Learn Piano with BACh: An Adaptive Learning Interface that Adjusts Task Difficulty Based on Brain State
Open accessAbstract
We present Brain Automated Chorales (BACh), an adaptive brain-computer system that dynamically increases the levels of difficulty in a musical learning task based on pianists' cognitive workload measured by functional near-infrared spectroscopy. As users' cognitive workload fell below a certain threshold, suggesting that they had mastered the material and could handle more cognitive information, BACh automatically increased the difficulty of the learning task. We found that learners played with significantly increased accuracy and speed in the brain-based adaptive task compared to our control condition. Participant feedback indicated that they felt they learned better with BACh and they liked the timings of the level changes. The underlying premise of BACh can be applied to learning situations where a task can be broken down into increasing levels of difficulty.
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Claims the paper makes
0 of 48 cited sources checked · 0 supported · 0 flagged
- Findingown data
The brain data effectively classified high versus low cognitive workload while participants played easy and hard pieces.
“We first verify that the brain data demonstrated effective classification of high and low cognitive workload while users played easy and hard pieces on the piano.”
Quote in the paperSubstantial claim - Findingreasoning
All participants segmented the pieces while learning in the normal condition.
“As a result, all participants segmented the pieces while they learnt during the normal condition.”
- Also stated in: § Learning Task Real Time Classification · plain
“As a consequence, all learners segmented the pieces while they learnt during the normal condition.”
Quote in the paperNot a substantial claim - Also stated in: § Learning Task Real Time Classification · plain
- Findingown data
Participants played the pieces faster in the BACh adaptive condition than in the control condition.
“We found that learners played with significantly increased accuracy and speed in the brain-based adaptive task compared to our control condition.”
- Also stated in: § INTRODUCTION · plain
“By analyzing performance data, we found that participants played the pieces faster and with increased accuracy when using our BACh system compared to how they would normally learn a piece of music.”
- Also stated in: § Performance Data · hedged
“Results indicated a pattern for two traits: 1) Increased Accuracy, and 2) Faster speed with BACh.”
Quote in the paperSubstantial claim - Also stated in: § INTRODUCTION · plain
- Findingown data
Participants played with significantly greater accuracy in the BACh adaptive condition than in the control condition.
“We found that learners played with significantly increased accuracy and speed in the brain-based adaptive task compared to our control condition.”
- Also stated in: § INTRODUCTION · plain
“By analyzing performance data, we found that participants played the pieces faster and with increased accuracy when using our BACh system compared to how they would normally learn a piece of music.”
- Also stated in: § Performance Data · hedged
“Results indicated a pattern for two traits: 1) Increased Accuracy, and 2) Faster speed with BACh.”
Quote in the paperSubstantial claim - Also stated in: § INTRODUCTION · plain
- Findingown data
The mean change in oxygenated hemoglobin was significantly higher when participants played hard pieces compared to easy pieces (t(29) = -2.42, p = .01).
“This revealed a significant difference between conditions when participants played an easy piece (µ = -0.068,σ = 0.077) versus a hard piece (µ = -0.00005,σ = 0.124) on the piano (t(29) = -2.42,p = .01).”
- Also stated in: § RESULTS fNIRS Data · plain
“The mean change in oxy-Hb was significantly higher in participants when they played an hard piece than an easy piece (p = .01).”
Quote in the paperSubstantial claim - Also stated in: § RESULTS fNIRS Data · plain
- Findingown data
Participants reported subjectively that they learned the pieces better with BACh.
“Participant feedback indicated that they felt they learned better with BACh and they liked the timings of the level changes.”
- Also stated in: § INTRODUCTION · plain
“Participants also subjectively reported that they played the pieces better, and liked the timings of the level changes.”
- Also stated in: § INTRODUCTION · boosted
“Demonstrate through questionnaire and interview data that participants felt they learnt the pieces better with BACh and they liked timings of the level changes.”
Quote in the paperSubstantial claim - Also stated in: § INTRODUCTION · plain
- Findingown data
Participants liked the timings of the level changes made by BACh.
“Participant feedback indicated that they felt they learned better with BACh and they liked the timings of the level changes.”
- Also stated in: § INTRODUCTION · plain
“Participants also subjectively reported that they played the pieces better, and liked the timings of the level changes.”
- Also stated in: § INTRODUCTION · boosted
“Demonstrate through questionnaire and interview data that participants felt they learnt the pieces better with BACh and they liked timings of the level changes.”
Quote in the paperNot a substantial claim - Also stated in: § INTRODUCTION · plain
- Findingown data
There was no significant difference between BACh and the normal condition in the number of extra notes played (Z = 0.8796, p = .3633).
“| Number of extra notes | 0.8796 0.3633 | - |”
Quote in the paperNot a substantial claim - Findingown data
There were individual differences between participants in the time spent within levels.
“While participants spent less time on easier levels and longer lengths of time on levels with increasing difficulty, we did find individual differences in time spent within levels.”
Quote in the paperNot a substantial claim - Findingown data
Participants played significantly fewer incorrect notes with BACh than in the normal condition (Z = 2.4401, p = .015).
“played significantly fewer incorrect notes (Z = 2.4401, p = .015)”
Quote in the paperSubstantial claim - Findingown data
Participants made significantly fewer errors with BACh than in the normal condition (Z = 3.0351, p = .004).
“and made fewer errors (Z = 3.0351, p = .004)with BACh (Table 1)”
Quote in the paperSubstantial claim - Findingown data
Participants performed significantly better with BACh than in the normal condition across all four conditions (p < .01).
“Participants showed significantly better performance with BACh in all four conditions (p < .01).”
Quote in the paperSubstantial claim - Findingown data
Participants took significantly less total time to play the whole piece with BACh than in the normal condition (Z = 2.5337, p = .009).
“participants had a faster overall time in the playing the whole piece (Z = 2.5337, p = .009)”
Quote in the paperSubstantial claim - Findingown data
The mean change in oxy-Hb was significantly higher when participants played a hard piece than an easy piece (p = .01).
“The mean change in oxy-Hb was significantly higher in participants when they played an hard piece than an easy piece (p = .01).”
Quote in the paperSubstantial claim - Findingown data
Participants had a significantly lower mean gap between notes with BACh than in the normal condition (Z = 2.482, p = .01).
“had a lower time in the mean gap between notes (Z = 2.482, p = .01)”
Quote in the paperSubstantial claim - Findingown data
Participants missed significantly fewer notes with BACh than in the normal condition (Z = 2.3151, p = .019).
“missed significantly fewer notes (Z = 2.3151, p = .019)”
Quote in the paperSubstantial claim - Findingown data
Piano players who were less skilled, indicated by more incorrect notes or more errors, tended to benefit more from BACh, shown by steeper slopes reflecting a larger difference between the two conditions.
“Figure 5 reveals that players who are less skilled (i.e.play more incorrect notes or make more errors) tend to benefit more greatly from BACh with steeper slopes demonstrating that there was a larger difference between the two conditions.”
Quote in the paperNot a substantial claim - Findingown data
The time spent on each level had high standard deviations: level 1 (μ = 58.58, σ = 33.47), level 2 (μ = 185.60, σ = 64.62), level 3 (μ = 248.49, σ = 151.59), and level 4 (μ = 407.32, σ = 179.80).
“length of time (in seconds) spent on level 1 (µ = 58.58,σ = 33.47),level 2 (µ = 185.60,σ = 64.62),level 3 (µ = 248.49,σ = 151.59),and level 4 (µ = 407.32,σ = 179.80)had high standard deviations”
Quote in the paperSubstantial claim - Findingown data
Participants had a significantly higher average beats-per-minute speed with BACh than in the normal condition (Z = -2.719, p = .005).
“and had a higher beats per minute (bpm) speed (Z = -2.719,p = .005)than in the normal condition”
Quote in the paperSubstantial claim - Findingown data
In the BACh condition, participants produced significantly more correct notes than in the normal condition (Z = -1.9689, p = .05).
“Results showed that participants played significantly more correct notes (Z = -1.9689,p = .05)”
Quote in the paperSubstantial claim - Premisecitation
Heart rate variability and electrodermal activity have been shown to be affected by cognitive workload.
“Heart rate variability (HRV) and electrodermal activity (EDA) (previously called galvanic skin response (GSR)) have been shown to be affected cognitive workload [14,28,41].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [14] 14. Tycho K. Fredericks, Sang D. Choi, Jason Hart, Steven E. Butt, and Anil Mital. 2005. An investigation of myocardial aerobic capacity as a measure of both physical and cognitive workloads. International Journal of Industrial Ergonomics 35 (2005), 1097-1107. DOI: http://dx.doi.org/10.1016/j.ergon.2005.06.002 source
Not checked in the free run.
- cites [28] 28. Taija M M Lahtinen, Jukka P. Koskelo, Tomi Laitinen, and Tuomo K. Leino. 2007. Heart rate and performance during combat missions in a flight simulator. Aviation Space and Environmental Medicine 78, 4 (2007), 387-391.
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- cites [41] 41. Wolfgang Schnotz and Christian Kürschner. 2007. A eeconsideration of cognitive load theory. Educational Psychology Review 19 (2007), 469-508. DOI: http://dx.doi.org/10.1007/s10648-007-9053-4
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- Premisecitation
Increasing-difficulty tasks are helpful if they adapt to learners individually rather than with fixed scheduling.
