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.
Video: The Wall Street Journal.
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.
Battenberg cake is one of my favourite cakes. But when I tell people that one of the things I like most about this cake is its E-number, they tend to look at me strangely.
It contains the raising agent E541, acidic sodium aluminium phosphate, which is one of the most restricted food additives in the UK. This is due to health concerns about human exposure to aluminium.
According to the UK government regulations, E541 is permitted in almost nothing except “sponge cakes produced from contrasting coloured segments held together by jam or spreading jelly and encased by a flavoured sugar paste”.
The reason for this curious exception is that Battenberg manufacturers managed to persuade the authorities that the additive was essential for achieving the cake’s chequered pattern, and that aluminium exposure from such a niche product wasn’t a health risk.
This level of specificity in the regulations surprises most people, who assume that additives are loosely regulated, casually thrown into food by manufacturers cutting corners. It’s part of a massive counter-movement against additives, driven by fears over ultra-processed foods and a desire for “clean” labels. Though these concerns are understandable, my view of the scientific evidence is that the reality is a little different.
Additives, of course, are compounds used to enhance foods in different ways, but mainly they are about control. When you cook in your home kitchen, the results can be very variable – which is often part of the fun. No-one expects two homemade cakes to look or taste the same, while breads out of your kitchen oven can be very different if you make them from different batches of flour.
For commercial foods, it’s a different story: industrial production relies on predictability, while consumers expect consistency. Few of us would be happy if supermarket breads, biscuits or burgers varied from batch to batch. So commercial foods are usually standardised — and additives help to achieve that.
Welcome to our series exploring the cutting edge of food science. From the latest advances in meat alternatives to weird and wonderful new additives, science is transforming what we eat like never before. This series will bring you up to speed on all the latest and give you plenty of, er, food for thought.
Their number one role is preservation: extending shelf life, making it possible to produce foods further away, and often making them more affordable in the process.
Propionic acid (E280) stops bread from going stale and mouldy. Antioxidants like vitamin C (E300, aka ascorbic acid) and vitamin E (E306/tocopherol) prevent fats from oxidising and going rancid. Emulsifiers stabilise products such as mayonnaise which would otherwise quickly separate.
Creating a specific texture and flavour is also very important. Thickeners improve the creaminess of yoghurts and some cheeses. Emulsifiers also help to make ice-cream smooth and chocolate soft. Citric acid (E330) can help zing up a salad dressing, while also dialling down the earthiness in some plant-based milks.
Another use of additives is to overcome the problem of raw products of different quality. Wheat from a wet, cold year can behave very differently from the same variety in a dry, hot year. This can mean differences in protein content and gluten formation during baking, which can affect how much breads rise, for instance.
Ascorbic acid and synthetic fats (mono and diglycerides of fatty acids (E471) can compensate for this and ensure the quality of bread is consistent. Likewise, adding E500 (bicarbonate of soda) and E336 (cream of tartar) – in other words, baking powder – ensures cakes and muffins are light and fluffy.
In the same way, jam depends on the pectin content of fruits, which is affected by factors that can’t be controlled – extra sunlight during growing reduces it, for instance. So manufacturers add pectin (E440) during jam-making to ensure it gels properly.
Or take meat, whose water retention can be improved by adding phosphates like E451. This ensures that natural fluids don’t leak out in the supermarket chiller section (phosphates are also more dubiously used by some meat producers to enable them to add water to cheaper cuts so that they look more attractive to consumers). As for milk products, you can avoid the risk of separation by adding carrageenan (E407), a seaweed gum, which strengthens their protein network.
Indeed, whole product categories would barely exist without certain additives. Many low-fat spreads rely on gelling agents to mimic the texture of butter. Without sweeteners, there would be a much smaller selection of low calorie and low sugar foods.
Very little of this is about deceiving consumers — and it should not be. The need to ensure the consistent quality of produce using additives is probably much greater than most people realise.
The remarkably long history of additives
While we’ve only referred to them as additives for about a century, these compounds have been around a great deal longer. Findings from the Qesem Cave in Israel suggest ash was being used to preserve foods as much as 200,000 years ago.
In times when the food supply has been unreliable, meaning most of our history, preserving food was essential to human survival. Entire empires have been built on the trade of another vital preservative, salt, to be used either as brine for pickling or as crystals/powder for drying.
Nitrates (E251) and nitrites (E250) have also been used as additives for many hundreds of years. In Roman times they were used as a constituent of rock salt, both for preserving meat and to keep it nice and pink. With a little more chemical know-how, these compounds are still used today.
Another preservative in use since Roman times is sulphur dioxide (E220), both for wine and foods such as fruits. The Romans obtained it by mining brimstone and heating it into a liquid that could be made into sulphur candles. These could then be burned to produce sulphur dioxide gas in the presence of whatever was being preserved.
