Efficient Multitasking through Sequence Learning
in collaboration with Fang Zhao and Robert Gaschler from FernUniversität in Hagen
contact: Kenneth zur Kammer
Introduction
This thesis will deal with the question of whether multitasking can be improved through
sequential learning. Although the topic of multitasking usually means a simultaneous implementation
of two or more tasks (Lee & Taatgen, 2002), many researchers see it as a rapid switch between these
actions (e.g. Oswald, Hambrick & Jones, 2007). Switching attention between actions comes with costs
of change, indicated through a higher number of mistakes and also a higher reaction time in stimulusreaction experiments (e.g. Monsell, 2003; Wylie & Apport, 2000). One of the most important
paradigms dealing with interferences in dual tasks is the psychological refractory period (Telford,
1931). Based on this paradigm, plenty of approaches attempt to explain the role of attention in switch
costs. These theories can be roughly categorized into bottleneck models and capacity sharing models
(Pashler, 1994). Pashler (1994) gives an overview about the differences between bottleneck and
capacity sharing models. Former assume that two parallel implemented tasks require the same single
processing mechanism, the result being that one or both tasks will be impaired. The latter postulate
limited attentional resources that have to be shared when more than one action has to be done.
In daily routine however, people usually have to coordinate ruled sequences of actions such as
playing an instrument, driving a car, or tying shoes (Gaschler, 2017; Schumacher & Schwarb, 2009).
Miller (1955) assumes that humans organize incoming stimuli to chunks to bypass the bottleneck.
What underlies this ability is a question of a longstanding debate in whích French, Addyman and
Meraschal (2011) contribute a mechanism called implicit chunk recognition (ICR). They describe ICR
a process based on the recognition of encountered subsequences of patterns. The ability to extract
sequences from patterns to chunks occurs often as an implicit process. It is not completely certain
what implicit learning actually includes, however most researchers agree on three criteria: the
incidental quality of the learning situation; that it is an unconscious process; that it is hardly or not at
all possible to verbalize the acquired knowledge (e.g. Cleeremans, Destrebecqz & Boyer, 1998;
Jiménez & Méndez, 1999). The most common method of measuring implicit sequence learning is the
serial reaction time task (SRT) developed by Nissen and Bullemer (1987). In their initial experiment,
they were able to show that when subjects were given fixed sequences (of which, they had not been
told) their reaction time decreased over time while the accuracy improved. However, under dual task
conditions, there were indications that multitasking impairs implicit learning (Schumacher and
Schwarb, 2009). They were able to show that sequence learning proceeds normally, when responseselection processes are performed serially.
Hypotheses
H1a: A practised sequence can decrease the reaction time compared to random sequences.
H1b: A practised sequence can decrease the error rate compared to random sequences.
H2a: Sequence learning can reduce multitasking costs in reaction time.
H2b: Sequence learning can reduce multitasking costs in error rates.
H3: Segmenting subsequences can support sequence learning reducing multitasking costs in reaction
time compared to non segmented subsequences.
Methods
Procedure and task
The experiment is enabled through a computer program. The subjects will sit in front of a
computer screen. The program is composed of seven blocks with 144 transitions each. In block one,
two and three, a single task will be implemented. This task is a serial reaction time task for the right
hand. With the arrow key, it has to react to the position of an X (up, down, left, right) at the screen.
The succession of the X-positions is a fixed sequence of eight elements, within which each of the four
positions will appear twice. Essentially, the point of the three blocks is to learn the sequence. For half
of the subjects (randomly allocated) the whole eight-element-long sequence will be repeated. For the
other half of the subjects, the sequence will be split into two parts so that sometimes the first part will
appear first, followed by the second part, and sometimes the first or second part will appear twice
consecutively.
In block four, five and six an additional task for the left hand will be added. Subjects will have
to react with the keys [1] or [2] to the “1” or “2” which appear on the screen (seen figure 1). These
blocks test whether sequence learning improves multitasking. Here, the eight element long sequence
will appear almost the entire time. Only in short parts of each of these blocks, a different sequence will
appear to test whether the reaction time will be shorter when the practised sequence appears.
In the seventh block, a single task will be implemented again. It indicates whether sequence
knowledge exists when no multitasking is to be implemented. In this block, both the practised
sequences and also two different sequences appear in the same amount. This allows detection of
whether the practised sequence will cause a decreased reaction time when no multitasking task is
supposed to be completed. The sequences that are randomly appropriated are the following ones:
3
4
1
2
1
3
2
4
1
4
2
3
4
3
1
2
3
2
1
4
1
3
4
2
Figure 1. Illustration of the task performance from Zhao, Gaschler, Nöhrling, Cremer, Röttger and Haider
(2016). The right hand reacts to the position of an X with the arrow key. The left hand reacts with the keys [1] or
[2] to the “1” or “2” appearing on the screen.
