How Your Power Affects My Impression of You

Abstract

In the present article, we investigate how a person’s power affects the way we infer traits from their behavior. In Experiment 1, our results suggest that, when faced with behavioral descriptions about others, participants infer both positive and negative traits about powerless actors, whereas for powerful and control (power irrelevant) actors, only positive but no negative traits are inferred, an effect we call the benevolence bias. In the second experiment, (a) we replicate this effect, (b) we show that it does not depend on the specific traits used in Experiment 1, and (c) we show that it is also detected when an implicit measure of inferences is used. Experiment 3 further shows that this effect generalizes to a more generic power manipulation. Theoretical explanations for these findings are discussed.

Keywords

power perception person perception spontaneous trait inference benevolence bias

Power is a social force constantly present in our day-to-day lives. It is present in countless interpersonal contexts such as in interactions between parents and children, professors and students, or in hierarchical dynamics in corporations. Power can be described as the interpersonal dynamic in which one person influences the outcomes or the behaviors of others (Fiske & Berdahl, 2007). The research about how having or lacking power affects our cognition, emotions, and decisions has been accumulating at an impressive rate (for a recent review, see Guinote, 2017). In stark contrast with this literature, very little research is done on how we perceive the power of others and how this perceived power affects the impressions we create about others. Here, we ask the following question: Does the perceived power of a person affect the way we infer personality traits from their behavior?

Personality traits are central constructs for describing and explaining other people’s behaviors, a phenomenon known as the correspondence bias (Jones & Harris, 1967). Moreover, the research has shown that when people are prompted with descriptions of trait-diagnostic behaviors of actors, they infer traits about the personality of those actors with no effort or awareness (Winter & Uleman, 1984; for a review, see Uleman, Saribay, & Gonzalez, 2008), a phenomenon known by the name of Spontaneous Trait Inferences. If you happen to read the following description: “Mary solved the mystery half-way through the book,” you will probably activate the trait “intelligent” without being asked to and, if later asked whether Mary is intelligent, you will probably agree more than you would if the behavioral description was never presented to you.

Although usually described as an efficient and unintended process, there is some empirical evidence that spontaneous trait inferences are affected by relevant information known about the actor. Wigboldus, Dijksterhuis, and van Knippenberg (2003) showed that when a behavior (e.g., “solved the mystery half-way through the book”) performed by an actor (“soccer player”) is inconsistent with the stereotype (soccer players tend to be perceived as unintelligent), the trait (“intelligent”) is less inferred (see also, Ramos, Garcia-Marques, Hamilton, Ferreira, & Van Acker, 2012; Wigboldus, Sherman, Franzese, & Knippenberg, 2004). Similar to the effect of stereotypes on spontaneous trait inferences, we are interested in the effect of power on this same phenomenon and, in particular, on how the actor’s power interacts with the valence of the trait being inferred. Power is an important source of respect and esteem (Ellemers & Barreto, 2001; Fiske & Berdahl, 2007) and, as such, some have argued that people have positive attitudes toward powerful actors (Brauer & Bourhis, 2006). In fact, powerful and high-status people are perceived as more moral (Overbeck, Tost, Wazlawek, 2013; as cited in Smith & Overbeck, 2014), more hardworking and intelligent (Humphrey, 1985), and more leader-like (Sande, Ellard, & Ross, 1986) than their powerless counterparts. On the same note, because people tend to think that the world is fair, they assume that those in positions of power must deserve it and are better people, while those that lack power do not deserve it and therefore are seen more negatively (Jost, Banaji, & Nosek, 2004). Based on this assumption, we hypothesize that the way people infer positive and negative traits from behaviors will be affected by the power of the actor performing these behaviors.

Overview of the Experiments

To test our hypothesis, we conducted three experiments. In all three, participants were presented with sentences describing behaviors. Each sentence was paired with the face of a person that was introduced as the actor performing that behavior. To manipulate the power of the actors, in Experiment 1, participants were told that the actors were employees with different positions in an organization. Specifically, the power differential was stressed both by stating that the managers have power over the employees and by listing their tasks in a way that reinforces this notion (e.g., that the managers decide the employees tasks; for similar manipulations, see Ames & Bianchi, 2008; Nesler, Aguinis, Quigley, & Tedeschi, 1993; Overbeck, Tiedens, & Brion, 2006). Moreover, an explicit trait-rating task was used to assess the inferences made. In the second and third experiments, we used a standard paradigm to measure spontaneous inferences—the false recognition paradigm. The third experiment used a communication network to manipulate power through control over information.

General Method

The three experiments followed the same general experimental method that started with a learning phase and ended with a testing phase. In the learning phase, participants were presented with behavioral descriptions and photographs of people (all males) who were said to be the actors of those behaviors. Participants were instructed to memorize the material for a later unspecified memory test (for similar instructions, see Goren & Todorov, 2009; Shimizu, Lee, & Uleman, 2017; Todorov & Uleman, 2002, 2003, 2004). By reading a behavioral description under memory instructions, the implied trait is expected to become activated and associated with the representation of the actor in the photograph (Carlston & Skowronski, 2005; Orghian, Garcia-Marques, Uleman, & Heinke, 2015; Todorov & Uleman, 2004). The behavior described was either positive or negative.¹ In the first two experiments, job titles (manager—powerful actor; employee—powerless actor; or visitor—power-irrelevant actor [control]) in a company were used to manipulate the power of the actors performing the behaviors (e.g., “The manager solved the mystery half-way through the book.”). In the third experiment, to manipulate power, we used a communication network in which the central node represented the most powerful individual due to their control over the information in the network.

