The Impact of Overconfidence and Environmental Conditions on Hazard Perception and Risk Assessment: An Experimental Study Using Video-Based Traffic Scenarios

The Impact of Overconfidence and Environmental Conditions on Hazard Perception and Risk Assessment: An Experimental Study Using Video-Based Traffic Scenarios | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Overconfidence and Environmental Conditions on Hazard Perception and Risk Assessment: An Experimental Study Using Video-Based Traffic Scenarios Elena Lupia, Alessandro Bortolotti, Riccardo Palumbo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8553534/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Human decisions are accompanied by internal confidence judgments about the likelihood of being correct. These judgments are not always well-calibrated. Miscalibration occurs when there is a discrepancy between self-reported confidence regarding the correctness of performance and the objective performance. As a result, cognitive bias such as overconfidence can emerge, shaping how individuals perceive and behave. Traffic represents a distinctive decision environment, in which individuals have to constantly monitor and interpret changing perceptual information. Although overconfidence has been linked to unsafe driving, prior research conceptualizes confidence through self-report questionnaires and group-based analyses. Using performance-based measures, the present study aims to capture confidence as a continuous variable, investigating whether this bias can impact both the hazard perception performance and subjective experience of risk. Sixty-five participants completed a novel adaptation of the Hazard Perception Task (HPT), which integrated trial-by-trial confidence judgments and risk estimation of driving scenarios, alongside scores from the Hazard Perception Questionnaire (HPQ) and the Environmental Sensitivity (HSP scale). Results showed that overconfidence significantly reduced hazard perception accuracy, whereas reaction times to hazard and risk estimation were unaffected. Response accuracy declined also during nighttime in safe scenarios but remained high in hazardous trials. Subjective risk estimation was driven by the presence of hazards and lightning conditions. Environmental Sensitivity (HSP scale) showed a significant positive correlation with risk estimation. No relationship emerged between overconfidence and driving experience. Understanding how confidence judgments and individual differences operate in this high-risk context is therefore critical for safety. Psychology confidence judgments perceptual categorization and identification risk estimations Figures Figure 1 Figure 2 1. Introduction Metacognition, or "thinking about thinking", is a higher-level cognitive process that involves regulating and assessing one's mental operations, which is essential for self-regulation and learning (Rhodes, 2019; Norman et al., 2019; Efklides & Misailidi, 2010a; Efklides & Misailidi, 2010b). Confidence judgments, a key aspect of metacognition, refer to the ability to assess the quality of one's performance (Metcalfe & Shimamura, 1994). This mechanism enables the evaluation of the correctness of one's own decisions and actions (Kepecs et al., 2012; Yeung & Summerfield, 2012; Fetsch et al., 2014). These judgments are not always accurate, and their miscalibration can lead to cognitive biases (Horrey et al., 2015). A relevant bias is overconfidence, which is commonly characterized by three distinct manifestations. Overestimation reflects the belief that one’s abilities or success probability exceed objective measures. Overplacement, or the Above-Average Effect, refers to individuals that see themselves as more skilled than peers, and overprecision, an excessive confidence in the accuracy of one’s beliefs (for a comprehensive review, see Moore & Healy, 2008). Research demonstrates that confidence in a decision can increase with the accumulation of evidence supporting that decision, together with the decrease of evidence for an alternative one (Yeung & Summerfield, 2012; Kiani et al., 2014; Murphy et al., 2015), and overconfidence often linked to confirmation bias. Metacognitive calibration (e.g. the ability to properly judge one’s decision or task performance; Horrey et al., 2015) improves when individuals consider disconfirming evidence (Koriat et al., 1980). These findings are well-supported in laboratory settings (Kiani et al., 2014; Chua & Solinger, 2015; Handel et al., 2020; Brus et al., 2021; for a review see Fleming, 2024). Confidence judgments are crucial in real-life settings, where sequential decisions must be made without immediate feedback (Fetsch et al., 2015). Within this context, overconfidence has serious implications in various professional fields, such as clinical judgment (Croskerry & Norman, 2008), investment decisions (Kumar & Prince, 2023), driving safety (Wohleber & Matthews, 2016), finance and trading (Chuang & Lee, 2006; Baker & Wurgler, 2013). The mobility domain is a complex adaptive system in which human decisions occur in the presence of considerable dynamism and variability (Groeger, 2013). Individuals have to rapidly process, interpret and adapt their decision to changing conditions in real time (Hills, 1980; Warren, 2006; Owsley & McGwin, 2010), and simultaneously they need to evaluate their performance while maintaining a calibrated judgment of correctness of one's own actions (Horrey et al., 2015). Based on Deery’s model (1999), young drivers are inclined to overestimate their driving abilities, while underestimating their risk of crash involvement. This tendency can lead individuals to believe they are less likely to experience negative outcomes than others (Matthews & Moran, 1986; Gregersen, 1996; Deery, 1999; Sümer et al., 2006; McKenna & Horswill, 2006). According to Horrey and colleagues (2015), overestimation reflects poor calibration, in which perceived and actual ability or performance diverge. In high-risk environments such as traffic, this miscalibration contributes to safety concerns by promoting increased risk-taking behaviors (Horrey et al., 2015), biased decision-making, and a distorted belief in one's capacity to control driving impairments, such as fatigue and distraction (Svenson, 1981; Matthews & Moran, 1986; Horswill & McKenna, 1999; Deery, 1999; Wohleber & Matthews, 2016). This manuscript primarily aims to investigate the relationship between confidence bias and perception in driving. A performance-based approach was adopted to explore whether this bias affects both hazard perception and the subjective experience of risk. To this end, we developed a novel adaptation of the video-based Hazard Perception Task (HPT), integrating trial-by-trial confidence judgments and subjective risk estimation of road scenarios. Also, it investigates how individual differences in Environmental Sensitivity relate to subjective risk estimation of potential threat in driving, and finally examines the relationship between overconfidence and driving experience. 1.1 The role of Confidence in Hazard Perception Driving involves a three-stage cognitive process: perceiving driving-related information, decision-making, and vehicle control actions (Groeger, 2013). Hazard Perception Skill (HPS) represents a critical component of the perception stage (Cao et al., 2022). In line with the definition proposed by Crundall and colleagues (2003), Hazard Perception Skill is defined as the “driver’s ability to detect and respond in time and appropriately to potentially dangerous events on the road” (Ābele et al., 2018). HPS is typically assessed through behavioral task, where participants observe dynamic traffic scenes from driver-perspective, and a response to the hazard is requested (e.g. Wetton et al., 2011; Zeuwts et al., 2017; Sun et al., 2021; Crundall et al., 2021). The behavioral measures include reaction time to hazards, response accuracy in detecting road hazards, and eye movements (for review, see Moran et al., 2019; Cao et al., 2022). Poor HPS is associated with increased rates of crash involvement and unsafe driving behavior (McKnight & McKnight, 2003; Horswill & McKenna, 2004; Fisher et al., 2006; Pollatsek et al., 2006; Cheng et al., 2011; Horswill et al., 2015). Hazard perception ability can be affected by several cognitive variables (Cao et al., 2022), as well as environmental factors such as nighttime visibility and weather conditions (Garay et al., 2004; Konstantopoulos et al., 2010; Asadamraji et al., 2019; Evans et al., 2020; Wang et al., 2025). Among human factors influencing this ability, driving experience has emerged as the most extensively studied variable (Sagberg & Bjørnskau, 2006; Scialfa et al., 2012; Crundall et al., 2012; Bonfiglio et al., 2014; Vlakveld, 2014; Crundall, 2016), with the role of (self-reported) confidence and its miscalibration (relative to performance outcomes) seldom being investigated. Literature regarding the consistency between subjectively and objectively measured hazard perception abilities has yielded mixed findings (Farrand and McKenna, 2001; Martinussen et al., 2017; Ābele et al., 2018). For instance, Farrand and McKenna (2001) found no association between self-reported questionnaires and behavioral performance, suggesting an independence between questionnaire and response latency measure. In contrast, Ābele and colleagues (2018) reported that young drivers who responded in time to visible hazards also scored higher on both subjective self-assessments and objective measures of HPS. Recent research has further investigated whether confidence-related bias may directly influence hazard perception performance (Sun et al., 2024; Hu et al., 2025). Sun and colleagues (2024), using a static-image paradigm of the hazard perception test, showed that overconfident drivers exhibit slower response times compared to controls. Nonetheless, static images provide a less ecologically valid representation of real-world driving. Addressing the limitations of this previous research, Hu, Sun, and Cheng (2025) used dynamic video stimuli and classified drivers into several confidence groups (rather than using a binary classification adopted by Sun and colleagues, 2024), based on their self-rated scores on the Hazard Perception Questionnaire (HPQ; White et al. 2011). Their results showed that underconfident drivers reported longer response times than both moderately and very confident drivers. It follows that both over- and under- confidence can impair hazard perception, highlighting the influence of confidence bias. Since previous studies primarily define confidence through self-reported questionnaires, the direct comparison between the ability to judge the correctness of one’s task performance and the actual performance outcomes remain overlooked. Addressing this gap, this study proposes a performance-based approach investigating the role of confidence bias, defined as a continuous variable, in shaping both hazard perception accuracy and subjective risk evaluation of road scenarios. 1.2 Individual differences in Environmental Sensitivity Individuals' responses may be shaped by their sensitivity to surrounding factors, with some individuals being more or less reactive to the same environmental conditions (Belsky & Pluess, 2009; Aron et al., 2012; Pluess, 2015). This manuscript took into consideration the role of Environmental Sensitivity, an individual trait that embodies genetic, neurophysiological, and behavioral differences in how people perceive and respond to stimuli (Pluess, 2015). Its theoretical foundation is grounded in three conceptual frameworks, comprising Aron and Aron's sensory processing sensitivity (1997), Belsky's differential susceptibility model (1997, 2009), and the biological sensitivity to context theory (Boyce & Ellis, 2005; Ellis et al., 2011). Recently, it has been conceptualized as an adaptive trait that allows individuals to respond to both negative and positive environmental conditions, serving as the foundation for human adaptability and neuroplasticity (Pluess et al., 2018). This evolution is reflected in the measurement of this trait, from the original Highly Sensitive Person scale (HSP, Aron & Aron, 1997) to more recent adaptation (Pluess et al., 2023). Although the original scale covers broad domains, it has been linked to heightened perceptual awareness and responsiveness to threats. As evidenced by findings from Rubaltelli and colleagues (2018), higher HSP scores predicted greater risk perception and a stronger psychophysiological response to threatening visual stimuli. To the best of our knowledge, its relationship with subjective risk estimation of potential road hazard has yet to be explored. 1.3 Overconfidence and Driving Experience Overconfidence and driving experience could demonstrate an association, though directionality remains exploratory due to conflicting evidence in prior research. On the one hand, according to the zero-risk theory (Näätänen & Summala, 1974), as drivers develop greater proficiency, the more they adapt to risks on the road. This exaggerated sense of control and overconfidence in one's abilities represent a significant risk factor for traffic safety, as it reduces the driver’s risk perception (Summala, 1988). Other studies have further corroborated that driver’s experience is positively correlated with increased confidence in their driving skills and negatively associated with safety concerns (Lajunen & Summala, 1995). On the other hand, Deery (1999) theorizes his model by emphasizing how young novice drivers often overestimate their driving abilities while underestimating the risks associated with hazardous situations. This overconfidence may be a key factor contributing to their higher involvement in accidents compared to experienced drivers. 1.4 The current study By considering confidence bias as a continuous predictor, this study attempts to examine its impact on hazard perception accuracy and subjective risk estimation, accounting for environmental factors (i.e. lighting conditions and hazard presence). It also takes into account individual differences, such as Environmental Sensitivity and driving experience. Based on the background discussed above, the following research aims are addressed. We examine the role of confidence bias, lighting conditions (daytime, nighttime), and hazard presence (hazard, safe) on hazard perception performance, specifically on response accuracy (RQ1a) and reaction times (RQ1b). We further evaluate how these same factors influence subjective risk estimation for potential traffic hazards (RQ2a), and explore if Environmental Sensitivity (HSP-12, Pluess et al., 2023) relates with individual differences in rating risk (RQ2b). Finally, we examine the association between overconfidence and driving experience (RQ3). 2. Method 2.1 Participants Sixty-five participants took part in the experiment (32 female, 1 non-binary and 32 male; age range 20–35 years, M = 26.31 years, SD = 4.33 years). They obtained a car driver's license from 1–17 years (M = 7.32 years; SD = 4.21 years) and were student volunteers from the D'Annunzio University of Chieti-Pescara. 2.2 Materials 2.2.1 Self-Report Questionnaires Socio-demographic information (including age, gender, level of education) and driving experience were collected via questionnaire. Driving experience refers to self-reported measures of road exposure, including years since obtaining a license, driving frequency, and annual kilometers driven (see Castro et al., 2020). Self-reported hazard perception skill was assessed using the Hazard Perception Questionnaire (HPQ-6) (White et al., 2011). Participants were asked to compare their ability in hazard perception to those of an average driver (e.g., item 1: "Compared to an average driver, how skilled are you at spotting hazards quickly?"). A 7-point Likert scale was used, ranging from 1 ("much less") to 7 ("much more"), with a midpoint of 4 ("the same"). In this study, the internal consistency reliability of the Hazard Perception Questionnaire was 0.88. The 12-item Highly Sensitive Person scale (HSP, Pluess et al., 2023) was used to measure the participants’ Environmental Sensitivity. They were asked to indicate their level of agreement with 12 items (e.g., Item 2: "Are you easily overwhelmed by things like bright lights, strong smells, coarse fabrics, or sirens close by?") on a 7-point Likert scale ranging from 1 ("Not at all") to 7 ("Extremely"). The internal consistency reliability of the Highly Sensitive Person Scale was 0.83. 2.2.2 Video-based Hazard Perception Task (HPT) The video-based Hazard Perception Task (Wetton et al., 2011) was programmed in E-Prime 3 (Psychology Software Tools, 2017; https://pstnet.com/products/e-prime/ ) , and contained 80 video clips selected from the Road Hazard Stimuli dataset (for details see Song et al., 2024). Each video showed a road scenario filmed using front-facing dashcams. The inclusion criteria for the videos required: an average duration of approximately 8 seconds (M = 7.78 s; SD = 1.25), adherence to right-hand traffic conventions (in line with Italian driving regulations), visual stability of the dashcam, absence of distracting text overlays, and a resolution of 1280 × 720 pixels (30 frames per second). Videos were selected, such that half depicted a hazardous event and half did not, balanced for daytime and nighttime conditions. Hazards include pedestrians, vehicles, animals, and obstacles in a range of environments (e.g., city streets, highways), weather (e.g., sunny, rainy, or snowy), and lighting conditions (daytime or nighttime). The clips were presented in random order. The videos have been used and validated in previous studies (Song & Wolfe, 2024; Guidi et al., 2024). Detailed categorical and temporal information on the Hazard Perception Task stimuli is provided in Supplementary Materials (Section A). Before starting the task, the following definition of hazard was given, as “any object, situation, occurrence or combination of these that introduce the possibility of the individual road user experiencing harm should be included […]. Harm may include damage to one’s vehicle, injury to oneself, damage to another’s property, or injury to another person" (Haworth et al. 2000, p. 3; Borowsky et al., 2010). During the task, participants observed driving scenes recorded from a driver’s perspective, imagining themselves as actors in the clips. They were instructed to press the spacebar only when they identified an hazardous situation that required action to avoid a crash (response), and to withhold responding otherwise. The video would stop in case of responding, and therefore were requested to respond only once to the hazard they identified. Response outcomes (whether participants pressed the spacebar or not) and Response Times to hazard (RTs, defined as the moment at which the spacebar was pressed to respond) were recorded for each trial of the Hazard Perception Task. After each video, participants provided trial-by-trial confidence judgments and subjective risk estimation (for details, see the following two sections). An example of a trial from this novel adaptation of the Hazard Perception Task is shown in Fig. 1 . 