Periodic mobile application (eMOM) with self-tracking of glucose and lifestyle improves treatment of diet-controlled gestational diabetes without human guidance: a randomized controlled trial.

BACKGROUND: Digitalization with minimal human resources could support self-management among women with gestational diabetes and improve maternal and neonatal outcomes. OBJECTIVE: This study aimed to investigate if a periodic mobile application (eMOM) with wearable sensors improves maternal and neonatal outcomes among women with diet-controlled gestational diabetes without additional guidance from healthcare personnel. STUDY DESIGN: Women with gestational diabetes were randomly assigned in a 1:1 ratio at 24 to 28 weeks' gestation to the intervention or the control arm. The intervention arm received standard care in combination with use of the periodic eMOM, whereas the control arm received only standard care. The intervention arm used eMOM with a continuous glucose monitor, an activity tracker, and a food diary 1 week/month until delivery. The primary outcome was the change in fasting plasma glucose from baseline to 35 to 37 weeks' gestation. Secondary outcomes included capillary glucose, weight gain, nutrition, physical activity, pregnancy complications, and neonatal outcomes, such as macrosomia. RESULTS: In total, 148 women (76 in the intervention arm, 72 in the control arm; average age, 34.1±4.0 years; body mass index, 27.1±5.0 kg/m2) were randomized. The intervention arm showed a lower mean change in fasting plasma glucose than the control arm (difference, -0.15 mmol/L vs -2.7 mg/mL; P=.022) and lower capillary fasting glucose levels (difference, -0.04 mmol/L vs -0.7 mg/mL; P=.002). The intervention arm also increased their intake of vegetables (difference, 11.8 g/MJ; P=.043), decreased their sedentary behavior (difference, -27.3 min/d; P=.043), and increased light physical activity (difference, 22.8 min/d; P=.009) when compared with the control arm. In addition, gestational weight gain was lower (difference, -1.3 kg; P=.015), and there were less newborns with macrosomia in the intervention arm (difference, -13.1 %; P=.036). Adherence to eMOM was high (daily use >90%), and the usage correlated with lower maternal fasting (P=.0006) and postprandial glucose levels (P=.017), weight gain (P=.028), intake of energy (P=.021) and carbohydrates (P=.003), and longer duration of the daily physical activity (P=.0006). There were no significant between-arm differences in terms of pregnancy complications. CONCLUSION: Self-tracking of lifestyle factors and glucose levels without additional guidance improves self-management and the treatment of gestational diabetes, which also benefits newborns. The results of this study support the use of digital self-management and education tools in maternity care.

Advanced workstations and collaborative robots: exploiting eye-tracking and cardiac activity indices to unveil senior workers' mental workload in assembly tasks.

Introduction: As a result of Industry 5.0's technological advancements, collaborative robots (cobots) have emerged as pivotal enablers for refining manufacturing processes while re-focusing on humans. However, the successful integration of these cutting-edge tools hinges on a better understanding of human factors when interacting with such new technologies, eventually fostering workers' trust and acceptance and promoting low-fatigue work. This study thus delves into the intricate dynamics of human-cobot interactions by adopting a human-centric view. Methods: With this intent, we targeted senior workers, who often contend with diminishing work capabilities, and we explored the nexus between various human factors and task outcomes during a joint assembly operation with a cobot on an ergonomic workstation. Exploiting a dual-task manipulation to increase the task demand, we measured performance, subjective perceptions, eye-tracking indices and cardiac activity during the task. Firstly, we provided an overview of the senior workers' perceptions regarding their shared work with the cobot, by measuring technology acceptance, perceived wellbeing, work experience, and the estimated social impact of this technology in the industrial sector. Secondly, we asked whether the considered human factors varied significantly under dual-tasking, thus responding to a higher mental load while working alongside the cobot. Finally, we explored the predictive power of the collected measurements over the number of errors committed at the work task and the participants' perceived workload. Results: The present findings demonstrated how senior workers exhibited strong acceptance and positive experiences with our advanced workstation and the cobot, even under higher mental strain. Besides, their task performance suffered increased errors and duration during dual-tasking, while the eye behavior partially reflected the increased mental demand. Some interesting outcomes were also gained about the predictive power of some of the collected indices over the number of errors committed at the assembly task, even though the same did not apply to predicting perceived workload levels. Discussion: Overall, the paper discusses possible applications of these results in the 5.0 manufacturing sector, emphasizing the importance of adopting a holistic human-centered approach to understand the human-cobot complex better.