In the testing phase, the photographs of the same individuals were presented again, this time, paired with a personality trait. This trait was either a trait implied in the sentence presented in the learning phase with the same actor (referred to as match trials from now on) or a trait that was not implied in the sentence presented with that actor, but instead implied by a sentence presented with a different actor within the same power condition (referred to as mismatch trials from now on). If a trait is inferred about an actor during the learning phase, we should be able to detect that by comparing the responses to the match and the mismatch trials in the testing phase. The valence of the mismatched traits was opposite to the valence of the inferred traits. The opposite valence procedure was employed to avoid any overlap between the representations of the two traits.² For instance, if the trait “intelligent” is inferred during the learning phase, then, in the testing phase, when the trait “intelligent” is presented with the same actor (match condition), the association between the trait and the person should be reflected in participants’ responses. In the mismatch condition, when the actor is paired, in the testing phase, with a negative trait (e.g., lazy), not only a lack of association is expected but also an incongruence between the impression formed about that person and the probe trait might be felt. This will lead to a larger difference between the match and the mismatch conditions, and, consequently, to a larger trait inference effect.

The task that participants performed in the testing phase of the first experiment was an explicit trait-rating task during which they were asked to indicate on a scale how much they thought that the trait belonged to that person. In the second and third experiments, participants performed a recognition test in which they had to indicate if the trait-word was part of the sentence presented with that actor in the learning phase.

Experiment 1A

Method

Participants

A total of 76 participants³ took part in this experiment, of which 26 were males. The average age was 23 years (SD = 4.69). They were compensated with a 5-euro voucher for taking part in the experiment.

Material

The material used in this experiment consisted of the following: 36 trait-implying sentences (critical sentences) previously pre-tested for the Portuguese language, 18 neutral sentences (filler sentences) that implied no traits, and 54 colorful photographs showing faces of individuals with neutral expressions. The sentences and the instructions of the experiments reported in this article can be found as Supplemental Materials on osf.io/e4ubq

Procedure

When recruited, participants were told the study was about how people memorize information about companies. The instructions started with a summary of the company and the three types of actors—managers, employees, and visitors, and their respective responsibilities in the company. To make the power differential more salient, we presented a picture with an example of a typical workspace of a manager, an employee, and an example of a reception desk where the visitors wait. The visitor is our control condition since the power of this person is irrelevant in the context of the company.

Subsequently, participants started the learning phase during which they had to memorize sentences and photographs. Before starting the experimental trials, participants performed four practice trials to get familiarized with the task. Next, 36 trait-implying and 18 filler trials followed. From the trait-implying sentences, 12 were randomly assigned to the manager condition, 12 to the employee, and 12 to the visitor. In each of these groups of 12, six sentences implied a positive trait and six implied a negative trait. The presentation order of the trials was randomized for each participant. The same was true for the 18 filler sentences with six sentences being randomly assigned to each power condition. The duration of the trials was self-paced and participants were instructed to press the space bar when ready to move to the next trial. The presentation of the sentence and the photograph was preceded by a 500 ms fixation dot. The inter-trials interval was of 100 ms. At the end of learning phase, a 2-min distraction task—word completion puzzle—followed.

In the testing phase, participants were presented with 36 pairs of photographs and traits. In half of the trials, the trait was a match and in the second half the trait was a mismatch (see Figure 1 for the full design). Participants were asked to indicate how much the trait belonged to the actor in the photograph. Note that in this phase, there was no information about the power of the actor. To answer, participants used a scale from 0 (not at all) to 9 (completely). The trials, in this phase, also started with a 500 ms fixation dot, ended with a 100 ms blank screen, and were self-paced.

Figure 1.

Experimental design and the number of trials per cell in Experiment 1.

The experimental design was a 3 Power Conditions (powerful vs. powerless vs. control) × 2 Valences of the Inferred Trait (positive vs. negative) × 2 Pairings at Test (match vs. mismatch), all the factors being within-subjects. Each cell in the design was populated with three trials (see Figure 1).

Results and discussion

A repeated-measures ANOVA was conducted, with power, valence of the inferred traits, and pairing as the independent variables and the ratings as the dependent variable. All the p-values reported in the current article are two-tailed. A main effect of pairing, F(1, 75) = 22.02, p < .001, 90% confidence interval (CI) = [0.10, 0.35], $η_{p}^{2} = . 23$ , and an interaction between power and valence, F(1, 75) = 16.17, p < .001, 90% CI = [0.06, 0.30], $η_{p}^{2} = . 18$ , were found. The three-way interaction between power, valence, and pairing was not statistically significant, F(1, 74) = 1.77, p = .178, 90% CI = [0.00, 0.10], $η_{p}^{2} = . 0234$ . Even though the three-way interaction did not reach significance, as our interest was to look at the effect of power on inferences, we decided to test for simple effects.