2.2.3 Driver’s Confidence Judgment Test In this study, the driver’s Confidence Judgment measure consisted of one question after each video trial of the Hazard Perception Task (HPT). Specifically, participants self-reported the confidence in their performance judgment accuracy, by answering the question: “ How confident are you that your decision [to press or not the spacebar] is correct? Please give a rate on a scale from 1 to 7 ”. 2.2.4 Subjective Risk Estimation Test Subjective risk estimation, designed to evaluate the perceived level of danger in each road scenario, was assessed immediately after each video trial of the Hazard Perception Task (HPT). Participants were prompted to answer the question: "How dangerous does this situation seem to you?" by selecting a response on a 7-point Likert scale ranging from 1 ("Not hazardous at all") to 7 ("Very hazardous") (see Castro et al., 2020). This measure is consistent with the definition of risk perception, as “a subjective judgment that categorizes an upcoming event as potentially dangerous” (Knuth et al., 2014). 2.2.5 Balloon Analogue Risk Task (BART) BART was used as a control variable to eliminate the effects of risk-taking behavior (Lejuez et al., 2002). Participants inflated a virtual computer-based balloon for earning points, with a random bursting threshold. They could stop inflating at any time to secure accumulated points. The task included one practice trial and ten critical trials. Risk tolerance was calculated as the percentage of burst balloons, which suggests risk-taking behavior. Higher percentages reflected a greater willingness to take risks, offering insights into individual differences in decision-making under uncertainty. 2.3 Experimental design A 2 (hazard presence: hazard, safe) x 2 (lighting conditions: daytime, nighttime) within subject factorial design was used, including confidence bias as a continuous between subject variable. The dependent variables were: (1) Response accuracy (binary outcome) in the HPT, (2) Reaction Times to hazard during the HPT, (3) Subjective risk estimation (Likert scale). 2.4 Procedure Upon arriving at the laboratory, participants received information about the experiment and signed an informed consent form. They completed three questionnaires: (1) demographic and driving experience; (2) HPQ-6; and (3) HSP-12. Then, participants were instructed to sit 80 cm from the screen of a 15.6'' HP Windows laptop, where the experimenter provided instructions for the behavioral tasks. E-Prime 3 software ( https://pstnet.com/products/e-prime/ ) was used to present the written instructions and record participants' behavioral responses. First, participants performed the Balloon Analog Risk Task (BART). Next, the instructions about how to proceed during the video-based Hazard Perception Task (HPT) and how to rate each trial in terms of confidence judgment and subjective risk estimation were given. Questionnaires were administered before the Video-based Hazard Perception Task to limit possible response bias created by driving scenarios (see Abele et al., 2018). The entire experiment lasted approximately 35 minutes. 2.5 Data Analysis 2.5.1 Hazard Perception Task (HPT) A total of 27 trials were removed due to a wrong keyboard case pressed during the task. Response accuracy was scored binary, marking the response as “correct” in the case of hazard identification (in the hazard videos) and omission of response (in the safe videos); misses and false alarms were marked as “incorrect”. For the hazard videos, Reaction Times (RTs) were calculated as the difference between the participant’s response time and the hazard onset time. The hazard onset time, as defined and pre-annotated in the Road Hazard Stimuli dataset (Song et al., 2024), refers to the first visible deviation point from the normal state at which it could be detected. It varied randomly across videos. A positive difference between the participant's response time and hazard onset indicates a delayed response, a negative difference reflects anticipation of the hazard, and finally, a value of zero indicates a response coinciding with the hazard appearance. Generalized linear mixed-effects models (GLMMs) and Linear Mixed Models (LMMs) were used to investigate the effects of hazard presence (hazard vs. safe), lighting condition (daytime vs. nighttime), and confidence bias on the dependent variables (response accuracy, RTs, subjective risk estimation). Response accuracy was examined using a GLMM with a binomial distribution and logit link function, whereas RTs (on hazard trials only) and subjective risk estimation were analyzed using LMMs. Random intercepts were included to account for between-participant variability. Confidence bias was treated as a continuous predictor. For response accuracy (binomial outcome), model based predicted probabilities were estimated on the response scale, and findings are reported as percentage-point ( pp ) differences relative to a baseline condition defined as safe daytime trials with confidence bias fixed at its sample mean. For RTs and subjective risk estimation, effects are reported as differences in estimated marginal means. 2.5.2 Confidence Bias Confidence bias was determined as the deviation between participants’ (self-reported) confidence judgments regarding the correctness of one’s task performance and their (objective) performance in the Hazard Perception Task. Adopting the method proposed by Sun and colleagues (2024), this measure was calculated, for each participant, as the difference between the percentage of confidence judgments and the percentage of response accuracy. A difference of zero indicates perfect calibration (i.e., the ability to correctly judge one’s task performance), while positive values reflect overconfidence. For descriptive analyses, the sample (N = 65) was classified into two groups: twenty-nine overconfident individuals (44.6%; positive deviation), and thirty-six not overconfident (55.4%; zero or negative deviation). In the main analysis, confidence bias was included as a continuous predictor. 2.5.3 Driving Experience Based on the criteria established by Castro and colleagues (2020), the sample was classified according to the driving experience. Twenty-six were considered experienced drivers (40%), as they had held a car driver's license for more than three years, drove at least twice a week, and covered more than 10,000 km per year. The remaining thirty-nine participants (60%), who did not meet these criteria, were categorized as novices. A Chi-square test of independence was performed to explore the association between confidence bias-based groups (overconfident vs. not overconfident) and driving experience-based groups (novice vs. experienced drivers). 3. Results 3.1 Descriptive statistics Table 1 shows the descriptive analysis of the total sample (N = 65) divided in two confidence bias-based groups. It includes demographic data (age and driving years), mean scores on the HPQ-6 questionnaires and HPS-12 scale, mean percentage of self-reported confidence judgments and mean percentage of exploded balloons in the BART. Overconfident Group Not Overconfident Group N = 29 N = 36 Mean (SD) % Mean (SD) % Age 26.29 (4.37) 26.32 (4.35) Driving Years 7.45 (4.14) 7.21 (4.34) HPQ-6 5.28 (0.83) 4.96 (0.87) HSP-12 4.43 (1.10) 4.66 (0.79) Confidence Judgments 95.26 86.57 BART (burst balloons) 23.87 27.94 Table 1 . Comparison of descriptive statistics between groups. 3.2 Behavioral data analysis Table 2 summarizes the results of the (G)LMM analyses. The main text only discusses the main significant effects. Full fixed-effects estimates are provided in the Supplementary Materials (Section B). Response Accuracy Confidence bias showed a significant effect on accuracy ( β = -0.04, SE = 0.01, t = -4.48, p 0) associated with a lower probability of a correct response. Also, as illustrated in Fig. 2 A, a significant hazard presence x lighting conditions interaction emerged ( p < .001). The baseline performance, defined as predicted accuracy in safe daytime trials with confidence bias fixed at its mean value, was estimated at 96.4%. During daytime, accuracy was slightly lower in hazardous scenarios compared to safe ones (-1.8 percentage points, p = .024). In contrast, during nighttime conditions hazard trials were associated with higher accuracy than safe trials (+ 8.9 percentage points, p < .001). In safe trials, accuracy decreased at night relative to daytime (-9.7 percentage points, p < .001), but no significant day-night difference was observed in hazardous trials ( p = .21). No interactions involving confidence bias reached statistical significance. Reaction Times (RTs) A significant main effect of lighting conditions was found ( β = -98.82, SE = 17.41, t = -5.67, p < .001). Estimated marginal means confirmed slower responses during nighttime scenarios (M = 934.8 ms, 95% CI [841.6, 1028.0]), compared to daytime (M = 737.1 ms, 95% CI [643.8, 830.5]). Confidence bias did not show a significant effect on RTs ( p = .45). Subjective Risk Estimation A significant hazard presence x lighting conditions interaction emerged ( p < .001; Fig. 2 B), suggesting that the effect of hazard presence on risk ratings differed between daytime and nighttime. In the baseline condition (safe daytime trials), the estimated marginal mean risk score was 1.74 (Likert) points (95% CI [1.58, 1.89]). Relative to this baseline, subjective risk estimation was higher in hazardous scenarios presented during daytime (+ 3.79, p < .001). Safe scenarios during nighttime showed a smaller but significant increase in rating risk (+ 0.63, p < .001). Hazardous nighttime scenarios were associated with the largest increase in rating risk relative to the baseline (+ 3.82, p < .001). Confidence bias did not exhibit a significant main effect on subjective risk estimation ( p = .68). Although the interaction between confidence bias and hazard presence reached statistical significance ( p < .001), conditional slopes analysis showed no reliable association between confidence bias and risk estimation in either hazardous trials (slope = 0.009, 95% CI [-0.010, 0.029]) or safe ones (slope = -0.0017, 95% CI [-0.037, 0.002]). No interaction between confidence bias and lighting condition emerged ( p = .86). Table 2 Model based effects relative to the safe-daytime baseline (except for confidence bias, which reflects the main effect of the continuous predictor). Findings are reported as percentage point (pp) difference for accuracy, milliseconds for RTs, and (Likert) point differences for subjective risk estimation. Significance: *: p < .05; **: p < .01; ***: p < .001. Dashes (—) indicate effects not estimated. Downward arrow (↓) indicates a negative effect. Accuracy ( pp ) RTs (ms) Risk Estimation (Likert point) Confidence bias ↓ *** ns ns Nighttime (Safe trials) − 9.7*** — + 0.63*** Nighttime (Hazard trials) — + 197.7*** — Hazard (Daytime) − 1.8* — + 3.79*** Hazard (Nighttime) + 8.9*** — + 3.82*** Environmental Sensitivity and Subjective Risk Estimation Pearson's correlation analysis revealed a significant positive correlation ( r = 0.028, p = 0.047, 95% CI [0.000, 0.055]) between the score on Highly Sensitive Person scale (HSP; Pluess et al., 2023) and risk ratings in driving scenarios. Overconfidence and Driving Experience Chi-Squared test of independence revealed a statistically non-significant association between these variables, χ²(1, 65) = 0.508, p = 0.47, indicating that in our sample the distribution of overconfidence does not significantly differ based on driving expertise. 4. Discussion The present study explored how confidence bias and environmental factors influence perception in driving, with regard to both subjective experience of risk and performance in a video-based Hazard Perception Task (HPT). First, a key contribution of this work is conceptualizing confidence bias as the difference between self-reported ratings of their task accuracy versus their objective task accuracy on each trial of HPT. Unlike previous studies, which relied primarily on self-report questionnaires representing an off-line global self-evaluation of driving abilities (Farrand & McKenna, 2001; Martinussen et al., 2017; Ābele et al., 2018; Händel et al., 2020; Hu et al., 2025), this work captures on-line trial-by-trial confidence judgments on correctness of one's own performance (Saraç & Karakelle, 2012). Second, by treating confidence bias as a continuous variable, it advances previous group-based studies (Sun et al., 2024; Hu et al., 2025), allowing us to gather individual variability. Task-level analysis of hazard perception performance showed that confidence bias was a significant predictor of response accuracy. Overconfident participants were associated with a lower probability of correct response in identifying hazards. This pattern supports the theoretical view that judgment miscalibration (Moore & Healy, 2008; Horrey et al., 2015) may undermine hazard identification in dynamic traffic environments. The effect of confidence bias on reaction times to road hazards was not observed, differing from previous studies based on confidence groups (Sun et al., 2024; Hu et al., 2025). This discrepancy likely reflects methodological differences in how overconfidence was operationalised across studies. Future work may benefit directly comparing questionnaire- and performance-based measures of confidence, along with continuous- and group-based approaches, within the same experimental paradigm. Beyond overconfidence, environmental factors significantly shaped hazard perception performance. As expected based on previous studies (Garay et al., 2004; Konstantopoulos et al., 2010; Asadamraji et al., 2019; Wang et al., 2025), performance was affected by both hazard presence and lighting conditions (see Fig. 2 A). Nighttime scenarios significantly elicited longer reaction times to hazards compared to daytime, which confirms that low luminance can impair visual perception (Wang et al., 2025). Accuracy in hazardous trials remained relatively high across lighting conditions, while it declined during nighttime in safe trials. Subjective risk estimation was primarily determined by the presence of hazard, and significantly modulated by lighting conditions (Deery, 1999; Horswill & McKenna, 2004; see Fig. 2 B). Confidence bias did not affect estimations of the perceived level of risk in a driving scenario. It’s important to point out that unlike previous evidence linked overconfidence to underestimation of personal crash risk involvement (Matthews & Moran, 1986; Gregersen, 1996; Deery, 1999; Sümer et al., 2006; McKenna & Horswill, 2006), the present study assessed context-specific risk ratings rather than self-referential risk. Within this framework, our findings would suggest that risk ratings of traffic scenarios depend more on context than on individual characteristics. Future research should further study how people evaluate the riskiness of scenes along with their own personal risk, and whether overconfidence affects them. Environmental Sensitivity (HSP scale) demonstrated a weak but significant correlation with subjective risk estimation of road scenarios. Specifically, individuals who scored higher on the Highly Sensitive Person scale (Pluess et al., 2023), tended to report slightly greater perceived risk. This finding is consistent with the literature suggesting that heightened environmental sensitivity reflects increased responsiveness to external cues (Pluess, 2015) and threat awareness (Rubaltelli et al., 2018). Despite the small effect size, this finding warrants further investigations. Within the task, a negative association emerged between response accuracy and subjective risk estimation, with accuracy significantly predicted perceived risk. This finding, though secondary, supports Deery’s (1999) model that links hazard detection to risk assessment. In this task, no significant relationship between overconfidence and driving experience was found. Notably, our sample did not include professional drivers, limiting generalizability to more advanced proficiency. Theoretical perspectives remain divided, with some suggesting greater experience increases overconfidence and reduces risk perception (Näätänen & Summala, 1974; Lajunen & Summala, 1995), while others highlight overconfidence in novice drivers (Deery, 1999). The lack of significance highlights the need for further research, including studies on professional drivers, to better understand how experience influences overconfidence. 