Supporting the Management of Gestational Diabetes Mellitus With Comprehensive Self-Tracking: Mixed Methods Study of Wearable Sensors.

BACKGROUND: Gestational diabetes mellitus (GDM) is an increasing health risk for pregnant women as well as their children. Telehealth interventions targeted at the management of GDM have been shown to be effective, but they still require health care professionals for providing guidance and feedback. Feedback from wearable sensors has been suggested to support the self-management of GDM, but it is unknown how self-tracking should be designed in clinical care. OBJECTIVE: This study aimed to investigate how to support the self-management of GDM with self-tracking of continuous blood glucose and lifestyle factors without help from health care personnel. We examined comprehensive self-tracking from self-discovery (ie, learning associations between glucose levels and lifestyle) and user experience perspectives. METHODS: We conducted a mixed methods study where women with GDM (N=10) used a continuous glucose monitor (CGM; Medtronic Guardian) and 3 physical activity sensors: activity bracelet (Garmin Vivosmart 3), hip-worn sensor (UKK Exsed), and electrocardiography sensor (Firstbeat 2) for a week. We collected data from the sensors, and after use, participants took part in semistructured interviews about the wearable sensors. Acceptability of the wearable sensors was evaluated with the Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. Moreover, maternal nutrition data were collected with a 3-day food diary, and self-reported physical activity data were collected with a logbook. RESULTS: We found that the CGM was the most useful sensor for the self-discovery process, especially when learning associations between glucose and nutrition intake. We identified new challenges for using data from the CGM and physical activity sensors in supporting self-discovery in GDM. These challenges included (1) dispersion of glucose and physical activity data in separate applications, (2) absence of important trackable features like amount of light physical activity and physical activities other than walking, (3) discrepancy in the data between different wearable physical activity sensors and between CGMs and capillary glucose meters, and (4) discrepancy in perceived and measured quantification of physical activity. We found the body placement of sensors to be a key factor in measurement quality and preference, and ultimately a challenge for collecting data. For example, a wrist-worn sensor was used for longer compared with a hip-worn sensor. In general, there was a high acceptance for wearable sensors. CONCLUSIONS: A mobile app that combines glucose, nutrition, and physical activity data in a single view is needed to support self-discovery. The design should support tracking features that are important for women with GDM (such as light physical activity), and data for each feature should originate from a single sensor to avoid discrepancy and redundancy. Future work with a larger sample should involve evaluation of the effects of such a mobile app on clinical outcomes. TRIAL REGISTRATION: Clinicaltrials.gov NCT03941652; https://clinicaltrials.gov/study/NCT03941652.

Comprehensive self-tracking of blood glucose and lifestyle with a mobile application in the management of gestational diabetes: a study protocol for a randomised controlled trial (eMOM GDM study).

