Psychocentricity and participant profiles: implications for lexical processing among multilinguals

Lexical processing among bilinguals is often affected by complex patterns of individual experience. In this paper we discuss the psychocentric perspective on language representation and processing, which highlights the centrality of individual experience in psycholinguistic experimentation. We discuss applications to the investigation of lexical processing among multilinguals and explore the advantages of using high-density experiments with multilinguals. High density experiments are designed to co-index measures of lexical perception and production, as well as participant profiles. We discuss the challenges associated with the characterization of participant profiles and present a new data visualization technique, that we term Facial Profiles. This technique is based on Chernoff faces developed over 40 years ago. The Facial Profile technique seeks to overcome some of the challenges associated with the use of Chernoff faces, while maintaining the core insight that recoding multivariate data as facial features can engage the human face recognition system and thus enhance our ability to detect and interpret patterns within multivariate datasets. We demonstrate that Facial Profiles can code participant characteristics in lexical processing studies by recoding variables such as reading ability, speaking ability, and listening ability into iconically-related relative sizes of eye, mouth, and ear, respectively. The balance of ability in bilinguals can be captured by creating composite facial profiles or Janus Facial Profiles. We demonstrate the use of Facial Profiles and Janus Facial Profiles in the characterization of participant effects in the study of lexical perception and production.

Social competence following mild head injury and moderate traumatic brain injury: Investigating the neuropsychological relationships between arousal, social decision-making and depression

Client-directed long-term rehabilitative goals and life satisfaction following head injury emphasize the importance of social inclusion, rather than cognitive or physical, outcomes. However, very little research has explored the socio-emotional factors that pose as barriers to social reintegration following injury. This study investigates social barriers following head injury (i.e., decision-making - Iowa Gambling Task [IGT] and mood – depression) and possible amelioration of those challenges (through treatment) in both highly functioning university students with and without mild head injury (MHI) and in individuals with moderate traumatic brain injury (TBI). An arousal manipulation using emotionally evocative stimuli was introduced to manipulate the subject’s physiological arousal state. Seventy-five university students (37.6% reporting a MHI) and 11 patients with documented moderate TBI were recruited to participate in this quasi-experimental study. Those with head injury were found to be physiologically underaroused (on measures of electrodermal activation [EDA] and pulse) and were less sensitive to the negative effects of punishment (i.e., losses) in the gambling task than those without head injury, with greater impairment being observed for the moderate TBI group. The arousal manipulation, while effective, was not able to maintain a higher state of arousal in the injury groups across trials (i.e., their arousal state returned to pre-manipulation levels more quickly than their non-injured cohort), and, subsequently, a performance improvement was not observed on the IGT. Lastly, head injury was found to contribute to the relationship between IGT performance and depressive symptom acknowledgment and mood status in persons with head injury. This study indicates the possible important role of physiological arousal on socio- emotional behaviours (decision-making, mood) in persons with even mild, non-complicated head injuries and across the injury severity continuum.

Educational Technology Decision-Making: A Case Study on Technology Acquisition for 746,000 Ontario Students

Very little research has examined K–12 educational technology decision-making in Canada. This collective case study explores the technology procurement process in Ontario’s publicly funded school districts to determine if it is informed by the relevant research, grounded in best practices, and enhances student learning. Using a qualitative approach, 10 senior leaders (i.e., chief information officers, superintendents, etc.) were interviewed. A combination of open-ended and closed-ended questions were used to reveal the most important factors driving technology acquisition, research support, governance procedures, data use, and assessment and return on investment (ROI) measures utilized by school districts in their implementation of educational technology. After participants were interviewed, the data were transcribed, member checked, and then submitted to “Computer-assisted NCT analysis” (Friese, 2014) using ATLAS.ti. The findings show that senior leaders are making acquisitions that are not aligned with current scholarship and not with student learning as the focus. It was also determined that districts struggle to use data-driven decision-making to support the governance of educational technology spending. Finally, the results showed that districts do not have effective assessment measures in place to determine the efficacy or ROI of a purchased technology. Although data are limited to the responses of 10 senior leaders, findings represent the technology leadership for approximately 746,000 Ontario students. The study is meant to serve as an informative resource for senior leaders and presents strategic and research-validated approaches to technology procurement. Further, the study has the potential to refine technology decision-making, policies, and practices in K–12 education.

GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct - the minimal discerning set of attributes - is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi--objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.

Tribunals and Guidelines: Exploring the Relationship Between Fairness and Legitimacy in Administrative Decision-Making

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Analogy in Decision-Making

In the context of decision making under uncertainty, we formalize the concept of analogy: an analogy between two decision problems is a mapping that transforms one problem into the other while preserving the problem's structure. We identify the basic structure of a decision problem, and provide a representation of the mappings that pre- serve this structure. We then consider decision makers who use multiple analogies. Our main results are a representation theorem for "aggregators" of analogies satisfying certain minimal requirements, and the identification of preferences emerging from analogical reasoning. We show that a large variety of multiple-prior preferences can be thought of as emerging from analogical reasoning.