Ambiguous Situations Research Articles

Толерантность к неопределенности у студентов-пользователей онлайн-платформ с разными статусами виртуальной идентичности

<p style="text-align: justify;"><strong>Objective. </strong>Assessment the contribution of virtual identity statuses and identity components to variation in uncertainty tolerance of ambiguity among users of online platforms in age of students. <br><strong>Background. </strong>The users of online platforms reconsider identity commitments and explore identity alternatives in conditions of information uncertainty and ambiguity. Previous studies have clearly established what non-adaptive processes of identity development in real environments compensating for by problematic use of online platforms and assessing with low levels of tolerance for uncertainty, but little is known about the impact of virtual identity on tolerance of ambiguity. <br><strong>Study design. </strong>We how ambiguity tolerance relates to virtual identity using a survey method among students who are users of online platforms. The associations of virtual identity and tolerance of ambiguity were examined using Pearson's r-test, and linear regression (Fisher's F-test), and structural equation modeling (SEM). <br><strong>Participants. </strong>The sample consisted of data obtained from 350 students (35% male; 65% female), aged 18 to 25 (<em>M</em> = 19,8; <em>SD</em> = 1,62), users of online platforms. <br><strong>Measurements. </strong>The study measured the indicators of virtual identity (VISI) and tolerance of ambiguity (MSTAT-I) using methods adapted to the Russian research pool. <br><strong>Results. </strong>Commitment of virtual identity increases tolerance of ambiguity – a general measure, uncertainty preference, of attitudes towards new, ambiguous, difficult to understand, uncertain and internally contradictory environmental stimuli. The Exploration of virtual identity is associated with a preference for uncertainty. We have found that age raised the levels of the understanding of complex information and reduces the levels of the interest in new stimuli decreases. The outcomes of this study revealed that age gradient was found to be more pronounced in males than females, such that users of the males demonstrated higher growth forecast of levels of a tolerance of ambiguity. Moreover, a virtual identity status influences tolerance for ambiguity, such that users with a diffuse virtual identity reported a tendency to perceive ambiguity (ambiguous and new) situations as sources of threat. <br><strong>Conclusions. </strong>The current study sparse existing literature of psychology with new data of whether tolerance of ambiguity relates robustly to identity, and support predictions of variability in tolerance of ambiguity depending on the virtual identity status, age and gender among users of online platforms in age of students. The pre-adaptive mechanism that links components of identity search and acceptance in virtual environments use to tolerance of ambiguity, this mechanism may be further moderated by age and gender factors.</p>

A mobile intervention to reduce anxiety among university students, faculty, and staff: Mixed methods study on users' experiences.

Anxiety is highly prevalent among college communities, with significant numbers of students, faculty, and staff experiencing severe anxiety symptoms. Digital mental health interventions (DMHIs), including Cognitive Bias Modification for Interpretation (CBM-I), offer promising solutions to enhance access to mental health care, yet there is a critical need to evaluate user experience and acceptability of DMHIs. CBM-I training targets cognitive biases in threat perception, aiming to increase cognitive flexibility by reducing rigid negative thought patterns and encouraging more benign interpretations of ambiguous situations. This study used questionnaire and interview data to gather feedback from users of a mobile application called "Hoos Think Calmly" (HTC), which offers brief CBM-I training doses in response to stressors commonly experienced by students, faculty, and staff at a large public university. Mixed methods were used for triangulation to enhance the validity of the findings. Qualitative data was collected through semi-structured interviews from a subset of participants (n = 22) and analyzed thematically using an inductive framework, revealing five main themes: Effectiveness of the Training Program; Feedback on Training Sessions; Barriers to Using the App; Use Patterns; and Suggestions for Improvement. Additionally, biweekly user experience questionnaires sent to all participants in the active treatment condition (n = 134) during the parent trial showed the most commonly endorsed response (by 43.30% of participants) was that the program was somewhat helpful in reducing or managing their anxiety or stress. There was overall agreement between the quantitative and qualitative findings, indicating that graduate students found it the most effective and relatable, with results being moderately positive but somewhat more mixed for undergraduate students and staff, and least positive for faculty. Findings point to clear avenues to enhance the relatability and acceptability of DMHIs across diverse demographics through increased customization and personalization, which may help guide development of future DMHIs.

