How We Type: Eye and Finger Movement Strategies in Mobile Typing

Jiang, Xinhui; Li, Yang; Jokinen, Jussi; Hirvola, Viet Ba; Oulasvirta, Antti; Ren, Xiangshi

doi:10.1145/3313831.3376711

Cited by 20 publications

(11 citation statements)

References 64 publications

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“…We set the scope for the typing model by way of three considerations. Firstly, the model should have the capacity to replicate main tendencies in an empirical dataset of typing Jiang et al (2020). Its policy should align closely with strategies and closely mirror key metrics and phenomena reported in the paper.…”

Section: Planning the Projectmentioning

confidence: 99%

A Workflow for building Computationally Rational Models of Human Behavior

Chandramouli,

Shi,

Putkonen

et al. 2024

Preprint

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Convergent progress in cognitive science and machine learning has given rise to a new approach to modeling human behavior, which can better account for its adaptive nature. This follows the theory of computational rationality, under which behavior adapts to maximize expected utility while constrained by the computational limits implied by their cognition. Building computational models of this theory involves three phases: i) specifying rewards and cognitive resources, ii) formulating a sequential decision-making problem to control such resources to maximize expected reward, and iii) using methods like deep reinforcement learning to approximate the optimal policy solution. However, despite their success in reproducing human-like adaptive behavior across everyday tasks, these models are not easy to build. The design of psychological assumptions and machine learning-decisions concerning policy optimization, parameter inference, and model selection, are all tangled processes rife with pitfalls that hinder the development of valid and effective models. The article, drawing from a decade of work on this approach, lays out a workflow for tackling this challenge and is accompanied with detailed discussion of pros and cons at key decision points.

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Section: Planning the Projectmentioning

confidence: 99%

A Workflow for building Computationally Rational Models of Human Behavior

Chandramouli,

Shi,

Putkonen

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…According to two large-scale analyses, people leave more errors when typing on mobile keyboards than when typing on desktop keyboards (2.34 and 1.17%, respectively) (Dhakal et al, 2018;Palin et al, 2019). Typing on mobile touchscreen keyboards, people are prone to make errors due to the small size of the keys and the absence of physical keys because without a physical reference point, visual attention is divided between the text display and the keyboard (Hoggan et al, 2008;Jiang et al, 2020). As a result, it is more difficult to detect errors and more cumbersome to correct them on a mobile device than on a non-mobile device (Jiang et al, 2020).…”

Section: Typographical Errorsmentioning

confidence: 99%

“…Typing on mobile touchscreen keyboards, people are prone to make errors due to the small size of the keys and the absence of physical keys because without a physical reference point, visual attention is divided between the text display and the keyboard (Hoggan et al, 2008;Jiang et al, 2020). As a result, it is more difficult to detect errors and more cumbersome to correct them on a mobile device than on a non-mobile device (Jiang et al, 2020). Furthermore, contextual factors such as typing while moving and the surrounding physical environments may cause more typographical errors (Lurie et al, 2018).…”

Section: Typographical Errorsmentioning

confidence: 99%

“…In terms of mobile cues, Carr and Stefaniak (2012) demonstrated that the presence of a cue of this kind (i.e., "Sent from my iPhone") can mitigate the negative effect of textual errors (i.e., grammatical errors) on evaluations of the email sender's professionalism. In a similar vein, when a review presents a cue indicating that it was written on/posted from a mobile device, based on their knowledge that mobile typing is more prone to typographical errors and requires more effort to fix the errors, viewers may attribute typographical errors to the situation (i.e., mobile typing) rather than to the reviewer (Jiang et al, 2020). Such a mitigation effect is likely to exist in the context of online reviews as well.…”

Section: Interaction Of Mobile Cues and Typographical Errorsmentioning

confidence: 99%

“…Moreover, the presence of typographical errors is expected to change the effect of a mobile cue on the perceived review writing effort. As noted, providing situational information that the review was written on and/or posted from a mobile device may mitigate the fundamental attribution error because people are familiar with the fact that typing on a mobile keyboard tends to produce more typographical errors and requires more time and effort to correct the errors than on non-mobile devices (Dhakal et al, 2018;Palin et al, 2019;Jiang et al, 2020). Thus, when a review includes typographical errors, a mobile cue may have a positive effect on the perceived review writing effort by mitigating the effect of typographical errors and so canceling out or even exceeding the negative effect of the mobile cue by itself.…”

Section: Perceived Review Writing Effort and Information Usefulness A...mentioning

confidence: 99%

See 2 more Smart Citations

Reviews via Mobile: The Role of Mobile Cues and Typographical Errors in Online Review Adoption

Lim

Masłowska

2022

Front. Psychol.

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Online consumer reviews are increasingly being written on and posted from mobile devices such that some platforms have started to indicate when this is the case with cues such as “via mobile” (i.e., mobile cue). Reviews from mobile devices differ from those from non-mobile devices; for example, reviews from mobile devices are more likely to include typographical errors. For this study, a web-based experiment was conducted to investigate viewers’ evaluation and adoption of online reviews in regard to a mobile cue and typographical errors. The results indicate an interaction effect between the presence of a mobile cue and typographical errors. When a review did not include typographical errors, the presence of a mobile cue negatively affected the evaluation and adoption of information (i.e., the viewer’s attitude toward the reviewed restaurant). However, the effects of a mobile cue were not significant for a review with typographical errors. Further, the results suggest that the viewer’s perception of the review writing effort and the review’s information usefulness are sequential mediators explaining the information adoption mechanism. The findings provide interesting insights into consumers’ perceptions of online reviews in the current media landscape in which the large-scale adoption of mobile devices is a well-recognized phenomenon.

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A Workflow for Building Computationally Rational Models of Human Behavior

Chandramouli,

Shi,

Putkonen

et al. 2024

Comput Brain Behav

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Computational rationality explains human behavior as arising due to the maximization of expected utility under the constraints imposed by the environment and limited cognitive resources. This simple assumption, when instantiated via partially observable Markov decision processes (POMDPs), gives rise to a powerful approach for modeling human adaptive behavior, within which a variety of internal models of cognition can be embedded. In particular, such an instantiation enables the use of methods from reinforcement learning (RL) to approximate the optimal policy solution to the sequential decision-making problems posed to the cognitive system in any given setting; this stands in contrast to requiring ad hoc hand-crafted rules for capturing adaptive behavior in more traditional cognitive architectures. However, despite their successes and promise for modeling human adaptive behavior across everyday tasks, computationally rational models that use RL are not easy to build. Being a hybrid of theoretical cognitive models and machine learning (ML) necessitates that model building take into account appropriate practices from both cognitive science and ML. The design of psychological assumptions and machine learning decisions concerning reward specification, policy optimization, parameter inference, and model selection are all tangled processes rife with pitfalls that can hinder the development of valid and effective models. Drawing from a decade of work on this approach, a workflow is outlined for tackling this challenge and is accompanied by a detailed discussion of the pros and cons at key decision points.

show abstract

How We Type: Eye and Finger Movement Strategies in Mobile Typing

Cited by 20 publications

References 64 publications

A Workflow for building Computationally Rational Models of Human Behavior

A Workflow for building Computationally Rational Models of Human Behavior

Reviews via Mobile: The Role of Mobile Cues and Typographical Errors in Online Review Adoption

A Workflow for Building Computationally Rational Models of Human Behavior

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