Analysis Approach

Due to space limitations, this chapter focuses on analysis of the offboard video data, with the aim of understanding in detail the kinds of interaction behaviors people displayed toward the robotic face. 315 total interactions occurred (over 500 total people, some who came in groups). Of those, 182 interactions (comprising 256 individuals) were included in the final analysis, about 60% of the total. The other ~40% of interactions were considered “non-interactions”, as the people only looked at the robot in

those cases, but did not attempt to interact with it. The overarching question is: what do people actually do when confronted with an interactive robotic face in naturalistic settings, can any useful interaction patterns be extracted from this data, and what might that tell us about how to design the robot and/or its interactive behavior?

Video was analyzed in two phases. First, an initial pass through the full set of videos was made, identifying each interaction that took place (whether individual people or groups of people) and marking the begin and end time of each interaction. Each interaction was also categorized by a number of variables related to the context and characteristics of the interaction and its participants: Exhibit Phase (opening night vs. “regular” daily exhibit), Group Size (alone or group), Age (adult or child), Gender (male or female), Interaction Zone (proximal or distal), and Primary (yes or no). Group Size, Age, and Gender are self-explanatory, and Exhibit Phase is explained in Section 6.2.3. As for the others, Interaction Zone refers to how close the person was to the robot, with “proximal” defined as being in the chair or within a few feet of the robot (the same distance as if they were in the chair). Primary distinguished whether the person was the main interactor during the interaction – generally this was the closest person to the robot who performed most of the interaction. This distinction was developed with the understanding that when another person is in front of and interacting with the robot, it limits what another participant can do, so we wanted to separate those conditions. Interactions generally had only one primary interactor, although in a few cases there were more than one or people took turns.

A second pass was then made on the video for each interaction (excluding non-interactions, see above), which entailed annotated coding of each video clip using Anvil (http://www.anvil-software.org/) to produce coded interaction behavior data (totaling roughly 235 minutes). Video coding was performed by two independent coders, with partially overlapping sets (approximately 10% of the total data), in order to calculate inter-rater reliability. Inter-class correlation was calculated via SPSS, with a Cronbach’s Alpha of 0.734, which is considered good. Deciding which behaviors to code was based on the most commonly observed behaviors during the first pass of video analysis. The final list of behavioral codes consisted of: Smiling, Frowning, Other Facial Expressions, Making Exaggerated Faces, Sticking Tongue

Out, Talking, Laughing, Mimic Robot (attempting to get the robot to mimic them, e.g. making the opposite expression of the robot), Communicative Hand Gestures (e.g. waving hello or goodbye), Attentional Hand Gestures (etc. snapping, pointing, finger-wagging), Feeding the Robot, Inspecting the Robot (examining the robot’s structure rather than making direct eye contact with it or interacting with it socially). Other than the facial expressions (a person could not smile and frown at the same time), these coded behaviors were not mutually exclusive, i.e. a person could smile and talk at the same time.

Behaviors were coded for both occurrences and time spent (in seconds), allowing us to analyze variations in both the number of people and the time spent per person engaging in each behavior across variables. For analysis purposes, time spent was scaled by the total duration of the interaction, since different people interacted for different lengths of time. In essence, this converted the time spent into a unit-free “time spent per second” value (i.e. what percentage of each second did the person spend doing behavior ‘x’), independent of the actual duration. This allowed us to directly compare different interactions.

We also transcribed and coded participant dialogue from the videos. The coding scheme included identification of Direct speech (e.g. “Hi, how are you doing?”), Anthropomorphism of the robot (e.g. “He’s so sad,” “He looked at me”), Mechanistic interpretation of the robot (e.g. “It has a camera”), ascription of Childlike behavior (e.g. “It’s like a baby”), and focus on what the robot is attending to (e.g. “Does it see me?”). The codes were not mutually exclusive. 161 (out of the total of 256) individuals had utterances recorded.

The analysis presented in Section 6.3 comes in two parts. First, we present an analysis of the variables of each interaction to examine differences across age, gender, etc. in interaction behaviors. We chose to test these demographic variables in this exploratory research because they are commonly evaluated in human-robot interaction studies as sources of differentiation in social behavior and attitudes toward robots (e.g. Schermerhorn, Scheutz, Crowell, 2008; Ezer, Fisk, & Rogers, 2009). Statistical hypothesis testing was performed using independent samples t-test in SPSS. We adopt the null hypothesis as our starting point, i.e. there would be no differences in interaction behavior across variables.

Second, we analyzed the coded interaction behavior data using unsupervised clustering to examine whether different groups of people adopted identifiable “interaction schemas” while interacting the robot, and whether such interaction schemas were emergent in the data. We utilized two-step clustering in SPSS

to identify such clusters

(http://10.110.22.85:49801/help/topic/com.ibm.spss.statistics.algorithms/alg_2step_cluster.htm), and assigned each interacting person to one of these clusters. Differences in interaction behaviors between clusters were evaluated via ANOVA. Clusters were then analyzed for differences across variables (age, gender, group size, etc.) using Chi-squared tests in SPSS.

6.3 Results