Biology subjects

Glimcher, P.

Publications and source records attributed to Glimcher, P..

5 recordsLinked to original sources

bioRxiv2026

Stochastic Decision Policy Is Optimal Under Costly Control

Human and animal decisions are often noisy. This variability is usually attributed to noise that corrupts the decision evidence, a defect that an ideal decision maker would eliminate. Here we ask whether stochastic choice can instead be optimal once the cost of implementing prescribed policies is counted. We treat the decision maker as an ideal observer that maximizes expected gain but pays costs to sharpen its policy away from a random default. Using sequential sampling, a setting in which the optimal policy is precisely defined, we show that this trade-off makes a stochastic policy optimal, replacing the hard decision boundaries of classical theory with soft stopping zones whose softness is set by the price of control. This soft policy aligns with the strategy used in cutting-edge artificial learning agents for robustness. Noise that corrupts the agent's belief, an important confound of noisy behavior, produces superficially similar yet distinctive signatures in behavioral policies. Leveraging this, we recovered the policies of two macaques with low-dimensional recurrent neural networks and found that their behavior carries both a stochastic policy and belief noise. By separating variability in constructing a decision belief from stochasticity in selecting actions, these results establish that stochastic decision policies can themselves be optimal, offering a common principle for understanding behavioral variability in biological and artificial decision makers.

animal behavior and cognition↗

bioRxiv2025

Decisional reference point pathology: a mechanism and marker for major depressive disorder in humans

The decisional reference point, the central mechanism of behavioral economics, conditions our evaluations of reinforcers. It determines whether a given event is experienced as positive or negative. Here we show, for the first time, a significant and pathological elevation of the reference point in patients with depression that correlates with disease severity. We also show that the mechanism for reference point setting is profoundly impaired in patients with depression. These findings link the previously demonstrated treatment of depression by deep brain stimulation to modulation of the reference point in the anterior cingulate cortex and identify pathology in the dynamics of reference point setting as a novel mechanism in the disorder. Finally, these results lay the foundation for a three-minute virtual diagnostic test for depression.

neuroscience↗

bioRxiv2024

A Tale of Two Environments: Divisive Normalization and the (In)Flexibility of Choice

The Divisive Normalization (DN) function has been described as a "canonical neural computation" in the brain that achieves efficient representations of sensory and choice stimuli. Recent theoretical work indicates that it efficiently encodes a specific class of Pareto-distributed stimuli. Does the brain shift to different encoding functions in other types of environments, or is there evidence for DN encoding in other types of environments? In this paper, using a within-subject choice experiment, we show evidence of the latter. Our subjects made decisions in two distinct choice environments with choice sets either drawn from a Pareto distribution or from a uniform distribution. Our results indicate that subjects choices are better described by a divisive coding strategy in both environments. Moreover, subjects appeared to calibrate a DN function to match, as closely as possible, the actual statistical properties of each environment. These results suggest that the nervous system may be constrained to use divisive representations under all conditions. Significance StatementHow does the frequency with which we encounter different kinds of decision problems affect how the brain represents those problems? Recent empirical findings suggest that we adapt our internal representations to match the environments in which we are making choices. Theoretical work has shown that one form of internal representation, called divisive normalization, provides an optimal adaptation when making choices in a specific class of environments. Using a stylized experimental design, subjects faced two distinct choice environments, each characterized by different statistical properties. Our findings show humans appear to use the same mechanism in both environments, suggesting that a divisive representation may be a fixed feature of human cognition.

neuroscience↗

bioRxiv2021

The nematode worm C. elegans chooses between bacterial foods exactly as if maximizing economic utility

In value-based decision making, options are selected according to subjective values assigned by the individual to available goods and actions. Despite the importance of this faculty of the mind, the neural mechanisms of value assignments, and how choices are directed by them, remain obscure. To investigate this problem, we used a classic measure of utility maximization, the Generalized Axiom of Revealed Preference, to quantify internal consistency of food preferences in Caenorhabditis elegans, a nematode worm with a nervous system of only 302 neurons. Using a novel combination of microfluidics and electrophysiology, we found that C. elegans food choices fulfill the necessary and sufficient conditions for utility maximization, indicating that nematodes behave as if they maintain, and attempt to maximize, an underlying representation of subjective value. Food choices are well-fit by a utility function widely used to model human consumers. Moreover, as in many other animals, subjective values in C. elegans are learned, a process we find requires intact dopamine signaling. Differential responses of identified chemosensory neurons to foods with distinct growth potentials are amplified by prior consumption of these foods, suggesting that these neurons may be part of a value-assignment system. The demonstration of utility maximization in an organism with a very small nervous system sets a new lower bound on the computational requirements for utility maximization and offers the prospect of an essentially complete explanation of value-based decision making at single neuron resolution in this organism.

neuroscience↗

bioRxiv2020

Quantifying the subjective cost of self-control in humans

Since Odysseus committed to resisting the Sirens, mechanisms to limit self-control failure have been a central feature of human behavior. Psychologists have long argued that the use of self-control is an effortful process and, more recently, that its failure arises when the cognitive costs of self-control outweigh its perceived benefits. In a similar way, economists have argued that sophisticated choosers can adopt "pre-commitment strategies" that tie the hands of their future selves in order to reduce these costs. Yet, we still lack an empirical tool to quantify and demonstrate the cost of self-control. Here, we develop and validate a novel economic decision-making task to quantify the subjective cost of self-control by determining the monetary cost a person is willing to incur in order to eliminate the need for self-control. We find that humans will pay to avoid having to exert self-control in a way that scales with increasing levels of temptation and that these costs are modulated both by motivational incentives and stress exposure. Our psychophysical approach allows us to index moment-to-moment self-control costs at the within-subject level, validating important theoretical work across multiple disciplines and opening new avenues of self-control research in healthy and clinical populations.

neuroscience↗