Markkula, G. orcid.org/0000-0003-0244-1582, Uludağ, Z., Wilkie, R. orcid.org/0000-0003-4299-7171 et al. (1 more author) (2020) Accumulation of continuously time-varying sensory evidence constrains neural and behavioral responses in human collision threat detection. [Preprint - PsyArXiv]
Abstract
Evidence accumulation models provide a dominant account of human decision-making, and have been particularly successful at explaining behavioral and neural data in laboratory paradigms using abstract, stationary stimuli. It has been proposed, but with limited in-depth investigation so far, that similar decision-making mechanisms are involved in tasks of a more embodied nature, such as movement and locomotion, by directly accumulating externally measurable sensory quantities of which the precise, typically continuously time-varying, magnitudes are important for successful behavior. Here, we leverage collision threat detection as a task which is ecologically relevant in this sense, but which can also be rigorously observed and modeled in a laboratory setting. Conventionally, it is assumed that humans are limited in this task by a perceptual threshold on the optical expansion rate — the visual looming — of the obstacle. Using concurrent recordings of EEG and behavioral responses, we disprove this conventional assumption, and instead provide strong evidence that humans detect collision threats by accumulating the continuously time-varying visual looming signal. Generalizing existing accumulator model assumptions from stationary to time-varying sensory evidence, we show that our model accounts for previously unexplained empirical observations and full distributions of detection response. We replicate a preresponse centroparietal positivity (CPP) in scalp potentials, which has previously been found to correlate with accumulated decision evidence. In contrast with these existing findings, we show that our model is capable of predicting the onset of the CPP signature rather than its buildup, suggesting that neural evidence accumulation is implemented differently, possibly in distinct brain regions, in collision detection compared to previously studied paradigms.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Keywords: | computational model; drift diffusion; looming threshold; sensory threshold; visual looming |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Psychology (Leeds) The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/S005056/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Jan 2025 15:46 |
Last Modified: | 30 Jan 2025 15:46 |
Identification Number: | 10.31234/osf.io/ca3h9 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:222631 |