How is the visual working memory load of driver influenced by information density of traffic signs?

The rapid development of urban roads has resulted in an increase in the amount of information that needs to be displayed on traffic signs. The overloading of information on traffic signs may increase drivers’ visual cognition burden and cause driving errors. It could be an important factor affecting driving safety and result in accidents. To enable drivers to cognize traffic signs, the information density that they could endure under reasonable workload levels should be determined. In this study, we designed and implemented trials to analyze drivers’ visual working memory load (VWML) caused by different information densities (including stimulus intensity (SI) and interval time of stimulus (IS)) of traffic signs. Twenty-four paid participants (12 females) with a mean age of 23.38 years (SD = 1.38) participated in laboratory simulation trials based on the memory retrieval approach. Subjective ratings (rating scale mental effort (RSME)), and performance measurement based on reaction time and accuracy of information-matching task were measured to reflect drivers’ VWML at different information densities. The results show that information density is an important factor affecting drivers’ VWML. The following are the significant observations of the study: (1) The subjective ratings on mental effort increased with increase in the SI. Meanwhile, it first decreased and then increased with increase in the IS. (2) The response accuracy results show that a high accuracy (≥0.7) was achieved by drivers in an IS of 15 s when the SI was at most 14.16 bit/m². Correspondingly, the drivers underwent less mental workload as a result of their better short-term memory characteristic. (3) The mean reaction time was over 1800 ms when the SI was over 14.16 bit/m². This indicated a reduction in the information-processing capability of the drivers. The results indicate that cognition of information of unreasonable density on traffic signs would increase drivers’ VWML. This would, in turn, result in an increase in their reaction time and decrease in accuracy. The threshold of information density was also determined through the experiment. It would satisfy the information demand of drivers and prevent information redundancy. The results contribute to an understanding of drivers’ VWML while cognizing traffic signs with different information densities and provide safety, effectiveness, and information continuity considerations for the design and setting of traffic signs.     

A data–information–knowledge cycle for modeling driving behavior

When talking about automation, “autonomous vehicles”, often abbreviated as AVs, come to mind. In transitioning from the “driver” mode to the different automation levels, there is an inevitable need for modeling driving behavior. This often happens through data collection from experiments and studies, but also information extraction, a key step in behavioral modeling. Particularly, naturalistic driving studies and field operational trials are used to collect meaningful data on drivers’ interactions in real–world conditions. On the other hand, information extraction methods allow to predict or mimic driving behavior, by using a set of statistical learning methods. In simple words, the way to understand drivers’ needs and wants in the era of automation can be represented in a data–information cycle, starting from data collection, and ending with information extraction. To develop this cycle, this research reviews studies with keywords “data collection”, “information extraction”, “AVs”, while keeping the focus on driving behavior. The resulting review led to a screening of about 161 papers, out of which about 30 were selected for a detailed analysis. The analysis included an investigation of the methods and equipment used for data collection, the features collected, the size and frequency of the data along with the main problems associated with the different sensory equipment; the studies also looked at the models used to extract information, including various statistical techniques used in AV studies. This paved the way to the development of a framework for data analytics and fusion, allowing the use of highly heterogeneous data to reach the defined objectives; for this paper, the example of impacts of AVs on a network level and AV acceptance is given. The authors suggest that such a framework could be extended and transferred across the various transportation sectors.     

Anxiety-related differences in affective categorizations of lexical stimuli

Effects of emotional valence on attention have been shown to occur even when stimuli are presented outside awareness. The impact of negative valence on stimulus processing has been demonstrated to be particularly salient in anxiety. Therefore, it has been hypothesized that compared to nonanxious individuals, anxious individuals have an enhanced ability to detect the valence of negative stimuli. However, it remains unclear whether anxious individuals are better at identifying the valence of threatening stimuli or, rather, more likely to label ambiguous stimuli as threatening. To investigate these hypotheses, high and low anxious participants categorized lexical stimuli as "safe" or "dangerous." Stimuli were presented at durations that allowed for both conscious (unmasked) and nonconscious (masked) processing. Results show that on masked trials, anxious individuals evidenced an enhanced ability to correctly classify threatening information, whereas nonanxious participants demonstrated an enhanced ability to correctly classify neutral or positive information. Signal detection analyses indicated results were explained by a response bias, whereby anxious individuals were more likely than nonanxious individuals to categorize masked words as threatening and nonanxious individuals were more likely to categorize masked words as nonthreatening. No group differences for nonword stimuli emerged, suggesting that anxiety-related response bias tendencies are activated only after detection of a weak semantic signal.     

