Research Projects

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What to do next? Human scheduling of cognitive resources

Information overload is an important psychological problem for two reasons: (a) Search: Relevant information becomes more difficult to find. (b) Processing: Decision making becomes less accurate and less efficient with increasing information. More data is created daily (> 2.5 billion gigabytes) than all the information contained in every published book many times over; this rate is predicted to quintuple by 2025. When timely decisions and actions are necessary, information overload can exacerbate the problem of what information to prioritise for completing tasks or making decisions.

We are interested in how people decide what information to prioritise when making decisions and what subtasks people choose to do next when completing a multi-component task. Optimal policies have been established in operations research for many cases; we are intereseted in how human cognitive and perceptual decision mechanisms and attention perform with respect to these optimal policies.

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Feature Processing in Perceptual Categorization

A hallmark of decision making is that information from multiple sources must be combined to achieve specific goals.Simple decisions are best thought of as the accumulation of information over time from a single source. For complex decisions, accumulation from multiple sources might occur simultaneously, making the decision process surprisingly simple, or sequentially, making the decision process complicated.

This project focuses on revealing the processes and representations that underlie decision making in perceptual categorization by focusing on detailed analyses of the time course of information processing. Our computational approach combines parametric model fitting with non-parametric analyses to strengthen inferences and avoid problems associated with model mimicry. The computational models synthesize the information accumulation approach used to understand simple decisions with mental architecture models of serial and parallel processing, enabling predictions at the level of full RT distributions. By incorporating mental architectures, the models address fundamental questions about whether multiple dimensions are processed sequentially in serial fashion or simultaneously in parallel or pooled into a common processing channel.

Funded by

Australian Research Council Grant DP120103120

Australian Research Council Grant DP160102360

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Understanding Human Information Processing

The goal of this project is to promote advances and applications in Systems Factorial Technology (SFT), a theory-driven methodology aimed at identifying fundamental characteristics of human information processing.  SFT was developed by James T. Townsend and colleagues. The Knowlab has contributed to the development of SFT over the past 10 years most notably with the publcation of Systems Factorial Technology: A Theory Driven Methodology for the Identification of Perceptual and Cognitive Mechanisms.

Further applications include the examination of information processing in speeded cued detection tasks, the extension of methods of workload capacity to deal with distractors and conflicting information, and the extension of SFT to deal with errors.

Funded by

Australian Research Council Grant DP160102360

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Unpacking the moment of insight
(with Dr Margaret Webb and Dr Simon Cropper)

The feeling of insight in problem solving is associated the sudden realization of a solution to a complex problem. This project investigates the phenomenology of insight examining aspects of "aha", surprise, impasse, and accuracy. We find that insight occurs across many different types of problems and examine individual differences in the experience of insight.

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Constellation Perception Across Cultures
(with Prof Charles Kemp, A/Prof Duane Hamacher, and Dr Simon Cropper)

There is a remarkable similarity in shape of constellations observed by different cultures across the world. In this project, we seek to expalin this similarity by modelling the visual mechanisms that give rise to perceptions of constellations.

Funded by

McCoy Grant Seed Funding 2019


Kemp, C., Hamacher, D. W., Little, D. R., & Cropper, S. J. (2022). Perceptual Grouping Explains Similarities in Constellations Across Cultures. Psychological Science, 33(3), 354-363.
Kemp, C., Hamacher, D. W., Little, D. R., & Cropper, S. J. (2022). Comparing constellations across cultures.Nature Astronomy, 6(4), 406-409.