Gaze following: why (not) learn it?

Triesch, Jochen; Teuscher, Christof; Deák, Gedeon O.; Carlson, Eric T.

doi:10.1111/j.1467-7687.2006.00470.x

Cited by 201 publications

(221 citation statements)

References 111 publications

(126 reference statements)

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“…As shown in previous studies [4], [5], the direction of the caregiver's gaze s f t leads to a predictable consequence of the robot's shifting its gaze a g t , that is, finding a salient object r o t+1 . Conversely, the robot's hand gestures, a h t , are not causal because the caregiver does not respond to them and her gaze direction does not lead to any predictable consequence related to them.…”

Section: B Transfer Entropy In Face-to-face Interactionmentioning

confidence: 99%

“…We simulate almost the same interaction as in previous studies [4], [5]; but, for the robot, we add actions such as hand gestures and perceptual variables such as types of objects not related to joint attention. This experiment confirms whether the robot can eliminate unrelated variables from candidates for the elements of the sensorimotor map for joint attention.…”

Section: Environmental Settingmentioning

confidence: 99%

“…A number of robotics researchers have pointed out that it may play an important role in enabling smooth communication between humans and robots [2], [3]. Synthetic studies have been also conducted to understand the underlying mechanisms of the development of joint attention [4], [5]. We can expect such studies to enable realizing adaptive robot to changes in an environment including humans as well as to help understanding of human development [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…Previous synthetic studies have argued that causality between gazing behaviors of an infant and its caregiver can be utilized to learn joint attention in such uncertain situations [4], [5]. These studies assumed that a shift in the caregiver's gaze implies something salient in the direction of gaze, and such an object would also be salient to an infant robot.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies [4], [5] have shown that a robot can acquire sensorimotor mapping to achieve joint attention by associating a pair of variables involved in such causal experiences, i.e., the action variable of shifting its gaze and the preceding perception variable of the direction of the caregiver's gaze. However, no work has, to our knowledge, presented a model to enable a robot to detect such causality.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Learning of Joint Attention from Detecting Causality Based on Transfer Entropy

Sumioka¹,

Yoshikawa²,

Asada³

2008

J. Robot. Mechatron.

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Abstract-Joint attention, i.e., the behavior of looking at the same object that another person is looking at, plays an important role in human and human-robot communication. Previous synthetic studies focusing on modeling the early developmental process of joint attention have proposed learning methods without explicit instructions for joint attention. In these studies, the causal structure between a perception variable (a caregiver's face direction or an individual object) and an action variable (gaze shift to a caregiver's face or to an object location) was given in advance to learn joint attention. However, such a structure is expected to be found by the robot through interaction experiences. In this paper, we investigates how transfer entropy, an information theory measure, is used to quantify the causality inherent in face-to-face interaction. In computer simulations of human-robot interaction, we examine which pair of perceptions and actions is selected as the causal pair and show that the selected pairs can be used for learning a sensorimotor map for joint attention.

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Section: B Transfer Entropy In Face-to-face Interactionmentioning

confidence: 99%

Section: Environmental Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Learning of Joint Attention from Detecting Causality Based on Transfer Entropy

Sumioka¹,

Yoshikawa²,

Asada³

2008

J. Robot. Mechatron.

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Neuroconstructivism

Westermann¹,

Thomas²,

Karmiloff‐Smith³

2010

The Wiley‐Blackwell Handbook of Childhood Cognitive Development

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Reward processing in autism

et al. 2010

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The social motivation hypothesis of autism posits that infants with autism do not experience social stimuli as rewarding, thereby leading to a cascade of potentially negative consequences for later development. While possible downstream effects of this hypothesis such as altered face and voice processing have been examined, there has not been a direct investigation of social reward processing in autism. Here we use functional magnetic resonance imaging to examine social and monetary rewarded implicit learning in children with and without autism spectrum disorders (ASD). Sixteen males with ASD and sixteen age- and IQ-matched typically developing (TD) males were scanned while performing two versions of a rewarded implicit learning task. In addition to examining responses to reward, we investigated the neural circuitry supporting rewarded learning and the relationship between these factors and social development. We found diminished neural responses to both social and monetary rewards in ASD, with a pronounced reduction in response to social rewards (SR). Children with ASD also demonstrated a further deficit in frontostriatal response during social, but not monetary, rewarded learning. Moreover, we show a relationship between ventral striatum activity and social reciprocity in TD children. Together, these data support the hypothesis that children with ASD have diminished neural responses to SR, and that this deficit relates to social learning impairments.

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Gaze following: why (not) learn it?

Cited by 201 publications

References 111 publications

Learning of Joint Attention from Detecting Causality Based on Transfer Entropy

Learning of Joint Attention from Detecting Causality Based on Transfer Entropy

Neuroconstructivism

Reward processing in autism

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