Biology subjects

Boundy-Singer, Z. M.

Publications and source records attributed to Boundy-Singer, Z. M..

5 recordsLinked to original sources

bioRxiv2025

Computational characterization of metacognitive ability in subjective decision-making

Metacognition is the process of reflecting on and controlling ones own thoughts and behaviors. Metacognitive ability is often measured through modeling the relationship between confidence reports and choice behavior in tasks where performance can be objectively measured. Previous work has explored whether metacognitive ability is conserved across different types of tasks, and across different domains such as perception and memory. However, it is unclear whether approaches to the assessment of metacognitive ability that have worked in these contexts can be extended to value-based decision-making where objective accuracy cannot be evaluated. Here, we compare metacognitive ability across different tasks spanning perception and valuation, using Bayesian hierarchical estimation of the parameters of a computational process model of confidence. This model captures metacognitive ability as the uncertainty of an individuals own decision uncertainty, and can do so in the space of any decision variable, regardless of whether it indexes external features or internal, subjective states. We find that metacognitive ability can be reliably estimated in both objective and subjective decision-making tasks, and is relatively well conserved across tasks, especially within domains and similar confidence reporting paradigms.

neuroscience↗

bioRxiv2024

Sensory population activity reveals confidence computations in the primate visual system

Perception is fallible1-3. Humans know this4-6, and so do some non-human animals like macaque monkeys7-14. When monkeys report more confidence in a perceptual decision, that decision is more likely to be correct. It is not known how neural circuits in the primate brain assess the quality of perceptual decisions. Here, we test two hypotheses. First, that decision confidence is related to the structure of population activity in sensory cortex. And second, that this relation differs from the one between sensory activity and decision content. We trained macaque monkeys to judge the orientation of ambiguous stimuli and additionally report their confidence in these judgments. We recorded population activity in the primary visual cortex and used decoders to expose the relationship between this activity and the choice-confidence reports. Our analysis validated both hypotheses and suggests that perceptual decisions arise from a neural computation downstream of visual cortex that estimates the most likely interpretation of a sensory response, while decision confidence instead reflects a computation that evaluates whether this sensory response will produce a reliable decision. Our work establishes a direct link between neural population activity in sensory cortex and the metacognitive ability to introspect about the quality of perceptual decisions.

neuroscience↗

bioRxiv2023

How does V1 population activity inform perceptual certainty?

Neural population activity in sensory cortex informs our perceptual interpretation of the environment. Oftentimes, this population activity will support multiple alternative interpretations. The larger the set of plausible alternatives, the more uncertain the selected perceptual interpretation. We test the hypothesis that the reliability of perceptual interpretations can be revealed through simple transformations of sensory population activity. We recorded V1 population activity in fixating macaques while presenting oriented stimuli under different levels of nuisance variability and signal strength. We developed a decoding procedure to infer from V1 activity the most likely stimulus orientation as well as the certainty of this estimate. Our analysis shows that response magnitude, response dispersion, and variability in response gain all offer useful proxies for orientation certainty. Of these three metrics, the last one has the strongest association with the decoders uncertainty estimates. These results clarify that the nature of neural population activity in sensory cortex provides downstream circuits with multiple options to assess the reliability of perceptual interpretations.

neuroscience↗

bioRxiv2021

Confidence as a noisy decision reliability estimate

Decisions vary in difficulty. Humans know this and typically report more confidence in easy than in difficult decisions. However, confidence reports do not perfectly track decision accuracy, but also reflect response biases and difficulty misjudgments. To isolate the quality of confidence reports, we developed a model of the decision-making process underlying choice-confidence data. In this model, confidence reflects a subjects estimate of the reliability of their decision. The quality of this estimate is limited by the subjects uncertainty about the uncertainty of the variable that informs their decision ("meta-uncertainty"). This model provides an accurate account of choice-confidence data across a broad range of perceptual and cognitive tasks, revealing that meta-uncertainty varies across subjects, is stable over time, generalizes across some domains, and can be manipulated experimentally. The model offers a parsimonious explanation for the computational processes that underlie and constrain the sense of confidence.

animal behavior and cognition↗

bioRxiv2019

Representation of uncertainty in macaque visual cortex

Uncertainty is intrinsic to perception. Neural circuits which process sensory information must therefore also represent the reliability of this information. How they do so is a topic of debate. We propose a view of visual cortex in which average neural response strength encodes stimulus features, while cross-neuron variability in response gain encodes the uncertainty of these features. To test our theory, we studied spiking activity of neurons in macaque V1 and V2 elicited by repeated presentations of stimuli whose uncertainty was manipulated in distinct ways. We show that gain variability of individual neurons is tuned to stimulus uncertainty, that this tuning is invariant to the source of uncertainty, and that it is specific to the features encoded by these neurons. We demonstrate that this behavior naturally arises from known gain-control mechanisms, and derive how downstream circuits can jointly decode stimulus features and their uncertainty from sensory population activity.

neuroscience↗