The Science of Cosiness: Why Winter Feels Better With Warm Light, Soft Sound and Ritual

4 December 2025

Paul Francis

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Somewhere between the first frosty morning and the second early sunset, many of us start craving the same things: warm light, hot drinks, familiar films, thick socks, and the sense that home is a refuge from the outdoors. We call it “cosy”, but the feeling is not just aesthetic. It is physical, psychological, and surprisingly practical.

Woman in a cozy sweater sits on a sofa holding a stuffed animal, near a decorated Christmas tree. Warm lights create a festive mood.

Cosiness is what happens when your body senses safety and steadiness. It is comfort, but with a particular flavour: warmth, softness, predictability and a gentle lowering of demands.

What “cosy” really is

Cosiness is often described like a mood, but it behaves more like an environment. It is created by a combination of signals that tell your nervous system, “you can relax now”.

Those signals tend to fall into a few familiar categories:

Warmth (temperature, blankets, hot food)
Softness (textures, cushions, knitted fabrics)
Low glare lighting (lamps, candles, fairy lights)
Low threat sound (quiet music, gentle voices, rain on windows)
Small rituals (tea at the same time, lighting a candle, a film tradition)

In winter, these cues work harder because the outside world feels harsher, darker, louder and colder. Cosiness becomes a way of counterbalancing.

Why winter makes us want it more

In the UK, winter hits in a very specific way: damp cold, short days, and long stretches of grey. Less daylight can affect energy levels and mood, partly because it disrupts sleep timing and daily routines. Even if you do not feel “sad”, you can still feel less motivated, a bit flatter, and more easily tired.

Cosy settings offer a gentle solution. They reduce stimulation, encourage rest, and help you slow down without needing to call it “self care”.

The comfort of warm light

Bright overhead lighting can feel harsh when it is dark outside. Warm, low lighting tends to feel safer and more flattering, but there is something deeper going on too. At night, the body is more suited to calm light rather than intense glare. Lamps and warm tones mimic firelight, which humans have used for thousands of years to signal rest and safety after dark.

If you want a quick cosy upgrade, change the lighting first. Even a single lamp can shift a room from “functional” to “inviting”.

Soft sound and the “safe noise” effect

Silence can be peaceful, but it can also make a home feel empty. Cosy sound is rarely loud. It is predictable, soft, and steady. Think: gentle playlists, radio voices, crackling fire videos, rain sounds.

This kind of audio does something important. It fills the background so your mind stops scanning for surprises. If you have had a stressful day, soft sound can make it easier to come down from that heightened state.

Texture is emotional, not decorative

Texture is one of the fastest ways to create cosiness because your skin reads it instantly. Rough, cold or synthetic textures can keep you feeling slightly “on guard”. Soft, warm fabrics can do the opposite.

You do not need to redesign a room. One throw, one thick hoodie, one pair of warm slippers can change the entire feel of a winter evening.

Why rituals feel powerful in December

Many cosy habits are rituals. A ritual is not just a routine. It has meaning. It marks a moment as special, even if the act is small.

In winter, rituals help because they provide:

Predictability when days feel rushed or chaotic
A sense of control when the outside world feels uncertain
A cue to rest, especially when you struggle to switch off

This is why seasonal rituals catch on so easily. The first mince pie, the first film night, the first tree decoration. They are small anchors that make the month feel structured.

How to build cosiness without buying loads

Cosiness can become a shopping trend, but it does not have to be.

A simple “cosy checklist” looks like this:

One warm light source (lamp, fairy lights, candles)
One comforting texture (throw, thick socks, soft hoodie)
One safe sound (quiet playlist or spoken radio)
One warm drink or meal
One small ritual you repeat

The point is not perfection. The point is signalling to yourself that you are allowed to slow down.

Cosiness is not laziness, and it is not just decoration. In winter, it can be a quiet form of adaptation. A way of restoring energy, lowering stress, and finding warmth when the season asks us to endure cold and darkness.

In a world that rarely stops shouting, the cosy moment is often the moment your body finally believes it is safe.

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Watching the Watchers: Is Live Facial Recognition Fit for Purpose?

Paul Francis
May 8
4 min read

In an age of rapid technological advancement, surveillance is no longer a passive act. Live Facial Recognition (LFR) technology has moved from science fiction into the heart of modern policing and commercial security systems. Able to scan faces in real time and match them to watchlists within seconds, it promises efficiency, safety, and even crime prevention. But with these promises come serious questions about legality, accuracy, ethics, and trust.

Futuristic officer with glowing green eyes and circuit-patterned uniform in a neon-lit corridor, exuding a cool, technological vibe.

As this technology continues to spread across public streets and private retail spaces alike, we must ask: is LFR ready for widespread use, or is it running ahead of the safeguards designed to protect our rights?

What is Live Facial Recognition?

