Communications Psychology最新文献

Recent advances in AI have enabled large language models to produce expressions that seem empathetic to human users, raising scientific and ethical questions about how people perceive and choose between human and AI sources of emotional support. Although an increasing number of studies have examined how people rate empathy generated by AI, little to no work has examined whether people would choose to receive empathy from AI. We conducted four studies investigating whether people prefer to receive empathetic expressions from humans or AI, and how they evaluate these expressions. Across diverse samples and stimuli, we found evidence for what we term the AI empathy choice paradox: participants significantly preferred to receive empathy from humans, yet they rated AI-generated empathetic responses as higher in quality, more effective at making them feel heard, and more effortful when they did choose them. These findings contribute to ongoing debates about AI empathy by demonstrating that while people may avoid AI as an empathy source, they nonetheless benefit from AI empathy when they experience it. Our results suggest potential applications for AI in supplementing human emotional support while highlighting the importance of respecting individual preferences for empathy sources.

The ability to transfer skills is critical for complex performance. However, performance in complex environments is often examined within single levels of analysis, neglecting interactions among characteristics of the task, person, and experience. Here, we examine how intervention-level factors (task consistency, stress), between-person differences (emotion-cognition traits, physiological traits), and within-person fluctuations (amount of practice) jointly influence transfer. Across six rounds of a gamified learning task, participants (N = 241) trained under stress or control conditions and in consistent or inconsistent task environments. They then either continued or switched to the other task environment. Results revealed that task consistency enhanced efficiency during learning, but switching to an inconsistent environment disrupted performance. Patterns in pre- to post-switch performance were shaped by physiological reactivity and emotion-cognition traits, including cognitive reappraisal and intolerance of uncertainty, revealing compensatory adaptations that group-level analyses may obscure. These findings advance existing transfer models by highlighting how emotional and physiological regulation interact with environment.

Anti-fat attitudes are pervasive and contribute to deleterious social and health outcomes. The following research investigates perceptual exposure (i.e., visual exposure to larger bodies) as a potential mechanism for reducing anti-fat attitudes across various contexts and methodologies. Perceptual exposure in primary environments (i.e., the body sizes of people encountered in daily life) was examined using aggregated county-level data from the United States. Regions with higher adult obesity rates, indicating greater exposure to larger bodies, were associated with lower explicit but higher implicit anti-fat attitudes. Perceptual exposure in media (i.e., the body sizes of people in advertisements) was assessed using France's ban on extremely thin fashion models. Prior to the ban, explicit anti-fat attitudes increased over time. Following the ban, and coinciding with increased representation of larger bodies in French media, explicit anti-fat attitudes decreased. The impact on implicit attitudes was inconsistent. To test the mechanism underlying the relationship between perceptual exposure and attitudes, a two-week longitudinal experiment exposed participants to thin or fat bodies for three minutes daily. Exposure to fat bodies increased the threshold for categorizing bodies as fat, whereas exposure to thin bodies lowered it. Attitudes did not significantly change after two weeks. Together, these findings suggest that perceptual exposure influences body size categorization thresholds and may, over time, contribute to improvements in explicit anti-fat attitudes. The differential effects on explicit and implicit attitudes, as well as limitations and future directions, are discussed.

Political identity shapes neural responses to political content, but how these responses change within individuals over time remains unexplored. Here, we tested this by leveraging a unique political crisis. We conducted two fMRI scans separated by two and a half years, during which 21 participants viewed identical political videos. This period coincided with political instability that potentially caused participants to shift their attitudes towards the videos. Analysis revealed a neural plasticity hierarchical pattern: primary sensory regions showed minimal changes, while limbic, reward, and memory networks exhibited the most substantial differences between sessions. Specifically, the amygdala, hippocampus, and caudate demonstrated activity patterns that tracked changes in interpretation. Notably, neural changes in these regions correlated with shifts in political in-group affiliations, but not statistically significantly with changes in ideological positions. These findings provide empirical support for the hypothesis that social and psychological processes shape neural responses to political content, rather than vice versa.

Concern about biased depictions of individuals, groups, and events in media has intensified across the political spectrum. At the same time, implicit attitudes have become an increasingly common explanation for discriminatory outcomes. The present study examines emotional, cognitive, and behavioral consequences of biased media depictions depending on whether they are attributed to journalists' implicit or explicit attitudes. A sample of U.S. participants (N = 350) read about biased media coverage of Muslim natural disaster victims that ostensibly reduced the public's donation behavior relative to other types of victims. The biased reporting was attributed to journalists' implicit (i.e., unconscious) or explicit (i.e., conscious) anti-Muslim attitudes and beliefs. After reading the report, participants in the implicit bias condition felt less outrage, guilt, and anger toward the journalists, held them less culpable, and revealed lower intentions and willingness to donate to similar victims of natural disasters in the future, compared with participants in the explicit bias condition. These findings point to compelling behavioral consequences of emphasizing the role that implicit, rather than more deliberate, biases play in producing discrimination, be it in media or other important societal domains.