For the powerful actors, a significant interaction between valence and pairing was found, F(1, 75) = 10.20, p = .002, 90% CI = [0.03, 0.24], $η_{p}^{2} = . 12$ . Participants inferred positive traits, MD = 1.08, SE = 0.23, p < .001, since the ratings in the match condition (M = 5.43, SD = 1.47) were larger than in the mismatch condition (M = 4.35, SD = 1.44), and did not infer negative traits, MD = 0.06, SE = 0.24, p = .814 (match: M = 4.77, SD = 1.73; mismatch: M = 4.71, SD = 1.40). The pattern is similar in the control condition, with a significant interaction between valence and pairing, F(1, 75) = 12.19, p = .001, 90% CI = [0.04, 0.26], $η_{p}^{2} = . 14$ . Positive inferences, reflected by a significant and positive difference between the match (M = 5.34, SD = 1.61) and mismatch trials (M = 4.09, SD = 1.41), MD = 1.25, SE = 0.24, p < .001, were detected. However, again, no negative inferences were found (match: M = 4.86, SD = 1.52; mismatch: M = 4.85, SD = 1.58), MD = 0.004, SE = .27, p = .987. For the powerless actors, the interaction did not reach significance, F(1, 75) = 2.79, p = .099, 90% CI = [0.00, 0.13], $η_{p}^{2} = . 04$ . Positive inferences were found (match: M = 5.21, SD = 1.44; mismatch: M = 4.31, SD = 1.48), MD = 0.90, SE = 0.24, p < .001, but marginal negative inferences were observed as well (match: M = 5.04, SD = 1.34; mismatch: M = 4.63, SD = 1.61), MD = 0.41, SE = 0.23, p = .082.

These results suggest that positive traits but no negative traits are inferred about control and powerful actors. This benevolence bias does not apply to powerless actors. Note, however, that only marginal effects support these findings. We suspect that this happened due to a limitation of the current experiment. Even though the traits were randomly assigned to the power condition for each participant, the number of traits relevant to the powerful and to the powerless actors might be unbalanced and the results can be a consequence of that. In other words, it is possible that our set of negative traits are more relevant to powerless actors, which might have led to the observed effect. To exclude this possibility, we conducted a short experiment to better understand the relationship between the traits used and the power conditions. Understanding this relationship is especially important since there are studies showing that powerful people are perceived as more competent and intelligent (Georgesen & Harris, 1998; Humphrey, 1985; Lambert, Hodgson, Gardner, & Fillenbaum, 1960; Sande et al., 1986), meaning that at least the traits of competence and intelligence are more relevant to the powerful condition. Experiment 1B deals with this issue.

Experiment 1B

Method

Participants and material

A total of 42 participants voluntarily took part in this experiment via an online survey. Their average age was 26 (SD = 6.82) years and eight were males. The 36 traits used in Experiment 1A constituted the material employed in this experiment.

Procedure

When recruited, participants were told that the goal of the experiment was to understand how people perceive the personalities of employees working in companies. They were asked to rate how plausible it is for a manager and for an employee to have a certain trait. For each question, they were presented with a trait (e.g., boring) and their task was to choose one of three options: (a) being boring makes it more plausible for the person to be a manager (or an employee), (b) being boring makes it less plausible for the person to be a manager (or an employee), and (c) it is not informative (does not make it more or less plausible) for the person to be a manager (or an employee). Each of the 36 traits was presented twice, once for the manager and once for the employee. The pairs were presented in random order.

Results and discussion

For each trait, we conducted two chi-square tests. The first test allowed us to verify whether choosing the third option (non-informative) versus one of the first two (more and less plausible) was affected by the power of the person judged (manager vs. employee). In the second test, the first option (more plausible) was compared with the second options (less plausible). This second test allowed us to divide the traits into three groups: (a) a first group with traits that did not interact with the power condition (N = 12; for example, careful, hardworking, liar, rude); (b) a second group consisting of traits considered by the participant as being more typical of managers (N = 9; for example, competent, confident, selfish, proud); and (c) a group with traits more typical of employees (N = 15; for example, honest, generous, clumsy, ignorant). The results of both tests are presented in the appendix.

Next, we re-analyzed the data from Experiment 1A after excluding, for each power condition, the trials where traits relevant for that power condition were used. Thus, in the powerful condition, the test trials with traits relevant to managers were excluded from the analyses. The same was done for the powerless condition. No trials were eliminated from the control condition.⁴ If the same pattern is observed now that the “problematic” traits were eliminated, it is because the lack of benevolence bias (i.e., inference of negative traits found for the powerless and absent for the powerful and control actors) cannot be explained by the relevance of the negative traits to this power condition.

A repeated-measures ANOVA was performed. A main effect of pairing, F(1, 55) = 13.38, p = .001, 90% CI = [0.06, 0.33], $η_{p}^{2} = . 20$ , an interaction between valence of the inferred traits and pairing, F(1, 55) = 7.06, p = .010, 90% CI = [0.02, 0.25], $η_{p}^{2} = . 11$ , and a three-way interaction, F(1, 55) = 3.11, p = .049, 90% CI = [0.00, 0.17], $η_{p}^{2} = . 05$ , were found. When decomposing the three-way interaction in simple interaction effects, a significant interaction between valence and pairing was found for the powerful actors, F(1, 55) = 8.09, p = .006, 90% CI = [0.02, 0.27], $η_{p}^{2} = . 13$ (see Figure 2). Participants inferred positive traits, since the ratings for match (M = 5.39, SD = 0.26) were larger than for mismatch trials (M = 4.41, SD = 0.19), MD = 0.97, SE = 0.31, p = .003.

