The effect of facial attractiveness on temporal perception

Previous research suggests that feelings of fear, dislike, shame and sadness affect our perception of duration (Droit-Volet et al., 2004; Gil et al., 2009). The current study sought to expand our understanding of the variables which moderate temporal perception by examining whether the attractiveness of a face influenced its perceived duration. Participants completed a verbal estimation task in which they judged the duration of attractive, unattractive and neutral faces. The results showed that participants underestimated the duration of unattractive faces relative to attractive and neutral faces. Estimates of unattractive faces were also less accurate than those of the attractive and neutral faces. The results are consistent with Gil et al.'s (2009) suggestion that the duration of disliked stimuli are underestimated relative to liked and neutral stimuli because they detract attention from temporal perception. Analysis of the slope and intercept of the estimation gradients supports Zakay and Block's (1997) suggestion that reduced attention to time results in a multiplicative underestimation of duration.     

A Thurstonian Analysis of Preference Change

This paper presents a Thurstonian model for the analysis of preference change. Preferences are expressed in the form of rankings, possibly with ties. A vector-autoregression framework is used to investigate relationships between past and current rankings. It is shown that this approach yields a parsimonious and easily interpretable representation of individual preference differences in time-dependent ranking data. A detailed analysis of the 1992 National Election Study illustrates the proposed approach.     

Identification and Testing of Modes in Beliefs

Characterizing diversity in beliefs is of paramount importance in empirical research on human behavior. In game theory, the focus is on the hypothesis formation. We present a data set with ideal properties for characterizing such diversity. The data consist of hypotheses on the empirical distribution of opponent choices. Using nonparametric techniques, we identify modes in the data corresponding to subpopulations of behavioral types and find the statistical significance of these modes in the underlying population distribution.     

大学生实时距、空时距估计的比较研究

本研究采用2(空时距、实时距)×8(1秒～8秒)的实验设计,以224名大学生为被试,考察了不同类型、不同长度的时距估计特点,结果发现;(1)时距类型在时距估计中存在主效应,在本实验中,1秒～8秒条件下实时距估计比空时距准确;(2)时距长度在时距估计中存在主效应,时距估计所产生的误差随时距长度的增加而增大;(3)时距类型与时距长度之间存在交互作用,对实时距、空时距估计进行曲线估计及预测,发现两类时距估计呈现动态特征,1845052秒之前实时距估计比空时距准确,之后空时距估计比实时距准确,两类时距估计曲线的变化速度也呈交替上升趋势。     

个人目标相关性对非临床抑郁者未来想象的影响

通过两个实验考查了非临床抑郁者未来想象的异常是否受到个人目标相关性的调节。实验1采用未来想象任务, 实验2采用可能性评估范式, 两个实验一致发现, 抑郁倾向者想象未来积极事件的异常, 受到了与个人目标相关性的调节：相对于非抑郁倾向者, 抑郁倾向者对未来与个人目标相关的积极事件的预期减弱, 而对未来与个人目标无关的积极事件的预期则没有表现出异常; 同时还发现, 抑郁倾向者表现出了对未来消极预期的普遍增强, 不受与个人目标相关性的影响。     

Efficient nonparametric approaches for estimating the operating characteristics of discrete item responses

Rationale and the actual procedures of two nonparametric approaches, called Bivariate P.D.F. Approach and Conditional P.D.F. Approach, for estimating the operating characteristic of a discrete item response, or the conditional probability, given latent trait, that the examinee's response be that specific response, are introduced and discussed. These methods are featured by the facts that: (a) estimation is made without assuming any mathematical forms, and (b) it is based upon a relatively small sample of several hundred to a few thousand examinees.Some examples of the results obtained by the Simple Sum Procedure and the Differential Weight Procedure of the Conditional P.D.F. Approach are given, using simulated data. The usefulness of these nonparametric methods is also discussed.This research was mostly supported by the Office of Naval Research (N00014-77-C-0360, N00014-81-C-0569, N00014-87-K-0320, N00014-90-J-1456).     

Dealing with Multiple Solutions in Structural Vector Autoregressive Models

Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.     

Estimation of the Coefficient of Variation with Minimum Risk: A Sequential Method for Minimizing Sampling Error and Study Cost

The coefficient of variation is an effect size measure with many potential uses in psychology and related disciplines. We propose a general theory for a sequential estimation of the population coefficient of variation that considers both the sampling error and the study cost, importantly without specific distributional assumptions. Fixed sample size planning methods, commonly used in psychology and related fields, cannot simultaneously minimize both the sampling error and the study cost. The sequential procedure we develop is the first sequential sampling procedure developed for estimating the coefficient of variation. We first present a method of planning a pilot sample size after the research goals are specified by the researcher. Then, after collecting a sample size as large as the estimated pilot sample size, a check is performed to assess whether the conditions necessary to stop the data collection have been satisfied. If not an additional observation is collected and the check is performed again. This process continues, sequentially, until a stopping rule involving a risk function is satisfied. Our method ensures that the sampling error and the study costs are considered simultaneously so that the cost is not higher than necessary for the tolerable sampling error. We also demonstrate a variety of properties of the distribution of the final sample size for five different distributions under a variety of conditions with a Monte Carlo simulation study. In addition, we provide freely available functions via the MBESS package in R to implement the methods discussed.     

Flexible representations in visual working memory and interactions with long‐term learning: Commentary on the special issue

This special issue of the British Journal of Psychology brings together cutting edge research on a range of topics in visual working memory (VWM). In this commentary, we attempt to summarize common themes in current VWM research exemplified in this issue. The articles include several reviews of important topics as well as empirical papers covering three main themes. The first concerns the nature of mental representations of memoranda in the commonly used delayed estimation task, where both fine‐grained and broad categorical details appear to be represented, and their susceptibility to interference. The second concerns interactions between VWM representations, both those that produce individuation of representations and those that create an overarching ensemble structure. Finally, the third main topic concerns the use of VWM during visual search and in the learning of repeated configurations in search displays. The work presented here, and other work in the field, points to a rich interplay between representations in VWM but also between VWM and information in long‐term memory. Opportunities for further investigation are highlighted throughout.     

The influence of number line estimation precision and numeracy on risky financial decision making

This study examined whether different aspects of mathematical proficiency influence one's ability to make adaptive financial decisions. “Numeracy” refers to the ability to process numerical and probabilistic information and is commonly reported as an important factor which contributes to financial decision‐making ability. The precision of mental number representation (MNR), measured with the number line estimation (NLE) task has been reported to be another critical factor. This study aimed to examine the contribution of these mathematical proficiencies while controlling for the influence of fluid intelligence, math anxiety and personality factors. In our decision‐making task, participants chose between two options offering probabilistic monetary gain or loss. Sensitivity to expected value was measured as an index for the ability to discriminate between optimal versus suboptimal options. Partial correlation and hierarchical regression analyses revealed that NLE precision better explained EV sensitivity compared to numeracy, after controlling for all covariates. These results suggest that individuals with more precise MNR are capable of making more rational financial decisions. We also propose that the measurement of “numeracy,” which is commonly used interchangeably with general mathematical proficiency, should include more diverse aspects of mathematical cognition including basic understanding of number magnitude.