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.     

Using logic programming for theory representation and scientific inference

The aim of this paper is to show that logic programming is a powerful tool for representing scientific theories and for scientific inference. In a logic program it is possible to encode the qualitative and quantitative components of a theory in first order predicate logic, which is a highly expressive formal language. A theory program can then be handed to an algorithm that reasons about the theory. We discuss the advantages of logic programming with regard to building formal theories and present a novel software package for scientific inference: Theory Toolbox. Theory Toolbox can derive any conclusions that are entailed by a theory, explain why a certain conclusion follows from a theory, and evaluate a theory with regard to its internal coherence and generalizability. Because logic is, or should be, a cornerstone of scientific practice, we believe that our paper can make an important contribution to scientific psychology.     

Pinning down polysemy: A formalisation for a Brazilian Portuguese preposition

This paper presents a formal definition for a number of distinct cases where the preposition “em” (in) can be applied in Brazilian Portuguese. The aim of this work is to establish a mathematical model using qualitative spatial reasoning formalisms within the idea of precisification from supervaluation semantics. Our long term goal is to implement this model into artificial intelligent systems to allow for seamless communication with humans in a common speech.     

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?”     

Verbal and practical intelligence in general anxiety,obsessive compulsive and major depression disorders

IntroductionPrevious researches have shown that anxiety symptoms are negatively associated with measures of intelligence. However, recent findings indicate possible positive relationships between Generalized Anxiety Disorders (GAD) and intelligence. Also, Obsessive Compulsive Disorder (OCD) is associated with a moderate degree of underperformance on cognitive tests, including deficient processing. There are inconsistent results to present the relationship between Major Depression Disorder (MDD) and IQ. The present study has three main aims. The first aim of this study is to investigate the difference between IQ in individuals with GAD, OCD and major depressive disorder, and normal group. The second purpose is to perform a comparative study between the GAD, OCD and MDD groups on verbal and non-verbal intelligence. The third aim of this study is to examine the relationships between GAD, OCD and MDD as well as their underlying cognitive processes, including worry, rumination, and post-event processing, with verbal and non-verbal intelligence.ObjectiveThe present study is performed on four groups of participants including those with GAD, OCD, MDD and Healthy Volunteer (HV) group consisting of individuals without psychiatric disorders.MethodThe number of 50 healthy volunteers as the control group, 45 patients with GAD, 20 patients with OCD and 25 patients with MDD (n = 140) were selected as the case-referent groups. The present study was a cross-sectional type and the research was performed based on the causal-comparative method. Verbal and non-verbal intelligence was measured with the Wechsler Adult Intelligence Scale-3rd edition (WAIS-III). Rumination and post-event processing were measured by PSWQ, RRS-BR, and PEPQ, respectively.ResultsThe results indicate that Verbal Intelligence and Verbal Comprehension Index in GAD patients have significant differences in comparison to the OCD, MDD and control groups. While, the value of the Working Memory Index (WMI) in the normal group is higher than the value of the same index in the GAD, OCD and MDD groups. Also, the values of the Processing Speed Index (PSI) in normal and GAD groups are higher than the OCD and MDD groups. The worry, rumination, and post-event processing in patients with GAD are positively correlated with general and verbal intelligence. But, verbal and non-verbal intelligence had a negative correlation with worry, rumination and post-event processing in healthy volunteers.ConclusionInvestigation of the possible connections between intelligence and the cognitive processes underlying emotional disorders can provide therapeutic strategies for smart individuals who are at risk for GAD.     

Modeling the emergence of informational content by adaptive networks for temporal factorisation and criterial causation

Propagated activation of neurons through their network is an important process in the brain. Another crucial part of neural processing concerns adaptation over time of characteristics of this network such as connection strengths or excitability thresholds. This adaptation can be slow, as in learning from a multiple experiences, or it can be fast, as in memory formation. These adaptive network characteristics can be considered informational criteria for activation of a neuron. This then is viewed as a form of emergent information formation. Activation of neurons is determined by such information via a process termed criterial causation. In the current paper, the relationship of criterial causation with the principle of temporal factorisation for the dynamics of the world in general is explored. Temporal factorisation describes how the world represents information about its past in its present state, which then in turn determines the world’s future. In the paper, it is shown how these processes are analysed in more detail and modeled by (adaptive) network models.