An Enhanced Mental Model Elicitation Technique to Improve Mental Model Accuracy

Memon, Tasneem; Lu, Jie; Hussain, Farookh Khadeer

doi:10.1007/978-3-642-42054-2_11

Cited by 6 publications

(10 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some researchers advocated for the use of textual data and argued that language is key to understanding mental models [12]. Others have pointed to the complex nature of mental models as a cognitive construct that makes it difficult for individuals to articulate them [66]. While we acknowledge that not all aspects of mental models can be verbalized, our results demonstrated that the parts that are verbalized provide much insight, especially about the mental model development of people with different behavioral profiles.…”

Section: Discussionmentioning

confidence: 58%

“…While we acknowledge that not all aspects of mental models can be verbalized, our results demonstrated that the parts that are verbalized provide much insight, especially about the mental model development of people with different behavioral profiles. We accomplished this by designing thought bubbles with an open-ended prompt to mitigate contextual and framing biases [66] and to avoid taking players out of context. Although one might be skeptical of players' engagement with thought bubbles in this format, despite a somewhat negative trend, we found evidence for continuous engagement across the eight prompts (see Appendix C).…”

Section: Discussionmentioning

confidence: 99%

“…Thought bubbles are a form of verbal elicitation that we utilize in situ. By allowing players to provide openresponse comments, we leverage the verbalization of players' thought processes to investigate their mental model development in the context of dynamic decision-making and without introducing framing bias [66,90].…”

Section: Mental Modelsmentioning

confidence: 99%

“…(see in Figure 2) and allowed them to provide open-ended input (see in Figure 2). We chose an open-ended question and response format to mitigate framing bias [90] and help players to articulate their thought process by engaging associative memory [66]. The analyses we present in this paper center around players' responses to this question.…”

Section: Thought Bubblesmentioning

confidence: 99%

See 3 more Smart Citations

Thought Bubbles: A Proxy into Players' Mental Model Development

Mohaddesi,

Chicoine,

Gong

et al. 2023

Preprint

View full text Add to dashboard Cite

Studying mental models has recently received more attention, aiming to understand the cognitive aspects of human-computer interaction. However, there is not enough research on the elicitation of mental models in complex dynamic systems. We present Thought Bubbles as an approach for eliciting mental models and an avenue for understanding players' mental model development in interactive virtual environments. We demonstrate the use of Thought Bubbles in two experimental studies involving 250 participants playing a supply chain game. In our analyses, we rely on Situation Awareness (SA) levels, including perception, comprehension, and projection, and show how experimental manipulations such as disruptions and information sharing shape players' mental models and drive their decisions depending on their behavioral profile. Our results provide evidence for the use of thought bubbles in uncovering cognitive aspects of behavior by indicating how disruption location and availability of information affect people's mental model development and influence their decisions.CCS Concepts: • Human-centered computing → Empirical studies in HCI; User studies.

show abstract

Section: Discussionmentioning

confidence: 58%

Section: Discussionmentioning

confidence: 99%

Section: Mental Modelsmentioning

confidence: 99%

Section: Thought Bubblesmentioning

confidence: 99%

See 2 more Smart Citations

Thought Bubbles: A Proxy into Players' Mental Model Development

Mohaddesi,

Chicoine,

Gong

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…As these “internal knowledge structures are not directly observable, researchers require adequate tools, instruments, and methodologies to allow people to externalize them” (Ifenthaler, 2010, p.161), yet methodology for mental model elicitation has been inconsistent. According to Memon, Lu, and Hussain (2013), there is a need for better and more efficient ways to articulate mental models and improve the accuracy of elicitation.…”

mentioning

confidence: 99%

A Conceptual Model for Eliciting Mental Models Using a Composite Methodology

Grenier

Dudzinska-Przesmitzki²

2015

Human Resource Development Review

View full text Add to dashboard Cite

Formed through experience, observation, and learning, mental models are used in decision making to understand, predict, and solve problems. The investigation and application of mental models research are a growing area of scholarly inquiry in human resource development (HRD) and adult education, yet debate about how to best elicit such knowing still persists. Therefore, exemplars for eliciting the mental models of individuals are a matter of importance. The goal of this article is to address some of the issues central to mental model research and the methodological problems faced by scholars and provide an approach to mental model elicitation that holds promise for attending to some of the issues raised in the article. Multi-method Mental Model Elicitation (MMME) overcomes some of the limitations of current approaches, thus helping to increase the utility of mental models research in the field of HRD. Keywords research methodologies, employee development, learning and development The mental models of individuals influence many workplace factors including decision making, work processes, and learning. A mental model encompasses those values, beliefs, and knowledge that create perspectives for filtering information and guiding problem solving (Eckert & Bell, 2005), is held internally, and has the ability to affect how a person acts (Rook, 2013). An individual holds multiple mental models simultaneously, and those are refined and built out over time through the integration of

show abstract