Early Attrition Prediction for Web-Based Interpretation Bias Modification to Reduce Anxious Thinking: A Machine Learning Study.

Digital mental health is a promising paradigm for individualized, patient-driven health care. For example, cognitive bias modification programs that target interpretation biases (cognitive bias modification for interpretation [CBM-I]) can provide practice thinking about ambiguous situations in less threatening ways on the web without requiring a therapist. However, digital mental health interventions, including CBM-I, are often plagued with lack of sustained engagement and high attrition rates. New attrition detection and mitigation strategies are needed to improve these interventions. This paper aims to identify participants at a high risk of dropout during the early stages of 3 web-based trials of multisession CBM-I and to investigate which self-reported and passively detected feature sets computed from the participants interacting with the intervention and assessments were most informative in making this prediction. The participants analyzed in this paper were community adults with traits such as anxiety or negative thinking about the future (Study 1: n=252, Study 2: n=326, Study 3: n=699) who had been assigned to CBM-I conditions in 3 efficacy-effectiveness trials on our team's public research website. To identify participants at a high risk of dropout, we created 4 unique feature sets: self-reported baseline user characteristics (eg, demographics), self-reported user context and reactions to the program (eg, state affect), self-reported user clinical functioning (eg, mental health symptoms), and passively detected user behavior on the website (eg, time spent on a web page of CBM-I training exercises, time of day during which the exercises were completed, latency of completing the assessments, and type of device used). Then, we investigated the feature sets as potential predictors of which participants were at high risk of not starting the second training session of a given program using well-known machine learning algorithms. The extreme gradient boosting algorithm performed the best and identified participants at high risk with macro-F1-scores of .832 (Study 1 with 146 features), .770 (Study 2 with 87 features), and .917 (Study 3 with 127 features). Features involving passive detection of user behavior contributed the most to the prediction relative to other features. The mean Gini importance scores for the passive features were as follows: .033 (95% CI .019-.047) in Study 1; .029 (95% CI .023-.035) in Study 2; and .045 (95% CI .039-.051) in Study 3. However, using all features extracted from a given study led to the best predictive performance. These results suggest that using passive indicators of user behavior, alongside self-reported measures, can improve the accuracy of prediction of participants at a high risk of dropout early during multisession CBM-I programs. Furthermore, our analyses highlight the challenge of generalizability in digital health intervention studies and the need for more personalized attrition prevention strategies.

Stability and Change in Intolerance of Uncertainty and Its Association with Interpretation Bias in Social Situations: A Longitudinal Study of Italian Adolescents

Background: Intolerance of Uncertainty (IU) is a transdiagnostic factor measured using the Intolerance of Uncertainty Scale-Revised (IUS-R). This study evaluated the stability and change in adolescents’ IU over a three-month period using a modified version of the scale. Methods: A two-wave study was conducted, with 290 adolescents responding to an online survey at baseline and 199 at follow-up. The original IUS-R was modified to probe the rating of the current perceived state of IU, rather than typical experience. The link between IU variability and the development of interpretation bias in ambiguous social situations at follow-up was explored. Structural Equation Modeling and Linear Mixed Model analyses were performed to assess the longitudinal measurement invariance and responsiveness of the Modified IUS-R scale. Results: The scale demonstrated good psychometric properties and full measurement invariance. Individual participants showed significant variability in baseline IU levels but not in the degree of change. A reliable change in scores was observed in 8% of adolescents. The Modified IUS-R predicted interpretation bias in social situations at follow-up. Conclusions: Significant inter-individual-level variation in IU suggests this tool may be useful for detecting changes in IU and predicting significant health outcomes. Future studies should further address the assessment of changing IU with longer timeframes.

