Back from VSS, off to AVA

Last week I was at the VSS conference in Naples, Florida. Went on a boat, met lots of interesting people, and my poster went down well. It was fun!  Here are some photos.

For the first time, I had my poster printed onto fabric by scienceposters.co.uk. It was great being able to fold it up in my luggage, instead of having to take a poster tube on lots of planes. The print quality is slightly below that of paper, but it’s a small sacrifice for the convenience, and it’s still more than readable. There were lots of other posters printed onto fabric this year, so I think it’s something that’s just about to go mainstream.

I saw lots of interesting birds, and this rather neat grating butterfly:

Something I noticed this year was the number of posters with QR codes. It’s neat – you just scan the code using a smart phone or iPad with a QR reader app, and it downloads a pdf of the poster. I recently upgraded to an iPad 3, which has a camera (unlike my first gen iPad), so I scanned a fair few that way. I’m off to an AVA conference in Cambridge next week, so I decided to include a QR code on my poster, created using Kaywa. It was a bit of a fiddle getting the code on the poster to point to the pdf of the poster, as I had to know the web address in advance of creating the code. Worked though

Face distortion is not due to lens distortion

Here is a series of photographs of my face, taken from different distances, using lenses with different focal lengths (see here and here for more examples). Because I covaried distance and focal length, my face appears about the same size in each image. However, the relative size and positions of my various facial features changes very markedly – in the last photo I have no ears! Why does this happen?

Photos of me using different focal length lenses (85-8mm on an APS-C sensor, so equivalent to 127.5-12mm on 35mm film) from five different distances (200-20cm). Note that the wide angle lens used for the last three photos is not a fisheye lens (it’s one of these).

Many people would probably tell you this was due to ‘lens distortion’ – implying that the wide angle lens used for the photos on the right somehow distorts reality, creating an imperfect image. This is totally incorrect. Actually, the only ‘distortions’ are caused by geometry, they are nothing to do with the lens or the camera.

In the leftmost image, the subject is far away (2 metres) from the camera. At this distance, each of my facial features is a similar distance from the camera – within a few percent of the total distance – so my face appears flat. In the rightmost image, I’m about 20cm from the camera. Because my nose is about 10cm away from my ears on my head, this means that there is a large proportional difference in the distance from the camera to my nose, and the distance from the camera to my ears. My nose appears much larger, because it is proportionally closer to the camera than the rest of my face.

The crazy thing about this is that it happens in real life too, we just don’t often notice it. If you look at yourself in the mirror from very close up (or get close to someone you’re intimate with), you get exactly the same distortions (closing one eye helps with this, as most people can’t maintain vergence that close). I find the middle image above to be the closest to how I think I look, probably because the distance of 40cm means it’s approximately what I see when I look in a mirror normally. Portrait photographers usually use long focal lengths (at least 85mm) because this is thought to produce a more ‘natural’ and flattering portrait.

All of this is important, and not just to make yourself look hot on Facebook (a hint ignored in both those links: don’t get too close to the camera!). Every time we go through passport control at an airport, the photo on your passport gets compared to the real life you, by someone who never met you before. Similarly, if you ever end up in court for something, CCTV evidence – usually shot from many metres away – might be used to identify you. It turns out that people are surprisingly bad at correctly identifying strangers in this way (e.g. see this paper). I wonder how many false convictions this has resulted in over the years….

Thoughts on academic social media websites

In recent years there have been numerous social media websites introduced geared towards academics. Many of these are aiming to become a ‘Facebook for researchers’, with varying degrees of success. I find this interesting, so I tend to sign up for sites when I become aware of them. I thought I’d summarise my thoughts on all of the ones I’m aware of, partly because several people have asked me about them, but also to make it easier to keep track of them all myself!

Academia.edu                                            Rating: 4/5 Worth a go

Probably the most widely used, and also the most similar to Facebook. You create a profile and populate it with your publication list, as well as job history, talks etc. You can ‘follow’ other researchers, and see their updates in a feed. I found the process of manually adding publications fairly straightforward, whereas the automated method was poor, probably because I have a fairly high frequency name. One unique feature is that it tells you if someone has searched Google and then clicked on your profile. This happens occasionally, and it’s interesting to see the search terms used and the searcher’s location.

