Given the aging highway infrastructure requiring extensive rebuilding and
enhancements, and the consequent rise in the number of work zones, there is an
urgent need to develop advanced safety systems to protect workers. While
Augmented Reality (AR) holds significant potential for delivering warnings to
workers, its integration into roadway work zones remains relatively unexplored.
The primary objective of this study is to improve safety measures within
roadway work zones by conducting an extensive analysis of how different
combinations of multimodal AR warnings influence the reaction times of workers.
This paper addresses this gap through a series of experiments that aim to
replicate the distinctive conditions of roadway work zones, both in real-world
and virtual reality environments. Our approach comprises three key components:
an advanced AR system prototype, a VR simulation of AR functionality within the
work zone environment, and the Wizard of Oz technique to synchronize user
experiences across experiments. To assess reaction times, we leverage both the
simple reaction time (SRT) technique and an innovative vision-based metric that
utilizes real-time pose estimation. By conducting five experiments in
controlled outdoor work zones and indoor VR settings, our study provides
valuable information on how various multimodal AR warnings impact workers
reaction times. Furthermore, our findings reveal the disparities in reaction
times between VR simulations and real-world scenarios, thereby gauging VR's
capability to mirror the dynamics of roadway work zones. Furthermore, our
results substantiate the potential and reliability of vision-based reaction
time measurements. These insights resonate well with those derived using the
SRT technique, underscoring the viability of this approach for tangible
real-world uses.

提高工地工人的道路工区安全措施对于日益老化的公路基础设施以及工区数量的增加具有迫切需求。本研究旨在通过对不同组合的多模态增强现实警告对工人反应时间的影响进行广泛分析，改善道路工区的安全措施。通过在真实环境和虚拟现实环境中模拟道路工区的特殊条件，本研究通过三个关键组件（先进的增强现实系统原型、工区环境内的虚拟现实模拟和用户体验同步的 Wizard of Oz 技术）进行了一系列实验。通过在控制的室外工区和室内虚拟现实环境中进行五项实验，我们的研究提供了关于各种多模态增强现实警告如何影响工人反应时间的宝贵信息。此外，我们的研究结果揭示了虚拟现实模拟和实际场景之间的反应时间差异，从而评估了虚拟现实模拟道路工区动态的能力，并证实了基于视觉的反应时间测量的潜力和可靠性。这些发现与使用简单反应时间技术得出的结果是一致的，强调了这种方法在实际应用中的可行性。

道路工作区增强现实警示：评估模态对工人反应时间的影响

Augmented Reality Warnings in Roadway Work Zones: Evaluating the Effect  of Modality on Worker Reaction Times

Subject-verb agreement in the presence of an attractor noun located between
the main noun and the verb elicits complex behavior: judgments of
grammaticality are modulated by the grammatical features of the attractor. For
example, in the sentence "The girl near the boys likes climbing", the attractor
(boys) disagrees in grammatical number with the verb (likes), creating a
locally implausible transition probability. Here, we parametrically modulate
the distance between the attractor and the verb while keeping the length of the
sentence equal. We evaluate the performance of both humans and two artificial
neural network models: both make more mistakes when the attractor is closer to
the verb, but neural networks get close to the chance level while humans are
mostly able to overcome the attractor interference. Additionally, we report a
linear effect of attractor distance on reaction times. We hypothesize that a
possible reason for the proximity effect is the calculation of transition
probabilities between adjacent words. Nevertheless, classical models of
attraction such as the cue-based model might suffice to explain this
phenomenon, thus paving the way for new research. Data and analyses available
at this https URL

主题动词一致性，吸引名词，转换概率，人工神经网络模型以及反应时间是本研究的主要关键词和研究领域，并基于数据和分析结果提出了有关吸引对主题动词一致性的影响及其可能原因的新研究方向。

评估吸引心理 - 动词距离对人类和语言模型中的语法一致性的影响

Assessing the influence of attractor-verb distance on grammatical  agreement in humans and language models

The meteoric rise in the adoption of deep neural networks as computational
models of vision has inspired efforts to "align" these models with humans. One
dimension of interest for alignment includes behavioral choices, but moving
beyond characterizing choice patterns to capturing temporal aspects of visual
decision-making has been challenging. Here, we sketch a general-purpose
methodology to construct computational accounts of reaction times from a
stimulus-computable, task-optimized model. Specifically, we introduce a novel
metric leveraging insights from subjective logic theory summarizing evidence
accumulation in recurrent vision models. We demonstrate that our metric aligns
with patterns of human reaction times for stimulus manipulations across four
disparate visual decision-making tasks spanning perceptual grouping, mental
simulation, and scene categorization. This work paves the way for exploring the
temporal alignment of model and human visual strategies in the context of
various other cognitive tasks toward generating testable hypotheses for
neuroscience.

本文介绍了一种通用方法，基于主观逻辑理论和重复视觉模型中的证据累积，构建对反应时间的计算机解释， 并证明它可以与人类的视觉决策方式进行对齐。

从稳定的循环视觉模型计算类人反应时度量

Computing a human-like reaction time metric from stable recurrent vision  models

Word frequency is a strong predictor in most lexical processing tasks. Thus,
any model of word recognition needs to account for how word frequency effects
arise. The Discriminative Lexicon Model (DLM; Baayen et al., 2018a, 2019)
models lexical processing with linear mappings between words' forms and their
meanings. So far, the mappings can either be obtained incrementally via
error-driven learning, a computationally expensive process able to capture
frequency effects, or in an efficient, but frequency-agnostic closed-form
solution modelling the theoretical endstate of learning (EL) where all words
are learned optimally. In this study we show how an efficient, yet
frequency-informed mapping between form and meaning can be obtained
(Frequency-informed learning; FIL). We find that FIL well approximates an
incremental solution while being computationally much cheaper. FIL shows a
relatively low type- and high token-accuracy, demonstrating that the model is
able to process most word tokens encountered by speakers in daily life
correctly. We use FIL to model reaction times in the Dutch Lexicon Project
(Keuleers et al., 2010) and find that FIL predicts well the S-shaped
relationship between frequency and the mean of reaction times but
underestimates the variance of reaction times for low frequency words. FIL is
also better able to account for priming effects in an auditory lexical decision
task in Mandarin Chinese (Lee, 2007), compared to EL. Finally, we used ordered
data from CHILDES (Brown, 1973; Demuth et al., 2006) to compare mappings
obtained with FIL and incremental learning. The mappings are highly correlated,
but with FIL some nuances based on word ordering effects are lost. Our results
show how frequency effects in a learning model can be simulated efficiently by
means of a closed-form solution, and raise questions about how to best account
for low-frequency words in cognitive models.

本研究使用区分词典模型，提出了一种称为频率知情学习的方法，能够更加高效地模拟频率效应，以及探究了如何在认知模型中更好地解释低频词。