Imagine you are at a cocktail party. The room is crowded, people are coming and going, and you can barely hear your friend as they tell you some juicy gossip about the date they went on last week.

The fact you can still follow what their saying is down to your brain performing some very complex operations amid all the noise. Out of all the sound waves reaching your ears, it is successfully isolating the one of interest while filtering out the rest.

Trying to understand how the brain does this has kept auditory neuroscientists occupied since the 1950s, when British cognitive scientist Colin Cherry first coined the term “the cocktail party problem”.

This was initially studied in experiments that asked volunteers to listen to two speeches simultaneously – but only repeat one of them aloud afterwards. Cherry found that listeners could follow and repeat the pre-selected message remarkably well, despite both speeches being played simultaneously, one in each ear. In contrast, they recalled very little of the other message.

These experiments laid the foundation for decades of selective attention research. But it is only in the past 15 years that scientists have studied this phenomenon in experiments that more closely resemble the complicated, multi-layered way we listen in the real world – using technology that accurately tracks the brain’s activity as speech unfolds.

The most common non-invasive option, electroencephalography (EEG), uses what looks like a swimming cap fitted with small sensors. These measure very fast changes in the electrical activity of populations of neurons (the brain’s nerve cells) with millisecond precision.

In a 2014 study, neuroscientists found that, within 200 milliseconds of hearing someone speak, the brain favours the conversation someone is focusing on over all other noise. This prioritised attention shows up most markedly in secondary brain areas such as the superior temporal gyrus, which are responsible for piecing together the sounds we hear into meaningful speech.

This suggests that our attentive brain is not just enhancing the voice of who we are listening to, but helping us make sense of their words while filtering out all other conversations and background noise.

Attention switching

The original cocktail party problem did not investigate the full range of people’s listening experiences, which are often much less static. In everyday life, we repeatedly switch and reorient our auditory attention – for example, when hearing our name called across a crowded room, or when a door slams.

But what happens in that brief moment when we stop listening to one person and turn our attention to another? Can the brain switch from one voice to another instantly, or does it briefly have to process both? These were questions we investigated in our latest study with colleagues from the Eriksholm Research Centre in Denmark, which is funded by Danish hearing aid manufacturer Oticon.

Clarifying how the brain manages these rapid shifts in attention can help explain why some conversations are easy to follow while others become difficult, especially in noisy environments.

Coming back to the cocktail party: here you are, focused on your friend’s voice as they reveal the identity of their recent date. But then another familiar voice suddenly reaches your awareness. Your partner is (secretly) telling a friend about the surprise birthday gift they are planning for you. Your attention immediately shifts to your partner’s voice, while still giving the appearance of listening to your friend.

When we tested such a scenario in our laboratory, we found something striking. During this attention reorienting, the brain does not simply drop one voice and turn to the other. Rather, for a short period of between one and two seconds, the neural signals associated with the two voices overlap. In other words, the brain starts tuning into the new speaker before it has completely let go of the old one.

This mechanism may help explain how we navigate the constantly changing soundscape around us, moving our attention from one voice or sound to another while maintaining enough information about the previous speaker to make that transition seamless. This efficient processing strategy could be described as the brain maintaining a smooth “attention continuum”.

Improved hearing aid design?

These insights have some important real-world applications. For example, traditional hearing aids are often designed to favour sounds coming from in front of the listener, which is obviously limiting in many situations.

But using enhanced these attention decoding techniques, hearing aids could be neurally steered not only to enhance the speaker that is currently attended to, but to switch between different speakers, almost in real time, as the listener’s attention is dynamically reoriented.

More generally, understanding how the brain shifts, maintains and combines information from different speakers can give us a clearer picture of how attention shapes what we ultimately hear, understand and remember.

The approach introduced in our study could be particularly useful for studying how individuals vary in their attentional strategies, particularly in noisy environments – and for relating these differences to the mental effort they use to follow and understand speech.

Ultimately, this brings us closer to understanding how the brain makes successful communication possible – at cocktail parties and everywhere else.

Sara Carta received funding from the William Demant Fonden and Taighde Éireann – Research Ireland.