4.1 Limitations and Implications The present research has some limitations. First, the absence of differentiation between latent and immediate hazards in the video stimuli used. Our definition of hazards did not account for latent hazards, which could have limited the generalizability of findings to scenarios involving less obvious dangers (Song et al., 2024). Second, hazards occurred in 50% of the stimulus set, which does not reflect the probabilities of occurrence in the real world. This manipulation may influence participants' expectations and responses. Future studies should address these limitations to improve the design of stimuli in the HPT. This study has practical applications. Findings indicate that confidence bias can impair response accuracy in perceiving hazards in driving scenarios, highlighting the key relevance of confidence calibration in potentially real-world driving performance. Although Hazard Perception Task is already implemented in driver licensing systems across several countries as an early indicator of driving skill (e.g., Crundall et al., 2021; Wetton et al., 2011), confidence measurement in judging task performance should be integrated into driving tests. Based on studies showing that feedback-based training can effectively reduce bias and overconfidence in self-assessments of driving abilities (e.g., Horswill et al., 2017), training programs targeting overconfidence could contribute to safe driving. 5. Conclusion In conclusion, this study reveals insight regarding drivers' hazard perception performance and subjective risk estimation based on confidence bias and environmental conditions in a video-based hazard perception task. Overconfidence (i.e., positive values of confidence bias) was associated with a reduced response accuracy in perceiving hazards, but it did not affect reaction times. Also, hazard perception performance and subjective risk estimation resulted from the interaction between the presence of hazard and lighting conditions. Confidence bias did not influence subjective risk estimation, suggesting that risk ratings of traffic scenarios may rely more on environmental cues. A weak positive association between Environmental Sensitivity (HSP scale) and subjective risk estimation suggests that this trait may amplify responsiveness to potential threats in traffic context. An absent link emerged between overconfidence and driving experience. The results suggest that integrating the measurement of confidence in performance accuracy into driver training and testing could improve awareness and safety. Declarations Funding Not applicable Competing interests The authors declare no conflicts of interest related to this study or its publication. The manuscript was assessed in line with the journal’s standard editorial processes, including its policy on competing interests. Ethical approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study received approval from the Institutional Review Board (IRB) of the University of Chieti-Pescara, by the Department of Neuroscience e Imaging. Consent to participate All participants provided informed consent, confirming voluntary participation, understanding of study details, and the right to withdraw at any time. They also approved the use of anonymized data for scientific purposes, ensuring no personal identification. Consent for publication Participants have explicitly granted permission for the publication of all anonymized data and findings derived from this study, ensuring compliance with ethical research standards. Availability of data and materials The datasets and materials used and/or analyzed during the current study are available on the Open Science Framework (OSF) at https://osf.io/jb72y/?view_only=2728ddd1a9ce457b87790de7026e99ee , ensuring adherence to data transparency practices. Code availability The code utilized for the analysis in this study is available on the Open Science Framework (OSF) at https://osf.io/jb72y/?view_only=2728ddd1a9ce457b87790de7026e99ee , ensuring adherence to data transparency practices. References Ābele, L., Haustein, S., Møller, M., & Martinussen, L. M. (2018). Consistency between subjectively and objectively measured hazard perception skills among young male drivers. Accident Analysis & Prevention , 118 , 214–220. https://doi.org/10.1016/j.aap.2018.02.022 Aron, E. N., & Aron, A. (1997). Sensory-processing sensitivity and its relation to introversion and emotionality. Journal of Personality and Social Psychology , 73 (2), 345–368. https://doi.org/10.1037/0022-3514.73.2.345 Aron, E. N., Aron, A., & Jagiellowicz, J. (2012). Sensory Processing Sensitivity: A Review in the Light of the Evolution of Biological Responsivity. Personality and Social Psychology Review , 16 (3), 262–282. https://doi.org/10.1177/1088868311434213 Asadamraji, M., Saffarzadeh, M., Ross, V., Borujerdian, A., Ferdosi, T., & Sheikholeslami, S. (2019). A novel driver hazard perception sensitivity model based on drivers’ characteristics: A simulator study. Traffic Injury Prevention , 20 (5), 492–497. https://doi.org/10.1080/15389588.2019.1607971 Baker, M., & Wurgler, J. (2013). Chapter 5 - Behavioral Corporate Finance: An Updated Survey. In G. M. Constantinides, M. Harris, & R. M. Stulz (Eds.), Handbook of the Economics of Finance (Vol. 2, pp. 357–424). Elsevier. https://doi.org/10.1016/B978-0-44-453594-8.00005-7 Belsky, J. (1997). Variation in susceptibility to environmental influence: An evolutionary argument. Psychological Inquiry , 8 (3), 182–186. https://doi.org/10.1207/s15327965pli0803_3 Belsky, J., & Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. Psychological Bulletin , 135 (6), 885–908. https://doi.org/10.1037/a0017376 Bonfiglio, D. J., Lee, Y.-C., LaVoie, N., & Parker, J. (2014). Progression of hazard perception knowledge, licensure status and driving experience among teen drivers. Proceedings of the Human Factors and Ergonomics Society Annual Meeting , 58 (1), 2136–2140. https://doi.org/10.1177/1541931214581449 Borowsky, A., Oron-Gilad, T., & Parmet, Y. (2010). The role of driving experience in hazard perception and categorization: A traffic-scene paradigm. World Academy of Science, Engineering and Technology , 66 , 305–309. https://www.scopus.com/pages/publications/79951530926 Boyce, W. T., & Ellis, B. J. (2005). Biological sensitivity to context: I. An evolutionary-developmental theory of the origins and functions of stress reactivity. Development and Psychopathology , 17 (2), 271–301. https://doi.org/10.1017/S0954579405050145 Brus, J., Aebersold, H., Grueschow, M., & Polania, R. (2021). Sources of confidence in value-based choice. Nature Communications , 12 (1), 7337. https://doi.org/10.1038/s41467-021-27618-5 Cao, S., Samuel, S., Murzello, Y., Ding, W., Zhang, X., & Niu, J. (2022). Hazard Perception in Driving: A Systematic Literature Review. Transportation Research Record , 2676 (12), 666–690. https://doi.org/10.1177/03611981221096666 Castro, C., Muela, I., Doncel, P., & García-Fernández, P. (2020). Hazard Perception and Prediction test for walking, riding a bike and driving a car: “Understanding of the global traffic situation.” PLoS ONE , 15 (10). https://doi.org/10.1371/journal.pone.0238605 Cheng, A. S. K., Ng, T. C. K., & Lee, H. C. (2011). A comparison of the hazard perception ability of accident-involved and accident-free motorcycle riders. Accident Analysis & Prevention , 43 (4), 1464–1471. https://doi.org/10.1016/j.aap.2011.02.024 Chua, E. F., & Solinger, L. A. (2015). Building metamemorial knowledge over time: insights from eye tracking about the bases of feeling-of-knowing and confidence judgments. Frontiers in Psychology , 6 . https://doi.org/10.3389/fpsyg.2015.01206 Chuang, W.-I., & Lee, B.-S. (2006). An empirical evaluation of the overconfidence hypothesis. Journal of Banking & Finance , 30 (9), 2489–2515. https://doi.org/10.1016/j.jbankfin.2005.08.007 Croskerry, P., & Norman, G. (2008). Overconfidence in Clinical Decision Making. The American Journal of Medicine , 121 (5), S24–S29. https://doi.org/10.1016/j.amjmed.2008.02.001 Crundall, D. (2016). Hazard prediction discriminates between novice and experienced drivers. Accident Analysis & Prevention , 86 , 47–58. https://doi.org/10.1016/j.aap.2015.10.006 Crundall, D., Chapman, P., Phelps, N., & Underwood, G. (2003). Eye movements and hazard perception in police pursuit and emergency response driving. Journal of Experimental Psychology: Applied , 9 (3), 163–174. https://doi.org/10.1037/1076-898X.9.3.163 Crundall, D., Chapman, P., Trawley, S., Collins, L., van Loon, E., Andrews, B., & Underwood, G. (2012). Some hazards are more attractive than others: Drivers of varying experience respond differently to different types of hazard. Accident Analysis & Prevention , 45 , 600–609. https://doi.org/10.1016/j.aap.2011.09.049 Crundall, D., van Loon, E., Baguley, T., & Kroll, V. (2021). A novel driving assessment combining hazard perception, hazard prediction and theory questions. Accident Analysis & Prevention , 149 , 105847. https://doi.org/10.1016/j.aap.2020.105847 Deery, H. A. (1999). Hazard and Risk Perception among Young Novice Drivers. Journal of Safety Research , 30 (4), 225–236. https://doi.org/10.1016/S0022-4375(99)00018-3 Efklides, A., & Misailidi, P. (2010a). Introduction: The Present and the Future in Metacognition. In A. Efklides & P. Misailidi (Eds.), Trends and Prospects in Metacognition Research (pp. 1–18). Springer US. https://doi.org/10.1007/978-1-4419-6546-2_1 Efklides, A., & Misailidi, P. (Eds.). (2010b). Trends and Prospects in Metacognition Research . Springer US. https://doi.org/10.1007/978-1-4419-6546-2 Ellis, B. J., Boyce, W. T., Belsky, J., Bakermans-Kranenburg, M. J., & Van Ijzendoorn, M. H. (2011). Differential susceptibility to the environment: An evolutionary–neurodevelopmental theory. Development and Psychopathology , 23 (1), 7–28. https://doi.org/10.1017/S0954579410000611 E-Prime® Stimulus Presentation Software | Psychology Software Tools . (2017, September 11). Psychology Software Tools | Solutions for Research, Assessment, and Education. https://pstnet.com/products/e-prime/ Evans, T., Stuckey, R., & Macdonald, W. (2020). Young drivers’ perceptions of risk and difficulty: Day versus night. Accident Analysis & Prevention , 147 , 105753. https://doi.org/10.1016/j.aap.2020.105753 Farrand, P., & McKenna, F. P. (2001). Risk perception in novice drivers: the relationship between questionnaire measures and response latency. Transportation Research Part F: Traffic Psychology and Behaviour , 4 (3), 201–212. https://doi.org/10.1016/S1369-8478(01)00024-9 Fetsch, C. R., Kiani, R., & Shadlen, M. N. (2014). Predicting the Accuracy of a Decision: A Neural Mechanism of Confidence. Cold Spring Harbor Symposia on Quantitative Biology , 79 , 185–197. https://doi.org/10.1101/sqb.2014.79.024893 Fisher, D. L., Pollatsek, A. P., & Pradhan, A. (2006). Can novice drivers be trained to scan for information that will reduce their likelihood of a crash? Injury Prevention: Journal of the International Society for Child and Adolescent Injury Prevention , 12 Suppl 1 (Suppl 1), i25-29. https://doi.org/10.1136/ip.2006.012021 Fleming, S. M. (2024). Metacognition and Confidence: A Review and Synthesis. Annual Review of Psychology , 75 (Volume 75, 2024), 241–268. https://doi.org/10.1146/annurev-psych-022423-032425 Garay, L., Fisher, D. L., & Hancock, K. L. (2004). Effects of Driving Experience and Lighting Condition on Driving Performance. Proceedings of the Human Factors and Ergonomics Society Annual Meeting , 48 (19), 2290–2294. https://doi.org/10.1177/154193120404801918 Gregersen, N. P. (1996). Young drivers’ overestimation of their own skill—an experiment on the relation between training strategy and skill. Accident Analysis & Prevention , 28 (2), 243–250. https://doi.org/10.1016/0001-4575(95)00066-6 Groeger, J. A. (2013). Understanding Driving: Applying Cognitive Psychology to a Complex Everyday Task . Routledge. https://doi.org/10.4324/9780203769942 Guidi, S., Kosovicheva, A., & Wolfe, B. (2024). Seeing the truck, but missing the cyclist: effects of blur on duration thresholds for road hazard detection. Cognitive Research: Principles and Implications , 9 (1), 32. https://doi.org/10.1186/s41235-024-00557-7 Händel, M., de Bruin, A. B. H., & Dresel, M. (2020). Individual differences in local and global metacognitive judgments. Metacognition and Learning , 15 (1), 51–75. https://doi.org/10.1007/s11409-020-09220-0 Haworth, N., Symmons, M., & Kowadlo, N. (2000). Hazard perception by inexperienced motorcyclists . https://trid.trb.org/View/732859 Hills, B. L. (1980). Vision, Visibility, and Perception in Driving. Perception , 9 (2), 183–216. https://doi.org/10.1068/p090183 Horrey, W. J., Lesch, M. F., Mitsopoulos-Rubens, E., & Lee, J. D. (2015). Calibration of skill and judgment in driving: Development of a conceptual framework and the implications for road safety. Accident Analysis & Prevention , 76 , 25–33. https://doi.org/10.1016/j.aap.2014.12.017 Horswill, M. S., Garth, M., Hill, A., & Watson, M. O. (2017). The effect of performance feedback on drivers’ hazard perception ability and self-ratings. Accident Analysis & Prevention, 101, 135–142. https://doi.org/10.1016/j.aap.2017.02.009 Horswill, M. S., Hill, A., & Wetton, M. (2015). Can a video-based hazard perception test used for driver licensing predict crash involvement? Accident Analysis & Prevention , 82 , 213–219. https://doi.org/10.1016/j.aap.2015.05.019 Horswill, M. S., & McKenna, F. P. (1999). The Effect of Perceived Control on Risk Taking. Journal of Applied Social Psychology , 29 (2), 377–391. https://doi.org/10.1111/j.1559-1816.1999.tb01392.x Horswill, M. S., & McKenna, F. P. (2004). Drivers’ hazard perception ability: situation awareness on the road. In S. Banbury & T. S. (Eds.), A cognitive approach to situation awareness: theory and application (pp. 155–175). Ashgate. https://centaur.reading.ac.uk/14289/ Hu, W., Sun, L., & Cheng, L. (2025). Effects of confidence level and hazard type on the visual search patterns and hazard response times of young drivers. Traffic Injury Prevention , 1–7. https://doi.org/10.1080/15389588.2025.2497521 Kepecs, A., & Mainen, Z. F. (2012). A computational framework for the study of confidence in humans and animals. Philosophical Transactions of the Royal Society B: Biological Sciences , 367 (1594), 1322–1337. https://doi.org/10.1098/rstb.2012.0037 Kiani, R., Corthell, L., & Shadlen, M. N. (2014). Choice certainty is informed by both evidence and decision time. Neuron , 84 (6), 1329–1342. https://doi.org/10.1016/j.neuron.2014.12.015 Knuth, D., Kehl, D., Hulse, L., & Schmidt, S. (2014). Risk perception, experience, and objective risk: a cross-national study with European emergency survivors. Risk Analysis: An Official Publication of the Society for Risk Analysis , 34 (7), 1286–1298. https://doi.org/10.1111/risa.12157 Konstantopoulos, P., Chapman, P., & Crundall, D. (2010). Driver’s visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers’ eye movements in day, night and rain driving. Accident Analysis & Prevention , 42 (3), 827–834. https://doi.org/10.1016/j.aap.2009.09.022 Koriat, A., Lichtenstein, S., & Fischhoff, B. (1980). Reasons for confidence. Journal of Experimental Psychology: Human Learning and Memory , 6 (2), 107–118. https://doi.org/10.1037/0278-7393.6.2.107 Kumar, J., & Prince, N. (2023). Overconfidence Bias in Investment Decisions: A Systematic Mapping of Literature and Future Research Topics. FIIB Business Review , 23197145231174344. https://doi.org/10.1177/23197145231174344 Lajunen, T., & Summala, H. (1995). Driving experience, personality, and skill and safety-motive dimensions in drivers’ self-assessments. Personality and Individual Differences , 19 (3), 307–318. https://doi.org/10.1016/0191-8869(95)00068-H Lejuez, C. W., Read, J. P., Kahler, C. W., Richards, J. B., Ramsey, S. E., Stuart, G. L., Strong, D. R., & Brown, R. A. (2002). Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task (BART). Journal of Experimental Psychology. Applied , 8 (2), 75–84. https://doi.org/10.1037//1076-898x.8.2.75 Martinussen, L. M., Møller, M., & Prato, C. G. (2017). Accuracy of young male drivers’ self-assessments of driving skill. Transportation Research Part F: Traffic Psychology and Behaviour , 46 , 228–235. https://doi.org/10.1016/j.trf.2017.03.001 Matthews, M. L., & Moran, A. R. (1986). Age differences in male drivers’ perception of accident risk: The role of perceived driving ability. Accident Analysis & Prevention , 18 (4), 299–313. https://doi.org/10.1016/0001-4575(86)90044-8 McKenna, F. P., & Horswill, M. S. (2006). Risk taking from the participant’s perspective: The case of driving and accident risk. Health Psychology , 25 (2), 163–170. https://doi.org/10.1037/0278-6133.25.2.163 McKnight, A. J., & McKnight, A. S. (2003). Young novice drivers: careless or clueless? Accident Analysis & Prevention , 35 (6), 921–925. https://doi.org/10.1016/S0001-4575(02)00100-8 Metcalfe, J., & Shimamura, A. P. (1994). Metacognition: Knowing about Knowing . MIT Press. Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological Review , 115 (2), 502–517. https://doi.org/10.1037/0033-295X.115.2.502 Moran, C., Bennett, J. M., & Prabhakharan, P. (2019). Road user hazard perception tests: A systematic review of current methodologies. Accident Analysis & Prevention , 129 , 309–333. https://doi.org/10.1016/j.aap.2019.05.021 Murphy, P. R., Robertson, I. H., Harty, S., & O’Connell, R. G. (2015). Neural evidence accumulation persists after choice to inform metacognitive judgments. eLife , 4 , e11946. https://doi.org/10.7554/eLife.11946 Näätänen, R., & Summala, H. (1974). A model for the role of motivational factors in drivers’ decision-making∗. Accident Analysis & Prevention , 6 (3), 243–261. https://doi.org/10.1016/0001-4575(74)90003-7 Norman, E., Pfuhl, G., Sæle, R. G., Svartdal, F., Låg, T., & Dahl, T. I. (2019). Metacognition in psychology. Review of General Psychology , 23 (4), 403–424. https://doi.org/10.1177/1089268019883821 Owsley, C., & McGwin, G. (2010). Vision and driving. Vision Research , 50 (23), 2348–2361. https://doi.org/10.1016/j.visres.2010.05.021 Pluess, M. (2015). Individual Differences in Environmental Sensitivity. Child Development Perspectives , 9 (3), 138–143. https://doi.org/10.1111/cdep.12120 Pluess, M., Assary, E., Lionetti, F., Lester, K. J., Krapohl, E., Aron, E. N., & Aron, A. (2018). Environmental sensitivity in children: Development of the Highly Sensitive Child Scale and identification of sensitivity groups. Developmental Psychology , 54 (1), 51–70. https://doi.org/10.1037/dev0000406 Pluess, M., Lionetti, F., Aron, E. N., & Aron, A. (2023). People differ in their sensitivity to the environment: An integrated theory, measurement and empirical evidence. Journal of Research in Personality , 104 , 104377. https://doi.org/10.1016/j.jrp.2023.104377 Pollatsek, A., Narayanaan, V., Pradhan, A., & Fisher, D. L. (2006). Using Eye Movements to Evaluate a PC-Based Risk Awareness and Perception Training Program on a Driving Simulator. Human Factors , 48 (3), 447–464. https://doi.org/10.1518/001872006778606787 Rhodes, M. G. (2019). Metacognition. Teaching of Psychology , 46 (2), 168–175. https://doi.org/10.1177/0098628319834381 Rubaltelli, E., Scrimin, S., Moscardino, U., Priolo, G., & Buodo, G. (2018). Media exposure to terrorism and people’s risk perception: The role of environmental sensitivity and psychophysiological response to stress. British Journal of Psychology , 109 (4), 656–673. https://doi.org/10.1111/bjop.12292 Sagberg, F., & Bjørnskau, T. (2006). Hazard perception and driving experience among novice drivers. Accident Analysis & Prevention , 38 (2), 407–414. https://doi.org/10.1016/j.aap.2005.10.014 Saraç, S., & Karakelle, S. (2012). On-line and off-line assessment of metacognition. International Electronic Journal of Elementary Education , 4 (2), 301–315. https://iejee.com/index.php/IEJEE/article/view/201 Scialfa, C. T., Borkenhagen, D., Lyon, J., Deschênes, M., Horswill, M., & Wetton, M. (2012). The effects of driving experience on responses to a static hazard perception test. Accident Analysis & Prevention , 45 , 547–553. https://doi.org/10.1016/j.aap.2011.09.005 Song, J., Kosovicheva, A., & Wolfe, B. (2024). Road Hazard Stimuli: Annotated naturalistic road videos for studying hazard detection and scene perception. Behavior Research Methods , 56 (4), 4188–4204. https://doi.org/10.3758/s13428-023-02299-8 Song, J., & Wolfe, B. (2024). Highly dangerous road hazards are not immune from the low prevalence effect. Cognitive Research: Principles and Implications , 9 (1), 6. https://doi.org/10.1186/s41235-024-00531-3 Sümer, N., Özkan, T., & Lajunen, T. (2006). Asymmetric relationship between driving and safety skills. Accident Analysis & Prevention , 38 (4), 703–711. https://doi.org/10.1016/j.aap.2005.12.016 Summala, H. (1988). Risk control is not risk adjustment: the zero-risk theory of driver behaviour and its implications. Ergonomics , 31 (4), 491–506. https://doi.org/10.1080/00140138808966694 Sun, L., Cheng, L., & Zhang, Q. (2021). The differences in hazard response time and driving styles of violation-involved and violation-free taxi drivers. Transportation Research Part F: Traffic Psychology and Behaviour , 82 , 178–186. https://doi.org/10.1016/j.trf.2021.08.011 Sun, L., Hu, W., Cheng, L., & Zhang, C. (2024). Effects of hazard type and confidence level on hazard perception in young male drivers: an ERP study. NeuroReport , 35 (5), 299. https://doi.org/10.1097/WNR.0000000000002007 Svenson, O. (1981). Are we all less risky and more skillful than our fellow drivers? Acta Psychologica , 47 (2), 143–148. https://doi.org/10.1016/0001-6918(81)90005-6 Vlakveld, W. P. (2014). A comparative study of two desktop hazard perception tasks suitable for mass testing in which scores are not based on response latencies. Transportation Research Part F: Traffic Psychology and Behaviour , 22 , 218–231. https://doi.org/10.1016/j.trf.2013.12.013 Wang, J., Li, J., Xiao, Y., & Song, K. (2025). Steering in the dark: The impact of environmental luminance on driver behavior through optical flow analysis. Attention, Perception, & Psychophysics . https://doi.org/10.3758/s13414-025-03146-y Warren, W. H. (2006). The dynamics of perception and action. Psychological Review , 113 (2), 358–389. https://doi.org/10.1037/0033-295X.113.2.358 Wetton, M. A., Hill, A., & Horswill, M. S. (2011). The development and validation of a hazard perception test for use in driver licensing. Accident Analysis & Prevention , 43 (5), 1759–1770. https://doi.org/10.1016/j.aap.2011.04.007 White, M. J., Cunningham, L. C., & Titchener, K. (2011). Young drivers’ optimism bias for accident risk and driving skill: Accountability and insight experience manipulations. Accident Analysis & Prevention , 43 (4), 1309–1315. https://doi.org/10.1016/j.aap.2011.01.013 Yeung, N., & Summerfield, C. (2012). Metacognition in human decision-making: confidence and error monitoring. Philosophical Transactions of the Royal Society B: Biological Sciences , 367 (1594), 1310–1321. https://doi.org/10.1098/rstb.2011.0416 Zeuwts, L. H. R. H., Vansteenkiste, P., Deconinck, F. J. A., Cardon, G., & Lenoir, M. (2017). Hazard perception in young cyclists and adult cyclists. Accident Analysis & Prevention , 105 , 64–71. https://doi.org/10.1016/j.aap.2016.04.034 Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterials.docx Supplementary Materials Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8553534","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571612922,"identity":"4dcb2a90-1565-43d9-b27a-9c1400286bdd","order_by":0,"name":"Elena Lupia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACdjBpwcDAzMD4gIFBgsGAgbEBvxZmMCkBYjAbQLU04tcD18LAwAYmgfrwW8PPzHzswQ8GicTt7MzPqnl3WMibMzC3P8CnRbKZLd2wB6hlZzOb2W3eMxKGOxsIOMzgMI+ZBA9Qy4bDDEAtbRIJBgcIaLE/zP9N8g9YC/u3YqK0GDDzsElDbOExYyZKi8RhNjNpGQMJ453NPMWSc9skDDccZmycgU8Lf3vzM8k3FTay2/mPb/zwtq1O3uB4+4MP+LRAnQdGUMBMWD1c1ygYBaNgFIwC7AAAylU/WKwqqe4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0004-3363-4074","institution":"University of Chieti-Pescara","correspondingAuthor":true,"prefix":"","firstName":"Elena","middleName":"","lastName":"Lupia","suffix":""},{"id":571612923,"identity":"bf6c7aa3-b31c-41e5-b9d4-71dc23bdb06c","order_by":1,"name":"Alessandro Bortolotti","email":"","orcid":"https://orcid.org/0000-0003-1890-1627","institution":"University of Chieti-Pescara","correspondingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Bortolotti","suffix":""},{"id":571612926,"identity":"d800b719-6111-4557-8e06-cef2c93410bf","order_by":2,"name":"Riccardo Palumbo","email":"","orcid":"https://orcid.org/0000-0003-4230-5817","institution":"University of Chieti-Pescara","correspondingAuthor":false,"prefix":"","firstName":"Riccardo","middleName":"","lastName":"Palumbo","suffix":""}],"badges":[],"createdAt":"2026-01-08 16:09:21","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8553534/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8553534/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100361916,"identity":"85de94e0-2260-4249-8a00-4cf83f1d5d65","added_by":"auto","created_at":"2026-01-16 07:45:56","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":732289,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8553534/v1/2312841f390c9e069c1b4492.docx"},{"id":100362206,"identity":"908126de-aca0-4e2d-a4ab-5e8be48d11c3","added_by":"auto","created_at":"2026-01-16 07:46:20","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs8553534.json","url":"https://assets-eu.researchsquare.com/files/rs-8553534/v1/6b7089644a3a403c140cbde5.json"},{"id":100018063,"identity":"bde2e628-5ace-4971-a54c-ac5b2608c613","added_by":"auto","created_at":"2026-01-12 07:13:34","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125985,"visible":true,"origin":"","legend":"","description":"","filename":"rs85535340enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8553534/v1/88c8c5ca64510175d567a23b.xml"},{"id":100018055,"identity":"77a730c6-e788-460f-aa97-17d0ed2ca71a","added_by":"auto","created_at":"2026-01-12 07:13:34","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66137,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8553534/v1/ea6fefa8ef1511320c9e6cee.png"},{"id":100362151,"identity":"6071685b-7c7a-4ea1-b6fd-7a33e3fbccf8","added_by":"auto","created_at":"2026-01-16 07:46:14","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19052,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8553534/v1/1aeee4354c0180d0b90e2026.png"},{"id":100018060,"identity":"df352ece-03aa-4722-80f2-218b848518d1","added_by":"auto","created_at":"2026-01-12 07:13:34","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123721,"visible":true,"origin":"","legend":"","description":"","filename":"rs85535340structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8553534/v1/b23467bcb35dfc0f4c0a30b7.xml"},{"id":100018064,"identity":"a9dbe3ee-7f41-4121-9874-2126b6525bc9","added_by":"auto","created_at":"2026-01-12 07:13:34","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136847,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8553534/v1/7c981bc43b39edbd2ccf0422.html"},{"id":100018062,"identity":"5ab2eb0f-8c45-4338-ac79-536390d19995","added_by":"auto","created_at":"2026-01-12 07:13:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75470,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExample of a single trial of the task. The timeline shows: the initial mask slide (duration 350 ms), followed by a road traffic video clip (approximately 8 seconds) during which participants could respond or not; after each video, they provided trial-by-trial rating of confidence judgment and subjective risk estimation on a scale from 1 to 7.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8553534/v1/e574dceebb0315ba75a80a4e.jpeg"},{"id":100362393,"identity":"1624a827-5d5a-4d97-8fbc-4178473492b4","added_by":"auto","created_at":"2026-01-16 07:46:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModel-based estimates from mixed effects model as a function of hazard presence and lighting conditions (95% CIs). Panels show: (A) predicted accuracy from the generalized linear mixed-effects model (GLMM), and (B) estimated marginal means of risk ratings from the linear mixed-effects model (LMM).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8553534/v1/f2138ea684810679b9e85291.png"},{"id":100380796,"identity":"a5722402-36e6-4e16-9128-460fedd5845a","added_by":"auto","created_at":"2026-01-16 10:34:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1083383,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8553534/v1/19647a73-469c-416a-bed8-70ff2b3bc396.pdf"},{"id":100362306,"identity":"bf5ef908-efb6-4396-967e-b4d8fedd8111","added_by":"auto","created_at":"2026-01-16 07:46:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":594370,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Materials\u003c/p\u003e","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8553534/v1/0add741380d60d48fa0a3b29.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Impact of Overconfidence and Environmental Conditions on Hazard Perception and Risk Assessment: An Experimental Study Using Video-Based Traffic Scenarios\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetacognition, or \"thinking about thinking\", is a higher-level cognitive process that involves regulating and assessing one's mental operations, which is essential for self-regulation and learning (Rhodes, 2019; Norman et al., 2019; Efklides \u0026amp; Misailidi, 2010a; Efklides \u0026amp; Misailidi, 2010b). Confidence judgments, a key aspect of metacognition, refer to the ability to assess the quality of one's performance (Metcalfe \u0026amp; Shimamura, 1994). This mechanism enables the evaluation of the correctness of one's own decisions and actions (Kepecs et al., 2012; Yeung \u0026amp; Summerfield, 2012; Fetsch et al., 2014). These judgments are not always accurate, and their miscalibration can lead to cognitive biases (Horrey et al., 2015). A relevant bias is overconfidence, which is commonly characterized by three distinct manifestations. Overestimation reflects the belief that one\u0026rsquo;s abilities or success probability exceed objective measures. Overplacement, or the Above-Average Effect, refers to individuals that see themselves as more skilled than peers, and overprecision, an excessive confidence in the accuracy of one\u0026rsquo;s beliefs (for a comprehensive review, see Moore \u0026amp; Healy, 2008). Research demonstrates that confidence in a decision can increase with the accumulation of evidence supporting that decision, together with the decrease of evidence for an alternative one (Yeung \u0026amp; Summerfield, 2012; Kiani et al., 2014; Murphy et al., 2015), and overconfidence often linked to confirmation bias. Metacognitive calibration (e.g. the ability to properly judge one\u0026rsquo;s decision or task performance; Horrey et al., 2015) improves when individuals consider disconfirming evidence (Koriat et al., 1980). These findings are well-supported in laboratory settings (Kiani et al., 2014; Chua \u0026amp; Solinger, 2015; Handel et al., 2020; Brus et al., 2021; for a review see Fleming, 2024). Confidence judgments are crucial in real-life settings, where sequential decisions must be made without immediate feedback (Fetsch et al., 2015). Within this context, overconfidence has serious implications in various professional fields, such as clinical judgment (Croskerry \u0026amp; Norman, 2008), investment decisions (Kumar \u0026amp; Prince, 2023), driving safety (Wohleber \u0026amp; Matthews, 2016), finance and trading (Chuang \u0026amp; Lee, 2006; Baker \u0026amp; Wurgler, 2013).\u003c/p\u003e \u003cp\u003eThe mobility domain is a complex adaptive system in which human decisions occur in the presence of considerable dynamism and variability (Groeger, 2013). Individuals have to rapidly process, interpret and adapt their decision to changing conditions in real time (Hills, 1980; Warren, 2006; Owsley \u0026amp; McGwin, 2010), and simultaneously they need to evaluate their performance while maintaining a calibrated judgment of correctness of one's own actions (Horrey et al., 2015). Based on Deery\u0026rsquo;s model (1999), young drivers are inclined to overestimate their driving abilities, while underestimating their risk of crash involvement. This tendency can lead individuals to believe they are less likely to experience negative outcomes than others (Matthews \u0026amp; Moran, 1986; Gregersen, 1996; Deery, 1999; S\u0026uuml;mer et al., 2006; McKenna \u0026amp; Horswill, 2006). According to Horrey and colleagues (2015), overestimation reflects poor calibration, in which perceived and actual ability or performance diverge. In high-risk environments such as traffic, this miscalibration contributes to safety concerns by promoting increased risk-taking behaviors (Horrey et al., 2015), biased decision-making, and a distorted belief in one's capacity to control driving impairments, such as fatigue and distraction (Svenson, 1981; Matthews \u0026amp; Moran, 1986; Horswill \u0026amp; McKenna, 1999; Deery, 1999; Wohleber \u0026amp; Matthews, 2016).\u003c/p\u003e \u003cp\u003eThis manuscript primarily aims to investigate the relationship between confidence bias and perception in driving. A performance-based approach was adopted to explore whether this bias affects both hazard perception and the subjective experience of risk. To this end, we developed a novel adaptation of the video-based Hazard Perception Task (HPT), integrating trial-by-trial confidence judgments and subjective risk estimation of road scenarios. Also, it investigates how individual differences in Environmental Sensitivity relate to subjective risk estimation of potential threat in driving, and finally examines the relationship between overconfidence and driving experience.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 The role of Confidence in Hazard Perception\u003c/h2\u003e \u003cp\u003eDriving involves a three-stage cognitive process: perceiving driving-related information, decision-making, and vehicle control actions (Groeger, 2013). Hazard Perception Skill (HPS) represents a critical component of the perception stage (Cao et al., 2022). In line with the definition proposed by Crundall and colleagues (2003), Hazard Perception Skill is defined as the \u0026ldquo;driver\u0026rsquo;s ability to detect and respond in time and appropriately to potentially dangerous events on the road\u0026rdquo; (Ābele et al., 2018). HPS is typically assessed through behavioral task, where participants observe dynamic traffic scenes from driver-perspective, and a response to the hazard is requested (e.g. Wetton et al., 2011; Zeuwts et al., 2017; Sun et al., 2021; Crundall et al., 2021). The behavioral measures include reaction time to hazards, response accuracy in detecting road hazards, and eye movements (for review, see Moran et al., 2019; Cao et al., 2022). Poor HPS is associated with increased rates of crash involvement and unsafe driving behavior (McKnight \u0026amp; McKnight, 2003; Horswill \u0026amp; McKenna, 2004; Fisher et al., 2006; Pollatsek et al., 2006; Cheng et al., 2011; Horswill et al., 2015). Hazard perception ability can be affected by several cognitive variables (Cao et al., 2022), as well as environmental factors such as nighttime visibility and weather conditions (Garay et al., 2004; Konstantopoulos et al., 2010; Asadamraji et al., 2019; Evans et al., 2020; Wang et al., 2025). Among human factors influencing this ability, driving experience has emerged as the most extensively studied variable (Sagberg \u0026amp; Bj\u0026oslash;rnskau, 2006; Scialfa et al., 2012; Crundall et al., 2012; Bonfiglio et al., 2014; Vlakveld, 2014; Crundall, 2016), with the role of (self-reported) confidence and its miscalibration (relative to performance outcomes) seldom being investigated.\u003c/p\u003e \u003cp\u003eLiterature regarding the consistency between subjectively and objectively measured hazard perception abilities has yielded mixed findings (Farrand and McKenna, 2001; Martinussen et al., 2017; Ābele et al., 2018). For instance, Farrand and McKenna (2001) found no association between self-reported questionnaires and behavioral performance, suggesting an independence between questionnaire and response latency measure. In contrast, Ābele and colleagues (2018) reported that young drivers who responded in time to visible hazards also scored higher on both subjective self-assessments and objective measures of HPS. Recent research has further investigated whether confidence-related bias may directly influence hazard perception performance (Sun et al., 2024; Hu et al., 2025). Sun and colleagues (2024), using a static-image paradigm of the hazard perception test, showed that overconfident drivers exhibit slower response times compared to controls. Nonetheless, static images provide a less ecologically valid representation of real-world driving. Addressing the limitations of this previous research, Hu, Sun, and Cheng (2025) used dynamic video stimuli and classified drivers into several confidence groups (rather than using a binary classification adopted by Sun and colleagues, 2024), based on their self-rated scores on the Hazard Perception Questionnaire (HPQ; White et al. 2011). Their results showed that underconfident drivers reported longer response times than both moderately and very confident drivers. It follows that both over- and under- confidence can impair hazard perception, highlighting the influence of confidence bias. Since previous studies primarily define confidence through self-reported questionnaires, the direct comparison between the ability to judge the correctness of one\u0026rsquo;s task performance and the actual performance outcomes remain overlooked. Addressing this gap, this study proposes a performance-based approach investigating the role of confidence bias, defined as a continuous variable, in shaping both hazard perception accuracy and subjective risk evaluation of road scenarios.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Individual differences in Environmental Sensitivity\u003c/h2\u003e \u003cp\u003eIndividuals' responses may be shaped by their sensitivity to surrounding factors, with some individuals being more or less reactive to the same environmental conditions (Belsky \u0026amp; Pluess, 2009; Aron et al., 2012; Pluess, 2015). This manuscript took into consideration the role of Environmental Sensitivity, an individual trait that embodies genetic, neurophysiological, and behavioral differences in how people perceive and respond to stimuli (Pluess, 2015). Its theoretical foundation is grounded in three conceptual frameworks, comprising Aron and Aron's sensory processing sensitivity (1997), Belsky's differential susceptibility model (1997, 2009), and the biological sensitivity to context theory (Boyce \u0026amp; Ellis, 2005; Ellis et al., 2011). Recently, it has been conceptualized as an adaptive trait that allows individuals to respond to both negative and positive environmental conditions, serving as the foundation for human adaptability and neuroplasticity (Pluess et al., 2018). This evolution is reflected in the measurement of this trait, from the original Highly Sensitive Person scale (HSP, Aron \u0026amp; Aron, 1997) to more recent adaptation (Pluess et al., 2023). Although the original scale covers broad domains, it has been linked to heightened perceptual awareness and responsiveness to threats. As evidenced by findings from Rubaltelli and colleagues (2018), higher HSP scores predicted greater risk perception and a stronger psychophysiological response to threatening visual stimuli. To the best of our knowledge, its relationship with subjective risk estimation of potential road hazard has yet to be explored.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Overconfidence and Driving Experience\u003c/h2\u003e \u003cp\u003eOverconfidence and driving experience could demonstrate an association, though directionality remains exploratory due to conflicting evidence in prior research. On the one hand, according to the zero-risk theory (N\u0026auml;\u0026auml;t\u0026auml;nen \u0026amp; Summala, 1974), as drivers develop greater proficiency, the more they adapt to risks on the road. This exaggerated sense of control and overconfidence in one's abilities represent a significant risk factor for traffic safety, as it reduces the driver\u0026rsquo;s risk perception (Summala, 1988). Other studies have further corroborated that driver\u0026rsquo;s experience is positively correlated with increased confidence in their driving skills and negatively associated with safety concerns (Lajunen \u0026amp; Summala, 1995). On the other hand, Deery (1999) theorizes his model by emphasizing how young novice drivers often overestimate their driving abilities while underestimating the risks associated with hazardous situations. This overconfidence may be a key factor contributing to their higher involvement in accidents compared to experienced drivers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e1.4 The current study\u003c/h2\u003e \u003cp\u003eBy considering confidence bias as a continuous predictor, this study attempts to examine its impact on hazard perception accuracy and subjective risk estimation, accounting for environmental factors (i.e. lighting conditions and hazard presence). It also takes into account individual differences, such as Environmental Sensitivity and driving experience. Based on the background discussed above, the following research aims are addressed.\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWe examine the role of confidence bias, lighting conditions (daytime, nighttime), and hazard presence (hazard, safe) on hazard perception performance, specifically on response accuracy (RQ1a) and reaction times (RQ1b).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWe further evaluate how these same factors influence subjective risk estimation for potential traffic hazards (RQ2a), and explore if Environmental Sensitivity (HSP-12, Pluess et al., 2023) relates with individual differences in rating risk (RQ2b).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFinally, we examine the association between overconfidence and driving experience (RQ3).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eSixty-five participants took part in the experiment (32 female, 1 non-binary and 32 male; age range 20\u0026ndash;35 years, M\u0026thinsp;=\u0026thinsp;26.31 years, SD\u0026thinsp;=\u0026thinsp;4.33 years). They obtained a car driver's license from 1\u0026ndash;17 years (M\u0026thinsp;=\u0026thinsp;7.32 years; SD\u0026thinsp;=\u0026thinsp;4.21 years) and were student volunteers from the D'Annunzio University of Chieti-Pescara.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Materials\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Self-Report Questionnaires\u003c/h2\u003e \u003cp\u003eSocio-demographic information (including age, gender, level of education) and driving experience were collected via questionnaire. Driving experience refers to self-reported measures of road exposure, including years since obtaining a license, driving frequency, and annual kilometers driven (see Castro et al., 2020).\u003c/p\u003e \u003cp\u003eSelf-reported hazard perception skill was assessed using the Hazard Perception Questionnaire (HPQ-6) (White et al., 2011). Participants were asked to compare their ability in hazard perception to those of an average driver (e.g., item 1: \"Compared to an average driver, how skilled are you at spotting hazards quickly?\"). A 7-point Likert scale was used, ranging from 1 (\"much less\") to 7 (\"much more\"), with a midpoint of 4 (\"the same\"). In this study, the internal consistency reliability of the Hazard Perception Questionnaire was 0.88.\u003c/p\u003e \u003cp\u003eThe 12-item Highly Sensitive Person scale (HSP, Pluess et al., 2023) was used to measure the participants\u0026rsquo; Environmental Sensitivity. They were asked to indicate their level of agreement with 12 items (e.g., Item 2: \"Are you easily overwhelmed by things like bright lights, strong smells, coarse fabrics, or sirens close by?\") on a 7-point Likert scale ranging from 1 (\"Not at all\") to 7 (\"Extremely\"). The internal consistency reliability of the Highly Sensitive Person Scale was 0.83.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Video-based Hazard Perception Task (HPT)\u003c/h2\u003e \u003cp\u003eThe video-based Hazard Perception Task (Wetton et al., 2011) was programmed in E-Prime 3 (Psychology Software Tools, 2017; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pstnet.com/products/e-prime/\u003c/span\u003e\u003cspan address=\"https://pstnet.com/products/e-prime/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, and contained 80 video clips selected from the \u003cem\u003eRoad Hazard Stimuli\u003c/em\u003e dataset (for details see Song et al., 2024). Each video showed a road scenario filmed using front-facing dashcams. The inclusion criteria for the videos required: an average duration of approximately 8 seconds (M\u0026thinsp;=\u0026thinsp;7.78 s; SD\u0026thinsp;=\u0026thinsp;1.25), adherence to right-hand traffic conventions (in line with Italian driving regulations), visual stability of the dashcam, absence of distracting text overlays, and a resolution of 1280 \u0026times; 720 pixels (30 frames per second). Videos were selected, such that half depicted a hazardous event and half did not, balanced for daytime and nighttime conditions. Hazards include pedestrians, vehicles, animals, and obstacles in a range of environments (e.g., city streets, highways), weather (e.g., sunny, rainy, or snowy), and lighting conditions (daytime or nighttime). The clips were presented in random order. The videos have been used and validated in previous studies (Song \u0026amp; Wolfe, 2024; Guidi et al., 2024). Detailed categorical and temporal information on the Hazard Perception Task stimuli is provided in Supplementary Materials (Section A).\u003c/p\u003e \u003cp\u003eBefore starting the task, the following definition of hazard was given, as \u0026ldquo;any object, situation, occurrence or combination of these that introduce the possibility of the individual road user experiencing harm should be included [\u0026hellip;]. Harm may include damage to one\u0026rsquo;s vehicle, injury to oneself, damage to another\u0026rsquo;s property, or injury to another person\" (Haworth et al. 2000, p. 3; Borowsky et al., 2010). During the task, participants observed driving scenes recorded from a driver\u0026rsquo;s perspective, imagining themselves as actors in the clips. They were instructed to press the spacebar only when they identified an hazardous situation that required action to avoid a crash (response), and to withhold responding otherwise. The video would stop in case of responding, and therefore were requested to respond only once to the hazard they identified.\u003c/p\u003e \u003cp\u003eResponse outcomes (whether participants pressed the spacebar or not) and Response Times to hazard (RTs, defined as the moment at which the spacebar was pressed to respond) were recorded for each trial of the Hazard Perception Task.\u003c/p\u003e \u003cp\u003eAfter each video, participants provided trial-by-trial confidence judgments and subjective risk estimation (for details, see the following two sections). An example of a trial from this novel adaptation of the Hazard Perception Task is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Driver\u0026rsquo;s Confidence Judgment Test\u003c/h2\u003e \u003cp\u003eIn this study, the driver\u0026rsquo;s Confidence Judgment measure consisted of one question after each video trial of the Hazard Perception Task (HPT). Specifically, participants self-reported the confidence in their performance judgment accuracy, by answering the question: \u0026ldquo;\u003cem\u003eHow confident are you that your decision\u003c/em\u003e [to press or not the spacebar] \u003cem\u003eis correct? Please give a rate on a scale from 1 to 7\u003c/em\u003e\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Subjective Risk Estimation Test\u003c/h2\u003e \u003cp\u003eSubjective risk estimation, designed to evaluate the perceived level of danger in each road scenario, was assessed immediately after each video trial of the Hazard Perception Task (HPT). Participants were prompted to answer the question: \"How dangerous does this situation seem to you?\" by selecting a response on a 7-point Likert scale ranging from 1 (\"Not hazardous at all\") to 7 (\"Very hazardous\") (see Castro et al., 2020). This measure is consistent with the definition of risk perception, as \u0026ldquo;a subjective judgment that categorizes an upcoming event as potentially dangerous\u0026rdquo; (Knuth et al., 2014).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Balloon Analogue Risk Task (BART)\u003c/h2\u003e \u003cp\u003eBART was used as a control variable to eliminate the effects of risk-taking behavior (Lejuez et al., 2002). Participants inflated a virtual computer-based balloon for earning points, with a random bursting threshold. They could stop inflating at any time to secure accumulated points. The task included one practice trial and ten critical trials. Risk tolerance was calculated as the percentage of burst balloons, which suggests risk-taking behavior. Higher percentages reflected a greater willingness to take risks, offering insights into individual differences in decision-making under uncertainty.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Experimental design\u003c/h2\u003e \u003cp\u003eA 2 (hazard presence: hazard, safe) x 2 (lighting conditions: daytime, nighttime) within subject factorial design was used, including confidence bias as a continuous between subject variable. The dependent variables were: (1) Response accuracy (binary outcome) in the HPT, (2) Reaction Times to hazard during the HPT, (3) Subjective risk estimation (Likert scale).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Procedure\u003c/h2\u003e \u003cp\u003e Upon arriving at the laboratory, participants received information about the experiment and signed an informed consent form. They completed three questionnaires: (1) demographic and driving experience; (2) HPQ-6; and (3) HSP-12. Then, participants were instructed to sit 80 cm from the screen of a 15.6'' HP Windows laptop, where the experimenter provided instructions for the behavioral tasks. E-Prime 3 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pstnet.com/products/e-prime/\u003c/span\u003e\u003cspan address=\"https://pstnet.com/products/e-prime/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to present the written instructions and record participants' behavioral responses. First, participants performed the Balloon Analog Risk Task (BART). Next, the instructions about how to proceed during the video-based Hazard Perception Task (HPT) and how to rate each trial in terms of confidence judgment and subjective risk estimation were given. Questionnaires were administered before the Video-based Hazard Perception Task to limit possible response bias created by driving scenarios (see Abele et al., 2018). The entire experiment lasted approximately 35 minutes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Analysis\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Hazard Perception Task (HPT)\u003c/h2\u003e \u003cp\u003eA total of 27 trials were removed due to a wrong keyboard case pressed during the task. Response accuracy was scored binary, marking the response as \u0026ldquo;correct\u0026rdquo; in the case of hazard identification (in the hazard videos) and omission of response (in the safe videos); misses and false alarms were marked as \u0026ldquo;incorrect\u0026rdquo;. For the hazard videos, Reaction Times (RTs) were calculated as the difference between the participant\u0026rsquo;s response time and the hazard onset time. The hazard onset time, as defined and pre-annotated in the \u003cem\u003eRoad Hazard Stimuli\u003c/em\u003e dataset (Song et al., 2024), refers to the first visible deviation point from the normal state at which it could be detected. It varied randomly across videos. A positive difference between the participant's response time and hazard onset indicates a delayed response, a negative difference reflects anticipation of the hazard, and finally, a value of zero indicates a response coinciding with the hazard appearance.\u003c/p\u003e \u003cp\u003eGeneralized linear mixed-effects models (GLMMs) and Linear Mixed Models (LMMs) were used to investigate the effects of hazard presence (hazard vs. safe), lighting condition (daytime vs. nighttime), and confidence bias on the dependent variables (response accuracy, RTs, subjective risk estimation). Response accuracy was examined using a GLMM with a binomial distribution and logit link function, whereas RTs (on hazard trials only) and subjective risk estimation were analyzed using LMMs. Random intercepts were included to account for between-participant variability. Confidence bias was treated as a continuous predictor. For response accuracy (binomial outcome), model based predicted probabilities were estimated on the response scale, and findings are reported as percentage-point (\u003cem\u003epp\u003c/em\u003e) differences relative to a baseline condition defined as safe daytime trials with confidence bias fixed at its sample mean. For RTs and subjective risk estimation, effects are reported as differences in estimated marginal means.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Confidence Bias\u003c/h2\u003e \u003cp\u003eConfidence bias was determined as the deviation between participants\u0026rsquo; (self-reported) confidence judgments regarding the correctness of one\u0026rsquo;s task performance and their (objective) performance in the Hazard Perception Task. Adopting the method proposed by Sun and colleagues (2024), this measure was calculated, for each participant, as the difference between the percentage of confidence judgments and the percentage of response accuracy. A difference of zero indicates perfect calibration (i.e., the ability to correctly judge one\u0026rsquo;s task performance), while positive values reflect overconfidence. For descriptive analyses, the sample (N\u0026thinsp;=\u0026thinsp;65) was classified into two groups: twenty-nine overconfident individuals (44.6%; positive deviation), and thirty-six not overconfident (55.4%; zero or negative deviation). In the main analysis, confidence bias was included as a continuous predictor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Driving Experience\u003c/h2\u003e \u003cp\u003eBased on the criteria established by Castro and colleagues (2020), the sample was classified according to the driving experience. Twenty-six were considered experienced drivers (40%), as they had held a car driver's license for more than three years, drove at least twice a week, and covered more than 10,000 km per year. The remaining thirty-nine participants (60%), who did not meet these criteria, were categorized as novices. A Chi-square test of independence was performed to explore the association between confidence bias-based groups (overconfident vs. not overconfident) and driving experience-based groups (novice vs. experienced drivers).