Virtual reality goggles for mice

by Paul Curzon, Queen Mary University of London

Conjure up a stereotypical image of a scientist and they likely will have a white coat. If not brandishing test tubes, you might imagine them working with mice scurrying around a maze. In future the scientists may well be doing a lot of programming, and the mice for their part will be scurrying around in their own virtual world wearing Virtual Reality goggles.

Scientists have long used mazes as away to test the intelligence of mice, to the point it has entered popular culture as a stereotypical thing that scientists in white lab coats do. Mazes do give ways to test intelligence of animals, including exploring their memory and decision making ability in controlled experiments. That can ultimately help us better understand how our brains work too, and give us a better understanding of intelligence. The more we understand animal cognition as well as human cognition, the more computer scientists can use that improved understanding to create more intelligent machines. It can also help neurobiologists find ways to improve our intelligence too.

Flowers for Algernon is a brilliant short story and later novel based on the idea, there using experiments on mice and humans to test surgery intended to improve intelligence. In a slightly different take on mice-maze experiments, Douglas Adams, in ‘The Hitchhikers Guide to the Galaxy’, famously claimed that the mice were actually pan-dimensional beings and these experiments were really incredibly subtle experiments the mice were performing on humans. Whatever the truth of who is experimenting on who, the experiments just took a great leap forward because scientists at Northwestern University have created Virtual Reality goggles for their mice.

For a long time researchers at Northwestern have used a virtual reality version of maze experiments, with mice running on treadmills with screens around them projecting what the researchers want them to see, whether mazes, predators or prey. This has the advantage of being much easier to control than using physical mazes, and as the mice are actually stationary the whole time , just running on a treadmill, brain-scanning technology can be used to see what is actually happening in their brains while facing these virtual trials. The problem though is that the mice, with their 180 degree vision, can still see beyond the edges of the screens. The screens also give no sense of 3 dimensions, when like us the mice naturally see in 3D. As the screens are not fully immersive, they are not fully natural and that could affect the behaviour of the mice and so invalidate the experimental results.

That is why the Northwestern researchers invented the mousey VR googles, the idea being that they would give a way to totally immerse the mice in their online world, and so improve the reliability of the experiments. In the current version the goggles are not actually worn by the mice, as they are still too heavy. Instead, the mouse’s head is held in place really close to them, but with the same effect of total immersion. Future versions may be small enough for the mice to wear them though.

The scientists have already found that the mice react more quickly to events, like the sight of a predator, than in the old set-up, suggesting that being able to see they were in a lab was affecting their behaviour. Better still, there are new kinds of experiment that can be done with this set up. In particular, the researchers have run experiments where an aerial predator like an owl appears from above the mice in a natural way. Mounting screens above them previously wasn’t possible as it got in the way of the brain scanning equipment. What does happen when a virtual owl appears? The mice either run faster or freeze, just as in the wild. This means that by scanning their brains while this is happening, how their perception of the threat works can be investigated, as well as how decision-making is taking place at the level of their brain activity. The scientists also intend to run similar experiments where the mouse is the predator, for example chasing a virtual fly too. Again this would not have been possible previously.

That in any case is what we think the purpose of these new experiments is. What new and infinitely subtle experiments it is allowing the pan-dimensional mice to perform on us remains to be seen.

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Exploring mazes, inventing algorithms (part I) 

by Paul Curzon, Queen Mary University of London

A maze with mouse searching for cheese.
Image by CS4FN

Computer science research in part involves inventing new algorithms or improving new ones. But what does that mean. Let’s explore some mazes to explore algorithms.

What does computer science research involve? It is very varied: from interviewing people to find out what the real problems that need solving in their lives or jobs are; to running experiments to find out what works and what doesn’t; to writing programs to solve problems.

Improving algorithms

A core part of much research is coming up with new and better algorithms that solve particular problems. The kind of algorithm could be anything from a new more secure cryptographic protocol, or a better way to rank the results of a search engine, to a new more effective machine learning algorithm that is less likely to make things up, or perhaps can better explain how it came to its conclusions.

What does it mean to come up with a better algorithm though? Once a problem is solved, isn’t it solved? Let’s explore a simple problem to see. Let’s explore mazes. Solve the simple maze puzzle above before you go on. Find a route that gets the mouse to the cheese.

