4,143 research outputs found
Defectors cannot be detected during"small talk" with strangers.
To account for the widespread human tendency to cooperate in one-shot social dilemmas, some theorists have proposed that cooperators can be reliably detected based on ethological displays that are difficult to fake. Experimental findings have supported the view that cooperators can be distinguished from defectors based on "thin slices" of behavior, but the relevant cues have remained elusive, and the role of the judge's perspective remains unclear. In this study, we followed triadic conversations among unacquainted same-sex college students with unannounced dyadic one-shot prisoner's dilemmas, and asked participants to guess the PD decisions made toward them and among the other two participants. Two other sets of participants guessed the PD decisions after viewing videotape of the conversations, either with foreknowledge (informed), or without foreknowledge (naïve), of the post-conversation PD. Only naïve video viewers approached better-than-chance prediction accuracy, and they were significantly accurate at predicting the PD decisions of only opposite-sexed conversation participants. Four ethological displays recently proposed to cue defection in one-shot social dilemmas (arms crossed, lean back, hand touch, and face touch) failed to predict either actual defection or guesses of defection by any category of observer. Our results cast doubt on the role of "greenbeard" signals in the evolution of human prosociality, although they suggest that eavesdropping may be more informative about others' cooperative propensities than direct interaction
Free Will of an Ontologically Open Mind
The problem of free will has persistently resisted a solution throughout centuries. There is reason to believe that new elements need to be introduced into the analysis in order to make progress. In the present physicalist approach, these elements are emergence and information theory in relation to universal limits set by quantum physics. Furthermore the common, but vague, characterization of free will as "being able to act differently" is, in the spirit of Carnap, rephrased into an explicatum more suitable for formal analysis. It is argued that the mind is an ontologically open system; a causal high-level system, the future of which cannot be reduced to the states of its associated low-level neural systems, not even if it is rendered physically closed. A positive answer to the question of free will is subsequently outlined
Autonomous decision-making for socially interactive robots
Mención Internacional en el título de doctorThe aim of this thesis is to present a novel decision-making system
based on bio-inspired concepts to decide the actions to make during
the interaction between humans and robots. We use concepts from
nature to make the robot may behave analogously to a living being
for a better acceptance by people. The system is applied to
autonomous Socially Interactive Robots that works in environments
with users. These objectives are motivated by the need of having
robots collaborating, entertaining or helping in educational tasks for
real situations with children or elder people where the robot has to
behave socially. Moreover, the decision-making system can be
integrated into this kind of robots in order to learn how to act
depending on the user profile the robot is interacting with. The
decision-making system proposed in this thesis is a solution to all
these issues in addition to a complement for interactive learning in
HRI. We also show real applications of the system proposed applying
it in an educational scenario, a situation where the robot can learn
and interact with different kinds of people. The last goal of this
thesis is to develop a robotic architecture that is able to learn how to
behave in different contexts where humans and robots coexist. For
that purpose, we design a modular and portable robotic architecture
that is included in several robots. Including well-known software
engineering techniques together with innovative agile software
development procedures that produces an easily extensible
architecture.El objetivo de esta tesis es presentar un novedoso sistema de toma de
decisiones basado en conceptos bioinspirados para decidir las acciones
a realizar durante la interacción entre personas y robots. Usamos
conceptos de la naturaleza para hacer que el robot pueda comportarse
análogamente a un ser vivo para una mejor aceptación por las personas.
