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Abstract Autonomous vehicles present fundamental challenges to tort liability systems designed for human drivers. This study examines whether laypeople exhibit systematic bias in fault attribution and damage awards when defendants operate autonomous vehicles rather than conventional vehicles, and whether information about tort law's preventive function moderates these judgments. We conduct a pre-registered randomized experiment with 4,200 participants using a 2×2 factorial design. The first factor varies information content: participants receive either general civil litigation information or information emphasizing tort law's preventive function in promoting safety investments. The second factor varies engagement: half of participants answer support questions about the preventive function of tort law while half do not. Following treatment, all participants complete six vignette-based liability scenarios as mock judges in traffic accident cases. Each scenario systematically manipulates defendant vehicle type (human-driven vs. autonomous), accident preventability, vehicle performance characteristics, and plaintiff fault levels. Our design addresses two research questions. First, we estimate whether autonomous vehicle defendants face higher fault attributions or damage awards compared to human drivers in otherwise identical accidents, testing for autonomous vehicle aversion in liability judgments. Second, we test whether information emphasizing tort law's deterrence function reduces or amplifies any observed bias, examining whether framing tort law as a safety incentive mechanism rather than purely compensatory affects liability judgments differently for autonomous versus human defendants. The vignette scenarios present realistic traffic accidents involving either two vehicles (intersection and highway settings) or vehicle-pedestrian collisions. We manipulate defendant vehicle technology (human-driven vs. fully autonomous), accident preventability (whether the defendant vehicle could have avoided the accident), autonomous vehicle performance capabilities (superior vs. equivalent to human drivers), and plaintiff baseline fault (equal to, lower than, or much lower than the defendant’s fault). Each participant evaluates six scenarios selected through constrained randomization to ensure exposure to different vehicle types while maintaining statistical power. Primary outcomes include fault allocation percentages (comparative negligence determination) and solatium amounts (non-pecuniary damage awards). We measure both point estimates and participants' reported confidence in their judgments. This confidence measure enables analysis of decision certainty, which may differ between autonomous and human defendant cases even when average judgments appear similar. We stratify randomization by age, gender, and metropolitan residence to enable analysis of heterogeneous treatment effects. These dimensions are policy-relevant because public acceptance of autonomous vehicles may vary systematically across demographic groups, and tort liability judgments by actual judges or juries may reflect similar heterogeneity. Our sample includes targeted recruitment from regions with ongoing autonomous vehicle development initiatives. The experimental design enables causal identification of information treatment effects on liability judgments while the within-subject vignette variation permits estimation of autonomous vehicle bias through difference-in-differences analysis. By varying both information framing and engagement depth, we test whether passive information exposure suffices or whether active processing through support questions is necessary to shift liability judgments. This study contributes to understanding how liability systems may need to adapt as autonomous vehicles become prevalent. If we observe systematic bias against autonomous vehicles, this suggests that current liability frameworks may inadvertently create excessive deterrence for autonomous vehicle deployment even when these vehicles demonstrably improve safety. Conversely, if information about tort law's preventive function reduces bias, this suggests that public education about liability system objectives could facilitate autonomous vehicle adoption while maintaining appropriate safety incentives. Autonomous vehicles present fundamental challenges to tort liability systems designed for human drivers. This study examines whether laypeople exhibit systematic bias in fault attribution and damage awards when defendants operate autonomous vehicles rather than conventional vehicles, and whether information about tort law's preventive function moderates these judgments. We conduct a pre-registered randomized experiment with 6,200 participants. The original study employs a 2×2 factorial design with 4,200 participants in a legal framing condition. An additional between-subject condition with 2,000 participants uses a non-legal framing, in which all legal terminology (e.g., comparative negligence, civil litigation) is replaced with everyday language centered on "responsibility," and no legal informational materials are provided. This non-legal condition was added post-registration (conducted March 10–31, 2026) to test whether the legal framing itself affects fault attribution and damage award judgments.The first factor varies information content: participants receive either general civil litigation information or information emphasizing tort law's preventive function in promoting safety investments. The second factor varies engagement: half of participants answer support questions about the preventive function of tort law while half do not. Following treatment, all participants complete six vignette-based liability scenarios as mock judges in traffic accident cases. Each scenario systematically manipulates defendant vehicle type (human-driven vs. autonomous), accident preventability, vehicle performance characteristics, and plaintiff fault levels. Our design addresses three research questions. First, we estimate whether autonomous vehicle defendants face higher fault attributions