State Tolerance of Ambiguity Drives Performance and Well-Being Outcomes Within Project Managers: Evidence from a Five-Wave Longitudinal Study

This study investigates Tolerance of Ambiguity in project managers, focusing on how their ability to navigate ambiguous situations varies over time and influences their performance. In a four-week study of 275 project managers, we tracked changes in Tolerance of Ambiguity in project managers and found that these temporary fluctuations were significantly associated with positive mood, adaptive performance, and project progress. Factors such as self-control, coping skills, creativity, and supportive leadership emerged as key drivers of these changes. This study highlights Tolerance of Ambiguity as a dynamic, context-dependent competency in project managers, offering novel insights for enhancing project managers’ ability to navigate ambiguous situations.

The outcome‐representation learning model of decision‐making capacity in mild cognitive impairment

AbstractBackgroundThe decision‐making capacity of persons with mild cognitive impairment (MCI) has not been fully explored. This study aimed to examine the decision‐making capacity in MCI using the outcome‐representation learning model.Method52 persons with MCI and 49 healthy controls were recruited in the study. The Iowa Gambling Task (IGT) was administered to measure decision‐making under ambiguity situations. Five parameters, including the reward learning rate, the punishment learning rate, forgetfulness, win perseverance and deck perseverance, were included in the outcome‐representation learning (ORL) model to capture changes in decision‐making abilities in MCI patients.ResultIndividuals with MCI exhibited reduced learning from losses outcomes and similar learning from wins outcomes compared to healthy controls. Additionally, MCI individuals exhibited less susceptibility to forgetfulness in the IGT compared to controls, suggesting that memory fading of choices made by MCI patients was not improved. Although MCI individuals still favored high‐win frequency decks like controls, their preference appeared to dimmish from the first to second completion of the IGT.ConclusionThe findings suggest that individuals with mild cognitive impairment have difficulty in learning lessons from decision‐making failures in complex contexts. It offers a potential novel computational neurocognitive marker for mild cognitive impairment.

Trickster teaching and the anthropocene: disrupting the explicitification of pedagogy, people and planet

In cultures committed to making everything explicit, calling for trickery in education seems suspicious. However, for better or worse, trickery is already prevalent in education. In particular, it quietly persists in assumptions it is possible and desirable to be explicit about many educational matters, including learning intentions, and knowledge of self and others. In this philosophical and autobiographical inquiry, we explore trickery’s role in disrupting and exposing this presumptive transparency, and in working with the educational possibilities that then arise. In parallel, we take note that the Anthropocene is an ambiguous and undetermined situation, which promises to trick whoever seeks secure diagnoses and prescriptions of what is at stake. There are therefore confluences between disrupting explicitification in classroom ecologies and ecologies of the broader world. Suspicious of habits that foreclose people’s ability to respond to events as they arise, trickery surfaces and engage ambiguities, contradictions, and potentials inherent in the invisible and assumed. Despite such antics, the trickster is no mere jokester. As she deals in duplicity, confusion, and concealment, she attends evermore carefully to sincerity, trust, and revelation, to the freedom of people and planet, and to the ongoing threats and promises of a perpetual return to harmony.

Decision making in ambiguous situations: Determining recipients of cash social assistance in Indonesia

