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Reflections
Published in Eduard Fosch-Villaronga, Robots, Healthcare, and the Law, 2019
Regardless of how technology looks like, companies want to make users use it, engage them more, and make their technological product indispensable. This is somehow normal: why would you buy something that you do not use? At some point, the founders of Facebook wondered about something similar: how could the platform consume a greater amount of users’ time? How can Facebook users pay more conscious attention to the platform? To achieve that, the social network designers intentionally created social-driven feedback loops. These loops help to promote the release of dopamine and to exploit in this way a vulnerability in the human brain. Dopamine confers motivational salience, i.e., a cognitive process in form of attention that motivates a certain behaviour towards or against an outcome. This is addictive. The pathological addiction to dopamine caused by technology has been called digital heroin and it can cause irritability, anger, aggressivity, and violence (Kardaras, 2016). This also makes users spend more time in front of their screens. The overuse of screen time, however, promotes the alteration and shrinking of the frontal cortex, something typically related to disorders such as autism disorder or bipolarity (Lin et al., 2012; Hong et al., 2013). There is thus a fine line between engagement and addiction, a confusion perpetuated through the inadvertence of the negative consequences of the idea of better engagement.
Connecting workspace, work characteristics, and outcomes through work design
Published in Oluremi B. Ayoko, Neal M. Ashkanasy, Organizational Behaviour and the Physical Environment, 2019
N-SEEV (Wickens, 1984) is a useful theory of attention from which to describe likely mechanisms that connect the external workspace with the experience of work through work design. N-SEEV stands for Noticing-Salience Effort Expectancy Value, and when taken together the theory includes different forms of attention. Salience refers to the ability of a visual stimuli to elicit attention by contrast or movement for example. Effort is the amount of work (cognitive, movement) needed to notice the stimuli. Both are bottom–up processes to influence visual attention, by attracting attention and dictating how easy it is to visually notice something. Workspace elements influence these bottom–up attentional processes. The expectancy and information value are top–down attention processes, and are the perceived probability of the occurrence of an important event, and value is the level of importance given to that event. These top–down processes likely are influenced by work design (e.g. previous feedback in relation to a co-worker makes one see that co-worker everywhere). Thus, visual attention viewed from the N-SEEV model framework may be particularly useful to delineate cognitive and neural processes underlying ways in which workspace impacts work outcomes through work design.
Industrial Engineering and Human Factors
Published in Adedeji B. Badiru, The Story of Industrial Engineering, 2018
It is also important to consider that attention can be drawn to channels that are relevant to the intended activities, but also to those that are irrelevant. Daydreaming is a common example of attention being focused on unessential information channels. Attention is driven in large part by the salience of each existing information channel. Salience can be defined as the attention-attracting properties of an object. It can be derived based on the intensity of a channel's output in various sensory modalities (Wickens and Hollands, 2000). For example, a loud alarm is more likely to draw attention than a quiet alarm. Salience can also be based on the semantic importance of the channel. An alarm that indicates a nuclear accident is more likely to draw attention than an alarm signaling lunchtime. Salience is the reason that workers tend to daydream when work intensity is low, such as long-duration monitoring of displays (control center operators, air travel, and security). When nothing is happening on the display, daydreams are more interesting and draw away the worker's attention. If something important happens, the worker may not notice.