Figure 2.

Ratings as a function of pairing and valence of inference in Experiment 1B.

They did not infer, however, negative traits for these actors, MD = 0.22, SE = 0.29, p = .463 (match: M = 4.65, SD = 0.24; mismatch: M = 4.87, SD = 0.26). The results are similar for the control condition, with a significant interaction between valence and pairing, F(1, 55) = 7.78, p = .007, 90% CI = [0.02, 0.26], $η_{p}^{2} = . 12$ . Participants inferred positive traits, MD = 1.25, SE = 0.30, p < .001 (match: M = 5.36, SD = 0.22; mismatch: M = 4.11, SD = 0.20), but did not infer negative traits for the visitors, MD = 0.10, SE = 0.27, p = .713 (match: M = 4.96, SD = 0.18; mismatch: M = 4.86, SD = 0.20). The pattern of results is different for the powerless actors, since no interaction was found between valence and pairing for these actors, F < 1. There was, however, a main effect of pairing in this condition, F(1, 55) = 7.33, p = .009, 90% CI = [0.02, 0.26], $η_{p}^{2} = . 12$ , with higher ratings for the match (M = 5.12, SD = 1.32) than the mismatch (M = 4.41, SD = 1.60) trials. This result indicates that the amount of inferences for powerless actors is not affected by the valence of the traits being inferred.

In sum, the results in this experiment suggest that power affects the inference of negative and positive traits differently. More precisely, for the control and the powerful actors, only positive inferences were observed—a benevolence bias—whereas for the powerless actors, both positive and negative inferences were observed. In other words, the powerless do not seem to benefit from the benevolence bias that powerful and control actors do.

In Experiment 1A, we used a trait-rating task to measure inferences. That, by itself, could have prompted explicit inference making at the testing moment. Consequently, the trait-rating task does not guarantee that the inferences are made in a spontaneous manner while the behavior is being encoded. To overcome this limitation, in the second and third experiments, we used a paradigm that allows to implicitly measure the inference of traits and their integration into the representation of the actors—the false recognition paradigm (Todorov & Uleman, 2002, 2003, 2004). Moreover, we conducted two pre-tests to create a set of materials (sentences and traits) irrelevant to power manipulated in an enterprise setting and used it in Experiment 2.

Experiment 2

Method

Participants

A total of 69 participants took part in this experiment, their average age was 24.84 (SD = 6.06) years and 23 were males. They were compensated for participating with a 5-euro voucher.

Material

The material used in this experiment consisted of 48 trait-implying sentences (see Figure 3 for more details) previously pre-tested for the Portuguese language and for their association with the roles used to transmit the power differential. For more information on the selection of the materials for this study, please consult the supplemental materials. The same colorful photographs as in Experiment 1 were used in this study.

Figure 3.

Experimental design and the number of trials per cell in Experiment 2.

Procedure

The instructions and method of this experiment are similar to the method described in Experiment 1, except that this time: (a) we only included two power conditions (the manager and the employee), (b) no neutral behavioral descriptions were used, and (c) 48 critical sentences were used instead of 36.

In the learning phase, the 48 sentences were randomly paired with 48 photographs. In total, 24 of these pairs were then quasi-randomly assigned to the powerful condition and the other 24 to the powerless condition (quasi due to the requirement of having an equal number of positive and negative sentences in the two conditions). Moreover, half of the trials in each of these two groups were trait-implying sentences, and the other half were filler trials. Filler sentences consisted of adaptations of the trait-implying sentences that explicitly mentioned the trait (e.g., “The manager/employee was so boring that he made everybody yawn with his story”). The filler sentences were necessary due to the task participants performed in the test phase.

In the testing phase, participants were presented with photographs paired with traits. Different from Experiment 1 in which participants performed a rating task, here, participants were asked to indicate whether the trait was presented in the sentence that appeared with that same actor in the learning phase. If the trait is inferred from the sentence, it is more likely that the participant will say during this phase that the word was part of the sentence when in fact it was only inferred (false recognition). In the filler trials, the correct answer was to indicate “yes” (the trait was part of the sentence). The inclusion of these trials is important because without them the correct answer would be “no” in all the trials. Participants’ task was to press the key “S” for answering “Yes” and “N” for “No,” as fast and accurately as possible. The trials started with a 500 ms fixation dot, followed by the presentation of the photograph and the trait, which remained on the screen until participants responded. Following the same reasoning as in Experiment 1, half of the trials (24 trials) were match trials and the other half were mismatch trials (24 trials; see Figure 3 for more details). Here again, we expected a higher rate of false recognition when the trait had previously been implied in the sentence presented with that actor (match trials) than when the trait was implied in the sentence presented with a different actor (mismatch trials). In addition, we developed two versions of the experiment, the two being identical in everything but in the pairing between the sentences and the actors. Participants were assigned randomly to one of the two versions.