“However, it has been found that increasing-difficulty task were helpful if the tasks adapted to the learners individually and not with fixed scheduling [52].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [52] 52. Dennis C. Wightman and Gavan Lintern. 1985. Part-Task Training for Tracking and manual control. Human Factors 27, 3 (1985), 267-283. source
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- Premisecitation
Research has shown that cognitive workload can be measured with fNIRS brain sensing while playing the piano.
“Research has shown that cognitive workload can indeed be measured with fNIRS brain sensing while playing the piano [55].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [55] 55. Making Music on the Brain #chi4good, CHI 2016, San Jose, CA, USA
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- Interpretationreasoning
fNIRS can be used to measure levels of cognitive activation in the anterior prefrontal cortex by placing sensors on the forehead.
“We can therefore use fNIRS to measure levels of cognitive activation in the anterior prefrontal cortex (PFC) by placing sensors on the forehead.”
Quote in the paperSubstantial claim - Interpretationcitation
Real-time adaptations using physiological sensing can help learning.
“This is a rare ex-ample of how real-time adaptations can help learning using physiological sensing.”
Quote in the paperSubstantial claim - Interpretationreasoning
A beginner pianist will have a higher cognitive workload than a skilled pianist while learning the same piece.
“In the example of a pianist, a skilled musician can cognitively group together notes to form chords in their schemata, whereas a beginner has to process each note individually, suggesting that the beginner will have a higher cognitive workload while learning the same piece as the skilled pianist.”
Quote in the paperSubstantial claim - Premisecitation
Increasing task difficulty is only helpful if the levels are increased adaptively to learners.
“Interestingly, meta-analyses have revealed that this is only helpful if the levels of difficulty are increased adaptively to learners [52,53].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [52] 52. Dennis C. Wightman and Gavan Lintern. 1985. Part-Task Training for Tracking and manual control. Human Factors 27, 3 (1985), 267-283. source
Not checked in the free run.
- cites [53] 53. Bin Xie and Gavriel Salvendy. 2000. Prediction of mental workload in single and multiple task environments. International journal of cognitive ergonomics 4 (2000), 213-242. source
Not checked in the free run.
- Premisecitation
Cognitive workload is limited in its capacity to handle information.
“The fundamental idea behind both Baddeley's working memory model [39] and CLT [46] is that cognitive workload is limited in its capacity to handle information.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [39] 39. Grega Repovs and Alan Baddeley. 2006. The multi-component model of working memory: Explorations in experimental cognitive psychology. Neuroscience 139, 1 (2006), 5-21. source
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- cites [46] 46. John Sweller. 1999. Instructional Design in Technical Areas. ACER Press.
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- Premisecitation
The measurement of cognitive workload is repeatedly highlighted as the weak link in cognitive load theory studies and learning studies.
“While these studies have made progress one factor is repeatedly highlighted as the weak link in CLT studies and learning studies in general: the measurement of cognitive workload [10,31,24,7].”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [10] 10. Ton de Jong. 2010. Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science 38 (2010), 105-134. DOI: http://dx.doi.org/10.1007/s11251-009-9110-0
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- cites [31] 31. Roxana Moreno. 2010. Cognitive load theory: more food for thought. Instructional Science 38, November 2009 (2010), 135-141. DOI: http://dx.doi.org/10.1007/s11251-009-9122-9 source
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- cites [24] 24. Slava Kalyuga. 2011. Cognitive Load Theory: How Many Types of Load Does It Really Need? Educational Psychology Review 23 (2011), 1-19. DOI: http://dx.doi.org/10.1007/s10648-010-9150-7 Making Music on the Brain #chi4good, CHI 2016, San Jose, CA, USA
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- cites [7] 7. Roland Brünken, Jan L Plass, and Detlev Leutner. 2003. Direct Measurement of Cognitive Load in Multimedia Learning. Educational Psychologist 38, 1 (2003), 53-61. DOI: http://dx.doi.org/10.1207/S15326985EP3801_7 source
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- Interpretationreasoning
An adaptive learning system that adjusts task difficulty based on the learner's actual cognitive state, rather than task performance, would address the weak link of cognitive workload measurement.
“An adaptive learning system that could adjust task difficulty based on the learner's actual cognitive state, rather than their task performance, would address this issue.”
Quote in the paperSubstantial claim - Interpretationreasoning
The two selected Bach chorales ought to be highly similar in musical difficulty level.
“While they are different pieces of music, they ought to be highly similar in musical difficulty level.”
Quote in the paperSubstantial claim - Interpretationcitation
The higher levels of oxy-Hb when playing harder pieces correspond with the hemodynamic literature, where increased cognitive activity leads to excess oxygen provision.
“The significantly higher levels of oxy-Hb when participants were playing harder pieces on the piano correspond with the hemodynamic literature, whereby, when there is increased cognitive activity in an area of the brain, excess oxygen is provided to that area.”
Quote in the paperNot a substantial claim - Premisecitation
Cognitive workload is a dynamic measure that fluctuates during a learning task.
“However, cognitive workload is a dynamic measure that fluctuates during a learning task [54].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [54] 54. Beste F. Yuksel, Daniel Afergan, Evan M. Peck, Garth Griffin, Lane Harrison, Nick Chen, Remco Chang, and Rob J.K. Jacob. 2015. BRAAHMS: A Novel Adaptive Musical Interface Based on Users' Cognitive State. Proc. NIME 2015 (2015). source
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- Premisecitation
Functional magnetic resonance imaging and positron emission tomography studies have established that cognitive workload can be measured in the prefrontal cortex.
“Functional magnetic resonance imaging (FMRI) and positron emission tomography (PET) studies have established that cognitive workload can be measured in the prefrontal cortex [11,29].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [11] 11. Mark D'Esposito, Bradley R. Postle, and Bart Rypma. 2000. Prefrontal cortical contributions to working memory: evidence from event-related fMRI studies. Experimental brain research 133, 1 (2000), 3-11. http://www.ncbi.nlm.nih.gov/pubmed/10933205
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- cites [29] 29. Dara S. Manoach, Gottfried Schlaug, Bettina Siewert, David G. Darby, Benjamin M. Bly, Andrew Benfield, Robert R. Edelman, and Steven Warach. 1997. Prefrontal cortex fMRI signal changes are correlated with working memory load. Neuroreport 8, 2 (1997), 545-549. http://www.ncbi.nlm.nih.gov/pubmed/9080445 source
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- Premisereasoning
Real-time monitoring of cognitive workload to act as a determinant in a learning task has not yet been explored.
“However, real-time monitoring of cognitive workload to act as determinant in a learning task has not yet been explored.”
Quote in the paperNot a substantial claim - Premisecitation
EEG has been shown to measure cognitive workload via changes in theta and alpha range signals.
“EEG has been shown to measure cognitive workload via changes in theta and alpha range signals (e.g.[18,17,16]).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [18] 18. Alexander Gundel and Glenn F. Wilson. 1992. Topographical changes in the ongoing EEG related to the difficulty of mental tasks. Brain topography 5, 1 (1992), 17-25.
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- cites [17] 17. Alan Gevins, Michael E. Smith, Linda McEvoy, and Daphne Yu. 1997. High-resolution EEG mapping of cortical activation related to working memory: Effects of task difficulty, type of processing, and practice. Cerebral Cortex 7 (1997), 374-385. DOI: http://dx.doi.org/10.1093/cercor/7.4.374 source
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- cites [16] 16. Alan Gevins, Michael E. Smith, Harrison Leong, Linda McEvoy, Susan Whitfield, Robert Du, and Georgia Rush. 1998. Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Human Factors: The Journal of the Human Factors and Ergonomics Society 40, 1 (1998), 79-91. source
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- Study design and conductreasoning
BACh's algorithm assessed learner cognitive workload using both the learner's brain data from the training task and brain data from the current level of difficulty, creating an individual threshold for that learner at that level.
“gorithm that would assess learner cognitive workload using both the learner's brain data from the training task and brain data from the current level of difficulty that the learner was on to create a threshold for that individual on that level of difficulty.”
Quote in the paperSubstantial claim - Premisecitation
Mapping from physiological signals to psychological states is a limitation of physiological computing.
“One of the limitations of physiological computing is that of mapping from physiological signals to psychological states [12].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [12] 12. Stephen H. Fairclough. 2009. Fundamentals of physiological computing. Interacting with Computers 21, 1-2 (Jan. 2009), 133-145. DOI: http://dx.doi.org/10.1016/j.intcom.2008.10.011 source
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- Interpretationown data
BACh effectively supported participants in learning by raising difficulty levels when they were ready and able to handle more information.
“Such comments suggest that BACh was able to effectively support participants in their learning process by increasing difficulty levels when they were ready and able to handle more information.”
Quote in the paperSubstantial claim - Interpretationown data
The individual variation in time spent within levels indicates that BACh was responding to individual differences.
“there was individual variation on time spent within levels, indicating that BACh was responding to individual differences.”
Quote in the paperSubstantial claim - Premisecitation
Studies in HCI have successfully used fNIRS to measure and adapt to cognitive workload in real-time.
“Studies in HCI have successfully used fNIRS to measure and adapt to cognitive workload in real-time [44,1,2,55].”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [44] 44. Erin Treacy Solovey, Paul Schermerhorn, Matthias Scheutz, Angelo Sassaroli, Sergio Fantini, and Robert J K Jacob. 2012. Brainput : Enhancing Interactive Systems with Streaming fNIRS Brain Input. Proceedings of the SIGCHI conference on Human Factors in Computing Systems (2012). source
Not checked in the free run.
