As intelligent machines begin muscling into daily life, a big issue remaining is how deeply people will trust them to take over critical tasks like driving, elder or child care, and even military operations.

Why it matters: Calibrating a human's trust to a machine's capability is crucial, as we've reported: Things go wrong if a person places too much or too little trust in a machine. Now, researchers are searching for ways of monitoring trust in real time so they can immediately alter a robot's behavior to match it.

The trouble is that trust is inexact. You can't measure it like a heart rate. Instead, most researchers examine people's behaviors for evidence of confidence.

But an ongoing project at Purdue University found more accurate indicators by peeking under the hood at people's brain activity and skin response.

In an experiment whose results were published in November, the Purdue team used sensors to measure how participants' bodies changed when they were confronted with a virtual self-driving car with faulty sensors.

Understanding a person's attitude toward a bot — a car, factory robot or virtual assistant — is key to improving cooperation between human and machine. It allows a machine to "self-correct" if it's out of sync with the person using it, Neera Jain, a Purdue engineering professor involved with the research, tells Axios.

Some examples of course-correcting robots:

An autonomous vehicle that would give a particularly skeptical driver more time to take control before reaching an obstacle that it can't navigate on its own.

An industrial robot that reveals its reasoning to boost confidence in a worker who might otherwise engage a manual override and potentially act less safely.

A military reconnaissance robot that gives a trusting soldier extra information about the uncertainty in a report to prevent harm.

Go deeper: