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Professor Andrew Neal
  – Director of Organisational Psychology program

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Professor Andrew Neal
Andrew joined the School in 1995, having obtained his PhD at UNSW. Andrew works at the intersection of organisational psychology and human factors. He leads basic and applied research into human performance and safety that have received more than $10 million in funding from ARC, NICTA, Federal and State Governments, and industry. His research examines the factors that enhance the performance, safety and effectiveness of people at work, and the mechanisms by which people manage competing demands in complex, dynamic environments.
McElwain 118
+61 7 3365 6372
+61 7 3365 4466
Postal Address:
School of Psychology
McElwain Building
The University of Queensland
St Lucia, QLD 4072

Picture of 'Professor Andrew Neal'
Professor Andrew Neal


Picture of 'Professor Andrew Neal'
Professor Andrew Neal
Research Activities:

Research Areas

I work at the intersection of three disciplines: Industrial & Organizational Psychology; Cognitive Psychology; and Human Factors. My research focuses on the motivational and cognitive determinants of performance, safety and effectiveness. My work is carried out in organizational settings and in the laboratory. I am interested in the interaction between people and the systems in which they work. My research is characterized by a concern for the temporal dynamics of complex systems, and the levels of analysis at which phenomena exist. This program is funded by both government and industry.

Current & Recent Projects

A general theory of multiple-goal pursuit

The aim of this project is to develop and test a formal theory that explains the mechanisms by which people make choices amongst competing goals in a dynamic and uncertain environment ("multiple goal pursuit"). People have to manage competing goals in a wide range of settings (e.g., work, education, sport), yet the mechanisms are poorly understood. Our theory integrates formal theories of self-regulation with formal theories of decision making, to provide a more general account of multiple goal pursuit. We test the predictions of the theory in a series of experiments in which people have to pursue two goals simultaneously. The experiments allow us to test competing views, and understand the mechanisms involved. 

Human Performance Modelling for Future Air Traffic Management Systems

There is a pressing need for the development of smart technologies to increase the capacity and efficiency of the national Air Traffic Management (ATM) system. The Australian Strategic ATM Group (ASTRA) predicts that air traffic will grow between 4.5% and 6% per annum in Australia over the next 20 years (ASTRA, 2007). Demand is projected to exceed capacity within a decade. This problem is not unique to Australia. Similar levels of growth are expected in the US and Europe (eg., FAA Air Traffic Organization, 2006). In its 2006 annual report, the US Federal Aviation Administration (FAA) Air Traffic Organization noted that:

 “using our current approach, air traffic controllers will not be able to handle traffic at 25 percent above today’s levels. Traffic may increase this much by 2016. Although our long-term goal is to use the Next Generation Air Transportation System (NextGen), we need interim solutions, as NextGen will not be completed until 2025.” (p32).

Our research group is developing the tools needed to evaluate the impact that technology will have on air traffic controllers.  We are developing methods for simulating the performance of expert air traffic controllers, and predicting the level of workload that they will experience when controlling traffic. These tools will help designers build and evaluate the decision support tools that are needed to support the implementation of these future operational concepts.

Developing and testing a dynamic model of the proximal and distal motivational processes responsible for the regulation of task-directed effort.

Motivation is one of the most widely studied concepts in psychology, yet there is a mismatch between the level of analysis inherent in motivation theory, and the level at which it is studied. Theoretically, motivation is a dynamic process that operates within individuals over time. However, most studies examine variability in motivation between people rather than variability within people. This project is examining how motivation changes over time and why the rate of change varies for different people in different contexts. It aims to improve our understanding of the dynamics of motivational processes.

Development of a Computational Model for the Prediction of Mental Workload inAir Traffic Control.

The aim of the project is to develop a computational model that can measure the flow of traffic through an air sector, and predict the level of workload that an air traffic controller will experience. The purpose is to develop a tool that can be used for sector redesign. The workload model has been derived empirically from data collected in both an operational environment and in high fidelity simulators based at Brisbane Centre. We have used multi-level modelling to identify the factors that produce changes in subjective workload both within and between sectors. Agent-based models have been built to simulate the tasks that the human controller carries out, allowing the workload model to run from flight plans, as well as from historical radar track data. The major focus of this modelling effort is currently on simulating how controllers detect and resolve conflicts.


