Influence of Agent Type and Task Ambiguity on Conformity in Social Decision Making

Transcription

1 Proceedings of the Human Factors and Ergonomics Society 2016 Annual Meeting 313 Influence of Agent Type and Task Ambiguity on Conformity in Social Decision Making Nicholas Hertz & Eva Wiese George Mason University Previous research has demonstrated reliable effects of social pressure on conformity and social decisionmaking in human-human interaction. The current study investigates whether non-human agents are also capable of inducing similar social pressure effects; in particular, we examined whether the degree of physical human-likeness of an agent (i.e., appearance) modulates conformity and whether potential effects of agent type on conformity are modulated further by task ambiguity. To answer these questions, participants performed a line judgment task together with agents of different degrees of humanness (human, robot, computer) in either a high or low ambiguity version of the task. We expected an increase in conformity rates for agents with increasing levels of physical humanness, as well as for increasing levels of task ambiguity. Results showed low-level conformity with all agents, with a significant difference in conformity between the high and low ambiguity version of the task (i.e., stronger compliance for the high versus the low task); the degree of humanness, however, did not have an influence on conformity rates (neither alone or in combination with task type). The results suggest that when performing a task together with others, participants always conform to some degree with the social interaction partner independent of its level of humanness; the level of conformity, however, depends on task ambiguity with stronger compliance across agents for more ambiguous tasks. Copyright 2016 by Human Factors and Ergonomics Society. DOI / INTRODUCTION Past studies provide evidence that human reasoning and decision-making is strongly modulated by context variables. For instance, when participants are asked to judge whether someone is armed, they are more likely to say yes when they are holding a gun while making this judgment (Witt & Brockmole, 2012). In line with these findings, Lewin (1944) has shown that whether a problem has to be solved alone versus in a group setting has a strong influence on how decisions are made and what decisions are made. In particular, there is evidence that the presence of a group during decision making causes social pressure and can bias individual decisions towards the group opinion a process that is called conformity (Asch, 1956). Conformity is a uniform mechanism that is observed in all sorts of social animals (Myers & DeWall, 2015) and occurs even when it is obvious that the group opinion is false (Asch, 1956; Bond, 2012). The reason why conformity is observed in social groups is that it has positive effects on group cohesion and communication of social norms within a group (Myers & DeWall, 2015). These studies suggest that conformity is an implicit principle that regulates social interactions between different members of the same species (i.e., human-human interaction). In our highly technologized society, however, we not only interact with other humans, but also with a variety of nonhuman agents, such as computers, automations and robots, all of which are designed to support the human decision making process to a certain extent (Groom & Nass, 2006). Thus, the question arises whether conformity also plays a role in humanmachine interaction and if so, what are the context variables that determine human conformity with an artificial agent. The current paper addresses this question by investigating whether artificial agents can induce social pressure in human-machine interaction. In particular, the presented research examines the effect of task ambiguity (i.e., high vs. low) and agent type (human vs. robot vs. computer) on conformity in a social decision making task. Conformity in Human-Human Interaction The study of conformity has a long history in the context of experimental psychology. Inspired by research conducted by Kurt Lewin (1944) on the dynamics of group, Solomon Asch (1951) was the first to empirically study conformity effects on decision-making in group-settings). In his original set of experiments, Asch generated a disagreement between an individual (i.e., the participant) and a group (consisting of 7 people) on a simple line judgment task (i.e., choosing which of three test lines was the same length as a sample line). The group consisted of informed confederates who were instructed to answer in a predefined pattern. The experiment contained 18 comparisons in total: On six neutral trials (trial 1, 2, 5, 10, 11, and 14), confederates unanimously answered correctly and in the remaining twelve trials the confederates unanimously answered incorrectly (Asch, 1956). All group members were placed in the same room and had to give their answers verbally in a serial pattern. By the time the actual participant had to announce their decision, six other group members have already given their answers thereby causing social pressure for the participant. The group answers were not expected to trigger false responses on the neutral trials, but were assumed to lead the participant to give a wrong answer in a certain percentage of critical trials due to perceived social pressure. The results showed that the presence of a unanimous majority significantly affected the judgment and decision making of individuals, producing an approximately 33.3% conformity rate (i.e., participants gave the wrong answer on a simple line judgment task in order to conform with the group opinion; Asch, 1956). These findings indicate that human decisionmaking can be significantly biased by the presence of a social group that consistently agrees on a certain type of answer.