The Romans also used additives to make dull, bland food more palatable. Garum — the ubiquitous Roman fish sauce – provided salty and umami flavours by fermenting fish guts. Lead acetates, obtained from boiling grape juice in leaden pots and kettles, were used as a non-calorific sweetener – in wine, for example. It wasn’t until many centuries later that it was proven that this additive was poisonous.
Towards the end of the 18th century, we developed a better understanding of how additives work thanks to advances in food chemistry. It also became possible to extract these compounds in a laboratory, such as lecithin from egg yolk to be used as an emulsifier, pectin from apples, and agar, another gelling agent, from seaweed.
A few decades later, chemists started synthesising additives, which often proved cheaper and easier than obtaining them naturally. Salt of hartshorn – also known as Baker’s ammonia (E503) – could be made by heating ammonium sulphate obtained from soot in combination with limestone (calcium carbonate).
Previously extracted from deer antlers, this was used as a raising agent and preservative for things like digestive biscuits and lebkuchen, a German gingerbread. Much like making aspirin in a laboratory instead of extracting it from willow bark, or digoxin instead of foxglove tea (for heart problems), synthetic and purified additives allowed much better control over their use.
Scientists’ better understanding of chemical and physical mechanisms in the 19th century also allowed new additives to be developed to suit the food industry and consumer demand. These included dyes like aniline purple and martius yellow, both obtained from coal tar, and new flavours like amyl acetate, a byproduct from fermenting alcohol, which gives a sense of pear or banana. These were unveiled at the Great Exhibition in London in 1851 for the first time.
Artificial pear and banana sweets were unveiled at London’s Great Exhibition in 1851.Wikimedia
Other additives allowed the use of cheaper ingredients or extended shelf lives. Borax, also known as sodium borate, was harvested from lake beds starting in the late 19th century and added to sub-standard milk and butter to mask sour tastes and kill bacteria.
Additives also began to allow the development of new products that could be used as cheap alternatives, such as margarine. Though originally made from beef fat and skimmed milk in the 1870s, it became vegetarian around the turn of the century after chemists worked out how to make food oils solidify by forcing hydrogen into them at high temperatures (known as hydrogenation).
Margarine solved one problem – the affordability of butter – but food scientists realised a few years later that it created another: it lacks the vitamins naturally found in dairy. So from the 1920s onwards, manufacturers also started fortifying it with vitamins, as well as making it more attractive by incorporating emulsifiers, flavours and colours.
Elsewhere, the use of additives has put consumers at risk, often due to unscrupulous characters. They made bread whiter with chalk, for instance, at a time when many people were already suffering from malnutrition. They sweetened wine with lead salt long after it was known to be toxic. There have even been cases in more recent times of wine being sweetened with anti-freeze.
One other notorious case was the Bradford sweets poisoning of 1858. At the time, sugar was very expensive due to high taxes, so confectionery manufacturers would mix it with plaster of Paris to keep costs down. In the Bradford case, an apprentice muddled up plaster of Paris and arsenic, which led to around 20 people being killed from eating toxic peppermint lozenges.
The modern era
The 20th century brought massive changes to the food system: a rapidly growing population needed to be fed, scientific advances provided a better understanding of food processing, and the rise of synthetic chemistry made hundreds of new compounds available. The upheaval of two world wars accelerated these processes as new products such as canned meats, sliced cheeses and even packets of crisps were all made possible by additives.
The same goes for British sliced bread. The Chorleywood process, named after the village near London where it was invented in 1961, revolutionised production of the daily loaf. By adding lard, ascorbic acid and extra yeast to dough, then mixing it at high speeds, there was no more need to wait hours for the mixture to rise.
This allowed the mass production of bread, which was extremely helpful in meeting ever growing demand. It also allowed the use of British-grown flour, which often has a lower protein content, meaning its dough traditionally didn’t rise as well. In subsequent years, other additives have also become common ingredients in this mass-produced bread, including E471 (mono- and diglycerides of fatty acids) as emulsifiers to improve dough handling and E282 (calcium propionate) as a preservative to enhance shelf life.
The post-war decades saw dramatic societal changes: rising incomes, longer working hours, and more women entering the workforce. These all created demand for foods that saved time and preparation. Ready meals, packet soups, instant desserts and salad dressings all reached supermarket shelves for the first time. This wouldn’t have been possible without emulsifiers, stabilisers, gelling agents and preservatives – even if such details were quietly hidden on labels that often sought to make these products look as traditional as possible.
