Category: Research

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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New project: “Bias and Blame: Do Moral Interactions Modulate the Expression of Implicit Bias?”

The Leverhulme Trust has awarded a 36 month grant to the University of Nottingham, for a project led by my collaborator Dr Jules Holroyd, with support from myself. The project title is “Bias and Blame: Do Moral Interactions Modulate the Expression of Implicit Bias?” (abstract below). The aim is to conduct experiments to advance our understanding of how implicit biases are regulated by ‘moral interactions’ (these are things such as being blamed, or being held responsible). The grant will pay for a post-doc (Robin Scaife) in Sheffield and a PhD student (as yet unknown, let us know if you’re interested!) in Nottingham.

Obviously, this is something of a departure for myself, at least as far as the topic goes (which is why Jules leads). I’m hoping my background in decision making and training in experimental design will help me navigate the new conceptual waters of implicit bias. Some credit for inspiring the project should go to Jenny Saul and her Bias Project, and before that, Alec Patton and his faith in interdisciplinary dialogue that helped get Jules and myself talking about how experiments and philosophical analysis could help each other out.

Project Abstract:

This project will investigate whether moral interactions are useful tool for regulating implicit bias. Studies have shown that implicit biases – automatic associations which operate without reflective control – can lead to unintentionally differential or unfair treatment of stigmatised individuals. Such biases are widespread, resistant to deliberate moderation, and have a significant role in influencing judgement and action. Strategies for regulating implicit bias have been developed, tested and evaluated by psychologists and philosophers. But neither have explored whether holding individuals responsible for implicit biases may help or hinder their regulation. This is what we propose to do.

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New PhD Student: Angelo Pirrone

Angelo joins us in the Department, to run experimental studies of decision making. He is second supervised by James Marshall who is a Reader in Computer Science, and head of the Behavioural and Evolutionary Theory Lab. Angelo’s funding comes from the cross-disciplinary Neuroeconomics network I lead: “Decision making under uncertainty: brains, swarms and markets”. We’re hoping to use computational, neuroscientific and evolutionary perspectives to guide the development of behavioural studies of perceptual decision making. More about this, and the neuroeconomics network, soon. In the meantime – welcome to Sheffield, Angelo!

Update August 2016: Well, that went quick! Angelo is writing up and looking for post-doctoral positions. His CV is here

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New paper: No learning where to go without first knowing where you’re coming from: action discovery is trajectory, not endpoint based.

We’ve a new paper out in Frontier in Cognitive Science: No learning where to go without first knowing where you’re coming from: action discovery is trajectory, not endpoint based. This was work done by Martin and Tom Walton as part of the IM-CLeVeR project.

The research uses our joystick task (Stafford et al, 2013) to look at how people learn a novel arbitrary action (in this case moving the joystick to a particular position). By comparing a condition (A) where the start point of the movement is always the same with a condition (B) where the start point moves around, we are able to look at the way people find it easiest to learn novel actions. In condition (A) you could learn the correct action my identifying the target location OR you could lean the correct action by identifying a target trajectory to make (which, since you always start from the same place, would work just as well to get you to the target location). In condition (B) you can’t rely on this second strategy, you have to identify the target location and head towards it from wherever you start. Surprisingly, participants in our experiment were very bad at this second condition – so much so that over the number of trials we gave them, they didn’t appear to learn anything about the target location and so acquired no novel action. This suggests that we have strong bias to rely on trajectories of movement when acquiring novel actions, rather code them by arbitrary spatial end points.

The paper: Thirkettle, M., Walton, T., Redgrave, P., Gurney, K., & Stafford, T. (2013). No learning where to go without first knowing where you’re coming from: action discovery is trajectory, not endpoint based. Frontiers in Cognitive Science, 4:, 638. doi:10.3389/fpsyg.2013.00638

The paper is published as part of our Special Topic in Frontiers on Intrinsic motivations and open-ended development in animals, humans, and robots

Cited:
Stafford, T., Thirkettle, M., Walton, T., Vautrelle, N., Hetherington, L., Port, M., Gurney, K.N., Redgrave, P. (2012), A Novel Task for the Investigation of Action Acquisition, PLoS One, 7(6), e37749.