or damage awards compared to human drivers in otherwise identical accidents, testing for autonomous vehicle aversion in liability judgments. Second, we test whether information emphasizing tort law's deterrence function reduces or amplifies any observed bias, examining whether framing tort law as a safety incentive mechanism rather than purely compensatory affects liability judgments differently for autonomous versus human defendants. Third, by comparing the legal and non-legal framing conditions, we examine whether embedding liability judgments in a legal context — with legal terminology, procedural framing, and tort law information — systematically shifts fault attribution and damage awards relative to judgments made using ordinary responsibility language. This addresses whether observed autonomous vehicle bias (if any) is specific to legal reasoning or reflects broader responsibility attribution patterns. The vignette scenarios present realistic traffic accidents involving either two vehicles (intersection and highway settings) or vehicle-pedestrian collisions. We manipulate defendant vehicle technology (human-driven vs. fully autonomous), accident preventability (whether the defendant vehicle could have avoided the accident), autonomous vehicle performance capabilities (superior vs. equivalent to human drivers), and plaintiff baseline fault (equal to, lower than, or much lower than the defendant’s fault). Each participant evaluates six scenarios selected through constrained randomization to ensure exposure to different vehicle types while maintaining statistical power. Participants in the non-legal framing condition evaluate the same six vignette scenarios, but all references to legal concepts (comparative negligence, civil liability, tort law) are replaced with non-legal equivalents (responsibility allocation, accountability). These participants receive no informational treatment about civil litigation or tort law's preventive function. This between-subject comparison isolates the effect of legal framing on liability judgments while holding the underlying accident scenarios constant. Primary outcomes include fault allocation percentages (comparative negligence determination) and solatium amounts (non-pecuniary damage awards). We measure both point estimates and participants' reported confidence in their judgments. This confidence measure enables analysis of decision certainty, which may differ between autonomous and human defendant cases even when average judgments appear similar. We stratify randomization by age, gender, and metropolitan residence to enable analysis of heterogeneous treatment effects. These dimensions are policy-relevant because public acceptance of autonomous vehicles may vary systematically across demographic groups, and tort liability judgments by actual judges or juries may reflect similar heterogeneity. Our sample includes targeted recruitment from regions with ongoing autonomous vehicle development initiatives. The experimental design enables causal identification of information treatment effects on liability judgments while the within-subject vignette variation permits estimation of autonomous vehicle bias through difference-in-differences analysis. By varying both information framing and engagement depth, we test whether passive information exposure suffices or whether active processing through support questions is necessary to shift liability judgments. This study contributes to understanding how liability systems may need to adapt as autonomous vehicles become prevalent. If we observe systematic bias against autonomous vehicles, this suggests that current liability frameworks may inadvertently create excessive deterrence for autonomous vehicle deployment even when these vehicles demonstrably improve safety. Conversely, if information about tort law's preventive function reduces bias, this suggests that public education about liability system objectives could facilitate autonomous vehicle adoption while maintaining appropriate safety incentives.
Last Published December 09, 2025 08:17 AM March 31, 2026 03:38 AM
Intervention (Public) This pre-registered study consists of a 2×2 factorial information experiment followed by a common vignette-based liability judgment task. In the first stage, participants are randomly assigned to watch either (i) a video providing general information about civil litigation procedures or (ii) a video emphasizing the preventive function of tort law. Within each information condition, they are further divided into (a) a group that proceeds directly to the next stage after watching the video and (b) a group that answers brief questions about the preventive function of tort law before moving on. In the second-stage vignette experiment, all participants act as mock judges. Traffic accident cases are presented as vignettes in 30 distinct scenarios that systematically vary whether the defendant operates a human-driven vehicle or an autonomous vehicle, whether the autonomous vehicle is equipped with state-of-the-art safety technology for accident prevention, whether its prediction-and-avoidance capabilities are superior to or equivalent to those of human drivers, and the plaintiff’s baseline level of fault (equal to, lower than, or much lower than the defendant’s fault). The 30 scenarios are categorized into five groups; for each participant, one scenario is randomly drawn from each group, and one additional scenario is randomly selected from the full set, so that each participant evaluates a total of six scenarios. For every scenario, participants allocate comparative fault between plaintiff and defendant, determine an amount for non-pecuniary damages, and report their confidence in each of these judgments. This pre-registered study employs a between-subject legal vs. non-legal framing design combined with a common vignette-based judgment task. [Legal Frame (N = 4,200)] Participants in the legal frame complete a 2×2 factorial information experiment followed by a liability judgment task. In the first stage, participants are randomly assigned to watch either (i) a video providing general information about civil