Happy to See Me,Aren’t You,Sally? Signal Detection Analysis of Emotion Detection in Briefly Presented Male and Female Faces

Using signal detection methods, possible effects of emotion type (happy, angry), gender of the stimulus face, and gender of the participant on the detection and response bias of emotion in briefly presented faces were investigated. Fifty-seven participants (28 men, 29 women) viewed 90 briefly presented faces (30 happy, 30 angry, and 30 neutral, each with 15 male and 15 female faces) answering yes if the face was perceived as emotional and no if it was not perceived as emotional. Sensitivity [d', z(hit rate) minus z(false alarm rate)] and response bias (β, likelihood ratio of "signal plus noise" vs. "noise") were measured for each face combination for each presentation time (6.25, 12.50, 18.75, 25.00, 31.25 ms). The d' values were higher for happy than for angry faces and higher for angry-male than for angry-female faces, and there were no effects of gender-of-participant. Results also suggest a greater tendency for participants to judge happy-female faces as emotional, as shown by lower β values for these faces as compared to the other emotion-gender combinations. This happy-female response bias suggests, at least, a partial explanation to happy-superiority effects in studies where performance is only measured as percent correct responses, and, in general, that women are expected to be happy.     

Modeling the effect of reward amount on probability discounting

The present study with college students examined the effect of amount on the discounting of probabilistic monetary rewards. A hyperboloid function accurately described the discounting of hypothetical rewards ranging in amount from $20 to $10,000,000. The degree of discounting increased continuously with amount of probabilistic reward. This effect of amount was not due to changes in the rate parameter of the discounting function, but rather was due to increases in the exponent. These results stand in contrast to those observed with the discounting of delayed monetary rewards, in which the degree of discounting decreases with reward amount due to amount-dependent decreases in the rate parameter. Taken together, this pattern of results suggests that delay and probability discounting reflect different underlying mechanisms. That is, the fact that the exponent in the delay discounting function is independent of amount is consistent with a psychophysical scaling interpretation, whereas the finding that the exponent of the probability-discounting function is amount-dependent is inconsistent with such an interpretation. Instead, the present results are consistent with the idea that the probability-discounting function is itself the product of a value function and a weighting function. This idea was first suggested by Kahneman and Tversky (1979), although their prospect theory does not predict amount effects like those observed. The effect of amount on probability discounting was parsimoniously incorporated into our hyperboloid discounting function by assuming that the exponent was proportional to the amount raised to a power. The amount-dependent exponent of the probability-discounting function may be viewed as reflecting the effect of amount on the weighting of the probability with which the reward will be received.     

Rational Choice and Moral Decision Making in Research

University psychology and sociology researchers rated the likelihood they would engage in misconduct as described in 9 research scenarios, while also making moral judgments and rating the likelihood of discovery and sanctions. Multiple regression revealed significant effects across various scenarios for moral judgment as well as shame and embarrassment on reducing misconduct. The effects on misconduct of the perceived probability of sanctions were conditioned on moral judgments in some scenarios. The results have implications for how universities address the prevention, detection, and sanctioning of research misconduct.     

What a feeling! Touching sexually laden stimuli makes women seek rewards

We argue that women's previously documented unresponsiveness to sexual primes when making economic decisions may be a consequence of the specific types of primes that have been used (i.e., visual primes). In three studies we show that presenting women with tactile sexual cues does influence their decisions about economic rewards. Similar to the effect found in men, the first study demonstrates that touching a pair of boxer shorts leads to a craving for monetary rewards in women. In the second study it is shown that touching a pair of boxers makes women less loss averse for both money and food. The third study explicitly focuses on the relative effectiveness of tactile versus visual sexual cues in altering women's economic decisions, and reveals that women's willingness-to-pay for economic rewards increases only when the sexual cue is tactile. We suggest that touching (vs. seeing) sexually laden stimuli prompts pre-programmed consummatory Pavlovian responses that promote approaching economic rewards.     

