWe aim to introduce the discussion on the crisis of confidence to sport and exercise psychology. We focus on an important aspect of this debate, the impact of sample sizes, by assessing sample sizes within sport and exercise psychology. Researchers have argued that publications in psychological research contain numerous false-positive findings and inflated effect sizes due to small sample sizes.MethodWe analyse the four leading journals in sport and exercise psychology regarding sample sizes of all quantitative studies published in these journals between 2009 and 2013. Subsequently, we conduct power analyses.ResultsA substantial proportion of published studies does not have sufficient power to detect effect sizes typical for psychological research. Sample sizes and power vary between research designs. Although many correlational studies have adequate sample sizes, experimental studies are often underpowered to detect small-to-medium effects.ConclusionsAs sample sizes are small, research in sport and exercise psychology may suffer from false-positive results and inflated effect sizes, while at the same time failing to detect meaningful small effects. Larger sample sizes are warranted, particularly in experimental studies.     

“涟漪效应”与“心理台风眼效应”：不同程度COVID-19疫情地区民众风险认知与焦虑的双视角检验

在因防控COVID-19疫情武汉“封城”后的一个半月内, 对全国34个省(区市)4833名民众的风险认知与焦虑进行了当事人视角和旁观者视角的调查研究。结果表明, (1)基于当事人视角时, 不同程度疫情地区民众的风险认知和焦虑存在类似“涟漪效应”的趋势, 疫情严重地区大于其他地区; (2)基于旁观者视角, 民众对武汉居民的焦虑和对所需心理咨询工作者和医生数量的评估存在“心理台风眼效应”; (3)基于旁观者视角, 在风险认知上, 低风险地区民众对武汉居民的评估显著低于高、中高及中风险地区的评估, 部分支持“涟漪效应”; 研究还发现了“边缘带效应”和“心理可控阈限”的存在。本研究一方面为“心理台风眼效应”研究的“当事人与旁观者”视角知觉差异提供了更为丰富的直接证据, 另一方面为突发公共卫生事件应急管理提供了有针对性的心理依据。     

Exceptional reported effects and data anomalies merit explanation from “A randomized controlled trial of coordination exercise on cognitive function in obese adolescents” by Liu et al. (2018)

We read the article in Psychology of Sport and Exercise by Liu et al. (“A randomized controlled trial of coordination exercise on cognitive function in obese adolescents”) with great interest. Our interest in the article stemmed from the extraordinary differences in obesity-related outcomes reported in response to a rope-jumping intervention. We requested the raw data from the authors to confirm the results and, after the journal editors reinforced our request, the authors graciously provided us with their data. We share our evaluation of the original data herein, which includes concerns that weight and BMI loss by the intervention appears extraordinary in both magnitude and aspects of the distributions. We request that the authors address our findings by providing explanations of the extraordinary data or correcting any errors that may have occurred in the original report, as appropriate.     

Is the Third‐Person Effect Real? A Critical Examination of Rationales,Testing Methods,and Previous Findings of the Third‐Person Effect on Censorship Attitudes

Regarding the effect of the third‐person perception on censorship attitudes, the present study first highlighted logical weaknesses of previously proposed rationales and limitations of previously used statistical models to test the effect. This study reanalyzed data from past research on the effect of the third‐person perception. In Study 1, the average effects of the other‐self perceptual gap in the media influence on censorship attitudes were estimated based on reports from 13 previous studies (total N = 6,414). Study 2 reanalyzed Schmierbach, Boyle, Xu, and McLeod's (2011) correlational data (N = 692). The results of these two studies showed that the presumed effect on others is a stronger predictor of censorship attitudes than the other‐self differential in perceived media effect.     

Base Rates,Deception Detection,and Deception Theory: A Reply to Burgoon (2015)

This essay continues a debate about the relative scientific merits of the Park‐Levine Model (PLM) and Interpersonal Deception Theory (IDT) with regard to accuracy in deception detection. Key points of disagreement include (a) the degree to which message recipients are sensitive to sender veracity and (b) the extent to which interactivity moderates the veracity and base‐rate effects specified by PLM. According to PLM, people are truth‐biased, truth‐bias makes people insensitive to deception, and as a consequence, base rates affect accuracy regardless of interactivity. IDT, in direct contrast, holds that people are sensitive to the veracity of other's communication and that interactivity is a key moderator. Consistent with PLM, data repeatedly show that people are insensitive to other's veracity and that this insensitivity is general across media affordances including interactivity. This rejoinder uses Burgoon's (2015) own interactive data to demonstrate the empirical superiority of PLM over IDT.     

The “other-accent” effect in voice recognition

The current paper examines an “other-accent” effect when recognising voices. English and Scottish listeners were tested with English and Scottish voices using a sequential lineup method. The results suggested greater accuracy for own-accent voices than for other-accent voices under both target-present and target-absent conditions. Moreover, self-rated confidence in response to target-absent lineups suggested greater confidence for own-accent voices than other-accent voices. As predicted, the other-accent effect noted here emerged more strongly for English listeners than for Scottish listeners, and these results are discussed within an expertise framework alongside both other-race effects in face recognition, and other-accent effects in word recognition. Given these results, caution is advised in the treatment of earwitness evidence when recognising a voice of another accent.     

Interpreting effect sizes when controlling for stability effects in longitudinal autoregressive models: Implications for psychological science

Effect sizes in longitudinal studies often are dramatically smaller than effect sizes in cross-sectional studies. Indeed, autoregressive models (which are often used in longitudinal studies but not in cross-sectional studies) control for past levels on the outcome (i.e., stability effects) in order to predict change in levels of the outcome over time and thus may greatly reduce the magnitude of the effect of a predictor on the outcome. Unfortunately, however, there have been no attempts to differentiate guidelines for interpreting effect sizes for longitudinal studies versus cross-sectional studies. Consequently, longitudinal effect sizes that fall below the universal guidelines for “small” may be incorrectly dismissed as trivial, when they might be meaningful. In the current paper, we first review the present guidelines for interpreting effect sizes. Next, we discuss several examples of how controlling for stability effects can dramatically attenuate effect sizes of other predictors, in order to support our argument that the current guidelines may be misleading for interpreting longitudinal effects. Finally, we conclude by making recommendations for researchers regarding the interpretation of effect sizes in longitudinal autoregressive models.     

A Gradual Effects Model for Single-Case Designs

Single-case designs are a class of repeated measures experiments used to evaluate the effects of interventions for small or specialized populations, such as individuals with low-incidence disabilities. There has been growing interest in systematic reviews and syntheses of evidence from single-case designs, but there remains a need to further develop appropriate statistical models and effect sizes for data from the designs. We propose a novel model for single-case data that exhibit nonlinear time trends created by an intervention that produces gradual effects, which build up and dissipate over time. The model expresses a structural relationship between a pattern of treatment assignment and an outcome variable, making it appropriate for both treatment reversal and multiple baseline designs. It is formulated as a generalized linear model so that it can be applied to outcomes measured as frequency counts or proportions, both of which are commonly used in single-case research, while providing readily interpretable effect size estimates such as log response ratios or log odds ratios. We demonstrate the gradual effects model by applying it to data from a single-case study and examine the performance of proposed estimation methods in a Monte Carlo simulation of frequency count data.