The case for a dual-process theory of transitive reasoning

Ever since its popularisation by Piaget around 60 years ago, transitive reasoning (deductively-inferring A > C from premises A > B and B > C) has been of psychological interest both as a mental phenomenon and as a tool in areas of psychological discourse. However, the focus of interest in it has shifted periodically first from child development, to learning disability, to non-humans and currently to cognitive and clinical neuroscience. Crucially, such shifts have always been plagued by one core question – the question of which of two competing paradigms (extensive-training paradigm versus non-training paradigm) is valid for assessing transitive reasoning as originally conceived in Piagetian research. The continued avoidance of this question potentially undermines several important findings recently reported: Such as about exactly what is involved in deducing transitive inferences, which brain regions are critical for reaching transitive inference, and what links exist between weakened deductive transitivity and mental illnesses like schizophrenia. Here, we offer the view that both of the competing paradigms are indexing transitivity, but each one tends to tap a different aspect of it. Then, we summarise studies from child and adult cognitive psychology, disabilities research, and from cognitive neuroscience. These, together with studies of non-human reasoning, seem to afford a theory of transitive reasoning that has two major components; one deductive but the other associative. It is proposed that only a dual-process theory of transitivity (having analytic versus intuitive routes approximate to deductive versus associative processing respectively) can account both for the variety of findings and the apparently-disparate paradigms. However, fuzzy-trace theory (“Gist” processes and representations), if not already embodying such a dual-process theory, will need to be incorporated into any complete theory.     

Safety at first sight? – Manual drivers’ experience and driving behavior at first contact with Level 3 vehicles in mixed traffic on the highway

Soon, manual drivers will interact with conditionally automated vehicles (CAVs; SAE Level 3) in a mixed traffic on highways. As of yet, it is largely unclear how manual drivers will perceive and react to this new type of vehicle. In a driving simulator study with N = 51 participants aged 20 to 71 years (22 female), we examined the experience and driving behavior of manual drivers at first contact with Level 3 vehicles in four realistic driving scenarios (highway entry, overtaking, merging, introduction of a speed limit) that Level 3 vehicles may handle alone once their operational domain extends beyond driving in congested traffic. We also investigated the effect of an external marking via a visual external human–machine interface (eHMI), with participants being randomly assigned to one of three experimental groups (none, correct, incorrect marking). Participants experienced each driving scenario four times, twice with a human-driven vehicle (HDV), and twice with a CAV. After each interaction, participants rated perceived driving mode of the target vehicle as well as perceived safety and comfort. Minimum time headways between participants and target vehicles served as an indicator of safety criticality in the interactions. Results showed manual driver can distinguish CAVs from HDVs based on behavioral differences. In all driving scenarios, participants rated interactions with CAVs at least as safe as interactions with HDVs. The driving data analysis showed that manual driver interactions with CAVs were largely uncritical. However, the CAVs’ strict rule-compliance led to short time headways of following manual drivers in some cases. The eHMI used in this study neither affected the subjective ratings of the manual drivers nor their driving behavior in mixed traffic. Thus, the results do not support the use of eHMIs on the highway, at least not for the eHMI design used in this study.     

How the initial level of trust in automated driving impacts drivers’ behaviour and early trust construction

Trust in Automation is known to influence human-automation interaction and user behaviour. In the Automated Driving (AD) context, studies showed the impact of drivers’ Trust in Automated Driving (TiAD), and linked it with, e.g., difference in environment monitoring or driver’s behaviour. This study investigated the influence of driver’s initial level of TiAD on driver’s behaviour and early trust construction during Highly Automated Driving (HAD). Forty drivers participated in a driving simulator study. Based on a trust questionnaire, participants were divided in two groups according to their initial level of TiAD: high (Trustful) vs. low (Distrustful). Declared level of trust, gaze behaviour and Non-Driving-Related Activities (NDRA) engagement were compared between the two groups over time. Results showed that Trustful drivers engaged more in NDRA and spent less time monitoring the road compared to Distrustful drivers. However, an increase in trust was observed in both groups. These results suggest that initial level of TiAD impact drivers’ behaviour and further trust evolution.     

Effects of traffic context on eHMI icon comprehension

External human–machine-interfaces (eHMIs) might support the interaction between automated vehicles and pedestrians. The messages conveyed by eHMIs need to be understood quickly and correctly by their addressees. If implemented in the future, pedestrians will repeatedly encounter eHMIs in situations that feature different traffic context. So far, little is known about the influence of contextual cues like regulatory elements or (presumed) model behavior of fellow road users on the comprehension of eHMIs. In order to investigate possible effects of such contextual cues on comprehension, we conducted a picture-based online study among German residents (N = 175). Participants repeatedly interpreted three eHMI icons (“you can cross”, “do not cross”, and “pedestrian detected”) either without any context (control group) or within varying degrees of relevant context (experimental group). Context facilitated comprehensibility in terms of accuracy and subjective certainty. Relevant context was especially beneficial at first encounter. As soon as an icon’s meaning was internalized, the necessity of relevant context decreased. The effect of context should therefore be considered in future eHMI research as real-world comprehension might be underestimated otherwise.     