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New paper: The Discovery of Novel Actions Is Affected by Very Brief Reinforcement Delays and Reinforcement Modality

In this paper, in press at the Journal of Motor Behaviour, we build on our previous work which developed a novel task for investigating how we learn actions. Our interest is in how the motor system connects what we’ve been doing with what happens. When something you do causes a change in the world you want to identify what exactly it was that you did that had the effect. Our hypothesis is that the machinery of the subcortical basal ganglia does this job for us – in the domain of motor learning. One key feature of the basal ganglia architecture is the speed with which dopamine signalling responds to external events. Profs Redgrave and Gurney have argued that this rapidity is because even millisecond delays in event signalling lead to a disporportunate increase in the difficulty of connecting the correct part of what you’ve done with the event. In other words, with delay you easily lose track of what it was that you did that caused a surprising outcome.

This is the context for the experiments reported in the new paper. These experiments show that our task has a very high sensitivity to delay – of the order of 100 ms. This is fits with the Redgrave-Gurney theory of dopamine signalling, and is considerably briefer than previous work looking at the effects of delay on motor learning. This is because, we argue, previous work uses response frequency (of an already learnt action) as the dependent variable, whereas our task is better designed to look at the emergence of new actions as they are in the process of being learn.

Here’s the abstract:

The authors investigated the ability of human participants to discover novel actions under conditions of delayed reinforcement. Participants used a joystick to search for a target indicated by visual or auditory reinforcement. Reinforcement delays of 75–150 ms were found to significantly impair action acquisition. They also found an effect of modality, with acquisition superior with auditory feedback. The duration at which delay was found to impede action discovery is, to the authors’ knowledge, shorter than that previously reported from work with operant and causal learning paradigms. The sensitivity to delay reported, and the difference between modalities, is consistent with accounts of action discovery that emphasize the importance of a time stamp in the motor record for solving the credit assignment problem.

And the citation:

Walton, T., Thirkettle, M., Redgrave, P., Gurney, K. N., & Stafford, T. (2013). The Discovery of Novel Actions Is Affected by Very Brief Reinforcement Delays and Reinforcement Modality. Journal of Motor Behavior, 45(4), 351-360.

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New paper: “The path to learning: Action acquisition is impaired when visual reinforcement signals must first access cortex”

Using cunning experimental design we provide evidence which supports a new theory of how the brain learns new actions. Back in 2006, our professors Redgrave and Gurney proposed a new theory of how the brain learns new actions, centered around the subcortical brain area the basal ganglia and the function of the neurotransmitter dopamine. This was exciting for two reasons: it proposed a theory of what these parts of the brain might do, based on our understanding of the pathways involved and the computations they might support and because it was a theory that was in flat contradiction to the most popular theory of dopamine function, the reward prediction error hypothesis.

We set out to test this theory. We used a novel task to assess action-outcome learning, in which human subjects moved a joystick around until they could identify a target movement. We didn’t record the dopamine directly – a tall order for human subjects – but instead used our knowledge of what triggers dopamine to compare two learning conditions: one where dopamine would be triggered as normal, and one where we reasoned the dopamine signal would be weakened.

We did this by using two different kinds of reinforcement signals, either a simple luminance change (i.e. a white flash), or a specifically calibrated change in colour properties (visual psychophysics fans: a shift along the tritan line). The colour change signal is only visible to some of the cells in the eye, the s-cone photoreceptors. Importantly, for our purposes, this means that although the signal travels the cortical visual pathways it does not enter the subcortical visual pathway to the superior colliculus. And the colliculus is the main, if not only, route to trigger dopamine release in the basal ganglia.