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eshows the descriptive analysis of the total sample (N\u0026thinsp;=\u0026thinsp;65) divided in two confidence bias-based groups. It includes demographic data (age and driving years), mean scores on the HPQ-6 questionnaires and HPS-12 scale, mean percentage of self-reported confidence judgments and mean percentage of exploded balloons in the BART.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOverconfident Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNot Overconfident Group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;29\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMean (SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eMean (SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.29 (4.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.32 (4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDriving Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.45 (4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.21 (4.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHPQ-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.28 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.96 (0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSP-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.43 (1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.66 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfidence Judgments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBART\u003c/p\u003e \u003cp\u003e(burst balloons)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eComparison of descriptive statistics between groups.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Behavioral data analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the results of the (G)LMM analyses. The main text only discusses the main significant effects. Full fixed-effects estimates are provided in the Supplementary Materials (Section B).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eResponse Accuracy\u003c/strong\u003e \u003cp\u003eConfidence bias showed a significant effect on accuracy (\u003cem\u003eβ\u003c/em\u003e = -0.04, SE\u0026thinsp;=\u0026thinsp;0.01, \u003cem\u003et\u003c/em\u003e = -4.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), with positive values of confidence bias (i.e., participants whose subjective confidence judgments exceeded their actual performance, deviation\u0026thinsp;\u0026gt;\u0026thinsp;0) associated with a lower probability of a correct response. Also, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, a significant hazard presence x lighting conditions interaction emerged (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The baseline performance, defined as predicted accuracy in safe daytime trials with confidence bias fixed at its mean value, was estimated at 96.4%. During daytime, accuracy was slightly lower in hazardous scenarios compared to safe ones (-1.8 percentage points, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.024). In contrast, during nighttime conditions hazard trials were associated with higher accuracy than safe trials (+\u0026thinsp;8.9 percentage points, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). In safe trials, accuracy decreased at night relative to daytime (-9.7 percentage points, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), but no significant day-night difference was observed in hazardous trials (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.21). No interactions involving confidence bias reached statistical significance.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eReaction Times (RTs)\u003c/strong\u003e \u003cp\u003eA significant main effect of lighting conditions was found (\u003cem\u003eβ\u003c/em\u003e = -98.82, SE\u0026thinsp;=\u0026thinsp;17.41, \u003cem\u003et\u003c/em\u003e = -5.67, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Estimated marginal means confirmed slower responses during nighttime scenarios (M\u0026thinsp;=\u0026thinsp;934.8 ms, 95% CI [841.6, 1028.0]), compared to daytime (M\u0026thinsp;=\u0026thinsp;737.1 ms, 95% CI [643.8, 830.5]). Confidence bias did not show a significant effect on RTs (\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;.45).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSubjective Risk Estimation\u003c/strong\u003e \u003cp\u003eA significant hazard presence x lighting conditions interaction emerged (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), suggesting that the effect of hazard presence on risk ratings differed between daytime and nighttime. In the baseline condition (safe daytime trials), the estimated marginal mean risk score was 1.74 (Likert) points (95% CI [1.58, 1.89]). Relative to this baseline, subjective risk estimation was higher in hazardous scenarios presented during daytime (+\u0026thinsp;3.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Safe scenarios during nighttime showed a smaller but significant increase in rating risk (+\u0026thinsp;0.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Hazardous nighttime scenarios were associated with the largest increase in rating risk relative to the baseline (+\u0026thinsp;3.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Confidence bias did not exhibit a significant main effect on subjective risk estimation (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.68). Although the interaction between confidence bias and hazard presence reached statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), conditional slopes analysis showed no reliable association between confidence bias and risk estimation in either hazardous trials (slope\u0026thinsp;=\u0026thinsp;0.009, 95% CI [-0.010, 0.029]) or safe ones (slope = -0.0017, 95% CI [-0.037, 0.002]). No interaction between confidence bias and lighting condition emerged (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.86).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eModel based effects relative to the safe-daytime baseline (except for confidence bias, which reflects the main effect of the continuous predictor). Findings are reported as percentage point (pp) difference for accuracy, milliseconds for RTs, and (Likert) point differences for subjective risk estimation. Significance: *: p\u0026thinsp;\u0026lt;\u0026thinsp;.05; **: p\u0026thinsp;\u0026lt;\u0026thinsp;.01; ***: p\u0026thinsp;\u0026lt;\u0026thinsp;.001. Dashes (\u0026mdash;) indicate effects not estimated. Downward arrow (\u0026darr;) indicates a negative effect.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccuracy (\u003cem\u003epp\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRTs (ms)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRisk Estimation (Likert point)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfidence bias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026darr; ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ens\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNighttime (Safe trials)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;9.7***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.63***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNighttime (Hazard trials)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;197.7***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHazard (Daytime)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;1.8*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;3.79***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHazard (Nighttime)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;8.9***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;3.82***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEnvironmental Sensitivity and Subjective Risk Estimation\u003c/strong\u003e \u003cp\u003ePearson's correlation analysis revealed a significant positive correlation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047, 95% CI [0.000, 0.055]) between the score on Highly Sensitive Person scale (HSP; Pluess et al., 2023) and risk ratings in driving scenarios.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOverconfidence and Driving Experience\u003c/strong\u003e \u003cp\u003eChi-Squared test of independence revealed a statistically non-significant association between these variables, χ\u0026sup2;(1, 65)\u0026thinsp;=\u0026thinsp;0.508, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.47, indicating that in our sample the distribution of overconfidence does not significantly differ based on driving expertise.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study explored how confidence bias and environmental factors influence perception in driving, with regard to both subjective experience of risk and performance in a video-based Hazard Perception Task (HPT). First, a key contribution of this work is conceptualizing confidence bias as the difference between self-reported ratings of their task accuracy versus their objective task accuracy on each trial of HPT. Unlike previous studies, which relied primarily on self-report questionnaires representing an off-line global self-evaluation of driving abilities (Farrand \u0026amp; McKenna, 2001; Martinussen et al., 2017; Ābele et al., 2018; H\u0026auml;ndel et al., 2020; Hu et al., 2025), this work captures on-line trial-by-trial confidence judgments on correctness of one's own performance (Sara\u0026ccedil; \u0026amp; Karakelle, 2012). Second, by treating confidence bias as a continuous variable, it advances previous group-based studies (Sun et al., 2024; Hu et al., 2025), allowing us to gather individual variability.\u003c/p\u003e \u003cp\u003eTask-level analysis of hazard perception performance showed that confidence bias was a significant predictor of response accuracy. Overconfident participants were associated with a lower probability of correct response in identifying hazards. This pattern supports the theoretical view that judgment miscalibration (Moore \u0026amp; Healy, 2008; Horrey et al., 2015) may undermine hazard identification in dynamic traffic environments. The effect of confidence bias on reaction times to road hazards was not observed, differing from previous studies based on confidence groups (Sun et al., 2024; Hu et al., 2025). This discrepancy likely reflects methodological differences in how overconfidence was operationalised across studies. Future work may benefit directly comparing questionnaire- and performance-based measures of confidence, along with continuous- and group-based approaches, within the same experimental paradigm. Beyond overconfidence, environmental factors significantly shaped hazard perception performance. As expected based on previous studies (Garay et al., 2004; Konstantopoulos et al., 2010; Asadamraji et al., 2019; Wang et al., 2025), performance was affected by both hazard presence and lighting conditions (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Nighttime scenarios significantly elicited longer reaction times to hazards compared to daytime, which confirms that low luminance can impair visual perception (Wang et al., 2025). Accuracy in hazardous trials remained relatively high across lighting conditions, while it declined during nighttime in safe trials.\u003c/p\u003e \u003cp\u003eSubjective risk estimation was primarily determined by the presence of hazard, and significantly modulated by lighting conditions (Deery, 1999; Horswill \u0026amp; McKenna, 2004; see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Confidence bias did not affect estimations of the perceived level of risk in a driving scenario. It\u0026rsquo;s important to point out that unlike previous evidence linked overconfidence to underestimation of personal crash risk involvement (Matthews \u0026amp; Moran, 1986; Gregersen, 1996; Deery, 1999; S\u0026uuml;mer et al., 2006; McKenna \u0026amp; Horswill, 2006), the present study assessed context-specific risk ratings rather than self-referential risk. Within this framework, our findings would suggest that risk ratings of traffic scenarios depend more on context than on individual characteristics. Future research should further study how people evaluate the riskiness of scenes along with their own personal risk, and whether overconfidence affects them.\u003c/p\u003e \u003cp\u003eEnvironmental Sensitivity (HSP scale) demonstrated a weak but significant correlation with subjective risk estimation of road scenarios. Specifically, individuals who scored higher on the Highly Sensitive Person scale (Pluess et al., 2023), tended to report slightly greater perceived risk. This finding is consistent with the literature suggesting that heightened environmental sensitivity reflects increased responsiveness to external cues (Pluess, 2015) and threat awareness (Rubaltelli et al., 2018). Despite the small effect size, this finding warrants further investigations. Within the task, a negative association emerged between response accuracy and subjective risk estimation, with accuracy significantly predicted perceived risk. This finding, though secondary, supports Deery\u0026rsquo;s (1999) model that links hazard detection to risk assessment.\u003c/p\u003e \u003cp\u003eIn this task, no significant relationship between overconfidence and driving experience was found. Notably, our sample did not include professional drivers, limiting generalizability to more advanced proficiency. Theoretical perspectives remain divided, with some suggesting greater experience increases overconfidence and reduces risk perception (N\u0026auml;\u0026auml;t\u0026auml;nen \u0026amp; Summala, 1974; Lajunen \u0026amp; Summala, 1995), while others highlight overconfidence in novice drivers (Deery, 1999). The lack of significance highlights the need for further research, including studies on professional drivers, to better understand how experience influences overconfidence.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Limitations and Implications\u003c/h2\u003e \u003cp\u003eThe present research has some limitations. First, the absence of differentiation between latent and immediate hazards in the video stimuli used. Our definition of hazards did not account for latent hazards, which could have limited the generalizability of findings to scenarios involving less obvious dangers (Song et al., 2024). Second, hazards occurred in 50% of the stimulus set, which does not reflect the probabilities of occurrence in the real world. This manipulation may influence participants' expectations and responses. Future studies should address these limitations to improve the design of stimuli in the HPT.\u003c/p\u003e \u003cp\u003eThis study has practical applications. Findings indicate that confidence bias can impair response accuracy in perceiving hazards in driving scenarios, highlighting the key relevance of confidence calibration in potentially real-world driving performance. Although Hazard Perception Task is already implemented in driver licensing systems across several countries as an early indicator of driving skill (e.g., Crundall et al., 2021; Wetton et al., 2011), confidence measurement in judging task performance should be integrated into driving tests. Based on studies showing that feedback-based training can effectively reduce bias and overconfidence in self-assessments of driving abilities (e.g., Horswill et al., 2017), training programs targeting overconfidence could contribute to safe driving.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study reveals insight regarding drivers' hazard perception performance and subjective risk estimation based on confidence bias and environmental conditions in a video-based hazard perception task. Overconfidence (i.e., positive values of confidence bias) was associated with a reduced response accuracy in perceiving hazards, but it did not affect reaction times. Also, hazard perception performance and subjective risk estimation resulted from the interaction between the presence of hazard and lighting conditions. Confidence bias did not influence subjective risk estimation, suggesting that risk ratings of traffic scenarios may rely more on environmental cues. A weak positive association between Environmental Sensitivity (HSP scale) and subjective risk estimation suggests that this trait may amplify responsiveness to potential threats in traffic context. An absent link emerged between overconfidence and driving experience. The results suggest that integrating the measurement of confidence in performance accuracy into driver training and testing could improve awareness and safety.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003cp\u003eCompeting interests\u003c/p\u003e \u003cp\u003eThe authors declare no conflicts of interest related to this study or its publication. The manuscript was assessed in line with the journal\u0026rsquo;s standard editorial processes, including its policy on competing interests.\u003c/p\u003e \u003cp\u003eEthical approval\u003c/p\u003e \u003cp\u003e All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study received approval from the Institutional Review Board (IRB) of the University of Chieti-Pescara, by the Department of Neuroscience e Imaging.\u003c/p\u003e \u003cp\u003eConsent to participate\u003c/p\u003e \u003cp\u003e All participants provided informed consent, confirming voluntary participation, understanding of study details, and the right to withdraw at any time. They also approved the use of anonymized data for scientific purposes, ensuring no personal identification.\u003c/p\u003e \u003cp\u003e Consent for publication\u003c/p\u003e \u003cp\u003eParticipants have explicitly granted permission for the publication of all anonymized data and findings derived from this study, ensuring compliance with ethical research standards.