INTRODUCTION: Gestational diabetes (GDM) causes various adverse short-term and long-term consequences for the mother and child, and its incidence is increasing globally. So far, the most promising digital health interventions for GDM management have involved healthcare professionals to provide guidance and feedback. The principal aim of this study is to evaluate the effects of comprehensive and real-time self-tracking with eMOM GDM mobile application (app) on glucose levels in women with GDM, and more broadly, on different other maternal and neonatal outcomes. METHODS AND ANALYSIS: This randomised controlled trial is carried out in Helsinki metropolitan area. We randomise 200 pregnant women with GDM into the intervention and the control group at gestational week (GW) 24-28 (baseline, BL). The intervention group receives standard antenatal care and the eMOM GDM app, while the control group will receive only standard care. Participants in the intervention group use the eMOM GDM app with continuous glucose metre (CGM) and activity bracelet for 1 week every month until delivery and an electronic 3-day food record every month until delivery. The follow-up visit after intervention takes place 3 months post partum for both groups. Data are collected by laboratory blood tests, clinical measurements, capillary glucose measures, wearable sensors, air displacement plethysmography and digital questionnaires. The primary outcome is fasting plasma glucose change from BL to GW 35-37. Secondary outcomes include, for example, self-tracked capillary fasting and postprandial glucose measures, change in gestational weight gain, change in nutrition quality, change in physical activity, medication use due to GDM, birth weight and fat percentage of the child. ETHICS AND DISSEMINATION: The study has been approved by Ethics Committee of the Helsinki and Uusimaa Hospital District. The results will be presented in peer-reviewed journals and at conferences. TRIAL REGISTRATION NUMBER: NCT04714762.

Behavior Change App for Self-management of Gestational Diabetes: Design and Evaluation of Desirable Features.

BACKGROUND: Gestational diabetes (GDM) has considerable and increasing health effects as it raises both the mother's and the offspring's risk for short- and long-term health problems. GDM can usually be treated with a healthier lifestyle, such as appropriate dietary modifications and sufficient physical activity. Although telemedicine interventions providing weekly or more frequent feedback from health care professionals have shown the potential to improve glycemic control among women with GDM, apps without extensive input from health care professionals are limited and have not been shown to be effective. Different features in personalization and support have been proposed to increase the efficacy of GDM apps, but the knowledge of how these features should be designed is lacking. OBJECTIVE: The aim of this study is to investigate how GDM apps should be designed, considering the desirable features based on the previous literature. METHODS: We designed an interactive GDM prototype app that provided example implementations of desirable features, such as providing automatic and personalized suggestions and social support through the app. Women with GDM explored the prototype and provided feedback in semistructured interviews. RESULTS: We identified that (1) self-tracking data in GDM apps should be extended with written feedback, (2) habits and goals should be highly customizable to be useful, (3) the app should have different functions to provide social support, and (4) health care professionals should be notified through the app if something unusual occurs. In addition, we found 2 additional themes. First, basic functionalities that are fast to learn by women with GDM who have recently received the diagnosis should be provided, but there should also be deeper features to maintain interest for women with GDM at a later stage of pregnancy. Second, as women with GDM may have feelings of guilt, the app should have a tolerance for and a supporting approach to unfavorable behavior. CONCLUSIONS: The feedback on the GDM prototype app supported the need for desirable features and provided new insights into how these features should be incorporated into GDM apps. We expect that following the proposed designs and feedback will increase the efficacy of GDM self-management apps. TRIAL REGISTRATION: ClinicalTrials.gov NCT03941652; https://clinicaltrials.gov/ct2/show/NCT03941652.

Increasing self-other similarity modulates ethnic bias in sensorimotor resonance to others' pain.

The tendency to simulate the pain of others within our own sensorimotor systems is a vital component of empathy. However, this sensorimotor resonance is modulated by a multitude of social factors including similarity in bodily appearance, e.g. skin colour. The current study investigated whether increasing self-other similarity via virtual transfer to another colour body reduced ingroup bias in sensorimotor resonance. A sample of 58 white participants was momentarily transferred to either a black or a white body using virtual reality technology. We then employed electroencephalography to examine event-related desynchronization (ERD) in the sensorimotor beta (13-23 Hz) oscillations while they viewed black, white and violet photorealistic virtual agents being touched with a noxious or soft object. While the noxious treatment of a violet agent did not increase beta ERD, amplified beta ERD in response to black agent's noxious vs soft treatment was found in perceivers transferred to a black body. Transfer to the white body dismissed the effect. Further exploratory analysis implied that the pain-related beta ERD occurred only when the agent and the participant were of the same colour. The results suggest that even short-lasting changes in bodily resemblance can modulate sensorimotor resonance to others' perceived pain.