Representative Publications:

Motivation, Self Regulation & Temporal Dynamics

Yeo, G., Fredericks, E., Kiewitz, K. & Neal, A. (2014). A dynamic, self-regulatory model of affect and performance interactions between states, traits and task demands. Motivation and Emotion, 38(3), 429-443. 

Yeo, G. & Neal, A. (2013). Revisiting the functional properties of self-efficacy: A dynamic perspective. Journal of Management, 39, 1385-1396.

Greche, M. R., Neal, A., Yeo, G., Humphreys. M. S. & Smith, S. (2009). Workload and Fatigue: An Examination of the Relationship within and Across Consecutive Days of Work. Journal of Occupational Health Psychology, 14(3), 231-242.

Yeo, G. & Neal, A. (2008). Subjective cognitive effort: A model of states, traits and time. Journal of Applied Psychology, 93(3), 617-631.

Yeo, G. & Neal, A. (2006). An examination of the dynamic relationship between self-efficacy and performance across levels of analysis and levels of specificity. Journal of Applied Psychology, 91(5), 1088-1101.

Yeo, G. & Neal, A. (2004). A multilevel analysis of effort, practice and performance: Effects of ability, conscientiousness and goal orientation. Journal of Applied Psychology, 89(2), 231-247.

Performance, Safety and Effectiveness at Work

Neal, A., Yeo, G., Koy, A. & Xiao, T. (2012). Predicting the form and direction of work role performance from the Big 5 model of personality traits. Journal of Organizational Behavior, 33(2), 175-192.

Griffin, M. A., Neal, A. & Parker, S. K. (2007). A new model of work role performance: Positive behavior in uncertain and interdependent contexts. Academy of Management Journal, 50(2), 327-347.

Neal, A., Godley, S. T., Kirkpatrick, T., Dewsnap, G., Joung, W. & Hesketh, B. (2006). An examination of learning processes during critical incident training: Implications for the development of adaptable trainees. Journal of Applied Psychology, 91(6), 1276-1291.

Neal, A. & Griffin, M. A. (2006). A study of the lagged relationships among safety climate, safety motivation, safety behavior, and accidents at the individual and group levels. Journal of Applied Psychology, 91(4), 946-953.

Griffin, M. A. & Neal, A. (2000). Perceptions of safety at work: A framework for linking safety climate to safety performance, knowledge, and motivation. Journal of Occupational Health Psychology, 5, 347-358.

Neal, A., Griffin, M. A., & Hart, P. M. (2000). The impact of organizational climate on safety climate and individual behavior. Safety Science, 34, 99-109.

Human Factors & Applied Cognition

Hannah, S. & Neal, A. (2014). On-the-fly scheduling as a manifestation of partial-order planning and dynamic task values. Human Factors, 56(6), 1093 - 1112.

Neal, A., Hannah, S., Sanderson, P. M., Bolland, S., Mooij, M. & Murphy, S. (2014). Development and validation of multilevel model for predicting workload under routine and non-routine conditions in an Air Traffic Management center. Human Factors, 56(2), 287-305.

Vuckovic, A., Kwantes, P. J., Humphreys, M. & Neal, A. (2014) A sequential sampling account of response bias and speed-accuracy tradeoffs in a conflict detection task. Journal of Experimental Psychology: Applied, 20(1), 55-68. 

Vuckovic, A., Kwantes, P. J. & Neal, A. (2013). Adaptive decision making in a dynamic environment: A test of a sequential sampling model of relative judgment. Journal of Experimental Psychology: Applied, 19, 266-284.