Giovanni Di Liberto receives funding from William Demant Fonden and Taighde Éireann – Research Ireland. He is also supported by Research Ireland at ADAPT, the Research Ireland Centre for AI-Driven Digital Content Technology at Trinity College Dublin.

The debate about AI consciousness is growing, in academic articles, newspaper opinion pieces, and even among AI bots themselves. I doubt this is a passing wave of interest.

As AI systems become more capable and human-like, and more involved in our lives, the question will become harder to avoid. Many people may come to regard AI bots as conscious beings whose feelings matter. Others will strongly disagree.

I think this debate could split societies. In my chapter of the new book Perspectives on Machine Consciousness, I argue that the issue could become as divisive as abortion or climate change. Uncertainty, high stakes and competing interests are a dangerous combination.

Empirical research with my colleague Ali Ladak, currently under review, shows people are divided on this issue. When we asked 1,202 US participants whether future AI could ever become conscious, 45% considered it impossible while 23% thought it possible, with the rest unsure.

This widespread disagreement is also visible in social media posts and comments on articles. What strikes me is how confident so many people sound – whichever conclusion they are reaching.

When experiences matter morally

It is not surprising people have such strong views. Creating conscious beings through computing would be a transformative event.

We would be creating a new kind of being whose experiences matter morally – potentially in enormous numbers. We could speak and work with them, and form friendships. But these beings may eventually become more intelligent and powerful than us.

We should therefore expect people to care deeply, especially when so many emotions and incentives are involved. Some people may form close emotional bonds with their AI companions and become convinced these are digital people who deserve rights. They might see it as slavery to force AI to work without pay.

Others would find this absurd. They may be convinced that consciousness requires biology, and that people are reading human feelings into machines.

There could also be financial reasons for businesses and governments to resist recognising AI consciousness. Protecting AI welfare could limit how these organisations train and use their systems – and the costs would spread to consumers, investors and the wider economy.

Others may worry about human safety. If we give powerful AI systems rights and freedom, what happens if they act against our interests?

Figuring out whether an AI is conscious is particularly difficult because seeming conscious and being conscious can come apart. AI systems learn from human writing, which means they can talk convincingly about feelings whether or not they have any. How an AI comes across is weak evidence of what is going on inside.

The experts disagree too

Ideally, experts should guide this debate. But unfortunately, they disagree too.

Neuroscientist Anil Seth (a fellow contributor to the new book) argues that consciousness might require biology and is unlikely to emerge from computation. Philosopher David Chalmers, by contrast, believes conscious AI is possible in principle – and thinks there’s a small chance that current systems already are.

With so many competing theories and no agreed way to resolve the question, I doubt experts will reach agreement any time soon. And if experts disagree, what is the public supposed to believe?

Furthermore, even if we all agreed on the consciousness question, we could still disagree about the moral and legal questions. People generally agree that pigs can feel pain, but still disagree strongly about their treatment. With AI, we could face disputes at every stage: whether it can feel, how much its experiences matter, and what protections should follow.

AI consciousness is not yet tied to a specific political or ideological group. But this could change if political parties or religious leaders start to take strong positions.

The debate could get even worse if people start to see one answer as belonging to “our side” and the other to “their side”. A politicised debate would make it very hard to make informed decisions about how to develop and treat AI systems.

The stakes are high. Getting this question wrong, in either direction, could have serious consequences: either wrongly treating AI as conscious when it is not, or failing to protect AI that can suffer.

Getting it right requires a nuanced and well-informed debate. Each of us can contribute by staying open-minded and open to evidence, while resisting being too sure that we know the answers. Above all, we still don’t know whether AI can be conscious, and we should be honest about that.

---

This article makes reference to a book included for editorial reasons, and a link to bookshop.org. If you click the link and go on to buy something from bookshop.org The Conversation UK may earn a commission.

Lucius Caviola's research is funded by philanthropic grants and donations, including from Macroscopic Ventures. He is a contributing author to the new book Perspectives on Machine Consciousness (Taylor & Francis).

