Judging robot ability: How people form implicit and explicit impressions of robot competence.

Robots' proliferation throughout society offers many opportunities and conveniences. However, our ability to effectively employ these machines relies heavily on our perceptions of their competence. In six studies (N = 2,660), participants played a competitive game with a robot to learn about its capabilities. After the learning experience, we measured explicit and implicit competence impressions to investigate how they reflected the learning experience. We observed two distinct dissociations between people's implicit and explicit competence impressions. Firstly, explicit impressions were uniquely sensitive to oddball behaviors. Implicit impressions only incorporated unexpected behaviors when they were moderately prevalent. Secondly, after forming a strong initial impression, explicit, but not implicit, impression updating demonstrated a positivity bias (i.e., an overvaluation of competence information). These findings suggest that the same learning experience with a robot is expressed differently at the implicit versus explicit level. We discuss implications from a social cognitive perspective, and how this work may inform emerging work on psychology toward robots. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Trait attribution explains human-robot interactions.

Clark and Fischer (C&F) claim that trait attribution has major limitations in explaining human-robot interactions. We argue that the trait attribution approach can explain the three issues posited by C&F. We also argue that the trait attribution approach is parsimonious, as it assumes that the same mechanisms of social cognition apply to human-robot interaction.

Algorithmic discrimination causes less moral outrage than human discrimination.

Companies and governments are using algorithms to improve decision-making for hiring, medical treatments, and parole. The use of algorithms holds promise for overcoming human biases in decision-making, but they frequently make decisions that discriminate. Media coverage suggests that people are morally outraged by algorithmic discrimination, but here we examine whether people are less outraged by algorithmic discrimination than by human discrimination. Eight studies test this algorithmic outrage deficit hypothesis in the context of gender discrimination in hiring practices across diverse participant groups (online samples, a quasi-representative sample, and a sample of tech workers). We find that people are less morally outraged by algorithmic (vs. human) discrimination and are less likely to hold the organization responsible. The algorithmic outrage deficit is driven by the reduced attribution of prejudicial motivation to algorithms. Just as algorithms dampen outrage, they also dampen praise-companies enjoy less of a reputational boost when their algorithms (vs. employees) reduce gender inequality. Our studies also reveal a downstream consequence of algorithmic outrage deficit-people are less likely to find the company legally liable when the discrimination was caused by an algorithm (vs. a human). We discuss the theoretical and practical implications of these results, including the potential weakening of collective action to address systemic discrimination. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Robots at work: People prefer-and forgive-service robots with perceived feelings.

Organizations are increasingly relying on service robots to improve efficiency, but these robots often make mistakes, which can aggravate customers and negatively affect organizations. How can organizations mitigate the frontline impact of these robotic blunders? Drawing from theories of anthropomorphism and mind perception, we propose that people evaluate service robots more positively when they are anthropomorphized and seem more humanlike-capable of both agency (the ability to think) and experience (the ability to feel). We further propose that in the face of robot service failures, increased perceptions of experience should attenuate the negative effects of service failures, whereas increased perceptions of agency should amplify the negative effects of service failures on customer satisfaction. In a field study conducted in the world's first robot-staffed hotel (Study 1), we find that anthropomorphism generally leads to higher customer satisfaction and that perceived experience, but not agency, mediates this effect. Perceived experience (but not agency) also interacts with robot service failures to predict customer satisfaction such that high levels of perceived experience attenuate the negative impacts of service failures on customer satisfaction. We replicate these results in a lab experiment with a service robot (Study 2). Theoretical and practical implications are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Holding Robots Responsible: The Elements of Machine Morality.

As robots become more autonomous, people will see them as more responsible for wrongdoing. Moral psychology suggests that judgments of robot responsibility will hinge on perceived situational awareness, intentionality, and free will, plus human likeness and the robot's capacity for harm. We also consider questions of robot rights and moral decision-making.

When there's a will, there's a way: Disentangling the effects of goals and means in emotion regulation.

