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.     

Is prospective memory related to depression and anxiety? A hierarchical MPT modelling approach

Prospective memory (PM) refers to remembering to perform an action in the future. One hundred and twenty-nine students completed a laboratory event–based PM task as well as depression and anxiety questionnaires. The data were analysed with the beta-MPT version of the multinomial processing tree model of event-based PM. Thereby, the prospective and retrospective components of PM were estimated for each participant and were then correlated with depression and anxiety. State anxiety was negatively correlated with the prospective component of PM. Neither depression nor trait anxiety were related to either component of PM.     

Strategic grouping in the spatial span memory task

Spatial short-term memory performance was examined in relation to participants’ strategies. A total of 20 adult participants viewed and reproduced sequences of locations that varied in length (five, six, seven, or eight locations) and spatial separability (a manipulation of the configurations). In trial-by-trial self-reports, participants described five types of strategies. Chunking the spatial sequences into groups of three or four locations was the sole strategy associated with increased accuracy. Participants demonstrated considerable variability in the strategies that they selected, suggesting that cognitive resources are allocated to strategy selection, execution, and monitoring in the spatial span task. Spatially separable sequences were more accurately recalled than nonseparable sequences, independent of strategic grouping, suggesting two levels of grouping in the spatial span task.     

Prior distributions for random choice structures

We study various axioms of discrete probabilistic choice, measuring how restrictive they are, both alone and in the presence of other axioms, given a specific class of prior distributions over a complete collection of finite choice probabilities. We do this by using Monte Carlo simulation to compute, for a range of prior distributions, probabilities that various simple and compound axioms hold. For example, the probability of the triangle inequality is usually many orders of magnitude higher than the probability of random utility. While neither the triangle inequality nor weak stochastic transitivity imply the other, the conditional probability that one holds given the other holds is greater than the marginal probability, for all priors in the class we consider. The reciprocal of the prior probability that an axiom holds is an upper bound on the Bayes factor in favor of a restricted model, in which the axiom holds, against an unrestricted model. The relatively high prior probability of the triangle inequality limits the degree of support that data from a single decision maker can provide in its favor. The much lower probability of random utility implies that the Bayes factor in favor of it can be much higher, for suitable data.     

Hierarchical models for relational event sequences

Interaction within small groups can often be represented as a sequence of events, each event involving a sender and a recipient. Recent methods for modeling network data in continuous time model the rate at which individuals interact conditioned on the previous history of events as well as actor covariates. We present a hierarchical extension for modeling multiple such sequences, facilitating inferences about event-level dynamics and their variation across sequences. The hierarchical approach allows one to share information across sequences in a principled manner—we illustrate the efficacy of such sharing through a set of prediction experiments. After discussing methods for adequacy checking and model selection for this class of models, the method is illustrated with an analysis of high school classroom dynamics from 297 sessions.     

Are the sources of interest the same for everyone? Using multilevel mixture models to explore individual differences in appraisal structures

How does personality influence the relationship between appraisals and emotions? Recent research suggests individual differences in appraisal structures: people may differ in an emotion's appraisal pattern. We explored individual differences in interest's appraisal structure, assessed as the within-person covariance of appraisals with interest. People viewed images of abstract visual art and provided ratings of interest and of interest's appraisals (novelty–complexity and coping potential) for each picture. A multilevel mixture model found two between-person classes that reflected distinct within-person appraisal styles. For people in the larger class (68%), the novelty–complexity appraisal had a stronger effect on interest; for people in the smaller class (32%), the coping potential appraisal had a stronger effect. People in the larger class were significantly higher in appetitive traits related to novelty seeking (e.g., sensation seeking, openness to experience, and trait curiosity), suggesting that the appraisal classes have substantive meaning. We conclude by discussing the value of within-person mixture models for the study of personality and appraisal.     

Overgeneral autobiographical memory predicts changes in depression in a community sample

This study investigated whether overgeneral autobiographical memory (OGM) predicts the course of symptoms of depression and anxiety in a community sample, after 5, 6, 12 and 18 months. Participants (N = 156) completed the Autobiographical Memory Test and the Depression Anxiety Stress Scales-21 (DASS-21) at baseline and were subsequently reassessed using the DASS-21 at four time points over a period of 18 months. Using latent growth curve modelling, we found that OGM was associated with a linear increase in depression. We were unable to detect changes over time in anxiety. OGM may be an important marker to identify people at risk for depression in the future, but more research is needed with anxiety.     

A New Model for Acquiescence at the Interface of Psychometrics and Cognitive Psychology

ABSTRACT

When measuring psychological traits, one has to consider that respondents often show content-unrelated response behavior in answering questionnaires. To disentangle the target trait and two such response styles, extreme responding and midpoint responding, Böckenholt (2012a Böckenholt, U. (2012a). Modeling multiple response processes in judgment and choice. Psychological Methods, 17, 665–678. doi:10.1037/a0028111[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]) developed an item response model based on a latent processing tree structure. We propose a theoretically motivated extension of this model to also measure acquiescence, the tendency to agree with both regular and reversed items. Substantively, our approach builds on multinomial processing tree (MPT) models that are used in cognitive psychology to disentangle qualitatively distinct processes. Accordingly, the new model for response styles assumes a mixture distribution of affirmative responses, which are either determined by the underlying target trait or by acquiescence. In order to estimate the model parameters, we rely on Bayesian hierarchical estimation of MPT models. In simulations, we show that the model provides unbiased estimates of response styles and the target trait, and we compare the new model and Böckenholt’s model in a recovery study. An empirical example from personality psychology is used for illustrative purposes.     

Evaluating Medical Marijuana Dispensary Policies: Spatial Methods for the Study of Environmentally-Based Interventions

In 1996, California was the first state to pass a Compassionate Use Act allowing for the legal use of marijuana for medical purposes. Here we review several current policy and land use environmental interventions designed to limit problems related to the influx of medical marijuana dispensaries across California cities. Then we discuss the special challenges, solutions, and techniques used for studying the effects of these place-based policies. Finally, we present some of the advanced spatial analytic techniques that can be used to evaluate the effectiveness of environmental interventions, such as those related to reducing problems associated with the proliferation of medical marijuana dispensaries. Further, using data from a premise survey of all the dispensaries in Sacramento, this study will examine what characteristics and practices of these dispensaries are related to crime within varying distances from the dispensaries (e.g., 100, 250, 500, and 1,000 feet). We find that some security measures, such as security cameras and having a door man outside, implemented by medical marijuana dispensary owners might be effective at reducing crime within the immediate vicinity of the dispensaries.     

The Mini-IPIP6: Tiny yet highly stable markers of Big Six personality

We assessed the stability of a short-form six-factor personality measure over a one-year period in a large national probability sample (N = 4289). Personality was assessed using the Mini-IPIP6—a short-form measure assessing Extraversion, Agreeableness, Conscientiousness, Neuroticism, Openness to Experience, and Honesty-Humility. Standardized estimates calculated using Bayesian Structural Equation Modelling (BSEM) indicated that all six personality dimensions were extremely stable. An alternative model using Maximum Likelihood estimation, in which residual item variances were associated over repeated assessments, yielded similar findings. These results highlight the stability of personality in the general population, even when assessed using short-form scales. The use of Bayesian models to examine the stability of personality and their application for study of change in specific developmental periods is discussed.