Category: Research

Cognitive Science Conference, Philadelphia

fitThis week, 10-13th August, I am a the Annual Cognitive Science Society Conference, in Philadelphia. While there I am presenting work which uses a large data set on chess players and their games.

Previously the phenomenon of ‘stereotype threat’ has been found in many domains where people’s performance suffers when they are made more aware of their identity as a member of a social group which is expected to perform poorly – for example there is a stereotype that men are better at maths, and stereotype threat has been reported for female students taking maths exams when their identity as a women is emphasised, even if only subtly (by asking them to declare their gender on the top of the exam paper, for example). This effect has been reported for Chess, which is heavily male dominated, especially among top players. However, the reports of stereotype threat in chess, like in many other domains, often rely on laboratory experiments with a small number of people (around or less than 100).

My data are more than 11 million games of chess: every tournament recorded with FIDE, the international chess authority, between 2008-2015. Using this data, I asked if it was possible to observe stereotype threat in this real world setting. If the phenomenon is real, however small it is, I should be able to observe it playing out in this data – the sheer number of games I can analyse allows me a very powerful statistical lens.

The answer is, no: there is no stereotype threat in international chess. To see how I determined this, and what I think it means, you can read the paper here, or see the Jupyter notebook which walks you through the key analysis. And if you’re at the Conference, come and visit the poster (as PDF, as PNG). Jeff Sonas, who was compiled the data, has been kind enough to allow me to make available a 10% sample of the data (still over 1 million games), and this, along with all the analysis code for the paper, is available via the Open Science Framework.

There’s lots more to come from this data – as well as analysing performance related effects, the data affords a fantastic opportunity to look at learning curves and try to figure out what affects how players’ performance changes over time.

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Why don’t we trust the experts?

During the EU referendum debate a friend of mine, who happens to be a Professor of European Law, asked in exasperation why so much of the country seems unwilling to trust experts on the EU. When we want a haircut, we trust the hairdresser. If we want a car fixed, we trust the mechanic. Now, when we need informed comment on EU, why don’t we trust people who have spent a lifetime studying the topic?

The question rattled around in my mind, until I realised I had actually done some research which provides part of the answer. During my post-doc with Dick Eiser we did a survey of people who lived on land which may have been contaminated with industrial pollutants. We asked people with houses on two such ‘brownfield’ sites, one in the urban north, and one in the urban south, who they trusted to tell them about the possible risks.

One group we asked about the perception of was scientists. The householders answered on scale which went from 1 to 5 (5 is the most trust). Here’s the distribution of answers:

trust_scientists

As you can see, scientists are highly trusted. Compare with the ratings of property developers:

trust_devs

We also asked our respondents about how they rated different communicators on various dimensions. One dimension was expertise about this topic. As you’d expect, scientists were rated as highly expert in the risks of possible brownfield pollution. We also asked people about whether they believed the different potential communicators of risks has their interests at heart, and whether they would be open with their beliefs about risks. With this information, it is possible, statistically, to analyse not just who is trusted, but why they are trusted.

The results, published in Eiser at al (2009), show that expertise is not the strongest determinate of who is trusted. Instead, people trust those who they believe have their best interests at heart. This is three to four times more important than perception of expertise (fig. 3 on p294 for those reading along with the paper in hand).

One way of making this clear is to pick out the people who have high trust in scientists (rating or 4 or 5), and compared them to people who have low trust (rating scientists a 1 or 2 for trust). The perceptions of their expertise differ, but not too much:

perc_expertiseEven those who don’t trust scientists recognise that they know about pollution risks. In other words, their actual expertise isn’t in question.

The difference is seen whether scientists are seen to have the householders’ interests at heart:

perc_interests

So those who didn’t trust the scientist tend to believe that the scientists don’t care about them.