情绪Stroop效应：威胁驱动还是反应偏差？

为了考查情绪Stroop效应的产生机制是威胁驱动还是反应偏差,本研究进行了两个实验.实验一用一般负性情绪词语和中性词语作为实验材料,实验二用威胁性的情绪图片和中性图片作为实验材料.每个实验均要求一组被试做按键反应,另一组被试做摇杆反应.结果发现,当实验材料为词语时,未出现情绪Stroop效应;当实验材料为威胁性图片时,无论是趋向性反应方式还是回避性反应方式,都出现了情绪Stroop效应.本研究结果支持威胁驱动机制.     

A method in modeling interactive pedestrian crossing and driver yielding decisions during their interactions at intersections

Investigating pedestrian crossing and driver yielding decisions should be an important focus considering the high risks of pedestrians in exposed to motorized traffic. Limitations, however, exist in previous studies – variables considered previously have been limited; how their behavior affect each other (defined as interactive impacts) were not sufficiently considered. This paper aims to provide a methodological approach for pedestrian crossing and driver yielding decisions during their interactions, considering of different variable types including interactive impact variables, traffic condition variables, road design variables, and environment variables. A Distance-Velocity (DV) framework proposed in an earlier study is introduced for definitions and concepts in studying pedestrian-vehicle interactions. Logistic regression, support vector machines, neural networks and random forests, are introduced as candidate models. A case study involving six crosswalk locations is conducted, focusing on interactions between pedestrians and right-turn vehicles. The proposed methodological approach is applied, with the performance of the four machine learning methods compared in terms of model generalization and confusion matrix. The model with the best performance is further compared to the typical gap-based model. Results show that random forest and logistic regression models performed the best in modeling pedestrian crossing and driver yielding decisions respective, in terms of model generalization. Besides, the DV-based modeling method (average accuracy of over 90% for pedestrians and 80% for drivers) outperformed the traditional gap-based method in all test seeds. As a key finding, interactive impacts from each other (the pedestrian and the driver) act as a key contributing variable on their decisions.     

No expectation bias in elite-level cricket umpires’ leg-before-wicket (LBW) decisions according to batting order position

ObjectivesPrevious studies have shown that sport officials’ decisions can be impacted by biases associated with expectations. The aim of this study was to determine whether elite cricket umpires’ decisions are also influenced by expectations associated with batter skill.MethodsLBW decisions (n = 5578) from actual elite level cricket matches in Australia between 2009 and 2016 were analysed in a multi-level binomial logistic regression paradigm. In our first model, we predicted the likelihood that an umpire will answer ‘out’ for batters in the top order (1–4), middle order (5–7), and low order (8-11). In our second model, we controlled for the correctness of a decision.ResultsUmpires were more likely to respond ‘out’ as the batting order progressed, however this did not appear to be due to biased decision-making. Instead, as batting order progressed, batters were more likely to actually be ‘out’.ConclusionsCricket umpires do not seem to be impacted by expectation bias associated with batting order. This study highlights the importance of controlling for the correctness of a decision when exploring bias in sport officials’ decisions.     

计算机化分类测验终止规则的类别、特点及应用

计算机化分类测验(Computerized Classification Testing, CCT)能够高效地对被试进行分类, 已广泛应用于合格性测验及临床心理学中。作为CCT的重要组成部分, 终止规则决定测验何时停止以及将被试最终划分到何种类别, 因此直接影响测验效率及分类准确率。已有的三大类终止规则(似然比规则、贝叶斯决策理论规则及置信区间规则)的核心思想分别为构造假设检验、设计损失函数和比较置信区间相对位置。同时, 在不同测验情境下, CCT的终止规则发展出不同的具体形式。未来研究可以继续开发贝叶斯规则、考虑多维多类别情境以及结合作答时间和机器学习算法。针对测验实际需求, 三类终止规则在合格性测验上均有应用潜力, 而临床问卷则倾向应用贝叶斯规则。