litigation procedures or (ii) a video emphasizing the preventive function of tort law. Within each information condition, they are further divided into (a) a group that proceeds directly to the next stage after watching the video and (b) a group that answers brief questions about the preventive function of tort law before moving on. In the second stage, all participants act as mock judges evaluating traffic accident vignettes framed in legal terminology (e.g., comparative negligence, plaintiff, defendant, solatium). [Non-Legal Frame (N = 2,000)] Participants in the non-legal frame receive no informational treatment — no video and no comprehension questions. They proceed directly to the vignette judgment task. All legal terminology is replaced with everyday language: references to comparative negligence are reframed as responsibility allocation, legal party designations are replaced with non-legal equivalents, and non-pecuniary damages are described using ordinary language. This condition serves as a baseline to identify whether the legal framing itself systematically affects fault attribution and damage award judgments. [Common Vignette Task] In both frames, participants evaluate traffic accident cases presented as vignettes across 30 distinct scenarios that systematically vary whether the defendant operates a human-driven vehicle or an autonomous vehicle, whether the autonomous vehicle is equipped with state-of-the-art safety technology for accident prevention, whether its prediction-and-avoidance capabilities are superior to or equivalent to those of human drivers, and the plaintiff's baseline level of fault (equal to, lower than, or much lower than the defendant's fault). The 30 scenarios are categorized into five groups; for each participant, one scenario is randomly drawn from each group, and one additional scenario is randomly selected from the full set, so that each participant evaluates a total of six scenarios. For every scenario, participants allocate fault between the two parties, determine an amount for non-pecuniary damages, and report their confidence in each of these judgments.
Experimental Design (Public) This pre-registered online survey experiment is conducted with 4,200 adults in South Korea aged 20 to 60, recruited from a professional online panel. Sample sizes are allocated across age groups to reflect the underlying population structure. Each participant is individually and randomly assigned to one of four treatment arms in a 2×2 factorial design that combines information content (general information about civil litigation vs. information emphasizing the preventive function of tort law) and the presence or absence of support questions about the preventive role of tort law. Participants first answer pre-treatment questions on AI and autonomous vehicles, then receive their randomly assigned information treatment, and subsequently read standardized explanations of key legal concepts such as fully autonomous vehicles, responsibility allocation, and non-pecuniary damages. They are then presented with six traffic accident vignette scenarios, randomly drawn from a set of 30 scenarios that systematically vary the defendant vehicle type, the presence of advanced safety technology for accident prevention (a latest-generation infrared camera), the accident-prediction and avoidance performance of the autonomous vehicle, and the plaintiff’s baseline fault level (medium/low/very low). For each scenario, participants determine the comparative responsibility allocation and the pain and suffering damages amount and report their confidence in these judgments. Finally, they complete a post-treatment questionnaire measuring demographic characteristics, litigation and driving experience, and psychological traits. This pre-registered online survey experiment is conducted with 4,200 adults in South Korea aged 20 to 60, recruited from a professional online panel. Sample sizes are allocated across age groups to reflect the underlying population structure. Each participant is individually and randomly assigned to one of four treatment arms in a 2×2 factorial design that combines information content (general information about civil litigation vs. information emphasizing the preventive function of tort law) and the presence or absence of support questions about the preventive role of tort law. Participants first answer pre-treatment questions on AI and autonomous vehicles, then receive their randomly assigned information treatment, and subsequently read standardized explanations of key legal concepts such as fully autonomous vehicles, responsibility allocation, and non-pecuniary damages. They are then presented with six traffic accident vignette scenarios, randomly drawn from a set of 30 scenarios that systematically vary the defendant vehicle type, the presence of advanced safety technology for accident prevention (a latest-generation infrared camera), the accident-prediction and avoidance performance of the autonomous vehicle, and the plaintiff’s baseline fault level (medium/low/very low). For each scenario, participants determine the comparative responsibility allocation and the pain and suffering damages amount and report their confidence in these judgments. Finally, they complete a post-treatment questionnaire measuring demographic characteristics, litigation and driving experience, and psychological traits. Post-registration amendment (March 2026): A non-legal framing condition was added as a between-subject arm with 2,000 new participants (conducted March 10–31, 2026). In this condition: All legal terminology is removed (e.g., "comparative negligence" → "responsibility allocation"; "plaintiff/defendant" → everyday equivalents) No informational video or text about civil litigation procedures is shown Participants evaluate the same six vignette scenarios as the legal framing conditions The outcome measures (fault allocation percentages and solatium amounts) remain identical This addition enables a between-subject comparison of legal vs. non-legal framing to test whether the legal context itself moderates fault attribution toward autonomous vehicle defendants.