Determining recipients of Direct Cash Assistance from Village Funds in Indonesia is a complex process often marked by various challenges, especially in ambiguous situations. In this situation, leadership communication strategies become very important to ensure that decisions are effective and acceptable to the community. This study aims to analyze leadership communication strategies in decision-making in ambiguous situations to determine recipients of direct cash assistance in Indonesia. This study was conducted in two village areas, namely Karangtengah village in Cilongok District, Banyumas Regency, Central Java, Indonesia as a rural area and Rawapanjang village in Bojonggede District, Bogor Regency, West Java, Indonesia as a semi-rural-area. This study uses a qualitative descriptive research design with a case study approach. Data analysis was conducted inductively with a thematic approach, where relevant parts of the data were given specific labels or codes. These codes are based on themes or categories that emerged from interviews with informants. The coded data is presented narratively and in tabular visualization. The study results show that Karangtengah village and Rawapanjang village, in the implementation of BLT-DD, have the power of sensemaking in the direct involvement of village leaders in the field. They do not only rely on data but also directly verify the conditions of residents so that the decisions taken are more relevant to the situation in the field. Overall, Karangtengah village excels in the power of sensemaking and leadership communication through a collaborative approach involving various stakeholders. Active community involvement in village deliberations creates a decision-making process that is more representative and responsive to the needs of residents.

Disagreements in Medical Ethics Question Answering Between Large Language Models and Physicians.

Medical ethics is inherently complex, shaped by a broad spectrum of opinions, experiences, and cultural perspectives. The integration of large language models (LLMs) in healthcare is new and requires an understanding of their consistent adherence to ethical standards. To compare the agreement rates in answering questions based on ethically ambiguous situations between three frontier LLMs (GPT-4, Gemini-pro-1.5, and Llama-3-70b) and a multi-disciplinary physician group. In this cross-sectional study, three LLMs generated 1,248 medical ethics questions. These questions were derived based on the principles outlined in the American College of Physicians Ethics Manual. The topics spanned traditional, inclusive, interdisciplinary, and contemporary themes. Each model was then tasked in answering all generated questions. Twelve practicing physicians evaluated and responded to a randomly selected 10% subset of these questions. We compared agreement rates in question answering among the physicians, between the physicians and LLMs, and among LLMs. The models generated a total of 3,744 answers. Despite physicians perceiving the questions' complexity as moderate, with scores between 2 and 3 on a 5-point scale, their agreement rate was only 55.9%. The agreement between physicians and LLMs was also low at 57.9%. In contrast, the agreement rate among LLMs was notably higher at 76.8% (p < 0.001), emphasizing the consistency in LLM responses compared to both physician-physician and physician-LLM agreement. LLMs demonstrate higher agreement rates in ethically complex scenarios compared to physicians, suggesting their potential utility as consultants in ambiguous ethical situations. Future research should explore how LLMs can enhance consistency while adapting to the complexities of real-world ethical dilemmas.

Synapses learn to utilize stochastic pre-synaptic release for the prediction of postsynaptic dynamics.

Synapses in the brain are highly noisy, which leads to a large trial-by-trial variability. Given how costly synapses are in terms of energy consumption these high levels of noise are surprising. Here we propose that synapses use noise to represent uncertainties about the somatic activity of the postsynaptic neuron. To show this, we developed a mathematical framework, in which the synapse as a whole interacts with the soma of the postsynaptic neuron in a similar way to an agent that is situated and behaves in an uncertain, dynamic environment. This framework suggests that synapses use an implicit internal model of the somatic membrane dynamics that is being updated by a synaptic learning rule, which resembles experimentally well-established LTP/LTD mechanisms. In addition, this approach entails that a synapse utilizes its inherently noisy synaptic release to also encode its uncertainty about the state of the somatic potential. Although each synapse strives for predicting the somatic dynamics of its postsynaptic neuron, we show that the emergent dynamics of many synapses in a neuronal network resolve different learning problems such as pattern classification or closed-loop control in a dynamic environment. Hereby, synapses coordinate themselves to represent and utilize uncertainties on the network level in behaviorally ambiguous situations.