The difference is made clear by one group that was highly trusted to communicate risks of brownfield land-  friends and family:

trust_frfam

Again, the same relationship between variables held. Trust in friends and family was driven more by a perception of shared interests than it was by perceptions of expertise. Remember, this isn’t a measure of generalised trust, but specifically of trust in their communications about pollution risks. Maybe your friends and family aren’t experts in pollution risks, but they surely have your best interests at heart, and that it why they are nearly as trusted on this topic as scientists, despite their lack of expertise.

So here we have a partial answer to why experts aren’t trusted. They aren’t trusted by people who feel alienated from them. My reading of this study would be that it isn’t that we live in a ‘post-fact’ political climate. Rather it is that attempts to take facts out of their social context won’t work. For me and my friends it seems incomprehensible to ignore the facts, whether about the science of vaccination, or the law and economics of leaving the EU. But me and my friends do very well from the status quo- the Treasury, the Bar, the University work well for us. We know who these people are, we know how they work, and we trust them because we feel they are working for us, in some wider sense. People who voted Leave do suffer from a lack of trust, and my best guess is that this is a reflection of a belief that most authorities aren’t on their side, not because they necessarily reject their status as experts.

The paper is written up as : Eiser, J. R., Stafford, T., Henneberry, J., & Catney, P. (2009). “Trust me, I’m a Scientist (Not a Developer)”: Perceived Expertise and Motives as Predictors of Trust in Assessment of Risk from Contaminated Land. Risk Analysis, 29(2), 288-297. I’ve just made the data and analysis for this post available here.

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New paper: Improving training for sensory augmentation using the science of expertise.

A few years ago, we started work on a device we called “the tactile helmet”  (Bertram et al, 2013). This would, the plan was, help you navigate without sight, using ultrasound sensors to give humans an extended sense of touch. Virtual rat-whiskers!

As well as doing some basic testing with the device (Kerdergari et al, 2014), Craig and I also reviewed the existing literature on similar sensory augmentation devices.

What we found was that there are many such devices, with little consistency in how their effectiveness is assessed. Critically, for us, research reports neglected to consider the ease and extent of training with a device. So some devices have users who have practiced with the device for thousands of hours (even decades!), while the results from others are described with users who have little more than a few minutes familiarisation.

In our new paper, Improving training for sensory augmentation using the science of expertise, we review existing sensory augmentation devices with an eye on how users can be trained to use them effectively. We make recommendations for which features of training should be reported, so fair comparisons can be made across devices. These aspects of training also provide a natural focus for how training can be optimised (because for each them, as cogntive scientists, we know how they can be adjusted so as to enhance learning. Our features of training are:

  • The total training duration
  • Session duration and interval
  • Feedback
  • The similarity of training to end use

We discuss each of these in turn, with reference to the psychology literature on skill acquisition, as well as discussing non-training factors which affect device usability.

A post-print of the paper is available here:

References:

Bertram, C., & Stafford, T. (2016). Improving training for sensory augmentation using the science of expertise. Neuroscience & Biobehavioral Reviews, 68, 234-244.

Bertram, C., Evans, M. H., Javaid, M., Stafford, T., & Prescott, T. (2013). Sensory augmentation with distal touch: the tactile helmet project. In Biomimetic and Biohybrid Systems (pp. 24-35). Springer Berlin Heidelberg.

Kerdegari, H., Kim, Y., Stafford, T., & Prescott, T. J. (2014). Centralizing bias and the vibrotactile funneling illusion on the forehead. In Haptics: Neuroscience, Devices, Modeling, and Applications (pp. 55-62). Springer Berlin Heidelberg.

