Seeing Minds in Others – Can Agents with Robotic Appearance Have Human-Like Preferences?

Molly C Martini; Christian A Gonzalez; Eva Wiese

doi:10.1371/journal.pone.0146310

. 2016 Jan 8;11(1):e0146310. doi: 10.1371/journal.pone.0146310

Seeing Minds in Others – Can Agents with Robotic Appearance Have Human-Like Preferences?

Molly C Martini ^1,^*, Christian A Gonzalez ¹, Eva Wiese ¹

Editor: Marina A Pavlova²

PMCID: PMC4706415 PMID: 26745500

Abstract

Ascribing mental states to non-human agents has been shown to increase their likeability and lead to better joint-task performance in human-robot interaction (HRI). However, it is currently unclear what physical features non-human agents need to possess in order to trigger mind attribution and whether different aspects of having a mind (e.g., feeling pain, being able to move) need different levels of human-likeness before they are readily ascribed to non-human agents. The current study addresses this issue by modeling how increasing the degree of human-like appearance (on a spectrum from mechanistic to humanoid to human) changes the likelihood by which mind is attributed towards non-human agents. We also test whether different internal states (e.g., being hungry, being alive) need different degrees of humanness before they are ascribed to non-human agents. The results suggest that the relationship between physical appearance and the degree to which mind is attributed to non-human agents is best described as a two-linear model with no change in mind attribution on the spectrum from mechanistic to humanoid robot, but a significant increase in mind attribution as soon as human features are included in the image. There seems to be a qualitative difference in the perception of mindful versus mindless agents given that increasing human-like appearance alone does not increase mind attribution until a certain threshold is reached, that is: agents need to be classified as having a mind first before the addition of more human-like features significantly increases the degree to which mind is attributed to that agent.

Introduction

Constructing artificial agents that can engage in intuitive social interactions with their human partners is not only an engineering endeavor, but one that requires a deeper understanding of how humans process and engage in interactions with non-human agents. In order to define what makes interactions intuitive, one must first understand how humans interact with each other, that is: what sort of signals they use for communication and what sort of knowledge is needed to predict and understand the behaviors of others. Psychological research has shown that when we interact with other people, we need to understand whom we are interacting with and what this person is going to do next [1]. Based on this knowledge, we make inferences about the person’s internal states (e.g., intentions, beliefs, feelings) in order to explain, understand and predict their behavior—a process that is commonly referred to as mentalizing [2]. Mentalizing is crucial for successful social interactions, given that they require profound understanding of how others think, feel, and act [3]. Identifying the different factors that contribute to an increased likelihood of mentalizing in interactions with non-human agents provides insight into how human observers may approach interacting with a social robot, which in turn can lead to design recommendations that may facilitate a more naturalistic interaction experience.

The current paper addresses this challenge in two ways: First, we present a theoretical framework that summarizes physical, cognitive, motivational and context variables that have been shown to increase the likelihood of attributing mental states to others (built upon the work of [3] and [4]; see Table 1). Second, we examine whether the physical appearance of an agent influences the likelihood with which mind is attributed and whether different internal states have different threshold levels before they are readily ascribed to that agent. In order to investigate this question, we created agent images of varying degrees of humanness ranging from mechanistic to humanoid and from humanoid to human and asked participants to rate these agents regarding their potential for having different internal states (e.g., feeling pain, having a sense of humor). Results from this study are used to formulate a mathematical model that determines the relationship between appearance and mind attribution and estimates appearance threshold levels for different internal states.

Table 1. Theoretical Framework of Factors that can Increase Mentalization of Social Robots.