- cites [1] 1. Daniel Afergan, Evan M. Peck, Erin T. Solovey, Andrew Jenkins, Samuel W. Hincks, Eli T. Brown, Remco Chang, and Robert J.K. Jacob. 2014a. Dynamic difficulty using brain metrics of workload. Proceedings of the SIGCHI conference on Human Factors in Computing Systems (2014), 3797-3806. source
Not checked in the free run.
- cites [2] 2. Daniel Afergan, Tomoki Shibata, Samuel W. Hincks, Evan M. Peck, Beste F. Yuksel, Remco Chang, and Robert J.K. Jacob. 2014b. Brain-Based Target Expansion. Proc. UIST 2014 (2014). source
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- cites [55] 55. Making Music on the Brain #chi4good, CHI 2016, San Jose, CA, USA
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- Interpretationown data
Less skilled piano players benefited more from learning with BACh than more skilled players.
“Our results actually suggested that less skilled piano players benefited more from learning with BACh, as demonstrated by the steeper lines in Figure 5 for players who made more mistakes.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 20
- May combine two claims. 24
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataEleven of 16 participants rated themselves as beginners; of the five intermediates, three no longer played piano; median piano-playing experience was 3 years.
“While eleven out of 16 participants rated themselves as beginners, out of the remaining five who rated themselves as intermediate, 3 of them no longer played piano.The median time our participants had played piano was 3 years.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataMost participants began learning with their right hand only, while one participant started with the left hand.
“Most participants started learning with their right hand only, one participant who was very good with his left hand started with his left hand first.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingown dataWhen cognitive workload measurement began before the full level of difficulty, BACh incorrectly determined readiness to advance earlier because it read workload for an easier level.
“We found that if we started measuring their cognitive workload earlier before the full level of difficulty, BACh would quite correctly think that they were ready to move on earlier as it was reading their cognitive workload for an easier difficulty level.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataParticipants spent less time on easier levels and longer on levels of increasing difficulty.
“While participants spent less time on easier levels and longer lengths of time on levels with increasing difficulty, we did find individual differences in time spent within levels.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataParticipants' interview feedback about the timing of difficulty changes was generally positive, with no standalone or overtly negative comments about the timings.
“their feedback was generally positive:”
- Also stated in: § Interview Data · plain
“Overall, there were no standalone or overtly negative comments about the timings of BACh.”
Quote in the paperSubstantial claim- Also stated in: § Interview Data · plain
May combine two claims.
Findingown dataParticipants' subjective ratings showed a noticeable preference for BACh over the normal condition in felt mastery of the piece, felt correctness of playing, and felt ease of learning.
“Figure 6 shows that participants showed noticeable preferences for BACh in how well they felt they mastered the piece, how correctly they felt they played, and how easy they felt the piece was to learn.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationGood teachers often guide their students into the zone of proximal development.
“Good teachers, whether they realize it or not, often guide their students into the zone of proximal development.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [51] 51. Lev Vygotsky. 1978. Interaction between learning and development. Mind and Society (1978), 79-91. 51. Christopher D. Wickens, Shaun. Hutchins, Thomas. Carolan, and John. Cumming. 2013. Effectiveness of Part-Task Training and Increasing-Difficulty Training Strategies: A Meta-Analysis Approach. Human Factors: The Journal of the Human Factors and Ergonomics Society (2013). DOI: http://dx.doi.org/10.1177/0018720812451994
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
PremisecitationCognitive load theory holds that working memory has a finite capacity, so instructional designers should avoid overloading students.
“It is based on idea that there is only a finite capacity for working memory available [39,46] so designers of learning material should avoid overloading students.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [39] 39. Grega Repovs and Alan Baddeley. 2006. The multi-component model of working memory: Explorations in experimental cognitive psychology. Neuroscience 139, 1 (2006), 5-21. source
Not checked in the free run.
- cites [46] 46. John Sweller. 1999. Instructional Design in Technical Areas. ACER Press.
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationComputer-based education has shown that students tend to learn more, take around 30% less time, and have increased enjoyment of their subject.
“CBE has shown that students tend to learn more, take around 30% less time and have increased enjoyment of their subject [27].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27] 27. James A. Kulik. 1994. Technology Assessment in Education and Training. Eva Baker and Harold F. O'Neil Jr, Chapter Meta-analytic Studies of findings on computer-based instruction., 9-34.
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Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationSecondary tasks can interfere considerably with the primary task, especially if the primary task is complex and requires much of the learner's limited cognitive capacity.
“An important disadvantage to this methodology is that the secondary task can interfere considerably with the primary task, especially if the primary task is complex and requires much of the learner's limited cognitive capacity [34,7].”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [34] 34. Fred Paas, Juhani E. Tuovinen, Huib Tabbers, and Pascal W.M. Van Gerven. 2003. Cognitive load measurement as a means to advance cognitive load theory. Educational psychologist 38, 1 (2003), 63-71.
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- cites [7] 7. Roland Brünken, Jan L Plass, and Detlev Leutner. 2003. Direct Measurement of Cognitive Load in Multimedia Learning. Educational Psychologist 38, 1 (2003), 53-61. DOI: http://dx.doi.org/10.1207/S15326985EP3801_7 source
Not checked in the free run.
Classified as probably not a substantial claim.
Interpretationnone givenThe underlying premise of BACh can be applied to learning situations where a task can be broken down into increasing levels of difficulty.
“The underlying premise of BACh can be applied to learning situations where a task can be broken down into increasing levels of difficulty.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationfNIRS has been demonstrated by HCI researchers to measure user cognitive workload in adaptive systems and is resilient to subject movement such as playing piano.
“Brain sensing using functional near-infrared spectroscopy (fNIRS) has recently been demonstrated by HCI researchers to measure user cognitive workload in adaptive systems [4,44,1] and is resilient to subject movement as might be seen while playing a piano [43].”
Quote in the paperSubstantial claim0 of 4 sources checked- cites [4] 4. Hasan Ayaz, Patricia A Shewokis, Scott Bunce, Kurtulus Izzetoglu, Ben Willems, and Banu Onaral. 2012. Optical brain monitoring for operator training and mental workload assessment. Neuroimage 59, 1 (2012), 36-47. source
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- cites [44] 44. Erin Treacy Solovey, Paul Schermerhorn, Matthias Scheutz, Angelo Sassaroli, Sergio Fantini, and Robert J K Jacob. 2012. Brainput : Enhancing Interactive Systems with Streaming fNIRS Brain Input. Proceedings of the SIGCHI conference on Human Factors in Computing Systems (2012). source
Not checked in the free run.
- cites [1] 1. Daniel Afergan, Evan M. Peck, Erin T. Solovey, Andrew Jenkins, Samuel W. Hincks, Eli T. Brown, Remco Chang, and Robert J.K. Jacob. 2014a. Dynamic difficulty using brain metrics of workload. Proceedings of the SIGCHI conference on Human Factors in Computing Systems (2014), 3797-3806. source
Not checked in the free run.
- cites [43] 43. Erin Treacy Solovey, Audrey Girouard, Krysta Chauncey, Leanne M Hirshfield, Angelo Sassaroli, Feng Zheng, Sergio Fantini, and Robert J K Jacob. 2009. Using fNIRS Brain Sensing in Realistic HCI Settings : Experiments and Guidelines. Proc. UIST 2009 (2009). source
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May combine two claims.
InterpretationreasoningPhysiological measures provide continuous data, which is especially important for adaptive tasks that react to learners' needs in real-time.
“This misses the advantage that physiological measures provide which is the continuous availability of data which is especially important for adaptive tasks that can react to learners' needs in real-time.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationreasoningThe segmentation behavior in the normal condition can be viewed as akin to self-judgment of when to increase task complexity while learning.
“This can be viewed as akin to self-judgment of when to increase task complexity while learning.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationPart-task training is only helpful with sequential material, not concurrent tasks.
“Meta-analyses have found parttask training is only helpful with sequential material, such as playing sequential notes on a piano (e.g.[3]), rather than concurrent tasks, such as playing two hands on a piano at the same time [52,53].”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [3] 3. Daniel W. Ash and Dennis H. Holding. 1990. Backward versus forward chaining in the acquisition of a keyboard skill. Human Factors 32 (1990), 139-146. source
Not checked in the free run.
- cites [52] 52. Dennis C. Wightman and Gavan Lintern. 1985. Part-Task Training for Tracking and manual control. Human Factors 27, 3 (1985), 267-283. source
Not checked in the free run.
- cites [53] 53. Bin Xie and Gavriel Salvendy. 2000. Prediction of mental workload in single and multiple task environments. International journal of cognitive ergonomics 4 (2000), 213-242. source
Not checked in the free run.
May combine two claims.
InterpretationreasoningAdaptive learning tasks are important, especially when using increasing-difficulty strategies.
“These findings highlight the importance of adaptive learning tasks, especially when using increasing-difficulty strategies.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningFor increasing-difficulty training to be adaptive, there must be some measurement of when the user is ready to move to the next level.
“In order for increasing-difficulty training to be adaptive, there has to be some measurement of when the user is ready to move on to the next level of difficulty.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Interpretationnone givenRemoving some notes meant that some minor counterpoint rules are no longer met, but the underlying structure and form of both pieces remain intact.
“We note that by removing some of the notes, some minor counterpoint rules are no longer met.However, the underlying structure and form of both pieces is still intact.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationThe self-reporting questionnaire has issues with sensitivity, reliability, and validity in measuring cognitive workload.