Biomed Experts                                           Rating 3/5 OK

One of the earliest websites I signed up to, and still going. It is interesting, as it creates profiles by using an algorithm to group PubMed entries which appear to be by the same person. You can then claim your profile, and fix any errors. A huge advantage of this is that it automatically adds new publications as they come out, and it also suggests relevant papers you might find interesting (much more successfully than other websites). It makes pretty, if pointless, network diagrams of people who collaborate with each other. I sometimes find it hard to tell if other people have actually signed up, or if their profiles are just auto-generated.

ResearchGate                                               Rating: 2/5 Waste of time

Kind of a mixture of the previous two websites. It calculates ‘impact points’, which turn out to be just the impact factor (from 2009) of the journal an article was published in. It adds these up for a ‘total impact’, and averages them too. Not terribly useful, and doesn’t do anything better than an alternative.

IAmScientist                                                 Rating: 1/5 Not worth it

Again, similar to the above three sites, with little to make it stand out. I actually don’t know anyone else on this one, and there doesn’t seem to be a mechanisms for ‘connecting’ with other people. There used to be a horrendous bug in the publication searching feature where it would add thousands of unwanted papers, which were then difficult to remove. Hopefully they’ve sorted that out by now. Possibly created by a non-native English speaker, as there are various peculiar turns of phrase (such as “Manage you publications”).

SciLink                                                           Rating: 1/5 Gone anyway

A primitive version of the above websites. It seems to have disappeared entirely, so I assume the founders went bankrupt. Basically the same concept, but with virtually no subscribers (I knew no one else on there). Also the founder of the site had a weird habit of trawling through message boards and leaving asinine comments, presumably to try and ‘stimulate conversation’, or maybe to make it look like someone gave a shit. Seemed to be ad-funded. Can’t say I’ve missed it.

NeuroTree                                                     Rating: 4/5 Simple, original, cool

A clever and informative ‘family tree’ for neuroscience researchers, with around 36000 people added. It shows who your academic ‘parents’ (PhD and postdoc supervisors) and ‘children’ (students) are, and goes back several generations. It’s pretty heavily dominated by vision researchers. You can update your own, or other people’s information, and add new people too, alive or dead. There is a feature for calculating the distance between two people, or find your nearest Nobel prize winner. Similar things exist for other disciplines, such as the famous Erdös number for mathematicians.

ResearcherID                                                  Rating: 3/5 Alright

Not exactly a networking website, this is Thompson’s tool for keeping track of citations to all your publications. Originally it was invite-only, but now anyone can sign up. You locate your papers in Web of Knowledge/Web of Science, assuming you have an institutional subscription. Subsequent papers have to be added manually through the same process. It then counts your cites, produces a pretty graph and tells you your H-index. Used to be the best way of doing this, but has since been eclipsed by Google Scholar (see below).

There is a nice feature for producing a ‘badge’ to stick on your website, to ‘pimp your H’. My main dislike is the continuous signing in process that it seems to insist on. Often I just want to see the ‘public’ version of my profile, without signing in. However, if it detects that you have previously signed in on the machine you’re using, it forces you to go through the laborious (and often malfunctioning) Shibboleth/Athens sign in rigmarole. This is such a pain, I usually don’t bother.

Google scholar citations                                     Rating: 5/5 Great

Very similar to ResearcherID, but it works better. Setting the whole thing up took literally under a minute – it found all my papers with only one false positive, which was easily discarded. It automatically adds new papers within a few days of being published. It’s more inclusive for citations, as it indexes the whole of the internet, rather than Thomson’s more limited database. This means my H-index is slightly higher than in ResearcherID. You can add your co-authors if they’re signed up too, and because it’s Google it’s free, and doesn’t require endless sign ins. The only thing I can think of to improve it would be a facility to ‘follow’ other people’s citations and H-index. This sort of exists, but it’s for email notifications – I’d prefer a summary on a web page instead. Otherwise, it’s great – Google have really outdone themselves with this and their journal citation tool (see my previous post).

Microsoft Academic Search (beta)                    Rating: 2/5 Not worth it

Essentially a poor copy of the previous two sites. Records are auto-generated, and (in my case) wildly wrong. You don’t claim your own profile (as with BioMed Experts), instead you submit change requests on your, or anyone else’s, profile. These then have to be approved, so they take around a week to go live. Calculates H-index, but seems to miss about 50% of legitimate cites from what I can make out. Hopefully it will get better, but I don’t see what it adds to any of the others.

