Sampling-based Causal Inference in Cue Combination and its Neural Implementation

09/03/2015
by   Zhaofei Yu, et al.
0

Causal inference in cue combination is to decide whether the cues have a single cause or multiple causes. Although the Bayesian causal inference model explains the problem of causal inference in cue combination successfully, how causal inference in cue combination could be implemented by neural circuits, is unclear. The existing method based on calculating log posterior ratio with variable elimination has the problem of being unrealistic and task-specific. In this paper, we take advantages of the special structure of the Bayesian causal inference model and propose a hierarchical inference algorithm based on importance sampling. A simple neural circuit is designed to implement the proposed inference algorithm. Theoretical analyses and experimental results demonstrate that our algorithm converges to the accurate value as the sample size goes to infinite. Moreover, the neural circuit we design can be easily generalized to implement inference for other problems, such as the multi-stimuli cause inference and the same-different judgment.

READ FULL TEXT
research
03/17/2020

ParKCa: Causal Inference with Partially Known Causes

Causal Inference methods based on observational data are an alternative ...
research
06/27/2023

Causal Inference via Predictive Coding

Bayesian and causal inference are fundamental processes for intelligence...
research
04/09/2020

A category theoretical argument for causal inference

The goal of this paper is to design a causal inference method accounting...
research
04/19/2021

Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis

Causal inference is the process of capturing cause-effect relationship a...
research
02/07/2022

Causal Inference Using Tractable Circuits

The aim of this paper is to discuss a recent result which shows that pro...
research
05/26/2017

Learning Causal Structures Using Regression Invariance

We study causal inference in a multi-environment setting, in which the f...
research
03/25/2021

User-Oriented Smart General AI System under Causal Inference

General AI system solves a wide range of tasks with high performance in ...

Please sign up or login with your details

Forgot password? Click here to reset