“De Jong [10] and Moreno [31] raise several issues with the scale's sensitivity, reliability and validity in measuring cognitive workload.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [10] 10. Ton de Jong. 2010. Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science 38 (2010), 105-134. DOI: http://dx.doi.org/10.1007/s11251-009-9110-0
Not checked in the free run.
- cites [31] 31. Roxana Moreno. 2010. Cognitive load theory: more food for thought. Instructional Science 38, November 2009 (2010), 135-141. DOI: http://dx.doi.org/10.1007/s11251-009-9122-9 source
Not checked in the free run.
May combine two claims.
InterpretationreasoningBrain sensing provides a more direct measurement of physiological changes during higher cognitive workload and can be measured continuously for use as a real-time determinant in adaptive learning.
“Brain sensing provides a more direct measurement of the physiological changes occurring in the brain during higher cognitive workload and can be measured continuously which can be used as a real-time determinant in adaptive learning studies.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationMeta-analyses show that students using intelligent tutoring systems and computer-based education tend to learn faster, more accurately, and with increased enjoyment.
“There have been promising results; meta-analyses revealed that students tend to learn faster and more accurately, and have increased enjoyment of their learning material [27].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [27] 27. James A. Kulik. 1994. Technology Assessment in Education and Training. Eva Baker and Harold F. O'Neil Jr, Chapter Meta-analytic Studies of findings on computer-based instruction., 9-34.
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationTask-evoked pupillary responses have been associated with changes in cognitive workload.
“Task-evoked pupillary responses (TEPRs) have also been associated with changes in cognitive workload [49,20].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [49] 49. Pascal W M Van Gerven, Fred Paas, Jeroen J G Van Merriënboer, and Henk G. Schmidt. 2004. Memory load and the cognitive pupillary response in aging. Psychophysiology 41 (2004), 167-174. DOI:http: //dx.doi.org/10.1111/j.1469-8986.2003.00148.x
Not checked in the free run.
- cites [20] 20. Shamsi T Iqbal, Xianjun Sam Zheng, and Brian P Bailey. 2004. Task-evoked pupillary response to mental workload in human-computer interaction. Extended abstracts of the 2004 conference on Human factors and computing systems CHI 04 (2004), 1477. DOI: http://dx.doi.org/10.1145/985921.986094 source
Not checked in the free run.
May combine two claims.
PremisecitationThe fNIRS signal is resilient to respiration, heartbeat, eye movement, minor head motion, and mouse and keyboard clicks.
“The fNIRS signal has been found to be resilient to respiration, heartbeat, eye movement, minor head motion, and mouse and keyboard clicks [43].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [43] 43. Erin Treacy Solovey, Audrey Girouard, Krysta Chauncey, Leanne M Hirshfield, Angelo Sassaroli, Feng Zheng, Sergio Fantini, and Robert J K Jacob. 2009. Using fNIRS Brain Sensing in Realistic HCI Settings : Experiments and Guidelines. Proc. UIST 2009 (2009). source
Not checked in the free run.
May combine two claims.
Study design and conductown dataEarly studies showed that a fixed percentage threshold for increasing difficulty did not work, because learners' brain measurements were highly individual.
“Early studies showed us very quickly that this was not going to work.Each learner was very individual in their brain measurements, and what worked very well for some, would not even trigger a fixed percentage threshold for others.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductreasoningBACh was designed not to interfere when learners' cognitive workload was high, and its adaptations never decreased difficulty during periods of high cognitive workload.
“We purposefully designed BACh not to interfere when learners' cognitive workload was high in order to avoid disrupting a period of possible learning.”
- Also stated in: § Modeling and Adapting to Cognitive Workload · plain
“This is the reason why the adaptations never decreased the difficulty level during periods of high cognitive workload, as this could have proven both disruptive and frustrating to the learners.”
Quote in the paperSubstantial claim- Also stated in: § Modeling and Adapting to Cognitive Workload · plain
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductreasoningIn the normal condition, learners were told to learn in any way they wished, and all of them segmented the pieces as they learned.
“Learners were told that they could learn the normal condition in any way they wished as they normally would.As a consequence, all learners segmented the pieces while they learnt during the normal condition.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataBACh was able to correctly respond to learner cognitive workload so as to improve playing speed and accuracy.
“By examining the performance data and participant feedback, it seems that BACh was able to correctly respond to learner cognitive workload to improve playing speed and accuracy.”
Quote in the paperSubstantial claimMay combine two claims.
Interpretationown dataBACh helps piano players play with significantly higher accuracy and speed than the normal condition.
“Taken together, these results suggest that BACh helps piano players to play with significantly higher accuracy and speed than the normal condition.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningBy using brain sensing, BACh acquires an objective view of the learner's cognitive state and adjusts the learning task to guide the learner into the zone of proximal development.
“By using brain sensing, BACh is able to acquire an objective view into the learner's cognitive state and adjust the learning task to best guide the learner into the zone of proximal development.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).
Interpretationnone givenThe underlying premise of BACh can be applied to any learning situation involving a complex task that can be broken into steps of increasing difficulty.
“the underlying premise of BACh can be applied to any learning situation for a complex task that can be broken down into steps of increasing difficulty.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationHarrison et al. found that oxygenation levels measured by fNIRS increased along with self-reports of increased cognitive workload as the number of aircraft under operator control increased.
“In an air traffic control simulation, they found oxygenation levels measured by fNIRS increased along with self-reports of increased cognitive workload as the number of aircrafts under operator control increased [19].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [19] 19. Joshua Harrison, Kurtulus Izzetoglu, Hasan Ayaz, Ben Willems, Sehchang Hah, Ulf Ahlstrom, Hyun Woo, Patricia Shewokis, Scott C. Bunce, and Banu Onaral. 2014. Cognitive workload and learning assessment during the implementation of a next-generation air traffic control technology using functional near-infrared spectroscopy. IEEE Transactions on Human-Machine Systems 44, 4 (2014), 429-440. source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductnone givenThe order of the two conditions alternated between participants.
“The order of the conditions alternated between participants.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationFixed scheduling of increases in task difficulty is not helpful.
“While meta-analyses have shown that fixed scheduling when increasing task difficulty is not helpful [52,53]”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [52] 52. Dennis C. Wightman and Gavan Lintern. 1985. Part-Task Training for Tracking and manual control. Human Factors 27, 3 (1985), 267-283. source
Not checked in the free run.
- cites [53] 53. Bin Xie and Gavriel Salvendy. 2000. Prediction of mental workload in single and multiple task environments. International journal of cognitive ergonomics 4 (2000), 213-242. source
Not checked in the free run.
May combine two claims.
Interpretationown dataBACh is targeted at and useful for beginner piano players.
“We, therefore, simply suggest that our system is targeted at and useful for beginner piano players.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
Study design and conductown dataAn expert musician who had taught piano rated participants' learning compared to groundtruth piano rolls.
“An expert musician who had taught piano in the past rated participants' learning compared to the groundtruth piano rolls.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
PremisecitationInstructional techniques that benefit beginners can have reverse effects on more experienced learners (the expertise reversal effect).
“Learning and CLT literature has explored a phenomenon called the expertise reversal effect whereby instructional techniques that are beneficial to beginners can have the reverse effects on more experienced learners [22,23].”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [22] 22. Slava Kalyuga. 2003. The expertise reversal effect. Educational psychologist 38, 1 (2003), 23-31.
Not checked in the free run.
- cites [23] 23. Slava Kalyuga. 2007. Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review 19, 4 (2007), 509-539. source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Study design and conductown dataShapiro-Wilk tests verified that the data was non-parametric, followed by Wilcoxon Signed-rank tests on all dependent variables.
“We carried out Shapiro-Wilk tests that verifed that the data was non-parametric, followed by Wilcoxon Signed-rank tests on all dependent variables (Table 1).”
Quote in the paperSubstantial claim
The paper relies on this without support.
When a learner's cognitive workload falls below a certain threshold, they have mastered the material and can handle more cognitive information.
“As users' cognitive workload fell below a certain threshold, suggesting that they had mastered the material and could handle more cognitive information, BACh automatically increased the difficulty of the learning task.”
“BACh increased the difficulty level by adding a full line of music when users' cognitive workload fell below a threshold, indicating that they could handle more information.”
The paper relies on this without support.
The normal condition, in which participants learned as they normally would, is a valid control for how they would learn without BACh.
“One chorale was presented in normal form and participants were instructed to learn the piece the way they normally would.”
“In the normal condition, learners were told they could learn the piece the way they normally would.”
The paper relies on this without support.
The expert musician's ratings accurately reflect participants' learning.
“An expert musician who had taught piano in the past rated participants' learning compared to the groundtruth piano rolls.”
The paper relies on this without support.
The measurement periods determined by pilot studies are appropriate for the main study.
“For each level of difficulty, we measured each learner's cognitive workload for a period of time determined by pilot studies.”
“We ran many pilot studies to create an algorithm that worked for beginners.”
The paper relies on this without support.
Piano players are typically classically taught to play pieces first with their right hand, then their left hand, and finally both together.
“Piano players are typically classically taught to play pieces first with their right hand, then their left hand, and finally both together.”
The paper relies on this without support.
A learner's cognitive workload, as measured by fNIRS brain sensing, reliably indicates whether that learner is able to handle more information and a higher level of difficulty.
