The classic mapping metaphor posits that children learn a word by mapping it onto a concept of an object or event. However, we believe that a mapping metaphor cannot account for word learning, because even though children focus attention on objects, they do not necessarily remember the connection between the word and the referent unless it is framed pragmatically, that is, within a task. Our theoretical paper proposes an alternative mechanism for word learning. Our main premise is that word learning occurs as children accomplish a goal in cooperation with a partner. We follow Bruner’s (1983) idea and further specify pragmatic frames as the learning units that drive language acquisition and cognitive development. These units consist of a sequence of actions and verbal behaviors that are co-constructed with a partner to achieve a joint goal. We elaborate on this alternative, offer some initial parametrizations of the concept, and embed it in current language learning approaches.
People are known to change their behavior and decisions to conform to others, even for obviously incorrect facts. Because of recent developments in artificial intelligence and robotics, robots are increasingly found in human environments, and there, they form a novel social presence. It is as yet unclear whether and to what extent these social robots are able to exert pressure similar to human peers. This study used the Asch paradigm, which shows how participants conform to others while performing a visual judgment task. We first replicated the finding that adults are influenced by their peers but showed that they resist social pressure from a group of small humanoid robots. Next, we repeated the study with 7-to 9-year-old children and showed that children conform to the robots. This raises opportunities as well as concerns for the use of social robots with young and vulnerable cross-sections of society; although conforming can be beneficial, the potential for misuse and the potential impact of erroneous performance cannot be ignored. Computers as social actors Reeves and Nass concluded from a number of social psychology experiments that "individuals' interactions with computers, television, and new media are fundamentally social and natural, just like interactions in real life" [(17), p. 5]. The CASA hypothesis is part of the media equation hypothesis (17), an overarching theory that additionally implies that people process experiences mediated by technology in the same way as they process unmediated experiences. Describing an unconscious and automatic response, the CASA hypothesis seems to apply to everyone regardless of expertise. The studies conducted by Reeves and Nass show that people treat technology like people, using the same social rules, expectations, beliefs, and behaviors toward technology as they would with other people: according them social behaviors (e.g., politeness and reciprocity), attributing human characteristics to them (e.g., gender), reacting to them as they would to human interaction partners, and so on (18, 19). Nass and colleagues found that, when a computer asked a user to evaluate itself, the user gave more positive feedback than when the user did the evaluation on a different computer (23). They also found that people showed gender stereotypes toward computers with male and female voices (24). Rules of attraction seemed to hold as well. Users were shown to like electronic partners better when they had the same personality as the user (17). Peer-driven normative conformity and the Asch paradigm Conformity describes the behavior of an individual who is complying with group norms. In the field of social psychology, two main varieties of conformity are considered: informational social conformity and normative social conformity. The former depicts the influence of others' responses as a source of information on one's own judgment when a task is ambiguous and the correct answer is not straightforward. The latter describes an influence of others on judgments in a task with unambiguous stim...
In this contribution, we describe a method of analysing and interpreting the direction and timing of a human's gaze over time towards a robot whilst interacting. Based on annotated video recordings of the interactions, this post-hoc analysis can be used to determine how this gaze behaviour changes over the course of an interaction, following from the observation that humans change their behaviour towards the robot on the time-scale of individual interactions. We posit that given these circumstances, this measure may be used as a proxy (among others) for engagement in the interaction or the human's attribution of social agency to the robot. Application of this method to a sample of unstructured childrobot interactions demonstrates its use, and justifies its utilisation in future studies.
Robot learning by imitation requires the detection of a tutor's action demonstration and its relevant parts. Current approaches implicitly assume a unidirectional transfer of knowledge from tutor to learner. The presented work challenges this predominant assumption based on an extensive user study with an autonomously interacting robot. We show that by providing feedback, a robot learner influences the human tutor's movement demonstrations in the process of action learning. We argue that the robot's feedback strongly shapes how tutors signal what is relevant to an action and thus advocate a paradigm shift in robot action learning research toward truly interactive systems learning in and benefiting from interaction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.