Wandering around mazes, finding algorithms

If you’ve ever been in a hedge maze in the garden of some stately home, or a corn field maze, the chances are you just dived in and wandered rather aimlessly. Perhaps you tried to remember which way you went at each junction, to avoid going down the same dead-ends more than once. How about solving the paper version of a maze puzzle like the one above? Now perhaps you looked ahead to spot dead-ends to avoid tracing wrong paths with your pencil.

Probably what you are doing is at least a little random. You could, in theory at least, end up going back over the same paths, never taking the right one and and never getting to the middle. Could we come up with an algorithm that guarantees to solve mazes? To be an algorithm it would need to guarantee you ended up finding a path to the centre of the maze if you followed the steps of the algorithm precisely. It should also work for any maze, or at least all mazes of a particular kind. Ideally, the algorithm gives you a path that can then be followed by anyone without them having to run the algorithm themselves. They can just follow the path generated by the algorithm for that maze.

Wall-following

In fact lots of maze algorithms have been invented. Perhaps the one most people have heard of, if they know of any maze algorithm, is called Wall-following. It is very simple to do, You just pick a wall at the entrance either to the left or right and then follow it, If in a garden maze, keep your hand on the hedge as you walk round. If doing a paper puzzle, draw the path sticking to the chosen wall. Try it on the following simple maze.

A simple maze with mouse and cheese.
Image by CS4FN

Simply connected

The wall-following algorithm will guarantee to get you to the centre of the maze, and back out again too, but only as long as the maze is what is called simply connected. That just means the maze is constructed from a single hedge (or one unbroken drawn line) not a series of unconnected hedges. If you look at both examples above you will see I created them by just drawing a single wiggly line.

If a maze is simply connected then it cannot have looping paths, so no going round in circles for ever. It will also only have one entrance/exit. That shows the first aspect of inventing algorithms that is important. They often only work for some situations, not all. You must be sure you know what situations they do and don’t work.

Often the earliest algorithms invented to solve a problem are like wall-following: they only work for simple situations. Other people then come along and find new algorithms that can cover more problems (here more mazes). Can you tweak the wall-following maze algorithm to work even if there are multiple exits from the maze, for example? As it stands our algorithm could just take you from the entrance straight out of another exit without exploring much of the maze at all! See the end for one simple way to tweak the algorithm. What if there are paths that take you round in circles? Can you come up with an algorithm to deal with that?

Some times the improvements invented just involve tweaking an existing algorithm as with dealing with multiple exits in a maze. Some times a whole new algorithm is needed.

Faster, higher, stronger?

Even for a simple constrained version of the problem, like simply connected mazes, people can invent better algorithms. What does better mean for a maze? Well one way you might have a better algorithm is if it is faster in coming up with a solution. Another is that the solution it comes up with is faster. For a maze that means a shorter (ideally the shortest) path to the centre. Wall following may get you in to the centre (and out again) but you probably will have discovered a very long path that takes you in and out of lots of dead-ends needlessly. You do find a path to the centre, but it may be a very long path. Can you come up with an algorithm that finds shorter paths?

We will explore an algorithm that does next.

More to come…

Some solutions

The result of wall following on our simple maze

A route for the mouse to follow that takes it to the cheese.
Image by CS4FN

One way to deal with multiple exits

To deal with a maze that has multiple exits, so multiple breaks in the outer wall, tweak the wall-following algorithm as follows. First mark the exit you use to enter the maze, so you know when you return to it. If you come to any other exit then pretend there is a gate there and keep following the wall as though it were unbroken and there were no exit.

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Competitive Zen

A hooded woman's intense concentration focussing on the eyes
Image by Walkerssk from Pixabay

To become a Jedi Knight you must have complete control of your thoughts. As you feel the force you start to control your surroundings and make objects move just by thinking. Telekinesis is clearly impossible, but could technology give us the same ability? The study of brain-computer interfaces is an active area of research. How can you make a computer sense and react to a person’s brain activity in a useful way?

Imagine the game of Mindball. Two competitors face each other across a coffee table. A ball sits at the centre. The challenge is to push the ball to your opponent’s end before they push it down to you. The twist is you can use the power of thought alone.

Sound like science fiction? It’s not! I played it at the Dundee Sensation Science Centre many, many years ago where it was a practical and fun demonstration of the then nascent area of brain-computer interfaces.