“The goal of BACh was to change the level of difficulty at a time when the participants' cognitive workload was low enough to handle more information and a higher level of difficulty.”
The paper relies on this without support.
The performance differences observed between the BACh and normal conditions are attributable to BACh's cognitive-workload-based adaptation rather than to other differences between the two conditions.
“We showed that BACh significantly increased speed and accuracy compared to a control condition where participants learned pieces the way they normally would.”
Statistics that don’t add up
Each reported p-value recomputed from its test statistic and degrees of freedom.
p-value fits an unstated one-tailed test: t(29) = -2.42,p = .01
MinorThe paper reports p = .01 here; recomputing from the test statistic and its degrees of freedom gives p = .022 (two-tailed). The reported value is what a one-tailed test gives, but the paper does not say near this result that a one-tailed test was used.
Reported p = .01 · recomputed p = .022
“t(29) = -2.42,p = .01”
Statistics recomputed
Every reported test result, recomputed from its test statistic and degrees of freedom. A result shows as not adding up only once an independent check has confirmed it.
- Doesn’t add upt(29) = -2.42reported p = .01 · recomputed p = .022
“t(29) = -2.42,p = .01”
- Not confirmedz = 2.5337reported p = .009 · recomputed p = .011
“(Z = 2.5337, p = .009”
- Not confirmedz = -2.719reported p = .005 · recomputed p = .007
“(Z = -2.719,p = .005”
- Consistentz = -1.9689reported p = .05 · recomputed p = .049
“(Z = -1.9689,p = .05”
- Consistentz = 2.3151reported p = .019 · recomputed p = .021
“(Z = 2.3151, p = .019”
The paper states an exact rank test
- Consistentz = 2.4401reported p = .015 · recomputed p = .015
“(Z = 2.4401, p = .015”
- Consistentz = 3.0351reported p = .004 · recomputed p = .002
“(Z = 3.0351, p = .004”
The paper states an exact rank test
- Consistentz = 2.482reported p = .01 · recomputed p = .013
“(Z = 2.482, p = .01”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Self-check results appear here once an independent check has confirmed them.
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“Szafir and Mutlu [48] monitored student attention with EEG and determined which lecture topic students would benefit from reviewing.”
BackgroundFredericks, T. K., Choi, S. D., Hart, J., Butt, S. E., & Mital, A. (2005) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2005.06.002 · Cited by 78 · see citing works
Harrison, J., Izzetoglu, K., Ayaz, H., Willems, B., Hah, S., Ahlstrom, U., Woo, H., Shewokis, P. A., Bunce, S. C., & Onaral, B. (2014) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2014.2319822 · Cited by 71 · cited 5× · see citing works
“[19] examined whether cognitive workload could be accurately monitored for incrementally changing task difficulty levels using fNIRS in an offline analysis.”
BackgroundMethodInfluential(2007) · PubMed · Cited by 38 · see citing works
Adaptive Hypermedia and Adaptive Web-Based Systems
Habieb-Mammar, H., & Tarpin-Bernard, F. (2004) · Lecture notes in computer science · doi:10.1007/b99480 · Cited by 34 · see citing works
Backward versus Forward Chaining in the Acquisition of a Keyboard Skill
Ash, D. W., & Holding, D. H. (1990) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/001872089003200202 · Cited by 26 · cited 1× · see citing works
“[3]), rather than concurrent tasks, such as playing two hands on a piano at the same time [52, 53].”
BackgroundAfergan, D., Shibata, T., Hincks, S. W., Peck, E. M., Yuksel, B. F., Chang, R., & Jacob, R. J. K. (2014) · Proceedings of the 27th annual ACM symposium on User interface software and technology · doi:10.1145/2642918.2647414 · Cited by 19 · cited 3× · see citing works
“Due to its general ease in setting up with users and its relative tolerance of minor motion, fNIRS is an increasingly popular method of brain sensing in the HCI community [43, 4, 44, 36, 1, 2, 19].”
BackgroundMethodSelf-citationBraahms: A Novel Adaptive Musical Interface Based On Users' Cognitive State
Yuksel, B. F., Afergan, D., Peck, E., Griffin, G., Harrison, L., Chen, N., Chang, R., & Jacob, R. (2015) · Zenodo · doi:10.5281/zenodo.1181417 · Cited by 13 · see citing works
Self-citationFriedman (2000) · doi:10.1002/1097-0029(20001001)51:1 · cited 1×
“More recently, brain imaging studies have examined changes in brain function associated with improvements in performance of tasks [15, 38, 5, 25].”
Background
Flagged first, then by citations
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Cited by
Sorted by citations
Eye tracking cognitive load using pupil diameter and microsaccades with fixed gaze
Krejtz, K., Duchowski, A. T., Niedzielska, A., Biele, C., & Krejtz, I. (2018) · PLoS ONE · doi:10.1371/journal.pone.0203629 · Cited by 352 · see citing works
A Survey on Measuring Cognitive Workload in Human-Computer Interaction
Kosch, T., Karolus, J., Zagermann, J., Reiterer, H., Schmidt, A., & Woźniak, P. W. (2023) · ACM Computing Surveys · doi:10.1145/3582272 · Cited by 304 · see citing works
“…52, 67, 70, 83, 86, 87, 100, 105, 106, 124, 130, 135, 138, 148, 150, 184, 197, 198, 207, 235, 243, 244, 268] EEG ERPs [23, 26, 59, 81, 184, 214, 227, 243] Hemodynamic Response (fNIRS) [3, 5, 32, 80, 96, 97, 104, 159, 161, 191, 192, 196, 221–223, 227, 261, 262] Electromagnetic Changes (fMRI) [220]”
Hinds, J. M., Williams, E., & Joinson, A. (2020) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102498 · Cited by 281 · see citing works
Context-Aware Online Adaptation of Mixed Reality Interfaces
Lindlbauer, D., Feit, A. M., & Hilliges, O. (2019) · Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology · doi:10.1145/3332165.3347945 · Cited by 254 · see citing works
“Existing approaches exploit this knowledge e. g., to schedule interrupts (e. g., Bailey et al. [3]) or to adapt user interfaces (e. g., Yuksel et al. [47]).”
BackgroundDuchowski, A. T., Krejtz, K., Krejtz, I., Biele, C., Niedzielska, A., Kiefer, P., Raubal, M., & Giannopoulos, I. (2018) · Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173856 · Cited by 228 · see citing works
Artificial intelligence-enabled adaptive learning platforms: A review
Tan, L. Y., Hu, S., Yeo, D. J., & Cheong, K. H. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100429 · Cited by 207 · see citing works
Hassib, M., Schneegaß, S., Eiglsperger, P., Henze, N., Schmidt, A., & Alt, F. (2017) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3025453.3025669 · Cited by 110 · see citing works
Measuring Mental Workload Variations in Office Work Tasks using fNIRS
Midha, S., Maior, H. A., Wilson, M. L., & Sharples, S. (2020) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2020.102580 · Cited by 90 · see citing works
Appriou, A., Cichocki, A. S., & Lotte, F. (2020) · IEEE Systems Man and Cybernetics Magazine · doi:10.1109/msmc.2020.2968638 · Cited by 88 · see citing works
“, video games with adaptive difficulty [9] or training applications with a sequence of exercises adapted to the cognitive capabilities of each learner [10].”
BackgroundKosmyna, N., & Maes, P. (2019) · Sensors · doi:10.3390/s19235200 · Cited by 79 · see citing works
“Another one, by the recent start-up BrainCo [31], provides real-time feedback to the teacher about the attention of the students who wear EEG headbands; however, reports or studies are yet to be published.”
BackgroundLiu, Y., Yu, Y., Ye, Z., Li, M., Zhang, Y., Zhou, Z., Hu, D., & Zeng, L. (2023) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2023.3235003 · Cited by 73 · see citing works
“, N-back task [11], Stroop-color-word test [12], air traffic management [13], [14], piano learning [15], arithmetic [16], piloting [17], [18], and gaming [19]).”
BackgroundGehrke, L., Akman, S., Lopes, P., Chen, A., Singh, A. K., Chen, H., Lin, C., & Gramann, K. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300657 · Cited by 68 · see citing works
“The idea to analyze the user’s brain response as an indicator of the user’s state has become increasingly popular at the intersection of neuroscience and HCI [5, 52, 54].”
BackgroundVisual Appearance Modulates Prediction Error in Virtual Reality
Singh, A. K., Chen, H., Cheng, Y.-F., King, J., Ko, L., Gramann, K., & Lin, C. (2018) · IEEE Access · doi:10.1109/access.2018.2832089 · Cited by 57 · see citing works
“., usually for the purpose of monitoring or evaluation [18, 19]. Most works from the HCI community utilize reactive and passive BCI, as rightfully predicted by Tan and Nijholt [10]. A series of works [1, 2, 23, 44, 48, 49, 50, 61] were recently undertaken, based on a relatively new brain imaging technology of function…”
MethodHow Stress and Mental Workload are Connected
Alsuraykh, N. H., Wilson, M. L., Tennent, P., & Sharples, S. (2019) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3329189.3329235 · Cited by 58 · see citing works
Predicting Cognitive Load in an Emergency Simulation Based on Behavioral and Physiological Measures
Appel, T., Sevcenko, N., Wortha, F., Tsarava, K., Moeller, K., Ninaus, M., Kasneci, E., & Gerjets, P. H. (2019) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3340555.3353735 · Cited by 52 · see citing works
Pandarova, I., Schmidt, T. D., Hartig, J., Boubekki, A., Jones, R. D., & Brefeld, U. (2019) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-019-00180-4 · Cited by 53 · see citing works
“…tutors/instructional games outside language learning have reported more positive results (Camp et al. 2001 and Salden et al. 2004 on air traffic control training; Kalyuga and Sweller 2005 on algebra; Yuksel et al. 2016 on music instruction), although some studies point in the opposite direction.”