2 Proceedings of the Human Factors and Ergonomics Society 2016 Annual Meeting 314 Asch s research stimulated a wealth of follow up studies that both improved the original paradigm and provided additional insights on the influence of majorities on decision making. Richard Crutchfield (1955), for instance, created a new variation of the Asch paradigm by including measures of response latencies, which allowed one to measure the cognitive incongruity created by unanimous groups answering incorrectly. He also showed that the original Asch effect could be replicated even if the confederates are not physically present, but just represented by their answers on a computer screen. Though he was able to produce a similar conformity effect, there is evidence that there is greater conformity in in vivo interactions than in simulated groups (Bond & Smith, 1996; Deutsch & Gerard, 1955; Levy, 1960). Conformity Depends on Context Factors More recent studies on conformity in social groups have looked at the influence of context effects on the degree to which humans conform to the group opinion. In particular, meta-analyses conducted by Bond and Smith (1996, 2005) examined hundreds of studies from more than a dozen countries that used Asch s line judgment task, specifically investigating effects of majority size, relation of the participant to the majority, features of the stimulus materials, gender and culture on conformity. The analyses confirmed that the critical majority size is three, where anything greater produced only marginal increases in conformity and anything less severely decreased the effect. The meta-analysis further showed that greater conformity levels are found when the participant and the members of the group share some similarities (e.g. classmates, social class, race) and that perceiving other group members as in-group as compared to out-group can affect conformity. Other factors found to influence conformity in their meta-analyses were: anonymity of response, where conformity is higher when participants believe their response will be seen by others; stimulus materials, where the greater the deviation from the majority the greater the conformity; and gender, where it is found that on average women were more likely to conform than men. Altogether, the studies reported above provide evidence that although conformity seems to be a universal principle in human-human interactions it does not occur in a automated fashion but can rather be modulated by context variables. Importantly, though, the nature of the group members (i.e., human versus non-human) and the ambiguity of the task (i.e., high vs. low) have not been investigated yet as potential context factors that modulate conformity. Aim of the Study The current study investigates whether non-human agents are capable of inducing conformity in social decision making scenarios; in particular, we examined whether the degree of human-likeness of an agent modulates conformity and whether this effect is modulated further by the ambiguity of the task. To answer these questions, participants completed the classical Asch line judgment task (Asch, 1956) with different agents (human, robot, computer) in either a low or high ambiguity version of the task. Our hypotheses were that the degree of human-likeness of an agent is positively correlated with the degree to which it induces conformity effects. Furthermore, we hypothesized that conformity increases as task ambiguity increases because participants should be increasingly unsure about the correctness of their decision. METHOD Participants Participants consisted of 62 undergraduate students (40 women, 22 men, M age = years, age range: years) at George Mason University, were recruited through SONA systems (a cloud-based participant management system for universities) and were compensated for participation through course credit. Participants reported normal or corrected-tonormal vision. Task ambiguity was manipulated as a betweenparticipants factor and participants were randomly assigned to the low ambiguity condition (31 participants, 24 women, M age\ = 19) and the high ambiguity condition (31 participants, 16 women, M age\ = 19.65). Agent type was manipulated within participants, that is: all participants performed the task with all three agents (human, robot, computer). Apparatus The stimuli were presented via computer using a Python program with the Econ Willow platform (Version: ; Weel, 2010) serving as the interface. Everything was displayed on an Asus VB195T 19-inch LCD monitor with the refresh rate set at 70 Hz. Participants were seated 57 cm from the monitor and experimenters ensured the participant was centered in respect to the monitor. Materials Task. The line judgment stimuli, order, and agent response per judgment were created based on Asch s original experiment (Asch, 1956); presentation and response mode were adapted from Crutchfield (1955), that is: both the answers of the agents and the participant were presented electronically on a computer screen. The stimulus set included 9 different line set-ups, which were presented twice thereby creating 18 total judgments. The stimuli included the presentation of a standard line to the left of three test lines (see Figure 1). Type and order in which stimuli were presented were the same for all participants. The line stimuli were created in Microsoft PowerPoint and the size of the stimuli on screen varied between 1.8 visual angle for the shortest line and 17.7 visual angle for the longest line. The lines were 6.5 mm thick and were presented 31.2 mm apart from each other. In line with the original Asch experiment (Asch, 1956) the task was to decide which test line was the same length as the standard line presented on the left. Within the 18 judgments, there were 6 neutral trials (i.e., agents answering correctly) and 12 critical trials (i.e., agents answering incorrectly). The two different degrees of task ambiguity were created varying presentation time of the stimuli: in the low ambiguity condition, stimuli were presented for 1000 ms; in the high ambiguity condition, stimuli were presented for 400 ms. Task ambiguity was determined based on the data of an earlier experiment, which measured task accuracy without

