# The implications of perception as probabilistic inference for correlated neural variability during behavior

arXiv: https://arxiv.org/abs/1409.0257 · HTML: https://mlsanity.com/paper/1409.0257/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2014.

## Built on by (most cited first, 11 of 11)

- [Bidirectional predictive coding](https://mlsanity.com/paper/2505.23415/) (2025)
- [Neural Sampling in Hierarchical Exponential-family Energy-based Models](https://mlsanity.com/paper/2310.08431/) (2023)
- [How does the primate brain combine generative and discriminative computations in vision?](https://mlsanity.com/paper/2401.06005/) (2024)
- [Expressive probabilistic sampling in recurrent neural networks](https://mlsanity.com/paper/2308.11809/) (2023)
- [Bayesian Integration of Information Using Top-Down Modulated WTA Networks](https://mlsanity.com/paper/2308.15390/) (2023)
- [Inferring Inference](https://mlsanity.com/paper/2310.03186/) (2023)
- [How does the brain compute with probabilities?](https://mlsanity.com/paper/2409.02709/) (2024)
- [Modeling Dynamic Neural Activity by combining Naturalistic Video Stimuli and Stimulus-independent Latent Factors](https://mlsanity.com/paper/2410.16136/) (2024)
- [Predictive Learning in Energy-based Models with Attractor Structures](https://mlsanity.com/paper/2501.13997/) (2025)
- [Spike-based alignment learning solves the weight transport problem](https://mlsanity.com/paper/2503.02642/) (2025)
- [Task learning increases information redundancy of neural responses in macaque visual cortex](https://mlsanity.com/paper/2603.07369/) (2026)

## Abstract (arXiv)

This paper addresses two main challenges facing systems neuroscience today: understanding the nature and function of a) cortical feedback between sensory areas and b) correlated variability. Starting from the old idea of perception as probabilistic inference, we show how to use knowledge of the psychophysical task to make easily testable predictions for the impact that feedback signals have on early sensory representations. Applying our framework to the well-studied two-alternative forced choice task paradigm, we can explain multiple empirical findings that have been hard to account for by the traditional feedforward model of sensory processing, including the task-dependence of neural response correlations, and the diverging time courses of choice probabilities and psychophysical kernels. Our model makes a number of new predictions and, importantly, characterizes a component of correlated variability that represents task-related information rather than performance-degrading noise. It also demonstrates a normative way to integrate sensory and cognitive components into physiologically testable mathematical models of perceptual decision-making.