Cognitive heuristics in multitasking performance

A cognitive approach towards the understanding of human multitasking is presented in this contribution. Four dual-task studies provide evidence for the development and application of cognitive strategies which we refer to as “heuristics”: these fast and frugal rules of thumb help to adaptively use the information and structure of the environment to successfully perform two tasks concurrently. This work aims to show that heuristics not only arise unconsciously, but also change the selective visual attention by allowing different behavioral mechanisms (i.e., perception and action) to be performed in parallel. Interestingly, using the cognitive heuristics leads to an increase in performance. We used a realistic scenario in a driving simulator (study I and study II) and a systematically controlled and cognitive enriched tracking task in the laboratory (study III and study IV). Throughout all four studies, the results support our assumptions and encourage us to extend our research focus to other domains in the field of human machine interaction. Multitasking: A never-ending story In the 1920s, early approaches in the context of Gestalt Psychology investigated task interruption and dual task situations (Ovsiankina, 1928; Telford, 1931). Half a century ago, Cherry (1953) referred to “human’ s natural ability to multitask”. While in the last two decades, main interest was on the psychological refractory period (PRP-studies, see Pashler, 1994; Meyer & Kieras, 1997a, 1997b) or the central bottleneck – debate (Schumacher et al., 2001; Hazeltine et al., 2002; Anderson et al., 2005), the presented work is interested in the management of multiple goals in a real-life context. From a cognitive modeling perspective, multitasking can be considered as “the ability to integrate, interleave, and perform multiple tasks and/or component subtasks of a larger complex task” (Salvucci, 2005). Lee & Taatgen (2002) understand multitasking as “the ability to handle the demands of multiple tasks simultaneously”. Pew & Mavor (1998) simply refer to multitasking as “doing several things at once”. Our approach does not aspire a precise definition about what multitasking is. We rather intend to reveal the cognitive processing when doing several task at the same time. A taxonomic classification of dual task studies Before we introduce the applied dual task scenario, it is necessary to classify dual-task-situations. For real-life scenarios, Salvucci (2005) proposes four possible models that are worth introducing. Models of discrete successive tasks The first category specifies situations where we switch from one task to another. In empirical investigations, this is often applied in alternating trials of simple choice-reaction tasks. Main interest thus is paid in respect of temporal costs for switching (Rogers & Monsell, 1995). Models of discrete concurrent tasks PRP-studies (Pashler, 2000) belong to the second category. Offset by a short delay, a second task starts before the first one ends. This delay leads to a short overlapping of the two applied tasks. Models of elementary continuous tasks The third category allows one continuous task (most experimental scenarios use tracking as main task). Short discrete tasks occasionally appear and thus initiate task concurrency. Models of compound continuous tasks If two or more simultaneous tasks appear, we talk about models of compound continuous tasks: each of the both tasks is an “ongoing continuous process”. In our eyes, this last category implies a high degree on ecological validity and reflects human behavior in typical real-life-situations. Especially in the context of human machine interaction, we often have to deal with the concurrency of two (and sometimes even more) tasks. We therefore consider the presented four empirical investigations as models of compound continuous tasks. Figure 1: Multiple resource model (Wickens, 2002) The boundedness of human cognitive resource In contrast to assumptions about one single resource to be responsible for the management of human resources (Kahneman, 1992), the reference frame for our studies is the four-dimensional multiple resource model proposed by Wickens (2002, 2004): processing stages (perception, cognition, responding), perceptual modalities (auditory, visual), visual channels (focal, ambient), and processing codes (spatial, verbal) rely on different physiological mechanism (Fig. 1). Following this model, two tasks do not influence each other and hence can be executed in parallel if they require different resources. Figure 2: In-car-scenario (Driving simulator) Empirical framework, Part A: Study I & II In four experiments with scenarios of compound continuous tasks, we analyze the behaviour in dynamical task environments. Main task in all studies was driving: in a simple tracking task in a driving simulator, students of the TU Berlin (aged 20 to 40) were instructed to keep the lane (Fig. 2). Study one: Identification of