Audiovisual time-to-collision estimation for accelerating vehicles: The acoustic signature of electric vehicles impairs pedestrians' judgments

To avoid collisions, pedestrians intending to cross a road need to accurately estimate the time-to-collision (TTC) of an approaching vehicle. For TTC estimation, auditory information can be considered particularly relevant when the approaching vehicle accelerates. The sound of vehicles with internal combustion engine (ICEVs) provides characteristic auditory information about the acceleration state (increasing rotational speed and engine load). However, for electric vehicles (EVs), the acoustic signature during acceleration is less salient. Although the auditory detection of EVs has been studied extensively, there is no research on potential effects of the altered acoustic signature of EVs on TTC estimation. To close this gap, we compared TTC estimates for ICEVs and for EVs with and without activated acoustic vehicle alerting system (AVAS). We implemented a novel interactive audiovisual virtual-reality system for studying the human perception of approaching vehicles. Using acoustic recordings of real vehicles as source signals, the dynamic spatial sound field corresponding to a vehicle approaching in an urban setting is generated based on physical modeling of the sound propagation between vehicle and pedestrian (listener) and is presented via sound field synthesis (higher-order Ambisonics). In addition to the auditory simulations, the scene was visually presented on a head-mounted display with head tracking. Participants estimated the TTC of vehicles that either approached at a constant speed or accelerated positively. In conditions with constant speed, TTC estimates for EVs with and without AVAS were similar to those for ICEVs. In contrast, for accelerating vehicles, there was a substantial effect of the vehicle type on the TTC estimates. For the EVs, the mean TTC estimates showed a significant overestimation. Thus, subjects on average perceived the time of arrival of the EV at their position as longer than it actually was. The extent of overestimation increased with acceleration and presented TTC. This pattern is similar to a first-order TTC estimation representing a failure to consider the acceleration, which is consistently reported in the literature for visual-only presentations of accelerating objects. In comparison, the overestimation of TTC was largely reduced for the accelerating ICEVs. The AVAS somewhat improved the TTC estimates for the accelerating EVs, but without reaching the same level of accuracy as for the ICEVs. In real traffic scenarios, overestimations of the TTC of approaching vehicles might lead to risky road-crossing decisions. Therefore, our finding that pedestrians are significantly less able to use the acoustic information emitted by accelerating EVs for their TTC judgments, compared to accelerating ICEVs, has important implications for road safety and for the design of AVAS technologies.     

Speak your mind and I will make it right: the case of “selection task”

ABSTRACT

The “Wason selection task” is still one of the most studied tasks in cognitive psychology. We argue that the low performance originally obtained seems to be caused by how the information of the task is presented. By systematically manipulating the task instructions, making explicit the information that participants are required to infer in accordance with the logical interpretation of the material implication “if, then”, we found an improvement in performance. In Experiment 1, the conditional rule has been formulated within a relevant context and in accordance with the conversational rules of communication, hence transmitting the actual meaning of the material implication. In Experiment 2, a similar improvement has been obtained even without the realistic scenario, only by making explicit the unidirectionality of the material implication. We conclude that task instructions are often formulate neglecting the conversational rules of communication, and this greatly reduces the possibility to succeed in the task.     

Conditionals and inferential connections: toward a new semantics*

Abstract

In previous published research (“Conditionals and Inferential Connections: A Hypothetical Inferential Theory,” Cognitive Psychology, 2018), we investigated experimentally what role the presence and strength of an inferential connection between a conditional’s antecedent and consequent plays in how people process that conditional. Our analysis showed the strength of that connection to be strongly predictive of whether participants evaluated the conditional as true, false, or neither true nor false. In this article, we re-analyse the data from our previous research, now focussing on the semantics of conditionals rather than on how they are processed. Specifically, we use those data to compare the main extant semantics with each other and with inferentialism, a semantics according to which the truth of a conditional requires the presence of an inferential connection between the conditional’s component parts.     