ResultA conceptual space for EEG-based brain-computer interfaces
Kosmyna, N., & Lécuyer, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0210145 · Cited by 50 · see citing works
Krol, L. R., Haselager, P., & Zander, T. O. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab5bb5 · Cited by 48 · see citing works
“This principle has also been used to make sure students sustain an adequate level of engagement (Yuksel et al 2016).”
MethodSevcenko, N., Appel, T., Ninaus, M., Moeller, K., & Gerjets, P. H. (2022) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-022-00398-y · Cited by 51 · see citing works
“Yuksel et al. [41] reported on the benefits of adaptively increasing task complexity on learning performance in pianists, While, Walter, et al. [5] observed significant improvement in learners’ math performance when using an electroencephalography (EEG)- based adaptive learning environment.”
MethodTheories and Methods for Labeling Cognitive Workload: Classification and Transfer Learning
McKendrick, R. D., Feest, B., Harwood, A. E., & Falcone, B. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00295 · Cited by 48 · see citing works
“In fNIRS, EEG, and fMRI, researchers have shown person generalizable models to be possible (Mitchell et al., 2004; Grimes et al., 2008; Wang et al., 2011; Yuksel et al., 2016).”
BackgroundInfluentialTowards Robust Neuroadaptive HCI
Appriou, A., Cichocki, A. S., & Lotte, F. (2018) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3170427.3188617 · Cited by 43 · see citing works
Cross-Task and Cross-Participant Classification of Cognitive Load in an Emergency Simulation Game
Appel, T., Gerjets, P. H., Hoffmann, S., Moeller, K., Ninaus, M., Scharinger, C., Sevcenko, N., Wortha, F., & Kasneci, E. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3098237 · Cited by 41 · see citing works
“Yuksel and colleagues created a brain-computer interface that adapted the difficulty of a musical learning task [26].”
BackgroundWorkload Alerts—Using Physiological Measures of Mental Workload to Provide Feedback During Tasks
Maior, H. A., Wilson, M. L., & Sharples, S. (2018) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3173380 · Cited by 40 · see citing works
“While some work focuses on adapting the task that users need to perform [1, 64], this work focuses on making participants more aware of their workload so that they can better manage e.”
BackgroundRabbani, M. H. R., & Islam, S. M. R. (2023) · Cognitive Neurodynamics · doi:10.1007/s11571-023-09986-4 · Cited by 42 · see citing works
“…interface (BCI) (Bagheri and Power 2022; Li et al. 2019; Appriou et al. 2018; Hollender et al. 2010), adaptive learning (Andrees-sen et al. 2020; Yuksel et al. 2016), diagnosis of neural diseases (Deligani et al. 2021; Peng et al. 2021), neurore-habilitation for patients with motor disabilities…”
Roy, R. N., Hinss, M. F., Darmet, L., et al. (2022) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2022.838342 · Cited by 41 · see citing works
“As such, passive BCIs (pBCIs) have been used for many applications, including the estimation of users’ mental workload (Roy and Frey, 2016), in order to adapt education material to students’ cognitive resources (Yuksel et al., 2016) or to prevent airplane pilots from being overloaded (Singh et al.”
MethodCho, Y. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445577 · Cited by 45 · see citing works
“Hence, many recent works in HCI have turned to machine learning for a better mapping of physiological signatures to psychological states [9,47,63,80].”
BackgroundNeurophysiological Measurements in Higher Education: A Systematic Literature Review
Darvishi, A., Khosravi, H., Sadiq, S. W., & Weber, B. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00256-0 · Cited by 38 · see citing works
“In addition, studies that have used terms such as cognitive states (*Hubbard et al. 2017; *Pham and Wang 2015), cognitive activity (*Qu et al. 2018a; *Zlokazov et al. 2017), and cognitive workload (*Harrison et al. 2014; *Mannaru et al. 2016; *Yuksel et al. 2016) are grouped in this category.”
BackgroundCho, Y. (2021) · UCL Discovery (University College London) · Cited by 34 · see citing works
Benerradi, J., Maior, H. A., Marinescu, A., Clos, J., & Wilson, M. L. (2019) · Halfway to the Future Symposium · doi:10.1145/3363384.3363392 · Cited by 36 · see citing works
DigituSync: A Dual-User Passive Exoskeleton Glove That Adaptively Shares Hand Gestures
Nishida, J., Tanaka, Y., Nith, R., & Lopes, P. (2022) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3526113.3545630 · Cited by 33 · see citing works
“Adaptive training is a training paradigm in which the task or the feedback is varied as a function of how well the learner performs [48], leading to better performance as well as more motivation [2, 23, 32, 42, 80, 96, 111].”
BackgroundMcCrary, J. M., & ALTENMÜLLER, E. O. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.727231 · Cited by 31 · see citing works
“…seated deep breathing; chronotropic incompetence (i.e., inability to reach 85% of maximum heart rate during exercise); and post-exercise heart rate recovery (Tsuji et al., 1994; Lauer et al., 1996; Curtis and O’Keefe, 2002; Cooney et al., 2010; Zhang et al., 2016; Gulgun, 2017; Qiu et al., 2017).”
BackgroundLane, H. C., & D’Mello, S. K. (2018) · Educational communications and technology: issues and innovations · doi:10.1007/978-3-030-02631-8_5 · Cited by 47 · see citing works
Putze, F., Putze, S., Sagehorn, M., Micek, C., & Solovey, E. (2022) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3490554 · Cited by 28 · see citing works
Decoding Mental Workload in Virtual Environments: A fNIRS Study using an Immersive n-back Task
Putze, F., Herff, C., Tremmel, C., Schultz, T., & Krusienski, D. J. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8856386 · Cited by 29 · see citing works
“[5] demonstrated”
Wang, B., Yang, M. Y., & Grossman, T. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445162 · Cited by 27 · see citing works
“However, existing music learning systems often run into limitations where the curriculum is predesigned, thus limiting the amount of teaching resources available [14, 44, 67].”
BackgroundKarolus, J., Schuff, H., Kosch, T., Woźniak, P. W., & Schmidt, A. (2018) · Designing Interactive Systems Conference · doi:10.1145/3196709.3196803 · Cited by 27 · see citing works
“The questionnaires contained questions (7-point Likert scale) based on the work of Yuksel et al. [14] and their piano tutoring system.”
MethodHow Presenters Perceive and React to Audience Flow Prediction In-situ
Sun, W., Li, Y., Tian, F., Fan, X., & Wang, H. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359264 · Cited by 28 · see citing works
“Based on the theory of Flow and previous research about emotion recognition [7, 22, 38, 42], we implement the learning state sensing component accordingly so that it can predict the learner’s state.”
BackgroundMethodGoing beyond performance scores
Sridhar, P. K., Chan, S., & Nanayakkara, S. C. (2018) · ACM International Conference on Interaction Design and Children (IDC) · doi:10.1145/3202185.3202739 · Cited by 26 · see citing works
“This loses its purpose of capturing the dynamic and fluctuating nature of load that is imposed during learning [59].”
BackgroundWangwiwattana, C., Ding, X., & Larson, E. C. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3161164 · Cited by 26 · see citing works
“For instance, Beste et al. [54] developed a piano learning tool called BACh that could automatically adjust musical score sheet difficulty levels based on the cognitive load of a user.”
BackgroundMethodA Cyber-Physical-Human System for One-to-Many UAS Operations: Cognitive Load Analysis
Planke, L. J., Lim, Y., Gardi, A., et al. (2020) · Sensors · doi:10.3390/s20195467 · Cited by 26 · see citing works
“Another study has contrarily modulated task load based on a mental underload case, where the difficulty presented to a pianist increased when an fNIR sensor detected that the presented material became too easy for the participant [18].”
BackgroundNeurophysiological Evaluation of Haptic Feedback for Myoelectric Prostheses
Thomas, N., Ung, G., Ayaz, H., & Brown, J. D. (2021) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2021.3066856 · Cited by 24 · see citing works
“This resulted in higher accuracy and speed over the nonadaptive control condition [60].”
BackgroundAyaz, H., & Dehais, F. (2021) · Wiley eBooks · doi:10.1002/9781119636113.ch31 · Cited by 25 · see citing works
Brain-Computer Interface for Generating Personally Attractive Images
Spapé, M., Davis, K. M., Kangassalo, L., Ravaja, N., Sovijärvi-Spapé, Z., & Ruotsalo, T. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3059043 · Cited by 23 · see citing works
“of a learning task to avoid overwhelming the user’s cognitive capacities [75], or transforming a player’s character based on brain signals [72].”