At the same time, synthetic coal-tar dyes made sweets vivid and soft drinks luminous. Artificial flavours like strawberry, banana, pineapple, sometimes not tasting remotely natural, could be added to almost anything at negligible cost. And artificial sweeteners made it possible to indulge without sin … or at least calories.
For several decades between the 1950s and 1970s, all these new products were seen as unambiguous progress. At a time when science was enabling everything from tower blocks to rocket ships to the pill, it made perfect sense that it was doing the same for our eating habits.
The backlash
The enthusiasm for this modern food was short lived. In 1973, Californian allergist Benjamin Feingold suggested that additives – in particular artificial dyes and flavourings – were responsible for hyperactivity in children. His book, Why Your Child is Hyperactive, became a bestseller. The so-called Feingold diet became popular among parents, eliminating artificial colours, flavours and other additives.
In Britain, Maurice Hanssen’s E for Additives (1984) was another major blow to this industry when it became a bestseller in the 1980s. It detailed the purpose and health risks of all the E-numbers, which were devised as a European mark of safety, so that only additives considered safe were allocated one. Now they became culturally inverted into a warning sign.
The case against additives wasn’t quite what many began to believe, however. There was little proof that Feingold’s diet could reduce hyperactivity, even after extensive research. This may have been because of the strong genetic component in the condition.
In 2007 the UK’s Food Standards Agency commissioned a research study to investigate the effect of some artificial food colourings on hyperactivity. Children in the study showed some increase in hyperactivity from consuming the compounds in question – tartrazine (E102), sunset yellow (E110), quinoline yellow (E104), carmoisine (E122), ponceau 4R (E124) and allura red (E129).
Yet the European Food Safety Authority decided there was sufficient uncertainty that there was no need to change the permitted use of these additives. Instead European lawmakers mandated that foods containing them carry the warning “may have an adverse effect on activity and attention in children”, which applies today in the UK and across the continent.
In parallel, the additives industry has tried to adapt to consumers’ concerns about the things that go into foods. For the past several decades, we have seen a plethora of products which claim to be free from “artificial colours and flavours” – and sometimes even “free from chemicals” (although that is impossible).
Much of this “clean-label” movement has been about replacing E-numbers with ingredients that sound more natural, though in many cases the manufacturers just replace additives with plant extracts that contain the same compounds.
Acerola extract contains vitamin C, for instance, but sounds better than E300. Seaweed extract sounds much more natural than E407 (carrageenan) and turmeric extract better than E100 (curcumin). Similarly, vegetable salts or celery extract in processed meat replace nitrite (E250). The nitrite in these compounds will still act — turning the meat more pink and killing bacteria – but the consumer is not always aware that this additive is even present.
In the EU and UK, whether something counts as an additive legally depends on what it does, not where it comes from. However, this often depends on interpretation. Acerola cherry extract, for example, is almost always added for its vitamin C content, which acts as an antioxidant. However, it usually appears on the label as a fruit ingredient rather than as ascorbic acid (E300). Clean labelling probably employs as many lawyers as food scientists.
One example of how this plays out is Naked Bacon, made by Northern Irish company Finnebrogue. The goal was to remove nitrite from bacon to make it healthier – not just create a nicer label. As an alternative to nitrite and nitrate, they used a proprietary fruit and spice extract called Natpre made by Spanish manufacturer Prosur. This was initially marketed as free from E-numbers. The Food Standards Agency, however, took the view that the ascorbic acid (vitamin C) in the mix was an additive that had to be declared. After a lengthy dispute the company agreed in 2018 to list “antioxidant: ascorbic acid” on the label. The remainder of the extract is still declared as “natural flavouring”.
As well as these kinds of uncertainties, there’s also a potential problem with plant extracts as alternatives to existing additives: a rigorously tested compound is being replaced by something less understood, and still potentially harmful, in the name of a cleaner label. They can contain natural toxins, for instance, while the levels of the additive can vary by batch.
The case for additives
Another part of the stigma against additives is that they enable industrially produced food, which has come to be seen by many as inherently unhealthy. This was abetted by the likes of US journalist Michael Pollan’s bestseller, In Defense of Food (2008), the documentary Food Inc (2008) and TV chefs like Jamie Oliver.
This mindset probably partly derives from all those unsavoury practices I mentioned in the early food industry, where chemicals were used to disguise spoilage, extend products or improve appearance.
Wrapped up in this is a romanticism of natural food, viewing chemical intervention with suspicion. Many people still see natural as better and healthier, though there is actually little evidence for this claim.
Whether industrially produced food is unhealthy depends very much on what is in the food. Foods high in fat, salt and sugar increase the risk of chronic disease, whether it is a home-baked cake or an industrially manufactured cookie. Sure enough, a recent study found no difference in health outcomes between processed and non-processed foods that meet current dietary guidelines.