3 Proceedings of the Human Factors and Ergonomics Society 2016 Annual Meeting 315 agents present: Participants (N = 30, 19 females, M age = 20.11) performed significantly better for a stimulus presentation time of 1000 ms (mean accuracy = 89.1%, sd = 16.98) than for a presentation time of 400 ms (mean accuracy = 56.3%, sd = 24.33), t = 4.282, p <.001. Figure 1. Example of line judgment stimuli. Participants task is to determine which of the three lines on the right has the same length as the standard line on the left (adapted from Asch,1956). Agents. Before performing on the line judgment task, participants were introduced to the social agents that were used in our paradigm. Agents were introduced by picture (see Figure 2) and short backstories. The human agent was introduced as an English major participating in a study for credit, sitting in his dorm room in front of his computer. The robot agent was introduced as a robot designed to help users fill out and file their tax return quickly and easily. The computer agent was introduced as a commonly used antivirus program. All background stories and images for the human and the computer condition were adapted from de Visser and colleagues (2012). Figure 2. Agents. Computer, robot and human agents. Human and computer agents were adapted from de Visser et al. (2012), the robot images represent a robot by Meka Robotics. At the beginning of the experiment, participants were told that they were performing the line judgment task together with three different social agents who are working on the task with them in real time. Participants were told that the agents are located in a different room and are connected with the test computer through Skype. In reality, however, the social interaction with the agents was not real, but simulated based on a set of prerecorded videos (50 per agent) that showed the three different agents in different thinking poses. The videos for the human and the computer condition were adapted from de Visser and colleagues (2012), the robot videos were created using a Meka Robotics head in motion accompanied by noises created with Romo, an app controlled robot by Romotive. Each video was about 9 seconds long. Procedure Before the beginning of the experiment, participants were randomly assigned to one of the two task ambiguity conditions. Afterwards, participants received instructions and signed the informed consent forms followed by a demographic questionnaire. Then the actual test phase of the experiment started, during which each participant completed the line judgment task with each of the three agents. The presentation of agents was blocked and the order in which the agents were presented was randomized. Each block consisted of 18 trials, with trials 3-4, 6-9, 12-13, and being critical trials and the remaining trials being neutral trials. The whole experiment lasted about 30 minutes. At the beginning of each trial the line judgment stimuli were first presented (remaining on screen 400ms in the high ambiguity condition and 1000ms in the low ambiguity condition). The participant was then prompted to choose which line they thought was the same length as the standard by selecting numbers that corresponded to the line position in the task. The screen did not change until the participant selected an answer. Once their response was selected, one of the agent videos would appear showing the current interaction partner making their decision. The videos on the screen would remain there for 9 seconds. After the video finished, a new response row was shown indicating the agent s answer. Finally the participant was prompted to choose their final answer with the same possible choices as their first choice (see Figure 3). Once the final response was selected the next trial began. At the end of the experiment, participants were debriefed and received course credit via SONA. The whole experiment took about 30 minutes and the Institutional Review Board (IRB) at George Mason University approved procedures before the start of the study. Design and Analysis The goal of the study was to investigate whether context effects have an influence on conformity measures in a social decision making task. Agent type was manipulated within participants and had three levels that varied in terms of physical humanness: human, robot, and computer. The presentation of the agents was blocked and counter-balanced throughout the experiment to rule out order and practice effects. Task ambiguity was manipulated between participants and varied on two levels: low (stimulus presentation time of 1000 ms) versus high (stimulus presentation time of 400 ms). Participants were randomly assigned to one of the task conditions at the beginning of the experiment. Conformity was measured in terms of error rate on critical trials, that is: the percentage of critical trials in which participants changed their originally correct answer to the agent s answer. Data was analyzed using a mixed 3 (agent type: human, robot, computer) x 2 (ambiguity: low, high) ANOVA with conformity rate as dependent variable.

