Subjective,physiological, and behavioural responses towards evaluatively conditioned stimuli

Evaluative Conditioning (EC) is commonly defined as the change in liking of a stimulus (conditioned stimulus, CS) due to its pairings with an affective unconditioned stimulus (US). In Experiment 1, we investigated effects of repeated stimulus pairings on affective responses, i.e. valence and arousal ratings, pupil size, and duration estimation. After repeatedly pairing the CSs with affective USs, a consistent pattern of affective responses emerged: The CS_negative was rated as being more negative and more arousing, resulted in larger pupils, and was temporally overestimated compared to the CS_neutral. In Experiment 2, the influence of a mere instruction about the contingency between a CS and US on affective responses was examined. After mere instruction about upcoming pairings between the CS and US, subjective ratings also changed, but there was neither evidence for differential pupillary responses nor for differential temporal processing. The results indicate that EC via pairings or instructions can change the affective responses towards formerly neutral stimuli and introduce pupil size as a physiological measure in EC research. However, Experiment 2 suggests that there might be moderating factors based on the type of EC procedure involved.     

Modeling effects of risk and social distance on vaccination choice

Despite vaccines' consistently demonstrated effectiveness, vaccination rates remain suboptimal due to vaccine refusal. Low vaccination rates are particularly problematic for individuals who cannot be vaccinated for medical reasons and thus must rely on herd immunity (i.e., protection of vulnerable individuals due to the high rate of vaccination of other—often socially distant—individuals). The current study uses a novel decision‐making task to examine how three variables impacted participants' highest acceptable probability of side effects to their children: 1) the severity of the side effects their children experience, 2) the social distance to the beneficiary of the vaccination, and 3) the probability that the vaccine will prevent disease for that designated beneficiary. Participants' willingness to risk potential side effects of vaccination systematically decreased as the 1) effectiveness of the vaccination decreased, 2) the beneficiary of the vaccination became more socially distant, and 3) the severity of side effects increased. These data were well‐described by behavioral economic models used to examine the discounting of other health behavior.     

Integrating Covariates into Social Relations Models: A Plausible Values Approach for Handling Measurement Error in Perceiver and Target Effects

The Social Relations Model (SRM) is a conceptual and analytical approach to examining dyadic behaviors and interpersonal perceptions within groups. In an SRM, the perceiver effect describes a person's tendency to perceive other group members in a certain way, whereas the target effect measures the tendency to be perceived by others in certain ways. In SRM research, it is often of interest to relate these individual SRM effects to covariates. However, the estimated individual SRM effects might not provide a very reliable measure of the true, unobserved SRM effects, resulting in distorted estimates of associations with other variables. This article introduces a plausible values approach that allows users to correct for measurement error when assessing the association of individual SRM effects with other individual difference variables. In the plausible values approach, the latent, true individual SRM effects are treated as missing values and are imputed from an imputation model by applying Bayesian estimation techniques. In a simulation study, the statistical properties of the plausible values approach are compared with two approaches that have been used in previous research. A data example from educational psychology is presented to illustrate how the plausible values approach can be implemented with the software WinBUGS.     

Do Bettors Correctly Perceive Odds? Three Studies of How Bettors Interpret Betting Odds as Probabilistic Information

This paper reports on three studies investigating how accurately bettors (=people who regularly bet on sports events) interpret the probabilistic information implied by betting odds. All studies were based on data collected by web surveys prompting a total of 186 experienced bettors to convert sets of representative odds into frequency judgments. Bayesian statistical methods were used to analyze the data. From the results, the following conclusions were made: (i) On the whole, the bettors produced well‐calibrated judgments, indicating that they have realistic perceptions of odds. (ii) Bettors were unable to consciously adjust judgments for different margins. (iii) Although their interval judgments often covered the estimates implied by the odds, the bettors tended to overestimate the variation of expected profitable bets between months. The results are consistent with prior research showing that people tend to make accurate probability judgments when faced with tasks characterized by constant and clear feedback. Copyright © 2015 John Wiley & Sons, Ltd.     

The estimation of vehicle speed and stopping distance by pedestrians crossing streets in a naturalistic traffic environment

The ability to estimate vehicle speed and stopping distance accurately is important for pedestrians to make safe road crossing decisions. In this study, a field experiment in a naturalistic traffic environment was conducted to measure pedestrians’ estimation of vehicle speed and stopping distance when they are crossing streets. Forty-four participants (18–45 years old) reported their estimation on 1043 vehicles, and the corresponding actual vehicle speed and stopping distance were recorded. In the speed estimation task, pedestrians’ performances change in different actual speed levels and different weather conditions. In sunny conditions, pedestrians tended to underestimate actual vehicle speeds that were higher than 40 km/h but were able to accurately estimate speeds that were lower than 40 km/h. In rainy conditions, pedestrians tended to underestimate actual vehicle speeds that were higher than 45 km/h but were able to accurately estimate speeds ranging from 35 km/h to 45 km/h. In stopping distance estimation task, the accurate estimation interval ranged from 60 km/h to 65 km/h, and pedestrians generally underestimated the stopping distance when vehicles were travelling over 65 km/h. The results show that pedestrians have accurate estimation intervals that vary by weather conditions. When the speed of the oncoming vehicle exceeded the upper bound of the accurate interval, pedestrians were more likely to underestimate the vehicle speed, increasing their risk of incorrectly deciding to cross when it is not safe to do so.     

The effects of a high-probability instruction sequence and response-independent reinforcer delivery on child compliance

We compared the effects of a high-probability (high-p) instruction sequence and a fixed-time (FT) schedule of reinforcement on the compliance of 2 typically developing children. A multielement experimental design with a reversal component was implemented according to a multiple baseline across participants arrangement. Both the high-p and FT conditions resulted in increased compliance for both participants during the multielement sessions. These results suggest that it may be possible to increase compliance without a response requirement of the type arranged in the high-p instruction sequence.     

Notes on discounting

In general, if a variable can be expressed as a function of its own maximum value, that function may be called a discount function. Delay discounting and probability discounting are commonly studied in psychology, but memory, matching, and economic utility also may be viewed as discounting processes. When they are so viewed, the discount function obtained is hyperbolic in form. In some cases the effective discounting variable is proportional to the physical variable on which it is based. For example, in delay discounting, the physical variable, delay (D), may enter into the hyperbolic equation as kD. In many cases, however, the discounting data are not well described with a single-parameter discount function. A much better fit is obtained when the effective variable is a power function of the physical variable (kDS in the case of delay discounting). This power-function form fits the data of delay, probability, and memory discounting as well as other two-parameter discount functions and is consistent with both the generalized matching law and maximization of a constant-elasticity-of-substitution utility     

Estimation and visualization of confusability matrices from adaptive measurement data

We present a simple but effective method based on Luce’s choice axiom [Luce, R.D. (1959). Individual choice behavior: A theoretical analysis. New York: John Wiley & Sons] for consistent estimation of the pairwise confusabilities of items in a multiple-choice recognition task with arbitrarily chosen choice-sets. The method combines the exact (non-asymptotic) Bayesian way of assessing uncertainty with the unbiasedness emphasized in the classical frequentist approach.We apply the method to data collected using an adaptive computer game designed for prevention of reading disability. A player’s estimated confusability of phonemes (or more accurately, phoneme-grapheme connections) and larger units of language is visualized in an easily understood way with color cues and explicit indication of the accuracy of the estimates. Visualization of learning-related changes in the player’s performance is considered.The empirical validity of the choice axiom is evaluated using the game data itself. The axiom appears to hold reasonably well although a small systematic violation is observable for the smallest choice-set sizes.     

A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.