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Recent advances in computer vision have opened the door for scalable eye tracking using only a webcam. Such solutions are particularly useful for online educational technologies, in which a goal is to respond adaptively to students' ongoing experiences. We used WebGazer, a webcam-based eye-tracker, to automatically detect covert cognitive states during an online reading-comprehension task related to task-unrelated thought and comprehension. We present data from two studies using different populations: (1) a relatively homogenous sample of university students (N = 105), and (2) a more diverse sample from Prolific (N = 173, with < 20% White participants). Across both studies, the webcam-based eye-tracker provided sufficiently accurate and precise gaze measurements to predict both task-unrelated thought and reading comprehension from a single calibration. We also present initial evidence of predictive validity, including a positive correlation between predicted rates of task-unrelated thought and comprehension scores. Finally, we present slicing analyses to determine how performance changed under certain conditions (lighting, glasses, etc.) and generalizability of the results across the two datasets (e.g., training on the data Study 1 and testing on data from Study 2, and vice versa). We conclude by discussing results in the context of remote research and learning technologies. [ABSTRACT FROM AUTHOR]

What differentiates the joke writing strategy employed by professional comedians from non-comedians? Previous MRI work found that professional comedians relied to a greater extent on "bottom-up processes," i.e., associations driven by the prompt stimuli themselves, while controls relied more on prefrontal lobe directed, "top-down" processes. In the present work, professional improv comedians and controls generated humorous captions to cartoons while their eye movements were tracked. Participants' visual fixation patterns were compared to predictions of the saliency model (Harel et al. in Adv Neural Inf Process Syst 19:545–552, 2007)—a computer model for identifying the most salient locations in an image based on visual features. Captions generated by the participants were rated for funniness by independent raters. Relative to controls, professional comedians' gaze was driven to a greater extent by the cartoons' salient visual features. For all participants, captions' funniness positively correlated with visual attention to salient cartoon features. Results suggest that comedic expertise is associated with increased reliance on bottom-up, stimulus-driven creativity, and that a bottom-up strategy results, on average, in funnier captions whether employed by comedians or controls. The cognitive processes underlying successful comedic creativity appear to adhere to the old comedians' adage "pay attention to the elephant in the room." [ABSTRACT FROM AUTHOR]

A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is widely used for this purpose, nearly all implementations of the log-rank test rely on an asymptotic approximation that is not appropriate in many genomics applications. This is because: the two populations determined by a genetic variant may have very different sizes; and the evaluation of many possible variants demands highly accurate computation of very small p-values. We demonstrate this problem for cancer genomics data where the standard log-rank test leads to many false positive associations between somatic mutations and survival time. We develop and analyze a novel algorithm, Exact Log-rank Test (ExaLT), that accurately computes the p-value of the log-rank statistic under an exact distribution that is appropriate for any size populations. We demonstrate the advantages of ExaLT on data from published cancer genomics studies, finding significant differences from the reported p-values. We analyze somatic mutations in six cancer types from The Cancer Genome Atlas (TCGA), finding mutations with known association to survival as well as several novel associations. In contrast, standard implementations of the log-rank test report dozens-hundreds of likely false positive associations as more significant than these known associations. [ABSTRACT FROM AUTHOR]

Cancers exhibit extensive mutational heterogeneity, and the resulting long-tail phenomenon complicates the discovery of genes and pathways that are significantly mutated in cancer. We perform a pan-cancer analysis of mutated networks in 3,281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a new algorithm to find mutated subnetworks that overcomes the limitations of existing single-gene, pathway and network approaches. We identify 16 significantly mutated subnetworks that comprise well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer, including cohesin, condensin and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, pan-cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types. [ABSTRACT FROM AUTHOR]