Plenary address – A year of living ‘dangerously’: Reflections on risk,trust, trauma and change

This paper examines the role of emotions in the construction and performance of mis/trustful relations; with medical professionals, their technologies, and ultimately, with oneself. Using personal experience of two common conditions as illustrative examples, it questions what it means and feels like to trust, and how, where and by whom such feelings can be enhanced or undermined. It explores some of the ways in which discourses of risk are mobilized and embodied to create a crisis of trust, asking; what kind of selves and emotionalities surface, and what are the health outcomes, when bodies are viewed as ‘at risk’? Visualizing technologies that probe the interior for data play an increasingly prominent role in healthcare, and are typically considered more trustworthy sources of knowledge about the body than anything that might be produced by the tech-free sensing self. However, not all (even ‘physical’) trauma can be seen or quantified, and not all information is equal. The paper reflects on the emotional dissonance that ensues when one's own perceptions and representations are at odds with those of medical experts for whom one is supposed to perform trust. It examines the feeling rules that are broken when we fail to appreciate our treatment at their hands, and asks: What happens when we resist expert author-ity by telling different stories about our embodied selves, ones that make space for emotions in contexts where they are rarely seen to count, and where only what can be measured matters?     

Entitlement and conscientiousness in the prediction of organizational deviance

Responding to Wu and LeBreton’s (2011) call for further study, this paper examines dispositional predictors of organizational deviance. In a sample of 428 participants, self-report data were collected anonymously. Using hierarchical regression, the dispositional variables of entitlement and conscientiousness were similarly strong and statistically significant predictors of organizational deviance. The total variance explained in deviance by these variables and some demographic variables was .31. Additionally, the specificity matching principle suggests that narrow band traits like entitlement are better at predicting narrowly measured behaviors like deviance than are broad band traits like conscientiousness. Using dominance analysis, entitlement was a stronger predictor of organizational deviance than is conscientiousness.     

Out on a limb? On multiple cognitive systems within the octopus nervous system

The idea that there can be only one cognitive system within any single given cognitive organism is an established albeit implicit one within cognitive science and related studies of the mind. The firm foothold of this notion is due largely to the immense corpus of empirical evidence for the correlation of a high level of cognitive sophistication with a centralized nervous system. However, it must be pointed out that these findings are sourced in large part from studies on vertebrates. This paper presents a potential counterexample to the notion that only one cognitive system can be realized within any single genuine cognitive organism. This counterexample is the octopus, an invertebrate with what initially appears to be a paradoxical combination of vertebrate-like cognitive and behavioral capacities and a functionally decentralized nervous system. The extensive relegation of sensorimotor processing and control responsibilities to the peripheral nervous system which controls the arms of the octopus raises principled reasons to believe that the octopus is an organism that may house multiple independent cognitive systems.     

Utilizing driving simulators for persons with multiple sclerosis: A scoping review

ObjectiveThis scoping review synopsized and mapped the evidence on the utility of driving simulators for drivers with Multiple Sclerosis (MS) including participant characteristics (e.g., demographic, clinical) suited to simulator use; and driving scenario environments, maneuvers, and outcomes reflective of driving performance.Data SourcesThe research team searched six databases for English studies on driving simulator performance in drivers with MS.Study SelectionFour reviewers independently screened 976 unique titles, 148 abstracts, and 18 full-text sources for study inclusion or exclusion. Inclusion criteria ensured that studies utilized measures of driving simulator performance in adults 18 years or older with MS.Data ExtractionReviewers independently charted, verified, and achieved consensus on 100% of the data in the extraction table.Data SynthesisThe scoping review included thirteen studies (twelve assessment, one intervention), published between 2001 and 2021. Studies included small samples (N = 11–50) of adults (M age = 36–50 years; 40–91% female), mostly with a relapsing-remitting (53–100%) diagnosis and low to moderate physical disability (Expanded Disability Status Scale score < 6.0). The assessment studies utilized driving scenarios to assess pre-driving (n = 1) or driving abilities (n = 2), responses to hazardous events in suburban (n = 1) or urban environments (n = 4), or the ability to maintain speed or lateral lane positioning during monotonous highway drives (n = 7), with four scenarios also including responses to secondary divided attention tasks. The intervention study utilized driving simulation as a training program to improve driving-related skills.ConclusionsThe evidence on the utility of driving simulators for adults with MS is limited to assessment of driving performance, mostly during monotonous highway drives. Further research should consider exploring how driving simulators can be utilized to assess driving performance in other environments or for intervention. Gender should be examined to support gender diverse populations.     

Analysis of Interactions and Nonlinear Effects with Missing Data: A Factored Regression Modeling Approach Using Maximum Likelihood Estimation

Abstract

When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a multivariate normal distribution, which is also the default in many statistical software packages. This distribution will in general be misspecified if predictors with missing data have nonlinear effects (e.g., x²) or are included in interaction terms (e.g., x·z). In the present article, we introduce a factored regression modeling approach for estimating regression models with missing data that is based on maximum likelihood estimation. In this approach, the model likelihood is factorized into a part that is due to the model of interest and a part that is due to the model for the incomplete predictors. In three simulation studies, we showed that the factored regression modeling approach produced valid estimates of interaction and nonlinear effects in regression models with missing values on categorical or continuous predictor variables under a broad range of conditions. We developed the R package mdmb, which facilitates a user-friendly application of the factored regression modeling approach, and present a real-data example that illustrates the flexibility of the software.     

An evolutionary approach to support decision making with linear decision models

In this paper we consider decision problems that can be described as linear decision models. These models have been traditionally solved using linear programming, fuzzy linear programming, multiple-objective linear programming or ‘what-if’ analysis. Using these approaches, one encounters a number of difficulties. We propose an ‘evolutionary approach’ to overcome these difficulties. In the proposed approach the decision maker does not have to precisely specify the model (i.e. the objective functions, the RHS values, etc.) at the beginning of the solution procedure. In fact, the model evolves as the solution procedure proceeds.     

Holding multiple category representations: The role of age,theory of mind,and rule switching in children’s developing cross-classification abilities

Cross-classification, the ability to categorize multifaceted entities in many ways, is a remarkable cognitive milestone for children. Past work has focused primarily on documenting the timeline for when children reach cross-classification competence. However, it is not well understood what cognitive factors underpin children’s improvements. The current study aimed to examine the contributions of age, theory of mind, and rule switching to children’s cross-classification development. We tested 3- to 5-year-old children (N = 75) using a cross-classification task, the Theory of Mind Task Battery, and the Three-Dimensional Change Card Sort test. The results revealed that age and theory of mind predict children’s cross-classification over and above the effects of rule switching. The results also revealed that advanced-level theory of mind reasoning is a particularly strong predictor of cross-classification development. These findings increase understanding of cross-classification within children’s broader cognitive development.