The role of attitudes in perceptions of bicycle facilities: A latent-class regression approach

While many professionals support bicycling for transportation to reduce traffic congestion and improve public health, many potential cyclists see the risks from traffic injuries as a major barrier. The purpose of this paper is to investigate the impact of bicycle facilities on perceptions of bikeability, and how these perceptions vary among different groups. Data comes from surveys (N = 2157) deployed in ten study neighborhoods as part of two research projects. Respondents were asked to rate a series of images of hypothetical roadways in terms of perceived comfort, perceived safety, and willingness to try bicycling. Latent-class regression was used to model these responses, with bicycle facility type and roadway characteristics as explanatory variables and sociodemographics and attitudinal factors (car preference, bike enjoyment, risk tolerance, and anti-exercise) as covariates for class membership. A recurring theme among the presented solutions is the emergence of two pro-bicycle classes - risk-embracing and risk-cautious - together with a pro-car class. Results indicate that the impact of protected bicycle facilities on perceptions are much higher for the pro-bike/risk-cautious group. The major implication of these findings is that there likely exists a sizeable segment of the population that, although perhaps just as interested in bicycling as the typical pro-bicycle class, is more cautious in their perceptions of comfort and safety. Those belonging to this subgroup generally view minimal facilities as much more unsafe and uncomfortable than their risk-accepting counterparts, but have comparable perceptions regarding high-quality protected bicycle facilities.     

Fatigue risk management based on self-reported fatigue: Expanding a biomathematical model of fatigue-related performance deficits to also predict subjective sleepiness

Biomathematical models of fatigue can be used to predict neurobehavioral deficits during sleep/wake or work/rest schedules. Current models make predictions for objective performance deficits and/or subjective sleepiness, but known differences in the temporal dynamics of objective versus subjective outcomes have not been addressed. We expanded a biomathematical model of fatigue previously developed to predict objective performance deficits as measured on the Psychomotor Vigilance Test (PVT) to also predict subjective sleepiness as self-reported on the Karolinska Sleepiness Scale (KSS). Four model parameters were re-estimated to capture the distinct dynamics of the KSS and account for the scale difference between KSS and PVT. Two separate ensembles of datasets – drawn from laboratory studies of sleep deprivation, sleep restriction, simulated night work, napping, and recovery sleep – were used for calibration and subsequent validation of the model for subjective sleepiness. The expanded model was found to exhibit high prediction accuracy for subjective sleepiness, while retaining high prediction accuracy for objective performance deficits. Application of the validated model to an example scenario based on cargo aviation operations revealed divergence between predictions for objective and subjective outcomes, with subjective sleepiness substantially underestimating accumulating objective impairment, which has important real-world implications. In safety-sensitive operations such as commercial aviation, where self-ratings of sleepiness are used as part of fatigue risk management, the systematic differences in the temporal dynamics of objective versus subjective measures of functional impairment point to a potentially significant risk evaluation sensitivity gap. The expanded biomathematical model of fatigue presented here provides a useful quantitative tool to bridge this previously unrecognized gap.     

Seeing Like a Geologist: Bayesian Use of Expert Categories in Location Memory

Memory for spatial location is typically biased, with errors trending toward the center of a surrounding region. According to the category adjustment model (CAM), this bias reflects the optimal, Bayesian combination of fine‐grained and categorical representations of a location. However, there is disagreement about whether categories are malleable. For instance, can categories be redefined based on expert‐level conceptual knowledge? Furthermore, if expert knowledge is used, does it dominate other information sources, or is it used adaptively so as to minimize overall error, as predicted by a Bayesian framework? We address these questions using images of geological interest. The participants were experts in structural geology, organic chemistry, or English literature. Our data indicate that expertise‐based categories influence estimates of location memory—particularly when these categories better constrain errors than alternative (“novice”) categories. Results are discussed with respect to the CAM.     

Incremental Bayesian Category Learning From Natural Language

Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words (e.g., chair is a member of the furniture category). We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: (a) the acquisition of features that discriminate among categories, and (b) the grouping of concepts into categories based on those features. Our model learns categories incrementally using particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference that sequentially integrates newly observed data and can be viewed as a plausible mechanism for human learning. Experimental results show that our incremental learner obtains meaningful categories which yield a closer fit to behavioral data compared to related models while at the same time acquiring features which characterize the learned categories. (An earlier version of this work was published in Frermann and Lapata 2014 .)     

3-D bone models to improve treatment initiation among patients with osteoporosis: A randomised controlled pilot trial

Objective: To investigate the efficacy of 3-D printed bone models as a tool to facilitate initiation of bisphosphonate treatment among individuals who were newly diagnosed with osteoporosis.