	Variables		References
Observer Features	Cognitive	Agent knowledge: mind attribution, mental states	Frith & Frith [1]
			Waytz et al. [3]
		Agent behavior: intentionality, motives, cause of behavior	Dennett [5]
			Wiese et al. [6]
			Ristic & Kingstone [19]
	Motivation	Effectance	Epley et al. [4]
		Sociality	Ames et al. [11]
			Davis et al. [12]
			Epley et al. [13]
		Sense of control	Kay et al. [10]
Agent Features	Behavior	Predictability	Waytz et al. [3]
			Rosset [9]
		Negative consequences	Morewedge [15]
		Motion pattern	Heider & Simmel [7]
			Abell et al. [16]
			Castelli et al. [17]
			Klein et al. [18]
	Appearance	Similarity to humans	Kiesler [20]
			Looser & Wheatley [21]
		Design type: functional and biologically inspired	Fong, Nourbakhsh, & Dautenhahn [22]

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Summarizes the work by Epley et al. [4], Waytz et al. [3], and more recent research.

Theoretical framework on variables that facilitate mind attribution

When we try to reason about the mental states of others, we first need to decide whether an agent has a mind and, therefore is in principle, capable of having internal states [1]. This question is easy to answer for human interaction partners who are intentional agents by definition and are believed to have mental states. Machines by contrast do not possess mental states, but are believed to be physical entities with pre-programmed behaviors. Beliefs about internal states of others are generally based on experience or pre-existing knowledge we have about the world [1] and bias us to adopt specific attitudes or stances towards other agents, which are then used as mental models to explain observed agent behavior [5]: For instance, beliefs that an agent has a mind trigger the adoption of the intentional stance, which involves treating the agent as a rational entity with beliefs, desires and goals. The intentional stance is thought to be the best strategy to predict the behavior of agents that are truly intentional. However, if there is doubt about the intentionality of an agent’s behavior, the likelihood for adopting the intentional stance decreases, and the adoption of other stances becomes more likely. One could, for instance, adopt the design stance in order to explain the behavior of machines that are designed to function in certain ways (e.g., select objects in production), or the physical stance in order to explain the behavior of simple objects based on the laws of physics and chemistry (e.g., ball falls to the ground due to gravity).

In consequence, the same agent behavior can be explained in different ways depending on which stance is adopted towards an agent. If a human agent is observed, the intentional stance is adopted and his/ her behavior is explained based on particular internal states, such as desires, feelings, and preferences. For instance, if a human agent is looking at a red apple, one may be tempted to think that the agent is hungry and wants to eat the apple. By contrast, if the agent is believed to be a machine, one would adopt the design stance and be less tempted to think that it wants to eat the apple because it is hungry. Instead, one might speculate that the machine was programmed to look at red objects in order to select them for production purposes. Crucially, depending on the inferences we draw about the specific motives of an agent, we ascribe more or less social relevance to its actions, which has been shown to strongly influence the amount of cognitive resources we are willing to invest in the interaction [6]. Thus, agents that trigger the adoption of the design stance as opposed to the intentional stance are less likely to be treated as agents whose actions are socially relevant. As a consequence, less cognitive resources would be invested in the interaction, which could negatively impact joint action performance in human-robot interaction [6].

Given the relationship between mind attribution (i.e., assuming that an agent has a mind) and cognitive performance, it is important to investigate under which conditions humans are willing to explain the behavior of non-human agents in terms of mental states. One straightforward assumption would be that the adoption of the intentional stance is a linear function of the agent’s human-likeness: the more human-like (machine-like) an agent is, the more (less) likely it is that the intentional stance is adopted. However, the difficulty with this approach is which factors are taken into consideration when judging the human-likeness of an agent: On the one hand, artificial agents share physical features with humans (e.g., facial features, body shape, having limbs), but on the other hand they are qualitatively different from humans in terms of their capability of actually having a mind. Given that artificial agents do not truly possess minds, their behavior would be better explained as machine-like (i.e. mechanistic) than human-like (i.e. intentional). However, adopting the design stance towards artificial agents is not what people seem to do when explaining or predicting their behavior [3]. Rather, it has been shown that the same artificial agent can be treated as machine- or human-like depending on one’s beliefs about the cause of the agent’s actions. Wiese et al. [6], for instance, demonstrated that participants are more willing to follow the gaze direction of a robot under the assumption that its eye movements represent human behavior rather than pre-programmed behavior, which resulted in better overall performance in the intentional, but not in the pre-programmed condition. Thus, mind attribution seems to not only take place for agents that actually have a mind (e.g. humans), but also for agents that do not have a mind (e.g., robots). In fact, studies from the 1940s have shown that mind can even be attributed to geometrical figures that do not have human-like features at all, as long as their movement patterns resemble social interactions, for instance a big triangle chasing a small triangle [7].