Live Facial Recognition (LFR) is a biometric surveillance tool that uses real-time video feeds to detect and identify faces. Unlike static facial recognition, which analyses images after an event has occurred, LFR operates live. Cameras scan crowds, extract facial features, and compare them to a database of preloaded images. If the system detects a potential match, it alerts a human operator to intervene or investigate.

LFR is being trialled and used by several police forces in the UK, including the Metropolitan Police and South Wales Police. Retailers, stadiums, and event organisers are also deploying the technology in an attempt to identify shoplifters or detect banned individuals before trouble starts.

A woman's face on a monitor with blue facial recognition lines, surrounded by software interface text, creates a tech-focused atmosphere.

How Does It Work? A Closer Look

LFR involves several distinct technical steps. At its core, it is powered by artificial intelligence and machine learning algorithms trained on vast datasets of facial images. The process typically unfolds as follows:

Face Detection

First, the system identifies a face within a video frame. This step uses computer vision models to detect facial structures such as the eyes, nose, and jawline. This is not identification yet; it is simply recognising that a face is present.

Alignment and Normalisation

Once detected, the system adjusts the face to account for differences in head tilt, lighting, or distance. This is known as normalisation. The aim is to ensure that all faces are processed in a similar format so that they can be compared reliably.

Feature Extraction

The system then uses a deep learning model, often a convolutional neural network, to extract features from the face. These are translated into a biometric template, a mathematical vector that represents the unique aspects of that person’s face.

Matching

This template is then compared against a watchlist. The system calculates a similarity score between the live face and each entry in the database. If the score passes a predefined threshold, the system flags it as a match. A human operator is usually involved at this stage to confirm or reject the result.

This entire process happens in seconds, enabling real-time surveillance across public or private spaces.

The Case For LFR

Proponents argue that LFR is a valuable tool for modern policing. It can identify wanted criminals, locate missing persons, and even prevent terrorist acts before they happen. In retail settings, it promises to reduce shoplifting and protect staff from repeat offenders. Unlike traditional methods, it allows for rapid identification without the need for physical interaction or delays.

The technology also allows for more efficient use of resources. Officers can be directed to individuals flagged by the system, rather than relying solely on observation or tip-offs. In theory, this reduces the burden on police and enhances public safety.

The Case Against LFR

Despite its promise, LFR is far from perfect. One of the main concerns is accuracy. Studies have shown that LFR systems are more likely to produce false positives for people with darker skin tones and for women. These errors are not trivial. A mistaken identity can result in an innocent person being stopped, searched, or even arrested.

There is also the issue of bias in training data. If an algorithm has been trained primarily on certain demographics, it will perform less effectively on others. In real-world conditions, such as low lighting or crowd movement, these problems can become even more pronounced.

Beyond technical flaws, legal and ethical questions loom large. In the United Kingdom, there is currently no specific law governing the use of LFR. Its deployment relies on a complex mesh of data protection laws, human rights principles, and operational guidance. Critics argue that this legal uncertainty leaves too much room for misuse.

A 2020 Court of Appeal ruling found South Wales Police’s use of LFR to be unlawful, citing insufficient safeguards, inadequate impact assessments, and the risk of discriminatory practices. The ruling did not ban the technology outright but signalled that current uses are walking a legal tightrope.

Potential Misuse and the Chilling Effect

One of the most troubling aspects of LFR is its capacity for mass surveillance. By scanning every face in a crowd, it treats everyone as a potential suspect. This blanket approach has been described as disproportionate and invasive by privacy groups such as Big Brother Watch and Liberty.

There is also the risk of function creep. A system introduced to identify serious offenders could, over time, be expanded to monitor protests, track political activists, or even control access to public spaces based on social or behavioural metrics.

Furthermore, the use of LFR by private companies raises concerns about data ownership and accountability. Retailers may share watchlists across multiple sites or even with law enforcement, all without the consent or knowledge of the individuals being scanned. This could lead to people being unfairly banned, blacklisted, or targeted, based on secretive and unchallengeable criteria.

Is It Fit for Purpose?

At present, the evidence suggests that Live Facial Recognition technology is not ready for widespread deployment. While it offers considerable potential, its use is outpacing the development of ethical, legal, and technical safeguards. In its current state, LFR is more likely to erode public trust than to enhance security.

Without robust legislation, transparent oversight, and significant improvements in accuracy and fairness, LFR risks doing more harm than good. Surveillance should not come at the cost of civil liberties or human dignity. As with all powerful technologies, its benefits must be balanced against the risks, and right now, that balance appears off.

LFR is a powerful tool with a fragile foundation. Its strengths lie in speed and scale, but its weaknesses—bias, error, and lack of transparency—cast a long shadow. Until these flaws are addressed, caution must guide its use.

In the race to embrace smart surveillance, we must not forget the human rights and democratic values that underpin our society. Watching the watchers may be just as important as watching the streets.

Images provided by Leonardo AI