The Hidden Power of Eye Contact: Unlocking Human Connection in Technology

For the first time, a groundbreaking study has revealed how and when we make eye contact plays a crucial role in understanding and responding to others, including robots. Led by cognitive neuroscientist Dr Nathan Caruana and his team at the HAVIC Lab at Flinders University, researchers discovered that a specific gaze sequence is most effective in signaling a request: looking at an object, making eye contact, then looking back at the same object.

This precise timing makes people most likely to interpret the gaze as a call for help. The study’s findings have significant implications for smarter, more human-centered technology and can inform how we build social robots and virtual assistants that are becoming increasingly ubiquitous in our daily lives.

“We found that it’s not just how often someone looks at you or if they look at you last in a sequence of eye movements,” says Dr Caruana. “But the context of their eye movements that makes that behavior appear communicative and relevant.”

The researchers also discovered that people responded in the same way whether the gaze behavior was observed from a human or a robot. This suggests that humans are broadly tuned to see and respond to social information, priming us to effectively communicate and understand robots and virtual agents if they display non-verbal gestures we’re used to navigating in everyday interactions with other people.

The study’s authors say their research can directly inform the development of social robots and virtual assistants, while also having broader implications beyond tech. Understanding how eye contact works could improve non-verbal communication training in high-pressure settings like sports, defense, and noisy workplaces, as well as support people who rely heavily on visual cues, such as those who are hearing-impaired or autistic.

The team is now expanding the research to explore other factors that shape how we interpret gaze, including the duration of eye contact, repeated looks, and our beliefs about who or what we’re interacting with (human, AI, or computer-controlled).

By understanding these subtle signals better, we can create technologies and training that help people connect more clearly and confidently. The HAVIC Lab is affiliated with the Flinders Institute for Mental Health and Wellbeing and a founding partner of the Flinders Autism Research Initiative.

A groundbreaking study by Simon Fraser University researchers has shed new light on why Parkinson’s disease medications don’t always work as intended. By using a novel approach to brain imaging, the team found that the main drug used in dopamine replacement therapy – levodopa – can have unintended “off-target” effects in some patients.

Parkinson’s is the second most prevalent neurodegenerative disorder worldwide and affects millions of people globally. While levodopa is often effective in improving symptoms for many patients, it doesn’t work as well for everyone. To better understand why this is the case, researchers used magnetoencephalography (MEG) technology to study how the drug affects brain signals.

“We can see how levodopa activates certain parts of the brain in a patient,” said Alex Wiesman, assistant professor in biomedical physiology and kinesiology at SFU. “This information can help inform a more personalized approach to treatment.”

The study was a collaboration with researchers at Karolinska Institute in Sweden, who collected data from 17 patients with Parkinson’s disease using MEG technology. This advanced non-invasive technique measures the magnetic fields produced by the brain’s electrical signals.

Researchers mapped participants’ brain signals before and after taking the drug to see how it impacted brain activity. The results showed that some patients experienced “off-target” effects, which got in the way of the helpful effects of levodopa.

“We found that those people who showed ‘off target’ effects are still being helped by the drug, but not to the same extent as others,” Wiesman said.

The study’s findings have significant implications for personalized medicine. By understanding how individual patients respond to levodopa, clinicians may be able to adjust dosages or try different medications to improve treatment outcomes.

“This might be really helpful for tracking individualized responses to these types of drugs and helping with prescribing and therapeutics,” Wiesman said.

The new type of brain imaging analysis developed by the researchers is not only for studying Parkinson’s disease; any medications that affect brain signaling can be studied using this method. SFU’s ImageTech Lab, at the Surrey Memorial Hospital, is home to the only MEG in western Canada.

“Our next step is to take our new approach and apply it to a larger patient group,” Wiesman said. “We also need to translate this research to more accessible brain imaging methods, like electroencephalogram (EEG). Ultimately, we want to make sure this technology is useful for a diverse population and more widely accessible to patients with Parkinson’s disease.”

The gene PTEN has emerged as one of the most significant autism risk genes. Variations in this gene are found in a significant proportion of people with autism who also exhibit brain overgrowth. Researchers at the Max Planck Florida Institute for Neuroscience have discovered how loss of this gene rewires circuits and alters behavior, leading to increased fear learning and anxiety in mice – core traits seen in ASD.

PTEN has been linked to alterations in the function of inhibitory neurons in the development of ASD. The researchers focused on the changes in the central lateral amygdala driven by loss of PTEN in a critical neuronal population – somatostatin-expressing inhibitory neurons. They found that deleting PTEN specifically in these interneurons disrupted local inhibitory connectivity in the amygdala by roughly 50% and reduced the strength of the remaining inhibitory connections.

This diminished connectivity between inhibitory connections within the amygdala was contrasted by an increase in the strength of excitatory inputs received from the basolateral amygdala, a nearby brain region that relays emotionally-relevant sensory information to the amygdala. Behavioral analysis demonstrated that this imbalance in neural signaling was linked to heightened anxiety and increased fear learning, but not alterations in social behavior or repetitive behavior traits commonly observed in ASD.

The results confirm that PTEN loss in this specific cell type is sufficient to induce specific ASD-like behaviors and provide one of the most detailed maps to date of how local inhibitory networks in the amygdala are affected by genetic variations associated with neurological disorders. Importantly, the altered circuitry did not affect all ASD-relevant behaviors – social interactions remained largely intact – suggesting that PTEN-related anxiety and fear behaviors may stem from specific microcircuit changes.

By teasing out the local circuitry underlying specific traits, researchers hope to differentiate the roles of specific microcircuits within the umbrella of neurological disorders, which may one day help in developing targeted therapeutics for specific cognitive and behavioral characteristics. In future studies, they plan to evaluate these circuits in different genetic models to determine if these microcircuit alterations are convergent changes that underlie heightened fear and anxiety expression across diverse genetic profiles.