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1. Domain-specific experience determines individual differences in holistic processing

   https://doi.org/10.1037/xge0000628  

   Chua, K.-W.; Gauthier, I. (January 2020, Journal of experimental psychology)

   null (Ed.)

   Holistic processing refers to the processing of objects as wholes rather than in a piecemeal, part-based fashion. Despite a suggested link between expertise and holistic processing, the role of experience in determining holistic processing of both faces and objects has been questioned. Here, we combine an individual differences approach with an experimental training study and parametrically manipulate experience with novel objects to examine the determinants of holistic processing. We also measure object-recognition ability. Our results show that although domain-general visual ability is a predictor of the ability to match object parts, it is the amount of experience people have individuating objects of a category that determines the extent to which they process new objects of this category in a holistic manner. This work highlights the benefits of dissociating the influences of domain-general ability and domain-specific experience, typically confounded in measures of performance or “expertise.” Our findings are consistent with those in recent work with faces showing that variability specific to experience is a better predictor of domain-specific effects than is variability in performance. We argue that individual differences in holistic processing arise from domain-specific experience and that these effects are related to similar effects of experience on other measures of selective attention. 

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2. Strengthening Children’s Privacy Literacy through Contextual Integrity

   https://doi.org/10.17645/mac.v8i4.3236  

   Kumar, Priya C.; Subramaniam, Mega; Vitak, Jessica; Clegg, Tamara L.; Chetty, Marshini (November 2020, Media and Communication)

   null (Ed.)

   Researchers and policymakers advocate teaching children about digital privacy, but privacy literacy has not been theorized for children. Drawing on interviews with 30 families, including 40 children, we analyze children’s perspectives on password management in three contexts—family life, friendship, and education—and develop a new approach to privacy literacy grounded in Nissenbaum’s contextual integrity framework. Contextual integrity equates privacy with appropriate flows of information, and we show how children’s perceptions of the appropriateness of disclosing a password varied across contexts. We explain why privacy literacy should focus on norms rather than rules and discuss how adults can use learning moments to strengthen children’s privacy literacy. We argue that equipping children to make privacy-related decisions serves them better than instructing them to follow privacy-related rules. 

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3. Classical Coding Problem from Transversal T Gates

   https://doi.org/10.1109/ISIT44484.2020.9174408  

   Rengaswamy, Narayanan; Calderbank, Robert; Newman, Michael; Pfister, Henry D. (June 2020, 2020 IEEE International Symposium on Information Theory (ISIT))

   null (Ed.)

   Universal quantum computation requires the implementation of a logical non-Clifford gate. In this paper, we characterize all stabilizer codes whose code subspaces are preserved under physical T and T † gates. For example, this could enable magic state distillation with non-CSS codes and, thus, provide better parameters than CSS-based protocols. However, among non-degenerate stabilizer codes that support transversal T, we prove that CSS codes are optimal. We also show that triorthogonal codes are, essentially, the only family of CSS codes that realize logical transversal T via physical transversal T. Using our algebraic approach, we reveal new purely-classical coding problems that are intimately related to the realization of logical operations via transversal T. Decreasing monomial codes are also used to construct a code that realizes logical CCZ. Finally, we use Ax's theorem to characterize the logical operation realized on a family of quantum Reed-Muller codes. This result is generalized to finer angle Z-rotations in https://arxiv.org/abs/1910.09333. 

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4. Comparison of Machine Learning Performance Using Analytic and Holistic Coding Approaches Across Constructed Response Assessments Aligned to a Science Learning Progression

   https://doi.org/10.1007/s10956-020-09858-0  

   Jescovitch, Lauren N.; Scott, Emily E.; Cerchiara, Jack A.; Merrill, John; Urban-Lurain, Mark; Doherty, Jennifer H.; Haudek, Kevin C. (September 2020, Journal of Science Education and Technology)

   null (Ed.)

   Abstract We systematically compared two coding approaches to generate training datasets for machine learning (ML): (i) a holistic approach based on learning progression levels and (ii) a dichotomous, analytic approach of multiple concepts in student reasoning, deconstructed from holistic rubrics. We evaluated four constructed response assessment items for undergraduate physiology, each targeting five levels of a developing flux learning progression in an ion context. Human-coded datasets were used to train two ML models: (i) an 8-classification algorithm ensemble implemented in the Constructed Response Classifier (CRC), and (ii) a single classification algorithm implemented in LightSide Researcher’s Workbench. Human coding agreement on approximately 700 student responses per item was high for both approaches with Cohen’s kappas ranging from 0.75 to 0.87 on holistic scoring and from 0.78 to 0.89 on analytic composite scoring. ML model performance varied across items and rubric type. For two items, training sets from both coding approaches produced similarly accurate ML models, with differences in Cohen’s kappa between machine and human scores of 0.002 and 0.041. For the other items, ML models trained with analytic coded responses and used for a composite score, achieved better performance as compared to using holistic scores for training, with increases in Cohen’s kappa of 0.043 and 0.117. These items used a more complex scenario involving movement of two ions. It may be that analytic coding is beneficial to unpacking this additional complexity. 

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5. Simultaneous selection of multiple important single nucleotide polymorphisms in familial genome wide association studies data

   https://doi.org/10.1038/s41598-023-35379-y  

   Majumdar, Subhabrata; Basu, Saonli; McGue, Matt; Chatterjee, Snigdhansu (December 2023, Scientific Reports)

   Abstract We propose a resampling-based fast variable selection technique for detecting relevant single nucleotide polymorphisms (SNP) in a multi-marker mixed effect model. Due to computational complexity, current practice primarily involves testing the effect of one SNP at a time, commonly termed as ‘single SNP association analysis’. Joint modeling of genetic variants within a gene or pathway may have better power to detect associated genetic variants, especially the ones with weak effects. In this paper, we propose a computationally efficient model selection approach—based on the e-values framework—for single SNP detection in families while utilizing information on multiple SNPs simultaneously. To overcome computational bottleneck of traditional model selection methods, our method trains one single model, and utilizes a fast and scalable bootstrap procedure. We illustrate through numerical studies that our proposed method is more effective in detecting SNPs associated with a trait than either single-marker analysis using family data or model selection methods that ignore the familial dependency structure. Further, we perform gene-level analysis in Minnesota Center for Twin and Family Research (MCTFR) dataset using our method to detect several SNPs using this that have been implicated to be associated with alcohol consumption. 

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