Reports of elapsed time: bounding and rounding processes in estimation

The present article concerns the way temporal information is represented in memory and the processes used in estimating when events occurred. In particular, we examine the sources of bias in reports of the time that has elapsed since a target event occurred. We find that reported times are less than actual times. Evidence is presented that this forward bias is not a result of misrepresentation of elapsed time in memory, but rather reflects two factors that arise in constructing reports from inexact information in memory. One factor is subjects' imposition of an upper boundary on reports, reflecting their notion of what would constitute reasonable answers to the question asked. This boundary truncates the distribution of reports, producing forward bias. The other factor is subjects' use of rounded (prototypic) values; these values, although stated in days, actually represent larger temporal categories (e.g., 14, 21, 30, 60 days ago). The distance between rounded values increases as the temporal categories become larger. Because of decreasing precision in memory and this increase in the distance between rounded values, a broader range of values is rounded down than up, thus producing forward bias.     

Development of a diary for use with ambulatory monitoring of mood,activities, and physiological function

This paper describes three studies developed and providing preliminary validation for a pocket-size, self-monitoring diary suitable for use infield research on relationships among psychological states, activities, and physiological events. In the first study, a principal-components analysis of the diary's mood and cognition scales yielded three factors—negative affect, engagement, and predictability. These showed moderate concordance across two samples. In the second study, diary self-ratings of events, moods, and cognitions were shown to be meaningfully correlated with ratings made by spouse observers, providing evidence of concurrent validity of the diary. In the third study, diary ratings made by the same subjects at home, at work, and in other locations differed from each other in expected ways, providing evidence for discriminative validity. Modifications to the diary are suggested, and possible uses and limitations of the instrument are discussed.This research was supported by USUHS protocol RO7233 and a grant from the John D. and Catherine T. MacArthur Foundation. The opinions and assertions expressed herein are those of the authors and should not be construed as reflecting those of the USUHS or the Department of Defense.     

The identity of Guanyin: Religion,convention and subversion

This paper explores the visual representation of Guanyin (Avalokiteshvara) and argues that this, understood in relation to the traditions and devotion to him/her, allows her to be read as a figure of subversive religiosity. Indeed, it is argued that, rather than being a symbol of tradition and convention, a subversive reading of her is not only more potent, but also inherent within her portrayals. To begin, the transformation of him/her from a figure of mainstream Mahayana tradition, in the Pure Land School and texts such as the Lotus Sutra, to the ‘Chinese Goddess of Compassion’ is explored, paying particular attention to the legends of Miaoshan. This is then extended to consider both why he/she may be considered a conventional figure, but also how and why he/she offers the potential for subversive readings centred around ideas of gender, identity, patriarchy and morality.     

Bayesian estimates of autocorrelations in single-case designs

Researchers in the single-case design tradition have debated the size and importance of the observed autocorrelations in those designs. All of the past estimates of the autocorrelation in that literature have taken the observed autocorrelation estimates as the data to be used in the debate. However, estimates of the autocorrelation are subject to great sampling error when the design has a small number of time points, as is typically the situation in single-case designs. Thus, a given observed autocorrelation may greatly over- or underestimate the corresponding population parameter. This article presents Bayesian estimates of the autocorrelation that greatly reduce the role of sampling error, as compared to past estimators. Simpler empirical Bayes estimates are presented first, in order to illustrate the fundamental notions of autocorrelation sampling error and shrinkage, followed by fully Bayesian estimates, and the difference between the two is explained. Scripts to do the analyses are available as supplemental materials. The analyses are illustrated using two examples from the single-case design literature. Bayesian estimation warrants wider use, not only in debates about the size of autocorrelations, but also in statistical methods that require an independent estimate of the autocorrelation to analyze the data.     

Spatial categories and the estimation of location

Four experiments are reported in which people organize a space hierarchically when they estimate particular locations in that space. Earlier work showed that people subdivide circles into quadrants bounded at the vertical and horizontal axes, biasing their estimates towards prototypical diagonal locations within those spatial categories (Psychological Review 98 (1991) 352). In this work Huttenlocher, Hedges, and Duncan showed that the use of such spatial categories can increase the accuracy of estimation of inexactly represented locations. The stimulus locations we examined were uniformly distributed across the circle. In the present study we explore whether variation in the distribution of locations affects how the circle is categorized. Other things being equal, categories that capture high density regions in a stimulus space should contribute most to accuracy of estimation. However, precision of boundaries is also important to accuracy; with imprecise boundaries stimuli may be misclassified, leading to large errors in estimation. We found that people use the same spatial categories regardless of the distribution of the locations. We argue that this spatial organization nevertheless can maximize the accuracy of estimates because vertical and horizontal category boundaries are the most exact, minimizing misclassification of stimuli.     

Adaptation Level as an Explanation of the Peak Shift in Generalization with Movement Stimuli

Two experiments examine the value of adaptation level theory as an explanation of the peak shift phenomenon in stimulus generalization using linear arm movements. The first experiment manipulates the differences in adaptation levels during training by varying the frequency of experience of the training stimulus. Significant shifts in the peaks of generalization gradients were subsequently identified. The peak shifts were in the direction predicted by adaptation level theory. The second experiment manipulates testing adaptation levels by biasing the generalization test movements. Three groups of subjects performed differentially weighted test sequences in which the relative number of short, medium, or long movements was manipulated. Significant peak shifts of generalization gradients were observed in the direction predicted by adaptation level theory.     

Within-category feature correlations and Bayesian adjustment strategies

To the extent that categories inform judgments about items, the accuracy with which categories capture the statistical structure of experience should affect judgment accuracy. The authors argue that representations of feature correlations can serve as Bayesian priors, increasing the accuracy of stimulus estimates by decreasing variability. Participants viewed a series of objects that varied on two dimensions that were either uncorrelated or correlated. They estimated each item by manipulating a response object to make it match the presented stimulus. Subsequent classification and featureinference tasks indicated that the correlation was detected. The pattern of variability in recollections of stimuli suggested that the feature correlation informed estimates as predicted by a Bayesian model of category effects on memory. This work was supported by NIMH Grant 1 F31 MH12072-01A1 to the first author.     

Estimating stimuli from contrasting categories: truncation due to boundaries

In this article, the authors present and test a formal model that holds that people use information about category boundaries in estimating inexactly represented stimuli. Boundaries restrict stimuli that are category members to fall within a particular range. This model posits that people increase the average accuracy of stimulus estimates by integrating fine-grain values with boundary information, eliminating extreme responses. The authors present 4 experiments in which people estimated sizes of squares from 2 adjacent or partially overlapping stimulus sets. When stimuli from the 2 sets were paired in presentation, people formed relative size categories, truncating their estimates at the boundaries of these categories. Truncation at the boundary of separation between the categories led to exaggeration of differences between stimuli that cross categories. Yet truncated values are shown to be more accurate on average than unadjusted values.