With today’s focus on ultra-processed foods, it is true that certain industrial additives like emulsifiers and artificial colours and flavours are sometimes used as evidence that a product falls into this category. But the health impact of these foods is more tied to issues like high calorie-density and low fibre content than the toxicity of individual ingredients, so the additives are rarely the primary culprits.
It’s also worth emphasising that without additives, natural food can spoil much more quickly. Almost 20% of foods are lost to spoilage in retail, and more than 30 million years of healthy lives are lost every year to food-borne diseases. Without additives, this situation would be much worse. And at a time when the cost of living is such a burden for so many people, it’s hard to deny that additives make food much more affordable.
Regulatory issues
Unlike with pharmaceuticals, there is no formal system for checking whether additives are causing harm after they start being used in food manufacturing. Regulators rely on the scientific literature to keep them up to date on any risks, as well as commissioning research studies if there are reasons for concern.
The situation isn’t helped by the different classification frameworks that exist. A recent example is aspartame. In 2023 the International Agency for Research on Cancer (IARC) classified this sweetener as possibly carcinogenic to humans, causing a huge amount of concern.
Yet at the same time Jecfa, the Joint FAO/WHO Expert Committee on Food Additives, whose pronouncements influence food regulation around the world, reaffirmed aspartame’s accepted daily intake of 40mg per kg of body weight. It concluded that there was no convincing evidence of adverse effects at current consumption levels.
This sounds contradictory, but actually the IARC and Jecfa ask very different questions. The IARC asks about hazard: could this substance possibly cause cancer? Jecfa asks about risk: could this substance possibly cause cancer at the amount people eat? Both can be right. A substance can probably cause cancer, only not at the amounts people generally consume.
The IARC, for instance, classes the Sun as a group 1 carcinogen, meaning it’s known to cause cancer in humans. Yet we still recommend sun exposure so that our bodies produce vitamin D. We manage the risk by reducing exposure — either by looking for shade or using sunscreen. As for aspartame, it’s worth noting that the IARC classification put it in the same category as aloe vera and pickled vegetables.
Emulsifiers are another additive currently receiving a lot of attention. Some studies have shown that they can alter the human gut microbiome and disrupt the intestinal barrier. Whether this causes harm is obviously an important question, but there’s much that isn’t yet clear: we don’t know if these effects are harmful, or whether they’re transient or lasting.
The trouble is that changes in the gut microbiome are difficult to interpret. It can change in response to all sorts of alterations to someone’s diet, and while there is some good data on links between the microbiome and health, we do not yet have a clear framework for interpreting what specific changes mean.
To be clear, change in itself isn’t a negative: fermented foods and prebiotics are recommended especially for their ability to change the gut microbiome. Regulators in the UK and elsewhere are actively investigating how to incorporate microbiome data into safety assessments, but the science is not yet at a stage where firm conclusions can be drawn.
It might still seem appealing to ban emulsifiers in the meantime as a precaution, but that might have consequences that eclipse the intended benefits. For example they could be replaced by natural lecithins from sources like egg yolks and sunflower seeds, but these are much more expensive. The consumer would bear the brunt – and there is no evidence that these alternatives are better.
There is also a question of consumer choice. Provided a food is safe to consume within established guidelines, there is a strong argument that individuals should be free to make their own choices, rather than having them imposed on the basis of uncertain evidence.
Alcohol, for instance, is another product categorised by the IARC as a group 1 carcinogen, and there is overwhelming evidence that drinking it can harm your health in numerous other ways. Yet while there are regular calls for tighter regulation of additives with a much better risk profile, calls for a ban on champagne or claret are much rarer.
This isn’t to say that the need for proportionality should always justify inaction. Regulatory agencies should and do act to protect consumers when the evidence warrants it. In 2007 data emerged that red 2G (E128), used as a colouring in sausages and burgers, was potentially carcinogenic and toxic to people’s genes. This was rightly considered serious enough that it was swiftly withdrawn in Europe and elsewhere.
The future of additives
One current area of rapid development has been plant-based foods, where achieving the texture, appearance and mouthfeel of meat and dairy requires a specific set of additives. For example, to produce plant-free dairy products that melt and taste like the real thing, food scientists add synthetic wheys and caseins made using a technique called precision fermentation. This involves inserting synthetic copies of dairy DNA into yeasts or fungi.
The first large-scale application of precision fermentation was actually in medicine in the early 1980s, producing insulin using genetically modified E. coli. The food industry got onboard in the early 1990s, with chymosin, an enzyme essential for cheesemaking to separate the curds and whey. It became the first major precision-fermentation product, and is now used in many milk-based cheeses instead of traditional rennet from calves’ stomachs, producing more consistent results and also vegetarian products into the bargain.