El sistema está desarrollado para que se pueda aplicar a los llamados
Robots Socialmente Interactivos que están destinados a entornos con
usuarios. Estos objetivos están motivados por la necesidad de tener
robots en tareas de colaboración, entretenimiento o en educación en
situaciones reales con niños o personas mayores en las cuales el robot
debe comportarse siguiendo las normas sociales. Además, el sistema
de toma de decisiones es integrado en estos tipos de robots con el fin
de que pueda aprender a actuar dependiendo del perfil de usuario con
el que el robot está interactuando. El sistema de toma de decisiones
que proponemos en esta tesis es una solución a todos estos desafíos
además de un complemento para el aprendizaje interactivo en la
interacción humano-robot. También mostramos aplicaciones reales del
sistema propuesto aplicándolo en un escenario educativo, una situación
en la que el robot puede aprender e interaccionar con diferentes
tipos de personas. El último objetivo de esta tesis es desarrollar un
arquitectura robótica que sea capaz de aprender a comportarse en
diferentes contextos donde las personas y los robots coexistan. Con ese
propósito, diseñamos una arquitectura robótica modular y portable
que está incluida en varios robots. Incluyendo técnicas bien conocidas
de ingeniería del software junto con procedimientos innovadores de
desarrollo de sofware ágil que producen una arquitectura fácilmente
extensible.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Fabio Bonsignorio.- Secretario: María Dolores Blanco Rojas.- Vocal: Martin Stoele
Beliefs about the Minds of Others Influence How We Process Sensory Information
Attending where others gaze is one of the most fundamental mechanisms of social cognition. The present study is the first to examine the impact of the attribution of mind to others on gaze-guided attentional orienting and its ERP correlates. Using a paradigm in which attention was guided to a location by the gaze of a centrally presented face, we manipulated participants' beliefs about the gazer: gaze behavior was believed to result either from operations of a mind or from a machine. In Experiment 1, beliefs were manipulated by cue identity (human or robot), while in Experiment 2, cue identity (robot) remained identical across conditions and beliefs were manipulated solely via instruction, which was irrelevant to the task. ERP results and behavior showed that participants' attention was guided by gaze only when gaze was believed to be controlled by a human. Specifically, the P1 was more enhanced for validly, relative to invalidly, cued targets only when participants believed the gaze behavior was the result of a mind, rather than of a machine. This shows that sensory gain control can be influenced by higher-order (task-irrelevant) beliefs about the observed scene. We propose a new interdisciplinary model of social attention, which integrates ideas from cognitive and social neuroscience, as well as philosophy in order to provide a framework for understanding a crucial aspect of how humans' beliefs about the observed scene influence sensory processing
Reinforcement Learning Approaches in Social Robotics
This article surveys reinforcement learning approaches in social robotics.
Reinforcement learning is a framework for decision-making problems in which an
agent interacts through trial-and-error with its environment to discover an
optimal behavior. Since interaction is a key component in both reinforcement
learning and social robotics, it can be a well-suited approach for real-world
interactions with physically embodied social robots. The scope of the paper is
focused particularly on studies that include social physical robots and
real-world human-robot interactions with users. We present a thorough analysis
of reinforcement learning approaches in social robotics. In addition to a
survey, we categorize existent reinforcement learning approaches based on the
used method and the design of the reward mechanisms. Moreover, since
communication capability is a prominent feature of social robots, we discuss
and group the papers based on the communication medium used for reward
formulation. Considering the importance of designing the reward function, we
also provide a categorization of the papers based on the nature of the reward.
This categorization includes three major themes: interactive reinforcement
learning, intrinsically motivated methods, and task performance-driven methods.
The benefits and challenges of reinforcement learning in social robotics,
evaluation methods of the papers regarding whether or not they use subjective
and algorithmic measures, a discussion in the view of real-world reinforcement
learning challenges and proposed solutions, the points that remain to be
explored, including the approaches that have thus far received less attention
is also given in the paper. Thus, this paper aims to become a starting point
for researchers interested in using and applying reinforcement learning methods
in this particular research field
Dog behaviour and ethology
Dogs engage in various interactions with artificial agents (UMOs) but it is not clear whether
they recognize UMOs as social agents. Jealous behaviour emerges when an important relationship
is threatened by another individual, but only when the intruder is a social agent. We investigated
whether UMOs elicit jealous behaviour in dogs. We tested three groups of 15 dogs, each group
observed different behaviour of the UMO: mechanistic movement, non-social or social behaviour.
Then, the owner interacted with another dog, the UMO and a magazine while ignoring the subject.
Dogs displayed more rival-oriented behaviour and attempt to interrupt the owner-rival interaction in
case of the other dog and UMO compared to the magazine (the latter mainly occurred in the Social
UMO group). However, they showed less owner- and interaction-oriented behaviour in case of the
UMO. Thus, although some elements of jealous behaviour emerged toward the UMO, the results are
not conclusive; Resumo:
Comportamento e Etologia Canina
Os cães interagem com agentes artificiais (UMOs), mas não sabemos se os reconhecem
como agentes sociais. O comportamento de ciúme surge quando uma relação importante é
ameaçada por outro indivíduo, mas apenas quando o rival é social. Investigámos se os UMOs geram
comportamento de ciúme nos cães. Testámos três grupos de 15 cães, cada grupo observou
diferentes comportamentos do UMO: comportamento mecânico, não-social ou social.
Posteriormente, o dono interagiu com o outro cão, o UMO e uma revista, enquanto ignorava a cobaia.
Os cães demonstraram mais comportamento orientado ao rival e tentaram interromper a interação
dono-rival mais vezes no caso do outro cão e do UMO comparado com a revista (principalmente no
grupo do UMO Social). Porém, os cães mostraram menos comportamento dirigido ao dono e à
interação no caso do UMO. Portanto, apesar de alguns elementos de comportamento de ciúme
surgirem com o UMO, os resultados não são conclusivos
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