Explore the vast range of titles available.

Artificial intelligence (AI) is already widely used in daily communication, but despite concerns about AI’s negative effects on society the social consequences of using it to communicate remain largely unexplored. We investigate the social consequences of one of the most pervasive AI applications, algorithmic response suggestions (“smart replies”), which are used to send billions of messages each day. Two randomized experiments provide evidence that these types of algorithmic recommender systems change how people interact with and perceive one another in both pro-social and anti-social ways. We find that using algorithmic responses changes language and social relationships. More specifically, it increases communication speed, use of positive emotional language, and conversation partners evaluate each other as closer and more cooperative. However, consistent with common assumptions about the adverse effects of AI, people are evaluated more negatively if they are suspected to be using algorithmic responses. Thus, even though AI can increase the speed of communication and improve interpersonal perceptions, the prevailing anti-social connotations of AI undermine these potential benefits if used overtly.

The introduction of artificial teammates in the form of autonomous social robots, with fewer social abilities compared to humans, presents new challenges for human–robot team dynamics. A key characteristic of high performing human-only teams is their ability to establish, develop, and calibrate trust over long periods of time, making the establishment of longitudinal human–robot team trust calibration a crucial part of these challenges. This paper presents a novel integrative model that takes a longitudinal perspective on trust development and calibration in human–robot teams. A key new proposed factor in this model is the introduction of the concept relationship equity . Relationship equity is an emotional resource that predicts the degree of goodwill between two actors. Relationship equity can help predict the future health of a long-term relationship. Our model is descriptive of current trust dynamics, predictive of the impact on trust of interactions within a human–robot team, and prescriptive with respect to the types of interventions and transparency methods promoting trust calibration. We describe the interplay between team trust dynamics and the establishment of work agreements that guide and improve human–robot collaboration. Furthermore, we introduce methods for dampening (reducing overtrust) and repairing (reducing undertrust) mis-calibrated trust between team members as well as methods for transparency and explanation. We conclude with a description of the implications of our model and a research agenda to jump-start a new comprehensive research program in this area.

Machines powered by artificial intelligence increasingly permeate social networks with control over resources. However, machine allocation behavior might offer little benefit to human welfare over networks when it ignores the specific network mechanism of social exchange. Here, we perform an online experiment involving simple networks of humans (496 participants in 120 networks) playing a resource-sharing game to which we sometimes add artificial agents (bots). The experiment examines two opposite policies of machine allocation behavior: reciprocal bots , which share all resources reciprocally; and stingy bots , which share no resources at all. We also manipulate the bot’s network position. We show that reciprocal bots make little changes in unequal resource distribution among people. On the other hand, stingy bots balance structural power and improve collective welfare in human groups when placed in a specific network position, although they bestow no wealth on people. Our findings highlight the need to incorporate the human nature of reciprocity and relational interdependence in designing machine behavior in sharing networks. Conscientious machines do not always work for human welfare, depending on the network structure where they interact.

Social robots are becoming increasingly influential in shaping the behavior of humans with whom they interact. Here, we examine how the actions of a social robot can influence human-to-human communication, and not just robot–human communication, using groups of three humans and one robot playing 30 rounds of a collaborative game (n = 51 groups). We find that people in groups with a robot making vulnerable statements converse substantially more with each other, distribute their conversation somewhat more equally, and perceive their groups more positively compared to control groups with a robot that either makes neutral statements or no statements at the end of each round. Shifts in robot speech have the power not only to affect how people interact with robots, but also how people interact with each other, offering the prospect for modifying social interactions via the introduction of artificial agents into hybrid systems of humans and machines.