In line with these observations, mind attribution seems to be a highly variable process that strongly depends on dispositional and situational factors on the observer’s side ([4], for a review) as well as the specific features of a given agent [8]. With regard to observer features, it has been shown that when uncertainty is high and no better explanation is available (e.g., lack of knowledge about computers), participants seem to revert to intentional strategies to explain behavior of artificial agents/machines [9]. Similar intentionality biases are observed when humans feel a lack of control over a situation or when being under the impression that the observed behavior might be random [10]. In addition to these cognitive factors, the motivation to understand behavior (i.e., effectance) has also been shown to have a strong influence on mind attribution [4]. For instance, if socially interacting with an agent is thought to increase the effectiveness of task execution or is assumed to take place repeatedly in the future, then behavior is more likely to be explained in intentional rather than in mechanistic terms [4]. Likewise, if motivation to engage in social interactions is high (i.e., sociality) or if the individual feels lonely but human interaction partners are not present, the behavior of non-human agents is more likely to be explained in intentional terms compared to conditions when social needs are low [11–13]. Consequently, certain individual traits (i.e., the need to belong) can be considered to be relevant observer features that influence mind attribution by increasing one’s motivation to understand observed agent behavior [14].

As for agent features, behavior and appearance seem to have the strongest influence on the degree to which mind is attributed to non-human agents. With regard to behavior, it has been shown that agents whose behavior is hard to predict trigger the need for control and appear to be more mindful than predictable agents [8]. By the same token, behavior resulting in negative consequences is more likely to be attributed to agents with minds than to agents without minds (e.g., Morewedge [15]). Heider and Simmel [7] also demonstrated that behavior of geometrical figures, which do not possess human-like appearance at all, can still be described in intentional terms as long as their motion patterns resemble social interaction scenarios [16–18]. Likewise, other studies have shown that the same stimulus can be perceived as an intentional or mechanistic agent depending on participants’ beliefs about the cause of the agent’s behavior [6]. In line with this, Ristic and Kingstone [19] showed that an ambiguous stimulus that could either be perceived as a non-social stimulus (i.e., car with wheels) or as a social stimulus (i.e., face with a hat) facilitated performance only in the social condition, but not in the non-social condition. Importantly, in both studies the perceptual features of the stimulus did not differ between conditions, but only beliefs about the stimuli varied. The studies outlined above provide evidence that agent behavior seems to influence mind attribution to non-human agents independent of the agent’s actual appearance.

However, appearance remains an influential factor for mind attribution as it has been shown that artificial agents who physically resemble human agents are more likely to trigger mind attribution [20], even if they do not show human-like behavior. For instance, research on face perception suggests that decisions on whether an agent has a mind and is alive is categorically based on holistic processing of an agent’s facial features with eyes being disproportionately informative compared to other physical features such as skin and nose [21]. Specifically, the authors found that morphs presented on a spectrum from puppet to human triggered the attribution of animacy and intentionality only after a categorical threshold biased towards the human end of the spectrum was passed (mind attribution was measured using a 7-point Likert scale).

Aim of the Study

The studies outlined above suggest that mind attribution is a complex process that depends on dispositional and situational features on the observer’s side [4], as well as on the behavioral and physical aspects of the agent to whom the mind should be attributed. These studies also show that agents who by definition have no minds (e.g., puppets, robots) can be treated as if they were possessing minds under the right circumstances. More importantly, attributing mind to artificial agents has been shown to improve cognitive performance with those agents [6], which makes identifying factors that trigger mind attribution to artificial agents an important topic for cognitive psychology.

