Physiological features have also shown considerable promise as reliable detectors of TUT in reading tasks. (Faber et al., 2018) used eye gaze to measure TUT during computerized reading and found that the proportional distributions of TUT predicted by the model and the self-reported TUT were similar and were significantly correlated, with a Pearson’s r value of 0.40. Many researchers have utilized gaze behaviors for detecting TUT during reading, as eye movements provide a reliable window into cognitive processing (Rayner, 1998 Reichle et al., 1998). Reading tasks have been the most popular domain for real-time TUT detection to date, likely because of the association between TUTs and comprehension outcomes (Smallwood, 2011 Smallwood et al., 2007). This is also the approach we use in the current work. All the work we review below has used this cross-validation approach, where the training and testing sets have been kept explicitly separate. Generalizability is commonly assessed using a cross-validation technique where the algorithm is trained on a set of people and then tested on a new person (or people) that it did not encounter in the testing phase. That is, a “good” detector is both accurate, and it is generalizable to people it has never encountered before. off task?) without needing on any prior information about the person. The goal of any “good” TUT detector is to provide a real-time assessment about one’s cognitive state (i.e., are they on vs. 1.1.1 TUTĭetecting TUT in real-time is a growing area of interest, presumably for both measurement purposes and due to the link between TUT and task performance. Second, we turn to a complementary body of work that has developed detectors of related cognitive-affective states in the context of language-relevant tasks (i.e., writing). First, we describe previous work on the development of TUT detectors-all of which have existed outside of conversational contexts thus far. 1.1 Related workīelow we review two relevant bodies of work. Here, we take the first steps toward this goal, which is to identify effective low-cost methods for TUT detection in interactive contexts. For example, knowing when and how often someone was off task in conversational contexts may eventually help facilitate more effective conversations, particularly in remote education and health contexts. Accurate real-time detection offers unique opportunities for interventions or personalized feedback (S. TUT is currently almost exclusively measured via self-reports using online experience sampling or ecological momentary assessments, both of which necessarily interrupt ongoing tasks to obtain real-time assessments. Cost-effective, stealth assessments of TUT in real-time offer novel ways to measure TUT without interruptions or task demands. The current work takes a step in this direction by presenting the first real-time detector of TUT in the context of computer-mediated conversations using keystroke analyses. Whether positive or negative, the frequency and consequences of TUT highlight the need for reliable methods to detect its occurrence-especially using low cost, unobtrusive metrics in everyday life tasks. TUT is not only remarkably frequent, it also has functional implications in our everyday lives, including affective (Killingsworth & Gilbert, 2010 Mills et al., 2021), educational (D’Mello & Mills, 2021 Smallwood et al., 2007), and clinical correlates (Arch et al., 2021), (Marchetti et al., 2016). TUT, often used synonymously with the term mind-wandering (Mills et al., 2018), occurs in virtually every scenario of our lives while we are awake (Killingsworth & Gilbert, 2010)-whether it be driving, reading or watching videos. These are both examples of what is known as task-unrelated thought (TUT), which is defined here as an internal mental state that is unrelated to one’s current task (Smallwood & Schooler, 2015). You may have started thinking about what you needed to do later that day, or something they said may have triggered you to think about a memory from the past. Despite your intentions to stay engaged with the conversation, your mind may have-and likely did-drift away from the conversation at times. Imagine a recent conversation with a friend.
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