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Crowdsourcing analysis, an alternative approach to scientific research

Crowdsourcing analysis, an alternative approach to scientific research: Many Hands make tight work

Guest Lecture by Raphael Silberzahn, IESE Business School, University of Navarra

11:00 – 12:00, 9th of December, 2015

Lecture Theatre 6, The Diamond (32 Leavygreave Rd, Sheffield S3 7RD)

Is soccer players’ skin colour associated with how often they are shown a red card? The answer depends on how the data is analysed. With access to a dataset capturing the player-referee interactions of premiership players from the 2012-13 season in the English, German, French and Spanish leagues we organised a crowdsourced research project involving 29 different research teams and 61 individual researchers. Teams initially exchanged analytical approaches — but not results — and incorporated feedback from other teams into their analyses. Despite, the teams came to a broad range of conclusions. The overall group consensus (that a correlation exists) was much more tentative than would be expected from a single-team analysis. Raphael Silberzahn will provide insights from his perspective as one of the project coordinators and Tom Stafford will speak about his experience as a participant in this project. We will discuss how also smaller research projects can benefit from bringing together teams of skilled researchers to work simultaneously on the same data and thereby balance discussions and provide scientific findings with greater validity.

Links to coverage of this research in Nature (‘Crowdsourced research: Many hands make tight work’), and on FiveThirtyEight (‘Science Isn’t Broken: It’s just a hell of a lot harder than we give it credit for’). Our group’s analysis was supported by some great data exploration and visualisation work led by Mat Evans. You can see an interactive notebook of this work here

 

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Bias mitigation

200px-Unbalanced_scales2.svgOn Friday I gave a talk on cognitive and implicit biases, to a group of employment tribunal judges. The judges were a great audience, far younger, more receptive and more diverse than my own prejudices had led me to expect, and I enjoyed the opportunity to think about the area of cognitive bias, and how some conclusions from that literature might be usefully carried over to the related area of implicit bias.

First off, let’s define cognitive bias versus implicit bias. Cognitive bias is a catch all term for systematic flaws in thinking. The phrase is associated with the ‘Judgement and decision making’ literature which was spearheaded by Daniel Kahneman and colleagues (and for which he received the Nobel Prize in 2002). Implicit bias, for our purposes, refers to a bias in judgements of other people which is both unduly influenced by social categories such as sex or ethnicity and in which the person making this biased judgement is either unaware or unable to control the undue influence.

So from the cognitive bias literature we get a menagerie of biases such as ‘the overconfidence effect‘, ‘confirmation bias‘, ‘anchoring‘, ‘base rate neglect‘, and on and on. From implicit bias we get findings such as that maths exam papers are marked higher when they carry a male name on the top, job applicants with stereotypically black American names have to send out twice as many CVs, on average, to get an interview or that people sit further away from someone they believe has a mental health condition such as schizophrenia. Importantly all these behaviours are observed in individuals who insist that they are not only not sexist/racist/prejudiced but are actively anti-sexism/racism/prejudice.

My argument to the judges boiled down to four key points, which I think build on one another:

1. Implicit biases are cognitive biases

There is slippage in how we identify cognitive biases compared to how we identify implicit biases. Cognitive biases are defined against a standard of rationality – either we know the correct answer (as in the Wason selection task, for example), or we feel able to define irrelevant factors which shouldn’t affect a decision (as in the framing effect found with the ‘Asian Disease problem‘). Implicit biases use the second, contrastive, standard. Additionally it is unclear whether the thing being violated is a standard of rationality, or a standard of equity. So, for example, it is unjust to allow the sex of a student influence their exam score, but is it irrational? (If you think there is a clear answer to this, either way, then you are more confident of the ultimate definition of rationality than a full century of scholars).

Despite these differences, implicit biases can usefully be thought of as a kind of cognitive bias. They are a habit of thought, which produces systematic errors, and which we may be unaware we are deploying (although elsewhere I have argued that the evidence for the unconscious nature of these process is over-egged). Once you start to think of implicit biases and cognitive biases as very similar, it buys some important insights.

Specifically:

2. Biases are integral to thinking

Cognitive biases exist for a reason. They are not rogue processes which contaminate what would be otherwise intelligent thought. They are the foundation of intelligent thought. To grasp this, you need to appreciate just how hard principled, consistent thought is. In a world of limited time, information, certainty and intellectual energy cognitive biases arise from necessary short-cuts and assumptions which keep our intellectual show on the road. Time and time again psychologists have looked at specific cognitive biases and found that there is a good reason for people to make that mistake. Sometimes they even find that animals make that mistake, demonstrating that even without the human traits of pride, ideological confusion and general self-consciousness the error persists – suggesting that there are good evolutionary reasons for it to exist.

