Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process
- University College Dublin, Ireland
- Trinity College Dublin, Ireland
Abstract
Decisions about noisy stimuli are widely understood to be made by accumulating evidence up to a decision bound that can be adjusted according to task demands. However, relatively little is known about how such mechanisms operate in continuous monitoring contexts requiring intermittent target detection. Here, we examined neural decision processes underlying detection of 1-second coherence-targets within continuous random dot motion, and how they are adjusted across contexts with Weak, Strong, or randomly Mixed Weak/Strong targets. Our prediction was that decision bounds would be set lower when Weak targets are more prevalent. Behavioural hit and false alarm rate patterns were consistent with this, and were well-captured by a bound-adjustable leaky accumulator model. However, Beta-band EEG signatures of motor preparation contradicted this, instead indicating lower bounds in the Strong-target context. We thus tested two alternative models in which decision bound dynamics were constrained directly by Beta measurements, respectively featuring leaky accumulation with adjustable leak, and non-leaky accumulation of evidence referenced to an adjustable sensory-level criterion. We found that the latter model best explained both behaviour and neural dynamics, highlighting novel means of decision policy regulation and the value of neurally-informed modelling.
Data availability
Code to recreated the Random Dot Motion task utilising Psychtoolbox is publicly available at https://github.com/AnnaCGeuzebroek/Context-Dependent-Detection. All code to recreated the behavioural and EEG data analysis as well as the modelling code can be found at https://github.com/AnnaCGeuzebroek/Continuous-Behavioural-Modelling. Pre-processed anonymised EEG and behavioural data is uploaded at OSFhttps://osf.io/yjvku/?view_only=7ed5aee5d09a4d5ca13de1ba169b0588
Article and author information
Author details
Anna Catharina Geuzebroek
Redmond G O'Connell
Funding
Science Fundation Ireland (15/CDA/3591)
- Anna Catharina Geuzebroek
- Simon P Kelly
Wellcome Trust (219572/Z/19/Z)
- Anna Catharina Geuzebroek
- Simon P Kelly
Horizon 2020 European Research Council Consolidator Grant Ind (865474)
- Redmond G O'Connell
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: All participants gave written consent prior to their participation and were compensated for their time with €25. The UCD Human Research Ethics Committee for Life Sciences approved all experimental procedures in accordance with the Declaration of Helsinki (LS-16-76-Craddock).
Copyright
© 2023, Geuzebroek et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Balancing true and false detection of intermittent sensory targets by adjusting the inputs to the evidence accumulation process
eLife 12:e83025.
https://doi.org/10.7554/eLife.83025
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Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAMs are jointly fitted to trial-level RTs and raw (pixel-space) visual stimuli from individual subjects in a unified Bayesian framework. Models fitted to large-scale cognitive training data from a stylized flanker task captured individual differences in congruency effects, RTs, and accuracy. We find evidence that the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations, demonstrating how our framework can be used to relate visual representations to behavioral outputs. Together, our work provides a probabilistic framework for both constraining neural network models of vision with behavioral data and studying how the visual system extracts representations that guide decisions.
