Biology subjects

Jehee, J. F. M.

Publications and source records attributed to Jehee, J. F. M..

4 recordsLinked to original sources

bioRxiv2023

Motion direction is represented as a bimodal probability distribution in the human visual cortex

How do humans infer motion direction from noisy sensory signals? We hypothesized that motion direction is computed not only from velocity but also spatial orientation signals - a streak created by moving objects. We implemented this hypothesis in a Bayesian model, which quantifies knowledge with probability distributions, and tested its predictions using psychophysics and fMRI. Using a probabilistic pattern-based analysis, we decoded probability distributions of motion direction from trial-by-trial activity in the human visual cortex. Corroborating the predictions, the decoded distributions had a bimodal shape, with peaks that predicted the direction and magnitude of behavioral errors. Interestingly, we observed similar bimodality in the distribution of the observers behavioral responses across trials. Together, these results suggest that observers use spatial orientation signals when estimating motion direction. More broadly, our findings indicate that the cortical representation of low-level visual features, such as motion direction, can reflect a combination of several qualitatively distinct signals.

neuroscience↗

bioRxiv2021

Subjective confidence reflects representation of Bayesian probability in cortex

What gives rise to the human sense of confidence? Here, we tested the Bayesian hypothesis that confidence is based on a probability distribution represented in neural population activity. We implemented several computational models of confidence, and tested their predictions using psychophysics and fMRI. Using a generative model-based fMRI decoding approach, we extracted probability distributions from neural population activity in human visual cortex. We found that subjective confidence tracks the shape of the decoded distribution. That is, when sensory evidence was more precise, as indicated by the decoded distribution, observers reported higher levels of confidence. We furthermore found that neural activity in the insula, anterior cingulate, and prefrontal cortex was linked to both the shape of the decoded distribution and reported confidence, in ways consistent with the Bayesian model. Altogether, our findings support recent statistical theories of confidence and suggest that probabilistic information guides the computation of ones sense of confidence.

neuroscience↗

bioRxiv2021

TAFKAP: An improved method for probabilistic decoding of cortical activity

Cortical activity can be difficult to interpret. Neural responses to the same stimulus vary between presentations, due to random noise and other sources of variability. This unreliable relationship to external stimuli renders any pattern of activity open to a multitude of plausible interpretations. We have previously shown that this uncertainty in cortical stimulus representations can be characterized using a probabilistic decoding algorithm, which inverts a generative model of stimulus-evoked cortical responses. Here, we improve upon this method in two important ways, which both target the precision with which the generative model can be estimated from limited, noisy training data. We show that these improvements lead to considerably better estimation of the presented stimulus and its associated uncertainty. Estimates of the presented stimulus are recovered with an accuracy that exceeds that of standard decoding methods (SVMs), and in some cases even approaches the behavioral accuracy of human observers. Moreover, the uncertainty in the decoded probability distributions better characterizes the precision of cortical stimulus information from trial to trial.

neuroscience↗

bioRxiv2019

Probabilistic representation in human visual cortex reflects uncertainty in serial decisions

How does the brain represent the reliability of its sensory evidence? Here, we test whether sensory uncertainty is encoded in cortical population activity as the width of a probability distribution - a hypothesis that lies at the heart of Bayesian models of neural coding. We probe the neural representation of uncertainty by capitalizing on a well-known behavioral bias called serial dependence. Human observers of either sex reported the orientation of stimuli presented in sequence, while activity in visual cortex was measured with fMRI. We decoded probability distributions from population-level activity and found that serial dependence effects in behavior are consistent with a statistically advantageous sensory integration strategy, in which uncertain sensory information is given less weight. More fundamentally, our results suggest that probability distributions decoded from human visual cortex reflect the sensory uncertainty that observers rely on in their decisions, providing critical evidence for Bayesian theories of perception.

neuroscience↗