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Abstract Persuasion plays a crucial role in human communication. Yet, convincing someone to change their mind is often challenging. Here, we demonstrate that a subtle linguistic device, generic-you (i.e., “you” that refers to people in general, e.g., “You win some, you lose some”), is associated with successfully shifting people’s pre-existing views in a naturalistic context. Leveraging Large Language Models, we conducted a preregistered study using a large ( $$N_{trials}$$ = 204,120) online debate dataset. Every use of generic-you in an argument was associated with an up to 14% percent increase in the odds of successful persuasion. These findings underscore the need to distinguish between the specific and generic uses of “you” in large-scale linguistic analyses, an aspect that has been overlooked in the literature. The robust association between generic-you and persuasion persisted with the inclusion of various covariates, and above and beyond other pronouns (i.e., specific-you, I or we). However, these findings do not imply causality. In Supplementary Experiment 2, arguments with generic-you (vs. first-person singular pronouns, e.g., I) were rated as more persuasive by open-minded individuals. In Supplementary Experiment 3, generic-you (vs. specific-you) arguments did not differentially predict attitude change. We discuss explanations for these results, including differential mechanisms, boundary conditions, and the possibility that people intuitively draw on generic-you when expressing more persuasive ideas. Together, these findings add to a growing literature on the interpersonal implications of broadening one’s perspective via a subtle shift in language, while motivating future research on contextual and individual differences that may moderate these effects.

Numerous devices have been designed to support the back during lifting tasks. To improve the utility of such devices, this research explores the use of preparatory muscle activity to classify muscle loading and initiate appropriate device activation. The goal of this study was to determine the earliest time window that enabled accurate load classification during a dynamic lifting task.Nine subjects performed thirty symmetrical lifts, split evenly across three weight conditions (no-weight, 10-lbs and 24-lbs), while low-back muscle activity data was collected. Seven descriptive statistics features were extracted from 100 ms windows of data. A multinomial logistic regression (MLR) classifier was trained and tested, employing leave-one subject out cross-validation, to classify lifted load values. Dimensionality reduction was achieved through feature cross-correlation analysis and greedy feedforward selection. The time of full load support by the subject was defined as load-onset.Regions of highest average classification accuracy started at 200 ms before until 200 ms after load-onset with average accuracies ranging from 80% (±10%) to 81% (±7%). The average recall for each class ranged from 69-92%.These inter-subject classification results indicate that preparatory muscle activity can be leveraged to identify the intent to lift a weight up to 100 ms prior to load-onset. The high accuracies shown indicate the potential to utilize intent classification for assistive device applications.Active assistive devices, e.g. exoskeletons, could prevent back injury by off-loading low-back muscles. Early intent classification allows more time for actuators to respond and integrate seamlessly with the user.