Young children help others in a range of situations, relatively indiscriminate of the characteristics of those they help. Recent results have suggested that young children’s helping behaviour extends even to humanoid robots. However, it has been unclear how characteristics of robots would influence children’s helping behaviour. Considering previous findings suggesting that certain robot features influence adults’ perception of and their behaviour towards robots, the question arises of whether young children’s behaviour and perception would follow the same principles. The current study (...) investigated whether two key characteristics of a humanoid robot (animate autonomy and friendly expressiveness) would affect children’s instrumental helping behaviour and their perception of the robot as an animate being. Eighty-two 3-year-old children participated in one of four experimental conditions manipulating a robot’s ostensible animate autonomy (high/low) and friendly expressiveness (friendly/neutral). Helping was assessed in an out-of-reach task and animacy ratings were assessed in a post-test interview. Results suggested that both children’s helping behaviour, as well as their perception of the robot as animate, were unaffected by the robot’s characteristics. The findings indicate that young children’s helping behaviour extends largely indiscriminately across two important characteristics. These results increase our understanding of the development of children’s altruistic behaviour and animate-inanimate distinctions. Our findings also raise important ethical questions for the field of child-robot interaction. (shrink)

It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings (...) of any letter length and any number of syllables. The model is evaluated on error rates and further validated on the results of a behavioral experiment designed to examine ambiguities in the processing of graphemes. The results show that the model (a) chooses graphemes from letter strings with a high level of accuracy, even when trained on only a small portion of the English lexicon; (b) chooses a similar set of graphemes as people do in situations where different graphemes can potentially be selected; (c) predicts orthographic effects on segmentation which are found in human data; and (d) can be readily integrated into a full-blown model of multi-syllabic reading aloud such as CDP++ (Perry, Ziegler, & Zorzi, 2010). Altogether, these results suggest that the model provides a plausible hypothesis for the kind of computations that underlie the use of graphemes in skilled reading. (shrink)