For an example, take confirmation bias. Although there are risks to preferring to seek information that confirms whatever you already believe, the strategy does provide a way of dealing with complex information, and a starting point (i.e. what you already suspect) which is as good as any other starting point. It doesn’t require that you speculate endless about what might be true, and in many situations the world (or other people) is more than likely to put contradictory evidence in front of you without you having to expend effort in seeking it out. Confirmation bias exists because it is an efficient information seeking strategy – certainly more efficient than constantly trying to disprove every aspect of what you believe.

Implicit biases concern social judgement and socially significant behaviours, but they also seem to share a common mechanism. In cognitive terms, implicit biases arise from our tendency towards associative thoughts – we pick up on things which co-occur, and have the tendency to make judgements relying on these associations, even if strict logic does not justify it. The scope of how associations are created and strengthened in our minds is beyond the scope of the post.

For now it is clear that making judgements based on circumstantial evidence is unjustified but practical. An uncontentious example might be you get sick after eating at a particular noodle bar. Maybe it was bad luck, you were going to get sick anyway or it was the sandwich you ate a lunch, but the odds are good you’ll avoid the noodle bar in the future. Why chance it, there are plenty of other restaurants? It would be impractical to never make some assumptions, and the assumption-laden (biased!) route offers a practical solution to the riddle of what you should conclude from your food poisoning.

3. There is no bias-free individual

Once you realise that our thinking is built on many fast, assumption-making, processes which may not be perfect – indeed which have systematic tendencies which produce the errors we identify as cognitive bias – you then realise that it would be impossible to have bias-free decision processes. If you want to make good choices today rather than a perfect choices in the distant future, you have to compromise and accept decisions which will have some biases in them. You cannot free yourself of bias, in this sense, and you shouldn’t expect to.

This realisation encourages some humility in the face of cognitive bias. We all have biases, and we shouldn’t pretend that we don’t or hope that we can free ourselves of them.

We can be aware of the biases we are exposed to and likely to harbour within ourselves. We can, with a collective effort, change the content of the biases we foster as a culture. We can try hard to identify situations where bias may play a larger role, or identify particular biases which are latent in our culture or thinking. We can direct our bias mitigation efforts at particularly important decisions, or decisions we think are particularly likely to be prone to bias. But bias-free thinking isn’t an option, it is part of who we are.

4. Many effective mitigation strategies will be supra-personal:

If humility in the face of bias is the first practical reaction to the science of cognitive bias, I’d argue that second is to recognise that bias isn’t something you can solve on your own at a personal psychological level. Obviously you have to start by trying your honest best to be clear-headed and reasonable, but all the evidence suggests that biases will persist, that they cannot be cut out of thinking and may even thrive when we think ourselves most objective.

The solution is to embed yourself in groups, procedures and institutions which help counter-act bias. Obviously, to a large extent, the institutions of law have evolved to counter personal biases. It would be an interesting exercise to review how legal cases are conducted from a psychological perspective, interpreting different features as to how they work with or against our cognitive tendencies (so, for example, the adversarial system doesn’t get rid of confirmation bias, but it does mean that confirmation bias is given equal and opposite opportunity to work in the minds of the two advocates).

Amongst other kinds of ‘ecological control‘ we might count proper procedure (following the letter of the law, checklists, etc), control of (admissible) information and the systematic collection of feedback (without which you may not ever come to realise that you are making systematically biased decisions).

Slides from my talk here as Google docs slides and as PDF. Thanks to Robin Scaife for comments on a draft of this post. Cross-posted to the blog of our Leverhulme trust funded project on “Bias and Blame“.