The experimental design was 2 Types of Trials (inference vs. fillers) × 2 Power Conditions (powerful vs. powerless) × 2 Trait Valences (positive vs. negative) × 2 Pairings (match vs. mismatch), all the factors being within-subjects.

Results and discussion

We present separate analyses for fillers and inference trials.

Starting with the trait-inference trials, a main effect of power, F(1, 68) = 4.01, p = .0491, 90% CI = [0.00, 0.16], $η_{p}^{2} = . 06$ , a main effect of pairing, F(1, 68) = 25.33, p < .001, 90% CI = [0.13, 0.40], $η_{p}^{2} = . 27$ , an interaction between pairing and valence, F(1, 68) = 8.69, p = .004, 90% CI = [0.02, 0.23], $η_{p}^{2} = . 11$ , and a three-way interaction between power, valence, and pairing, F(1, 68) = 5.647, p = .020, 90% CI = [0.01, 0.19], $η_{p}^{2} = . 08$ , were found. Further analyses were conducted to decompose this three-way interaction.

In the powerful condition, a strong interaction between the valence of the trait inferred and pairing was observed, F(1, 68) = 15.65, p < .001, 90% CI = [0.07, 0.32], $η_{p}^{2} = . 19$ . A large inference was detected for positive traits (match: M = 0.50, SD = 0.36; mismatch: M = 0.28, SD = 0.33), MD = 23, SE = 0.05, p = .001. However, for negative traits, no difference was found in the false recognition rate between match (M = 0.32, SD = 0.27) and mismatch trials (M = 0.35, SD = 0.30), MD = 0.03, SE = 0.04, p = .357 (see Figure 4). In the powerless condition, a main effect of pairing was found, F(1, 68) = 16.98, p < .001, 90% CI = [0.07, 0.33], $η_{p}^{2} = . 20$ , with a larger false recognition rate in the match (M = 0.49, SD = 0.27) than in the mismatch (M = 0.33, SD = 0.27) conditions. Critically, no interaction between valence and pairing was found, F < 1.

Figure 4.

False recognition rate (for the inference trials, in the two panels on the top of the figure) and rate of “yes” responses (for the filler trials, in the two bottom panels) as a function of the power, trait valence, and pairing in Experiment 2.

A repeated-measures ANOVA was also conducted for the filler trials. A main effect of type of pairing, F(1,68) = 56.76, p < .000, 90% CI = [0.31, 0.56], $η_{p}^{2} = . 46$ (match: M = 0.67, SD = 0.17; mismatch: M = 0.43, SD = 0.24) was observed. No other effects were found. The lack of a three-way interaction, F < 1, suggests that the effects found for the inference trials apply only to inferences and do not apply to the cases where the trait is explicitly provided in the sentence (see Figure 4).

This experiment suggests that power impacts spontaneous trait inferences, even when these are measured with an implicit methodology. In addition, by replicating the lack of benevolence bias found for the powerless with a different paradigm it adds external validity to the finding.

Experiments 1 and 2 have an important limitation in that the way the power was manipulated might be confounded with organizational role.⁴ While power does vary with the role, other variables might vary as well. To overcome this limitation, in Experiment 3, a different type of power manipulation is used.

Experiment 3

Method

Experiment 3 is in all equal to Experiment 2 except for the power manipulation. Here, the actors were represented as nodes in a communication network. The position in the network defined the power via the control the node had over the communication in the network and over the information that other nodes received. This manipulation was inspired by Leavitt’s (1951) work in which a wheel-shaped communication network (one with a central node that connects the remaining nodes) led to the identification of the central node as being the leader of the network (see also Bavelas, 1950).

Participants

A total of 123 participants⁵ took part in this experiment for a 5-euro voucher compensation. In total, 45 were males and the average age of the whole sample was 21.04 (SD = 5.73) years.

Material

The material was the same as the one used in Experiment 2 except for the fact that the sentences did not mention any organizational role (e.g., “He made everybody yawn with his story.”).

Procedure

When recruited, participants were told that the objective was to investigate how people memorize information about communication networks. The instructions introduced the concept of communication networks by emphasizing the power differential between the central and the peripheral nodes in the network. A picture reinforced this idea with the central node being connected to more nodes than any other (to access the picture see the instruction in supplemental material). Participants were told that they would be presented with a sentence describing a behavior and that inside a communication network diagram, one node would show a photograph of the actor of that behavior. Participants were asked to memorize all the information (including whether the node was central or peripheral) for a later, unspecified, memory test.

The method of this experiment followed the method described in Experiment 2, both in the learning phase and in the testing phase (see Figure 3). In the learning phase, each trial started with a 500 ms fixation dot that was followed by the simultaneous presentation of the network diagram, the photograph, and the sentence. This display remained on the screen for 8 s. Each trial ended with a 100 ms blank screen. The experimental trials were preceded by three practice trials. After the learning phase, participants performed a distraction task that was followed by the test phase. The test phase was equal to the test phase in Experiment 2. The position of the actor in the network was not revealed to the participants in this phase.

Finally, the participants responded to four questions aimed at checking if the manipulation of power with the communication network was successful (“How much power do you think the central node has over the remaining ones?”; “How much control do you think the central node has over the remaining ones?”; “How much power do you think the peripheral nodes have over the central one?”; “How much control do you think the peripheral nodes have over the central one?”; 1 = None, 6 = A lot).