The association between aberrant salience and psychotic experiences in general population twins, and genetic vulnerability as a modifier

BackgroundPrevious studies assessing the hypothesis that the construct of ‘aberrant salience’ is associated with psychosis and psychotic symptoms showed conflicting results. For this reason, the association between measures to index aberrant salience and subclinical psychotic symptoms in a general population sample was analysed. In addition, genetic vulnerability was added to the analysis as a modifier to test the hypothesis that modification by genetic vulnerability may explain variability in the results.MethodsThe TwinssCan project obtained data from general population twins (N = 887). CAPE (Community Assessment of Psychic Experience) scores were used to index psychotic experiences. Aberrant salience was assessed with white noise task and ambiguous situations task.ResultsMeasures of aberrant salience were not associated with psychotic experiences, nor was there evidence for an interaction with genetic predisposition in this association (Z = 1.08, p = 0.282).ConclusionsVarious studies including the present could not replicate the association between aberrant salience and psychotic experiences in general population samples. The conflicting findings might be explained by moderation by genetic vulnerability, but results are inconsistent. If there was evidence for a main effect or interaction, this was in the positive symptom scale only. On the other hand, the association was more robust in so-called ‘ultra-high risk’ patients and first episode psychosis patients. Thus, this association may represent a state-dependent association, present only at the more severe end of the psychosis spectrum.

Classical Logic, Some of its Limits, and the Psychological Normality

This article identifies some situations of linguistic ambiguities and how general logic tries to solve them and it analyzes the influence it can have in particular situations. Since the topic is vast, we stopped at the ambiguities of language caused by identity. First, the attempt to solve the problem was the enunciation of the laws of logic, namely the principle of identity, and we followed its limits. There are analyzed three aspects emphasizing the degree of remaining ambiguity: the relationship between intension and extension, vague terms, and symbolical-metaphorical thinking. Each of the examples given accentuated the limits of classical logic in the face of the natural language ambiguity problems. Ambiguous situations do not only have effects on logical or communicative levels; we also showed the impact on the fields of psycho-social interventions: therapy, counseling, and education.

Acceptability of artificial intelligence in dental radiology among patients in India: are we ready for this revolution?

In recent times, artificial Intelligence (AI) has gained popularity in medical as well as dental radiology. Studies have been conducted among medical and dental students and professionals about the knowledge and understanding towards AI. The aim of this study was to investigate the perceptions and acceptability of AI in dental radiology among a group of Indian patients seeking dental treatment. A cross-sectional research was planned with a validated questionnaire, containing ten close ended questions amongst 1562 patients. Their sociodemographic characters, opinions and attitudes regarding AI and feasibility of acceptance of AI-based dental radiological diagnosis among patients was evaluated. The study sample was divided in various groups on the basis of their age; group-1(16-30years), group-2(31-45years) and group-3(>45years), educational status and urban/rural background. Statistical analysis was done by Chi-square test with significance value set at p< 0.005. The participants possessed impressive knowledge about AI. Patients' awareness, attitudes and acceptability towards AI for dental radiographic diagnosis were substantially influenced by age, education level and residential background. Although many of them, especially the urban and more educated participants believed that AI could be more accurate, they preferred the human judgement. Overall, a negative attitude in terms of acceptability of AI in dental radiology was observed in this study. Participants opined that AI should only be used as an auxiliary tool and valued clinical judgment over AI in ambiguous situations. It is recommended that this promising technological advancement can be used for initial screening in dental radiology.

Signless Laplacian energy aware decision making for electric car batteries based on intuitionistic fuzzy graphs.

Fuzzy graphs (FGs) contain dual-nature characteristics that may be extended to intuitionistic fuzzy graphs. These FGs are better at capturing ambiguity in situations in reality involving decision-making than FGs. In this paper, we address decision-making problems based on intuitionistic fuzzy preference relations (IFPRs) by utilizing Signless Laplacian energy (SLE), intuitionistic fuzzy weighted averaging (IFWA), and intuitionistic fuzzy weighted averaging geometric (IFWAG). The paper suggests an approach that makes use of intuitionistic fuzzy graphs (IFG) and IFPR to optimize batteries for electric vehicles. Electric vehicles (EVs) performance, range, and efficiency are all dependent on battery technology. Research and technological developments may help remove adoption hurdles and increase public interest in EVs. Producers of batteries and automakers are investing in recycling and cost-cutting measures for manufacture. With the use of carbon nanotube electrodes, battery power may be increased tenfold beyond existing capabilities. In a procedure called group decision-making, experts evaluate and choose the best options based on present standards. This method provides crucial data for well-informed decision-making by capturing ambiguity and uncertainty in real-world decision-making. The strategy improves decision-making and maximizes profits, giving investors a useful foundation for choosing environmentally friendly electric vehicle batteries.

