Mean response times: fingerprints of dual-task interference.
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(A) Sketch of the PRP paradigm. Stimulus S1 is mapped to R1, and stimulus S2 to response R2. RT1 is defined as the time between S1 onset and the response R1. RT2 is defined as the time between the onset of S2 and the response R2. The SOA - defined as the time between onsets of S1 and S2 - is systematically varied, typically between 0 and 1000 ms. (B) Scheme of the mathematical formalism traditionally used to explain the delay in RT2 during the PRP. The vertical axis labels RT. The column on the left indicates the first task, and each colored box within the column represents a different stage of processing: Perceptual component (red), Central component (grey), and Motor component (blue). The series of columns on the right indicate the processing time for task 2 at different SOA, labeled on the x-axis. For each column, the three different boxes represent the three different stages of task 2: Perceptual component (green), Central component (grey), and Motor component (brown). As SOA progresses, the Perceptual component starts later. All components can be performed in parallel except for the Central component, which establishes a bottleneck. (C) Effect of SOA manipulations in response times for the proposed neural architecture. Average response times to the second task show a dependency on SOA similar to observations from PRP experiments: RT2 decreased with SOA within the interference range with a slope of −1, and is constant in the non-interference regime. RT1 is unaffected by SOA manipulations. In most PRP studies, response times are measured from the onset of the corresponding stimulus (T1 or T2). Other studies have used a different convention in which response times to both tasks are reported from trial onset (i.e., onset of T1). Here we show the PRP effect under both conventions, by defining the variable R2 = RT2 + SOA. The PRP effect is observed as an invariance of R2 with SOA for short SOA values, and a linear increase of R2 with SOA for large SOA values. Data points show averages across 300 trials. Error bars depict the standard error of the mean. (D–G) Effect of task complexity and SOA in response times. Each panel (containing two plots) defines the manipulation type (perceptual or central) and the affected task. Human data (taken from ) is shown to the left in each panel. To maintain the convention adopted in the experimental study , response times are shown relative to the onset of the first task. In each plot both easy (without manipulation, solid line) and difficult (with manipulation, dashed line) conditions are shown. RT1 is shown in grey, and R2 is shown in black. (D,F) We first varied the response complexity of the stimulus, changing the layer of the sensory hierarchy which feeds the integrator (Perceptual). This effect resulted in an increase of (RT1) when this factor affected the first task (F) which propagated to the second task (increase in RT2) within the interference regime. When the factor affected the second task (D) we observed no change in the first task, and a change in RT2 only outside of the interference regime, indicating that this manipulation can be absorbed during the PRP. This is exactly what is expected in the classic PRP model from a ‘pre-bottleneck’ manipulation . (E,G) We also varied the stimulus ambiguity (i.e. the relative input currents to each of the two competing sensory populations) (Central). When the ambiguity of the first task was increased (G), we observed an increase of (RT1) which propagated to the second task (increase in RT2) within the interference regime. When the factor affected the second task (E) we observed an effect on RT2 both inside and outside the interference regime. This is exactly what is expected in the classic PRP model from a ‘bottleneck’ manipulation . Data points show averages across 200 trials, except the baseline data (easy condition) that were averaged across 300 trials.