Design: Fifty eight participants with estimated fracture risk above that at which guidelines recommend pharmacological intervention were randomised to receive either a standard physician interview or an interview augmented by the presentation of 3-D bone models.

Main outcome measures: Participants’ beliefs about osteoporosis and bisphosphonate treatment, initiation of bisphosphonate therapy assessed at two months using self-report and pharmacy dispensing data.

Results: Individuals in the 3-D bone model intervention condition were more emotionally affected by osteoporosis immediately after the interview (p = .04) and reported a greater understanding of osteoporosis at follow-up (p = .04), than the control group. While a greater proportion of the intervention group initiated an oral bisphosphonate regimen (alendronate) (52%) in comparison with the control group (21%), the overall initiation of medication for osteoporosis, including infusion (zoledronate), did not differ significantly (intervention group 62%, control group 45%, p = .19).

Conclusion: The presentation of 3-D bone models during a medical consultation can modify cognitive and emotional representations relevant to treatment initiation among people with osteoporosis and might facilitate commencement of bisphosphonate treatment.     

Eye Movements in Strategic Choice

In risky and other multiattribute choices, the process of choosing is well described by random walk or drift diffusion models in which evidence is accumulated over time to threshold. In strategic choices, level‐k and cognitive hierarchy models have been offered as accounts of the choice process, in which people simulate the choice processes of their opponents or partners. We recorded the eye movements in 2 × 2 symmetric games including dominance‐solvable games like prisoner's dilemma and asymmetric coordination games like stag hunt and hawk–dove. The evidence was most consistent with the accumulation of payoff differences over time: we found longer duration choices with more fixations when payoffs differences were more finely balanced, an emerging bias to gaze more at the payoffs for the action ultimately chosen, and that a simple count of transitions between payoffs—whether or not the comparison is strategically informative—was strongly associated with the final choice. The accumulator models do account for these strategic choice process measures, but the level‐k and cognitive hierarchy models do not. © 2015 The Authors. Journal of Behavioral Decision Making published by John Wiley & Sons Ltd.     

First Among Equals: Hybridization of Cognitive Diagnostic Assessment and Evidence-Centered Game Design

The objective of the present article is to explore differences and similarities between cognitive diagnostic assessment (CDA) and evidence-centered game design (ECgD) in the service of intentional hybridization. Although some testing specialists might argue that both are essentially the same given their origins in principled assessment design and equivalency of measurement models, this view misses differences in their focus and operationalization. Given the strengths of both CDA and ECgD, there is motivation to consider ways in which each can deliberately inform the other. The intentional hybridization of CDA and ECgD has, at least in principle, significant advantages to produce a stronger offspring than either parent alone. The article includes four sections: (1) conceptual differences between CDA and ECgD, (2) conceptual similarities between CDA and ECgD, (3) challenges with CDA and ECgD, including narrowness of cognitive models, fidelity with learning, ocean of data, sensitivity to diverse learners, reliance on multidimensional psychometric models, and how hybridization may help, and (4) implications for educational assessment in the twenty-first century around the globe.     

风险决策的神经机制:基于啮齿类动物研究

风险决策是在不同选项的结果确定、并且结果出现的概率已知情况下的决策。啮齿类风险决策模型研究发现前额皮层-杏仁核-伏隔核神经环路联系是决定风险决策的选择倾向的关键。前额皮层中的眶额皮层和内侧前额皮层参与风险决策的策略形成和策略转换有关;而皮层下核团中的杏仁核、腹侧纹状体的伏隔核等结构参与策略保持、价值判断,影响决策偏向和行动强度;眶额皮层、伏隔核神经元和多巴胺神经元编码风险决策过程中的概率、风险等因素;此外,多巴胺等单胺类神经递质及受体在风险决策中有复杂的作用。     

Measuring Creativity with Planned Missing Data

This paper introduces a method for the assessment of creativity that relies on creativity tasks, a subjective evaluation procedure, and a planned missing data design that offers a drastic reduction in the overall implementation costs (administration time and scoring procedure). This method was tested on a sample of 149 people, using three creativity tasks as a basis. Participants were instructed to produce several ideas in each task and then to select what they considered to be their best two ideas (i.e., “Top 2” procedure; Silvia, Winterstein, Willse, Barona, et al., Psychology of Aesthetics, Creativity, and the Arts, 2 , 2008 and 68). These ideas were then evaluated by a panel of peers and experts. Creativity ratings were analyzed with structural equations; measurement models were estimated for each task and correlations between factor-scores across the three tasks were investigated. Further insights regarding validity are provided through systematic investigation of the relationship between fluency scores, creativity ratings, intelligence tasks, self-reported idea generation abilities, and creative activities and achievements. Overall, the results support the viability of this new approach, providing evidence of convergent and discriminant validity. They are discussed in relation to past research and avenues for further extension are proposed.