PosterPDF Available

Memory-related cognitive load effects in abstract symbol learning tasks: A model-based explanation

Authors:

Abstract

Introduction of the current state of my cognitive modeling project at the Cognitive Modeling Spring School 2019
https://maria-wirzberger.de | https://www.is.mpg.de/person/mwirzberger
Max Planck Institute for Intelligent Systems
Existing modeling work
Experimental task (Wirzberger et al., 2017)
Behavioral data (Wirzberger et al., submitted)
fMRI predictions (Wirzberger et al., submitted)
Existing ACT-R model Future modeling work
Experimental task (Wirzberger et al., 2018)
Behavioral data
Research focus
References
Wirzberger, M., Borst, J. P., Krems, J. F., & Rey, G. D. (submitted). Memory-related cognitive load effects
in an interrupted learning task: A model-based explanation. Journal of Memory and Language.
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, 3041. doi:10.1016/j.actpsy.2017.07.001
Wirzberger, M., Herms, R., Esmaeili Bijarsari, S., Eibl, M., & Rey, G. D. (in press). Schema-related
cognitive load influences performance, speech, and physiology in a dual-task setting: A continuous
multi-measure approach. Cognitive Research: Principles and Implications, 3:46. doi: 10.1186/s41235-
018-0138-z
References
Future ACT-R model
Memory-related cognitive load effects in abstract
symbol learning tasks: A model-based explanation
Dr. Maria Wirzberger
maria.wirzberger@tuebingen.mpg.de
116 student participants
learned four easy or difficult
symbol combinations with
five interruptions due to a
visual search task
Explaining behavioral patterns in human data
Validating neural patterns from existing model
Inspecting effects of task order prioritization
More complex symbol combinations result in slower
learning and less interruption effects
ACT-R model with spreading activation and partial
matching can map results best
Learning effects are visible on a neural level: less
investment of cognitive resources with task progress
123 student participants
performed a dual-task:
learning easy or complex
symbol combinations while
memorizing five-digit
sequences
More complex symbol combinations result in slower
learning and lower levels of accuracy
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Article
Full-text available
Schema acquisition processes comprise an essential source of cognitive demands in learning situations. To shed light on related mechanisms and influencing factors, this study applied a continuous multi-measure approach for cognitive load assessment. In a dual-task setting, a sample of 123 student participants learned visually presented symbol combinations with one of two levels of complexity while memorizing auditory presented number sequences. Learners’ cognitive load during the learning task was addressed by secondary task performance, prosodic speech parameters (pauses, articulation rate), and physiological markers (heart rate, skin conductance response). While results revealed increasing primary and secondary task performance over the trials, decreases in speech and physiological parameters indicate a reduction in the overall level of cognitive load with task progression. In addition, the robustness of the acquired schemata was confirmed by a transfer task that required participants to apply the obtained symbol combinations. Taken together, the observed pattern of evidence supports the idea of a logarithmically decreasing progression of cognitive load with increasing schema acquisition, and further hints on robust and stable transfer performance, even under enhanced transfer demands. Finally, theoretical and practical consequences are discussed considering evidence on desirable difficulties in learning as well as the potential of multimodal cognitive load detection in learning applications.
Article
Background The Cognitive Load Theory provides a well-established framework for investigating aspects of learning situations that demand learners’ working memory resources. However, the interplay of these aspects at the cognitive and neural level is still not fully understood. Method We developed four computational models in the cognitive architecture ACT-R to clarify underlying memory-related strategies and mechanisms. Our models account for human data of an experiment that required participants to perform a symbol sequence learning task with embedded interruptions. We explored the inclusion of subsymbolic mechanisms to explain these data and used our final model to generate fMRI predictions. Results The final model indicates a reasonable fit for reaction times and accuracy and links the fMRI predictions to the Cognitive Load Theory. Conclusions Our work emphasizes the influence of task characteristics and supports a process-related view on cognitive load in instructional scenarios. It further contributes to the discussion of underlying mechanisms at a neural level.
Article
Cognitive processes related to schema acquisition comprise an essential source of demands in learning situations. Since the related amount of cognitive load is supposed to change over time, plausible temporal models of load progression based on different theoretical backgrounds are inspected in this study. A total of 116 student participants completed a basal symbol sequence learning task, which provided insights into underlying cognitive dynamics. Two levels of task complexity were determined by the amount of elements within the symbol sequence. In addition, interruptions due to an embedded secondary task occurred at five predefined stages over the task. Within the resulting 2x5-factorial mixed between-within design, the continuous monitoring of efficiency in learning performance enabled assumptions on relevant resource investment. From the obtained results, a nonlinear change of learning efficiency over time seems most plausible in terms of cognitive load progression. Moreover, different effects of the induced interruptions show up in conditions of task complexity, which indicate the activation of distinct cognitive mechanisms related to structural aspects of the task. Findings are discussed in the light of evidence from research on memory and information processing.