Considering the different ways agent appearance can be manipulated, the current study aims to better examine how physically human an agent needs to appear before intentionality is bestowed onto it (Experiment 1), and if mentalizing about different internal states (e.g., preferences, emotional states) has different appearance thresholds before humans begin to see these agents as social, intentional beings (Experiment 2). To answer these questions, we created images of agents that had different degrees of human-like appearance and asked participants to subjectively rate each morph in terms of its ability to have different internal states. The images of the agents were created by morphing in 10% increments the image of a mechanistic robot with a humanoid robot and then morphing the humanoid robot with a human image. As a result, we got a morph spectrum that ranged from 100% mechanistic robot to 100% humanoid robot and then to 100% human (see Fig 1). With this manipulation we were able to measure the relationship between the degree of physical humanness of an agent and ratings regarding its believed mental states.

Fig 1 — The morph spectrum in 10% increments ranging from 100% mechanistic (top far left) to 100% humanoid (top far right) and then from 100% humanoid (repeated bottom far left) to 100% human (bottom far right).

The results of this study are interesting in two ways: First, the question of how physical appearance and mind attribution interact is important for cognitive psychology because it helps to understand to which degree humans take appearance into account when making judgments about another’s internal states. Second, the study provides information for social roboticists on how to design artificial agents that will reliably trigger the attribution of mental states. Given how difficult and costly it is to emulate realistic human appearance in social robots, defining the minimum level of physical humanness required to trigger the attribution of different mental states will also enable designers to more reliably determine the sufficient level of humanness a robot needs to display in order to trigger the attribution of mental states.

Experiment 1

The goal of Experiment 1 is to determine if mind attribution is dependent on humanlike appearance and to model this potential relationship with an appropriate mathematical description. In order to address this issue, morphed agents with differing degrees of humanness were presented and rated on five items representing five different internal states. Qualtrics was used to create an online survey containing images and question items that could be linked to Amazon Mechanical Turk, an online marketplace where individuals can select to participant in various studies for small monetary rewards.

Methods and Materials

Participants

One hundred and sixty Amazon Mechanical Turk workers completed the 5-item per image online mental attribution survey. The survey took eleven minutes to complete and participants received $0.20 for their participation via their Mechanical Turk account. Participants were majority female (N = 94), white (N = 115), in their late-thirties (Age_avg = 39.07, SD_age = 13.60), and high school educated or above. All were reportedly native English speakers except for seven who each had 15 or more years of experience speaking English.

Ethics Statement

The Office of Research Integrity and Assurance (ORIA) at George Mason University approved the design of this experiment and determined the project to be exempt from IRB review according to federal regulations. As the research presented no more than minimal risk of harm to the participants and the data was analyzed anonymously, a waiver of signature of informed consent was granted. While no written consent was obtained, a consent form approved by ORIA was shown at the beginning of the study informing participants of their rights. Participants had to acknowledge their consent via a response item before they could continue on to the survey.

Materials

FantaMorph software was used to create a morphing spectrum across the three different agents using 10% increments (Fig 1). The mechanical agent represents the robot EDDIE, which was developed at the TU Munich and the humanoid agent represents the S2 humanoid head developed by Meka robotics. The image of the human agent was taken from the Karolinska Directed Emotional Faces database [23]. Each image within the mechanistic to humanoid range was presented at 248 x 259 pixels on Qualtrics while the humanoid to human images were presented at 248 x 277 pixels to account for the human head being larger in height than the other two agent images.

In order to measure the degree to which mental states were ascribed to different agents we used a questionnaire consisting of five items that asked participants to rate the social skills of a given agent on a 7-point Likert-scale (see Table 2 below). The first two questions were taken from a social identity and mind perception study by Hackel, Looser, and Van Bavel [24]. The two questions asked participants to rate how much each face looked alive (“Alive”) and how much each face looked like it had a mind (“Mind”) on a 7-point scale (1 = Definitely not alive/Definitely has no mind to 7 = Definitely alive/Definitely has a mind). Participants were told in the instructions that a human mind differs from that of an animal or a machine, and an animal is alive while a rock is not to help clarify the meaning of these items (see [24] for a more detailed description).

Table 2. The Five Internal States of Experiment 1 and Their Corresponding Items.

Internal State	Item
Alive	Please rate how much this face looks alive.
Mind	Please rate how much this face looks like it has a mind.
Feels Pain	Do you think this agent would feel pain if it tripped and fell on hard ground?
Hangs Out	Do you think this agent likes to hang out with friends?
Converses	Do you think this agent would be an interesting conversationalist?