Eye movements and spoken language comprehension: effects of visual context on syntactic ambiguity resolution

When participants follow spoken instructions to pick up and move objects in a visual workspace, their eye movements to the objects are closely time-locked to referential expressions in the instructions. Two experiments used this methodology to investigate the processing of the temporary ambiguities that arise because spoken language unfolds over time. Experiment 1 examined the processing of sentences with a temporarily ambiguous prepositional phrase (e.g., "Put the apple on the towel in the box") using visual contexts that supported either the normally preferred initial interpretation (the apple should be put on the towel) or the less-preferred interpretation (the apple is already on the towel and should be put in the box). Eye movement patterns clearly established that the initial interpretation of the ambiguous phrase was the one consistent with the context. Experiment 2 replicated these results using prerecorded digitized speech to eliminate any possibility of prosodic differences across conditions or experimenter demand. Overall, the findings are consistent with a broad theoretical framework in which real-time language comprehension immediately takes into account a rich array of relevant nonlinguistic context.     

Precision of working memory for speech sounds

Memory for speech sounds is a key component of models of verbal working memory (WM). But how good is verbal WM? Most investigations assess this using binary report measures to derive a fixed number of items that can be stored. However, recent findings in visual WM have challenged such “quantized” views by employing measures of recall precision with an analogue response scale. WM for speech sounds might rely on both continuous and categorical storage mechanisms. Using a novel speech matching paradigm, we measured WM recall precision for phonemes. Vowel qualities were sampled from a formant space continuum. A probe vowel had to be adjusted to match the vowel quality of a target on a continuous, analogue response scale. Crucially, this provided an index of the variability of a memory representation around its true value and thus allowed us to estimate how memories were distorted from the original sounds. Memory load affected the quality of speech sound recall in two ways. First, there was a gradual decline in recall precision with increasing number of items, consistent with the view that WM representations of speech sounds become noisier with an increase in the number of items held in memory, just as for vision. Based on multidimensional scaling (MDS), the level of noise appeared to be reflected in distortions of the formant space. Second, as memory load increased, there was evidence of greater clustering of participants' responses around particular vowels. A mixture model captured both continuous and categorical responses, demonstrating a shift from continuous to categorical memory with increasing WM load. This suggests that direct acoustic storage can be used for single items, but when more items must be stored, categorical representations must be used.     

Automatic semantic encoding in verbal short-term memory: Evidence from the concreteness effect

The concreteness effect in verbal short-term memory (STM) tasks is assumed to be a consequence of semantic encoding in STM, with immediate recall of concrete words benefiting from richer semantic representations. We used the concreteness effect to test the hypothesis that semantic encoding in standard verbal STM tasks is a consequence of controlled, attention-demanding mechanisms of strategic semantic retrieval and encoding. Experiment 1 analysed the effect of presentation rate, with slow presentations being assumed to benefit strategic, time-dependent semantic encoding. Experiments 2 and 3 provided a more direct test of the strategic hypothesis by introducing three different concurrent attention-demanding tasks. Although Experiment 1 showed a larger concreteness effect with slow presentations, the following two experiments yielded strong evidence against the strategic hypothesis. Limiting available attention resources by concurrent tasks reduced global memory performance, but the concreteness effect was equivalent to that found in control conditions. We conclude that semantic effects in STM result from automatic semantic encoding and provide tentative explanations for the interaction between the concreteness effect and the presentation rate.