Biology subjects

Lange, R. D.

Publications and source records attributed to Lange, R. D..

5 recordsLinked to original sources

bioRxiv2026

Reassessing Choice Probability: What 59 Macaque Studies Tell Us About Decision-Related Activity in Visual Cortex

Choice probability (CP) quantifies the trial-by-trial covariation between a sensory neurons response and perceptual reports. Despite extensive interest in CP as a window into how perception is linked to the activity of sensory neurons, the usefulness of CP remains debated. On one hand, reported CP magnitudes vary widely across seemingly equivalent studies, questioning its utility as a metric. On the other hand, the absence of clear patterns in CP variability has made it difficult to use it to adjudicate between competing models of visual perception. Here, we performed a meta-analysis of 150 CP estimates from 59 macaque neurophysiology studies to identify factors that systematically influence CP. We confirmed the positive relationship between CP and neuronal sensitivity both across and within individual studies. When controlled for sensitivity, we found remarkable consistency in CP across varying tasks and brain regions with two notable exceptions. First, CPs were higher in tasks involving bistable percepts, reinforcing the link between CP magnitude and subjective perception. Second, CPs were smaller in area V1, supporting prior suggestions about V1s special role in visual processing. We further found a significant effect of stimulus duration on CP, providing evidence against strictly feedforward models and favoring models with substantial feedback and recurrent processing. Finally, we offer recommendations for future studies to enhance the cross-study comparability and theoretical utility of choice signals as the field transitions to large-scale population recordings. More broadly, our findings demonstrate the benefits of meta-studies that expose patterns across many different tasks and animals - yielding insights that complement large-scale population recordings.

neuroscience↗

bioRxiv2020

Bayesian Encoding and Decoding as Distinct Perspectives on Neural Coding

One of the most influential, and controversial, ideas in neuroscience has been to understand the brain in terms of Bayesian computations. Unstated differences in how Bayesian ideas are operationalized across different models make it difficult to ascertain both which empirical data support which models, and how Bayesian computations might be implemented by neural circuits. In this paper, we make one such difference explicit by identifying two distinct philosophies that underlie existing neural models of Bayesian inference: one in which the brain recovers experimenter-defined structures in the world from sensory neural activity (Decoding), and another in which the brain represents latent quantities in an internal model that explains its inputs (Encoding). These philosophies require profoundly different assumptions about the nature of inference in the brain, and lead to different interpretations of empirical data. Here, we characterize and contrast both philosophies in terms of motivations, empirical support, and relationship to neural data. We also show that this implicit difference in philosophy underlies some of the debate on whether neural activity is better described as a sampling-based, or a parametric, distributional code. Using a simple model of primary visual cortex as an example, we show mathematically that it is possible that the very same neural activity can be described as probabilistic inference by neural sampling in the Encoding framework while also forming a linear probabilistic population code (PPC) in the Decoding framework. This demonstrates that certain families of Encoding and Decoding models are compatible with each other rather than competing explanations of data. In sum, Bayesian Encoding and Bayesian Decoding are distinct, non-exclusive philosophies, and appreciating their similarities and differences will help organize future work and allow for stronger empirical tests about the nature of inference in the brain.

neuroscience↗

bioRxiv2018

A confirmation bias in perceptual decision-making due to hierarchical approximate inference

Making good decisions requires updating beliefs according to new evidence. This is a dynamical process that is prone to biases: in some cases, beliefs become entrenched and resistant to new evidence (leading to primacy effects), while in other cases, beliefs fade over time and rely primarily on later evidence (leading to recency effects). How and why either type of bias dominates in a given context is an important open question. Here, we study this question in classic perceptual decision-making tasks, where, puzzlingly, previous empirical studies differ in the kinds of biases they observe, ranging from primacy to recency, despite seemingly equivalent tasks. We present a new model, based on hierarchical approximate inference and derived from normative principles, that not only explains both primacy and recency effects in existing studies, but also predicts how the type of bias should depend on the statistics of stimuli in a given task. We verify this prediction in a novel visual discrimination task with human observers, finding that each observers temporal bias changed as the result of changing the key stimulus statistics identified by our model. By fitting an extended drift-diffusion model to our data we rule out an alternative explanation for primacy effects due to bounded integration. Taken together, our results resolve a major discrepancy among existing perceptual decision-making studies, and suggest that a key source of bias in human decision-making is approximate hierarchical inference.

animal behavior and cognition↗

bioRxiv2017

Review: Characterizing the influence of ‘internal states’ on sensory activity

The concept of a tuning curve has been central for our understanding of how the responses of cortical neurons depend on external stimuli. Here, we describe how the influence of unobserved internal variables on sensory responses, in particular correlated neural variability, can be understood in a similar framework. We suggest that this will lead to deeper insights into the relationship between stimulus, sensory responses, and behavior. We review related recent work and discuss its implication for distinguishing feedforward from feedback influences on sensory responses, and for the information contained in those responses.\n\nHighlightsO_LIRe-interpretation of neural correlations in terms of internal variables...\nC_LIO_LI...can clarify whether they limit or enhance information\nC_LIO_LIInfluence of internal variables can be captured by interpretable tuning functions\nC_LIO_LIEstimation of both internal variables and tuning possible from population recordings\nC_LI

neuroscience↗

bioRxiv2016

Inferring the brain’s internal model from sensory responses in a probabilistic inference framework

Perception can be characterized as an inference process in which beliefs are formed about the world given sensory observations. The sensory neurons implementing these computations, however, are classically characterized with firing rates, tuning curves, and correlated noise. To connect these two levels of description, we derive expressions for how inferences themselves vary across trials, and how this predicts task-dependent patterns of correlated variability in the responses of sensory neurons. Importantly, our results require minimal assumptions about the nature of the inferred variables or how their distributions are encoded in neural activity. We show that our predictions are in agreement with existing measurements across a range of tasks and brain areas. Our results reinterpret task-dependent sources of neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function. HighlightsO_LIGeneral connection between neural covariability and approximate Bayesian inference based on variability in the encoded posterior density. C_LIO_LIOptimal learning of a discrimination task predicts top-down components of noise correlations and choice probabilities in agreement with existing data. C_LIO_LIDifferential correlations are predicted to grow over the course of perceptual learning. C_LIO_LINeural covariability can be used to reverse-engineer the subjects internal model. C_LI

neuroscience↗