Effects of contraction bias on the decision process in the macaque prefrontal cortex.

Our representation of magnitudes such as time, distance, and size is not always veridical because it is affected by multiple biases. From a Bayesian perspective, estimation errors are considered to be the result of an optimization mechanism for the behavior in a noisy environment by integrating previous experience with the incoming sensory information. One influence of the distribution of past stimuli on perceptual decisions is represented by the regression toward the mean, a type of contraction bias. Using a spatial discrimination task with 2 stimuli presented sequentially at different distances from the center, we show that this bias is also present in macaques when comparing the magnitude of 2 distances. We found that the contraction of the first stimulus magnitude toward the center of the distribution accounted for some of the changes in performance, even more so than the effect of difficulty related to the ratio between stimulus magnitudes. At the neural level in the dorsolateral prefrontal cortex, the coding of the decision after the presentation of the second stimulus reflected the effect of the contraction bias on the discriminability of the stimuli at the behavioral level.

Misremembering emotion: Inductive category effects for complex emotional stimuli.

Memories of objects are biased toward what is typical of the category to which they belong. Prior research on memory for emotional facial expressions has demonstrated a bias towards an emotional expression prototype (e.g., slightly happy faces are remembered as happier). We investigate an alternate source of bias in memory for emotional expressions - the central tendency bias. The central tendency bias skews reconstruction of a memory trace towards the center of the distribution for a particular attribute. This bias has been attributed to a Bayesian combination of an imprecise memory for a particular object with prior information about its category. Until now, studies examining the central tendency bias have focused on simple stimuli. We extend this work to socially relevant, complex, emotional facial expressions. We morphed facial expressions on a continuum from sad to happy. Different ranges of emotion were used in four experiments in which participants viewed individual expressions and, after a variable delay, reproduced each face by adjusting a morph to match it. Estimates were biased toward the center of the presented stimulus range, and the bias increased at longer memory delays, consistent with the Bayesian prediction that as trace memory loses precision, category knowledge is given more weight. The central tendency effect persisted within and across emotion categories (sad, neutral, and happy). This article expands the scope of work on inductive category effects to memory for complex, emotional stimuli.

A Bayesian perspective on Likert scales and central tendency.

The central tendency bias is a robust finding in data from experiments using Likert scales to elicit responses. The present paper offers a Bayesian perspective on this bias, explaining it as a natural outcome of how participants provide point estimates of probability distributions over the items on a Likert scale. Two studies are reported that support this Bayesian explanation.

Category effects on stimulus estimation: Shifting and skewed frequency distributions-A reexamination.

Duffy, Huttenlocher, Hedges, and Crawford (Psychonomic Bulletin & Review, 17(2), 224-230, 2010) report on experiments where participants estimate the lengths of lines. These studies were designed to test the category adjustment model (CAM), a Bayesian model of judgments. The authors report that their analysis provides evidence consistent with CAM: that there is a bias toward the running mean and not recent stimuli. We reexamine their data. First, we attempt to replicate their analysis, and we obtain different results. Second, we conduct a different statistical analysis. We find significant recency effects, and we identify several specifications where the running mean is not significantly related to judgment. Third, we conduct tests of auxiliary predictions of CAM. We do not find evidence that the bias toward the mean increases with exposure to the distribution. We also do not find that responses longer than the maximum of the distribution or shorter than the minimum become less likely with greater exposure to the distribution. Fourth, we produce a simulated dataset that is consistent with key features of CAM, and our methods correctly identify it as consistent with CAM. We conclude that the Duffy et al. (2010) dataset is not consistent with CAM. We also discuss how conventions in psychology do not sufficiently reduce the likelihood of these mistakes in future research. We hope that the methods that we employ will be used to evaluate other datasets.