Humans are remarkably efficient at adapting to different contextual demands by exerting optimal levels of cognitive flexibility versus stability for switching between different tasks. However, empirical findings document that these control adaptation processes can occur across multiple timescales, suggesting that different computational mechanisms may be involved. Here, we developed a recurrent neural network model to simulate behavioral indices of cognitive flexibility versus stability and investigated its dynamics. Throughout four simulation studies, we revealed that control adaptations that benefit from recent control states (activation-based) can support fast adjustments in cognitive flexibility, whereas control adaptations that depend on changes in control settings (weight-based) evolve more slowly over learning. Interestingly, like humans, the model further learned to associate contextual features to different control settings and use this knowledge to shift along a flexibility-stability continuum when encountering these same contexts. We further verified model-specific predictions through an analysis of an existing human dataset (N = 102). In sum, our work integrates fast and slow control adaptations within a unified framework and advances our understanding on human adaptative behavior and its regulatory mechanisms through the lens of activation-based versus weight-based adaptations.

The term 'inertia' refers to the seemingly common Autistic experience of remaining in a state of rest or a state of motion until there is some form of external intervention. While a heavily discussed phenomenon in the Autistic community, it has been scarcely acknowledged in the academic literature. The present study aimed to advance knowledge of Autistic inertia by analysing a large qualitative sample of naturalistic discourse on the topic from Autistic online communities on the social media platform, 'Reddit'. We identified 501 relevant posts shared between 2005 and 2023, including 9,955 comments. We analysed the posts using reflexive thematic analysis with an inductive approach. We identified four themes, centred on the "all or nothing" extremes of inertia (Theme 1), the range of factors that intersect with and exacerbate it (Theme 2), its joyful and often highly-disabling impacts (Theme 3), and the varied ways in which Reddit users manage it (Theme 4). Our findings corroborated those from existing interview-based studies and also uncovered additional insights, elaborating on 'the vicious cycle' of inertia, its fatiguing effects and its interaction with other commonly co-occurring conditions. We discuss these less-reported experiences and identify what we know - and are still yet to understand - about the key features of Autistic inertia.

Common-pool resource dilemmas are group resource sustainability problems that are sensitive to over-extraction. While human strategies for overcoming common-pool resource dilemmas are well studied, the comparative evolutionary perspective has received little attention. Here, we compare resource management of chimpanzees (N = 15) grouped as dyads and quartets using an original experimental paradigm. The participants could use sticks to feed from a pool of yoghurt. The number of sticks equalled the number of players, and removing all of the sticks triggered resource collapse, thereby creating a social dilemma. Quartets were found to maintain the resource longer than dyads. Quartets', but not dyads', success was positively associated with social tolerance. Furthermore, quartets were more successful when the dominant ape acquired the relative lowest payoff. These results suggest that chimpanzees respond differently to cooperative sustainability problems depending on group size, with social tolerance playing an important role. The findings have implications for studying the evolution and diversity of hominid cooperation, in particular, highlighting that group size should be carefully considered in the design of non-human primate cooperation experiments.

Deciding whether, when, and which information to sample is critical for making effective decisions, yet the cognitive mechanisms of this process are not well understood. Here, we propose that key aspects of human information demand are explained by non-linear subjective perceptions of probabilistic losses or gains. Using behavioral testing and quantitative model comparisons across three independent participant samples (N = 50, 50, and 150), we show that a model that incorporates non-linear probability and value perception outperforms a model based on a linear mixture of motives in explaining instrumental and non-instrumental information demand. Moreover, individual non-linearities that best explained information demand were correlated with personality traits and with non-linearities explaining risk seeking/aversion in standard choice tasks. The results suggest that a computational framework rooted in the subjective perception of probability furthers our understanding of information demand and its relationship with decision making under risk and uncertainty.

Decision scientists have grown increasingly interested in how people adaptively control their decision making, exploring how metacognitive factors influence how people accumulate evidence and commit to a choice. A recent study proposed a novel form of such adaptive control, whereby the values of one's options  contribute to both the formation of a decision and the effortful invigoration of a response. In this framework, the control process was operationalized in a drift diffusion model as the lowering of the decision threshold on difficult trials. Reanalyzing the data from this experiment, we establish alternative explanations for these findings. We show that the reported evidence for controlled threshold adjustments can be explained away by task confounds, time-dependent collapses in decision thresholds, and stimulus-driven dynamics in an alternative form of evidence accumulation. Our findings challenge the specific evidence for this new theory of motivated control while at the same time revealing paths and pitfalls in computational approaches to a more general understanding when and how control guides decision-making.