Driver Eye Movement During a Route Navigation Information Search

Author(s):  
Guangyan Li ◽  
Helen Middleton
2020 ◽  
Vol 31 (3) ◽  
pp. 675-691 ◽  
Author(s):  
Jella Pfeiffer ◽  
Thies Pfeiffer ◽  
Martin Meißner ◽  
Elisa Weiß

How can we tailor assistance systems, such as recommender systems or decision support systems, to consumers’ individual shopping motives? How can companies unobtrusively identify shopping motives without explicit user input? We demonstrate that eye movement data allow building reliable prediction models for identifying goal-directed and exploratory shopping motives. Our approach is validated in a real supermarket and in an immersive virtual reality supermarket. Several managerial implications of using gaze-based classification of information search behavior are discussed: First, the advent of virtual shopping environments makes using our approach straightforward as eye movement data are readily available in next-generation virtual reality devices. Virtual environments can be adapted to individual needs once shopping motives are identified and can be used to generate more emotionally engaging customer experiences. Second, identifying exploratory behavior offers opportunities for marketers to adapt marketing communication and interaction processes. Personalizing the shopping experience and profiling customers’ needs based on eye movement data promises to further increase conversion rates and customer satisfaction. Third, eye movement-based recommender systems do not need to interrupt consumers and thus do not take away attention from the purchase process. Finally, our paper outlines the technological basis of our approach and discusses the practical relevance of individual predictors.


2012 ◽  
Vol 41 (1) ◽  
pp. 30-37 ◽  
Author(s):  
Sören Kliem* ◽  
Christoph Kröger* ◽  
Nico Bayat Sarmadi ◽  
Joachim Kosfelder
Keyword(s):  

Zusammenfassung. Theoretischer Hintergrund: Bei der Behandlung der posttraumatischen Belastungsstörung (PTBS) nach einem Typ-II-Trauma werden im klinischen Alltag gegenwärtig unterschiedliche traumabearbeitende Interventionen eingesetzt. Fragestellung: Wie werden die Verbesserungen in verschiedenen Symptombereichen (plötzliches Wiedererleben, Vermeidung, Übererregung, Dissoziation und zusätzliche Symptomatik) in Abhängigkeit von dem Einsatz unterschiedlicher traumabearbeitender Interventionen von den Behandlern retrospektiv eingeschätzt? Methode: Aus einer Umfrage unter Psychologischen Psychotherapeuten (N = 272) wurden die Fälle ausgewählt, bei denen die Therapeuten (1) ein Ereignis nannten, das einem Typ-II-Trauma zugeordnet werden konnte, und (2) angaben, traumabearbeitende Interventionen gemäß der traumafokussierenden, kognitiven Verhaltenstherapie (TF-KVT), der Methode des Eye Movement Desensitization and Reprocessing (EMDR-Methode) oder der Psychodynamisch-imaginativen Traumatherapie (PITT) durchgeführt zu haben (n = 37). Außerdem beurteilten die Therapeuten retrospektiv die Verbesserungen in den Symptombereichen zu Therapieende. Ergebnisse: Über 40% der Therapeuten gaben an, die Vorstellungsübungen bzw. Bearbeitung des Täter Introjekts gemäß der PITT eingesetzt zu haben, gefolgt von den traumabearbeitenden Interventionen der TF KVT (35.1%) und der EMDR Methode (21.6%). Die Therapeuten, die Interventionen eines der beiden zuletzt genannten Verfahren einsetzten, schätzten die Verbesserungen in den verschiedenen Symptombereichen höher ein als diejenigen, die angaben, eine Intervention gemäß der PITT durchgeführt zu haben. Schlussfolgerungen: Die retrospektiven Einschätzungen der Verbesserungen durch die Therapeuten stehen im Einklang mit den Empfehlungen der Behandlungsleitlinien zur PTBS.


2019 ◽  
Vol 24 (4) ◽  
pp. 297-311
Author(s):  
José David Moreno ◽  
José A. León ◽  
Lorena A. M. Arnal ◽  
Juan Botella

Abstract. We report the results of a meta-analysis of 22 experiments comparing the eye movement data obtained from young ( Mage = 21 years) and old ( Mage = 73 years) readers. The data included six eye movement measures (mean gaze duration, mean fixation duration, total sentence reading time, mean number of fixations, mean number of regressions, and mean length of progressive saccade eye movements). Estimates were obtained of the typified mean difference, d, between the age groups in all six measures. The results showed positive combined effect size estimates in favor of the young adult group (between 0.54 and 3.66 in all measures), although the difference for the mean number of fixations was not significant. Young adults make in a systematic way, shorter gazes, fewer regressions, and shorter saccadic movements during reading than older adults, and they also read faster. The meta-analysis results confirm statistically the most common patterns observed in previous research; therefore, eye movements seem to be a useful tool to measure behavioral changes due to the aging process. Moreover, these results do not allow us to discard either of the two main hypotheses assessed for explaining the observed aging effects, namely neural degenerative problems and the adoption of compensatory strategies.


2020 ◽  
Vol 19 (2) ◽  
pp. 63-74
Author(s):  
Klaus Moser ◽  
Hans-Georg Wolff ◽  
Roman Soucek

Abstract. Escalation of commitment occurs when a course of action is continued despite repeated drawbacks (e.g., maintaining an employment relationship despite severe performance problems). We analyze process accountability (PA) as a de-escalation technique that helps to discontinue a failing course of action and show how time moderates both the behavioral and cognitive processes involved: (1) Because sound decisions should be based on (hopefully unbiased) information search, which requires time to gather, the effect of PA on de-escalation increases over time. (2) Because continuing information search creates behavioral commitment, the debiasing effect of PA on information search diminishes over time. (3) Consistent with the tunnel vision notion, the effects of less biased information search on de-escalation decrease over time.


1975 ◽  
Vol 30 (3) ◽  
pp. 315-330 ◽  
Author(s):  
Laurence R. Young ◽  
David Sheena

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