Prediction and Decision-Making Lab
Welcome to the Prediction and Decision-Making Lab
Welcome to the Prediction and Decision-Making Laboratory at the University of Melbourne, led by Dr. Daniel Feuerriegel as Principal Investigator.
The lab investigates how we use our prior knowledge about the world to guide our perception and decision-making. We are constantly learning associations between different events in our environment and storing these associations in memory. For example, we have learned that, if we hear a dog barking, we are likely to see a dog soon afterward. This allows us to form predictions about what we will see or hear in the immediate future.
We use psychophysical experiments in combination with recordings of neural data to investigate what occurs when these predictions are fulfilled, and what happens when they are violated by surprising events. We apply computational modelling and machine learning techniques to behavioural and electroencephalographic (EEG) data to understand how predictive processes shape how decisions unfold in the brain.
We also investigate a broad range of topics, ranging from basic decision-making processes to early childhood neurodevelopment. Take a look at our projects page to see what we are currently working on!
Lab News
Update (17-08-2021): Daniel Feuerriegel has been awarded a DECRA to continue his research on decision-making in the wake of surprising events. Watch this space for 3 more years of exciting, ARC-funded research from our lab!
- Dr Daniel Feuerriegel
Principal Investigator
Dr Daniel Feuerriegel
My research is focused on how our decisions are influenced by our history of prior choices and experiences. I use a variety of neuroimaging techniques, such as electroencephalography (EEG), in combination with computational modelling and machine learning approaches, to investigate how decisions are formed in the brain.
I completed my PhD at the University of South Australia (UniSA) under the supervision of Assoc. Prof. Hannah Keage. During my PhD I investigated how learned expectations shape activity in the visual system, and how these processes interact with other phenomena such as neural adaptation. I also visited the laboratory of Prof. Bruno Rossion at UC Louvain in Belgium to learn the Fast Periodic Visual Stimulation technique.
I commenced my postdoctoral research in the Decision Neuroscience Lab under the supervision of Assoc. Prof. Stefan Bode. Since then my research has focused on how decisons unfold over time, and how we can rapidly change our minds or adjust our decision-making strategies in response to events in our environment. I was awarded a DECRA by the ARC in 2021 to investigate how our expectations about future events influence our decision-making.
- Jie Sun
PhD Student, Co-Supervised with Dr. Adam Osth
- Claudia Locatelli
Honours Student, Co-Supervised with Assoc. Prof. Stefan Bode
Collaborators
- A/Prof Stefan Bode – Decision Neuroscience Lab, University of Melbourne
- Prof Robert Hester – Cognitive Neuroimaging Lab, University of Melbourne
- A/Prof Katherine Johnson – Attention Dynamics Lab, University of Melbourne
- Dr. Hinze Hogendoorn – Timing in Brain and Behaviour Lab, University of Melbourne
- Dr. Adam Osth – Computational Memory Lab, University of Melbourne
- Prof Philip Smith – Vision and Attention Lab, University of Melbourne
- Dr. Simon Lilburn – Category Lab, Vanderbilt University
- Dr. Luke Smillie - Personality Processes Lab, University of Melbourne
- A/Prof Hannah Keage – CAIN Lab, University of South Australia
- Dr. Ashleigh Smith – ARENA, University of South Australia
- A/Prof Sant-Rayn Pasricha – Walter + Eliza Hall Institute
- Dr. Genevieve Quek – Donders Institute, Radboud University
- Prof Gyula Kovacs - University of Jena
- Prof Rufin Vogels - KU Leuven
Lab Alumni
Honours / PhD Students
Vinay Mepani (Honours / 2020)
Jie Sun (Honours / 2020)
Research Assistants / Interns
Jane Yook (RA / 2020)
Predictive processes in the visual system and beyond
Our brains are adept at learning from repeating patterns in our environment. We can easily learn associations between successive events, such as hearing a dog bark and the appearance of a dog. Our expectations are also guided by other types of knowledge. For example, we know that our visual world is stable over time – if we stare at our coffee cup, we don’t expect it to suddenly change into something completely different. We use a variety of neuroimaging techniques, including EEG and fMRI, to investigate how the brain forms and evaluates predictions about visual events. We probe what happens when these predictions are fulfilled, and what happens when they are violated by an unexpected event. We are also interested in how predictive processes relate to other phenomena such as neural adaptation and attention.
Joint modelling of behavioural and neural data to track unfolding decision processes
Making even a simple decision recruits extensive networks that are distributed throughout the brain. For example, when judging whether this text is black or white, information is transmitted through the retina, through the visual system and to a vast network of parietal, frontal and motor areas in the brain. We use electroencephalography (EEG) in combination with computational modelling of behaviour to understand how decisions unfold in the brain, and how we adjust our decision-making strategies following surprising or mentally effortful events.
How our predictions shape our conscious experience
We tend to see what we expect to see. When there are strong expectations to see a certain image, people have a tendency to report seeing that image, even in cases where it didn’t appear. We are investigating how the brain biases our conscious perception toward the predicted and away from the unexpected.
Best practices for performing multivariate pattern analysis (MVPA) on EEG data
The practice of 'decoding' perceptual and cognitive variables based on distributed patterns of neural activity has become wildly popular in cognitive neuroscience. We are systematically investigating key features of these multivariate pattern analysis techniques in order to help ourselves and others to design more optimal experiments and analysis approaches. We are also co-developing a freely-available toolbox for performing these analyses on EEG data with Assoc. Prof. Stefan Bode (see our Code + Data page).