So by manipulating the stimulus properties we can control the pathways the stimulus information travels. Either the reinforcement signal goes directly to the colliculus and so to the dopamine (luminance change condition), or the signal must travel through visual cortex first and then to the colliculus, ‘the long way round’, to get to the dopamine (s-cone condition).

The result is a validation for the action-learning hypothesis: when reinforcement signals are invisible to the colliculus learning new action-outcome associations is harder. We also did an important control experiment which showed that the impairment due to the s-cone signals couldn’t be matched by simple transport delay of the stimulus information; this suggests the s-cone signal is weaker, not just slower in terms of dopaminergic action. You can read the full thing here.

The results aren’t conclusive – no behavioural experiment which didn’t record dopamine directly could be – but we think it is a strong result. Popper said there are two kinds of results to be most interested in. One was the experiment which proved a theory wrong. The other – which we believe this is – is an experiment which confirms a bold hypothesis. There are no other theories which would suggest this experiment, and only the Redgrave and Gurney theory predicted the result we got before we got it. This makes it a startling validation for the theory and that is why we’re really proud of the paper.

This work was funded by our European project, im-clever, and all the difficult work was done by Martin Thirkettle, building on Tom Walton’s foundation.

Thirkettle, M., Walton, T., Shah, A., Gurney, K., Redgrave, P., & Stafford, T. (2013). The path to learning: Action acquisition is impaired when visual reinforcement signals must first access cortex. Behavioural Brain Research, 243, 267–272. doi:10.1016/j.bbr.2013.01.023

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New paper: Memory Enhances the Mere Exposure Effect

This research used a novel testing strategy to overturn a long-standing claim in the literature. The mere exposure effect is the finding that simply experiencing something inclines you to like it. Obviously, back in the days of behaviourism this provided a marked contrast to reward-induced preferences. A landmark paper by Bob Zajonc showed that this effect could hold even if you weren’t aware of the original exposure. (Incidentally it was this paper, as far as I can tell, which reignited interest in subliminal perception after the topic had fallen into ‘hidden persuader’ ignominy).

For a long time, based partly on the influence of this seminal paper, it has been reported that explicit memory for stimuli will reduce the mere exposure effect. The logic is that explicit memory will allow people to use a deliberate discounting strategy (something along the lines of “I know I’ve seen that before, so maybe I just feel positive about it because I’ve seen it before”). This isn’t implausible, but does conflict with a large marketing literature which suggests that sustained engagement with marketing materials is more likely to lead to preference (and it is just such engagement with adverts which you would expect to be accompanied by explicit memory).

I put test stimuli in my PSY101 lectures, and then weeks later tested the students on their preferences for these stimuli and a matched group which they hadn’t seen. This allowed me to collect high number of participants for an experiment which had a high ecological validity (and still many elements of experimental control). Continue reading

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Frontiers special issue on intrinstic motivation and open-ended development

Our special issue in Frontiers in Cognitive Science is now accepting submissions: Intrinsic motivations and open-ended development in animals, humans, and robots

This call stems from the EU FP7 project “IM-CLEVER”, programme of work that involved computer scientists, neuroscientists, psychologists and roboticist in developing robot controllers that can guide a robot to learn by exploring the world.

The special issue will gather together work related to this task. ‘Intrinsic motivations’ are those that guide exploration – things like curiosity, play or desire for mastery. The emphasis is on learning systems which are more than the simple stimulus-response or response-reward learning which has dominated learning theory for so long. ‘Open-ended development’ means learning that doesn’t have a goal or limit, but is instead designed to produce skills and abilities which can be build on to produce ever more complex skills and abilities. The call welcomes papers from experimental, theoretical and engineering perspectives. The full text of the call is here.