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Power analysis for a between-sample experiment

Understanding statistical power is essential if you want to avoid wasting your time in psychology. The power of an experiment is its sensitivity – the likelihood that, if the effect tested for is real, your experiment will be able to detect it.

Statistical power is determined by the type of statistical test you are doing, the number of people you test and the effect size. The effect size is, in turn, determined by the reliability of the thing you are measuring, and how much it is pushed around by whatever you are manipulating.

Since it is a common test, I’ve been doing a power analysis for a two-sample (two-sided) t-test, for small, medium and large effects (as conventionally defined). The results should worry you.

power_analysis2

This graph shows you how many people you need in each group for your test to have 80% power (a standard desirable level of power – meaning that if your effect is real you’ve an 80% chance of detecting it).

Things to note:

  • even for a large (0.8) effect you need close to 30 people (total n = 60) to have 80% power
  • for a medium effect (0.5) this is more like 70 people (total n = 140)
  • the required sample size increases drammatically as effect size drops
  • for small effects, the sample required for 80% is around 400 in each group (total n = 800).

What this means is that if you don’t have a large effect, studies with between groups analysis and an n of less than 60 aren’t worth running. Even if you are studying a real phenomenon you aren’t using a statistical lens with enough sensitivity to be able to tell. You’ll get to the end and won’t know if the phenomenon you are looking for isn’t real or if you just got unlucky with who you tested.

Implications for anyone planning an experiment:

  • Is your effect very strong? If so, you may rely on a smaller sample (For illustrative purposes the effect size of male-female heigh difference is ~1.7, so large enough to detect with small sample. But if your effect is this obvious, why do you need an experiment?)
  • You really should prefer within-sample analysis, whenever possible (power analysis of this left as an exercise)
  • You can get away with smaller samples if you make your measure more reliable, or if you make your manipulation more impactful. Both of these will increase your effect size, the first by narrowing the variance within each group, the second by increasing the distance between them

Technical note: I did this cribbing code from Rob Kabacoff’s helpful page on power analysis. Code for the graph shown here is here. I use and recommend Rstudio.

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New grant: Reduced habitual intrusions : an early marker for Parkinson’s Disease?

SurprisalDensityPlotFor4CharacterWindowI have very pleased to announce that the Michael J Fox Foundation have funded a project I lead titled ‘Reduced habitual intrusions : an early marker for Parkinson’s Disease?’. The project is for 1 year, and is a collaboration between a psychologist (myself), a neuroscientist (Pete Redgrave), a clinician specialising in Parkinson’s (Jose Obeso, in Spain) and a computational linguist (Colin Bannard, in Liverpool). Mariana Leriche will be joining us a post-doc.

The idea of the project stems from hypothesis that Parkinson’s Disease will be specifically characterised by a loss of habitual control in the motor system. This was proposed by Pete, Jose and others in 2010. Since my PhD I’ve been interested automatic processes in behaviour. One phenomenon which seems to offer particular promise for exploring the interaction between habits and deliberate control is the ‘action slip’. This is an error where a habit intrudes into the normal stream of intentional action – for example, such as when you put the cereal in to the fridge, or when someone greets you by asking “Isn’t it a nice day?” and you say “I’m fine thank you”. An interesting prediction of the Redgrave et al theory is people with Parkinson’s should make fewer action slips (in contrast to all other types of movement errors, which you would expect to increase as the disease progresses).

The domain we’re going to look at this in is typing, which I’ve worked with before, and which – I’ve argued – is a great domain for looking at how skill, intention and habit combine in an everyday task which generates lots of easily coded data.

I feel the project reflects exactly the kind of work I aspire to do – cognitive science which uses precise behavioural measurement, informed by both neuroscientific and computational perspectives, and in the service of am ambitious but valuable goal. Now, of course, we actually have to get on and do it.

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New paper: wiki users get higher exam scores

Just out in Research in Learning Technology, is our paper Students’ engagement with a collaborative wiki tool predicts enhanced written exam performance. This is an observational study which tries to answer the question of how students on my undergraduate cognitive psychology course can improve their grades.