Labome.org                                                           Rating: 1/5 Pointless

I stumbled across this the other day. It appears to be just a list of publications, which is mostly correct for me but has a few missing. I don’t see what it’s for, there’s no way to ‘join’ or actually do anything with the information. Not even sure how to pronounce it – is it ‘ome’ to rhyme with ‘dome’, or a contracted version of “Lab of me”? Possibly an oblique scheme for hawking antibodies, as Labome.com seems to be a company that sells the same thing as lots of other companies who spam me all the time. They are the science version of “Canadian pharmacies” selling viagra.

Facebook

With lots of these websites trying to be like Facebook, part of me thinks, why bother? Facebook already exists, and virtually everyone who owns a computer already has an account. So, if you want to interact with your colleagues, maybe ask them something, why not do it on Facebook? In practise, this happens quite a lot these days – people post about conferences, plug their papers, ask each other questions. Google+ also seems to have a fair number of people I know through work (more than non-work friends actually), and so that’s had some work-related chat recently. Of course, there’s no way of showing off all your papers, but you can always add a link to your website for anyone interested.

I guess I’d be surprised if many of these websites are still around in 5 years. They are trying to fill a gap that doesn’t really exist, and most of them aren’t doing it particularly well.

New paper in PLoS ONE

Last week I had a new paper out in PLoS ONE. It’s about how perceived contrast depends on the phase relationship across the eyes. We were inspired to do this work by another paper which seemed to show that there was no relationship between phase and perceived contrast. We investigated a wider range of phase and contrast conditions, and find a clear effect, which we explain using a computational model:

This is the first paper I’ve published in a PLoS journal. Although the review process for PLoS ONE is supposed to focus on methodological soundness, I found it comparable to other places I’ve published, like Journal of Vision, which is also open access. Given the recent highlighting of all the bad things about for-profit publishers, I think it’s important to support not-for-profit open-access journals like these. It also has a decent impact factor, which doesn’t hurt.

Google h5 vs Thomson Impact Factor

I really like bibliometrics. Imagine my delight then, when I learnt that Google have recently introduced a new journal metric as an alternative to the impact factor. It’s called the h5 index, and you can read more about it here.

Basically, it’s equivalent to the Hirsch index, but calculated for a journal rather than an author, over a 5 year period. So, an h5 of 10 means that during the past five years a journal has published 10 articles which were each cited at least ten times (and many more articles which were cited fewer than 10 times).

I thought it would be interesting to see how this compares to the impact factor for various journals of relevance to perception researchers. So, here is a graph showing the relationship between the two metrics:

As you can see, there’s a strong correlation over the lower end of the range (approximately h5 = 12*IF), but some divergence at the top end. In particular, some journals with very different impact factors have a similar h5 index (e.g. PLoS ONE and Nature Neuroscience). I suspect this is to do with volume of papers published and scope of the journals involved (e.g. PLoS ONE publishes thousands of articles in many disciplines, so it’s not surprising it has a high h5).

Despite the many problems people have with impact factors, the hard reality is that journal metrics are useful for a range of things. I’ll be interested to see how widely the h5 index gets used over the next few years, especially given the strong correlation for the low-to-medium impact journals.

In particular, if I were Thomson I’d be very worried indeed. In the past six months or so, Google have, seemingly out of nowhere, produced viable competitors to both the Journal Citation Reports database and the ResearcherID tool for calculating an individual’s h-index. In fact, for a variety of reasons I prefer Google’s versions (they’re free, not behind an annoying paywall, much faster, more transparent, and my h-index is higher according to Google!). Although Thomson may have pioneered bibliometrics, remember that Google didn’t invent internet search – they just did it better than everyone else . . . .

Transatlantic spam

So, I’m used to getting spam emails, trying to sell me viagra, or do some protein sequencing for me. Recently though, I’ve received some old fashioned spam, sent in the post from America. The culprits are Thomson Reuters, the company behind the Web of Science/Knowledge website, journal impact factors and so on.

I’ve had two packages now, each sent from Philadelphia, USA to Birmingham, UK, at a cost of around $5 each in postage. Now, you’d think that if you’re sending something such a long way, and paying a fair bit to do so, that it should be something good.

Not so:

Here is spam item 1 – a jigsaw.  The package contained only a short leaflet and a nine-piece jigsaw. Would you pay $5 to send this across the Atlantic ocean?