Facing with Collaborative Robots: The Subjective Experience in Senior and Younger Workers.

In the past few years, collaborative robots (i.e., cobots) have been largely adopted within industrial manufacturing. Although robots can support companies and workers in carrying out complex activities and improving productivity, human factors related to cobot operators have not yet been thoroughly investigated. The present study aims to understand the subjective experience of younger and senior workers interacting with an industrial collaborative robot. Results show that workers' acceptance of cobots is high, regardless of age and control modality used. Interesting differences between seniors and younger adults emerged in the evaluations of user experience, usability, and perceived workload of participants and are detailed and commented in the last part of the work.

Characterizing the Quality of Insight by Interactions: A Case Study.

Understanding the quality of insight has become increasingly important with the trend of allowing users to post comments during visual exploration, yet approaches for qualifying insight are rare. This article presents a case study to investigate the possibility of characterizing the quality of insight via the interactions performed. To do this, we devised the interaction of a visualization tool-MediSyn-for insight generation. MediSyn supports five types of interactions: selecting, connecting, elaborating, exploring, and sharing. We evaluated MediSyn with 14 participants by allowing them to freely explore the data and generate insights. We then extracted seven interaction patterns from their interaction logs and correlated the patterns to four aspects of insight quality. The results show the possibility of qualifying insights via interactions. Among other findings, exploration actions can lead to unexpected insights; the drill-down pattern tends to increase the domain values of insights. A qualitative analysis shows that using domain knowledge to guide exploration can positively affect the domain value of derived insights. We discuss the study's implications, lessons learned, and future research opportunities.

Integrating neurophysiologic relevance feedback in intent modeling for information retrieval.

The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first-of-its-kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology-based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).

The semiotics of the message and the messenger: How nonverbal communication affects fairness perception.

Nonverbal communication determines much of how we perceive explicit, verbal messages. Facial expressions and social touch, for example, influence affinity and conformity. To understand the interaction between nonverbal and verbal information, we studied how the psychophysiological time-course of semiotics-the decoding of the meaning of a message-is altered by interpersonal touch and facial expressions. A virtual-reality-based economic decision-making game, ultimatum, was used to investigate how participants perceived, and responded to, financial offers of variable levels of fairness. In line with previous studies, unfair offers evoked medial frontal negativity (MFN) within the N2 time window, which has been interpreted as reflecting an emotional reaction to violated social norms. Contrary to this emotional interpretation of the MFN, however, nonverbal signals did not modulate the MFN component, only affecting fairness perception during the P3 component. This suggests that the nonverbal context affects the late, but not the early, stage of fairness perception. We discuss the implications of the semiotics of the message and the messenger as a process by which parallel information sources of "who says what" are integrated in reverse order: of the message, then the messenger.

Motivational intensity and visual word search: Layout matters.

Motivational intensity has been previously linked to information processing. In particular, it has been argued that affects which are high in motivational intensity tend to narrow cognitive scope. A similar effect has been attributed to negative affect, which has been linked to narrowing of cognitive scope. In this paper, we investigated how these phenomena manifest themselves during visual word search. We conducted three studies in which participants were instructed to perform word category identification. We manipulated motivational intensity by controlling reward expectations and affect via reward outcomes. Importantly, we altered visual search paradigms, assessing the effects of affective manipulations as modulated by information arrangement. We recorded multiple physiological signals (EEG, EDA, ECG and eye tracking) to assess whether motivational states can be predicted by physiology. Across the three studies, we found that high motivational intensity narrowed visual attentional scope by altering visual search strategies, especially when information was displayed sparsely. Instead, when information was vertically listed, approach-directed motivational intensity appeared to improve memory encoding. We also observed that physiology, in particular eye tracking, may be used to detect biases induced by motivational intensity, especially when information is sparsely organised.

Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge.

Motivation: Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large. Results: We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach. Availability and implementation: Source code implementing the introduced computational methods is freely available at https://github.com/AaltoPML/knowledge-elicitation-for-precision-medicine. Supplementary information: Supplementary data are available at Bioinformatics online.

MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection.

BACKGROUND: Dispersed biomedical databases limit user exploration to generate structured knowledge. Linked Data unifies data structures and makes the dispersed data easy to search across resources, but it lacks supporting human cognition to achieve insights. In addition, potential errors in the data are difficult to detect in their free formats. Devising a visualization that synthesizes multiple sources in such a way that links between data sources are transparent, and uncertainties, such as data conflicts, are salient is challenging. RESULTS: To investigate the requirements and challenges of uncertainty-aware visualizations of linked data, we developed MediSyn, a system that synthesizes medical datasets to support drug treatment selection. It uses a matrix-based layout to visually link drugs, targets (e.g., mutations), and tumor types. Data uncertainties are salient in MediSyn; for example, (i) missing data are exposed in the matrix view of drug-target relations; (ii) inconsistencies between datasets are shown via overlaid layers; and (iii) data credibility is conveyed through links to data provenance. CONCLUSIONS: Through the synthesis of two manually curated datasets, cancer treatment biomarkers and drug-target bioactivities, a use case shows how MediSyn effectively supports the discovery of drug-repurposing opportunities. A study with six domain experts indicated that MediSyn benefited the drug selection and data inconsistency discovery. Though linked publication sources supported user exploration for further information, the causes of inconsistencies were not easy to find. Additionally, MediSyn could embrace more patient data to increase its informativeness. We derive design implications from the findings.

Manipulating Bodily Presence Affects Cross-Modal Spatial Attention: A Virtual-Reality-Based ERP Study.

Earlier studies have revealed cross-modal visuo-tactile interactions in endogenous spatial attention. The current research used event-related potentials (ERPs) and virtual reality (VR) to identify how the visual cues of the perceiver's body affect visuo-tactile interaction in endogenous spatial attention and at what point in time the effect takes place. A bimodal oddball task with lateralized tactile and visual stimuli was presented in two VR conditions, one with and one without visible hands, and one VR-free control with hands in view. Participants were required to silently count one type of stimulus and ignore all other stimuli presented in irrelevant modality or location. The presence of hands was found to modulate early and late components of somatosensory and visual evoked potentials. For sensory-perceptual stages, the presence of virtual or real hands was found to amplify attention-related negativity on the somatosensory N140 and cross-modal interaction in somatosensory and visual P200. For postperceptual stages, an amplified N200 component was obtained in somatosensory and visual evoked potentials, indicating increased response inhibition in response to non-target stimuli. The effect of somatosensory, but not visual, N200 enhanced when the virtual hands were present. The findings suggest that bodily presence affects sustained cross-modal spatial attention between vision and touch and that this effect is specifically present in ERPs related to early- and late-sensory processing, as well as response inhibition, but do not affect later attention and memory-related P3 activity. Finally, the experiments provide commeasurable scenarios for the estimation of the signal and noise ratio to quantify effects related to the use of a head mounted display (HMD). However, despite valid a-priori reasons for fearing signal interference due to a HMD, we observed no significant drop in the robustness of our ERP measurements.

Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals.

Finding relevant information from large document collections such as the World Wide Web is a common task in our daily lives. Estimation of a user's interest or search intention is necessary to recommend and retrieve relevant information from these collections. We introduce a brain-information interface used for recommending information by relevance inferred directly from brain signals. In experiments, participants were asked to read Wikipedia documents about a selection of topics while their EEG was recorded. Based on the prediction of word relevance, the individual's search intent was modeled and successfully used for retrieving new relevant documents from the whole English Wikipedia corpus. The results show that the users' interests toward digital content can be modeled from the brain signals evoked by reading. The introduced brain-relevance paradigm enables the recommendation of information without any explicit user interaction and may be applied across diverse information-intensive applications.

The self in conflict: actors and agency in the mediated sequential Simon task.