\u003c/p\u003e \u003cp\u003eAvailability of data and materials\u003c/p\u003e \u003cp\u003eThe datasets and materials used and/or analyzed during the current study are available on the Open Science Framework (OSF) at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/jb72y/?view_only=2728ddd1a9ce457b87790de7026e99ee\u003c/span\u003e\u003cspan address=\"https://osf.io/jb72y/?view_only=2728ddd1a9ce457b87790de7026e99ee\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, ensuring adherence to data transparency practices.\u003c/p\u003e \u003cp\u003eCode availability\u003c/p\u003e \u003cp\u003eThe code utilized for the analysis in this study is available on the Open Science Framework (OSF) at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/jb72y/?view_only=2728ddd1a9ce457b87790de7026e99ee\u003c/span\u003e\u003cspan address=\"https://osf.io/jb72y/?view_only=2728ddd1a9ce457b87790de7026e99ee\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, ensuring adherence to data transparency practices.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eĀbele, L., Haustein, S., M\u0026oslash;ller, M., \u0026amp; Martinussen, L. M. (2018). Consistency between subjectively and objectively measured hazard perception skills among young male drivers. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e118\u003c/em\u003e, 214\u0026ndash;220. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2018.02.022\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAron, E. N., \u0026amp; Aron, A. (1997). Sensory-processing sensitivity and its relation to introversion and emotionality. \u003cem\u003eJournal of Personality and Social Psychology\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(2), 345\u0026ndash;368. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1037/0022-3514.73.2.345\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAron, E. N., Aron, A., \u0026amp; Jagiellowicz, J. (2012). Sensory Processing Sensitivity: A Review in the Light of the Evolution of Biological Responsivity. \u003cem\u003ePersonality and Social Psychology Review\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(3), 262\u0026ndash;282. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1177/1088868311434213\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsadamraji, M., Saffarzadeh, M., Ross, V., Borujerdian, A., Ferdosi, T., \u0026amp; Sheikholeslami, S. (2019). A novel driver hazard perception sensitivity model based on drivers\u0026rsquo; characteristics: A simulator study. \u003cem\u003eTraffic Injury Prevention\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(5), 492\u0026ndash;497. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1080/15389588.2019.1607971\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaker, M., \u0026amp; Wurgler, J. (2013). Chapter\u0026nbsp;5 - Behavioral Corporate Finance: An Updated Survey. In G. M. Constantinides, M. Harris, \u0026amp; R. M. Stulz (Eds.), \u003cem\u003eHandbook of the Economics of Finance\u003c/em\u003e (Vol. 2, pp. 357\u0026ndash;424). Elsevier. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/B978-0-44-453594-8.00005-7\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelsky, J. (1997). Variation in susceptibility to environmental influence: An evolutionary argument. \u003cem\u003ePsychological Inquiry\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(3), 182\u0026ndash;186. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1207/s15327965pli0803_3\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelsky, J., \u0026amp; Pluess, M. (2009). Beyond diathesis stress: Differential susceptibility to environmental influences. \u003cem\u003ePsychological Bulletin\u003c/em\u003e, \u003cem\u003e135\u003c/em\u003e(6), 885\u0026ndash;908. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1037/a0017376\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonfiglio, D. J., Lee, Y.-C., LaVoie, N., \u0026amp; Parker, J. (2014). Progression of hazard perception knowledge, licensure status and driving experience among teen drivers. \u003cem\u003eProceedings of the Human Factors and Ergonomics Society Annual Meeting\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(1), 2136\u0026ndash;2140. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1177/1541931214581449\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorowsky, A., Oron-Gilad, T., \u0026amp; Parmet, Y. (2010). The role of driving experience in hazard perception and categorization: A traffic-scene paradigm. \u003cem\u003eWorld Academy of Science, Engineering and Technology\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e, 305\u0026ndash;309. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://www.scopus.com/pages/publications/79951530926\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoyce, W. T., \u0026amp; Ellis, B. J. (2005). Biological sensitivity to context: I. An evolutionary-developmental theory of the origins and functions of stress reactivity. \u003cem\u003eDevelopment and Psychopathology\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(2), 271\u0026ndash;301. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1017/S0954579405050145\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrus, J., Aebersold, H., Grueschow, M., \u0026amp; Polania, R. (2021). Sources of confidence in value-based choice. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(1), 7337. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1038/s41467-021-27618-5\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao, S., Samuel, S., Murzello, Y., Ding, W., Zhang, X., \u0026amp; Niu, J. (2022). Hazard Perception in Driving: A Systematic Literature Review. \u003cem\u003eTransportation Research Record\u003c/em\u003e, \u003cem\u003e2676\u003c/em\u003e(12), 666\u0026ndash;690. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1177/03611981221096666\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastro, C., Muela, I., Doncel, P., \u0026amp; Garc\u0026iacute;a-Fern\u0026aacute;ndez, P. (2020). Hazard Perception and Prediction test for walking, riding a bike and driving a car: \u0026ldquo;Understanding of the global traffic situation.\u0026rdquo; \u003cem\u003ePLoS ONE\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(10). \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1371/journal.pone.0238605\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, A. S. K., Ng, T. C. K., \u0026amp; Lee, H. C. (2011). A comparison of the hazard perception ability of accident-involved and accident-free motorcycle riders. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(4), 1464\u0026ndash;1471. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2011.02.024\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChua, E. F., \u0026amp; Solinger, L. A. (2015). Building metamemorial knowledge over time: insights from eye tracking about the bases of feeling-of-knowing and confidence judgments. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.3389/fpsyg.2015.01206\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChuang, W.-I., \u0026amp; Lee, B.-S. (2006). An empirical evaluation of the overconfidence hypothesis. \u003cem\u003eJournal of Banking \u0026amp; Finance\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(9), 2489\u0026ndash;2515. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.jbankfin.2005.08.007\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCroskerry, P., \u0026amp; Norman, G. (2008). Overconfidence in Clinical Decision Making. \u003cem\u003eThe American Journal of Medicine\u003c/em\u003e, \u003cem\u003e121\u003c/em\u003e(5), S24\u0026ndash;S29. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.amjmed.2008.02.001\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrundall, D. (2016). Hazard prediction discriminates between novice and experienced drivers. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e, 47\u0026ndash;58. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2015.10.006\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrundall, D., Chapman, P., Phelps, N., \u0026amp; Underwood, G. (2003). Eye movements and hazard perception in police pursuit and emergency response driving. \u003cem\u003eJournal of Experimental Psychology: Applied\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), 163\u0026ndash;174. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1037/1076-898X.9.3.163\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrundall, D., Chapman, P., Trawley, S., Collins, L., van Loon, E., Andrews, B., \u0026amp; Underwood, G. (2012). Some hazards are more attractive than others: Drivers of varying experience respond differently to different types of hazard. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e, 600\u0026ndash;609. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2011.09.049\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrundall, D., van Loon, E., Baguley, T., \u0026amp; Kroll, V. (2021). A novel driving assessment combining hazard perception, hazard prediction and theory questions. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e149\u003c/em\u003e, 105847. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2020.105847\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeery, H. A. (1999). Hazard and Risk Perception among Young Novice Drivers. \u003cem\u003eJournal of Safety Research\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4), 225\u0026ndash;236. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/S0022-4375(99)00018-3\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEfklides, A., \u0026amp; Misailidi, P. (2010a). Introduction: The Present and the Future in Metacognition. In A. Efklides \u0026amp; P. Misailidi (Eds.), \u003cem\u003eTrends and Prospects in Metacognition Research\u003c/em\u003e (pp. 1\u0026ndash;18). Springer US. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1007/978-1-4419-6546-2_1\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEfklides, A., \u0026amp; Misailidi, P. (Eds.). (2010b). \u003cem\u003eTrends and Prospects in Metacognition Research\u003c/em\u003e. Springer US. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1007/978-1-4419-6546-2\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllis, B. J., Boyce, W. T., Belsky, J., Bakermans-Kranenburg, M. J., \u0026amp; Van Ijzendoorn, M. H. (2011). Differential susceptibility to the environment: An evolutionary\u0026ndash;neurodevelopmental theory. \u003cem\u003eDevelopment and Psychopathology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 7\u0026ndash;28. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1017/S0954579410000611\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eE-Prime\u0026reg; Stimulus Presentation Software | Psychology Software Tools\u003c/em\u003e. (2017, September 11). Psychology Software Tools | Solutions for Research, Assessment, and Education. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://pstnet.com/products/e-prime/\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans, T., Stuckey, R., \u0026amp; Macdonald, W. (2020). Young drivers\u0026rsquo; perceptions of risk and difficulty: Day versus night. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e147\u003c/em\u003e, 105753. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2020.105753\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarrand, P., \u0026amp; McKenna, F. P. (2001). Risk perception in novice drivers: the relationship between questionnaire measures and response latency. \u003cem\u003eTransportation Research Part F: Traffic Psychology and Behaviour\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 201\u0026ndash;212. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/S1369-8478(01)00024-9\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFetsch, C. R., Kiani, R., \u0026amp; Shadlen, M. N. (2014). Predicting the Accuracy of a Decision: A Neural Mechanism of Confidence. \u003cem\u003eCold Spring Harbor Symposia on Quantitative Biology\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e, 185\u0026ndash;197. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1101/sqb.2014.79.024893\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFisher, D. L., Pollatsek, A. P., \u0026amp; Pradhan, A. (2006). Can novice drivers be trained to scan for information that will reduce their likelihood of a crash? \u003cem\u003eInjury Prevention: Journal of the International Society for Child and Adolescent Injury Prevention\u003c/em\u003e, \u003cem\u003e12 Suppl 1\u003c/em\u003e(Suppl 1), i25-29. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1136/ip.2006.012021\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleming, S. M. (2024). Metacognition and Confidence: A Review and Synthesis. \u003cem\u003eAnnual Review of Psychology\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e(Volume 75, 2024), 241\u0026ndash;268. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1146/annurev-psych-022423-032425\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaray, L., Fisher, D. L., \u0026amp; Hancock, K. L. (2004). Effects of Driving Experience and Lighting Condition on Driving Performance. \u003cem\u003eProceedings of the Human Factors and Ergonomics Society Annual Meeting\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(19), 2290\u0026ndash;2294. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1177/154193120404801918\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGregersen, N. P. (1996). Young drivers\u0026rsquo; overestimation of their own skill\u0026mdash;an experiment on the relation between training strategy and skill. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(2), 243\u0026ndash;250. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/0001-4575(95)00066-6\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGroeger, J. A. (2013). \u003cem\u003eUnderstanding Driving: Applying Cognitive Psychology to a Complex Everyday Task\u003c/em\u003e. Routledge. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.4324/9780203769942\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuidi, S., Kosovicheva, A., \u0026amp; Wolfe, B. (2024). Seeing the truck, but missing the cyclist: effects of blur on duration thresholds for road hazard detection. \u003cem\u003eCognitive Research: Principles and Implications\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1), 32. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1186/s41235-024-00557-7\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026auml;ndel, M., de Bruin, A. B. H., \u0026amp; Dresel, M. (2020). Individual differences in local and global metacognitive judgments. \u003cem\u003eMetacognition and Learning\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 51\u0026ndash;75. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1007/s11409-020-09220-0\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaworth, N., Symmons, M., \u0026amp; Kowadlo, N. (2000). \u003cem\u003eHazard perception by inexperienced motorcyclists\u003c/em\u003e. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://trid.trb.org/View/732859\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHills, B. L. (1980). Vision, Visibility, and Perception in Driving. \u003cem\u003ePerception\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 183\u0026ndash;216. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1068/p090183\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorrey, W. J., Lesch, M. F., Mitsopoulos-Rubens, E., \u0026amp; Lee, J. D. (2015). Calibration of skill and judgment in driving: Development of a conceptual framework and the implications for road safety. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e, 25\u0026ndash;33. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2014.12.017\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorswill, M. S., Garth, M., Hill, A., \u0026amp; Watson, M. O. (2017). The effect of performance feedback on drivers\u0026rsquo; hazard perception ability and self-ratings. Accident Analysis \u0026amp; Prevention, 101, 135\u0026ndash;142. https://doi.org/10.1016/j.aap.2017.02.009\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorswill, M. S., Hill, A., \u0026amp; Wetton, M. (2015). Can a video-based hazard perception test used for driver licensing predict crash involvement? \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e82\u003c/em\u003e, 213\u0026ndash;219. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2015.05.019\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorswill, M. S., \u0026amp; McKenna, F. P. (1999). The Effect of Perceived Control on Risk Taking. \u003cem\u003eJournal of Applied Social Psychology\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(2), 377\u0026ndash;391. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1111/j.1559-1816.1999.tb01392.x\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorswill, M. S., \u0026amp; McKenna, F. P. (2004). Drivers\u0026rsquo; hazard perception ability: situation awareness on the road. In S. Banbury \u0026amp; T. S. (Eds.), \u003cem\u003eA cognitive approach to situation awareness: theory and application\u003c/em\u003e (pp. 155\u0026ndash;175). Ashgate. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://centaur.reading.ac.uk/14289/\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, W., Sun, L., \u0026amp; Cheng, L. (2025). Effects of confidence level and hazard type on the visual search patterns and hazard response times of young drivers. \u003cem\u003eTraffic Injury Prevention\u003c/em\u003e, 1\u0026ndash;7. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1080/15389588.2025.2497521\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKepecs, A., \u0026amp; Mainen, Z. F. (2012). A computational framework for the study of confidence in humans and animals. \u003cem\u003ePhilosophical Transactions of the Royal Society B: Biological Sciences\u003c/em\u003e, \u003cem\u003e367\u003c/em\u003e(1594), 1322\u0026ndash;1337. