This study, introduces and evaluates different countermeasures using real-time eye-tracking data. The countermeasures detect when driver gaze deviates from the road for longer than a predetermined threshold and then redirect the driver's attention back to the road. The countermeasures include bimodal and trimodal alerts using combinations of auditory, tactile, and visual modalities. These countermeasures showcase the utility of adopting eye-tracking technologies in the context of driver monitoring and advanced driver's assistance systems. They enhance safety as a safeguard for the increased use of devices such as in-vehicle infotainment systems. Results show that countermeasures effectively redirect drivers’ attention to the road, with higher on-road gaze time. Additionally, bimodal alerts that include the visual modality are less effective at redirecting participants’ gaze on-road and result in poorer driving performance.

Practitioners in many fields of human-computer interaction are now using physiological data to measure different aspects of user experience. The dynamic nature of physiological data offers a continuous window to the users and allows a better understanding of their experience while interacting with a system. However, in order to be truly informative, physiological signals need to be closely linked to users’ behaviors and interaction states. This paper presents an analysis method that provides a direct visual interpretation of users’ physiological signals when interacting with an interface. The proposed physiological heatmap tool uses eyetracking data along with physiological signals to identify regions where users are experiencing different emotional and cognitive states with a higher frequency. The method was evaluated in an experiment with 44 participants. Results show that physiological heatmaps are able to identify emotionally significant regions within an interface better than standard gaze heatmaps. Applications of the method to different fields of HCI research are also discussed.

Information seeking activities have been broadly categorized into two types: exploratory and directed search. Likewise, product-related information is processed either by alternative or by attribute. The current literature does not specifically describe the effect of online information-seeking activities on the type of information processing employed by a consumer. The present study seeks to investigate this relationship, as well as the effect of repeat visits to a website as an antecedent of information seeking. An eye-tracking experiment was performed to test these relationships. Results indicate that participants with a greater proportion of time spent looking at directed-search elements had longer visual sweeps across the attributes of a song, indicating a greater degree of alternative-based processing. Additionally, repeat website visits resulted in a marginally higher proportion of time spent performing directed search. The implication of the findings in relation to website customization according to users’ information searching behavior is discussed.

In this paper, we present an off-the-shelf UX evaluation tool which contextualizes users' physiological and behavioral signals while interacting with a system. The proposed tool triangulates users' gaze data with inferred users' cognitive and emotional states to produce user experience (UX) heatmaps, which show where users were looking when they experienced specific cognitive and emotional states. Results show that for a given cognitive state (i.e., cognitive load), the proposed UX heatmap was able to effectively highlight the areas where users experienced different levels of cognitive load on an interface. The proposed tool enables the visual analysis of users' various emotional and cognitive states for specific areas on a given interface, and also to compare users' states across multiple interfaces, which should be useful for both UX researchers and practitioners.

Based on extensive discussion groups, this research qualitatively investigates the success factors of product recommendation agents' sites as defined by users. Four higher-order factors emerged from the analysis: users appear to require a recommendation agent service that is friendly, smart, trusted and informational. Determinants of these factors are summarized. The success factors and their determinants were largely invariant to age group (older versus youth users). The results hold several implications particularly to online marketers and researchers.