In the last couple of years, companies are now offering the entire suite of dairy proteins including whey, casein and lactoglobulins made in a fermentor as opposed to a cow. This is enabling products that are bio-identical to dairy rather than just plant-based alternatives, such as Bored Cow milk in the US. The line between additives and food itself is also increasingly blurring as different companies and research groups compete not just to produce individual proteins but entire muscle tissue in fermentation tanks.
Meanwhile, AI is somewhat inevitably being leveraged to help drive additives forward. One promising line of enquiry is discovering compounds that can fool human receptors into thinking they’re consuming salt or sugar that isn’t really there, or mask other tastes such as bitterness.
Also promising is a technique called nano-encapsulation, which raises the prospect of wrapping additives in microscopic biopolymers or bubbles of lipid (fat) to prevent them from spoiling. This could prevent colours from fading on supermarket shelves, or vitamins from being damaged by heat, for instance.
Whatever you think of these developments, food additives will probably be around for as long as humans need to consume food that is not freshly prepared. They are neither inherently good nor inherently bad, just tools that can be used to make food safer, more affordable and longer lasting.
Next time you enjoy a slice of Battenberg cake, indulge me by sparing a thought for E541 — and for the system that decided exactly where it could be eaten.
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Gunter Kuhnle has received research funding from Mars, Inc., a company engaged in flavanol research and flavanol-related commercial activities. As a previous member of the EFSA ANS panel and the Committee on Toxicity (FSA), he was involved in the evaluation of food additives. He is currently a member of ACNFP. Gunter also had discussions with Finnebrogue ahead of the release of their Naked Bacon product. He has no financial interest in either Finnebrogue or Mars. The views expressed are his own.
Imagine it’s the dead of night. Your phone pings with a text – it’s a government emergency alert: “Leave your home immediately! Only take with you enough supplies for the next three days.” What would you want in your go-bag?
Our new survey of UK adults (results not yet published) has found a surprising number of people prioritised sentimental belongings such as a childhood teddy bear.
In the face of climate change and growing geopolitical instability, governments have started to advise citizens to prepare now for disaster. In 2024, the UK government launched its Prepare initiative. This was followed by preparEU, a flagship EU project which argued: “Today’s threats are fast, complex and interconnected; our response must be more proactive.”
These initiatives typically recommend that people should have enough food, water and medicine to last 72 hours. In addition, they need ready access to documents, cash, batteries or power banks and a wind-up radio, plus a working knowledge of emergency support in their local area.
But when that message pings in the night, will you already be packed, with documents, food and medicine in a go-bag? Or will you be scrambling to find things, hoping you have enough essentials to keep you going?
In April 2026, as part of the After the End project – a Wellcome Trust-funded research project exploring how people experience and recover from crises – we surveyed 1,000 UK adults to understand how prepared they were for a sudden crisis or disaster.
We found that most people have less than half of what is recommended by the UK government and the US Federal Emergency Management Agency. Only four out of 1,000 people reported being fully prepared.
However, the focus on practical items and supplies may not capture what people need to withstand the emotional toll of crisis and disaster.
When asked what other items they would take with them during a crisis, many participants felt that personal items would be critical to their post-disaster wellbeing. They expressed a strong desire to keep sentimental items such as jewellery, family photographs and objects from their childhood close to them during crisis.
As one participant wrote: “My childhood teddy bear. He is going to the grave with me. Apart from that, nothing matters much.”
Pets also featured heavily in people’s responses. Animals such as cats and dogs were often described as members of the family. Some participants even found comfort in the idea of taking the ashes of deceased pets with them if they needed to flee in an emergency.
A concept in psychology called extended self theory explains why these types of items hold such importance during everyday life – and also during crisis. It suggests that our sense of self is fragile, and continually built and maintained through our connections with the world around us.
In this way, the things we surround ourselves with do more than serve practical functions. They help us construct, stabilise and make sense of our identity. Consciously or subconsciously, our possessions become part of who we are.
A childhood teddy bear, therefore, becomes a possession imbued with memories and emotions which tell a tale of who we are, where we have come from, and who we care about. Objects can reinforce continuity of identity over time and space.
A strong sense of self
Having a strong sense of self can be beneficial during times of crisis. If we enter such a period with a sense of identity that is stable and coherent, it can act as a psychological anchor. A strong identity helps us cope in a challenging situation, and reduces the likelihood of ruminating about negative experiences.
Research also suggests that our ability to connect with items from pre-disaster periods influences our ability to cope and recover psychologically following an emergency. So, salvaging photographs and other items of sentimental value during disaster could help to preserve and rebuild our memory and identity.