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The remaining three items asked participants to rate if they thought the agent would feel pain if it tripped and fell on hard ground (“Feels Pain”), if the agent likes to hang out with friends (“Hangs Out”), and if the agent would be an interesting conversationalist (“Converses”). These three items were also rated on a 7-point scale (1 = Definitely Not and 7 = Definitely).

Design and Procedure

Participants were told that they would be shown a series of faces representing different agents and that their task was to rate the perceived social skills of each agent. The presentation of the morphed images (Fig 1) was randomized and for each image participants answered the five items described above. Upon completing the agent ratings, participants filled out a demographic questionnaire and then received a code to enter on the Amazon Mechanical Turk site to collect their payment.

Data Analysis

The data analysis process was conducted in two stages. In the first stage we attempted to identify the best fitting model of the relationship between the degree of humanness of the morph and the perceived likelihood of the morph possessing a specific internal state. In the second stage we applied the best fitting model to each specific item in order to examine threshold differences across the five internal states.

In order to narrow the potential models to test, we first plotted mean ratings of perceived likelihood of the morph possessing each internal state by the degree of humanness of the morph (See S1 Data for the raw data of Experiment 1). Fig 2 depicts this relationship to be nonlinear as increases in human-likeness yielded negligible increases in perceptions of intentionality along the morphs between mechanistic and humanoid agents, but yielded large increases in perceived intentionality along the morphs between the humanoid and human agents. Based on this observed non-monotonic relationship we considered three different potential models: 1) a quadratic model 2) an exponential model and 3) a two-linear model.

Fig 2 — Each increment on the x-axis is equivalent to a 10% increase along the mechanistic to humanoid to human spectrum.

A quadratic model defines the relationship between an independent variable, X, and an outcome variable, Y, as a function of the linear and squared components of X as well as a constant or intercept term. In formula 1 below, a represents the constant parameter, b represents the linear parameter and c represents the squared parameter.

Y ~ a + b X + c X^{2}

(1)

An exponential model defines the relationship between an independent variable, X, and an outcome variable, Y, as the product between a constant term and base raised to the X power. In formula 2 below, a represents the constant parameter and b represents the base parameter.

Y ~ a b^{X}

(2)

Lastly, a two-linear model defines the relationship between an independent variable, X, and an outcome variable, Y, and results in two separate linear relationships each with their respective linear and constant terms. In formula 3 below, a represents the constant term of line 1, b represents the slope of line 1, e represents the constant term of line 2, c represents the slope of line 2 and d represents the break point between line 1 and line 2.

\begin{array}{l} IF \\ X < d \\ Y ~ a + b X \\ ELSE \\ Y ~ e + c X \end{array}

(3)

Model fitting

Each of the three models described above was fit with nonlinear least-squares implemented in the easynls R package [25]. We set the average ratings of perceived intentionally as the outcome variable and degree of humanness as reflected by the morph number as the single independent variable. In order to determine the best fitting model we computed multiple fit measures for each model: adjusted R² and Akiake Information Criterion (AIC) (Table 3 and Fig 3). Adjusted R² provides an estimate of the proportion of the variance in the outcome variable explained by a given model while AIC represents the relative amount of information lost given a particular model. AIC is often used in model selection because it accounts for potential overfitting by complex models with many parameters [26]. In both cases, lower AIC indicates a better model fit.

Table 3. The Akiake Information Criterion (AIC) and Adjusted R² Values for Each Model Providing a Measure of Fit for Experiment 1.

	AIC	Adj. R²
Linear	232.51	0.70
Quadratic	74.14	0.93
Exponential	170.28	0.83
Two-Linear	73.77	0.94

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Fig 3 — Results of the four different model fits of the relationship between average ratings of perceived intentionality by degree of humanness for the five different internal states.