Randomization Method Random assignment in the experiment is conducted by a computer program. At the point when participants complete the pre-survey and proceed to the information treatment stage, each participant is independently assigned to one of the four treatment conditions. The platform’s randomization algorithm is designed to maintain approximate balance across the four treatment groups, taking into account key demographic characteristics of the Korean population (such as age, gender, and region). Random assignment in the experiment is conducted by a computer program. At the point when participants complete the pre-survey and proceed to the information treatment stage, each participant is independently assigned to one of the four treatment conditions. The platform’s randomization algorithm is designed to maintain approximate balance across the four treatment groups, taking into account key demographic characteristics of the Korean population (such as age, gender, and region). Participants in the non-legal framing condition were recruited as a separate sample and were not randomized into the 2×2 information treatment design. Within the non-legal condition, each participant was assigned six vignette scenarios through the same constrained randomization procedure used in the legal framing conditions, ensuring comparable exposure to different vehicle types.
Planned Number of Observations 4,200 participants 6,200 participants (Legal 4,200 + Non-legal 2,000)
Sample size (or number of clusters) by treatment arms Approximately 1,050 participants per treatment arm: - Arm 1: Civil Litigation Information Only (1,050 participants) - Arm 2: Civil Litigation Information + Comprehension Questions (1,050 participants) - Arm 3: Preventive Function Information Only (1,050 participants) - Arm 4: Preventive Function Information + Comprehension Questions (1,050 participants) Since each participant evaluates six vignette scenarios, this yields a total of approximately 25,200 scenario-level observations (about 6,300 per treatment arm). * Legal frame (4,200 participants) Approximately 1,050 participants per treatment arm: - Arm 1: Civil Litigation Information Only (1,050 participants) - Arm 2: Civil Litigation Information + Comprehension Questions (1,050 participants) - Arm 3: Preventive Function Information Only (1,050 participants) - Arm 4: Preventive Function Information + Comprehension Questions (1,050 participants) Since each participant evaluates six vignette scenarios, this yields a total of approximately 25,200 scenario-level observations (about 6,300 per treatment arm). * Non-Legal Frame (2,000 participants) All participants are assigned to a single condition: Arm 5: No legal information, no legal terminology — all legal terms replaced with everyday responsibility language (2,000 participants) Since each participant evaluates six vignette scenarios, this yields a total of approximately 12,000 scenario-level observations.
Intervention (Hidden) We implement a 2×2 factorial design with four treatment arms, followed by a common vignette-based judgment task. Information Content Manipulation (Factor 1) Participants are randomly assigned to view one of two 30-40 second videos: - Treatment A (Civil Litigation Information) Video presents factual information about civil litigation procedures in South Korea, including case categorization by claim amount (small claims under 30 million won, single-judge cases from 30 million to 500 million won, and collegiate panel cases exceeding 500 million won), the three-tier appellate system (district court, high court or collegiate district court panel, and Supreme Court), and the progression of cases through these levels based on appeals and legal interpretation disputes. - Treatment B (Preventive Function Information) Video emphasizes tort law's preventive function in reducing accidents and promoting safety. Content explains that tort liability serves not only to compensate victims but also to incentivize accident prevention by establishing behavioral standards, imposing liability for violations that cause harm, and encouraging both individuals and firms to invest in safety measures including safer designs and quality control. Engagement Manipulation (Factor 2) Within each information content condition, participants are randomly assigned to: - No Questions Condition Participants watch the assigned video and proceed immediately to the vignette scenarios. - Questions Condition After watching the video, participants answer two multiple-choice questions testing support for the preventive function of tort law before proceeding to scenarios. This creates four treatment arms with equal allocation: (1) Civil Litigation Information Only, (2) Civil Litigation Information + Questions, (3) Preventive Function Information Only, (4) Preventive Function Information + Questions. Vignette-Based Judgment Task (Common to All Arms) Following information treatment, all participants complete six traffic accident vignettes as mock judges. Each vignette describes a collision between a plaintiff and defendant vehicle (or pedestrian plaintiff), provides demographic information about parties, displays a 30-40 second animated video of the accident, and describes injury severity and economic damages. We systematically vary the following factors to construct a total of 30 distinct scenarios: - Plaintiff type: human-driven vehicle, pedestrian - Defendant vehicle type: human-driven vehicle, fully autonomous vehicle - Plaintiff–defendant pairing: .. human-driven vehicle vs. human-driven vehicle .. pedestrian vs. human-driven vehicle .. human-driven vehicle vs. autonomous vehicle .. pedestrian vs. autonomous vehicle - Potential accident preventability through advanced safety technology on the autonomous defendant vehicle (hereafter ‘preventability’ or ‘preventable’): whether the autonomous vehicle is equipped with state-of-the-art safety technology for nighttime accident prevention (e.g., a latest-generation infrared camera), yielding technology-equipped versus non-equipped autonomous vehicles - Autonomous vehicle performance (prediction and avoidance capability) (hereafter ‘performance’): when the defendant is an autonomous vehicle, whether its accident prediction and avoidance capabilities are equivalent to, or superior to, those of human drivers - Accident location: nighttime intersection settings - Plaintiff’s baseline fault level: .. Medium: for example, at night, at an intersection without traffic lights, the plaintiff attempts a right turn and collides with the defendant, who is making a left turn into the plaintiff’s lane. .. Low: for example, at night, at an intersection without traffic lights, the plaintiff is driving straight through the intersection when the defendant attempts a left turn at the intersection and collides with the plaintiff. .. Very low: for example, at night, at an intersection where traffic lights are installed only on one approach, the defendant enters the intersection by driving straight through a red light and collides with the plaintiff, who is turning right from the cross street that has no traffic light. Each participant evaluates six scenarios selected through constrained randomization: one scenario with human-driven defendant, two scenarios with preventable autonomous defendant, two scenarios with non-preventable autonomous defendant, and one scenario randomly selected from the full set. This ensures within-subject variation in vehicle type while preventing participant fatigue. For each scenario, participants make four judgments: 1. Fault allocation percentage: Using a slider from 0-100%, participants allocate responsibility between plaintiff and defendant, where higher percentages indicate greater defendant responsibility 2. Fault allocation confidence: 0-100% scale of certainty in the responsibility allocation judgment 3. Non-pecuniary damages amount: Monetary award for non-pecuniary damages (pain and suffering, emotional distress) in units of 10,000 Korean won, given fixed economic damages of 30 million won 4. Non-pecuniary damages confidence: 0-100% scale of certainty in the damage award Pre-Treatment and Post-Treatment Measures Before information treatment, participants complete pre-survey measures including eight questions assessing AI knowledge, AI attitudes, autonomous vehicle familiarity, and autonomous vehicle feature usage. After completing all vignette judgments, participants answer 27 post-treatment questions measuring litigation experience, trust in courts, traffic accident history, driving experience, openness to experience personality traits, risk attitudes, and fairness preferences. Pilot Study A pilot study with 300 participants (100 per information content condition) was conducted in November 2025. Pilot results informed refinement of vignette content, confirmation of randomization procedures, and validation of outcome measure distributions. Based on pilot analysis, we reduced the total number of unique scenarios from 45 to 30 by eliminating highway accident scenarios and modifying one high-plaintiff-fault scenario. The main study implements these refined materials with the full 2×2 factorial design including the engagement manipulation. We implement a 2×2 factorial design with four treatment arms in a legal framing condition (N = 4,200), followed by a common vignette-based judgment task. An additional non-legal framing condition (N = 2,000) was added post-registration (conducted March 10–31, 2026) as a between-subject comparison arm. Information Content Manipulation (Factor 1) Participants are randomly assigned to view one of two 30-40 second videos: - Treatment A (Civil Litigation Information) Video presents factual information about civil litigation procedures in South Korea, including case categorization by claim amount (small claims under 30 million won, single-judge cases from 30 million to 500 million won, and collegiate panel cases exceeding 500 million won), the three-tier appellate system (district court, high court or collegiate district court panel, and Supreme Court), and the progression of cases through these levels based on appeals and legal interpretation disputes. - Treatment B (Preventive Function Information) Video emphasizes tort law's preventive function in reducing accidents and promoting safety. Content explains that tort liability serves not only to compensate victims but also to incentivize accident prevention by establishing behavioral standards, imposing liability for violations that cause harm, and encouraging both individuals and firms to invest in safety measures including safer designs and quality control. Engagement Manipulation (Factor 2) Within each information content condition, participants are randomly assigned to: - No Questions Condition Participants watch the assigned video and proceed immediately to the vignette scenarios. - Questions