Today it got even worse:

This cardboard box contained another leaflet, two large bits of cardboard, and a very small USB stick. The USB stick was embedded in a big bit of rubber, shaped like a jigsaw. I had to remove the rubber with scissors to plug it into my computer. It is a 1GB memory stick, with around 40MB of promotional files for Thomson Reuters – mostly pdfs of posters which I don’t care about. They could have sent me an email with links to download these files, but instead paid $5 to send it to me all the way from America.

I don’t understand why I’m being sent this stuff, or who would ever want to be sent things like this.

I don’t understand why any company has a marketing budget that allows them to do this, or how they can be so gratuitously wasteful.

If you ever wondered why subscriptions to Thomson Reuters ‘products’ cost as much as they do, now you know.

Paper accepted

Heard last night that we’ve had a paper accepted at iPerception, which is nice. Should be out in a few weeks, open access.

At the moment I’m working on a poster for a conference next week in Cardiff. Conference review and PDF of poster coming soon.

Masking

The previous post outlined a basic experiment for measuring sensitivity to contrast at detection threshold for a simple target. In this post, I’ll describe how detection thresholds can be affected by other stimuli, which are termed ‘masks’. You can think of a mask as ‘getting in the way’ of a target, and making it harder to detect. I’ll describe two varieties of masking (though several others exist) – when the mask is similar or identical to the target (pedestal masking) and when the mask is very different from the target (cross-channel masking).

Pedestal masking

In a standard 2AFC detection experiment, the target is shown in one temporal (or spatial) interval, but is absent in the other. This is still the case in a masking experiment, but there is also a mask, which is presented in both intervals. When the mask is spatially identical to the target (i.e. it’s the same image, but probably at a different contrast) it is known as a pedestal. The pedestal affects detection thresholds in interesting ways. For low contrast pedestals, thresholds are reduced (i.e. performance gets better). A good analogy is with height – if detection occurs when someone’s head is visible above a wall, standing them on a box (a pedestal) will make it more likely that their head pops up over the wall. Contrast detection works in a similar way, and the improvement in threshold is often referred to as facilitation. For high contrast pedestals, the task becomes contrast discrimination: the pedestal is visible in both intervals, but the target is added only in one. This is like judging the height of two huge skyscrapers – you’ll only notice the difference if one is substantially taller than the other. So, with high contrast pedestals, thresholds increase, and performance gets worse than at detection threshold (with no pedestal) – this is masking. The interaction of these two effects (masking and facilitation) produces a characteristic ‘dipper’ shaped function, as shown by the red symbols below. In the figure, the dashed horizontal line indicates detection threshold. Note that DHB (left panel) shows a clearer dip, whereas LP (right panel) shows clearer masking.

If the visual system were entirely linear, detection would be unaffected by a pedestal. So, the presence of the dipper reveals a nonlinearity of some kind in the system. Two main candidates for this nonlinearity have been proposed over the years, and there is not yet a consensus amongst researchers over which is truly responsible. It is likely that both are correct to some degree, or perhaps that they are both equally valid descriptions at different levels of analysis. The first is that there is a nonlinear transducer (e.g. Legge & Foley, 1980) or gain control, of the form:

C^2.4/(1 + C²)

where C is the input contrast. This equation produces a sigmoidal (s-shaped) contrast response function (see panel B below). The dipper function is determined by the gradient of the contrast response at a given input (pedestal) contrast, because this governs how much contrast the target must add to the pedestal to produce a given increase in output. When the contrast response function is steep, thresholds are low (detection, and the dip region of the dipper). When the function is shallow, target contrast must be higher to produce the same increase in response, so thresholds increase (the handle region of the dipper).

Model dipper function and contrast response function.

The other explanation for dipper functions was proposed by Pelli (1985). This account has two parts: the first explains facilitation (the dip) and the second explains masking (the handle). Pelli proposed that observers are uncertain about exactly which internal detecting mechanism(s) will respond to the target. Their strategy is to monitor many (linear) mechanisms, and select the most responsive. Because the mechanisms are noisy, when the target contrast is low observers will often select the wrong one. However, the pedestal raises the activity of the correct detecting mechanism above the background noise level (right panel below). This improves thresholds, producing the facilitation effect, as shown by the red curve in the left panel below.