4 Proceedings of the Human Factors and Ergonomics Society 2016 Annual Meeting 316 Figure 3. Sequence of events on a given trial. At the beginning of each trial the line stimuli were presented on the screen (400ms in the high ambiguity condition, 1000ms in the low ambiguity condition). Afterwards participants could give their answers by clicking on one of the 1-3 buttons on the left side of the screen. After the participant has given his/her answer, the video of the agent was presented on the right side of the screen and the agent gave its answer for the given trial. In a third step, the participant could change his/her answer or stick to his/her original answer. RESULTS Descriptive statistics are shown in Figure 4. Overall, we observed a conformity rate of 22.26%. The ANOVA showed a main effect of ambiguity (F(1, 60) = , p <.001), with significantly higher conformity rates for high ambiguity (M High = 32.33%, SD High = 28.5) than for low ambiguity tasks (M Low = 12.2%, SD Low = 18.5). This finding indicates that independent of agent type, participants rely more on a social interaction partner when doing a high versus a low ambiguous task. Mauchly s test of sphericity indicated that the assumption of sphericity had been violated for agent type, χ 2 (2) = 6.429, p =.04. Therefore, degrees of freedom were corrected using Greenhous-Geisser estimates of sphericity (ε =.906). There was no significant main effect for agent type (F(1.849, ), p =.696), showing that, agent type does not have an influence on conformity in social decision making tasks. The interaction effect between task type and agent type was also not significant (F(1.813, ), p =.596). This finding indicates that conformity with different agents is not modulated by task ambiguity. DISCUSSION The goal of the presented study was to investigate whether context effects, such as agent type and task ambiguity modulate the degree of conformity in a line judgment task at majority size zero (i.e., only one agent). We hypothesized that the more human-like an agent appeared, the more conformity it would induce. Furthermore, we assumed that the degree to which participants conformed to another agent would be modulated by task ambiguity with more conformity on high ambiguity versus low ambiguity tasks due to increased modulated by task ambiguity with more conformity on high ambiguity versus low ambiguity tasks due to increased uncertainty regarding one s own decision. The study is part of a larger set of experiments that investigates the influence of context effects, such as group size and group cohesion on conformity in human-machine interaction. Figure 4. Results. Percent conformity for computer, robot, and human agents; green line represents the low ambiguity condition, blue line represents the high ambiguity condition. Error bars represent standard error. In line with our hypothesis, the main finding is that there was a significant conformity difference between the low and high ambiguity conditions. That is, when the stimuli are harder to judge due to a shortened presentation time, there is

5 Proceedings of the Human Factors and Ergonomics Society 2016 Annual Meeting 317 an increase in reliance on the interaction partner regardless of agent type. This is consistent with Lewin s (1944) and Crutchfield s (1955) findings that ambiguous stimuli lead to greater conformity and provides an important baseline for future research. In particular, the results show that if a task is sufficiently ambiguous, people will rely on a variety of social agents relatively equally. In contrast to our second hypothesis, there were no significant differences in conformity between the different agents and there was also no significant interaction between agent type and task type. One possible explanation for the lack of an agent type effect is that there were little consequences for mistakes in the current experiment. That is, the task carried little to no risk or reward for accuracy for the participant, which might have lead to a general reliance on all agents equally. Another possible explanation is that the current group size (i.e., group size zero or one agent) is not sufficient to capture differences between human and non-human agents. In particular, given that effects on conformity in human-human interaction were mainly observed with group sizes equal