The theory of affordances

The cultural origins of human cognition

Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

A sensorimotor account of vision and visual consciousness-Authors' Response-Acting out our sensory experience

While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.

The Act of Creation

人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

This paper presents a constructive model by which a robot acquires the ability of joint attention with a human caregiver based on its embedded mechanisms of visual attention and learning with self-evaluation. The former is to look at a salient object in the robot's view, and the latter is to learn sensorimotor co-ordination when visual attention has succeeded. Since the success of visual attention does not always correspond to the success of joint attention, the robot has incorrect learning data for joint attention as well as correct data. However, the robot is expected statistically to lose incorrect data as outliers since such data do not have any correlation in the sensorimotor co-ordination while correct data have a correlation. The robot consequently acquires the ability of joint attention by finding the correlation in the sensorimotor co-ordination even if multiple objects are placed at random positions in an environment and a human caregiver does not provide any task evaluation to the robot. The experimental results show that the proposed model makes the robot reproduce the developmental process of infants' joint attention. Therefore, the proposed model could be one of the models to explain how infants develop the ability of joint attention.

A constructive model for the development of joint attention

Cognitive scientists and developmental psychologists have suggested that development in perceptual, motor and memory functions of human infants as well as adaptive evaluation by caregivers facilitate learning for cognitive tasks by infants. This article presents a robotic approach to understanding the mechanism of how learning for joint attention can be helped by such functional development. A robot learns visuomotor mapping needed to achieve joint attention based on evaluations from a caregiver. The caregiver adjusts the criterion for evaluating the robot's performance from easy to difficult as the performance improves. At the same time, the robot also gradually develops its visual function by sharpening input images. Experiments reveal that the adaptive evaluation by the caregiver accelerates the robot's learning and that the visual development in the robot improves the accuracy of joint attention tasks due to its well-structured visuomotor mapping. These results constructively explain what roles synchr...

Learning for joint attention helped by functional development

Collaborative robots are quickly gaining momentum in real-world settings. This has motivated many new research questions in human-robot collaboration. In this paper, we address the questions of whether and when a robot should take initiative during joint human-robot task execution. We develop a system capable of autonomously tracking and performing table-top object manipulation tasks with humans and we implement three different initiative models to trigger robot actions. Human initiated help gives control of robot action timing to the user; robot-initiated reactive help triggers robot assistance when it detects that the user needs help; and robot-initiated proactive help makes the robot help whenever it can. We performed a user study (N=18) to compare these trigger mechanisms in terms of task performance, usage characteristics, and subjective preference. We found that people collaborate best with a proactive robot, yielding better team fluency and high subjective ratings. However, they prefer having control of when the robot should help, rather than working with a reactive robot that only helps when it is needed.

/pdf/initiative-in-robot-assistance-during-collaborative-task-59zhb5qs2r.pdf

Initiative in Robot Assistance during Collaborative Task Execution

A difficulty in robot action learning is that robots do not know where to attend when observing action demonstration. Inspired by human parent-infant interaction, we suggest that parental action demonstration to infants, called motionese, can scaffold robot learning as well as infants'. Since infants' knowledge about the context is limited, which is comparable to robots, parents are supposed to properly guide their attention by emphasizing the important aspects of the action. Our analysis employing a bottom-up attention model revealed that motionese has the effects of highlighting the initial and final states of the action, indicating significant state changes in it, and underlining the properties of objects used in the action. Suppression and addition of parents' body movement and their frequent social signals to infants produced these effects. Our findings are discussed toward designing robots that can take advantage of parental teaching.

Computational Analysis of Motionese Toward Scaffolding Robot Action Learning

In developmental research, tutoring behavior has been identified as scaffolding infants' learning processes. It has been defined in terms of child-directed speech (Motherese), child-directed motion (Motionese), and contingency. In the field of developmental robotics, research often assumes that in human-robot interaction (HRI), robots are treated similar to infants, because their immature cognitive capabilities benefit from this behavior. However, according to our knowledge, it has barely been studied whether this is true and how exactly humans alter their behavior towards a robotic interaction partner. In this paper, we present results concerning the acceptance of a robotic agent in a social learning scenario obtained via comparison to adults and 8-11 months old infants in equal conditions. These results constitute an important empirical basis for making use of tutoring behavior in social robotics. In our study, we performed a detailed multimodal analysis of HRI in a tutoring situation using the example of a robot simulation equipped with a bottom-up saliency-based attention model [1]. Our results reveal significant differences in hand movement velocity, motion pauses, range of motion, and eye gaze suggesting that for example adults decrease their hand movement velocity in an Adult-Child Interaction (ACI), opposed to an Adult-Adult Interaction (AAI) and this decrease is even higher in the Adult-Robot Interaction (ARI). We also found important differences between ACI and ARI in how the behavior is modified over time as the interaction unfolds. These findings indicate the necessity of integrating top-down feedback structures into a bottom-up system for robots to be fully accepted as interaction partners.

/pdf/people-modify-their-tutoring-behavior-in-robot-directed-5dpymuxpy0.pdf

Yukie Nagai

Papers

A constructive model for the development of joint attention

Learning for joint attention helped by functional development

Initiative in Robot Assistance during Collaborative Task Execution

Computational Analysis of Motionese Toward Scaffolding Robot Action Learning

People modify their tutoring behavior in robot-directed interaction for action learning