Toward a Taxonomy of Affective Computing

Schwark, Jeremy

doi:10.1080/10447318.2015.1064638

Cited by 35 publications

(16 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, willingness to engage in a transaction is potentially related to the breadth and depth of consumer search behaviours (Moe 2003) or the time spent at a particular address (Kim and Yum 2011;Kim et al 2005). Such developments increase the likelihood that a computer system will be able to understand a user's mental status (Picard 1995;Schwark 2015).…”

Section: Consumer Trackingmentioning

confidence: 98%

“…The capacity to understand user emotions will be an important starting point (Schwark 2015;Wu, Huang, and Hwang 2015), but there will be little point customising system response if it does not improve human-computer interaction. Hopefully, providing computers with a capacity to understand emotion (Picard 1995) might improve interfaces in the manner that better social skills improve interpersonal interaction (see Welford 1966Welford , 1987.…”

Section: Consumer Trackingmentioning

confidence: 99%

“…Hence, negative affect could be associated with a failure to take up systems (Schwark 2015), the propagation of complaint within networks (Hornik et al 2015), and impairments in cognitive control (Engelhardt, Hilgard, and Bartholow 2015).…”

Section: Losingmentioning

confidence: 99%

“…Even so, an understanding of a computer user's affective state potentially allows a customisation of system response (e.g. warnings, advertisements, and recommendations) to user requirements (Schwark 2015;Tao and Tan 2005), but there will be little point customising system response if users do not heed this information (Whitworth 2005).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic changes in the use of online advice in response to task success or failure

Phillips

Landon

2016

Behaviour & Information Technology

View full text Add to dashboard Cite

A range of responsible gambling systems have been developed, but they are not necessarily used by consumers. This requires an understanding of factors (e.g. risk and loss) influencing response to advice. Therefore, an experiment examined the use of decisional support in response to induced success or failure. A total of 21 university students played computer Blackjack at two levels of risk, and were informed as to whether the odds were in their favour ('card count'), and were given online advice as to card selection (Basic). Winning streaks led to overconfidence, as indicated by larger mean bets, more Basic errors, and a failure to adjust wagering upon cessation of the winning streak. Participants were more cautious in response to losses, betting less, and were more likely to comply with advice. Participants attended and complied with advice earlier at low risk, but at high risk noticed advice later and did not comply. Induced emotional states (e.g. winning or losing) are important as they influence the likelihood that people will listen to online advice. Designers of Behaviour Change Support Systems should consider methods of contingently implementing advice. ARTICLE HISTORY

show abstract

Section: Consumer Trackingmentioning

confidence: 98%

Section: Consumer Trackingmentioning

confidence: 99%

Section: Losingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic changes in the use of online advice in response to task success or failure

Phillips

Landon

2016

Behaviour & Information Technology

View full text Add to dashboard Cite

show abstract

“…Four classes were identified in the previous section from a review of the literature: learning (White, 1973;Kazanas and Chawhan, 1975;Gatti-Petito et al, 2013), intelligence (Roberts, 1976;Schwark, 2015;Goksu and Gulcu, 2016;Nkhoma et al, 2017), autonomy (Dorais et al, 1999;Goodrich et al, 2001;Wiest, 2012), and communication (Bateman et al, 1990;Jäger and Rogers, 2012). We have chosen to base the learning subclasses on Gagne's Hierarchical Theory of Learning (Soulsby, 2006), a common hierarchy for learning.…”

Section: Classes and Subclassesmentioning

confidence: 99%

Towards an Ontology of Cognitive Assistants

Maier

Menold²,

McComb³

2019

Proc. Int. Conf. Eng. Des.

View full text Add to dashboard Cite

Cognitive assistants such as IBM Watson and Siri are at the forefront of social and technological innovation and have the potential to solve many unique problems. However, the lack of standardization and classification within the field impedes critical analysis of existing cognitive assistants and may further inhibit their growth into more useful applications. This paper discusses the development of an ontology, its classes, and subclasses that may serve as a foundation for defining and differentiating CAs. Specifically, the four suggested classes include: learning, intelligence, autonomy, and communication. Various assistants are described and categorized using the proposed system. Our novel ontological framework is the first step towards a classification system for this burgeoning field.

show abstract

Personalisation, Emotion, and Nudging

Kalpokas

2019

Algorithmic Governance

View full text Add to dashboard Cite

Toward a Taxonomy of Affective Computing

Cited by 35 publications

References 58 publications

Dynamic changes in the use of online advice in response to task success or failure

Dynamic changes in the use of online advice in response to task success or failure

Towards an Ontology of Cognitive Assistants

Personalisation, Emotion, and Nudging

Contact Info

Product

Resources

About