Once we determined that a two-linear model provided the best overall fit to our data we applied the model to the ratings of each of the five internal states (Fig 4). Specifically, we were interested in determining if there were significant differences in the breakpoint (parameter d) between internal states, which would suggest that the distinction between “having a mind” and “not having a mind” is in part dependent on the internal state being judged. In addition, significant differences in the average rating of intentionality for the fully mechanistic morph (parameter a) would suggest baseline differences in perceptions of intentionality depending on which internal state is judged. Significant differences in the slope of line 1 for mechanistic to humanoid morphs (parameter b) would suggest differences in the effect additional human characteristics, specifically a shell covering the wiring of the robot and creating an oval face structure, have on perceptions of intentionality between different internal states. Finally, significant differences in the slope of line 2 for humanoid to human morphs (parameter c) would suggest differences in the effect of physical humanness on perceptions of intentionality between internal states. As we were not concerned with parameter e, the constant for line 2 and baseline differences in perceptions of intentionality for the humanoid agent, it was excluded from further analysis. Refer to Table 4 below for the definitions of the two linear parameters.

Fig 4 — The light blue vertical line shows where the breakpoint occurs between the two linear functions.

Table 4. Description of the Five Parameters Estimated from a Two-Linear Model.

Parameter	Description	Interpretation
a	Intercept for Line 1	Value of internal state rating for fully mechanistic agents
b	Slope of Line 1	Increase in ratings (slope) of each internal state before breakpoint
c	Slope of Line 2	Increase in ratings (slope) of each internal state after breakpoint
d	Breakpoint of Line 1 and 2	Morph value which separates Line 1 from Line 2
e	Intercept for Line 2	Constant term for Line 2

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Parameter e, the y-intercept of line 2 was not estimated nor of interest in our analyses.

Internal state comparisons

Rather than comparing each internal state to every other internal state per each parameter (which would have resulted in 60 separate comparisons), we compared the 95% confidence intervals of each parameter estimate to the grand mean of the average parameter estimates for each internal state. A parameter was considered significantly different from the grand mean if a 95% confidence interval around the observed parameter did not include the grand mean. In comparison, if the 95% confidence interval crossed the grand mean or overlapped with the 95% confidence interval of another parameter, then those parameters were not considered significantly different. Table 5 contains parameter estimates, standard errors and confidence intervals for each internal state. Fig 5 depicts the 95% confidence intervals of each parameter estimate to the grand mean of the parameter estimates for each internal state.

Table 5. Two-Linear Model Results from Experiment 1.

Parameter	Question	Estimate	SE	2.50%	97.50%
a	Alive	2.01	0.15	1.72	2.30
	Mind	2.15	0.14	1.88	2.42
	Feels Pain	1.61	0.14	1.34	1.88
	Hangs Out	1.94	0.14	1.67	2.21
	Converses	2.42	0.11	2.20	2.64
b	Alive	0.02	0.02	-0.02	0.06
	Mind	0.03	0.02	-0.01	0.07
	Feels Pain	0.05	0.02	0.01	0.09
	Hangs Out	0.02	0.02	-0.02	0.06
	Converses	0.01	0.01	-0.01	0.03
c	Alive	0.44	0.03	0.38	0.50
	Mind	0.44	0.03	0.38	0.50
	Feels Pain	0.59	0.05	0.49	0.69
	Hangs Out	0.48	0.04	0.40	0.56
	Converses	0.35	0.03	0.29	0.41
d	Alive	11.88	0.46	10.98	12.78
	Mind	12.81	0.44	11.95	13.67
	Feels Pain	14.74	0.38	14.00	15.48
	Hangs Out	13.75	0.45	12.87	14.63
	Converses	12.76	0.44	11.90	13.62

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Estimate is maximum-likelihood estimate, SE is the standard error of the estimate, 2.5% and 97.5% indicates the lower and upper bounds of a 95% confidence interval around the estimate. Parameter a is the y-intercept of Line 1, parameter b is the slope of Line 1, parameter c is the slope of Line 2, parameter d is the breakpoint separating Line 1 and 2.

