There are modes of presentation of a person in thought corresponding to the first and third person pronouns. This paper proposes that there is also thought involving a second person mode of presentation of another, which might be expressed by an utterance involving ‘you’, but need not be expressed linguistically. It suggests that co-operative activity is the locus for such thought. First person thought is distinctive in how it supplies reasons for the subject to act. In co-operative action there is (...) first person plural intending and judging. So there is a way of thinking of another, when openly co-operating with him or her, which plays the distinctive role of giving reason for contribution to the co-operative activity. In slogan form, ‘you’ is ‘we minus I’. The way children learn to use second and third person pronouns is naturally explained on this view. Contrasting less sophisticated kinds of co-operative activity with more sophisticated forms, and considering some issues about common knowledge and commo.. (shrink)

In everyday interactions, humans naturally exhibit behavioral cues, such as gaze and head movements, that signal their intentions while interpreting the behavioral cues of others to predict their intentions. Such intention prediction enables each partner to adapt their behaviors to the intent of others, serving a critical role in joint action where parties work together to achieve a common goal. Among behavioral cues, eye gaze is particularly important in understanding a person's attention and intention. In this work, we seek to (...) quantify how gaze patterns may indicate a person's intention. Our investigation was contextualized in a dyadic sandwich-making scenario in which a “worker” prepared a sandwich by adding ingredients requested by a “customer.” In this context, we investigated the extent to which the customers' gaze cues serve as predictors of which ingredients they intend to request. Predictive features were derived to represent characteristics of the customers' gaze patterns. We developed a support vector machine-based (SVM-based) model that achieved 76% accuracy in predicting the customers' intended requests based solely on gaze features. Moreover, the predictor made correct predictions approximately 1.8 s before the spoken request from the customer. We further analyzed several episodes of interactions from our data to develop a deeper understanding of the scenarios where our predictor succeeded and failed in making correct predictions. These analyses revealed additional gaze patterns that may be leveraged to improve intention prediction. This work highlights gaze cues as a significant resource for understanding human intentions and informs the design of real-time recognizers of user intention for intelligent systems, such as assistive robots and ubiquitous devices, that may enable more complex capabilities and improved user experience. (shrink)