Modeling cognitive load effects in an interrupted learning task: An ACT-R approach Maria Wirzberger1, Günter Daniel Rey1, Josef F. Krems2 1Psychology
of Learning with Digital Media, 2Cognitive and Engineering Psychology
Theoretical Background
Model Concept
Research focus
Learning task
Interrupting task
▪ Cognitive processes and mechanisms that correspond with demands arising from factors inherent in learning situations
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Use of problem state for memorizing symbols (Borst et al., 2010; Nijboer et al., 2016)
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Saliency of task switch triggered bottom-up via blue color (Wirzberger et al., 2015)
Cognitive load theory (CLT; Sweller et al., 2011)
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Random choice of response in case of retrieval failure
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▪ Learners‘ cognitive resources demanded by task complexity (intrinsic cognitive load; ICL), situational constraints (extraneous cognitive load; ECL) and schema acquisition (germane cognitive load; GCL)
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Anticipative vs. reactive task solving strategy in difficult task condition retrieval of response after encoding first vs. second symbol
Use of problem state during counting creates resource interference (Borst et al., 2010; Nijboer et al., 2016)
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Activation decay of declarative and goal chunks during interruption (Trafton et al., 2003)
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Pre-attentive processing of symbol shape simulated by distinct colors (Wickens et al., 2013) Pop-out effect
▪ New approach on considering temporal aspect of GCL as process that results in changing pattern of resource demands over task and depends on structural influences from ICL and ECL (Wirzberger et al., 2017)
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Rebuilt goal after task switch by declarative memory or problem state
ACT-R (Anderson & Lebiere, 1998; Anderson, 2007) ▪ Cognitive architecture with modular organization of information processing structures on biologically vested background
Preliminary Model Results
Experimental Setting
Easy task condition ▪ Obvious loss after interruption in learning task for both model and human performance (see dashed red lines in Fig. 2) RMSSD = 3.22, R2 = .40
Sample ▪ 116 university students, M = 23.2 years, SD = 4.3, Range: 18-44, 77.6% female Task & Design ▪
Learning of four combinations of abstract geometric symbols over 64 trials
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ICL: Variation of task complexity by combinations consisting of two (easy) vs. three (difficult) symbols
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ECL: Interrupting task after five predefined trials (irregularly distributed over the task)
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GCL: Continuous efficiency score (correct answers per second) represents learning-related demands on mental resources
Core results Fig. 1. Schematic structure of learning trial followed by interruption in easy task condition
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Significant differences in resumption efficiency between easy and difficult task and over time
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No significant differences in interruption efficiency between conditions, but over time
Fig. 2. Model-data comparison for learning task (easy)
Fig. 3. Model-data comparison for interrupting task (easy)
▪ Observable differences between model and human performance in interrupting task (see Fig. 3) RMSSD = 5.45, R2 = .28
Difficult task condition ▪ Model using reactive strategy not suitable to map development in human performance over task (see Fig. 4) RMSSD = 3.65, R2 = .17 ▪ Model and human performance substantially differ as well in interrupting task (see Fig. 5) RMSSD = 7.38, R2 = .22 Fig. 4. Model-data comparison for learning task (difficult)
Fig. 5. Model-data comparison for interrupting task (difficult)
Outlook Future steps ▪ Improvement in model performance for learning and interrupting tasks in both conditions necessary to optimize model fit
Literature
▪ Generation of predictions on model performance in new experimental task setting and validation with human sample
Anderson, J. R., & Lebiere, C. J. (1998). The atomic components of thought. New York: Psychology Press. Anderson, J. R. (2007). How can the human mind occur in the physical universe? New York, NY, USA: Oxford University Press. Borst, J. P., Taatgen, N. A., & van Rijn, H. (2010). The Problem state: A cognitive bottleneck in multitasking. Journal of Experimental Psychology: Learning, Memory and Cognition, 36, 363-382. Nijboer, M., Borst, J., van Rijn, H., & Taatgen, N. (2016). Contrasting single and multi-component working-memory systems in dual tasking. Cognitive Psychology, 86, 1-26. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York, NY, USA: Springer Science + Business Media. Wirzberger, M., Esmaeili Bijarsari, S., & Rey, G. D. (2017). Embedded interruptions and task complexity influence schema-related cognitive load progression in an abstract learning task. Acta Psychologica, 179, 30-41. Wirzberger, M., & Russwinkel, N. (2015). Modeling interruption and resumption in a smartphone task: An ACT-R approach. i-com, 14, 147-154.
Open questions ▪ Appropriate parameters, mechanisms or processes that could be useful for adjusting model performance? ▪ Required extent of similarity between tasks for model predictions? Expectable amount of “transfer” between tasks?
Contact: Maria Wirzberger, M.Sc. – Email:
[email protected] – Phone: +49 371 531 31652 – Office: Reichenhainer Str. 70, 09126 Chemnitz, Germany