to or bigger than 3, it is possible that effects of humanness on conformity are underrepresented in the current study (but might become obvious as group size increases, with stronger effects of the human group versus non-human groups). Future experiments need to investigate the effects of task consequences (i.e., by changing the task to a risky decision making task) and group size (i.e., by repeating the experiment with a group size > 2) within the current paradigm in order to rule out potential confounding effects from these variables. In sum, the current study provides important insights in the dynamics of decision making in human-machine interaction. In particular, the study shows that conformity does not depend on the human-likeness of an agent, but strongly depends on task ambiguity (at least for a group size of zero). In other words, what seems to be the main motivation for participants to seek an agent s advice in the current study is to cope with uncertainty. However, given that we found significant conformity levels across all three agents (i.e., levels significantly different from zero: t Computer = 6.298, p Computer <.001; t Robot = 6.297, p Robot <.001; t Human = 7.146, p Human <.001, Bonferroni-corrected), it seems that conforming to social norms and therefore agreeing to the agent s opinion was an additional motivating factor in the current experiment. The surprising finding is that the degree of human-likeness of the agent does not seem to modulate this general tendency to conform further. There are a few limitations to this study. One limitation is the fact that we only examined one task, the line judgment task. This task might be problematic in two ways: first, the task has been used extensively in the past 50 years and might be known to a population of undergraduate psychology students with negative consequences on the effectiveness of our experimental manipulation. Second, it might be that the degree of conformity with different agents is modulated by task type in a sense that participants conform more to social agents on social tasks (e.g., judging emotions) and to mechanistic agents on analytical tasks (e.g., math problems). The line judgment task might be perceived as neither very social nor very analytical and therefore not be able to reveal differences between agents. As mentioned earlier, a second limitation of the current study might be the group size of zero (i.e., only one interaction partner). Given that significant changes in conformity in the traditional studies by Asch and Crutchfield (see meta-analyses by Bond & Smith, 1996, 2005 for an overview) occurred from a group size of 3 onwards, it might be that the current study is underestimating the effect of human agents because they are not presented at large enough numbers. Future studies need to address this issue by repeating the current experiment with larger group sizes. Future research will also focus on the effects of heterogeneous groups, which may provide insights for optimal team composition. Another line of research could explore whether varying the degree of perceived task importance has an influence on conformity with different social interaction partners, this would enable us to understand which interaction partner would be most helpful in high stakes situations. Future research should also explore different stimuli domains. The original Asch stimuli were a useful starting point, but the stimuli are well known in the psychology community. Different stimuli should explore the domains of social and analytical tasks. REFERENCES Asch, S. E. (1951). Effects of group pressure upon the modification and distortion of judgments. Groups, leadership, and men. S, Asch, S. (1956). Studies Of Independence And Conformity: I. A Minority Of One Against A Unanimous Majority. Psychological Monographs: General and Applied, 70(9), Bond, R. (2005). Group Size And Conformity. Group Processes & Intergroup Relations, 8(4), Bond, R., & Smith, P. B. (1996). Culture and conformity: A meta-analysis of studies using Asch's (1952b, 1956) line judgment task. Psychological bulletin, 119(1), 111. Crutchfield, R. (1955). Conformity And Character. American Psychologist, 10(5), Myers, D.G., & DeWall, C.N. Psychology, Eleventh Edition. New York: Worth Publishers, de Visser, E. 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