CURRENT ISSUES OF IMPORT OF MEDICAL DEVICES AND EQUIPMENT TO THE RUSSIAN FEDERATION

The import of medical products remains an important topic at present, given the relevance of healthcare in connection with the sanctions policy of Western countries and the ambiguous situation with foreign supplies of medical products. The situation with the dominant share of imported medical products in the Russian market highlights the dependence on international supplies and technologies in the healthcare sector. The policies and sanctions imposed by the European Union and other countries regarding the supply of medical equipment to Russia cause certain restrictions and difficulties due to the current political situation. Although these goods are classified as vital and do not fall under sanctions, many companies from Europe and the United States have suspended deliveries due to the so-called «moral embargo». Lack of access to modern equipment and medicines can lead to a deterioration in the quality of medical care, an increase in waiting times for treatment, as well as possible complications and a deterioration in the prognosis of the disease in patients. The article analyzes the dynamics of the medical products market over the past few years, and identifies changes in the structure of the medical devices market. The share in the import of medical equipment to Russia was also studied. In addition, the prospects for the development of the medical industry are considered and the current situation is assessed. To solve the problem in the short term, the necessary measures have been proposed to include vital medical devices and equipment in the list of the Ministry of Industry and Trade for its import according to the rules of parallel import.

Orbitofrontal Cortex Mediates Sustained Basolateral Amygdala Encoding of Cued Reward-Seeking States.

Basolateral amygdala (BLA) neurons are engaged by emotionally salient stimuli. An area of increasing interest is how BLA dynamics relate to evolving reward-seeking behavior, especially under situations of uncertainty or ambiguity. Here, we recorded the activity of individual BLA neurons in male rats across the acquisition and extinction of conditioned reward seeking. We assessed ongoing neural dynamics in a task where long reward cue presentations preceded an unpredictable, variably time reward delivery. We found that, with training, BLA neurons discriminated the CS+ and CS- cues with sustained cue-evoked activity that correlated with behavior and terminated only after reward receipt. BLA neurons were bidirectionally modulated, with a majority showing prolonged inhibition during cued reward seeking. Strikingly, population-level analyses revealed that neurons showing cue-evoked inhibitions and those showing excitations similarly represented the CS+ and behavioral state. This sustained population code rapidly extinguished in parallel with conditioned behavior. We next assessed the contribution of the orbitofrontal cortex (OFC), a major reciprocal partner to the BLA. Inactivation of the OFC while simultaneously recording in the BLA revealed a blunting of sustained cue-evoked activity in the BLA that accompanied reduced reward seeking. Optogenetic disruption of BLA activity and OFC terminals in the BLA also reduced reward seeking. Our data indicate that the BLA represents reward-seeking states via sustained, bidirectional cue-driven neural encoding. This code is regulated by cortical input and is important for the maintenance of vigilant reward-seeking behavior.

Shortest Path Problem under Pythagorean Fuzzy Network Using Dijkstra’s Algorithm

In this article investigates the Shortest Path Problem (SPP) in uncertain settings using Pythagorean fuzzy numbers (PFNs). A heuristic methodology using modified Dijkstra's algorithm is developed to exploit error, uncertainty, and partial truth for a low-cost solution. The method calculates minimum distance between starting and destination nodes and evaluates its effectiveness. Pythagorean fuzzy graphs can handle ambiguous situations.

