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.
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
- Anna Catharina Geuzebroek
- Simon P Kelly
- Anna Catharina Geuzebroek
- Simon P Kelly
- 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.
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).
- Valentin Wyart, Inserm, France
- Received: August 26, 2022
- Accepted: August 29, 2023
- Accepted Manuscript published: August 30, 2023 (version 1)
© 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.
Consumption of food and water is tightly regulated by the nervous system to maintain internal nutrient homeostasis. Although generally considered independently, interactions between hunger and thirst drives are important to coordinate competing needs. In Drosophila, four neurons called the interoceptive subesophageal zone neurons (ISNs) respond to intrinsic hunger and thirst signals to oppositely regulate sucrose and water ingestion. Here, we investigate the neural circuit downstream of the ISNs to examine how ingestion is regulated based on internal needs. Utilizing the recently available fly brain connectome, we find that the ISNs synapse with a novel cell-type bilateral T-shaped neuron (BiT) that projects to neuroendocrine centers. In vivo neural manipulations revealed that BiT oppositely regulates sugar and water ingestion. Neuroendocrine cells downstream of ISNs include several peptide-releasing and peptide-sensing neurons, including insulin producing cells (IPCs), crustacean cardioactive peptide (CCAP) neurons, and CCHamide-2 receptor isoform RA (CCHa2R-RA) neurons. These neurons contribute differentially to ingestion of sugar and water, with IPCs and CCAP neurons oppositely regulating sugar and water ingestion, and CCHa2R-RA neurons modulating only water ingestion. Thus, the decision to consume sugar or water occurs via regulation of a broad peptidergic network that integrates internal signals of nutritional state to generate nutrient-specific ingestion.
Complex behaviors depend on the coordinated activity of neural ensembles in interconnected brain areas. The behavioral function of such coordination, often measured as co-fluctuations in neural activity across areas, is poorly understood. One hypothesis is that rapidly varying co-fluctuations may be a signature of moment-by-moment task-relevant influences of one area on another. We tested this possibility for error-corrective adaptation of birdsong, a form of motor learning which has been hypothesized to depend on the top-down influence of a higher-order area, LMAN (lateral magnocellular nucleus of the anterior nidopallium), in shaping moment-by-moment output from a primary motor area, RA (robust nucleus of the arcopallium). In paired recordings of LMAN and RA in singing birds, we discovered a neural signature of a top-down influence of LMAN on RA, quantified as an LMAN-leading co-fluctuation in activity between these areas. During learning, this co-fluctuation strengthened in a premotor temporal window linked to the specific movement, sequential context, and acoustic modification associated with learning. Moreover, transient perturbation of LMAN activity specifically within this premotor window caused rapid occlusion of pitch modifications, consistent with LMAN conveying a temporally localized motor-biasing signal. Combined, our results reveal a dynamic top-down influence of LMAN on RA that varies on the rapid timescale of individual movements and is flexibly linked to contexts associated with learning. This finding indicates that inter-area co-fluctuations can be a signature of dynamic top-down influences that support complex behavior and its adaptation.