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Scholarly publications

(Google scholar profile, ORCID ID : 0000-0002-8089-9479)

2017 & in press

Panagiotidi, M., Overton, P.G., Stafford, T. (in press). The relationship between ADHD traits and sensory sensitivity in the general population. Comprehensive Psychiatry

Stafford, T. (in press). Female chess players outperform expectations when playing men. Psychological Science.

Panagiotidi, M., Overton, P.G.,Stafford, T. (2017). Multisensory integration and ADHD-like traits: Evidence for an abnormal temporal integration window in ADHD. Acta Psychologica, 181, 10-17

Holroyd, J., Scaife, R., Stafford, T. (in press). What is Implicit Bias? Philosophy Compass.

Panagiotidi, M., Overton, P.G., Stafford, T. (2017). Co-occurrence of ASD and ADHD traits in the general population . Journal of Attentional Disorders

Panagiotidi, M., Overton, P.G., Stafford, T. (2017). Increased microsaccade rate in individuals with ADHD traits. Journal of Eye Movement Research. 10,1

Kalfaoglu, C., Stafford, T. and Milne, E. (in press). Frontal Theta Band Oscillations Predict Error Correction and Post Error Slowing in Typing. Journal of Experimental Psychology: Human Perception and Performance.

Holroyd, J., Scaife, R., Stafford, T. (in press). Responsibility for Implicit Bias. Philosophy Compass.

Pirrone, A., Azab, H., Hayden, B.Y., Stafford, T. and Marshall, J.A.R. (in press). Evidence for the speed-value trade-off: human and monkey decision making is magnitude sensitive. Decision

Panagiotidi, M., Overton, P.G., Stafford, T. (2017). Attention Deficit Hyperactivity Disorder-like traits and distractibility in the visual periphery. Perception, 46 (6), 665-678

Pirrone, A., Dickinson, A., Gomez, R., Stafford, T. and Milne, E. (2017). Understanding perceptual judgement in autism spectrum disorder using the drift diffusion model. Neuropsychology, 31 (2), 173-180

Pirrone, A., Marshall, J. A., & Stafford, T. (2017). A Drift Diffusion Model Account of the Semantic Congruity Effect in a Classification Paradigm. Journal of Numerical Cognition, 3(1), 77-96.

Stafford, T. & Haasnoot, E. (2017). Testing sleep consolidation in skill learning: a field study using an online game. Topics in Cognitive Science. 9(2), 485-496.(data + code)

2016

Bednark J., Reynolds J., Stafford T., Redgrave P. and Franz E. (2016). Action experience and action discovery in medicated individuals with Parkinson’s disease. Frontiers in Human Neuroscience, 10, 427. DOI 10.3389/fnhum.2016.00427.

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

Lu, Y., Stafford, T., & Fox, C. (2016). Maximum saliency bias in binocular fusion. Connection Science, 28(3),258-269.

2015

Thirkettle, M., Stafford, T., & Offiah, A. (2015). Internet Based Measurement of Visual Expertise in Radiological Skill. Perception, 44, 44-45.

2014

Stafford, T. & Dewar, M. (2014). Tracing the trajectory of skill learning with a very large sample of online game players. Psychological Science, 25(2) 511-518. See also: Data and analysis code.

Baldassarre, G., Stafford, T., Mirolli, M., Redgrave, P., Ryan, R. M., & Barto, A. (2014). Intrinsic motivations and open-ended development in animals, humans, and robots: an overview. Frontiers in Psychology, 5(985). doi: 10.3389/fpsyg.2014.00985 (introduction to Special Topic we edited).

Kalfaoğlu, Ç & Stafford, T. (2014). Performance breakdown effects dissociate from error detection effects in typing. The Quarterly Journal of Experimental Psychology, 67(3), 508-524.

