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120 Socially Intelligent Agents

Figure 14.2. Eye gaze behaviours of seven children who interacted with the interactive robot
and a passive toy truck in a comparative study. Shown is the percentage of time during which
the behaviour occurred in the particular time interval analysed (%), as well as the number of
times the behaviour was observed (#). Note, that the length of the trial sections can vary.

robot very frequently but briefly. Chris, Sean and Tim direct slightly more eye
gaze behaviour towards the toy truck. The quantitative results nicely point out
individual differences in how the children interact with the robot, data that will
help us in future developments. Future evaluations with the full list of criteria
discussed above will allow us to characterise the interactions and individual
differences in more detail.

2.3 A Qualitative Approach
This section considers the organisation of interaction in the social setting
that involves the child, the robot and adults who are present. The following
analysis draws on the methods and findings of Conversation Analysis (CA) an
approach developed by Harvey Sacks and colleagues (e.g. [13]) to provide a
systematic analysis of everyday and institutional talk-in-interaction. Briefly,
CA analyses the fine details of naturalistic talk-in-interaction in order to iden-
tify the practices and mechanisms through which sequential organisation, so-
cial design and turn management are accomplished. For overviews and tran-
scription conventions see [5], [11]. This requires an inductive analysis that
reaches beyond the scope of quantitative measures of simple event frequency.
A basic principle of CA is that turns at talk are “context-shaped and context-
renewing” ([4], p. 242). This has a number of ramifications, one of which is
that the action performed by an utterance can depend on not just what verbal
or other elements it consists of, but also its sequential location. Consider for
example how a greeting term such as “hello” is unlikely to be heard as “doing