Executive control refers to the ability to withstand interference in order to achieve task goals. The effect of conflict adaptation describes that after experiencing interference, subsequent conflict effects are weaker. However, changes in the source of conflict have been found to disrupt conflict adaptation. Previous studies indicated that this specificity is determined by the degree to which one source causes episodic retrieval of a previous source. A virtual reality version of the Simon task was employed to investigate whether changes in a visual representation of the self would similarly affect conflict adaptation. Participants engaged in a mediated Simon task via 3D "avatar" models that either mirrored the participants' movements, or were presented statically. A retrieval cue was implemented as the identity of the avatar: switching it from a male to a female avatar was expected to disrupt the conflict adaptation effect (CAE). The results show that only in static conditions did the CAE depend on the avatar identity, while in dynamic conditions, changes did not cause disruption. We also explored the effect of conflict and adaptation on the degree of movement made with the task-irrelevant hand and replicated the reaction time pattern. The findings add to earlier studies of source-specific conflict adaptation by showing that a visual representation of the self in action can provide a cue that determines episodic retrieval. Furthermore, the novel paradigm is made openly available to the scientific community and is described in its significance for studies of social cognition, cognitive psychology, and human-computer interaction.

The meaning of the virtual Midas touch: an ERP study in economic decision making.

The Midas touch refers to the altruistic effects of a brief touch. Though these effects have often been replicated, they remain poorly understood. We investigate the psychophysiology of the effect using remotely transmitted, precisely timed, tactile messages in an economic decision-making game called Ultimatum. Participants were more likely to accept offers after receiving a remotely transmitted touch. Furthermore, we found distinct effects of touch on event-related potentials evoked by (a) feedback regarding accepted and rejected offers, (b) decision cues related to proposals, and (c) the haptic and auditory cues themselves. In each case, a late positive effect of touch was observed and related to the P3. Given the role of the P3 in memory-related functions, the results indicate an indirect relationship between touch and generosity that relies on memory. This hypothesis was further tested and confirmed in the positive effects of touch on later proposals.

Evaluating User Experience of Augmented Reality Eyeglasses.

Augmented reality based applications have been experimented with in various contexts. Typically, the interaction is supported by handled devices, which, in specific scenarios, may hinder the interaction and spoil the experience of use, as the user is forced to hold the device and to keep her eyes on it at all times. The recent launch on the market of light-weight, unobtrusive head-mounted displays may change this circumstance. Nevertheless, investigations are needed to understand if such head-worn devices effectively outperform handheld devices in terms of comfort and pleasant experience of use. Here we present two experiments aimed at assessing the comfort of wearing a head-worn, see-through AR viewer in both a controlled and a natural setting. Besides the comfort of wearing the device, aspects related to the user experience were also investigated in the field evaluation. Our findings suggest that the head-mounted display examined is comfortable to wear regardless of the context of use. Interestingly in the field trails, participants did not express concern for the impression they would have made on other people and the experience of use was overall pleasant. Possible issues related to visual fatigue emerged.

Keep your opponents close: social context affects EEG and fEMG linkage in a turn-based computer game.

In daily life, we often copy the gestures and expressions of those we communicate with, but recent evidence shows that such mimicry has a physiological counterpart: interaction elicits linkage, which is a concordance between the biological signals of those involved. To find out how the type of social interaction affects linkage, pairs of participants played a turn-based computer game in which the level of competition was systematically varied between cooperation and competition. Linkage in the beta and gamma frequency bands was observed in the EEG, especially when the participants played directly against each other. Emotional expression, measured using facial EMG, reflected this pattern, with the most competitive condition showing enhanced linkage over the facial muscle-regions involved in smiling. These effects were found to be related to self-reported social presence: linkage in positive emotional expression was associated with self-reported shared negative feelings. The observed effects confirmed the hypothesis that the social context affected the degree to which participants had similar reactions to their environment and consequently showed similar patterns of brain activity. We discuss the functional resemblance between linkage, as an indicator of a shared physiology and affect, and the well-known mirror neuron system, and how they relate to social functions like empathy.