One of the great misconceptions about sudying is that you just need to learn the material. Courses and exams which encourage regurgitation don’t help. In fact, as well as memorising content, you also need to understand it and reflect that understanding in writing. That is what the exam tests (and what an undergraduate education should test, in my opinion). A few years ago I realised, marking exams, that many students weren’t fulfilling their potential to understand and explain, and were relying too much on simply recalling the lecture and textbook content.

To address this, I got rid of the textbook for my course and introduced a wiki – an editable set of webpages, using which the students would write their own textbook. An inspiration for this was a quote from Francis Bacon:

Reading maketh a full man,
conference a ready man,
and writing an exact man.

(the reviewers asked that I remove this quote from the paper, so it has to go here!)

Each year I cleared the wiki and encouraged the people who took the course to read, write and edit using the wiki. I also kept a record of who edited the wiki, and their final exam scores.

The paper uses this data to show that people who made more edits to the wiki scored more highly on the exam. The obvious confound is that people who score more highly on exams will also be the ones who edit the wiki more. We tried to account for this statistically by including students’ scores on their other psychology exams in our analysis. This has the effect – we argue – of removing the general effect of students’ propensity to enjoy psychology and study hard and isolate the additional effect of using the wiki on my particular course.

The result, pleasingly, is that students who used the wiki more scored better on the final exam, even accounting for their general tendancy to score well on exams (as measured by grades for other courses). This means that even among people who generally do badly in exams, and did badly on my exam, those who used the wiki more did better. This is evidence that the wiki is beneficial for everyone, not just people who are good at exams and/or highly motivated to study.

Here’s the graph, Figure 1 from our paper:

wikigraph

This is a large effect – the benefit is around 5 percentage points, easily enough to lift you from a mid 2:2 to a 2:1, or a mid 2:1 to a first.

Fans of wiki research should check out this recent paper Wikipedia Classroom Experiment: bidirectional benefits ofstudents’ engagement in online production communities, which explores potential wider benefits of using wiki editing in the classroom. Our paper is unique for focussing on the bottom line of final course grades, and for trying to address the confound that students who work harder at psychology are likely to both get higher exam scores and use the wiki more.

The true test of the benefit of the wiki would be an experimental intervention where one group of students used a wiki and another did something else. For a discussion of this, and discussion of why we believe editing a wiki is so useful for learning, you’ll have to read the paper.

Thanks go to my collaborators. Harriet reviewed the literature and Herman instaled the wiki for me, and did the analysis. Together we discussed the research and wrote the paper.

Full citation:
Stafford, T., Elgueta, H., Cameron, H. (2014). Students’ engagement with a collaborative wiki tool predicts enhanced written exam performance. Research in Learning Technology, 22, 22797. doi:10.3402/rlt.v22.22797

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New paper: Performance breakdown effects dissociate from error detection effects in typing

This is the first work on typing that has come out of C’s PhD thesis. C’s idea, which inspired his PhD, was that typing would be an interesting domain to look at errors and error monitoring. Unlike most discrete trial tasks which have been used to look at errors, typing is a continuous performance task (some of subjects can type over 100 words per minutes, pressing around 10 keys a second!). Futhermore the response you make to signal an error is highly practiced – you press the backspace. Previous research on error signalling hasn’t been able to distinguished between effects due to the error and effects due having to make an unpracticed response to signal that you know you made the error.

For me, typing is a fascinating domain which contradicts some notions of how actions are learnt. The dichotomy between automatic and controlled processing doesn’t obviously apply to typing, which is rapid and low effort (like habits), but flexible and goal-orientated (like controlled processes). A great example of how typing can be used to investigate the complexity of action control comes from this recent paper by Gordan Logan and Matthew Crump (this).