Chinese handwriting while driving: Effects of handwritten box size on in-vehicle information systems usability and driver distraction
Published in Traffic Injury Prevention, 2023
Another valuable finding was that drivers performing Chinese handwriting tasks on IVISs with small HBSs not only deteriorated textual performance but also induced a greater extent of driver distraction, mainly manifested by significant lane position variation, which is unacceptable for safe driving. According to the SEEV model, which has been successfully applied to model drivers’ visual attention allocation while interacting with IVISs (Horrey et al. 2006), visual attention is determined by four factors of the stimulus: Salience, Expectancy, Effort, and Value. Salience represents the highlighting of a stimulus involving a range of physical properties such as size, color, and shape. Returning to this study, we carefully inferred that in virtue of this bottom-up attention mechanism, larger HBS (more salient stimuli) would occupy lower visual resources initially assigned to driving-related tasks, which was reflected in the driver’s off-road glance behavior. Furthermore, another reasonable explanation plausible explanation comes from research by Eren et al. (2018), who indicated larger HBS would promote peripheral visual interaction mechanisms, thereby reducing foveal visual resources. In summary, to enter text quickly and accurately during dual-task driving, drivers must often switch their eyes back and forth from the road ahead to the IVISs screen. Thus, potential visual resources competition existed between the textual and the driving task (Wickens 2008). Although each HBS condition had indistinguishable mean glance time, smaller HBS would cause extended total glance time, more frequent off-road glances, and more off-road glances exceeding 1.6 s. This result is reproduced in the study of Kim et al. (2014) and can be seen as an adaptive glance behavior (Peng and Boyle 2015). In addition, the driving speed of dual-task conditions is lower than the baseline condition, which generally was seen as an adaptive longitudinal driving behavior (Onate-Vega et al. 2020). However, there is no difference throughout all dual-task driving conditions. We discreetly inferred that it is the simple and easy settings (i.e., traffic flow, roadway alignment, driving speed, etc.) and the nature of simulated driving that make these young drivers too “overconfident” about their driving skills to take no compensatory measures (Zhong et al. 2022a; Zhong et al. 2022b). Furthermore, the result that the “physical demand” sub-scale scores lowest and has non-significant differences can also be explained. Finally, consistent with previous research (Zhong et al. 2022a), the blind interaction mechanism that handwriting can acquire good usability even without the involvement of vision (Costagliola et al. 2017) in the static context was also not repeated in this experiment, which highlights that these young drivers might find it hard to handwriting on IVISs as their eyes fall on the road ahead under driving conditions.
Computer models of saliency alone fail to predict subjective visual attention to landmarks during observed navigation
Published in Spatial Cognition & Computation, 2021
Demet Yesiltepe, Ayse Ozbil Torun, Antoine Coutrot, Michael Hornberger, Hugo Spiers, Ruth Conroy Dalton
Winter, Tomko, Elias and Sester (2008) stated that salient features are defined as landmarks. This indicates that if an object is more salient than others, it is more likely to be remembered (Cenani, Arentze & Timmermans, 2017) or used by people for navigation, orientation, and learning purposes. One of the most significant saliency categorization was developed by Sorrows and Hirtle (1999): they described three types of landmarks: visual, cognitive and structural landmarks. Visual landmarks can be distinguished based on their physical characteristics such as size, shape or color. Cognitive landmarks are more personal; they have a cultural or historical meaning so that even if an object does not have any visual attractiveness it can still be used by an observer to define a destination or to way-find. A structural landmark is about the location of objects in an environment. Various studies argue that if an object is highly accessible (for instance if an object is located at a decision point (Burnett, Smith & May, 2001; Cenani et al., 2017; Evans, Smith & Pezdek, 1982; Lynch, 1960; Miller & Carlson, 2011)), then the object is more likely to be used as a landmark. For instance, Burnett et al. (2001) defined characteristics of preferred landmarks for navigation and they mentioned that landmarks would be more useful if they are located close to decision points. In another study, researchers used en-route landmarks, off-route landmarks, decision-point landmarks and street facades and they observed that landmarks located at a decision point are more likely to be recognized (Cenani et al., 2017). The saliency definition was improved by Caduff and Timpf (2008) as they mentioned that Sorrows and Hirtle’s method was unable to characterize landmarks quantitatively. They defined perceptual, cognitive and contextual salience and offered measures to analyze them. Nothegger, Winter and Raubal (2004) added the concept of visibility to this definition and more recently Von Stülpnagel and Frankenstein (2015) referred to configurational salience, which is related to Space Syntax1Space Syntax is a technique used to analyze environments quantitatively and to understand the human and space relations (Hillier & Hanson, 1984). By using Space Syntax line based and visibility based analysis, it is possible to measure the environments objectively and compare different results to understand the most accessible-visible points. By using visibility graph analysis, researchers defined all accessible places with grids and they measured landmark size – number of grids/cells they occupy-, visibility of landmarks – number of grids/cells they are visible from- and integration – the average visual distance to all grids/cells. visibility graph analysis (VGA).