A Gricean Interpretation of Nāgārjuna’s Catuṣkoṭi and the No-Thesis View

Nāgārjuna (c. 150–250 CE), the famous founder of the Madhyamika School, proposed the positive catu?ko?i in his seminal work, Mūlamadhyamakakārikā: ‘All is real, or all is unreal, all is both real and unreal, all is neither unreal nor real; this is the graded teaching of the Buddha’. He also proposed the negative catu?ko?i: ‘“It is empty” is not to be said, nor “It is non-empty,” nor that it is both, nor that it is neither; [“empty”] is said only for the sake of instruction’ and the no-thesis view: ‘No dharma whatsoever was ever taught by the Buddha to anyone’. In this essay, I adopt Gricean pragmatics to explain the positive and negative catu?ko?i and the no-thesis view proposed by Nāgārjuna in a way that does not violate classical logic. For Nāgārjuna, all statements are false as long as the hearer understands them within a reified conceptual scheme, according to which (a) substance is a basic categorical concept; (b) substances have svabhāva, and (c) names and sentences have svabhāva.     

Empirical Bayes Derivative Estimates

Abstract

A dynamic system is a set of interacting elements characterized by changes occurring over time. The estimation of derivatives is a mainstay for exploring dynamics of constructs, particularly when the dynamics are complicated or unknown. The presence of measurement error in many social science constructs frequently results in poor estimates of derivatives, as even modest proportions of measurement error can compound when estimating derivatives. Given the overlap in the specification of latent differential equation models and latent growth curve models, and the equivalence of latent growth curve models and mixed models under some conditions, derivatives could be estimated from estimates of random effects. This article proposes a new method for estimating derivatives based on calculating the Empirical Bayes estimates of derivatives from a mixed model. Two simulations compare four derivative estimation methods: Generalized Local Linear Approximation, Generalized Orthogonal Derivative Estimates, Functional Data Analysis, and the proposed Empirical Bayes Derivative Estimates. The simulations consider two data collection scenarios: short time series (≤10 observations) from many individuals or occasions, and long individual time series (25–500 observations). A substantive example visualizing the dynamics of intraindividual positive affect time series is also presented.     

Rectifying the Mischaracterization of Logic by Mental Model Theorists

Khemlani et al. (2018) mischaracterize logic in the course of seeking to show that mental model theory (MMT) can accommodate a form of inference (, let us label it) they find in a high percentage of their subjects. We reveal their mischaracterization and, in so doing, lay a landscape for future modeling by cognitive scientists who may wonder whether human reasoning is consistent with, or perhaps even capturable by, reasoning in a logic or family thereof. Along the way, we note that the properties touted by Khemlani et al. as innovative aspects of MMT-based modeling (e.g., nonmonotonicity) have for decades been, in logic, acknowledged and rigorously specified by families of (implemented) logics. Khemlani et al. (2018) further declare that is “invalid in any modal logic.” We demonstrate this to be false by our introduction (Appendix A) of a new propositional modal logic (within a family of such logics) in which is provably valid, and by the implementation of this logic. A second appendix, B, partially answers the two-part question, “What is a formal logic, and what is it for one to capture empirical phenomena?”     

WHAT IS CONSCIOUSNESS AND HAS IT EVOLVED?

Research into consciousness has now become respectable, and much has been written about it. Is consciousness the exclusive property of human beings, or can it be found also in animals? Can machines become conscious? Is consciousness an illusion, and are all mental states ultimately reducible to the movement of molecules? If consciousness is other than matter, what connection does it have with matter? These and others like them are now serious scientific questions in the West. This article discusses consciousness within the frame of the following assertions: Consciousness has evolved from earlier states of awareness to be found in lower forms of life. The current scientific method is too restrictive for the study of conscience and its evolution. In particular classical logic leads scientists to ignore or reject consciousness as a legitimate field of study. Mind and matter, generalized as knowing and being, have equal status.