If everything around us falls apart, a treasured item can help us maintain some sense of normality. This is perhaps why survivors of the 2011 Japan tsunami frequently described the loss of family photos as one of their greatest emotional losses.
Sentimental items can maintain or even improve our belief that we are capable of achieving our goals. Psychologists refer to this as self-efficacy. And research suggests higher levels of self-efficacy are associated with greater preparedness for, and better coping after, a disaster – particularly in vulnerable groups such as those with disability.
Our analysis found that people who reported greater self-efficacy also said they had more of the items and knowledge recommended by the UK’s Prepare initiative.
Sentimental items can also help to rebuild communities. Heirlooms and meaningful everyday items from before a crisis become points of storytelling afterwards. So, remember to save a little space in your backpack for something that means something to you.
Whether it is a childhood teddy bear, your late mother’s engagement ring or a beer coaster from a first date, these items are more than just “things”. When so much else is uncertain, the small objects that remind us who we are may be among the most valuable items we can take with us.
Ruth Ogden receives funding from British Academy, the Wellcome Trust, the Economic and Social Research Council, CHANSE and Horizon 2020.
Anna Powell receives funding from the Wellcome Trust through the After the End Discovery Award, and from The Pandemic Institute through a partnership with CSL Seqirus UK.
Patricia Kingori receives funding from the Wellcome Trust through the After the End Discovery Award and the Antitheses Research platform.
Vast quantities of plastic and fishing gear are washing up along the shores of the Seychelles, destroying fragile ecosystems and causing significant economic problems.
Aldabra Atoll, a Unesco world heritage site in this archipelago nation of 115 islands, is estimated to have accumulated more than 500 tonnes of plastic on its shores alone. Most of this plastic debris continues to pile up along its beaches today. It was estimated that removing it would cost approximately US$4.68 million (£3.46 million) and would require 18,000 hours of labour.
Small island developing states are particularly vulnerable to plastic debris on their beaches. It can damage tourism and fishing in these coastal communities, which is essential to their economies. Clean-up costs can be prohibitive.
Fish and other marine life can also be killed by getting entangled with or ingesting plastic debris.
It is impossible to tackle this growing problem without knowing where this pollution is coming from and who is responsible. We investigated this in our recent study using computer simulations and observations, focusing on pollution in the western Indian Ocean from fisheries and shipping.
To do this, we used satellite tracking data from fishing and shipping vessels over five years (from 2018 to 2022) to estimate activity across the region. Using this map, we simulated the drift of billions of virtual pollution fragments and followed where they ended up.
A map showing debris fishing gear.Author's own.
From this we built a map showing how likely a given vessel location is to pose a risk to the Seychelles’ islands. We did this separately for fishing and shipping.
Our research shows, based on previous fishing activity, that most fishing gear that is swept up on the beaches of the Seychelles comes from fishing within its own exclusive economic zone. These are the waters, extending 200 nautical miles (370km) offshore, over which a state has exclusive control over marine resources, including fisheries.
These waters are full of tuna, and the Seychelles’ economy leans heavily on industrial fishing, both as an employer and as a large source of foreign currency.
Where does the waste come from?
It is likely that some of the gear and plastic waste is coming from EU vessels. The Seychelles’ waters are heavily fished by two types of European fishing boats, purse-seiners and longliners. This falls under a longstanding agreement between the EU and the Seychelles granting them the right to fish here. Purse-seiners circle around tuna schools with a large net, and longliners trail baited hooks over many kilometres.
Indeed, a report presented to the Indian Ocean Tuna Commission found that 76% of 214 of the drifting fish aggregating devices used by tuna fisheries to attract shoals of fish in the Seychelles’ exclusive economic zone were from Spanish-owned vessels. This is an industry making significant profits, while leaving behind environmental damage.
A turtle on the Aldabra Atoll next to lost fishing gear and plastic.April Burt and Seychelles Islands Foundation
Recently, the Seychelles–EU fishing agreement was renewed for 2026–30 at €5,750,000 per year (£4.89 million), allowing 38 EU fishing vessels to operate in these waters. The Seychelles government is believed to have pushed for a 30% rise in this annual payment from the EU but it is believed they settled on 7%, according to some reports.
Given that the cost associated with cleaning up a single island already runs into millions, the Seychelles-EU deal is small compared to the indirect damages imposed on the nation.
Other proposed solutions
International shipping creates other issues. Our study suggests that more than 90% of plastic being swept up on the Seychelles’ coast from shipping comes from just five lanes crossing the western Indian Ocean. The origin and destination ports of these lanes line up with the countries named on labels of plastic bottles washed ashore on Aldabra.