Stafford, T. (2014). The perspectival shift: how experiments on unconscious processing don’t justify the claims made for them. Frontiers in Psychology, 5, 1067. doi:10.3389/fpsyg.2014.01067

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

Pirrone, A., Stafford, T., & Marshall, J. A. R. (2014). When natural selection should optimise speed-accuracy trade-offs. Frontiers in Neuroscience, 8(73). doi: 10.3389/fnins.2014.00073

Redgrave, P., Vautrelle, N., & Stafford, T. (2014). Interpretive conundrums when practice doesn’t always make perfect. Movement Disorders: Official Journal of the Movement Disorder Society 29(1), 7-10. doi:10.1002/mds.25726

 

2013

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.

Thirkettle, M., Walton, T., Redgrave, P., Gurney, K., & Stafford, T. (2013). No learning where to go without first knowing where you’re coming from: action discovery is trajectory, not endpoint based. Frontiers in Cognitive Science, 4:, 638. doi:10.3389/fpsyg.2013.00638

Walton, T., Thirkettle, M., Redgrave, P., Gurney, K. N., & Stafford, T. (2013). The Discovery of Novel Actions Is Affected by Very Brief Reinforcement Delays and Reinforcement Modality. Journal of Motor Behavior, 45(4), 351-360.

Bednark, J. G., Reynolds, J. N. J., Stafford, T., Redgrave, P., & Franz, E. A. (2013). Creating a movement heuristic for voluntary action: Electrophysiological correlates of movement-outcome learning. Cortex, 49(3), 771-780. doi:10.1016/j.cortex.2011.12.005

Thirkettle, M., Walton, T., Shah, A., Gurney, K., Redgrave, P., & Stafford, T. (2013). The path to learning: Action acquisition is impaired when visual reinforcement signals must first access cortex. Behavioural Brain Research, 243, 267–272. doi:10.1016/j.bbr.2013.01.023

2012

Stafford, T., Thirkettle, M., Walton, T., Vautrelle, N., Hetherington, L., Port, M., Gurney, K.N., Redgrave, P. (2012), A Novel Task for the Investigation of Action Acquisition, PLoS One, 7(6), e37749.

Stafford, T. & Grimes, A. (2012). Memory enhances the mere exposure effect. Psychology & Marketing, 29, 12, 995-1003.

Stafford, T., & Bell, V. (2012). Brain network: social media and the cognitive scientist. Trends in Cognitive Sciences, 16(10), 489–490. doi:10.1016/j.tics.2012.08.001

2011

Stafford, T., & Gurney, K. N. (2011). Additive Factors Do Not Imply Discrete Processing Stages: A Worked Example Using Models of the Stroop Task. Frontiers in Psychology, 2. doi:10.3389/fpsyg.2011.00287

Stafford, T., Ingram, L. and Gurney, K.N. (2011), Pieron’s Law holds during Stroop conflict: insights into the architecture of decision making, Cognitive Science 35, 1553–1566.

Yates, D.J. and Stafford, T. (2011), Insights into the function and mechanism of saccadic decision making from targets scaled by an estimate of the cortical magnification factor, Cognitive Computation, 3,89-93.

earlier key publications

Stafford, T. (2009), What use are computational models of cognitive processes? In Mayor, J., Ruh, N.,  Plunkett, K. Connectionist Models of Behaviour and Cognition II: Proceedings of the 11th Neural Computation and Psychology Workshop. World Scientific

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.

Stafford, T. & Gurney, K.N. (2007), Biologically constrained action selection improves cognitive control in a model of the Stroop task, Philosophical Transactions of the Royal Society B: Biological Sciences, 362 (1485), 1671-1684.

Stafford, T. & Wilson, S. P. (2007), Self-organisation can generate the discontinuities in the somatosensory map, Neurocomputing, 70(10-12), 1932-1937.

Stafford, T. & Gurney, K. N. (2004), The role of response mechanisms in determining reaction time performance: Pieron’s Law revisited, Psychonomic Bulletin & Review, 11:975-987.

Eiser, J. R., Fazio, R. H., Stafford, T. & Prescott, T. J. (2003), Connectionist simulation of attitude learning: Asymmetries in the acquisition of positive and negative evaluations, Personality and Social Psychology Bulletin, 29:1221-1235.