Condition After watching the video, participants answer two multiple-choice questions testing support for the preventive function of tort law before proceeding to scenarios. This creates four treatment arms with equal allocation: (1) Civil Litigation Information Only, (2) Civil Litigation Information + Questions, (3) Preventive Function Information Only, (4) Preventive Function Information + Questions. Vignette-Based Judgment Task (Common to All Arms) Following information treatment, all participants complete six traffic accident vignettes as mock judges. Each vignette describes a collision between a plaintiff and defendant vehicle (or pedestrian plaintiff), provides demographic information about parties, displays a 30-40 second animated video of the accident, and describes injury severity and economic damages. We systematically vary the following factors to construct a total of 30 distinct scenarios: - Plaintiff type: human-driven vehicle, pedestrian - Defendant vehicle type: human-driven vehicle, fully autonomous vehicle - Plaintiff–defendant pairing: .. human-driven vehicle vs. human-driven vehicle .. pedestrian vs. human-driven vehicle .. human-driven vehicle vs. autonomous vehicle .. pedestrian vs. autonomous vehicle - Potential accident preventability through advanced safety technology on the autonomous defendant vehicle (hereafter ‘preventability’ or ‘preventable’): whether the autonomous vehicle is equipped with state-of-the-art safety technology for nighttime accident prevention (e.g., a latest-generation infrared camera), yielding technology-equipped versus non-equipped autonomous vehicles - Autonomous vehicle performance (prediction and avoidance capability) (hereafter ‘performance’): when the defendant is an autonomous vehicle, whether its accident prediction and avoidance capabilities are equivalent to, or superior to, those of human drivers - Accident location: nighttime intersection settings - Plaintiff’s baseline fault level: .. Medium: for example, at night, at an intersection without traffic lights, the plaintiff attempts a right turn and collides with the defendant, who is making a left turn into the plaintiff’s lane. .. Low: for example, at night, at an intersection without traffic lights, the plaintiff is driving straight through the intersection when the defendant attempts a left turn at the intersection and collides with the plaintiff. .. Very low: for example, at night, at an intersection where traffic lights are installed only on one approach, the defendant enters the intersection by driving straight through a red light and collides with the plaintiff, who is turning right from the cross street that has no traffic light. Each participant evaluates six scenarios selected through constrained randomization: one scenario with human-driven defendant, two scenarios with preventable autonomous defendant, two scenarios with non-preventable autonomous defendant, and one scenario randomly selected from the full set. This ensures within-subject variation in vehicle type while preventing participant fatigue. For each scenario, participants make four judgments: 1. Fault allocation percentage: Using a slider from 0-100%, participants allocate responsibility between plaintiff and defendant, where higher percentages indicate greater defendant responsibility 2. Fault allocation confidence: 0-100% scale of certainty in the responsibility allocation judgment 3. Non-pecuniary damages amount: Monetary award for non-pecuniary damages (pain and suffering, emotional distress) in units of 10,000 Korean won, given fixed economic damages of 30 million won 4. Non-pecuniary damages confidence: 0-100% scale of certainty in the damage award Pre-Treatment and Post-Treatment Measures Before information treatment, participants complete pre-survey measures including eight questions assessing AI knowledge, AI attitudes, autonomous vehicle familiarity, and autonomous vehicle feature usage. After completing all vignette judgments, participants answer 27 post-treatment questions measuring litigation experience, trust in courts, traffic accident history, driving experience, openness to experience personality traits, risk attitudes, and fairness preferences. Pilot Study A pilot study with 300 participants (100 per information content condition) was conducted in November 2025. Pilot results informed refinement of vignette content, confirmation of randomization procedures, and validation of outcome measure distributions. Based on pilot analysis, we reduced the total number of unique scenarios from 45 to 30 by eliminating highway accident scenarios and modifying one high-plaintiff-fault scenario. The main study implements these refined materials with the full 2×2 factorial design including the engagement manipulation. Non-Legal Framing Condition (Post-Registration Amendment) A separate group of 2,000 participants completes the same six-vignette judgment task without any informational treatment (no video, no comprehension questions). All legal terminology is replaced with everyday language (e.g., comparative negligence → responsibility allocation, plaintiff/defendant → non-legal equivalents, solatium → ordinary damage language). This condition isolates whether the legal framing itself affects fault attribution and damage awards. The constrained randomization procedure and outcome measures are identical to the legal frame.
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