A Non-Humanoid Robot in the “Uncanny Valley”: Experimental Analysis of the Reaction to Behavioral Contingency in 2–3 Year Old Children

Infants' sensitivity to social or behavioral contingency has been examined in the field of developmental psychology and behavioral sciences, mainly using a double video paradigm or a still face paradigm. These studies have shown that infants distinguish other individuals' contingent behaviors from non-contingent ones. The present experiment systematically examined if this ability extends to the detection of non-humanoids' contingent actions in a communicative context. We examined two- to three-year-olds' understanding of contingent actions produced by a non-humanoid robot. The robot either responded contingently to the actions of the participants (contingent condition) or programmatically reproduced the same sequence of actions to another participant (non-contingent condition). The results revealed that the participants exhibited different patterns of response depending on whether or not the robot responded contingently. It was also found that the participants did not respond positively to the contingent actions of the robot in the earlier periods of the experimental sessions. This might reflect the conflict between the non-humanlike appearance of the robot and its humanlike contingent actions, which presumably led the children to experience the uncanny valley effect

Different impressions of other agents obtained through social interaction uniquely modulate dorsal and ventral pathway activities in the social human brain

Internal (neuronal) representations in the brain are modified by our experiences, and this phenomenon is not unique to sensory and motor systems. Here, we show that different impressions obtained through social interaction with a variety of agents uniquely modulate activity of dorsal and ventral pathways of the brain network that mediates human social behavior. We scanned brain activity with functional magnetic resonance imaging (fMRI) in 16 healthy volunteers when they performed a simple matching-pennies game with a human, human-like android, mechanical robot, interactive robot, and a computer. Before playing this game in the scanner, participants experienced social interactions with each opponent separately and scored their initial impressions using two questionnaires. We found that the participants perceived opponents in two mental dimensions: one represented “mind-holderness” in which participants attributed anthropomorphic impressions to some of the opponents that had mental functions, while the other dimension represented “mind-readerness” in which participants characterized opponents as intelligent. Interestingly, this “mind-readerness” dimension correlated to participants frequently changing their game tactic to prevent opponents from envisioning their strategy, and this was corroborated by increased entropy during the game. We also found that the two factors separately modulated activity in distinct social brain regions. Specifically, mind-holderness modulated activity in the dorsal aspect of the temporoparietal junction (TPJ) and medial prefrontal and posterior paracingulate cortices, while mind-readerness modulated activity in the ventral aspect of TPJ and the temporal pole. These results clearly demonstrate that activity in social brain networks is modulated through pre-scanning experiences of social interaction with a variety of agents. Furthermore, our findings elucidated the existence of two distinct functional networks in the social human brain. Social interaction with anthropomorphic or intelligent-looking agents may distinctly shape the internal representation of our social brain, which may in turn determine how we behave for various agents that we encounter in our society

Text Segmentation Based On Similarity Between Words

This paper proposes a new indicator of text structure, called the lexical cohesion profile (LCP), which locates segment boundaries in a text. A text segment is a coherent scene; the words in a segment are linked together via lexical cohesion relations. LCP records mutual similarity of words in a sequence of text. The similarity of words, which represents their cohesivehess, is computed using a semantic ]letwork. Comparison with the text segments marked by a number of subjects shows that LCP closely correlates with the bu- ]nan judgments. LCP may provide valuable information for resolving anaphora and ellipsis

Computing Lexical Cohesion as a Tool for Text Analysis

Recognizing coherent structure of a text is an essential task in natural language understanding. It is necessary, for example, to resolve anaphora, ellipsis, and ambiguity. One of the dominant factors of coherence of the text structure is lexical cohesion, namely the dependency relationship between words based on associative relations in common knowledge. This thesis proposes an objective and computationally feasible method for measuring lexical cohesion, especially semantic relations, between words. Lexical cohesion between words is computed on a semantic network constructed systematically from a subset of an ordinary English dictionary. Spreading activation on the semantic network analyses the meaning of a word into a 2,851-dimensional semantic space and computes the strength of lexical cohesion between any two words in the dictionary. As an evaluation of the measurement of lexical cohesion, this thesis then presents a quantitative indicator, Lexical Cohesion Profile (LCP), for segme..