Summary
Pinpointing the mechanistic origins of inter-individual differences in decision making is a central goal of modern psychology and a considerable challenge because even elementary perceptual choices rely on a multitude of sensory, cognitive, motivational and motoric processes. For this reason, researchers have relied heavily on a set of mathematical ‘sequential sampling’ models that are designed to parse the latent psychological processes driving variations in choice behaviour. Although these models have been fruitfully employed in thousands of theoretical and neurophysiological investigations, they suffer from several limitations that particularly undermine their utility in inter-individual or -group comparisons including: A) parameter values are estimated on a relative, within-subject scale; B) the models come in many forms that can make identical behavioural predictions despite invoking fundamentally different mechanisms (‘model mimicry’); and C) they deal in abstract psychological constructs that are themselves dependent on multiple neural processes. The objective of this proposal is to address each of these issues by pioneering a ground-breaking decision modelling framework in which models are constructed and evaluated based on their ability to explain key observable aspects of the neural implementation of the human decision process in addition to its behavioural output. This ambitious goal is made possible by recent advances in non-invasive electrophysiology which enable direct observation, measurement and manipulation of the decision process as it unfolds in the human brain. Across a series of empirical investigations that will use adult aging as a test-bed for studying inter-individual and -group differences, this research will yield new methods for directly comparing model parameter values across subjects, resolve prominent theoretical debates regarding decision making algorithms and gain important new insights into their susceptibility to cognitive aging.
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| Web resources: | https://cordis.europa.eu/project/id/865474 |
| Start date: | 01-01-2021 |
| End date: | 31-12-2025 |
| Total budget - Public funding: | 1 996 310,00 Euro - 1 996 310,00 Euro |
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Original description
Pinpointing the mechanistic origins of inter-individual differences in decision making is a central goal of modern psychology and a considerable challenge because even elementary perceptual choices rely on a multitude of sensory, cognitive, motivational and motoric processes. For this reason, researchers have relied heavily on a set of mathematical ‘sequential sampling’ models that are designed to parse the latent psychological processes driving variations in choice behaviour. Although these models have been fruitfully employed in thousands of theoretical and neurophysiological investigations, they suffer from several limitations that particularly undermine their utility in inter-individual or -group comparisons including: A) parameter values are estimated on a relative, within-subject scale; B) the models come in many forms that can make identical behavioural predictions despite invoking fundamentally different mechanisms (‘model mimicry’); and C) they deal in abstract psychological constructs that are themselves dependent on multiple neural processes. The objective of this proposal is to address each of these issues by pioneering a ground-breaking decision modelling framework in which models are constructed and evaluated based on their ability to explain key observable aspects of the neural implementation of the human decision process in addition to its behavioural output. This ambitious goal is made possible by recent advances in non-invasive electrophysiology which enable direct observation, measurement and manipulation of the decision process as it unfolds in the human brain. Across a series of empirical investigations that will use adult aging as a test-bed for studying inter-individual and -group differences, this research will yield new methods for directly comparing model parameter values across subjects, resolve prominent theoretical debates regarding decision making algorithms and gain important new insights into their susceptibility to cognitive aging.Status
SIGNEDCall topic
ERC-2019-COGUpdate Date
27-04-2024
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