Debris from international shipping lanes requires a different solution. Unlike the tuna fleets fishing under the agreement between the Seychelles and the EU, this is international traffic passing by. A solution to the plastic waste coming from these ships would be more rigorous enforcement of existing international rules against dumping waste at sea, and bilateral cooperation between the Seychelles and the many nations whose ships use these routes.
Even simple measures could cut litter. For instance, ensuring vessels carry enough safe drinking water to reduce the use of plastic bottles should cut some debris found on Aldabra.
The true cost of cleaning up this huge level of plastic pollution tends not to fall on those who have left it there. The Seychelles needs to work out the cost of the damage being done by these mountains of waste to get a better deal in future negotiations with the EU.
That should help fund clean ups and repair the fragile ecosystems of the islands, helping to preserve them for the future.
Noam Vogt-Vincent received funding from the Natural Environment Research Council and National Oceanic and Atmospheric Administration.
Alex Albinski does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.
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.
Neuroscientist Anil Seth discusses his reservations about AI ‘consciousness’. Video: TED.
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?
Philosopher David Chalmers believes conscious AI is possible. Video: Union College.
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).
Right now, your brain is recognising the shapes of these letters, analysing their meaning, and holding a stream of thought together – all on about 20 watts of power. That’s roughly what it takes to run a lightbulb.
Yet reproducing even a small fraction of the brain’s capabilities using today’s AI systems can require vast computing infrastructure and energy.
Neuromorphic computing – also known as “brain-inspired computing” – is an attempt to close that gap. Not by making today’s computers slightly more efficient, but by fundamentally rethinking how they should work.
The human brain holds roughly 86 billion neurons, wired to each other through junctions called synapses. A neuron receives signals from its neighbours; when their combined effect crosses a threshold, it will fire a brief electrical pulse known as a spike.
Two things make this process efficient. First, energy is spent only where it’s needed, not everywhere constantly. Second, memory and processing are intertwined in the strength of each synapse’s response. The brain doesn’t have to pause and go find information, saving both time and energy.
In contrast, in an ordinary computer chip, the processor and memory are physically separate, so every calculation entails transferring data between them. This can account for a substantial share of energy use – known as the Von Neumann bottleneck, a term first used by US computer scientist John Backus in 1978 to describe this computing inefficiency.
Introduction to neuromorphic computing. Video: UCL Electronic and Electrical Engineering.
Neuromorphic computing tackles this inefficiency in several ways: bringing memory and processing closer together, using “sparse representations” (data models where most values are zero), and performing computation only when events occur.
This is known as event-driven computing – and we are already seeing some exciting applications.
Sensors modelled on human retina
Event cameras are sensors modelled on the human retina. Their pixels respond individually and only when they detect a change in the scene – rather than capturing a full frame dozens of times a second, like a smartphone or video camera.
The result is a neuromorphic sensor that uses a fraction of the power, handles fast motion without blur, and works equally well in bright sunlight and near-total darkness.
In autonomous vehicles, this combination of low latency and reliable performance in glare or darkness could be the difference between detecting a pedestrian in time, or not. And in space, where power is scarce and lighting extremes are common, event cameras have already been deployed for object and debris tracking.
An event-based vision sensor explained. Video: Sony.
It might be tempting to ask if neuromorphic hardware could do for computing what Nvidia’s graphics processing units (GPUs) have done for AI. But these are fundamentally different kinds of chip.
Nvidia’s GPUs are exceptionally good at the dense, repetitive maths behind deep learning, which has made them the default choice for almost any AI task. In contrast, neuromorphic computing’s event-driven approach looks likely to spawn a wide range of specialised chips, rather than a single winner-takes-all design that everyone adopts at once.
Australia’s BrainChip already sells a commercial neuromorphic processor for powering cameras and sensors that need to run at very low power. Expect neuromorphic hardware to sit alongside conventional processors and GPUs rather than replacing them, taking on the jobs where its efficiency edge is decisive.
Sensitive information
But there is another advantage to neuromorphic computer technology beyond energy saving.
Because neuromorphic chips can process data right where it’s generated, sensitive information from wearables, smart cameras, and medical sensors can stay on the device instead of travelling to the cloud, potentially reducing privacy and cybersecurity risks associated with data transmission.
Devices can also work offline, without needing to send data to a distant server and wait for a reply. Cutting out that round trip matters wherever split-second decisions are required, such as in autonomous vehicles, drones, and robots operating beyond reliable signal.
This won’t make data centres redundant. But fewer tasks may need one in future, and those that remain could become dramatically more efficient. IBM has already demonstrated energy savings of up to 10,000-fold on event-driven tasks, compared with conventional digital architectures, with its TrueNorth chip.
