Biology subjects

Kane, G. A.

Publications and source records attributed to Kane, G. A..

6 recordsLinked to original sources

bioRxiv2025

Regulation of Decision Threshold by the Locus Coeruleus

A fundamental challenge for decision-making under uncertainty lies in regulating the balance between speed and accuracy. Humans and animals solve this problem by adjusting their decision thresholds -- the criterion that determines how much information is required before committing to a choice. While brain regions associated with this process have been identified, the neural circuits able to directly alter decision thresholds remain to be identified. Here we investigate the role of the locus coeruleus (LC) norepinephrine (NE) system in controlling the balance between speed and accuracy during decision making. Through cell-type specific chemogenetic manipulations, we discovered that LC-NE activation increased decision thresholds. We further demonstrated that this effect is replicated by systemic administration of the 2 adrenergic receptor (2 AR) agonist, clonidine. Notably, 2 AR activation drove changes in decision threshold specifically, without reproducing other LC-NE activation effects such as promoting task engagement. Together, these results suggest that LC-NE regulates decision thresholds via activation of downstream 2 ARs. HighlightsO_LILC-NE activation increases reaction time and accuracy of perceptual decisions C_LIO_LIBehavioral effects are consistent with an elevation of decision thresholds C_LIO_LIHeightened decision thresholds are replicated by selective activation of 2 ARs C_LIO_LIActivation of LC-NE and 2 ARs differently alter task engagement C_LI

animal behavior and cognition↗

bioRxiv2024

A cross-species framework for investigating perceptual evidence accumulation

Cross-species studies are important for a comprehensive understanding of brain functions. However, direct quantitative comparison of behaviors across species presents a significant challenge. To enable such comparisons in perceptual decision-making, we developed a synchronized evidence accumulation task for human and non-human animals, by aligning mechanics, stimuli, and training. The task was readily learned by rats, mice and humans, with each species exhibiting qualitatively similar performance. Quantitative model comparison revealed that all three species employed an evidence accumulation strategy, but differed in speed, accuracy, and key decision parameters. Human performance prioritized accuracy, whereas rodent performance was limited by internal time-pressure. Rats optimized reward rate, while mice appeared to switch between evidence accumulation and other strategies trial-to-trial. Together, these results reveal striking similarities and species-specific priorities in decision-making. Furthermore, the synchronized behavioral framework we present may facilitate future studies involving cross-species comparisons, such as evaluating the face validity of animal models of neuropsychiatric disorders. HighlightsO_LIDevelopment of an evidence accumulation task for rats and mice C_LIO_LISynchronized video game allows direct comparisons with humans C_LIO_LIRat, mouse and human behavior are well fit by the same decision models C_LIO_LIModel parameters reveal species-specific priorities in accumulation strategy C_LI

animal behavior and cognition↗

bioRxiv2022

Assessing evidence accumulation and rule learning in humans with an online game

Evidence accumulation, how the brain integrates sensory information over time, is an essential component of perception and decision making. In humans, evidence accumulation is commonly modeled as a diffusion process in which noise accumulates linearly with the incoming evidence. However, recent studies in rodents have shown that during perceptual decision making, noise scales non-linearly with the strength of accumulated evidence. The question of whether nonlinear noise scaling also holds for humans has been clouded by differences in the methodologies typically used to collect and analyze human and rodent data. For example, whereas humans are typically given explicit instructions in these tasks, rodents are trained using feedback. Therefore, to evaluate how perceptual noise scales with accumulated evidence, we developed an online evidence accumulation game and nonverbal training pipeline for humans inspired by pulse-based evidence accumulation tasks for rodents. Using this game, we collected and analyzed behavioral data from hundreds of participants trained either with an explicit description of the relevant decision rule or merely with experiential feedback. Across all participants, performance was well described by an accumulation process, in which stimuli were integrated equally across time. Participants trained using feedback alone learned the game rules rapidly and used similar strategies to those who received explicit instructions. Decisions in both groups were influenced in similar ways by biases and perceptual noise, suggesting that explicit instructions did not reduce bias or noise in pulse-based accumulation tasks. Finally, by leveraging data across all participants, we show that perceptual noise during evidence accumulation was best described by a non-linear model of noise scaling, consistent with previous animal studies, but inconsistent with diffusion models widely used in human studies. These results challenge the conventional description of humans accumulation process and suggest that online games inspired by evidence accumulation tasks provide a valuable large-scale behavioral assessment platform to examine perceptual decision making and learning in humans. In addition, the feedback-based training pipeline developed for this game may be useful for evaluating perceptual decision making in human populations with difficulty following verbal instructions. HighlightsO_LIDevelopment and validation of an online video game to measure perceptual decision making. C_LIO_LIHumans trained using a feedback-based pipeline exhibit similar strategies and performance compared with those receiving instructions. C_LIO_LIPerceptual noise increases superlinearly with sensory evidence. C_LI