The association between belief in a just world and endorsing conspiracy theories is moderated by ambiguity tolerance, but not scientific reasoning

Both the belief in a just world (BJW) and conspiracy theory (CT) endorsement assist people to make sense of their world when they encounter ambiguous situations. When one endorses BJW as a generalised framework for understanding their world, however, they may be less motivated to endorse more contextualised CTs. The present study tests this theoretical assertion and explores the extent to which ambiguity tolerance—a preference for black-and-white thinking—and scientific reasoning skills might modify the association. Findings indicate that people with low ambiguity tolerance and higher BJW were less likely to endorse CTs. However, when ambiguity tolerance is high, there was no such association. Scientific reasoning did not moderate the association between BJW and CT endorsement. This research provides support for the buffering effect of BJW against the endorsement of conspiracy theories when ambiguity tolerance is low and regardless of people's scientific reasoning abilities. We discuss the benefits of endorsing adaptive worldviews as a protective factor against engaging in more detrimental beliefs.

Hello, is it me you're Stopping for? The Effect of external Human Machine Interface Familiarity on Pedestrians' Crossing Behaviour in an Ambiguous Situation.

We investigated how different deceleration intentions (i.e. an automated vehicle either decelerated for leading traffic or yielded for pedestrians) and a novel (Slow Pulsing Light Band - SPLB) or familiar (Flashing Headlights - FH) external Human Machine Interface (eHMI) informed pedestrians' crossing behaviour. The introduction of SAE Level 4 Automated Vehicles (AVs) has recently fuelled interest in new forms of explicit communication via eHMIs, to improve the interaction between AVs and surrounding road users. Before implementing these eHMIs, it is necessary to understand how pedestrians use them to inform their crossing decisions. Thirty participants took part in the study using a Head-Mounted Display. The independent variables were deceleration intentions and eHMI design. The percentage of crossings, collision frequency and crossing initiation time across trials were measured. Pedestrians were able to identify the intentions of a decelerating vehicle, using implicit cues, with more crossings made when the approaching vehicles were yielding to them. They were also more likely to cross when a familiar eHMI was presented, compared to a novel one or no eHMI, regardless of the vehicle's intention. Finally, participants learned to take a more cautious approach as trials progressed, and not to base their decisions solely on the eHMI. A familiar eHMI led to early crossings regardless of the vehicle's intention but also led to a higher collision frequency than a novel eHMI. To achieve safe and acceptable interactions with AVs, it is important to provide eHMIs that are congruent with road users' expectations.

Autistic Adults Avoid Unpredictability in Decision-Making.

Decision-making under unpredictable conditions can cause discomfort in autistic persons due to their preference for predictability. Decision-making impairments might furthermore be associated with a dysregulation of sex and stress hormones. This prospective, cross-sectional study investigated decision-making in 32 autistic participants (AP, 14 female) and 31 non-autistic participants (NAP, 20 female) aged 18-64 years. The Iowa Gambling Task (IGT) and the Cambridge Risk Task (CRT) were used to assess decision-making under ambiguity and under risk with known outcome probabilities, respectively. Cortisol, estradiol, and testosterone serum levels were related to decision-making performance. Groups did not differ in overall IGT and CRT performance, but compared with NAP, AP preferred less profitable card decks with predictable outcomes while avoiding those with unpredictable outcomes. AP required more time to reach decisions compared to NAP. Additionally, AP without comorbid depression performed significantly worse than NAP in the IGT. Estradiol and cortisol concentrations were significant predictors of CRT scores in NAP, but not in AP. The study results imply that AP are 'risk-averse' in decision-making under ambiguity as they avoided choice options with unpredictable losses in comparison to NAP. Our findings highlight the intolerance for uncertainty, particularly in ambiguous situations. Thus, we recommend being as transparent and precise as possible when interacting with autistic individuals.Future research should explore decision-making in social situations among individuals with ASD, factoring in person-dependent variables such as depression.