Facilitating Game Play: How Others Affect Performance at and Enjoyment of Video Games

Bowman, Nicholas David; Weber, René; Tamborini, Ron; Sherry, John F.

doi:10.1080/15213269.2012.742360

Cited by 96 publications

(79 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It would present a fuller picture to make a case that the frustration occurred due to a failure of the character to perform to expectation rather than the fear factor. This matches what has been observed in human-players when they become noticeably frustrated in other gaming or social instances [6,[10][11][12][13]. Fear, instead, is the cause of the failure to perform.…”

Section: Frustration As It Has Been Observed In Human Playerssupporting

confidence: 66%

“…These values were used to affect a dramatic shift in the determination index to represent the emotive effects of certain events. Such sudden spikes of pique are commonly found in observations of frustration in human players [8,10,18,19]. Consequently, these values act as a means to shift from specific sets of feed-forward coefficients to other extreme values that were chosen to represent a better-suited altered emotive state.…”

Section: Implementing Frustration Modifiers Through the Emotive Effecmentioning

confidence: 99%

See 1 more Smart Citation

The Right to Remember: Implementing a Rudimentary Emotive-Effect Layer for Frustration on AI Agent Gameplay Strategy

Stallwood

Ranchhod

2017

Computers

View full text Add to dashboard Cite

AI (Artificial Intelligence) is often looked at as a logical way to develop a game agent that methodically looks at options and delivers rational or irrational solutions. This paper is based on developing an AI agent that plays a game with a similar emotive content like a human. The purpose of the study was to see if the incorporation of this emotive content would influence the outcomes within the game Love Letter. In order to do this an AI agent with an emotive layer was developed to play the game over a million times. A lower win/loss ratio demonstrates that, to some extent, this methodology was vindicated and a 100 per cent win for the AI agent did not happen. Machine learning techniques were modelled purposely so as to match extreme models of behavioural change. The results demonstrated a win/loss ratio of 0.67 for the AI agent and, in many ways, reflected the frustration that a normal player would exhibit during game play. As was hypothesised, the final agent investment value was, on average, lower after match play than its initial value.

show abstract

Section: Frustration As It Has Been Observed In Human Playerssupporting

confidence: 66%

Section: Implementing Frustration Modifiers Through the Emotive Effecmentioning

confidence: 99%

The Right to Remember: Implementing a Rudimentary Emotive-Effect Layer for Frustration on AI Agent Gameplay Strategy

Stallwood

Ranchhod

2017

Computers

View full text Add to dashboard Cite

show abstract

“…3 Although previous studies involving skill level used median splits for analyses (Bowman & Tamborini, 2008;Bracken & Skalski, 2009), the current study opted for three skill levels in hopes of observing more nuance.…”

Section: Primary Analysesmentioning

confidence: 98%

“…Specifically, Huh, Rosaen, Sherry, and Bowman (2006) show that performance at each analog test predicts performance in corresponding video games (e.g., targeting and performance at first-person shooter games) and flow during game play (Huh et al, 2006). Additionally, for those with higher cognitive skills, having an audience increases game performance for less challenging games (Bowman & Tamborini, 2008).…”

Section: Individual Differences and Skillmentioning

confidence: 99%

Too good to care: The effect of skill on hostility and aggression following violent video game play

Matthews

2015

Computers in Human Behavior

View full text Add to dashboard Cite

“…For all of the discussion of video games engagement potential, this was indicative of early scholarly works specifically discussing this engagement potential. Indeed, Turkle's work The Second Self is perhaps the first instance in which Mihaly Csikszentmihalyi's theory of 'flow', now ubiquitous in Serious Games research [89,90], was specifically applied to video games.…”

Section: From Analogue To Digital Serious Gamesmentioning

confidence: 99%

A Brief History of Serious Games

Wilkinson

2016

Lecture Notes in Computer Science

112

View full text Add to dashboard Cite

Abstract. Serious Games are now an established field of study. In this field most would attribute the rise of Serious Games to Clark C Abt's creation of the term in 1970, or indeed Ben Sawyer's popularization of it in 2002. However, considering the rich history of purposing non-digital games, itself preceded by discussions of purposing play that are traceable to the work of Plato, it can be said that Serious Games is a contemporary manifestation of centuries old theories and practices. In this chapter, we explore the pre-history of Serious Games, beginning with the suggested purpose, and purposing of play. Throughout this historical review we identify key in research and practice that are apparent in the contemporary Serious Games field.

show abstract

Facilitating Game Play: How Others Affect Performance at and Enjoyment of Video Games

Cited by 96 publications

References 33 publications

The Right to Remember: Implementing a Rudimentary Emotive-Effect Layer for Frustration on AI Agent Gameplay Strategy

The Right to Remember: Implementing a Rudimentary Emotive-Effect Layer for Frustration on AI Agent Gameplay Strategy

Too good to care: The effect of skill on hostility and aggression following violent video game play

A Brief History of Serious Games

Contact Info

Product

Resources

About