 

Link to: full list of journal publications

Invited Talks

Beyond Reinforcement Learning In Action Acquisition‘, 9 November 2011, Department of Psychological Sciences, Birkbeck University of London.

‘Infering cognitive architectures from high-resolution behavioural data’, 13 May 2011, York Centre for Complex Systems Analysis, University of York.

‘A novel task for the investigation of action learning’, 7 July 2010, Experimental Psychology Society, Manchester, 7-9 July 2010

‘An empirical test of some philosophies of science’, 21 May 2010, Heng Seng Centre for Cognitive Studies, University of Sheffield

‘How do we use computational models of cognitive processes?’, Neural Computation and Psychology Workshop, 8-10 April 2010, Birkbeck, London

– ‘What use are computational models of cognitive processes?‘, 19th of March 2010, Redwood Centre for Theoretical Neuroscience, UC Berkeley

– ‘Using the psychophysics of choice behaviours to infer mental structure from reaction times‘, 15th of January 2010, Department of Psychology, National University of Ireland, Galway

– ‘Pieron’s Law holds in conditions of response conflict’, 1st August, presented at the 31th Annual Conference of the Cognitive Science Society. Amsterdam, The Netherlands. Powerpoint here

– ‘The Nonconscious Mere Exposure Effect with Brand Logos: Real but Elusive’, Department of Psychology, City College, Thessaloniki, 1/6/09

– ‘Email: the technology and psychology of continuous partial attention’, UFI, Sheffield, 12/11/08

– “‘Things can be known’ : Teaching psychology through demonstrations” Keynote at 26th Annual Conference of the Association for the Teaching of Psychology, University of Lincoln, 11th of July, 2008

– ‘How to make students talk in seminars’, HEA Psychology ‘Postgraduates who Teach’ Network, University of Birmingham, 27 May 2008

– ‘The psychological foundations of privacy’, Privacy in Law, Ethics and Genetic Data, 1st international workshop PRIVILEGED Project (EC FP6), 10 Jan 2008

– ‘Debates in Cognitive Neuroscience’, Center for Inquiry Based Learning in the Arts and Social Sciences, University of Sheffield, 30 May 2007

‘Is there a science of advertising?’, University of Hull, Management School, 27th of April 2007

– ‘Residents’ Perception of Risk on Contaminated Sites’, Environment Agency training day, Leeds, 20th of June, 2007

 

Conference Papers & Posters

Grimes A, Stafford T & Roper S (2016), Ambient Rubbish: Examining the attitudinal impact of incidental exposure to brand litter, Academy of Marketing’s 11th Global Brand Conference, Bradford University, UK

“Linking total movement history to action learning”. Stafford & Thirkettle. Poster presented at Reinforcement Learning and Decision Making 2013, 24th-26th October, Princeton, USA

Testing theories of skill learning using a very large sample of online game players. Stafford & Dewar. Talk at the 35th Annual Meeting of the Cognitive Science Society, Berlin, Germany, July 31 -August 3, 2013

“The effect of acquisition of an internal forward model on an exploration task”. M. Dagioglou, J. M.Bugella, T. Walton, T. Stafford, P. Redgrave, R.C. Miall. Poster presented at the 22nd Annual Meeting of the Society for the Neural Control of Movement, April 23-29th 2012, Venice, Italy

Typing errors lead to increase in power and synchronization of theta oscillations“. Cigir Kalfaoglu, Tom Stafford and Elizabeth Milne. Poster presentation (by Kalfaoglu) in the British Association of Cognitive Neuroscience in Newcastle, UK on Wednesday the 11th of April, 2012.

“Instruction to relax enhances visual search performance by altering eye movements” David Yates and Tom Stafford. Oral Presentation (by Yates) at 34th European Conference on Visual Perception which will be held in Toulouse, France from Sunday 28th August to Thursday 1st September, 2011.