Each player wears a headband containing electrodes that pick up your brain waves – specifically alpha and theta waves. They are shown as lines on a monitor for all to see. The more relaxed you are, the more you can shut down your brain, the more your brain wave lines fall to the bottom of the screen and start to flatline together. This signals are linked to a computer that drives competing magnets in the table. They pull the metal ball more strongly towards the most agitated person. The more you relax the more the ball moves away from you…unless of course your opponent can out relax you.

Of course it’s not so easy to play. All around the crowd heckle, cheering on their favourite and trying to put off the opponent. You have to ignore it all. You have to think of nothing. Nothing but calm.

The ball gradually edges away from you. You see you are about to win but your excitement registers, and that makes it all go wrong! The ball hurtles back towards you. Relax again. See nothing. Make everything go black around you. Control your thoughts. Stay relaxed. Millimetre by millimetre the ball edges away again until finally it crosses the line and you have won.

Its not just a game of course. There are some serious uses. It is about learning to control your brain – something that helps people trying to overcome stress, addiction and more. Similar technology can also be used by people who are paralysed, and unable to speak, to control a computer. The most recent systems, combining this technology with machine learning to learn what thoughts correspond to different brain patterns can pick up words people are thinking.

For now though it’s about play. It’s a lot of fun, just moving a ball apparently by telekinesis. Imagine what mind games will be like when embedded in more complex gaming experiences!

– Paul Curzon, Queen Mary University of London (updated from the archive)

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Creating great game worlds

by Wateen Aliady, Queen Mary University of London

Are you a PUBG or Fortnite addict? Maybe you enjoy playing Minecraft? Have you thought how these games are created? Could you create a game yourself? It is all done using something called a “Game Engine”.

Games and films are similar as they require creativity and effort to make. Every movie is created by a talented cinema director who oversees everything involved in creating the film. Game creators use a special set of tools instead that similarly allow them to make compelling video game worlds, stories, and characters. These tools are called game engines and they bring your creative ideas to life! They are now even used to help make films too. So, whether you’re playing a game or watching a movie, get ready to be amazed as game creators and movie directors, the masterminds behind these incredible works, deliver captivating experiences that will leave us speechless.

Imagine a group of talented people working together to create a great video game. Miracles happen when a team’s mission becomes one. Every member in the team has a certain role, and when they work together, amazing things can happen. A key member in the group is the graphics whiz. They make everything look stunning by creating pretty scenery and characters with lots of details. Then, we have the physics guru who makes sure objects move realistically, like how they would in real life. They make things fall, bounce, and hit each other accurately. For example, they ensure the soccer ball in the game behaves like a real soccer ball when you kick it. Next, the sound expert who adds all the sounds to the game. The game engine takes on all these roles, so the experience and skill of all those people is built into the game engine, so now one person driving it can use it to create a stunning detailed backdrop, with physics that just works, integrated sound and much more.

Game creators use game engines to make all kinds of games. They have been used to create popular games like Minecraft and Fortnite. When you play a game, you enter a completely different world. You can visit epic places with beautiful views and secrets to discover. You can go on big adventures, solve tricky problems, and be immersed in thrilling fights. Game engines allow game developers to make fun and engaging games that people of all ages enjoy playing by looking after all the detail, leaving the developer to focus on the overall experience.

Anyone can learn to use a game engine even powerful industry standard ones like Unity used to create Pokemon Go, Monument Valley and Call of Duty: Mobile. Game engines could help you to create your own novel and creative games. These amazing tools can help you in creating characters, scenes, and adding fun features like animation and music. You can turn your ideas into fun games that you and your friends can play together. You might create a new video game that becomes massively popular, and people love all around the world. All it takes is for you to have the motivation and be willing to put in the time to learn the skills of driving a game engine and to develop your creativity. Interested? Then get started. You can do anything you want in a game world, so use your imagination and let the game engine help you make amazing games!

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The gender shades audit

by Jo Brodie, Queen Mary University of London

Face recognition technology is used widely, such as at passport controls and by police forces. What if it isn’t as good at recognising faces as it has been claimed to be? Joy Buolamwini and Timnit Gebru tested three different commercial systems and found that they were much more likely to wrongly classify darker skinned female faces compared to lighter or darker skinned male faces. The systems were not reliable.

