Micro and Macro Predictions: Using SGOMS to Predict Phone App Game Playing and Emergency Operations Centre Responses

West, Robert; Ward, Lawrence M.; Dudzik, Kate; Nagy, Nathan; Karimi, Fraydon

doi:10.1007/978-3-319-91122-9_41

Cited by 4 publications

(10 citation statements)

References 22 publications

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“…The results show that the model accurately predicts the Planning Unit condition and the Unit Task condition. The faster time for the Planning Unit prediction is a better fit than found in West et al, (2018), showing that the improvements to the model were warranted. (2018) study compared to the model predictions for all the conditions that match the conditions of the current study.…”

Section: Chapter 5: Resultsmentioning

confidence: 88%

“…Note. The West et al (2018) study had different conditions that did not produce easily interpretable results, so I redesigned the game to improve the experimental design. I also added a visual and motor module to fully account for all cognitive processes.…”

Section: 4mentioning

confidence: 99%

“…The SGOMS ACT-R game model (see Appendix A) that I built is similar to the model used by West et al, (2018). The reference model used separate estimates for the action of responding and the action of looking then responding.…”

Section: Chapter 4: the Modelmentioning

confidence: 99%

“…(3) analyze the data of each participant separately. The current study builds on a previous study by West et al(2018). In the previous study two participants were extensively trained to be high performing experts in a speeded memory game.…”

Section: Chapter 1: Introductionmentioning

confidence: 99%

“…To further the research started by West et al (2018), I decided to follow a similar experimental design and developed a version of the game without the conditions where the two subjects varied, to see if this part of the game causes participants to use the same micro strategies. In addition, although the essential elements of the game were retained, the stimulus response patterns and the input output interface were altered to see if this makes a difference.…”

Section: Chapter 1: Introductionmentioning

confidence: 99%

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Using ACT-R and SGOMS to Predict Micro Strategies Used by Experts During Routine Tasks

Greve¹

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Micro strategies refer to fast, low level, unconscious strategies that constitute a control structure for information processing within a specific task (Newell, 1973). Micro strategies determine how we process low level information, such as perceptual inputs (Shiffrin & Cousineau, 2004) or procedural information (Gray & Boehm-Davis, 2000). Using an SGOMS/ACT-R model I was able to predict expert, individual performance on a stimulus-response reaction time game involving a memorized hierarchical structure. I found that slightly different micro strategies were used by the participants, indicating that micro strategies have a very real influence on individual performance. These findings represent an understudied area of research that has larger implications for many Psychology and Cognitive Science studies. 2Chapter: The Game……………………………………………………………….……..3 2.1The Methods………………………………………………………………………4 2.2The Unit Tasks…………………………………………………………………….4 2.3The Planning Units………………………………………………………………..5 2.4The Conditions…..………………………………………………………………..6 2.5 App/ Game Details………………………………………………………………..7 3 Chapter: Methodology………………………………………………………………….10 3.1 Data Cleaning…………………………………………………………………….11 4 Chapter: The Model……………………………………………………………………11 4.1 Code Examples for each Condition……………………………………………...13

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Section: Chapter 5: Resultsmentioning

confidence: 88%

Section: 4mentioning

confidence: 99%

Section: Chapter 4: the Modelmentioning

confidence: 99%

Section: Chapter 1: Introductionmentioning

confidence: 99%

Section: Chapter 1: Introductionmentioning

confidence: 99%

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Using ACT-R and SGOMS to Predict Micro Strategies Used by Experts During Routine Tasks

Greve¹

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Developing a macro cognitive common model test bed for real world expertise

Dudzik

West

Young

2018

Procedia Computer Science

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Modelling Programming Problem Solving in Python ACT-R

Vorobeva¹

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Cognitive architectures such a Python ACT-R have been used to model human problemsolving strategies and behaviours in complex domains such as programming. However, to date, models of programming have not investigated various strategies for generating programs. To address this, the present thesis describes the construction of five cognitive models that represented different novice and expert strategies for solving a programming problem in Python. To aid in the design of the models, I conducted a talk-aloud study with expert and novice programmers. The models use a set of goals and steps that were identified in the study transcripts and solutions produced by the programmers in the study. Expert and competent novices were best modelled by the model utilizing an SGOMS framework. The SGOMS framework incorporated the ability to formalize the relationships between goals of the problem and allowed the model to structure the solution in the same way as experts and competent novices.iii vi Appendices .

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Micro and Macro Predictions: Using SGOMS to Predict Phone App Game Playing and Emergency Operations Centre Responses

Cited by 4 publications

References 22 publications

Using ACT-R and SGOMS to Predict Micro Strategies Used by Experts During Routine Tasks

Using ACT-R and SGOMS to Predict Micro Strategies Used by Experts During Routine Tasks

Developing a macro cognitive common model test bed for real world expertise

Modelling Programming Problem Solving in Python ACT-R

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