In this paper, we asked skilled touch-typists to copy type some set sentences and analysed the speed of typing before, during and after errors. We found, in contrast to some previous work which had used unpracticed discrete trial tasks to study errors, that there was no change in speed before an error. We did find, however, that typing speeds before errors did increase in variability – something we think signals a loss of control, something akin to slipping “out of the zone” of concentration. A secondary analysis compared errors which participants corrected against those they didn’t correct (and perhaps didn’t even notice they made). This gave us evidence that performance breakdown before an error isn’t just due to the processes that notice and correct errors, but – at least to the extent that error correction is synonymous with error detection – performance breakdown occurs independently of error monitoring.

Here’s the abstract

Mistakes in skilled performance are often observed to be slower than correct actions. This error slowing has been associated with cognitive control processes involved in performance monitoring and error detection. A limited literature on skilled actions, however, suggests that preerror actions may also be slower than accurate actions. This contrasts with findings from unskilled, discrete trial tasks, where preerror performance is usually faster than accurate performance. We tested 3 predictions about error-related behavioural changes in continuous typing performance. We asked participants to type 100 sentences without visual feedback. We found that (a) performance before errors was no different in speed than that before correct key-presses, (b) error and posterror key-presses were slower than matched correct key-presses, and (c) errors were preceded by greater variability in speed than were matched correct key-presses. Our results suggest that errors are preceded by a behavioural signature, which may indicate breakdown of fluid cognition, and that the effects of error detection on performance (error and posterror slowing) can be dissociated from breakdown effects (preerror increase in variability)

Citation and download: Kalfaoğlu, Ç., & Stafford, T. (2013). Performance breakdown effects dissociate from error detection effects in typing. The Quarterly Journal of Experimental Psychology, 67(3), 508-524. doi:10.1080/17470218.2013.820762

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Tracing the Trajectory of Skill Learning With a Very Large Sample of Online Game Players

I am very excited about this work, just published in Psychological Science. Working with a online game developer, I was able to access data from over 850,000 players. This allowed myself and Mike Dewar to look at the learning curve in an unprecedented level of detail. The paper is only a few pages long, and there are some great graphs. Using this real-world learning data set we were able to show that some long-established findings from the literature hold in this domain, as well as confirm a new finding from this lab on the value of exploration during learning.

However, rather than the science, in this post I’d like to focus on the methods we used. When I first downloaded the game data I thought I’d be able to use the same approach I was used to using with data sets gathered in the lab – look at the data, maybe in a spreadsheet application like Excel, and then run some analyses using a statistics package, such as SPSS. I was rudely awakened. Firstly, the dataset was so large that my computer couldn’t load it all into memory at one time – meaning that you couldn’t simply ‘look’ at the data in Excel. Secondly, the conventional statistical approaches I was used to, and programming techniques, either weren’t appropriate or didn’t work. I spent five solid days writing matlab code to calculate the practice vs mean performance graph of the data. It took two days to run each time and still didn’t give me the level of detail I wanted from the analysis.

Enter, Mike Dewar, dataist and currently employed in the New York Times R&D Lab. Speaking to Mike over Skype, he knocked up a Python script in two minutes which did in 30 seconds what my matlab script had taken two days to do. It was obvious I was going to have to learn to code in Python. Mike also persuaded me that the data should be open, so we started a github repository which holds the raw data and all the analysis scripts.

This means that if you want to check any of the results in our paper, or extend them, you can replicate our exact analysis, inspecting the code for errors or interrogating the data for patterns we didn’t spot. There are obvious benefits to the scientific community of this way of working. There are even benefits to us. When one of the reviewers questioned a cut-off value we had used in the analysis, we were able to write back that the exact value didn’t matter, and invited them to check for themselves by downloading our data and code. Even if the reviewer didn’t do this, I’m sure our response carried more weight since they knew they could have easily checked our claim if they had wanted. (Our full response to the first reviews, as well as a pre-print of the paper is available via the repository also).

Paper: Stafford, T. & Dewar, M. (2014). Tracing the Trajectory of Skill Learning With a Very Large Sample of Online Game Players. Psychological Science

Data and Analysis code: github.com/tomstafford/axongame

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