Interview with Tony Kenyon, director of the Neuroware Innovation and Knowledge Centre. Video: Curious with Ezra Chapman.
Rethinking how computing works
Computing has been through a transition like this before. The first computers filled entire rooms and drew as much power as a small factory. Today, however, a chip smaller than a fingernail in the phone in your pocket is far more capable.
Neuromorphic computing raises the possibility of a similar shift – driven less by cramming in more transistors (the tiny switches that make up a chip’s circuitry) than by rethinking how computing should work in the first place.
With the global market for neuromorphic technology forecast to nearly quadruple to US$20 billion (£14.8 billion) by 2030, the UK needs open-access facilities to prototype new chips, coordinated investment, common standards – and a workforce trained to bridge neuroscience, electronics and computer science.
The foundations are already here. What’s missing is the infrastructure to take neuromorphic computing from supercomputers such as the University of Manchester’s brain simulator, SpiNNaker, to the billions of ordinary devices that could one day use brain-inspired hardware for themselves.
Your brain solved this problem millions of years ago. It’s taken science a long time to appreciate just how well it works.
Aysha Asif Riaz works for NeuroWare Innovation Knowledge Centre as a Research Fellow. NeuroWare is funded by the Engineering and Physical Sciences Research Council under grant UKRI2784.
When asked about the greatest innovation of humans, people will often say language or fire. A new paper suggests that these innovations may be more interlinked than we previously assumed.
Understanding how human language evolved is one of the hardest problems in science to unpick, since writing is a relatively recent innovation. Also, the vocal tracts and brains of our ancestors did not fossilise as they were made of soft tissue. We are left, then, with indirect evidence about how humans and other apes communicate today.
One of the most prominent theories about the origins of human language is that our ancestors started to communicate using gestural language. This theory is based on our understanding of the abilities of apes, as evidence suggest both chimpanzees and gorillas have gestural communication systems.
We also know that one of the best strategies for communicating when you don’t have a shared language is gesture. We often do this when communicating on holiday with someone who doesn’t speak our language; we point, mime and emote to get our message across.
Some of my previous research has shown that we do this because gestures or sign languages are often more iconic than words in spoken languages. This means their meaning is easier to guess for someone without any previous knowledge of the gesture or language being spoken; perfect for creating a new language from scratch.
Did fireside storytelling aid the evolution of human language?
If human language started with gestures, then why are the vast majority of languages today spoken? One reason for this shift might lie in the limits of gestural language. You need a light source to see a gesture, and communication would have been necessary for our ancestors after dark.
An article by researchers at the University of St Andrews argues that by the dim light of a campfire, we might find an explanation for why we moved from gesture to spoken language. While a light source, firelight is potentially too dim for us to rely on it for the meaningful bits of early gestural language. For fully expressive stories in the dark, we would need spoken language but still keep gestural elements to emphasise and emote our speech.
The study highlights how storytelling links to some of the features of human language that make it unique. Stories have complex structures and are often displaced in time and space, meaning they talk about things not immediately present, in the past, or sometimes not even real.
The article suggests that social storytelling is what enabled us to surpass the communicative abilities of other living apes, into the rich, expressive tapestry of human language. Storytelling aids our social bonds and helps us understand the world and how to behave. It can help us develop our imagination, identities and cultural belief systems.
The idea that language emerged for social bonding is not new; however, the authors of the new article highlight that fireside context might have been crucial. They argue that the colour spectrum of fire is well suited for human vision without suppressing the melatonin that helps us sleep, like modern screen light can.
The study also highlights that human neurons synchronise to the frequency of flickering firelight, which the authors argue can improve shared social experience and bonding through connected rhythmic activities that can promote empathy.
How about storytelling today?
Other research, reviewed in my own work, has shown that stories from our early oral traditions share similar traits to one another. This is thought to happen because the social transmission of stories acts like evolution. Instead of survival of the fittest, we get survival of the most memorable aspects of stories as they are shared. Stories survive when they have more social, human-oriented content, counterintuitive elements, create negative emotions and have elements of survival.
Researchers think this happens because swapping stories about social bonds, unexpected dangers and survival are advantageous for our evolution.
Storytelling is still one of the primary ways we understand the world even now, through the news and social media. This content is full of human interest, negative emotions and surprise.
Storytelling in the modern world is now often about getting the most clicks and attention. It is often about keeping people up past their bedtime looking at the melatonin suppressing blue light of our screens.
I’m left wondering, after reading about the potential fireside origins of our language, that we might feel more connection and trust by going back to sharing stories around a fire.
Hannah Little receives funding from the British Academy and the Royal Society.
22% of 18- to 21-year-olds have sought emotional support from generative AI.oatawa/Shutterstock
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.
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