The experimental design was 2 Types of Trials (inference vs. fillers) × 2 Power Conditions (powerful vs. powerless) × 2 Implied Trait Valences (positive vs. negative) × 2 Pairings (match vs. mismatch), all the factors being within-subjects.

Results and discussion

Manipulation check

We aggregated the answers to the first two questions (How much power do you think the central node has over the remaining ones? How much control do you think the central node has over the remaining ones?) to form a measure of perceived power of the central node over the remaining ones (α = .72) and the answers to the last two questions (How much power do you think the peripheral nodes have over the central one? How much control do you think the peripheral nodes have over the central one?) to form a measure of perceived power of the peripheral nodes (α = .78) over the central node. Participants gave significantly higher ratings to the central node (M = 4.39, SD = 1.08) than to the peripheral ones (M = 2.22, SD = 0.83), t(115) = 19.24, p < .001. These results confirm that the communication network manipulation of power used in this experiment did affect the perceived power of the implicated actors in the expected way.

Next, we conducted separate analyses for the inference trials and for the fillers trials.

For the inference trials, a main effect of pairing, F(1, 122) = 13.61, p < .001, 90% CI = [0.03, 0.19], $η_{p}^{2} = . 10$ , an interaction between pairing and valence, F(1, 122) = 12.58, p = .001, 90% CI = [0.03, 0.18], $η_{p}^{2} = . 09$ , and a three-way interaction, F(1, 122) = 4.07, p =.046, 90% CI = [0.0003, 0.10], $η_{p}^{2} = . 03$ , were observed. To decompose the three-way interaction, simple effects and simple interaction effects are reported below. For the powerful condition, and as depicted in Figure 5, the interaction between valence of the inference and pairing was significant, F(1, 122) = 14.96, p < .001, 90% CI = [0.04, 0.20], $η_{p}^{2} = . 11$ , with a strong inference of positive traits (match: M = 0.40, SD = 0.34; mismatch: M = 0.23, SD = 0.28), MD = 0.18, SE = 0.04, p < .001, and no inference of negative traits (match: M = 0.29, SD = 0.29; mismatch: M = 0.31, SD = 0.31), MD = 0.03, SE = 0.04, p = .448. For the powerless condition, the interaction between pairing and valence did not reach significance, F(1, 122) = 1.85, p= .177, 90% CI = [0.00, 0.07], $η_{p}^{2} = . 01$ , but there was an overall significant difference between the match (M = 0.31, SD = 0.22) and mismatch trials (M = 0.23, SD = 0.21), F(1, 122) = 5.23, p= .024, 90% CI = [0.0029, 0.11], $η_{p}^{2} = . 04$ . This result replicates the findings from the previous experiments. Once again participants inferred traits regardless of the valence for the powerless actors and inferred only positive traits for the powerful actors.

Figure 5.

False recognition rate in function of the power, type of inference, and pairing in Experiment 3.

The same analysis was conducted for the filler trials (see Figure 5, bottom panels). A main effect of pairing, F(1, 122) = 74.45, p < .001, 90% CI = [0.27, 0.47], $η_{p}^{2} = . 38$ , was found, with more “yes” responses in the match trials (M = 0.59, SD = 0.18) than in the mismatch trials (M = 0.41, SD = 0.19). An interaction between power and valence was also found, F(1, 122) = 5.53, p = .020, 90% CI = [0.0036, 0.11], $η_{p}^{2} = . 04$ . A higher rate of “yes” responses was found for negative (M = 0.54, SD = 0.22) than for positive traits (M = 0.47, SD = 0.21) for powerless actors, MD = 0.07, SE = 0.02, p = .002, and no difference between the responses for negative (M = 0.49, SD = 0.21) and positive traits (M = 0.50, SD = 0.22) was observed for the powerful actors, MD = 0.01, SE = 0.03, p = .840. Finally, an interaction between valence and pairing was also found, F(1, 122) = 4.92, p = .028, 90% CI = [0.0022, 0.11], $η_{p}^{2} = . 04$ , with a larger difference between match (M = 0.20, SD = 0.22) and mismatch (M = 0.41, SD = 0.22) for the negative traits, MD = 0.21, SE = 0.03, p < .001, than the difference between the match (M = 0.55, SD = 0.22) and the mismatch (M = 0.42, SD = 0.24) trials for the positive traits, MD = 0.14, SE = 0.03, p < .001. More important, the three-way interaction did not reach significance, F < 1. Consistent with the results found for the filler trials in Experiment 2, the power manipulation had no impact (no three-way interaction) when, instead of inferring the traits, participants were explicitly provided in the sentences.