Vuckovic, A., Sanderson, P. M., Neal, A., Gaukrodger, S. & Wong, W. (2013). Relative Position Vectors: An alternative approach to conflict detection in Air Traffic Control. Human Factors, 55, 946-964

Loft, S., Bolland, S., Humphreys, M.S. & Neal, A. (2009) A theory and model of conflict detection in air traffic control. Journal of Experimental Psychology: Applied, 15, 106-124.

Neal, A. & Kwantes, P. J. (2009). An evidence accumulation model for conflict detection performance in a simulated air traffic control task. Human Factors, 51(2), 164-180.

Loft, S., Neal, A., Humphreys, M. S. (2007). The development of a general associative learning account of skill acquisition in a conflict detection task. Journal of Experimental Psychology: Human Perception & Performance, 33(4), 938-959.

Loft, S., Sanderson, P. M., Neal, A. & Mooij, M. (2007). A critical review of modelling and predicting mental workload in en-route air traffic control. Human Factors, 49(3), 376-399.

Kwantes, P. J. & Neal, A. (2006). Why people underestimate y when extrapolating in linear functions. Journal of Experimental Psychology: Learning, Memory & Cognition, 32(5), 1019-1030.

Loft, S., Humphreys, M. & Neal, A. (2004). The Influence of Memory for Prior Instances on Performance in a Conflict Detection Task. Journal of Experimental Psychology: Applied, 10 (3), 173-187.

Neal, A. & Hesketh, B. (1997). Episodic knowledge and implicit learning. Psychonomic Bulletin & Review, 4, 24-37.

Neal, A., Hesketh, B. & Andrews, S. (1995). Instance-based categorisation: Intentional versus automatic forms of retrieval. Memory & Cognition, 23, 227-242.


Open source ATC simulator (ATC-Lab Advanced)

Our research group has developed an open source Air Traffic Control simulator for use in experimental studies. The simulator is designed for use in both laboratory and field settings, and can be used by naive participants as well as expert controllers. Studies using this simulator have examined:

  • Factors influencing the performance of expert and novice controllers on conflict detection tasks;
  • Strategies used by expert controllers for resolving conflicts under varying levels of workload;
  • Cognitive and motivational determinants of skill acquisition; and
  • Prospective memory.

A paper describing the development of the simulator is now in press:

Fothergill, S., Loft, S. & Neal, A. (2009). ATC-labAdvanced: An Air Traffic Control Simulator with Realism and Control. Behavior Research Methods, Instruments & Computers, 41(1), 118-127.  

Source code can be downloaded from:

Course Coordinator:
  • Semester 1, 2014
    PSYC4171 - Personnel Assessment
  • Semester 1, 2014
    PSYC7424 - Job & Organisational Design
  • Semester 2, 2009
    PSYC4161 - Personnel Training
  • Semester 2, 2009
    PSYC7424 - Job & Organisational Design

Note: Coordinator roles prior to 2009 and tutor roles prior to 2006 are not included.

Research Area:

If you do your honours year in my lab, you will get to work in a team with other honours students, PhDs and postdocs on one of the following research programs: 

1. Developing and testing a general theory of multiple goal pursuit

The aim of this program is to develop and test a formal theory that explains the mechanisms by which people make choices amongst competing goals in a dynamic and uncertain environment ("multiple goal pursuit"). People have to manage competing goals in a wide range of settings (e.g., work, education, sport), yet the mechanisms are poorly understood. Our theory integrates formal theories of self-regulation with formal theories of decision making, to provide a more general account of multiple goal pursuit. We test the predictions of the theory in a series of experiments in which people have to pursue two goals simultaneously. The experiments allow us to test competing views, and understand the mechanisms involved. 

2. Modelling human decision making in complex environments 

The project aims to extend state-of-the art models of simple choice tasks to decision making with complex stimuli in complex environments. These new models will provide a comprehensive account of behaviour, including the choices that are made, how long it takes to make them, and how choices and choice times vary within and between decision makers. The models will explain how people adapt to changes in task demands when dealing with multiple stimuli or performing multiple tasks concurrently under time pressure. The project will provide the basic research that is needed to extend psychological models of choice to complex ‘real-world’ tasks, such air traffic control and maritime surveillance. 

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