Formation of expectations

Some of the examples have already touched on the question of formation of expectations. In the following, this issue will be discussed in detail. One classic experiment in social psychology that illustrates the formation of expectations was reported by Kelley (1950). In the experiment, students were told that a guest instructor was either warm or cold hearted. After the lecture, students judged the instructor. Their judgments clearly showed assimilation effects: the group of students that were told that the instructor was warm hearted, rated him as being more considerate, informal, sociable, popular, good-natured, humorous, and humane. These students were also more open for a discussion with the instructor (56% in the warm hearted condition vs.

32% in the cold hearted condition). Another classic example for this phenomenon is Langer and Abelson’s study (1974) on therapists’ impressions of “patients” and “job applicants” (see Biernat (2005) for an overview of work in this field).

In these examples, the expectations were primed by communication from other people before the actual encounter (see also Section 2.2.5.2 on priming of schemas). Besides communication from other people (indirect experience), Olson, Roese and Zanna (1996) name two more sources of expectations: direct personal experience and beliefs that are inferred from other beliefs.

Every expectation is based on at least one of these sources, all of which can be biased (Darley &

Fazio, 1980). Direct personal experience tends to have a greater influence on expectation formation than indirect experience (Fazio & Zanna, 1981) and leads to semantic and episodic expectancies. According to Roese and Sherman (2007), semantic expectancies are: “preexisting knowledge structures that are extracted from ongoing experience, stored in memory, and retrieved when needed.” (p.95). In contrast, episodic expectancies are based on single events that someone experiences. While semantic expectancies are an efficient way of using past experiences, episodic memories provide depth by including information from the concrete situation. Semantic and episodic expectations interact and can be applied in parallel. Usually, people at the outset have specific expectations, which become more general over time, and are finally balanced at mid-levels. Based on this concept, one can assume that expectations change over time depending on the situation. Since users in first-contact situations do not have any experience in interacting with the robot, their confidence, for example, that it answers when greeted, should be low in the beginning but increase during the interaction if the expectation is confirmed. Users should first form episodic memories of the robot’s behavior. For the analysis of the studies below, it is thus hypothesized that users are able to reflect concrete experiences in much more detail after a first-contact situation with a robot based on their episodic memories than after long-term interaction.

It can be concluded that the situation influences the expectations. Heckhausen (1977) has introduced a model that depicts this connection. The model encompasses a four-stage sequence of events (see Figure 2-3). Based on these stages, Heckhausen (1977) names different kinds of expectancies, each of which can be allocated to one stage of the model. From top to bottom the respective expectancies are: situation-outcome expectancies, action-outcome expectancies (these two are typically addressed in motivation theory, for example, by Tolman, 1932), action-by-situation-outcome expectancies, and outcome-consequence expectancies.

Figure 2-3. Model of the influence of the situation on expectancies (Heckhausen, 1977)

Out of these four kinds of expectancies, Maddux (1999) focuses on action-outcome expectancies and situation-outcome expectancies which he calls behavior-outcome expectancy and stimulus-outcome expectancy, respectively. Behavior-outcome expectancies are beliefs that a specific behavior results in a specific outcome or set of outcomes. Behavior-outcome expectancies have been included, for example, in the theory of reasoned action (Fishbein &

Ajzen, 1975) and the theory of planned behavior (Ajzen, 1988).⁹ They can be distinguished based on the outcomes that can be external (environmental events) or internal (nonvolitional responses). In the case of greeting the robot, the user’s action of saying “hello” is the behavior and the expected outcome is the robot’s answer, which is an environmental event. Behavior-outcome expectancies are important with respect to the central questions of this thesis: what do users do and what do they expect the robot to do in response?

While behavior-outcome expectancies focus on behaviors that lead to certain outcomes, stimulus-outcome expectancies “are concerned with beliefs that certain events provide a signal or cue for the possible occurrence of other events” (Maddux, 1999, p.23). Thus, a stimulus signals whether a certain event will occur or not if a person engages in a certain behavior.