BackgroundEEG Correlates of Difficulty Levels in Dynamical Transitions of Simulated Flying and Mapping Tasks
Jao, P.-K., Chavarriaga, R., Dell’Agnola, F., et al. (2020) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2020.3038339 · Cited by 23 · see citing works
Online Multimodal Inference of Mental Workload for Cognitive Human Machine Systems
Planke, L. J., Gardi, A., Sabatini, R., Kistan, T., & Ezer, N. (2021) · Computers · doi:10.3390/computers10060081 · Cited by 20 · see citing works
“Such measurements have been extensively researched for detecting physiological and behavioral responses associated with MWL, including the use of sensors, such as Electroencephalogram (EEG) [12], Functional Near Infrared Spectroscopy (fNIR) [13], eye activity tracking [14], Electrocardiogram (ECG) [15], Galvanic Skin…”
MethodNeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences
Baradari, D., Kosmyna, N., Petrov, O., Kaplun, R., & Maes, P. (2025) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3719160.3736623 · Cited by 24 · see citing works
Cortes, C., Lin, C., Do, T., et al. (2023) · IEEE Conference on Virtual Reality and 3D User Interfaces (VR) · doi:10.1109/vr55154.2023.00025 · Cited by 20 · see citing works
A Survey of Augmented Piano Prototypes: Has Augmentation Improved Learning Experiences?
Deja, J. A., Mayer, S., Pucihar, K. Č., & Kljun, M. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3567719 · Cited by 20 · see citing works
“In the next section, we provide recommendations for future development, implementations, and research of the augmented piano prototypes.”
BackgroundYang, J., Barragan, J. A., Farrow, J. M., Sundaram, C. P., Wachs, J. P., & Yu, D. (2022) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208221129940 · Cited by 21 · see citing works
“For example, pausing certain subtasks or adjusting the current task ’ s dif fi culty (Yuksel et al., 2016) is not feasible and may be deleterious to patient safety.”
BackgroundTraining 21st-century workers: Facts, fiction and memory illusions
Abadzi, H. (2016) · International Review of Education · doi:10.1007/s11159-016-9565-6 · Cited by 20 · see citing works
Heinze, R. A., Vanzella, P., Morais, G. A. Z., & Sato, J. R. (2019) · Cognitive Processing · doi:10.1007/s10339-019-00925-y · Cited by 17 · see citing works
“2015) and to optimize learning of piano skills (Yuksel et al. 2016).”
BackgroundZhao, M., Qiu, D., & Zeng, Y. (2023) · Journal of Engineering Design · doi:10.1080/09544828.2023.2249216 · Cited by 17 · see citing works
“…observed in many fields including industry, education andmedicine (Hinds, Roberts, and Jones 2004; Malone 2018; Vesin, Mangaroska, and Giannakos 2018; Whittaker 2005; Yuksel et al. 2016), which could lead to various consequences due to the mismatch between workload and the limited time resource.”
BackgroundArtificial Intelligence in Science and Society: The Vision of USERN
Dorigo, T., Brown, G. D., Casonato, C., Cerdà, A., Ciarrochi, J., Lio, M. D., D’Souza, N., Gauger, N. R., Hayes, S. C., Hofmann, S. G., et al. (2025) · IEEE Access · doi:10.1109/access.2025.3529357 · Cited by 19 · see citing works
Davanzo, N., & Avanzini, F. (2020) · IEEE Access · doi:10.1109/access.2020.3019978 · Cited by 16 · see citing works
“for intelligent music tutoring systems [100] and automatic accompaniment [101].”
BackgroundfNIRS-based adaptive visuomotor task improves sensorimotor cortical activation
Zheng, Y., Tian, B., Zhuang, Z., Zhang, Y., & Wang, D. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac823f · Cited by 12 · see citing works
“Previous studies have demonstrated the feasibility of fNIRS as a platform for the development of NF systems to increase neural activity associated with motor imagery [47–50] or cognitive functions [51–54], as well as dynamic difficulty adjustment systems for mental arithmetic training [55] or musical learning [56].”
Passive Bci-Based Neuroadaptive Systems
Krol, L. R., & Zander, T. O. (2020) · TUGraz OPEN Library (Graz University of Technology) · doi:10.3217/978-3-85125-533-1-46 · Cited by 17 · see citing works
“[32] demonstrated closedloop adaptation of educational material.”
Brain-Computer Interfaces for Artistic Expression
Nijholt, A., Jacob, R. J. K., Andujar, M., et al. (2018) · doi:10.1145/3170427.3170618 · Cited by 14 · see citing works
“Adaptive musical interfaces based on a user’s brain state have also been introduced [12,13].”
BackgroundSelf-citationWho is in Control? Understanding User Agency in AR-assisted Construction Assembly
Yang, X., Sasikumar, P., Amtsberg, F., Menges, A., Sedlmair, M., & Nanayakkara, S. C. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713765 · Cited by 14 · see citing works
Schöni, L., Carles, V., Strohmeier, M., Mayer, P. B., & Zimmermann, V. (2024) · European Symposium on Usable Security (EuroUSEC) · doi:10.1145/3688459.3688460 · Cited by 13 · see citing works
Revisiting embodiment for brain–computer interfaces
Serim, B., Spapé, M., & Jacucci, G. (2023) · Human-Computer Interaction · doi:10.1080/07370024.2023.2170801 · Cited by 14 · see citing works
“An example is the use of specific frequencies of the EEG spectrum to infer the “mental workload” of users and make various system adaptations in the background (Afergan et al., 2014; Yuksel et al., 2016).”
Lee, K., Yang, D. S., Singh, K., & Nishida, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713821 · Cited by 14 · see citing works
Multimodal Coordination Measures to Understand Users and Tasks
Chen, S., & Epps, J. (2020) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3412365 · Cited by 12 · see citing works
“2011 ], adaptive learning interfaces [Yuksel et al. 2016 ], or multimodal-multisensor interfaces [Dumas et al. 2009 ].”
BackgroundMethodBosworth, A., Russell, M. J., & Jacob, R. J. K. (2019) · Photonics · doi:10.3390/photonics6030090 · Cited by 12 · see citing works
“…the data collection process; our lab has approached this task by using di ff erent methods, including low pass and third-degree polynomial filtering [14], time-domain band-pass filtering [11], and most recently, by using a near source-detector pair LMS adaptive filtration method [9] from the method…”
MethodSelf-citationTowards Commoditised Near Infrared Spectroscopy
Klakegg, S., Gonçalves, J., van Berkel, N., Luo, C., Hosio, S. J., & Kostakos, V. (2017) · Designing Interactive Systems Conference · doi:10.1145/3064663.3064738 · Cited by 13 · see citing works
“[54] develop a system that automatically adjusts musical learning tasks based on the user’s cognitive workload measured by fNIRS.”
BackgroundTretow-Fish, T. A. B., & Khalid, M. S. (2023) · The Electronic Journal of e-Learning · doi:10.34190/ejel.21.5.3088 · Cited by 13 · see citing works
“Yuksel et al. (2016) presented a study on an adaptive brain-computer system (BACh).”
BackgroundMethodInfluentialTaming fNIRS-based BCI Input for Better Calibration and Broader Use
Wang, L., Huang, Z., Zhou, Z., McKeon, D., Blaney, G. P., Hughes, M. C., & Jacob, R. J. K. (2021) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3472749.3474743 · Cited by 13 · see citing works
“for design of this work are found in such previous experimental fNIRS-based systems as: Brainput for robot automation [56], air traffic control [2], music learning [67], and bubble cursor usage [3].”
BackgroundMethodSelf-citationStrauch, C., Barthelmäs, M., Altgassen, E., & Huckauf, A. (2020) · ACM Symposium on Eye Tracking Research and Applications (ETRA) · doi:10.1145/3379157.3388934 · Cited by 12 · see citing works
“Participants rated the employed system as favourable and were satisfied with the timing of level changes [Yuksel et al. 2016].”
MethodBrainCoDe: Electroencephalography-based Comprehension Detection during Reading and Listening
Schneegass, C., Kosch, T., Baumann, A., Rusu, M., Hassib, M. E., & Hußmann, H. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376707 · Cited by 11 · see citing works
Cognitive load associations when utilizing auditory display within image-guided neurosurgery
Plazak, J., DiGiovanni, D. A., Collins, D. L., et al. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-01970-w · Cited by 11 · see citing works
Sridhar, P., Chan, S. W. T., Chua, Y., Quin, Y. W., & Nanayakkara, S. (2019) · Int. J. Child Comput. Interact. · doi:10.1016/j.ijcci.2019.04.002 · Cited by 10 · see citing works
Towards Attentive Speed Reading on Small Screen Wearable Devices
Guo, W., & Wang, J. (2018) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3242969.3243009 · Cited by 11 · see citing works
“Inspired by Yuksel et al [59], we plan to adjust the displaying speed of SmartRSVP in a binary manner from low speed to high speed; Fourth, it will be interesting to invent a mixed-initiative approach for the fine-grained control of display speed, where both users and the intelligent interface can change or confirm th…”
BackgroundMethodInfluentialDecoding Cognitive Load: Eye-Tracking Insights into Working Memory and Visual Attention
Jin, X., Bai, Y., Xu, L., Ma, S., Shi, D., Yu, L., & Fan, M. (2025) · ACM Symposium on Eye Tracking Research and Applications (ETRA) · doi:10.1145/3715669.3725864 · Cited by 10 · see citing works
“1994], EEG [Gevins and Smith 2003], and fNIRS [Yuksel et al. 2016] provide more precise measurements, they are expensive and impractical for widespread use in educational settings.”
NeuroCHI: Are We Prepared for the Integration of the Brain with Computing?
Tanaka, Y., Vujic, A., Maes, P., Jacob, R. J. K., Blanke, O., Nakagome, S., & Lopes, P. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3643973 · Cited by 9 · see citing works
“Applications have spanned attention management [12, 16], meditation techniques [2, 47], tailored learning experiences [48], interactive gaming [5, 9], assistive technologies [8] and the emerging field of emotion modulation [43].”
