Assessing cross-modal interference in the detection response task☆
Section snippets
Background
Multi-tasking has a deleterious effect on performance due to people’s limited capacity for processing information (Kahneman, 1973). Tasks that require more resources interfere to a greater extent than others, while automated tasks require no resources, and therefore have no impact on other tasks (Wickens, 2002). However, a unitary model of processing capacity where all tasks draw on a single pool of resources cannot explain findings relating to multi-modal multi-tasking—situations in which
Participants
Fifty undergraduates from the University of Newcastle (F 25, M 25) participated in the study. Mean age of participants was 22.4 years (SD 5.6 years). Participants were remunerated with course credit. The study was approved by the University of Newcastle Human Research Ethics Committee.
Design
The task had two independent variables: primary task load, which was manipulated by changing the number of targets to be tracked and had three levels (High load — two targets to track simultaneously, LowLEFT
Results
Seven participants were excluded due to technical malfunction, low DRT accuracy (¡33% overall hit rate), and non-responses to primary tracking task trials.
Discussion
The DRT produced results in line with those expected from previous studies, with faster responses under conditions of low load than high load. The result of faster responses to high-salience stimuli compared to low-salience stimuli was also predicted. The primary finding from mean DRT RT was that participants who were presented an auditory signal responded faster than those who were presented a visual signal. This could be a result of the auditory pathway generally producing faster RTs than the
Conclusion
Cognitive models were applied to both a continuous primary tracking task and a software-based DRT to assess the differential impact of an auditory and visual DRT stimulus on user workload. The analysis of primary tracking task performance utilised SFT in a novel application, subjecting continuous tracking data and transformed pseudo-RT distributions to capacity analysis. Both measures agreed in their finding of limited capacity. A shifted-Wald model was applied to DRT data, which identified a
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This research was supported by an Australian Research Council grant DP160102360 to AE and JTT, and by an Australian Government Research Training Program (RTP) Scholarship awarded to Alexander Thorpe. We thank Mario Fific and an anonymous reviewer for their helpful feedback on earlier versions of this manuscript. We thank Bryan Paton for his invaluable contribution.