Biology subjects

Sanders, J. I.

Publications and source records attributed to Sanders, J. I..

3 recordsLinked to original sources

bioRxiv2024

Bounded optimality of time investments in rats, mice, and humans

Time is our scarcest resource. Allocating time optimally presents a universal challenge for all organisms because the future benefits of time investments are uncertain. We developed a normative framework for assessing bounded optimality in time allocation, emphasizing the accuracy of future predictions, independent of subjective costs and benefits. In a common decision task across humans, rats, and mice, we varied uncertainty by titrating ambiguous sensory evidence and measured the time each subject was willing to invest post-decision. We observed that all species and subjects invested more time when they were more likely to be correct, which reflected a statistical confidence of uncertain evidence. Time allocation strategy approached the lower bound of optimality, indicating an accurate decision-by-decision assessment of confidence in the likelihood that waiting will pay off - independent of the subjective payoff values and time costs. We demonstrate that an elementary algorithm based on a drift-diffusion process algorithm can implement this optimal time investment strategy. These results illuminate the computational mechanisms governing rational time investment, showing that humans, rats, and mice can maximize payoffs via confidence-guided time allocation. HighlightsO_LIComputational and behavioral framework to assess bounded optimality of investments. C_LIO_LIHumans, rats, and mice invest more time to obtain more likely payoffs, in proportion to statistical confidence. C_LIO_LITime investment was close to optimal model predictions, reflecting bounded optimality of investments under uncertainty. C_LIO_LIBounded-optimal time investment may be an evolutionary ancient adaptive behavioral strategy. C_LI

animal behavior and cognition↗

bioRxiv2020

Rapid learning of the 5-choice serial reaction time task in an automated rodent training system

Experiments aiming to understand sensory-motor systems, cognition and behavior often require animals trained to perform complex tasks. Traditional training protocols require lab personnel to move the animals between home cages and training chambers, to start and end training sessions, and in some cases, to hand-control each training trial. Human labor not only limits the amount of training per day, but also introduces several sources of variability and may increase animal stress. Here we present an automated training system for the 5-choice serial reaction time task (5CSRTT), a classic rodent task often used to test sensory detection, sustained attention and impulsivity. We found that fully automated training without human intervention greatly increased the speed and efficiency of learning, and decreased stress as measured by corticosterone levels. Introducing training breaks did not cancel these beneficial effects of automated training, and mice readily generalized across training systems when transferred from automated to manual protocols. Additionally, we validated our automated training system with mice implanted with wireless optogenetic stimulators, expanding the breadth of experimental needs our system may fulfill. Our automated 5CSRTT system can serve as a prototype for fully automated behavioral training, with methods and principles transferrable to a range of rodent tasks.

neuroscience↗

bioRxiv2019

Foraging fruit flies mix navigational and learning-based decision-making strategies

Animals often navigate environments that are uncertain, volatile and complex, making it challenging to locate reliable food sources. Therefore, it is not surprising that many species evolved multiple, parallel and complementary foraging strategies to survive. Current research on animal behavior is largely driven by a reductionist approach and attempts to study one particular aspect of behavior in isolation. This is justified by the huge success of past and current research in understanding neural circuit mechanisms of behaviors. But focusing on only one aspect of behaviors obscures their inherent multidimensional nature. To fill this gap we aimed to identify and characterize distinct behavioral modules using a simple reward foraging assay. For this we developed a single-animal, trial-based probabilistic foraging task, where freely walking fruit flies experience optogenetic sugar-receptor neuron stimulation. By carefully analyzing the walking trajectories of flies, we were able to dissect the animals foraging decisions into multiple underlying systems. We show that flies perform local searches, cue-based navigation and learn task relevant contingencies. Using probabilistic reward delivery allowed us to bid several competing reinforcement learning (RL) models against each other. We discover that flies accumulate chosen option values, forget unchosen option values and seek novelty. We further show that distinct behavioral modules -learning and navigation-based systems-cooperate, suggesting that reinforcement learning in flies operates on dimensionality reduced representations. We therefore argue that animals will apply combinations of multiple behavioral strategies to generate foraging decisions.

neuroscience↗