Emotion regulation involves activating an emotion goal (e.g., decrease negative emotions) and using an emotion regulation strategy (e.g., cognitive reappraisal) to pursue it. We propose that activating emotion goals and implementing means can independently affect emotion regulation. People are not always motivated to regulate emotions or to regulate them in a prohedonic manner. Therefore, activating prohedonic emotion goals is consequential. Furthermore, merely activating an emotion goal may trigger accessible means, leading to emotional changes. We tested these ideas by disentangling effects of pursuing prohedonic emotion goals and implementing cognitive reappraisal. First, we show that individuals perceive measures and manipulations of cognitive reappraisal as signaling the activation of specific emotion goals (i.e., decrease unpleasant or increase pleasant emotions) and the implementation of specific means (i.e., think differently about emotion-eliciting events). Second, we decomposed a classic measure of cognitive reappraisal to show that previously documented benefits of reappraisal might be because of the frequency of either pursuing prohedonic goals or using cognitive reappraisal. Third, in 2 empirical studies, we separately manipulated prohedonic goals (without specifying the means), cognitive reappraisal (without specifying the goal), and gave classic reappraisal instructions (specifying both the goal and the means). In both studies, activating prohedonic goals was as effective in decreasing negative emotions as was activating prohedonic goals with reappraisal instructions. Thus, activating emotion goals is essential, and sometimes even sufficient, for successful regulation. Finally, we demonstrate that the confound between goals and means is pervasive in the cognitive reappraisal literature, and offer recommendations for avoiding it. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

People are averse to machines making moral decisions.

Do people want autonomous machines making moral decisions? Nine studies suggest that that the answer is 'no'-in part because machines lack a complete mind. Studies 1-6 find that people are averse to machines making morally-relevant driving, legal, medical, and military decisions, and that this aversion is mediated by the perception that machines can neither fully think nor feel. Studies 5-6 find that this aversion exists even when moral decisions have positive outcomes. Studies 7-9 briefly investigate three potential routes to increasing the acceptability of machine moral decision-making: limiting the machine to an advisory role (Study 7), increasing machines' perceived experience (Study 8), and increasing machines' perceived expertise (Study 9). Although some of these routes show promise, the aversion to machine moral decision-making is difficult to eliminate. This aversion may prove challenging for the integration of autonomous technology in moral domains including medicine, the law, the military, and self-driving vehicles.

Expectations influence how emotions shape behavior.

Emotions shape behavior, but there is some debate over the manner in which they do so. The authors propose that how emotions shape behavior depends, in part, on how people expect emotions to shape behavior. In Study 1, angry (vs. calm) participants made more money in a negotiation when they expected anger to be beneficial. In Study 2, angry (vs. calm) participants killed more enemies in a computer game when they expected anger (but not calmness) to promote performance. In Study 3, excited (vs. calm) participants were more creative when they expected excitement to promote performance, whereas calm (vs. excited) participants were more creative when they expected calmness to promote performance. These findings demonstrate that, at least sometimes, what emotions do depends on what we expect them to do. (PsycINFO Database Record

When less is more: Effects of the availability of strategic options on regulating negative emotions.

Research in several domains suggests that having strategic options is not always beneficial. In this paper, we tested whether having strategic options (vs. not) is helpful or harmful for regulating negative emotions. In 5 studies (N = 151) participants were presented with 1 or more strategic options prior to watching aversive images and using the selected strategic option. Across studies, we found that people reported less intense negative emotions when the strategy they used to regulate their emotions was presented as a single option, rather than as 1 of several options. This was regardless of whether people could choose between the options (Studies 3-5) or not (Studies 1, 2, and 4), and specific to negative (but not neutral) images (Study 5). A sixth study addressed an explanation based on demand characteristics, showing that participants expected to feel more positive when having more than 1 option. The findings indicate that having strategic options for regulating negative emotions can sometimes be costly. (PsycINFO Database Record

Successful emotion regulation requires both conviction and skill: beliefs about the controllability of emotions, reappraisal, and regulation success.