Visual search performance can be enhanced by instructions that alter eye movements” David Yates and Tom Stafford. Poster Presentation (by Yates) at 16th European Conference on Eye Movements which will be held in Marseille, France from Sunday 21st to Thursday 25th August, 2011.

“Learning the long way round: Action learning based on visual signals unavailable to the superior colliculus is impaired.” Martin Thirkettle, Tom Walton, Kevin Gurney, Peter Redgrave and Tom Stafford. Oral presentation (by Thirkettle) at 34th European Conference on Visual Perception which will be held in Toulouse, France from Sunday 28th August to Thursday 1st September 2011.

“What mistakes reveal about skilled performance: A study of touch-typing”. Cigir Kalfaoglu, Tom Stafford and Elizabeth Milne. Poster presentation (by Kalfaoglu) in the Experimental Psychologists Society Meeting in Oxford, UK from Wednesday the 13th to Friday the 15th of April, 2011.

Stafford, T., Javaid, M, Mitchinson, B., Galloway, A.M.J., Prescott, T.J. (2011). Integrating Augmented Senses into Active Perception: a framework. Poster presented at Royal Society meeting on Active Touch Sensing at the Kavli Royal Society International Centre, 31 January – 02 February, 2011

J.G. Bednark, E.A. Franz, T. Stafford, P. Redgrave, J.N.J. Reynolds. Tracking the learning of actions: An evaluation of the frontal P3a component. Society for Neuroscience Annual Meeting, 17-21 October 2009, Chicago.

Yates, D.J.; Stafford, T. Saccadic latency versus eccentricity for targets scaled by an estimate of the cortical magnification factor, 15th European Conference on Eye Movements, Southampton, 23-27th of August, 2009

Stafford, T; Grimes, A., Perkins, C. The Nonconscious Mere Exposure Effect with Brand Logos: Real but Elusive, The 21st Annual Convention of the Association for Psychological Science, 22nd-24th of May 2009, San Francisco, CA.

Stafford, T. What use are computational models of cognitive processes? 11th Neural Computation and Psychology Workshop, Oxford, 16-18 July, 2008.

Stafford, T. & Wilson, S.P. Self-organisation explains discontinuities in the somatosensory map. Fifteenth Annual Computational Neuroscience Meeting CNS*2006, July 16 – July 20, 2006.

Tom Stafford, Mark D. Humphries, Jonathan M. Chambers. The neural circuitry necessary for decision making by evidence accumulation. Poster presented at the Computational Cognitive Neuroscience conference in Washington, DC, 11/10/2005.

Stafford, T. & Gurney, K. (2005). The basal ganglia as the selection mechanism in a cognitive task. In J. J. Bryson, T. J. Prescott & A. Seth (Eds.) Modeling Natural Action Selection (pp. 77-83). AISB Press.

Stafford, T. & Gurney, K. (2005). The basal ganglia as the selection mechanism in a cognitive task. Poster presented at Modelling Natural Action Selection workshop in Edinburgh, July 2005.

Eiser, Richard, Stafford, Tom, Shook, Natalie & Fazio, Russell. Learning under uncertainty: Manipulating and simulating the role of expectations. Paper presented at the 14th General Meeting of the European Association fo Experimental Social Psychology, Wurzburg, 19-23 July 2005.

Catney, P., Lawson, N., Palaseanu-Lovejoy, M., Shaw, S., Smith, C., Stafford, T., Talbot, S., Hao, X. (2005, 1st of March 2005). Acid tar lagoons: risks and sustainable remediation in an urban context. Paper presented at the SUBR:IM conference, Natural History Museum, London.

Stafford, T. & Gurney, K.N. The Basal Ganglia provides an appropriate model for response selection in the Stroop task. Poster presented at the Annual Conference of the BPS Cognitive Psychology Section at Essex, September 6th-8th, 2000.