SignificanceIt is well-known that the presence of others drastically changes how we behave, yet a majority of social neuroscience studies are limited to single-person neuroimaging experiments. Using simultaneous imaging of multiple interacting brains (also known as hyperscanning), recent studies have started to examine the brain basis underlying social cognition beyond passive observation. Here we performed a three-person functional MRI hyperscanning study to investigate the brain-to-brain coupling using a social game of Pictionary. Our results highlight the importance of the theory-of-mind network, especially a brain region in the right temporo-parietal junction for improved triadic collaboration. Our work lays the foundation for studying the brain basis of complex and truly social group interactions. Humans have an extraordinary ability to interact and cooperate with others. Despite the social and evolutionary significance of collaboration, research on finding its neural correlates has been limited partly due to restrictions on the simultaneous neuroimaging of more than one participant (also known as hyperscanning). Several studies have used dyadic fMRI hyperscanning to examine the interaction between two participants. However, to our knowledge, no study to date has aimed at revealing the neural correlates of social interactions using a three-person (or triadic) fMRI hyperscanning paradigm. Here, we simultaneously measured the blood-oxygenation level-dependent signal from 12 triads (n = 36 participants), while they engaged in a collaborative drawing task based on the social game of Pictionary. General linear model analysis revealed increased activation in the brain regions previously linked with the theory of mind during the collaborative phase compared to the independent phase of the task. Furthermore, using intersubject correlation analysis, we revealed increased synchronization of the right temporo‐parietal junction (R TPJ) during the collaborative phase. The increased synchrony in the R TPJ was observed to be positively associated with the overall team performance on the task. In sum, our paradigm revealed a vital role of the R TPJ among other theory-of-mind regions during a triadic collaborative drawing task.

Background Despite great advances in proliferative diabetic retinopathy (PDR) therapy over the last decades, one third of treated patients continue to lose vision. While resident vitreous macrophages called hyalocytes have been implicated in the pathophysiology of vitreoretinal proliferative disease previously, little is known about their exact role in PDR. In this study, we address molecular and cellular alterations in the vitreous of PDR patients as a means towards assessing the potential contribution of hyalocytes to disease pathogenesis. Results A total of 55 patients were included in this study encompassing RNA-Sequencing analysis of hyalocytes isolated from the vitreous of PDR and control patients, multiplex immunoassay and ELISA analyses of vitreous samples from PDR and control patients, as well as isolation and immunohistochemical staining of cultured porcine hyalocytes. Transcriptional analysis revealed an enhanced inflammatory response of hyalocytes contributing to the cytokine pool within the vitreous of PDR patients by expressing interleukin-6, among others. Further, increased angiopoietin-2 expression indicated that hyalocytes from PDR patients undergo a proangiogenic shift and may thus mediate the formation of retinal neovascularizations, the hallmark of PDR. Finally, RNA-Sequencing revealed an upregulation of factors known from hemoglobin catabolism in hyalocytes from PDR patients. By immunohistochemistry, cultured porcine hyalocytes exposed to red blood cells were shown to engulf and phagocytose these, which reveals hyalocytes’ potential to dispose of erythrocytes. Thus, our data suggest a potential role for vitreous macrophages in erythrophagocytosis and, thereby, clearance of vitreous hemorrhage, a severe complication of PDR. Conclusion Our results strongly indicate a critical role for vitreous hyalocytes in key pathophysiological processes of proliferative diabetic retinopathy: inflammation, angiomodulation and erythrophagocytosis. Immunomodulation of hyalocytes may thus prove an essential novel therapeutic approach in diabetic vitreoretinal disease. Graphical Abstract In the left-hand side panel, a human eye of a patient with proliferative diabetic retinopathy with retinal neovascularizations (RNV) growing in the preretinal vitreous is schemed. Hyalocytes, the macrophages of the vitreous, are located mainly in the posterior part of the eye, close to the vitreo-retinal interface and, thus, RNV. Diabetic hyalocytes (on the right) modulate angiogenesis by expressing factors such as interleukin-6 (IL-6) and angiopoietn-2 (ANGPT2), contribute to fibrosis by transdifferentiation in myofiboblasts secreting extracellular matrix (ECM) components, and participate in erythrophagocytosis, as shown in this study