The subjects will be recruited by the author. The elicitation will take place in the branches of
the University of Hagen, in the laboratory of the University of Hagen or at the subjects home. The
implementation will need about 30 minutes per subject.
Planned Sample
The planned minimum of the sample size is 16 subjects. The age of the subjects will range
between 18 and 60 years. The subjects will also be asked whether they are male or female and whether
they are right or left handed.
According to the statistical power analysis G*Power (Faul, Erdfelder, Lang, & Buchner, 2007)
a power of 80% for the within interaction is achieved with a sample size of 36 subjects, a power of
90% is achieved with a sample size of 48 subjects and a power of 95% is achieved with a sample size
of 56 subjects.
The data collection will be stopped after three weeks. Only one person is required to select the
planned sample size. No money will be offered.
Existing data
No existing data
Variables
Manipulated variable
- independent variables: task condition (Multitasking: yes/no)
sequences (random/ fixed)
segmentation (yes/no)
blocks
Measured variables
- demographics: age, gender, handedness
- dependent variables: reaction time; error rate
Indices
The mean number of mistakes and the reaction time will be calculated for each person.
Design Plan
Study type: Experiment - randomly assigned assessments to study subjects
Blinding: The setting of the sequences will not be mentioned to the subjects, but they will be asked
afterwards if they recognized any sequences.
Study design: This study is realized in a 2 (segmentation yes vs. no) x 2 (single task vs. dual task) x 2
(sequences vs. random) x blocks mixed design. The hypotheses 1 and 2 can be realized in a withinsubject design. The hypotheses 3 will be realized in a between-subject design.
Randomization: This study is randomized over the repeated within-subjects design.
Materials
For this experiment, an experimental program is presented, which is contained in a data file sent by the
University of Hagen. It will be performed by using a computer with a computer screen and a computer
keyboard.
Analysis Plan
Statistical models: An analysis of variance (ANOVA) for multiple factors will be used.
Transformations: Data will be inspected for a normal distribution.
Interference criteria: p-values (p < .05) will be used.
Follow-up analysis: If the interaction effect will be significant, an one factorial ANOVA will be used.
Data exclusion: Data that reaches a value that differs in two standard deviations of the mean will be
excluded.
Missing data: Missing data will be excluded.
References
Cleeremans, A., Destrebecqz, A. & Boyer, M. (1998). Implicit learning: News from the front. Trends
in Cognitive Science, 2(10), 406-416.
Gaschler, R. (2017). Implizites Lernen. In M. A. Wirtz (Hrsg.), Dorsch – Lexikon der Psychologie.
Abgerufen am 29.01.2017, von https://portal.hogrefe.com/dorsch/implizites-lernen/
Faul, F., Erdfelder, E., Lang, A.- G., & Buchner, A. (2007). G*Power 3: A flexible statistical power
analysis program for the social, behavioral, and biomedical sciences. Behavior Research
Methods, 39, 175-191.
French, R. M., Addyman, C. & Mareschal, D. (2011). TRACX: A recognition-based connectionist
framework for sequence segmentation and chunk extraction. Psychological Review, 118(4),
614-636.
Jiménez, L & Méndez, C. (1999). Which attention is needed for implicit sequence learning? Journal of
Experimental Psychology: Learning, memory, and Cognition, 25(1), 236-259.
Lee, F. J. & Taatgen, N. A. (2002). Multi-tasking as Skill Acquisition. Proceedings of the twentyfourth annual conference of the cognitive science society (S. 572-577). Mahwah, NJ: Erlbaum.
Miller, G. A. (1955). The magical number seven, plus or minus two: some limits on our capacity for
processing information. Psychological Review, 101(2), 343-352.
Monsell, S. (2003). Task switching. Trends in Cognitive Science, 7(3), 134-140.
Nissen, M. J., & Bullemer, P. (1987). Attentional requirements of learning: Evidence from
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and predicting multitasking performance. In D. H. Jonassen (Hrsg.), Learning
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Pashler, H. (1994). Dual-task interference in simple tasks: Data and theory. Psychological Bulletin,
116(2), 220-244.
Schumacher, E. H. & Schwarb, H. (2009). Parallel response selection disrupts sequence learning under
dual-task conditions. Journal of Experimental Psychology, 138(2), 270-290.
Telford, C. W. (1931). The refractory phase of voluntary and associative responses. Journal of
Experimental Psychology, 14(1), 1-36.
Wylie, G. & Allport, A. (2000). Task switching and the measurement of “switch costs”. Psychological
Research, 63, 212–233.
Zhao, F., Gaschler, R., Nöhrling, O., Cremer, N., Röttger, E. & Haider, H. (2016). Can sequence
knowledge influence the cross-task congruency effect? Poster session presented at SPSS
Workshop and Meeting, Aachen.