BackgroundMethodInfluentialSelf-citationJoie: a Joy-based Brain-Computer Interface (BCI)
Vujic, A., Nisal, S., & Maes, P. (2023) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3586183.3606761 · Cited by 10 · see citing works
“Other BCI in HCI works discussed in this paper used passive and not active protocols to help their users achieve a goal state, such as Prinzel et al. [43], Learn Piano with BACh [61], and BrainPut [49].”
BackgroundMethodInfluentialLiu, X., & Zhang, Y. (2024) · British Journal of Educational Psychology · doi:10.1111/bjep.12729 · Cited by 9 · see citing works
An Electroencephalography Study on Cognitive Load in Visual and Textual Programming
Thorgeirsson, S., Zhang, C., Weidmann, T. B., Weidmann, K.-H., & Su, Z. (2024) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3632620.3671124 · Cited by 8 · see citing works
Adaptive Visualisations Using Spatiotemporal and Heuristic Models to Support Piano Learning
Deja, J. A. (2021) · doi:10.1145/3450613.3459656 · Cited by 8 · see citing works
“[33] introduced an adaptive learning interface that adjusted itself based on the current brain state (EEG signal) of the learner.”
BackgroundLet’s Frets! Mastering Guitar Playing with Capacitive Sensing and Visual Guidance
Marky, K., Weiß, A., Müller, F., et al. (2021) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451536 · Cited by 8 · see citing works
“Further support can be provided through in-situ assistance with mixed reality [8, 10], or the actuation of the current playing style through implicit gestures and physiological measures [2, 3, 14].”
BackgroundEEG-Based Online Regulation of Difficulty in Simulated Flying
Jao, P.-K., Chavarriaga, R., & Millán, J. D. R. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3059688 · Cited by 9 · see citing works
“, although not using EEG signals, combined both fNIRS and task performance to improve learning experience of piano [18].”
BackgroundSchütz, L., Dehghani, S., Sommersperger, M., Faridpooya, K., & Navab, N. (2025) · Scientific Reports · doi:10.1038/s41598-025-07670-7 · Cited by 8 · see citing works
Grimmer, J., Simon, L. S., & Ehlers, J. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451662 · Cited by 7 · see citing works
“We apply a dual task scenario as this technique has been proven successful to determine mental workload in the recent past [6].”
BackgroundMethodNeurofeedback for Personalized Adaptive Training
Mark, J., Thomas, N., Kraft, A. E., Casebeer, W. D., Ziegler, M. D., & Ayaz, H. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-60642-2_8 · Cited by 7 · see citing works
Guo, W., & Wang, J. (2017) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3027063.3053176 · Cited by 6 · see citing works
“In addition to the current binary speed adaptation mechanism commonly used in today’s adaptive interfaces [23], we are also interested in exploring the feasibility of continuous speed adaption via physiological feedback.”
MethodHarborth, D., & Pape, S. (2020) · Future Internet · doi:10.3390/fi12120220 · Cited by 6 · see citing works
Yuksel, B. F., Oleson, K. B., Chang, R., & Jacob, R. J. K. (2019) · Springer series on cultural computing · doi:10.1007/978-3-319-92069-6_11 · Cited by 6 · see citing works
Self-citationHow Stress Affects Functional Near-Infrared Spectroscopy (fNIRS) Measurements of Mental Workload
Alsuraykh, N. H., Maior, H. A., Wilson, M. L., et al. (2018) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3170427.3188646 · Cited by 6 · see citing works
“Recent work has shown fNIRS to be more tolerant of natural conditions than other brain sensors [10, 16], and has been used to evaluate usability [8], and even musical performance [19].”
BackgroundHuman-centered Behavioral and Physiological Security
Alt, F., Hassib, M., & Distler, V. (2023) · New Security Paradigms Workshop · doi:10.1145/3633500.3633504 · Cited by 6 · see citing works
“While neuroimaging techniques had remained for decades confined to lab settings, portable and light-weight EEG/fNIRS sensors are becoming more available and enable their usage in more realistic setups (e.g., in workplaces [41], in lectures [42], while playing piano [94], and while driving [40]).”
BackgroundPiano Learning and Improvisation through Adaptive Visualisation and Digital Augmentation
Deja, J. A. (2022) · doi:10.1145/3532104.3571464 · Cited by 5 · see citing works
“[39] developed an adaptive learning interface that changed according to the learner’s EEG signal, which measures their current brain state.”
BackgroundYe, X., & Long, J. (2022) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2022.3182910 · Cited by 5 · see citing works
“The BCI with feedback is provided that can significantly improve performance in task [9], [10].”
MethodEncouraging Improvisation in Piano LearningUsing Adaptive Visualisations and Spatiotemporal Models
Deja, J. A. (2021) · doi:10.1145/3447527.3474865 · Cited by 5 · see citing works
“[33] introduced an adaptive learning interface that adjusted itself based on the current brain state (EEG signal) of the learner.”
BackgroundMark, J., Onaral, B., & Ayaz, H. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39955-3_22 · Cited by 5 · see citing works
“Moreover, if brain measures are integrated into the task design’s automated difficulty adjustment algorithms alongside the current smart analytics, they can improve the efficiency [31] and recovery speed as well as patients’ compliance [32].”
BackgroundSridhar, P. K., & Nanayakkara, S. C. (2020) · Education Sciences · doi:10.3390/educsci10070177 · Cited by 4 · see citing works
“This loses its purpose of capturing the dynamic and fluctuating nature of the load that is imposed during learning [35].”
BackgroundLow-Cost Real-Time Mental Load Adaptation for Augmented Reality Instructions - A Feasibility Study
Wolf, D. A., Wagner, T., & Rukzio, E. (2019) · doi:10.1109/ismar-adjunct.2019.00015 · Cited by 4 · see citing works
“Previous research on assessing a user’s cognitive or mental load relied mostly on expensive medical-grade sensors such as EEGs [2] and functional near-infrared spectroscopy (fNIRS) [1, 11], limiting not only the accessibility to the community and but also the potential application areas such as mobile scenarios.”
BackgroundIt's a Match - Enhancing the Fit between Users and Phishing Training through Personalisation
Schöni, L., Roch, N., Sievers, H., Strohmeier, M., Mayer, P. B., & Zimmermann, V. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713845 · Cited by 6 · see citing works
HuBar: A Visual Analytics Tool to Explore Human Behavior Based on fNIRS in AR Guidance Systems
Castelo, S., Rulff, J., Solunke, P., et al. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3456388 · Cited by 3 · see citing works
“Similarly, Yuksel et al. demonstrate how adjusting task difficulty based on cognitive load readings and behavioral measurements can significantly improve learning efficiency, as seen in their adaptive piano training program [43].”
BackgroundPredictions of task using neural modeling
Fox, E. L., Ugolini, M. H., & Houpt, J. W. (2022) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2022.1007673 · Cited by 3 · see citing works
A Neurophysiological Sensor Suite for Real-Time Prediction of Pilot Workload in Operational Settings
Grant, T., Dhruv, K., Eloy, L., Hayne, L., Durkee, K. T., & Hirshfield, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60128-7_5 · Cited by 3 · see citing works
Cognitive state monitoring for neuroadaptive information visualization
Idesis, S., Kassinopoulos, M., Barreda-Ángeles, M., Leiva, L. A., & Arapakis, I. (2026) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2026.1793651 · Cited by 2 · see citing works
“…in video games (Bontchev, 2016) and museum tours (Abdelrahman et al., 2015), facilitate learning in educational environments (Gerjets et al., 2014; Yuksel et al., 2016), and improve the behavior of robotic agents (Kim et al., 2017; Visualization patterns help calibrating information exposure.”
Eye Tracking-Based Adaptive User Interfaces in Virtual Reality Eye Surgery Training
Funk, M.-P., Navab, N., & Schütz, L. (2025) · Mensch und Computer (MuC) · doi:10.1145/3743049.3748551 · Cited by 2 · see citing works
EyePiano: Leveraging Gaze For Reflective Piano Learning
Karolus, J., Sylupp, J., Schmidt, A., & Woźniak, P. W. (2023) · Designing Interactive Systems Conference · doi:10.1145/3563657.3596065 · Cited by 2 · see citing works
“While there already exists a number of systems supporting different kind of instruments, such as the guitar [26, 34], the violin [25], and the piano [46, 62], we envision our work not as a strict tutoring”
BackgroundInfluential
- Authors
- Yuksel, B. F., Oleson, K. B., Harrison, L., Peck, E. M., Afergan, D., Chang, R., & Jacob, R. J.
- Affiliations
- Tufts University (US)
- Worcester Polytechnic Institute (US)
- Bucknell University (US)
- Venue
- Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2016-05-07
- Pages
- 5372-5384
- OpenAlex
- W2402939184
- Cited by
- 137
- References
- 56
- Citations by year
- 2026: 14 · 2025: 10 · 2024: 10 · 2023: 12 · 2022: 10 · 2021: 21 · 2020: 16 · 2019: 21 · 2018: 14 · 2017: 6 · 2016: 3
- FWCI
- 7.90
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Funders
- National Science Foundation (IIS-1218170 IIS-1065154)
- Google Inc.
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroscience, Education and Cognitive Function
- Keywords
- piano learning, adaptive learning, brain-computer interface, mental workload, functional near-infrared spectroscopy, music learning, user feedback
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