Face recognition systems are trained to detect, classify and even recognise faces based on a bank of photographs of people. Joy and Timnit examined two banks of images used to train the systems and found that around 80 percent of the photos used were of people with lighter coloured skin. If the photographs aren’t fairly balanced in terms of having a range of people of different gender and ethnicity then the resulting technologies will inherit that bias too. The systems examined were being trained to recognise light skinned people.

The pilot parliaments benchmark

Joy and Timnit decided to create their own set of images and wanted to ensure that these covered a wide range of skin tones and had an equal mix of men and women (‘gender parity’). They did this using photographs of members of parliaments around the world which are known to have a reasonably equal mix of men and women. They selected parliaments both from countries with mainly darker skinned people (Rwanda, Senegal and South Africa) and from countries with mainly lighter skinned people (Iceland, Finland and Sweden).

They labelled all the photos according to gender (they had to make some assumptions based on name and appearance if pronouns weren’t available) and used a special scale called the Fitzpatrick scale to classify skin tones (see Different Shades below). The result was a set of photographs labelled as dark male, dark female, light male, light female, with a roughly equal mix across all four categories: this time, 53 per cent of the people were light skinned (male and female).

Testing times

Joy and Timnit tested the three commercial face recognition systems against their new database of photographs (a fair test of a wide range of faces that a recognition system might come across) and this is where they found that the systems were less able to correctly identify particular groups of people. The systems were very good at spotting lighter skinned men, and darker skinned men, but were less able to correctly identify darker skinned women, and women overall. The tools, trained on sets of data that had a bias built into them, inherited those biases and this affected how well they worked.

As a result of Joy and Timnit’s research there is now much more recognition of the problem, and what this might mean for the ways in which face recognition technology is used. There is some good news, though. The three companies made changes to improve their systems and several US cities have already banned the use of this technology in criminal investigations, with more likely to follow. People worldwide are more aware of the limitations of face recognition programs and the harms to which they may be (perhaps unintentionally) put, with calls for better regulation.

Different Shades
The Fitzpatrick skin tone scale is used by skin specialists to classify how someone’s skin responds to ultraviolet light. There are six points on the scale with 1 being the lightest skin and 6 being the darkest. People whose skin tone has a lower Fitzpatrick score are more likely to burn in the sun and are at greater risk of skin cancer. People with higher scores have darker skin which is less likely to burn and have a lower risk of skin cancer. A variation of the Fitzpatrick scale, with five points, is used to create the skin tone emojis that you’ll find on most messaging apps in addition to the ‘default’ yellow.

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Collecting mini-beasts and pocket monsters

by Paul Curzon, Queen Mary University of London

Satoshi Tajiri created one of the biggest money-making media franchises of all time. It all started with his love of nature and, in particular, mini-beasts. It also eventually took gamers back into the fresh air.

As a child, Satoshi Tajiri, loved finding and collecting minibeasts, so spent lots of time outside, exploring nature. But, as Japan became more and more built up, his insect searching haunts disappeared. As the natural world disappeared he was drawn instead inside to video game arcades and those games became a new obsession. He became a super-fan of games and even created a game fanzine called Game Freak where he shared tips on playing different games. It wasn’t just something he sold to friends either: one issue sold 10,000 copies. An artist, Ken Sugimori, who started as a reader of the magazine, ultimately joined Satoshi, illustrating the magazine for him.

Rather than just writing about games, they wanted to create better ones themselves, so morphed Game Freak into a computer game company, ultimately turning it into one of the most successful ever. The cause of that success was their game Pokemon, designed by Satoshi with characters drawn by Ken. It took the idea of that first obsession, collecting minibeasts, and put it into a fun game with a difference.

It wasn’t about killing things, but moving around a game world searching for, taming and collecting monsters. The really creative idea, though, came from the idea of trading. There were two versions of the game and you couldn’t find all the creatures in your own version. To get a full set you had to talk to other people and trade from your collection. It was designed to be a social game from the outset.

It has been suggested that Satoshi is neuro-diverse. Whether he is or not, autistic people (as well as everyone else) found that Pokemon was a great way to make friends, something autistic people often find difficult. Pokemon, also became more than just a game, turning into a massive media franchise, with trading cards to collect, an animated series and a live action film. It also later sparked a second game craze when Pokemon Go was released. It combined the original idea with augmented reality, taking all those gamers back outside for real, searching for (virtual) beasts in the real world.

 

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