In this scheme, masking occurs because each detecting mechanism is noisy, with the amount of noise being proportional to the activity in the channel (called signal-dependent, or multiplicative noise). So, for a high pedestal contrast, the mechanism will be more noisy, meaning more target contrast is required to overcome the noise. This produces masking, as shown by the blue curve in the left panel below. The dipper function (green) comes from the combination of uncertainty and multiplicative noise.

Cross-channel masking

Example of a vertical mask (left) and mask + horizontal target (right)

When the mask is very different from the target, they will activate different detecting mechanisms. This means that the within-channel processes which produce dipper functions do not occur. But masking still happens, for example when the mask is orthogonal (at 90 degrees) to the target (see above stimulus example, and green data points in the top graphs). The most common explanation for this masking is that mechanisms sensitive to different stimuli inhibit each other. This inhibition can be modeled as a divisive process as part of a gain control equation (Heeger, 1992),

C^2.4/(1 + C² + wX)

where X refers to activity in mechanisms other than that which responds to the target, and w is a weight which determines the level of suppression. Note that this form of masking is very different from masking by a pedestal, and does not typically include facilitation.

Summary

Masking experiments are important, because they reveal nonlinear properties of the visual system. Pedestal masking tells us about the gradient of the contrast response function, whereas cross-channel masking tells us about interactions between different detecting mechanisms (or channels). Masking also occurs in other sensory domains, such as hearing or touch, and along other visual dimensions, like spatial frequency (size) or speed discrimination.

References

Heeger, D.J. (1992). Normalization of cell responses in cat striate cortex. Vis Neurosci, 9, 181-197.

Legge, G.E. & Foley, J.M. (1980). Contrast masking in human vision. J Opt Soc Am, 70, 1458-1471.

Pelli, D.G. (1985). Uncertainty explains many aspects of visual contrast detection and discrimination. J Opt Soc Am A, 2, 1508-1532.

Visual psychophysics for beginners

This post will outline some of the basic methods vision scientists use in experiments, in plain everyday language. I’m anticipating that future posts will refer back to this as a sort of glossary, so pay attention, this will be on the test…

Aims of vision research

Although it seems effortless, vision is pretty complicated. Your brain processes a huge amount of information very, very quickly to allow you to interpret the world. We’re years away from building a computer that can do the same job anywhere near as fast or as well. So, the aim of vision science is to understand the processing (or algorithms) that the brain uses to simplify and interpret the input from the eye. If we can understand this, we’ll know a lot more about how the brain works in general, and also be able to reproduce its functions in a computer program or even a robot. There are many potential applications for an accurate computer model of human vision, in areas such as advertising, image and movie compression, airport security, photography – even building artificial eyes for the blind.

Psychophysical methods: overview

Some vision research uses direct methods, such as single cell recording (usually in animals) or imaging (e.g. fMRI). However, a less invasive (and expensive!) method is to present observers with visual stimuli (pictures or movies) and ask them questions about them. These questions are not typically open-ended (how does this picture make you feel . . .) but are instead very simple. For example, we might show an observer two images of some stripes, and ask which has the highest contrast (i.e. the biggest difference in brightness between the dark and light regions). In the example below, it’s clear that the stimulus on the left is higher in contrast, though in real experiments the judgement might be much more difficult. Such simple questions have the advantage that they take very little time to respond to, and responses can often be given via a mouse or computer keyboard and repeated many times (often many thousands of times).

Example visual stimuli of different contrasts. The image on the left is higher in contrast than the one on the right.

Using these responses, we can gain insight into how the brain is working. Sometimes, an experiment might be designed to distinguish between two alternative theories. Other experiments might report on a surprising new finding, and still others collect data to inform the construction of computer models of the visual system. By systematically varying the stimuli in a controlled manner, we can find out about the limitations on performance (i.e. how good subjects are at doing a task) as well as their subjective experience of perception. This works for all sorts of stimulus dimensions: contrast, luminance, colour, motion, depth, size, tilt and many others. Psychophysical methods are used for other senses too – hearing and touch in particular.

A simple detection experiment

A fundamental question we can ask is how intense a target stimulus must be before we can see it. This intensity could be along lots of possible dimensions (luminance, colour, motion etc.), but the example here will be for contrast. So, how high must the contrast of a stimulus be before it can be reliably detected? We can find this out by presenting the target at a range of contrasts, and seeing how accurate an observer’s performance is at each contrast level. When the contrast is high, they should get it right all the time, as the target will be clearly visible. When the contrast is very low (definitely invisible) the observer will be guessing, so performance will be at chance levels. Somewhere in between these two extremes we should be able to find the contrast level where the target is just able to be detected.