In order to mitigate the influence of the halo effect (see the “General Method” section), in the mismatch conditions we used traits that were opposite in valence to the traits implied in the sentences. However, doing so might lead to answers based on evaluative judgments rather than inferential processes as a generic valence-based inference would suffice to accept the trait in the match and to reject it in the mismatch condition (for more on evaluative judgment in spontaneous trait inferences, see Schneid, Carlston, & Skowronski, 2015). So, an existing bias toward any of the groups (positive for the powerful and negative for the powerless) could explain participants’ responses without requiring trait inferences. To ensure that participants’ answers reflect more than simple valence-based responses, we conducted an additional analysis which addressed this problem. Briefly, we used the mismatch trials (the ones in which the correct answer is always “no” as the trait was not implied by the sentence associated to the target) to calculate participants bias. We calculated the difference between responses in the positive and negative conditions within each power condition. Participants who show differences between the positive and the negative mismatch conditions are answering based on a valence-based bias as there is no other reason for them to relate the mismatched traits to the targets. When such participants are excluded from the analysis, the same pattern of results is obtained (for more details about this analysis, see the supplemental material).

If there is, for example, a positive bias toward powerful people, then, regardless of the inference, there will be more false recognitions for positive than for negative traits. A possible way of detecting inferences beyond the bias is to analyze only the participants who do not show this bias.

General Discussion

In three experiments, using two different power operationalizations and two different trait inferences paradigms, we found that people infer more negative traits for powerless others than for powerful or power-irrelevant others. In the first experiment, participants were asked to memorize trait-implying behavioral descriptions about powerful, control (power irrelevant), and powerless actors. The results suggest that people are benevolent when inferring traits about powerful and control actors, but lack this benevolence when it comes to powerless actors. In other words, when the actor is a manager or a visitor, participants infer positive traits and do not infer negative traits, but when the actor is a powerless employee, participants infer both positive and also negative traits. In this first experiment, explicit trait-ratings were used. To evaluate whether such effect happens spontaneously, Experiment 2 provided evidence that the same effect is found when using an implicit paradigm—the false recognition paradigm. Finally, in Experiment 3, using a communication network to manipulate power, external validity is increased as results suggest this effect is not specific to a particular power manipulation.

These results show, in the first place, a robust positivity bias for powerful actors and for actors whose power is irrelevant to the situation. We know that positivity biases are frequently reported in psychology literature. We know that people tend to evaluate individuals more positively than groups (person-positivity bias; Miller & Felicio, 1990), that people use positive words more frequently than negative words, an effect also known as Pollyanna hypothesis (Boucher & Osgood, 1969), and that people respond faster to positive than to negative stimuli (Unkelbach, Fiedler, Bayer, Stegmüller, & Danner, 2008). Some argue that positivity biases exist because positive information is structurally simpler than negative information. Unkelbach and colleagues (2008), in an article where they proposed the density hypothesis, argued that positive stimuli are more alike (for a similar claim on face perception, see Potter, Corneille, Ruys, & Rhodes, 2007) due to their high density and consequent proximity in our cognitive “space.” Because of this high density, they are easier and faster to activate. Negative stimuli/traits, on the other hand, are less related and more different from each other and are, therefore, more difficult to activate. That should be especially true when the information given by the context (the power of the actor in the case of the current experiments) is incongruent in valence with the to-be-inferred negative trait. It follows that, when processing a negative trait-implying behavior, if there is nothing in the context of the behavior that the trait can easily relate to, its instantiation will be none or very weak. If there is something in the context of the sentence that is already negative in valence (the powerless actor), then the inference of the negative trait finds a congruent context that makes it possible for a coherent and stable integration of the inference into the memory trace of that event to occur. This sequence of events might explain the presence of negative inferences found for powerless actors. Indeed, the importance of coherence for the occurrence of inferences was previously acknowledged in the past in the constructionist theory (Graesser, Singer, & Trabasso, 1994) and by the minimalist hypothesis (McKoon & Ratcliff, 1992). In the minimalist approach, it is argued that an inference only occurs if it is easily available in memory or if it is necessary for local coherence. If we do happen to have a negative attitude toward powerless people, mentioning the lack of power of an actor might be activating a general negative evaluation of the situation, which, consequently, will make negative traits more easily available. This higher availability of negative traits will lead to a facilitation in the inferential process and subsequent negative inferences about powerless people.

Our findings might seem inconsistent with the immense research on the diagnosticity of negative information (e.g., Reeder & Brewer, 1979; Skowronski & Carlston, 1989). A negativity bias is usually observed in people’s evaluations of others, that is, negative attributes influence more these evaluations than do the positive attributes (e.g., Wojciszke, Bazinska, & Jaworski, 1998; Wojciszke, Brycz, & Borkenau, 1993; Ybarra, 2001). Carlston and Skowronski (2005), for instance, found that negative traits are more inferred than positive traits. Note that we found exactly the opposite, inferences of positive traits for all groups (visitors, managers, and employees) and no negative inferences for two (visitors and managers) out of three groups. However, this negativity effect reported by Carlston and Skowronski when applied to trait inferences seems to be somewhat unstable (e.g., McCarthy & Skowronski, 2011). Rim, Min, Uleman, Chartrand, and Carlston (2013), for example, found a positivity bias (in their baseline condition) with more spontaneous trait inferences for positive than for negative traits. Previous studies have also shown that people are more likely to falsely recognize positive than negative traits (Matlin & Stang, 1978; Todorov, 2002). One possible explanation for this discrepancy in results is the type of measures used (trait-ratings in Carlston & Skowronski’s studies and the false recognition paradigm in Rim & colleagues’ & Todorov’s studies) as well as the context of the task as suggested by Skowronski (2002). The conditions in which negative and positive biases occur in the context of spontaneous trait inferences should be further investigated and disentangled in future research.

