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.
Metrics
-
- 453
- views
-
- 66
- downloads
-
- 1
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
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
Further reading
-
- Neuroscience
Multiplexed error-robust fluorescence in situ hybridization (MERFISH) allows genome-scale imaging of RNAs in individual cells in intact tissues. To date, MERFISH has been applied to image thin-tissue samples of ~10 µm thickness. Here, we present a thick-tissue three-dimensional (3D) MERFISH imaging method, which uses confocal microscopy for optical sectioning, deep learning for increasing imaging speed and quality, as well as sample preparation and imaging protocol optimized for thick samples. We demonstrated 3D MERFISH on mouse brain tissue sections of up to 200 µm thickness with high detection efficiency and accuracy. We anticipate that 3D thick-tissue MERFISH imaging will broaden the scope of questions that can be addressed by spatial genomics.
-
- Neuroscience
Learning alters cortical representations and improves perception. Apical tuft dendrites in cortical layer 1, which are unique in their connectivity and biophysical properties, may be a key site of learning-induced plasticity. We used both two-photon and SCAPE microscopy to longitudinally track tuft-wide calcium spikes in apical dendrites of layer 5 pyramidal neurons in barrel cortex as mice learned a tactile behavior. Mice were trained to discriminate two orthogonal directions of whisker stimulation. Reinforcement learning, but not repeated stimulus exposure, enhanced tuft selectivity for both directions equally, even though only one was associated with reward. Selective tufts emerged from initially unresponsive or low-selectivity populations. Animal movement and choice did not account for changes in stimulus selectivity. Enhanced selectivity persisted even after rewards were removed and animals ceased performing the task. We conclude that learning produces long-lasting realignment of apical dendrite tuft responses to behaviorally relevant dimensions of a task.