Our results revealed no significant differences in parameter d, as exhibited by the overlapping confidence intervals in Fig 5 and Table 5. This suggests that regardless of the internal state being considered, the breakpoint between mechanistic to humanoid morphs and humanoid to human morphs remains the same and is a qualitative, not quantitative difference. In addition, our results revealed no significant differences in parameter b. This suggests that regardless of the internal state being considered, the relationship between degree of humanness and perceived intentionality of mechanistic to humanoid morphs remains the same.

However, as indicated in Table 5 and Fig 5, we found significant differences on parameter a, suggesting that baseline differences in intentionality ratings may exist between different internal states (i.e., the same agent gets lower ratings on “feeling pain” compared to “having a mind”). Specifically, the ratings for conversations skills of the mechanistic agent were significantly higher than ratings regarding the other four categories (as compared to grand mean of parameter a). Our results also revealed significant differences in parameter c, suggesting that the degree of humanness has differential effects on the ratings of different internal states (i.e., slope for “feeling pain” is steeper than slope for “having a mind”). Specifically, significantly higher estimates of parameter c were observed for the relationship between degree of humanness and perceptions of feeling pain as compared to ratings of other internal rates (i.e., a one unit increase in humanness results in a greater increase in perceptions of feeling pain compared to the other internal states). Conversely, we observed a significantly lower estimate of parameter c for the relationship between degree of humanness and perceptions of conversation skills (i.e., a one unit increase in humanness results in a smaller increase in perceptions of conversation skills compared to the other internal states).

Discussion

In accordance with previous studies (e.g., Hackel et al. [24]), the results of Experiment 1 suggest that the degree of human appearance of an agent is related to the degree to which mental states are attributed to that agent. However, in addition to previous studies, our findings revealed that this relationship is not linear: Increases in physical humanness from mechanistic to humanoid agents yielded only negligible increases in mind attribution, while increases in humanness from humanoid to human agents yielded large increases in mind attribution. After considering three different nonlinear models of this relationship, a two-linear model seems to fit the data best, which represents the relationship between degree of humanness and mind attribution as arising from two linear relationships separated by a breakpoint. The fact that the two-linear model fits the data best suggests a qualitative difference between mindful and mindless agents. The location of this breakpoint can be used to estimate how much human-likeness an agent needs before adding more humanness to its appearance results in significant changes of mind attribution. In Experiment 1, the breakpoint was located at around 25% humanness and there were no differences between different internal states regarding the location of this breakpoint on the spectrum. This finding implies that when judging an agent regarding a particular internal state, it does not seem to matter which internal state is evaluated, but more whether or not we think that the agent is in general capable of having a mind. The tendency to answer this question with “yes” seems to increase significantly after the breakpoint is passed and does not seem to depend on which internal state is evaluated.

However, one limitation of Experiment 1 could be that each internal state was operationalized only by one item (e.g., Having emotions → “feeling pain”), which may have resulted in a lack of sensitivity in our measurements and ultimately be responsible for not being able to detect breakpoints across different internal states. In addition, using a single broad category, e.g. “Do you think this agent has a mind?”, may have produced a higher degree of variability in average ratings by failing to provide examples of specific behaviors which a mindful agent might engage in, such as “Do you think this agent can learn?”. Finally, the small number of internal states participants responded to may not have been sufficiently diverse in order to detect differences in their breakpoints. Thus in order to validate the results of Experiment 1, we conducted a second experiment to explore the potential differences between a greater number of internal states captured by multiple items.

Experiment 2

Experiment 2 was conducted to further expand upon the findings of Experiment 1 by examining whether the effects of physical appearance on mind attribution can be generalized to different categories of internal states (e.g., agency, animacy, emotions, goals). Participants were shown a subset of the morphed images of Experiment 1 and asked to rate the degree to which they believed a given agent possessed a given internal state. However, instead of answering five questions per image, participants were now asked to respond to 39 items per image categorized under eight different mental states in order to increase the specificity of the model. If the pattern observed in Experiment 1 persisted for an increased number and wider range of internal states, it would provide further evidence that the relationship between humanlike appearance and perceived ratings of intentionality follows the general pattern of a two-linear model. More importantly, it would support the view that intentionality is only conferred onto a robotic agent if its physical design contains some degree of humanlike appearance.