[DSRP Evidence](https://dsrpevidence.org/)

# Neural dynamics of causal inference in the macaque frontoparietal circuit

## Details

**Authors** Qi et al.

**Year** 2022

**Publisher** eLife

**Discipline** Neuroscience

[Read it at the publisher](https://doi.org/10.7554/eLife.76145) 
10.7554/eLife.76145

## In authors' words

### Abstract

Natural perception relies inherently on inferring causal structure in the environment. However, the neural mechanisms and functional circuits essential for representing and updating the hidden causal structure and corresponding sensory representations during multisensory processing are unknown. To address this, monkeys were trained to infer the probability of a potential common source from visual and proprioceptive signals based on their spatial disparity in a virtual reality system. The proprioceptive drift reported by monkeys demonstrated that they combined previous experience and current multisensory signals to estimate the hidden common source and subsequently updated the causal structure and sensory representation. Single-unit recordings in premotor and parietal cortices revealed that neural activity in the premotor cortex represents the core computation of causal inference, characterizing the estimation and update of the likelihood of integrating multiple sensory inputs at a trial-by-trial level. In response to signals from the premotor cortex, neural activity in the parietal cortex also represents the causal structure and further dynamically updates the sensory representation to maintain consistency with the causal inference structure. Thus, our results indicate how the premotor cortex integrates previous experience and sensory inputs to infer hidden variables and selectively updates sensory representations in the parietal cortex to support behavior. This dynamic loop of frontal-parietal interactions in the causal inference framework may provide the neural mechanism to answer long-standing questions regarding how neural circuits represent hidden structures for body awareness and agency.

### What they set out to do (purpose)

To identify circuit-level mechanisms by which the primate brain performs causal inference during reaching.

### What they found (results)

Single-unit recordings showed premotor cortex computes the probability that visual and proprioceptive signals share a common source, while parietal cortex updates its representation based on that inferred causal structure.

## Commentary

### In short

The brain infers whether distinct signals share a common cause.

### In more detail

The brain implements relating as an active computation: it infers whether distinct signals share a common cause and updates its representations accordingly. Purpose: To identify circuit-level mechanisms by which the primate brain performs causal inference during reaching. Finding: Single-unit recordings showed premotor cortex computes the probability that visual and proprioceptive signals share a common source, while parietal cortex updates its representation based on that inferred causal structure. [Added 2026-07-30 as new evidence beyond the original 109; DSRP structural basis: R axiom (Coimplication of action/reaction): the brain actively infers cause→effect coupling between signals.]

**Patterns it shows** R

**Added** 2026-07-30

**How to cite this** Qi et al. (2022). Neural dynamics of causal inference in the macaque frontoparietal circuit. eLife.