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      Using Behavioural Information to Help Owners Gather Requirements from their Dogs’ Responses to Media Technology

      ,

      Proceedings of the 30th International BCS Human Computer Interaction Conference (HCI)

      Fusion

      11 - 15 July 2016

      Animal Computer Interaction, Dog Computer Interaction, Data gathering, Evaluative method

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          Abstract

          The study of non-human animals’ interactions with technology is referred to as Animal- Computer Interaction (ACI). Data gathering with these non- human animal users typically relies on the owner as a proxy to gather requirements and feedback from the animal’s behavioural reactions. These owners, however, may provide poor information, as they are habitually not knowledgeable in animal behaviour. To improve data gathering in Dog-Computer Interaction (DCI) research, we present a Dog Information Sheet (DISH) for owners which contains known dog physical behaviours and their potential cognitive reactions. This is used to create a more informed dog owner observer in order to improve feedback in ACI. DISH’s effect on owner evaluations is assessed by gauging their own dog’s behavioural reactions to persuasively designed media. The findings established that when using DISH, owners were better at identifying both the behaviour perceived and at reasoning behind their dogs’ reactions. However, owners using the DISH were unable to recognize the different dogs’ behavioral states unless they considered themselves experts at dog behaviour. Whilst this research is centred on collecting data on dogs to improve User Experience (UX) in a Dog-Computer Interaction (DCI) context, the method presented behind the DISH can be applied to both ACI and Human - Computer Interaction (HCI) field to help interpret behaviours during requirement gathering and evaluative practices for non-vocal and limited cognitive users.

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          Most cited references 60

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          Eye Tracking in HCI and Usability Research

          Eye tracking is a technique whereby an individual’s eye movements are measured so that the researcher knows both where a person is looking at any given time and the sequence in which the person’s eyes are shifting from one location to another. Tracking people’s eye movements can help HCI researchers to understand visual and display-based information processing and the factors that may impact the usability of system interfaces. In this way, eye-movement recordings can provide an objective source of interface-evaluation data that can inform the design of improved interfaces. Eye movements also can be captured and used as control signals to enable people to interact with interfaces directly without the need for mouse or keyboard input, which can be a major advantage for certain populations of users, such as disabled individuals. We begin this article with an overview of eye-tracking technology and progress toward a detailed discussion of the use of eye tracking in HCI and usability research. A key element of this discussion is to provide a practical guide to inform researchers of the various eye-movement measures that can be taken and the way in which these metrics can address questions about system usability. We conclude by considering the future prospects for eye-tracking research in HCI and usability testing.
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            Implicit human computer interaction through context

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              Dogs do look at images: eye tracking in canine cognition research.

              Despite intense research on the visual communication of domestic dogs, their cognitive capacities have not yet been explored by eye tracking. The aim of the current study was to expand knowledge on the visual cognition of dogs using contact-free eye movement tracking under conditions where social cueing and associative learning were ruled out. We examined whether dogs spontaneously look at actual objects within pictures and can differentiate between pictures according to their novelty or categorical information content. Eye movements of six domestic dogs were tracked during presentation of digital color images of human faces, dog faces, toys, and alphabetic characters. We found that dogs focused their attention on the informative regions of the images without any task-specific pre-training and their gazing behavior depended on the image category. Dogs preferred the facial images of conspecifics over other categories and fixated on a familiar image longer than on novel stimuli regardless of the category. Dogs' attraction to conspecifics over human faces and inanimate objects might reflect their natural interest, but further studies are needed to establish whether dogs possess picture object recognition. Contact-free eye movement tracking is a promising method for the broader exploration of processes underlying special socio-cognitive skills in dogs previously found in behavioral studies.
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                Author and article information

                Contributors
                Conference
                July 2016
                July 2016
                : 1-13
                Affiliations
                University of Central Lancashire

                Fylde Road, Preston, Lancashire, England
                Article
                10.14236/ewic/HCI2016.42
                © Douglas et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2016 Conference Fusion, Bournemouth, UK

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Proceedings of the 30th International BCS Human Computer Interaction Conference
                HCI
                30
                Bournemouth University, Poole, UK
                11 - 15 July 2016
                Electronic Workshops in Computing (eWiC)
                Fusion
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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