animal behavior and cognition↗

bioRxiv2021

Rat anterior cingulate cortex continuously signals decision variables in a patchforaging task

In patch foraging tasks, animals must decide whether to remain with a depleting resource or to leave it in search of a potentially better source of reward. In such tasks, animals consistently follow the general predictions of optimal foraging theory (the Marginal Value Theorem; MVT): to leave a patch when the reward rate in the current patch depletes to the average reward rate across patches. Prior studies implicate an important role for the anterior cingulate cortex (ACC) in foraging decisions based on MVT: within single trials, ACC activity increases immediately preceding foraging decisions, and across trials, these dynamics are modulated as the value of staying in the patch depletes to the average reward rate. Here, we test whether these activity patterns reflect dynamic encoding of decision-variables and whether these signals are directly involved in decision-making. We developed a leaky accumulator model based on the MVT that generates estimates of decision variables within and across trials, and tested model predictions against ACC activity recorded from rats performing a patch foraging task. Model predicted changes in MVT decision variables closely matched rat ACC activity. Next, we pharmacologically inactivated ACC to test the contribution of these signals to decision-making. ACC inactivation had a profound effect on rats foraging decisions and response times yet rats still followed the MVT decision rule. These findings suggest that the ACC encodes foraging-related variables for reasons unrelated to patch-leaving decisions.

neuroscience↗

bioRxiv2020

Real-time, low-latency closed-loop feedback using markerless posture tracking

The ability to control a behavioral task or stimulate neural activity based on animal behavior in real-time is an important tool for experimental neuroscientists. Ideally, such tools are noninvasive, low-latency, and provide interfaces to trigger external hardware based on posture. Recent advances in pose estimation with deep learning allows researchers to train deep neural networks to accurately quantify a wide variety of animal behaviors. Here we provide a new DeepLabCut-Live! package that achieves low-latency real-time pose estimation (within 15 ms, >100 FPS), with an additional forward-prediction module that achieves zero-latency feedback, and a dynamic-cropping mode that allows for higher inference speeds. We also provide three options for using this tool with ease: (1) a stand-alone GUI (called DLC-Live! GUI), and integration into (2) Bonsai and (3) AutoPilot. Lastly, we benchmarked performance on a wide range of systems so that experimentalists can easily decide what hardware is required for their needs.

neuroscience↗

bioRxiv2018

Rats exhibit similar biases in foraging and intertemporal choice tasks

Animals, including humans, consistently exhibit myopia in two different contexts: foraging, in which they harvest locally beyond what is predicted by optimal foraging theory, and intertemporal choice, in which they exhibit a preference for immediate vs. delayed rewards beyond what is predicted by rational (exponential) discounting. Despite the similarity in behavior between these two contexts, previous efforts to reconcile these observations in terms of a consistent pattern of time preferences have failed. Here, via extensive behavioral testing and quantitative modeling, we show that rats exhibit similar time preferences in both contexts: they prefer immediate vs. delayed rewards and they are sensitive to opportunity costs -- delays to future decisions. Further, a quasi-hyperbolic discounting model, a form of hyperbolic discounting with separate components for short-and long-term rewards, explains individual rats time preferences across both contexts, providing evidence for a common mechanism for myopic behavior in foraging and intertemporal choice.

animal behavior and cognition↗