This work contributes to the spontaneous trait inferences literature. It is already known that spontaneous trait inferences are affected by a diverse set of contextual variables such as processing goals (Uleman & Moskowitz, 1994), affiliation goals (Rim et al., 2013), need for structure (Moskowitz, 1993), mind-set abstractness (Rim, Uleman, & Trope, 2009), approach versus avoidance movements (Crawford, McCarthy, Kjærstad, & Skowronski, 2013), mood (Wang, Xia, & Yang, 2015), culture (e.g., Na & Kitayama, 2011; Shimizu, 2012; Zárate, Uleman, & Voils, 2001; Zhang & Wang, 2013), social class (Lillard & Skibbe, 2005; Varnum, Na, Murata, & Kitayama, 2012), and power of the perceiver (Yang & Wang, 2016). We nominate power of the actor to this list. Similarly, this work contributes to the power literature which, while being vastly developed in terms of the effects of social power on the self, is still at its infancy when it comes to the impact of perceived power in others.

Finally, this work bridges the power and the spontaneous trait inferences literature in an original manner. In doing so, it raises many interesting questions that future research might want to look at. For instance, what are the boundary conditions of these findings? How does the power of the perceiver affect the inferences of powerful and powerless targets? Perhaps more importantly: what mechanisms explain this effect?

The mechanisms underlying the effect reported in this article are still unclear. However, the patterns seem consistent: there is a benevolence bias toward powerful and power-irrelevant others, whereas no such benevolence is observed for powerless others. To infer more negative traits for the powerless than for others means to attribute their negative actions mainly to their personality. In other words, when powerless people do something negative, it will affect more negatively the impressions others create about them. This is especially unfortunate because being in a powerless position is already negative for the self in a variety of ways (e.g., Guinote, 2007; Obligacion, 1996; TenHouten, 2016) and this bias might be part of the core reasons why it is hard for powerless people to climb the social ladder and gain power.

Supplemental Material

Orghian_onlineappendix – Supplemental material for How Your Power Affects My Impression of You

Supplemental material, Orghian_onlineappendix for How Your Power Affects My Impression of You by Diana Orghian, Filipa de Almeida, Sofia Jacinto, Leonel Garcia-Marques and Ana Sofia Santos in Personality and Social Psychology Bulletin

Footnotes

Appendix

Table A2.

Results of the Statistical Test in Study 1b.

Trait	Informative vs. non-informative		Choice 1 vs. 2		Classification
Trait	Chi-square	p-value	Chi-square	p-value	Classification
Anti-social	2.791	.095	3.593	.058	NR
Boring	1.42	.233	1.418	.234	NR
Careful	0.571	.45	2.387	.122	NR
Clumsy	1.278	.258	6.671	.01	PL R
Confident	9.722	.022	17.581	0	PF R
Competent	2.275	.131	7.547	.006	PF R
Courageous	1.779	.182	12.594	0	PF R
Extroverted	3.048	.081	8.311	.004	PF R
Fearful	2.791	.095	13.847	0	PL R
Funny	0.985	.321	4.48	.034	PL R
Generous	0	1	10.133	.001	PL R
Hard-working	0.105	.745	0.502	.479	NR
Honest	0.198	.657	10.317	.001	PL R
Humble	0.857	.355	14.047	0	PL R
Ignorant	1.587	.208	14.12	0	PL R
Incompetent	0.819	.365	8.272	.004	PL R
Insecure	6.574	.01	29.325	0	PL R
Intelligent	8.021	.005	10.184	.001	PF R
Irresponsible	6.098	.014	12.375	0	PL R
Lazy	2.275	.131	11.854	.001	PL R
Liar	0.19	.663	1.736	.188	NR
Loyal	0.055	.815	4.994	.025	PL R
Modern	4.85	.028	4.542	.033	PF R
Nervous	1.867	.172	17.941	0	PL R
Nice	0.194	.659	0.008	.931	NR
Old-fashioned	0.985	.321	0.976	.323	NR
Proud	2.345	.126	8.619	.003	PF R
Relaxed	0.051	.821	4.668	.031	PL R
Responsible	8.571	.003	7.179	.007	PF R
Rude	0.891	.345	0.306	.58	NR
Selfish	1.197	.274	10.219	.001	PF R
Shy	3.733	.053	27.356	0	PL R
Spender	1.779	.182	0.002	.966	NR
Thrifty	0.049	.825	0.048	.826	NR
Unfaithful	0.857	.355	1.053	.305	NR
Well-mannered	0.051	.821	0.315	.575	NR

Note. The classification concerns the groups of traits we created based on the chi-square test. NR stands for non-relevant for any of the power conditions; PF R stands for relevant for the powerful condition; and PL R stands for relevant for the powerless condition.

Authors’ Note

Diana Orghian and Filipa de Almeida are first authors.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We thank Fundação para a Ciência e Tecnologia (Portuguese national foundation for Science and Technology, Grant #: SFRH/BD/87044/2012) for funding this research.

Notes

Supplemental Material

Supplemental material is available online with this article.

ORCID iD

Diana Orghian

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