Effect of verbal messages with reminders to communicate driving situations to alter driver behavior in conditional driving automation

In conditionally automated driving, drivers are relieved of steering (hands-off), accelerating, and braking actions as well as of continuous monitoring of driving situations and the system operation status (eyes off). This enables continuously engagement in non-driving-related activities. Managing the allocation of a driver’s attention to the surrounding environment and automation status presents a major challenge in human–machine system design. In this study, we propose a verbal message with a reminder (monitoring request) to divert the driver’s attention from non-driving-related activities to peripheral monitoring under conditionally automated driving. When the system encounters events related to weather, traffic, and road geometry, it provides a verbal message pertaining to the road surroundings (e.g., “It is foggy outside”) to the driver. After three seconds, the system provides a reminder message (i.e., “Did you confirm it?”) to the driver. We explore two questions: (1) how does the message with the reminder affect the driver’s attention allocation, and (2) how does the message with the reminder affect the driver behavior in response to a request to intervene (RTI). With a driving simulator, we designed a repeated measures mixed design with a between-participant factor of “Driving condition” and within-participant factors of “Event type” and “Measurement time”. Three driving conditions were established as follows: no messages, messages without reminders, and messages with reminders. Twenty-seven drivers participated as participants in the driving simulator experiment. Results showed that the reminder message was effective in allocating the participants’ attention to the surrounding environment, and the participants took over the driving task after spending more time understanding the take-over situation in the condition of messages with reminders compared to those in the condition of no messages. We conclude that the proposed reminder message can direct drivers’ attention to the road surroundings during conditionally automated driving. In the future, we plan to design adaptive verbal monitoring requests to adjust the reminder message according to the situation.     

Why would people want to travel more with automated cars?

The use of automated vehicles (AVs) may enable drivers to focus on non-driving related activities while travelling and reduce the unwanted efforts of the driving task. This is expected to make using a car more attractive, or at least less unpleasant compared to manually driven vehicles. Consequently, the number and length of car trips may increase. The aim of this study was to identify the main contributors to travelling more by AV.We analysed the L3Pilot project’s pilot site questionnaire data from 359 respondents who had ridden in a conditionally automated car (SAE level 3) either as a driver or as a passenger. The questionnaire queried the respondents’ user experience with the automated driving function, current barriers of travelling by car, previous experience with advanced driving assistance systems, and general priorities in travelling. The answers to these questions were used to predict willingness to travel more or longer trips by AV, and to use AVs on currently undertaken trips. The most predictive subset of variables was identified using Bayesian cumulative ordinal regression with a shrinkage prior (regularised horseshoe).The current study found that conditionally automated cars have a substantial potential to increase travelling by car once they become available. Willingness to perform leisure activities during automated driving, experienced usefulness of the system, and unmet travel needs, which AVs could address by making travelling easier, were the main contributors to expecting to travel more by AV. For using AVs on current trips, leisure activities, trust in AVs, satisfaction with the system, and traffic jams as barriers to current car use were important contributors. In other words, perceived usefulness motivated travelling more by AV and using AVs on current trips, but also other factors were important for using them on current trips. This suggests that one way to limit the growth of traffic with private AVs could be to address currently unmet travel needs with alternative, more sustainable travel modes.     

The potential of gamification for user education in partial and conditional driving automation: A driving simulator study

Drivers must establish adequate mental models to ensure safe driver-vehicle interaction in combined partial and conditional driving automation. To achieve this, user education is considered crucial. Since gamification has previously shown positive effects on learning motivation and performance, it could serve as a measure to enhance user education on automated vehicles. We developed a tablet-based instruction involving gamified elements and compared it to instruction without gamification and a control group receiving a user manual. After instruction, participants (N = 57) experienced a 30-minute automated drive on a motorway in a fixed-base driving simulator. Participants who received the gamified instruction reported a higher level of intrinsic motivation to learn the provided content. The results also indicate that gamification promotes mental model formation and trust during the automated drive. Taken together, including gamification in user education for automated driving is a promising approach to enhance safe driver-vehicle interaction.     

The initial representation in reasoning towards an interpretation of conditional sentences

All accounts of human reasoning (whether presented at the symbolic or subsymbolic level) have to reckon with the temporal organization of the human processing systems and the ephemeral nature of the representations it uses. We present three new empirical tests for the hypothesis that people commence the interpretational process by constructing a minimal initial representation. In the case of if A then C the initial representation captures the occurrence of the consequent, C, within the context of the antecedent, A. Conditional inference problems are created by a categorical premise that affirms or denies A or C. The initial representation allows an inference when the explicitly represented information matches (e.g., the categorical premise A affirms the antecedent “A”) but not when it mismatches (e.g., “not-A” denies A). Experiments 1 and 2 confirmed that people tend to accept the conclusion that “nothing follows” for the denial problems, as indeed they do not have a determinate initial-model conclusion. Experiment 3 demonstrated the other way round that the effect of problem type (affirmation versus denial) is reduced when we impede the possibility of inferring a determinate conclusion on the basis of the initial representation of both the affirmation and the denial problems.     

Evaluating the contribution of different item features to the effect size of the gender difference in three-dimensional mental rotation using automatic item generation

In complex three-dimensional mental rotation tasks males have been reported to score up to one standard deviation higher than females. However, this effect size estimate could be compromised by the presence of gender bias at the item level, which calls the validity of purely quantitative performance comparisons into question. We hypothesized that the effect of gender bias at the level of distinct item design features could lead to either an over- or underestimation of reported effect sizes of the gender difference in three-dimensional mental rotation. Using automatic item generation we conducted a series of psychometric experiments in which we independently manipulated one out of four different item design features that have exhibited a gender bias in the previous studies (study 1). This was done in a between-subjects design. The results indicated that gender bias caused by item design features linked to the perceptual stadium of mental rotation led to an overestimation of the effect size of the gender difference while item design features associated with the encoding and transformational stadium resulted in an underestimation of the effect size of the gender difference. In study 2 we tested the hypothesis that the gender difference still remains while controlling for the item design features causing gender bias. The results suggest that a significant portion of the gender difference may be attributable to perceptual and encoding processes involved in mental rotation.