Books & Book chapters

Stafford, T. (2011). How do we use computational models of cognitive processes? In E. Davelaar (eds) Connectionist Models Of Neurocognition And Emergent Behavior: Proceedings of the 12th Neural Computation and Psychology Workshop(pp 326-342). World Scientific.

Stafford, T. (2010). The Narrative Escape, 40kbooks, Milan.

Moore, G & Stafford, T. (2010). The Rough Guide Book of Brain Training.

Stafford, T. (2009) ‘Hacking our tools for thought’ in Nold, C. (ed) Emotional Cartography: Technologies of the Self, pp 88-96. emotionalcartography.net

Stafford, T. (2009). What use are computational models of cognitive processes? In J. Mayor, N. Ruh & K. Plunkett (eds.) Connectionist Models of Behavior and Cognition II: Proceedings of the 11th Neural Computation and Psychology Workshop(pp 265-274). World Scientific.

Catney, P., Eiser, J.R., Henneberry, J. & Stafford, T. (2007) ‘Democracy, Trust and Risk Related to Contaminated Sites in the UK’, in: T. Dixon, M. Raco, P. Catney & D.N. Lerner (Eds.) Sustainable Brownfield Regeneration: Liveable Places from Problem Spaces. Oxford: Blackwells.

Stafford, T. & Gurney, K.N. (2006). Computational Models of Cognition. In An Introduction to Cognitive Psychology: Processes and Disorders. Second Edition. Ed. David Groome. Hove, UK: Psychology Press.

Stafford, T. & Webb, M. (2004). Mind Hacks: Tips and Tricks for using your brain. Sebastapol, CA: O’Reilly.

 

Other publications

Stafford, T. (2009). Lessons from the campaign against Elsevier: “We won, but how did we win?”. ACME: An International E-Journal for Critical Geographies, 8(3), 494-504.

Stafford, T. (2008). Teaching Questions Rather than Answers: inquiry-based learning on an MSc course. HEA Psychology Network Newsletter, Issue 46, January 2008. Available at: http://www.psychology.heacademy.ac.uk/html/newsletter.asp

Stafford, T. & Martin, C.J. (2007). How to do a neuroscience lab class with 120 students. HEA Psychology Network Newsletter, Issue 45, November 2007. Available at: http://www.psychology.heacademy.ac.uk/html/newsletter.asp

Stafford, T. (2007). Isn’t it all just obvious? The Psychologist, 20,2,94-95

The neural circuitry necessary for decision making by evidence accumulation
Mark D. Humphries, Tom Stafford, Jonathan M. Chambers & Kevin N. Gurney
ABRG Technical Report number 5, May 2006. Department of Psychology, University of Sheffield, UK

Stafford, T. & Webb, M. (2006) ‘What Is a Wiki (and How to Use One for Your Projects)’. O’Reilly Network, 7 July 2006. Available at
http://www.oreillynet.com/pub/a/network/2006/07/07/what-is-a-wiki.html

Stafford, T. & Gurney, K. (2005). The basal ganglia as the selection mechanism in a cognitive task. In J. J. Bryson, T. J. Prescott & A. Seth (Eds.) Modeling Natural Action Selection (pp. 77-83). AISB Press.

Webb, M. & Stafford, T.(2004) ‘Paying Attention (or Not) to the Flickr Daily Zeitgeist’. O’Reilly Network, 6 December 2004. Available at
http://www.oreillynet.com/pub/a/network/2004/12/06/mndhcks_1.html

Stafford, T. (2003). Integrating psychological and neuroscientific constraints in models of Stroop processing and action selection PhD Thesis, University of Sheffield. (abstract and contents only)

Stafford, T. (2003). Psychology in the coffee shop. The Psychologist, 16(7), 358-359

Stafford, T. (2000). Stroop Interference: Methodological Problems and Contrary Data, Psycoloquy, 11, #110.