Why are dual-task costs reduced with ideomotor (IM) compatible tasks (see e.g. Greenwald & Shulman, 1973)? In a series of experiments, I tested the way in which task structure affects dual-task performance (Halvorson et al., 2012). The results suggest that in some cases, typical dual-task costs arise from task structure rather than response limitations. Further examination of this question has shown that dual-task costs cannot be predicted solely on the basis of the relationship between the stimuli and the responses; the relationship between the tasks, or the task pairing, plays a critical role in whether the tasks overlap and performance is impaired. A series of experiments using novel task pairings showed that when one task uses a spatial central code and the other uses a verbal central code, dual-task costs are eliminated.

Multi-robot systems have the potential to improve performance through parallelism. Unfortunately, interference often diminishes those returns. Starting from the earliest multi-robot research, a variety of arbitration mechanisms have been proposed to maximize speed-up. Vaughan and his collaborators demonstrated the effectiveness of an arbitration mechanism inspired by biological signalling where the level of aggression displayed by each agent effectively prioritizes the limited resources. But most often these arbitration mechanisms did not do any principled consideration of environmental constraints or task structure, signalling cost and precision of the outcome. These factors have been taken into consideration in this research and a taxonomy of the arbitration mechanisms have been presented. The taxonomy organizes prior techniques and newly introduced novel techniques. The latter include theoretical and practical mechanisms (from minimalist to especially efficient). Practicable mechanisms were evaluated on physical robots for which both data and models are presented. The arbitration mechanisms described span a whole gamut from implicit (in case of robotics, entirely without representation) to deliberately coordinated (via an established Biological model, reformulated from a Bayesian perspective). Another significant result of this thesis is a systematic characterization of system performance across parameters that describe the task structure: patterns of interference are related to a set of strings that can be expressed exactly. This analysis of the domain has the important (and rare) property of completeness, i.e., all possible abstract variations of the task are understood. This research presents efficiency results showing that a characterization for any given instance can be obtained in sub-linear time. It has been shown, by construction, that: (1) Even an ideal arbitration mechanism can perform arbitrarily poorly; (2) Agents may manipulate task-structure for individual and collective good; (3) Task variations affect the influence that initial conditions have on long-term behaviour; (4) The most complex interference dynamics possible for the scenario is a limit cycle behaviour.

Organizations today face complex problems requiring individuals to work in groups to develop insightful solutions efficiently through coordination, sharing, and integration of distributed knowledge. However, very little research has investigated group problem solving, specifically in terms of incentives and problem structure. This research uses laboratory experiments to investigate the effects of individual versus group goal conflict on collaborative behaviour and performance in group problem solving process. The experiments study 4-person problem solving groups, in which the group solution emerges through coordination and information sharing. The design of the experiment is a 3 by 3 design with two factors, incentive and task structure. Experiments manipulated the relative weights of individual and group rewards using three ratios (0:100, 50:50, 100:0). Three experimental tasks differing in structure were used to investigate the incentive conflict effect on different kind of problems; in particular, problems containing detours and requiring restructuring. One-hundred and sixty-four undergraduate students participated in this study. The group problem solving process is viewed as a process towards increased structural balance based on Heider’s balance theory. This method captures both incremental search and cognitive restructuring during the problem solving process. Results report the effects of group versus individual goal conflict on group performance and behaviour. Results show that incentive influenced group performance and behaviour by affecting strategies groups used to approach the problem. Individual incentive encouraged the group to focus on the solution state while group incentives encouraged random exploration, and this difference is most significant under the complex problem structure. Results also show that task structure influenced group performance and behaviour by varying the amount of incremental search and restructuring required to solve the problem. Individual incentive weakened difference on performances among three problem structures, while group incentive amplified differences on performance and behaviour among three problem structures.

Organizations today face complex problems requiring individuals to work in groups to develop insightful solutions efficiently through coordination, sharing, and integration of distributed knowledge. However, very little research has investigated group problem solving, specifically in terms of incentives and problem structure. This research uses laboratory experiments to investigate the effects of individual versus group goal conflict on collaborative behaviour and performance in group problem solving process. The experiments study 4-person problem solving groups, in which the group solution emerges through coordination and information sharing. The design of the experiment is a 3 by 3 design with two factors, incentive and task structure. Experiments manipulated the relative weights of individual and group rewards using three ratios (0:100, 50:50, 100:0). Three experimental tasks differing in structure were used to investigate the incentive conflict effect on different kind of problems; in particular, problems containing detours and requiring restructuring. One-hundred and sixty-four undergraduate students participated in this study. The group problem solving process is viewed as a process towards increased structural balance based on Heider’s balance theory. This method captures both incremental search and cognitive restructuring during the problem solving process. Results report the effects of group versus individual goal conflict on group performance and behaviour. Results show that incentive influenced group performance and behaviour by affecting strategies groups used to approach the problem. Individual incentive encouraged the group to focus on the solution state while group incentives encouraged random exploration, and this difference is most significant under the complex problem structure. Results also show that task structure influenced group performance and behaviour by varying the amount of incremental search and restructuring required to solve the problem. Individual incentive weakened difference on performances among three problem structures, while group incentive amplified differences on performance and behaviour among three problem structures.

A fundamental question of human existence is how much control we have on our behaviour. This dissertation aims to add to our understanding of cognitive control by characterising how a particular failure of performance, Goal Neglect (GN), is affected by different forms of complexity manipulations. In Chapter 2, I develop a new task to test GN and unlike previous studies, I manipulate complexity qualitatively by altering the instructional cues - the cues instructing the participant to shift to a different rule set. GN was sensitive to this kind of complexity manipulation and this is linked to a failure in recognizing the significance of the instructional cues. In Chapter 3, I propose a new entropy-like measure to quantify the temporal clustering of GN and use this to test the differential temporal patterns that are predicted by two theoretical models of GN. The results suggest that both models are likely to be operant, but with their relative dominance being different across time: GN early on in the task appears to be mostly driven by failures which are “task model” like, whilst GN which manifests later on is better aligned with the “monitoring” account. Chapter 2 also revealed that GN can be sensitive to manipulations of complexity during task performance, which motivated the question of whether previously published studies suggesting the contrary, were perhaps due to insufficient complexity. Hence, in Chapter 4, using the new GN task, I investigate this further. Overall, the results were mixed and indicated that complexity does not appear to affect GN unless the complexity manipulation is more closely associated to the critical event. Throughout this dissertation, I refer to models and empirical evidence from the Prospective Memory (PM) literature given the apparent similarity between PM and GN experimental paradigms. In Chapter 5, I take this further and investigate how PM failures and GN are different, if at all, with the broader aim to integrate what are otherwise isolated domains. I found a mixture of null findings which suggest that it is not entirely clear if GN and PMf reflect different capacities. Nonetheless, while investigating the differences between GN and PMf, a much more interesting question emerged with respect to what structural features of a task predict different signatures of GN-like and PMf-like errors. The key finding to this theory-neutral approach was a general rule about task structure: a combination of extended practice and low frequency of critical events predict both a larger amount of errors and with more of these occurring late in the task. Overall, this research has shed further light on task conditions that may result in different error signatures and that may reflect different cognitive resources.

This research is based on the premises that teams can be designed to optimize its performance, and appropriate team coordination is a significant factor to team outcome performance. Contingency theory argues that the effectiveness of a team depends on the right fit of the team design factors to the particular job at hand. Therefore, organizations need computational tools capable of predict the performance of different configurations of teams. This research created an agent-based model of teams called the Team Coordination Model (TCM). The TCM estimates the coordination load and performance of a team, based on its composition, coordination mechanisms, and job’s structural characteristics. The TCM can be used to determine the team’s design characteristics that most likely lead the team to achieve optimal performance. The TCM is implemented as an agent-based discrete-event simulation application built using JAVA and Cybele Pro agent architecture. The model implements the effect of individual team design factors on team processes, but the resulting performance emerges from the behavior of the agents. These team member agents use decision making, and explicit and implicit mechanisms to coordinate the job. The model validation included the comparison of the TCM’s results with statistics from a real team and with the results predicted by the team performance literature. An illustrative 26-1 fractional factorial experimental design demonstrates the application of the simulation model to the design of a team. The results from the ANOVA analysis have been used to recommend the combination of levels of the experimental factors that optimize the completion time for a team that runs sailboats races. This research main contribution to the team modeling literature is a model capable of simulating teams working on complex job environments. The TCM implements a stochastic job structure model capable of capturing some of the complexity not capture by current models. In a stochastic job structure, the tasks required to complete the job change during the team execution of the job. This research proposed three new types of dependencies between tasks required to model a job as a stochastic structure. These dependencies are conditional sequential, single-conditional sequential, and the merge dependencies.

In der Arbeit werden mit der Bedienungstheorie eine analytische und mit der Simulationstechnik eine experimentelle Methode zur Gestaltung von Mehrstellenarbeit in der Teilefertigung behandelt. Der theoretische Kontext wird über die Arbeitspsychologie mit der Handlungsregulationstheorie und den Operativen Abbildsystemen als Erklärungsmodell sowie den verschiedenen Ebenen der psychischen Regulation von Arbeitstätigkeiten hergestellt. Ein Praxisbeispiel zur Planung und Bewertung eines Arbeitsbereiches einer Teilefertigung mit Mehrstellenarbeit hinterlegt das Theoriegerüst.

In der Arbeit werden mit der Bedienungstheorie eine analytische und mit der Simulationstechnik eine experimentelle Methode zur Gestaltung von Mehrstellenarbeit in der Teilefertigung behandelt. Der theoretische Kontext wird über die Arbeitspsychologie mit der Handlungsregulationstheorie und den Operativen Abbildsystemen als Erklärungsmodell sowie den verschiedenen Ebenen der psychischen Regulation von Arbeitstätigkeiten hergestellt. Ein Praxisbeispiel zur Planung und Bewertung eines Arbeitsbereiches einer Teilefertigung mit Mehrstellenarbeit hinterlegt das Theoriegerüst.