Exploring the Link Between Cognitive Abilities and Speech Recognition in the Elderly Under Different Listening Conditions

Elderly listeners are known to differ considerably in their ability to understand speech in noise. Several studies have addressed the underlying factors that contribute to these differences. These factors include audibility, and age-related changes in supra-threshold auditory processing abilities, and it has been suggested that differences in cognitive abilities may also be important. The objective of this study was to investigate associations between performance in cognitive tasks and speech recognition under different listening conditions in older adults with either age appropriate hearing or hearing-impairment. To that end, speech recognition threshold (SRT) measurements were performed under several masking conditions that varied along the perceptual dimensions of dip listening, spatial separation, and informational masking. In addition, a neuropsychological test battery was administered, which included measures of verbal working and short-term memory, executive functioning, selective and divided attention, and lexical and semantic abilities. Age-matched groups of older adults with either age-appropriate hearing (ENH, n = 20) or aided hearing impairment (EHI, n = 21) participated. In repeated linear regression analyses, composite scores of cognitive test outcomes (evaluated using PCA) were included to predict SRTs. These associations were different for the two groups. When hearing thresholds were controlled for, composed cognitive factors were significantly associated with the SRTs for the ENH listeners. Whereas better lexical and semantic abilities were associated with lower (better) SRTs in this group, there was a negative association between attentional abilities and speech recognition in the presence of spatially separated speech-like maskers. For the EHI group, the pure-tone thresholds (averaged across 0.5, 1, 2, and 4 kHz) were significantly associated with the SRTs, despite the fact that all signals were amplified and therefore in principle audible

Effects of situational characteristics on drivers' merging into freeway traffic

Applying a model-based approach to the design of assistance and automation systems within the automotive domain requires the availability of a valid driver model that can be used to assess the effect of system prototypes on human driving behaviour in simulations. This paper presents two empirical studies conducted within the project IMoST (Integrated Modeling for Safe Transportation) focussing on drivers’ merging into freeway traffic and how the performance of this manoeuvre is influenced by situational characteristics. Additionally, it is briefly sketched how these results have been used to construct a cognitive driver model being able to perform this manoeuvre

The race model inequality for censored reaction time distributions

Abstract The race model inequality (RMI) introduced i

Designing Driver Assistance Systems with Crossmodal Signals: Multisensory Integration Rules for Saccadic Reaction Times Apply

<div><p>Modern driver assistance systems make increasing use of auditory and tactile signals in order to reduce the driver's visual information load. This entails potential crossmodal interaction effects that need to be taken into account in designing an optimal system. Here we show that saccadic reaction times to visual targets (cockpit or outside mirror), presented in a driving simulator environment and accompanied by auditory or tactile accessories, follow some well-known spatiotemporal rules of multisensory integration, usually found under confined laboratory conditions. Auditory nontargets speed up reaction time by about 80 ms. The effect tends to be maximal when the nontarget is presented 50 ms before the target and when target and nontarget are spatially coincident. The effect of a tactile nontarget (vibrating steering wheel) was less pronounced and not spatially specific. It is shown that the average reaction times are well-described by the stochastic “time window of integration” model for multisensory integration developed by the authors. This two-stage model postulates that crossmodal interaction occurs only if the peripheral processes from the different sensory modalities terminate within a fixed temporal interval, and that the amount of crossmodal interaction manifests itself in an increase or decrease of second stage processing time. A qualitative test is consistent with the model prediction that the probability of interaction, but not the amount of crossmodal interaction, depends on target–nontarget onset asynchrony. A quantitative model fit yields estimates of individual participants' parameters, including the size of the time window. Some consequences for the design of driver assistance systems are discussed.</p></div