One possible method is to show observer a single presentation, and ask whether or not they saw the target (and repeat many times for different contrast levels). This is called a yes/no task (for obvious reasons) and under some circumstances it is an OK method to use. However, there are a few technical reasons why it might not be the best choice of task. Instead, a technique called two-alternative forced choice (2AFC) is often better. This is very similar, except that there are two intervals (indicated by beeps), one of which contains the target, and the other contains a blank screen. The observer says which interval they thought contained the target (actually, this can also be done by using different areas of the screen, like left and right sides, instead of different temporal intervals), and the computer records their response. We repeat this for many many trials, over a range of contrasts, and calculate on what percentage of trials the observer was correct at each contrast level.

The psychometric function

The graph above shows some example data (circles) from an experiment like the one described. The y-axis tells us what percentage of trials were answered correctly at each contrast level (given on the x-axis in logarithmic (dB) units). The function the data describe is called a psychometric function. Because there are two intervals in our task, even when the target is invisible and the observer is guessing they will still be right half (50%) of the time. You can see that at the left hand side of the graph, where the contrast is low, the data points cluster around 50% correct. At the other extreme, when contrast is high, the observer is right all of the time – on 100% of the trials. The intermediate contrasts are the interesting ones, as here performance is somewhere between chance and perfect.

We usually decide to call a specific level of performance the ‘threshold’. A good level (for 2AFC) is 75% correct, as it is half way between chance and perfect performance – it’s when the observer can just see the target. You might notice that there is a data point very close to 75% correct, at about 6dB of contrast. If we didn’t care about details, this would be a good approximation of threshold. However, sometimes we want to be a bit more exact, so we fit a curve to the data points (using a computer program) and find out where the curve passes through 75% correct. For this example, it’s just under 6dB – this is our threshold.

Thresholds for different targets

Measuring just one threshold on its own isn’t really very interesting. But if we vary something about the target we can see how performance gets better or worse by measuring lots of thresholds. A classic example is the contrast sensitivity function (CSF). This measures thresholds (or sensitivity, which is 1/threshold) for grating stimuli (like the ones above) at a range of bar sizes (spatial frequencies). Low spatial frequencies are big, with wide bars, whereas high spatial frequencies are small, with very narrow bars. The graph below plots sensitivity as a function of spatial frequency. You can see there is a peak between 1 and 4 c/deg – these are the frequencies at which we are most sensitive. On either side of this, sensitivity falls off, meaning we need more contrast to reach threshold. The contrast sensitivity function is our window of visibility on the world – stimuli within the window are visible, those outside it are not. It is often used in clinical research to understand the source of a visual problem, or the limitations it causes for a patient.

A blog about vision

I am Daniel Baker, a postdoctoral research fellow at Aston University (UK). I live in Stoke-on-Trent with my girlfriend Laura, our two cats (Olive & River) and rabbit (Flossy).

I’ve wondered about starting a blog for a while, but was largely put off by the name (which makes me imagine someone vomiting), and the fact that it’s always seemed rather narcissistic to assume that anyone is interested in what I think. However, I’ve decided that even if nobody reads any of this it will still be a useful place for me to note down some ideas. Also, these days publicly funded researchers are supposed to ‘engage’ with the public, and this seems like as good a way of doing it as any other.

My intention is to write mostly about work and work-related things, but also include posts on my other interests occasionally, like photography and science fiction books/films. Work involves behavioural experiments on human observers to investigate low-level visual processing. I use a combination of psychophysical techniques and computational modelling. My main research interests are:

Binocular vision (including binocular rivalry and amblyopia)
Spatial vision
Adaptation
Masking

I intend to post about some of these things here, as well as papers I’m working on, and conferences I go to. Since I’m a sort of proto-academic, and at some point in the next two years will be job-hunting again, there might be bits & pieces about that too. I had a quick search before I signed myself up to WordPress, and couldn’t find anyone else writing specifically about low-level vision, so I hope this fills some sort of niche. Hopefully I’ll update frequently, as things like this tend to wither and die without regular attention. I have a lengthy daily commute, and an iPad, both of which should help!