To succeed in self-regulation, people need to believe that it is possible to change behaviour and they also need to use effective means to enable such a change. We propose that this also applies to emotion regulation. In two studies, we found that people were most successful in emotion regulation, the more they believed emotions can be controlled and the more they used an effective emotion regulation strategy - namely, cognitive reappraisal. Cognitive reappraisal moderated the link between beliefs about the controllability of emotion and success in emotion regulation, when reappraisal was measured as a trait (Study 1) or manipulated (Study 2). Such moderation was found when examining the regulation of disgust elicited by emotion-inducing films (Study 1), and the regulation of anger elicited by real political events (Study 2). We discuss the implications of our findings for research and practice in emotion regulation.

The road to heaven is paved with effort: Perceived effort amplifies moral judgment.

If good intentions pave the road to hell, what paves the road to heaven? We propose that moral judgments are based, in part, on the degree of effort exerted in performing the immoral or moral act. Because effort can serve as an index of goal importance, greater effort in performing immoral acts would lead to more negative judgments, whereas greater effort in performing moral acts would lead to more positive judgments. In support of these ideas, we found that perceived effort intensified judgments of both immoral (Studies 1-2) and moral (Studies 2-7) agents. The effect of effort on judgment was independent of the outcome (Study 3) and of perceptions of the outcome extremity (Study 6). Furthermore, the effect of effort on judgment was mediated by perceived goal importance (Studies 4-6), even when controlling for perceived intentions (Studies 5-6). Finally, we demonstrate that perceived effort can influence actual behavior, such as the assignment of monetary rewards (Study 7). We discuss the possible implications of effort as a causal motivational factor in moral judgment and social retribution. (PsycINFO Database Record

God rest our hearts: Religiosity and cognitive reappraisal.

Although religiosity is often accompanied by more intense emotions, we propose that people who are more religious may be better at using 1 of the most effective emotion regulation strategies-namely, cognitive reappraisal. We argue that religion, which is a meaning-making system, is linked to better cognitive reappraisal, which involves changing the meaning of emotional stimuli. Four studies (N = 2,078) supported our hypotheses. In Study 1, religiosity was associated with more frequent use of cognitive reappraisal in 3 distinct religions (i.e., Islam, Christianity, Judaism). In Studies 2A-2B, we replicated these findings using 2 indices of cognitive reappraisal and in a large representative sample. In Studies 3-4, individuals more (vs. less) religious were more effective in using cognitive reappraisal in the laboratory. We discuss how these findings inform our understanding of the psychology of religion and of emotion regulation.

Yes I can: Expected success promotes actual success in emotion regulation.

People who expect to be successful in regulating their emotions tend to experience less frequent negative emotions and are less likely to suffer from depression. It is not clear, however, whether beliefs about the likelihood of success in emotion regulation can shape actual emotion regulation success. To test this possibility, we manipulated participants' beliefs about the likelihood of success in emotion regulation and assessed their subsequent ability to regulate their emotions during a negative emotion induction. We found that participants who were led to expect emotion regulation to be more successful were subsequently more successful in regulating their emotional responses, compared to participants in the control condition. Our findings demonstrate that expected success can contribute to actual success in emotion regulation.

An expectancy-value model of emotion regulation: implications for motivation, emotional experience, and decision making.

According to expectancy-value models of self-regulation, people are motivated to act in ways they expect to be useful to them. For instance, people are motivated to run when they believe running is useful, even when they have nothing to run away from. Similarly, we propose an expectancy-value model of emotion regulation, according to which people are motivated to emote in ways they expect to be useful to them, regardless of immediate contextual demands. For instance, people may be motivated to get angry when they believe anger is useful, even when there is nothing to be angry about. In 5 studies, we demonstrate that leading people to expect an emotion to be useful increased their motivation to experience that emotion (Studies 1-5), led them to up-regulate the experience of that emotion (Studies 3-4), and led to emotion-consistent behavior (Study 4). Our hypotheses were supported when we manipulated the expected value of anxiety (Study 1) and anger (Studies 2-5), both consciously (Studies 1-4) and unconsciously (Study 5). We discuss the theoretical and pragmatic implications of the proposed model.