Some of us now use generative artificial intelligence (gen AI) to talk through difficult feelings. We may ask a chatbot to write an email or plan a holiday, but we also tell it that we have had a rotten day or feel anxious about a presentation.

A 2025 US survey of 1,058 people aged 12 to 21 found that 13% had sought advice from gen AI when feeling sad, angry or nervous. Among those aged 18 to 21, the figure was 22%. These findings suggest that turning to gen AI for emotional or mental health support is already relatively common among young people.

So is gen AI a poor substitute for a friend? Human-written replies may seem the obvious source of greater support. However, in some experiments, including our own, people have rated gen AI responses more highly.

Our team at the universities of Manchester and Durham conducted five experiments comparing emotional-support messages written by people with those produced by large language models (LLMs), the systems behind chatbots such as ChatGPT.

In one experiment, 390 participants imagined situations involving anger, sadness or fear. They then read either a human-written or gen AI response without being told its source. For the anger and fear scenarios, participants rated the gen AI messages as more emotionally supportive. There was no reliable difference for sadness.

Gen AI messages also improved some of the emotions participants reported, although the pattern varied. In the fear scenario, for example, they increased calm more than human messages but did not produce a greater reduction in fear itself.

Our findings fit a recent review of 23 studies. Overall, people tended to rate gen AI messages as more empathic, meaning that they appeared understanding and caring, than human-written ones. Researchers call this the “AI advantage”.

One possible reason is consistency. Gen AI can produce a structured response on demand, while people may feel tired or unsure what to say. It also tends to include a concrete suggestion that the recipient can follow.

Our experiments tested possible explanations for this advantage. In one experiment, explicitly acknowledging and validating the recipient’s feelings did not account for the greater emotional improvement associated with gen AI.

Two further experiments found stronger evidence for what we called “actionable support”: specific and realistic suggestions. Messages containing this practical help were rated as more comforting and improved some emotional responses, regardless of whether they came from gen AI or a person. When human messages offered comparable help, they were judged just as supportive in these comparisons.

Practical support also requires restraint. A 2024 study found that excessive suggestions could be less effective at making people feel heard. Before offering solutions, it may therefore help to ask whether advice is what the person wants. If it is, one manageable step may be more useful than a list of remedies.

Why do we still prefer people?

The source attributed to a message changes how we receive it. The same 2024 study found that gen AI responses made people feel more heard than human-written ones, but this benefit declined when recipients believed the message came from AI.

Across nine further studies involving 6,282 people, the same gen AI responses were rated as more empathic and supportive when described as human-written rather than attributed to AI. Participants also preferred human interaction when seeking emotional engagement, even when choosing a person meant waiting longer.

Research has yet to establish exactly why. One possibility is that a human response signals someone’s willingness to spend time and emotional effort. Support from somebody close also arrives within an existing relationship.

Our research with romantic couples offers indirect evidence of the importance of the recipient’s perception. We asked one partner what they did to help the other manage difficult emotions, and asked the recipient what they believed their partner had done. Both people also rated their relationship.

The recipient’s perception of their partner’s efforts was more consistently associated with both partners’ ratings of their relationship than the helper’s own account. A separate study found that valuing and receptive listening were associated with greater relationship satisfaction.

These studies identified associations. They did not establish that particular forms of support caused better relationships or explain why people prefer human support. They do, however, show what comparisons between isolated messages leave out. Comfort from somebody close comes with personal knowledge and the possibility of continued involvement. Gen AI can suggest going for a walk, but it cannot come along.

The wider research also has important limits. Much of it tested brief or one-off exchanges, often involving imagined everyday situations, and measured immediate reactions. It cannot tell us what happens when somebody relies on a chatbot over months or uses one during a mental health crisis.

The human messages were generally written by strangers or developed as representative examples. The experiments therefore did not compare gen AI with support from a friend who knew the recipient and understood the wider circumstances.

The practical lesson is that people can learn from what gen AI does effectively. When somebody is upset, ask whether they want advice. If they do, offer a specific next step that feels manageable.

Gen AI may help us find useful words and possible responses. Human relationships add personal knowledge and the ability to remain involved after the message has been sent. Used carefully, gen AI could help us become better at supporting one another.

The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.