Stimulus-outcome expectancies trigger behavior-outcome expectancies. Comparable to behavior-outcome expectancies, they can signal whether an environmental event (stimulus-stimulus expectancy) or a nonvolitional response ((stimulus-stimulus-response expectancy) will occur. It is interesting to note that the distinction between behavior-outcome expectancies and stimulus-outcome expectancies again points to the sub-units of a situation – event and stimulus – as introduced by Magnusson (1981a) (see Section 2.1.1.1). Also the robot can produce events, i.e.

behaviors, and stimuli that signal the occurrence of other events to the user. Therefore, both are important for the following analysis.

A third kind of expectancy is the self-efficacy expectancy (Maddux, 1999). It describes someone’s belief in the ability “to perform a specific behavior or set of behaviors under specific conditions” (p.24). The self-efficacy expectancy addresses cognitive abilities as well as motor abilities. In the example provided above, the self-efficacy expectancy of the users is that they are able to trigger an appropriate answer from the robot when greeting it. Since the robot interacts using natural modalities, i.e., speech as used in HHI, this expectancy should be rather high. However, it depends on the users’ expectations towards robots in general and BIRON in particular. Accordingly, Jones (1990) distinguishes target-based and category-based expectations. If an actor forms an expectation during the interaction with a certain robot, it is target-based. An expectation toward BIRON could be that it understands speech because the users have experienced the robot understanding an utterance before. In contrast, prior expectations brought into the interaction are rather category-based which means that people have certain expectations because this is what they would expect of all members of that group.

The robot BIRON belongs to a group of robots in general or social robots in particular. A category-based expectation towards the robot would also be that it understands speech but not

9Young et al. (2009) state that both theories can help to determine the willingness of people to adopt robots for application in domestic environments and, thus, stress the value of transferring theories from social psychology to HRI. The theory of reasoned action points to the utility, effectiveness and price of the robots. The theory of planned behavior points to the importance of perceived behavioral control; however, not only to control of the robot (which is especially important because the robot is physically present and might be mobile) but, for example, also to control of how owning it affects social status.

because the users have heard the robot speak before but because they assume that all robots (or social robots) do understand speech. In general, in HHI category-based expectations are discarded in favor of more target-based expectations during the interaction (Jones, 1990).

Category-based expectations can be divided into dispositional and normative expectations (Jones, 1990). Dispositional expectations are based on the belief that different members of a group share certain characteristics (dispositions); for example, people participating in HRI studies are interested in robotics and technology in general, or social robots are able to understand speech. Dispositional expectations are more probabilistic than target-based expectations. They tell us what might happen but when the probability is not close to certain, allow us to turn to another expectation quickly. Dispositional expectations are a combination of several expectations about the situation while normative expectations are more strongly bound to the situation in focus.

Target-based expectations vary on a dimension from replicative to structural:

replicative: we expect a person to behave in the same way again if a situation is rather similar

structural: expectations based on a complex cognitive schema or an implicit theory of personality that allows predictions for people with certain traits To conclude, expectation formation is guided by many factors such as the information that is taken into account. Once expectations are formed they are not necessarily stable but part of the dynamic memory that changes with the experiences that agents make. This is true especially for target-based expectations which are less probabilistic than category-based expectations and more likely to be replaced in the case of disconfirmation. For the analyses presented below, it seems reasonable to assume that the users’ expectations are mainly target-based because they have not experienced the interaction with other members of the group, i.e., they have never interacted with a robot before. Therefore, in the model that is introduced below, expectations are regarded as being highly dynamic. Moreover, the expectations are assumed to be replicative, i.e., the users believe that the robot behaves the same in a similar situation because it has done so before. Thus, it can be assumed that the users do not change their behavior if it has turned out to lead to a satisfying result before. The users might also attribute traits to a robot which would be in favor of the structural dimension. However, they barely know the robot. Moreover, transferring different personality traits from HHI to HRI and applying complex cognitive schema would cause additional cognitive load. This is underlined by the finding that most participants found it quite difficult to judge the personality of the robot in a personality questionnaire.¹⁰