Nutrition and early childhood neurocognitive development
Micronutrients, especially iron, play a critical role in the developing infant brain. However, many infants around the world do not get adequate access to iron or other micronutrients during critical periods of development. We are investigating how iron and micronutrient deficiency affects the developing brain as assessed using behavioural and neural measures of cognition.
If you are interested in contributing to any of these projects, or have another idea you would like to discuss, please get in touch!
We gratefully acknowledge funding from the following sources:
Publications by members of the Prediction and Decision-Making Lab since the founding of the lab in 2020.
Preprints
Ko, Y. H., Feuerriegel, D. C., Turner, W., Overhoff, H., Niessen, E., Stahl, J., ... & Bode, S. (2021). Divergent effects of absolute evidence magnitude on decision accuracy and confidence in perceptual judgements. bioRxiv. (link)
Bode, S., Feuerriegel, D.C., Schubert, E., & Hogendoorn, H. (2021). Decoding continuous variables from EEG data using linear support vector regression (SVR) analysis with the Decision Decoding Toolbox (DDTBOX). bioRxiv. (link)
Turner, W., Feuerriegel, D., Hester, R., & Bode, S. (2020). An initial 'snapshot' of sensory information biases the likelihood and speed of subsequent changes of mind. bioRxiv. (link)
2021
Overhoff, H., Ko, Y. H., Feurriegel, D. C., Fink, G. R., Stahl, J., Weiss, P.H., Bode, S., & Niessen, E. (2021). Neural correlates of metacognition across the adult lifespan. Neurobiology of Aging. (link)
Andrejević, M., Smillie, L., Feuerriegel, D., Turner, W., Laham, S., & Bode, S. (2021). How do basic personality traits map onto moral judgements of fairness-related actions? Accepted at Social Psychological and Personality Science. (link to preprint)
Feuerriegel, D., Vogels, R., & Kovács, G. (2021). Evaluating the evidence for expectation suppression in the visual system. Neuroscience & Biobehavioral Reviews, 126, 368-381. (link)
Feuerriegel, D., Jiwa, M., Turner, W. F., Andrejević, M., Hester, R., & Bode, S. (2021). Tracking dynamic adjustments to decision making and performance monitoring processes in conflict tasks. NeuroImage, 118265. (link)
Feuerriegel, D., Blom, T., & Hogendoorn, H. (2021). Predictive activation of sensory representations as a source of evidence in perceptual decision-making. Cortex, 136, 140-146. (link)
Turner, W., Feuerriegel, D., Andrejević, M., Hester, R., & Bode, S. (2021). Perceptual change-of-mind decisions are sensitive to absolute evidence magnitude. Cognitive Psychology, 124, 101358. (link)
Turner, W., Angdias, R., Feuerriegel, D., Chong, T. T. J., Hester, R., & Bode, S. (2021). Perceptual decision confidence is sensitive to forgone physical effort expenditure. Cognition, 207, 104525. (link)
Feuerriegel, D., Yook, J., Quek, G. L., Hogendoorn, H., & Bode, S. (2021). Visual mismatch responses index surprise signalling but not expectation suppression. Cortex, 134, 16-29. (link)
2020
Andrejević, M., Feuerriegel, D., Turner, W., Laham, S., & Bode, S. (2020). Moral judgements of fairness-related actions are flexibly updated to account for contextual information. Scientific Reports, 10(1), 1-17. (link)
Rostalski, S. M., Amado, C., Kovács, G., & Feuerriegel, D. (2020). Measures of repetition suppression in the Fusiform Face Area are inflated by co-occurring effects of statistically learned visual associations. Cortex, 131, 123-136. (link)
Jach, H. K., Feuerriegel, D., & Smillie, L. D. (2020). Decoding personality trait measures from resting EEG: An exploratory report. Cortex, 130, 158-171. (link)
Schubert, E., Agathos, J. A., Brydevall, M., Feuerriegel, D., Koval, P., Morawetz, C., & Bode, S. (2020). Neural patterns during anticipation predict emotion regulation success for reappraisal. Cognitive, Affective, & Behavioral Neuroscience, 20(4), 888-900. (link)
Keage, H. A., Feuerriegel, D., Greaves, D., Tregoweth, E., Coussens, S., & Smith, A. E. (2020). Increasing objective cardiometabolic burden associated with attenuations in the P3b event-related potential component in older adults. Frontiers in Neurology, 11, 643. (link)
Blom, T., Feuerriegel, D., Johnson, P., Bode, S., & Hogendoorn, H. (2020). Predictions drive neural representations of visual events ahead of incoming sensory information. Proceedings of the National Academy of Sciences of the U.S.A., 117(13), 7510-7515. (link)
ABC News article on mask wearing and risk perception
A journalist from the ABC caught up with Daniel to discuss how people use different sources of information to evaluate their level of COVID risk. (link)
Article in Pursuit about predicting personality based on patterns of neural activity.
Can the activity in your brain tell us something about your personality? We explain the findings of a study designed to probe that question, in collaboration with Dr. Luke Smillie and Hayley Jach from the Personality Processes Lab. (link)
Dr. Daniel Feuerriegel
Redmond Barry Building, Room 807
The University of Melbourne
Parkville, VIC 3010, Australia
Email: dfeuerriegel@unimelb.edu.au
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Twitter: @danfeuerriegel