cognitive strategies 24 participants joined the first study. Driving was instructed as priority task, secondary task was a modification of the D2 test of attention by Brickenkamp (2001). We refer to this implemented in-car-version as D2-Drive (Urbas et al., 2005): goal of this test is to verify whether a specific pattern contains the letter d and additionally two strokes (yesresponse) or whether not (no-response). Fig. 3 illustrates the three versions: subjects performed either the pattern in the middle (version A), or the complete line (version B and C). Please note that version C also contains a memory task: the number of the line to be performed on the next screen had to be memorized. The D2-Drive test measures the individual amount of visual attention, it can be interrupted and easily be learned. This makes it a perfect candidate to identify performance strategies under multitasking. Figure 3: D2-Drive test (Urbas et al., 2005) The sequence of the complete study was as follows: after a training period (for driving as well as for the D2-Drive test), both tasks were presented in single task and dual task condition. Task complexity in the secondary task (Fig. 3) was treated as within-subject variable. Dependent variables were driving performance (lane derivation) and D2-Driveperformance (number of correct patterns and performance time). Eye-tracking data was recorded in all possible conditions. After each test episode, workload was measured using the NASA TLX (Hart & Staveland, 1988). The study ended with a structured interview in which subjects were asked about general task processing, perceived task complexity and applied cognitive strategies. Core results of the first study After a short training, subjects performed the D2-Drive test error-free. Under dual task condition, driving performance did not decrease. It is worth mentioning that performance in D2-Drive did increase slightly under multitasking from trial to trial. In particular, this is true for version B: here, the task configuration promotes the merging of several patterns. This approach is confirmed by the structured interviews and justifies a deeper analysis of the strategic processing. Cognitive strategies under multitasking Starting with the D2-Drive test under single task condition, subjects performed pattern by pattern. This stepwise approach goes in line with people doing a task for the first time, as described by Taatgen (2005). Under dual task condition (and for some people, this is even true at the end of the single task condition), subjects intuitively understand that part of the secondary task (more precisely, the respondaction) does not require focal visual attention, but only manual action (Fig. 4). Manual and visual activities are decoupled: when entering the sequence of responses (from 2 up to 4 patterns), the visual channel is free. Consequently, during this time subjects are able to afford a control view at the street. This finding goes in line with the theoretical assumptions derived by the Wickens’ model of multiple resources (Wickens, 2002). Figure 4: Development of a cognitive strategy Please note that subjects do not necessarily have conscious access to the described cognitive strategies, as indicated by the structured interviews. For this reason, we refer to them as “cognitive heuristics”: heuristics are replicable methods often discovered in the field of complex problem solving or learning and provide a high efficiency in performance. In this article, the words “strategy” and “heuristic” are treated synonymously, even though the term “heuristic” captures a broader sense. To verify the use of cognitive heuristics such as the one example represented in Fig. 4, we did a second study in the driving simulator. Study two: The influence of experience Goal of the second study was to confirm the results of the first study. Despite the rather weak increase in performance, we expected a more pronounced effect if subjects are more experienced in a concrete multitasking situation. This is derived from studies reported by Taatgen (2005) as well as Salvucci & Taatgen (submitted). Consequently the dual task conditions were temporarily doubled, each subject attended two driving laps. Figure 5: Performance in the D2-Drive test in study II Performance increase as a result of experience As expected, performance increased under multitasking for both version A (slightly significant, p < .05) and version B (high significant, p < .01). Interestingly, even in version C, there is no decrease in performance (Fig. 5). Although observational data (eye tracking) as well as interviews support the proposed cognitive heuristics, we decided to formalize our hypotheses and thus make it revisable. To do so, we used the method of cognitive modeling. The benefit of Cognitive Modeling: Psychological research in general starts with a theory, for instance on human multitasking (see Salvucci, 2006). The derived hypotheses (assumptions) subsequently are tested (empirical investigation): r