Background The IL-6 cytokine family, with its crucial and pleiotropic intracellular signaling pathway STAT3, is a promising target for treating vasoproliferative retinal diseases. Previous research has shown that IL-6 cis-signaling (via membrane-bound receptors) and trans-signaling (via soluble receptors) can have distinct effects on target cells, leading to their application in various disease treatments. While IL-6 has been extensively studied, less is known about the angiogenic effects of IL-11, another member of the IL-6 family, in the retina. Therefore, the aim of this study was to characterize the effects of IL-11 on retinal angiogenesis. Main text In vitreous samples from proliferative diabetic retinopathy (PDR) patients, elevated levels of IL-11Rα, but not IL-11, were detected. In vitro studies using vascular endothelial cells revealed distinct effects of cis- and trans-signaling: cis-signaling (IL-11 alone) had antiangiogenic effects, while trans-signaling (IL-11 + sIL-11Rα) had proangiogenic and pro-migratory effects. These differences can be attributed to their individual signaling responses and associated transcriptomic changes. Notably, no differences in cis- and trans-signaling were detected in primary mouse Müller cell cultures. STAT3 and STAT1 siRNA knockdown experiments revealed opposing effects on IL-11 signaling, with STAT3 functioning as an antiproliferative and proapoptotic player while STAT1 acts in opposition to STAT3. In vivo, both IL-11 and IL-11 + sIL-11Rα led to a reduction in retinal neovascularization. Immunohistochemical staining revealed Müller cell activation in response to treatment, suggesting that IL-11 affects multiple retinal cell types in vivo beyond vascular endothelial cells. Conclusions Cis- and trans-signaling by IL-11 have contrasting angiomodulatory effects on endothelial cells in vitro. In vivo, cis- and trans-signaling also influence Müller cells, ultimately determining the overall angiomodulatory impact on the retina, highlighting the intricate interplay between vascular and glial cells in the retina.

Inhibitory control, or inhibition, is one of the core executive functions of humans. It contributes to our attention, performance, and physical and mental well-being. Our inhibitory control is modulated by various factors and therefore fluctuates over time. Being able to continuously and unobtrusively assess our inhibitory control and understand the mediating factors may allow us to design intelligent systems that help manage our inhibitory control and ultimately our well-being. The aim of this study is to investigate whether we can assess individuals' inhibitory control using an unobtrusive and scalable approach to identify digital markers that are predictive of changes in inhibitory control. We developed InhibiSense, an app that passively collects the following information: users' behaviors based on their phone use and sensor data, the ground truths of their inhibition control measured with stop-signal tasks (SSTs) and ecological momentary assessments (EMAs), and heart rate information transmitted from a wearable heart rate monitor (Polar H10). We conducted a 4-week in-the-wild study, where participants were asked to install InhibiSense on their phone and wear a Polar H10. We used generalized estimating equation (GEE) and gradient boosting tree models fitted with features extracted from participants' phone use and sensor data to predict their stop-signal reaction time (SSRT), an objective metric used to measure an individual's inhibitory control, and identify the predictive digital markers. A total of 12 participants completed the study, and 2189 EMAs and SST responses were collected. The results from the GEE models suggest that the top digital markers positively associated with an individual's SSRT include phone use burstiness (P=.005), the mean duration between 2 consecutive phone use sessions (P=.02), the change rate of battery level when the phone was not charged (P=.04), and the frequency of incoming calls (P=.03). The top digital markers negatively associated with SSRT include the standard deviation of acceleration (P<.001), the frequency of short phone use sessions (P<.001), the mean duration of incoming calls (P<.001), the mean decibel level of ambient noise (P=.007), and the percentage of time in which the phone was connected to the internet through a mobile network (P=.001). No significant correlation between the participants' objective and subjective measurement of inhibitory control was found. We identified phone-based digital markers that were predictive of changes in inhibitory control and how they were positively or negatively associated with a person's inhibitory control. The results of this study corroborate the findings of previous studies, which suggest that inhibitory control can be assessed continuously and unobtrusively in the wild. We discussed some potential applications of the system and how technological interventions can be designed to help manage inhibitory control.

Recognition studies are a mainstay in HRI. Such studies are often used to test whether a robot’s behavior is interpreted as intended by the designer. When designing recognition studies, researchers have to make important methodological decisions about the empirical study design (e.g., within-/between-subject design) and response format (e.g., forced choice, open text). Using the example of emotional expression recognition studies in HRI, we discuss (i) how theoretical conceptualizations determine methodological choices, (ii) the implications of the designs and response formats. We demonstrate in two experiments ( n = 2654 and n = 419) that conclusions drawn from recognition studies are heavily dependent on study design and response format. We conclude with a set of recommendations for researchers employing recognition studies in their research.