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1098/rstb.2012.0037\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiani, R., Corthell, L., \u0026amp; Shadlen, M. N. (2014). Choice certainty is informed by both evidence and decision time. \u003cem\u003eNeuron\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e(6), 1329\u0026ndash;1342. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.neuron.2014.12.015\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnuth, D., Kehl, D., Hulse, L., \u0026amp; Schmidt, S. (2014). Risk perception, experience, and objective risk: a cross-national study with European emergency survivors. \u003cem\u003eRisk Analysis: An Official Publication of the Society for Risk Analysis\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(7), 1286\u0026ndash;1298. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1111/risa.12157\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonstantopoulos, P., Chapman, P., \u0026amp; Crundall, D. (2010). Driver\u0026rsquo;s visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers\u0026rsquo; eye movements in day, night and rain driving. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(3), 827\u0026ndash;834. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2009.09.022\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoriat, A., Lichtenstein, S., \u0026amp; Fischhoff, B. (1980). Reasons for confidence. \u003cem\u003eJournal of Experimental Psychology: Human Learning and Memory\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2), 107\u0026ndash;118. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1037/0278-7393.6.2.107\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, J., \u0026amp; Prince, N. (2023). Overconfidence Bias in Investment Decisions: A Systematic Mapping of Literature and Future Research Topics. \u003cem\u003eFIIB Business Review\u003c/em\u003e, 23197145231174344. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1177/23197145231174344\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLajunen, T., \u0026amp; Summala, H. (1995). Driving experience, personality, and skill and safety-motive dimensions in drivers\u0026rsquo; self-assessments. \u003cem\u003ePersonality and Individual Differences\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(3), 307\u0026ndash;318. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/0191-8869(95)00068-H\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLejuez, C. W., Read, J. P., Kahler, C. W., Richards, J. B., Ramsey, S. E., Stuart, G. L., Strong, D. R., \u0026amp; Brown, R. A. (2002). Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task (BART). \u003cem\u003eJournal of Experimental Psychology. Applied\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(2), 75\u0026ndash;84. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1037//1076-898x.8.2.75\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinussen, L. M., M\u0026oslash;ller, M., \u0026amp; Prato, C. G. (2017). Accuracy of young male drivers\u0026rsquo; self-assessments of driving skill. \u003cem\u003eTransportation Research Part F: Traffic Psychology and Behaviour\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e, 228\u0026ndash;235. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.trf.2017.03.001\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatthews, M. L., \u0026amp; Moran, A. R. (1986). Age differences in male drivers\u0026rsquo; perception of accident risk: The role of perceived driving ability. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(4), 299\u0026ndash;313. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/0001-4575(86)90044-8\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKenna, F. P., \u0026amp; Horswill, M. S. (2006). Risk taking from the participant\u0026rsquo;s perspective: The case of driving and accident risk. \u003cem\u003eHealth Psychology\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(2), 163\u0026ndash;170. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1037/0278-6133.25.2.163\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKnight, A. J., \u0026amp; McKnight, A. S. (2003). Young novice drivers: careless or clueless? \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(6), 921\u0026ndash;925. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/S0001-4575(02)00100-8\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMetcalfe, J., \u0026amp; Shimamura, A. P. (1994). \u003cem\u003eMetacognition: Knowing about Knowing\u003c/em\u003e. MIT Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore, D. A., \u0026amp; Healy, P. J. (2008). The trouble with overconfidence. \u003cem\u003ePsychological Review\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e(2), 502\u0026ndash;517. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1037/0033-295X.115.2.502\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoran, C., Bennett, J. M., \u0026amp; Prabhakharan, P. (2019). Road user hazard perception tests: A systematic review of current methodologies. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e129\u003c/em\u003e, 309\u0026ndash;333. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2019.05.021\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy, P. R., Robertson, I. H., Harty, S., \u0026amp; O\u0026rsquo;Connell, R. G. (2015). Neural evidence accumulation persists after choice to inform metacognitive judgments. \u003cem\u003eeLife\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, e11946. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.7554/eLife.11946\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN\u0026auml;\u0026auml;t\u0026auml;nen, R., \u0026amp; Summala, H. (1974). A model for the role of motivational factors in drivers\u0026rsquo; decision-making\u0026lowast;. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(3), 243\u0026ndash;261. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/0001-4575(74)90003-7\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorman, E., Pfuhl, G., S\u0026aelig;le, R. G., Svartdal, F., L\u0026aring;g, T., \u0026amp; Dahl, T. I. (2019). Metacognition in psychology. \u003cem\u003eReview of General Psychology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(4), 403\u0026ndash;424. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1177/1089268019883821\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwsley, C., \u0026amp; McGwin, G. (2010). Vision and driving. \u003cem\u003eVision Research\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(23), 2348\u0026ndash;2361. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.visres.2010.05.021\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePluess, M. (2015). Individual Differences in Environmental Sensitivity. \u003cem\u003eChild Development Perspectives\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), 138\u0026ndash;143. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1111/cdep.12120\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePluess, M., Assary, E., Lionetti, F., Lester, K. J., Krapohl, E., Aron, E. N., \u0026amp; Aron, A. (2018). Environmental sensitivity in children: Development of the Highly Sensitive Child Scale and identification of sensitivity groups. \u003cem\u003eDevelopmental Psychology\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(1), 51\u0026ndash;70. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1037/dev0000406\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePluess, M., Lionetti, F., Aron, E. N., \u0026amp; Aron, A. (2023). People differ in their sensitivity to the environment: An integrated theory, measurement and empirical evidence. \u003cem\u003eJournal of Research in Personality\u003c/em\u003e, \u003cem\u003e104\u003c/em\u003e, 104377. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.jrp.2023.104377\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePollatsek, A., Narayanaan, V., Pradhan, A., \u0026amp; Fisher, D. L. (2006). Using Eye Movements to Evaluate a PC-Based Risk Awareness and Perception Training Program on a Driving Simulator. \u003cem\u003eHuman Factors\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(3), 447\u0026ndash;464. https://doi.org/10.1518/001872006778606787\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhodes, M. G. (2019). Metacognition. \u003cem\u003eTeaching of Psychology\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(2), 168\u0026ndash;175. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1177/0098628319834381\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubaltelli, E., Scrimin, S., Moscardino, U., Priolo, G., \u0026amp; Buodo, G. (2018). Media exposure to terrorism and people\u0026rsquo;s risk perception: The role of environmental sensitivity and psychophysiological response to stress. \u003cem\u003eBritish Journal of Psychology\u003c/em\u003e, \u003cem\u003e109\u003c/em\u003e(4), 656\u0026ndash;673. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1111/bjop.12292\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSagberg, F., \u0026amp; Bj\u0026oslash;rnskau, T. (2006). Hazard perception and driving experience among novice drivers. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(2), 407\u0026ndash;414. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2005.10.014\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSara\u0026ccedil;, S., \u0026amp; Karakelle, S. (2012). On-line and off-line assessment of metacognition. \u003cem\u003eInternational Electronic Journal of Elementary Education\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), 301\u0026ndash;315. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://iejee.com/index.php/IEJEE/article/view/201\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScialfa, C. T., Borkenhagen, D., Lyon, J., Desch\u0026ecirc;nes, M., Horswill, M., \u0026amp; Wetton, M. (2012). The effects of driving experience on responses to a static hazard perception test. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e, 547\u0026ndash;553. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2011.09.005\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, J., Kosovicheva, A., \u0026amp; Wolfe, B. (2024). Road Hazard Stimuli: Annotated naturalistic road videos for studying hazard detection and scene perception. \u003cem\u003eBehavior Research Methods\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(4), 4188\u0026ndash;4204. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.3758/s13428-023-02299-8\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, J., \u0026amp; Wolfe, B. (2024). Highly dangerous road hazards are not immune from the low prevalence effect. \u003cem\u003eCognitive Research: Principles and Implications\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1), 6. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1186/s41235-024-00531-3\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026uuml;mer, N., \u0026Ouml;zkan, T., \u0026amp; Lajunen, T. (2006). Asymmetric relationship between driving and safety skills. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(4), 703\u0026ndash;711. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2005.12.016\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSummala, H. (1988). Risk control is not risk adjustment: the zero-risk theory of driver behaviour and its implications. \u003cem\u003eErgonomics\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(4), 491\u0026ndash;506. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1080/00140138808966694\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, L., Cheng, L., \u0026amp; Zhang, Q. (2021). The differences in hazard response time and driving styles of violation-involved and violation-free taxi drivers. \u003cem\u003eTransportation Research Part F: Traffic Psychology and Behaviour\u003c/em\u003e, \u003cem\u003e82\u003c/em\u003e, 178\u0026ndash;186. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.trf.2021.08.011\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, L., Hu, W., Cheng, L., \u0026amp; Zhang, C. (2024). Effects of hazard type and confidence level on hazard perception in young male drivers: an ERP study. \u003cem\u003eNeuroReport\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(5), 299. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1097/WNR.0000000000002007\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSvenson, O. (1981). Are we all less risky and more skillful than our fellow drivers? \u003cem\u003eActa Psychologica\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(2), 143\u0026ndash;148. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/0001-6918(81)90005-6\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVlakveld, W. P. (2014). A comparative study of two desktop hazard perception tasks suitable for mass testing in which scores are not based on response latencies. \u003cem\u003eTransportation Research Part F: Traffic Psychology and Behaviour\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 218\u0026ndash;231. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.trf.2013.12.013\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J., Li, J., Xiao, Y., \u0026amp; Song, K. (2025). Steering in the dark: The impact of environmental luminance on driver behavior through optical flow analysis. \u003cem\u003eAttention, Perception, \u0026amp; Psychophysics\u003c/em\u003e. https://doi.org/10.3758/s13414-025-03146-y\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarren, W. H. (2006). The dynamics of perception and action. \u003cem\u003ePsychological Review\u003c/em\u003e, \u003cem\u003e113\u003c/em\u003e(2), 358\u0026ndash;389. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1037/0033-295X.113.2.358\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWetton, M. A., Hill, A., \u0026amp; Horswill, M. S. (2011). The development and validation of a hazard perception test for use in driver licensing. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(5), 1759\u0026ndash;1770. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2011.04.007\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite, M. J., Cunningham, L. C., \u0026amp; Titchener, K. (2011). Young drivers\u0026rsquo; optimism bias for accident risk and driving skill: Accountability and insight experience manipulations. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(4), 1309\u0026ndash;1315. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2011.01.013\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeung, N., \u0026amp; Summerfield, C. (2012). Metacognition in human decision-making: confidence and error monitoring. \u003cem\u003ePhilosophical Transactions of the Royal Society B: Biological Sciences\u003c/em\u003e, \u003cem\u003e367\u003c/em\u003e(1594), 1310\u0026ndash;1321. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1098/rstb.2011.0416\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeuwts, L. H. R. H., Vansteenkiste, P., Deconinck, F. J. A., Cardon, G., \u0026amp; Lenoir, M. (2017). Hazard perception in young cyclists and adult cyclists. \u003cem\u003eAccident Analysis \u0026amp; Prevention\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e, 64\u0026ndash;71. \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ehttps://doi.org/10.1016/j.aap.2016.04.034\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Chieti-Pescara","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"confidence judgments, perceptual categorization and identification, risk estimations ","lastPublishedDoi":"10.21203/rs.3.rs-8553534/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8553534/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman decisions are accompanied by internal confidence judgments about the likelihood of being correct. These judgments are not always well-calibrated. Miscalibration occurs when there is a discrepancy between self-reported confidence regarding the correctness of performance and the objective performance. As a result, cognitive bias such as overconfidence can emerge, shaping how individuals perceive and behave. Traffic represents a distinctive decision environment, in which individuals have to constantly monitor and interpret changing perceptual information. Although overconfidence has been linked to unsafe driving, prior research conceptualizes confidence through self-report questionnaires and group-based analyses. Using performance-based measures, the present study aims to capture confidence as a continuous variable, investigating whether this bias can impact both the hazard perception performance and subjective experience of risk. Sixty-five participants completed a novel adaptation of the Hazard Perception Task (HPT), which integrated trial-by-trial confidence judgments and risk estimation of driving scenarios, alongside scores from the Hazard Perception Questionnaire (HPQ) and the Environmental Sensitivity (HSP scale). Results showed that overconfidence significantly reduced hazard perception accuracy, whereas reaction times to hazard and risk estimation were unaffected. Response accuracy declined also during nighttime in safe scenarios but remained high in hazardous trials. Subjective risk estimation was driven by the presence of hazards and lightning conditions. Environmental Sensitivity (HSP scale) showed a significant positive correlation with risk estimation. No relationship emerged between overconfidence and driving experience. Understanding how confidence judgments and individual differences operate in this high-risk context is therefore critical for safety.\u003c/p\u003e","manuscriptTitle":"The Impact of Overconfidence and Environmental Conditions on Hazard Perception and Risk Assessment: An Experimental Study Using Video-Based Traffic Scenarios","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 07:13:29","doi":"10.21203/rs.3.rs-8553534/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fcec0b7e-450f-4a58-8146-f8d16f802b8c","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60830144,"name":"Psychology"}],"tags":[],"updatedAt":"2026-01-12T07:13:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 07:13:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8553534","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8553534","identity":"rs-8553534","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}