A happy consequence of the media exposure I have received is that all sorts of people contact me when they have questions about déjà vu. Often, people want to find out about personal experiences they or those they know have had, but every now and again, school students will contact me for help with their projects.

One student who contacted me earlier this year was Cyril Vivek Subramanian, from Sydney. Cyril Vivek was researching a video to enter for the University of Sydney Sleek Geeks Science Eureka Prize, and I had a couple of conversations with him and his mother via email and Skype to help him with this. He was keen to do a lot of background research himself, and I found myself thankful for being able to refer him to the Frontiers for Young Minds article I’d previously written with Julia Teale.

The video didn’t end up being shortlisted, but I was thoroughly impressed with it, and delighted to be able to share it here. It is always great to be able to guide and work with young people, and a privilege to see students like Cyril Vivek excited about science and able to communicate it so well. Bravo!

Come to St Andrews and figure out why déjà vu experiences decrease with age, with me and Ines Jentzsch.

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Please email (aro2@st-andrews.ac.uk) or tweet (@akiraoc) me if you’d like to speak more about this project.  If you’d like to speak to anyone about doing a PhD with me, please get in touch with Mags Pitt (3rd yr PhD), Bjorn Persson (3rd yr PhD) or Ravi Mill (completed PhD) via the People section of the blog.

CNS Poster
Ravi Mill presenting simultaneous EEG fMRI data at CNS 2014

Project Description

BBSRC Theme: Word class underpinning Bioscience

Adaptive cognition involves both the completion of a set of mental operations and the awareness that these operations have been completed so that the next stage of cognition can be engaged. During successful memory decision-making these two steps, memory retrieval and retrieval awareness, go hand in hand. However, they can occasionally fragment, leading to a set of experiences termed introspective memory phenomena (IMPs; e.g. déjà vu and jamais vu). During déjà vu positive retrieval awareness arises in the absence of true retrieval, yielding the overall sensation of inappropriate familiarity (O’Connor & Moulin, 2010). Jamais vu is the opposite–negative retrieval awareness in the presence of true retrieval. IMPs signal conflict within the cognitive system, and thus may play a crucial role in error correction (we do not act on IMPs in the way that we do act on false memories). However, beyond some curious demographic associations (they occur more in those who are well-travelled and well-educated), IMP occurrence is not known to be associated with any existing cognitive or psychological traits.

IMPs are not experienced uniformly across the population but peak in those in their mid-20s, before declining with age thereafter. They are also thought to be driven by dopaminergic over-activity such that some pharmacological and recreational drugs (e.g. dopaminergic flu medications) have been reported as causing persistent déjà vu (Taiminen & Jääskeläinen, 2001). Interestingly, these characteristics mirror what is known about neurophysiological markers of inhibitory control and response monitoring more generally (e.g. Strozyk & Jentzsch, 2012), which show the same lifespan trajectory with an age-related decrease in the dopaminergic functions mediated by the frontal cortex. These links suggest that IMP occurrence may be underpinned by basic neurocognitive characteristics integral to healthy cognition. Thus, the importance of IMPs may not lie in the fragmentation of the memory decision-making system, but in the capacity for our response monitoring systems to detect it and stop us making decisions based on faulty information.

We propose a systematic programme of research to establish the role of error-monitoring in the generation of IMPs. Using i) retrospective questioning to verify the recent occurrence of IMPs and ii) established procedures for their laboratory generation, we will explore individual differences in IMP experience and neurophysiological markers of response monitoring. These experiments will be a) developed in young adults and extended to b) primary school children (age 8-11; the age at which IMPs are first reported by children) and c) older adults (age 55 and older). We will also conduct opportunistic case-studies on d) patients who present themselves to Dr O’Connor over the course of the PhD (UK-based patients typically get in touch at a rate of 1-2/year). This systematic programme will allow us to establish any potential links between basic neurocognitive characteristics and the tendency to experience dissociative memory sensations which are not known to have any other psychological correlates.

This project will benefit from the joint multi-disciplinary expertise of Dr O’Connor, an internationally recognized expert in the area of metacognition and introspective memory phenomena and Dr Jentzsch, a biophysicist and electrophysiologist by training, who specialized in studying the neural underpinnings dopaminergic functions such as action and conflict control. Together, we will provide the prospective student conceptual knowledge of metacognitive models of memory and changes to these functions with healthy ageing integrating behavioural methods and physiological measures of brain function in humans. The student will learn about experimental design, programming (Matlab), data collection and behavioural analysis techniques such as signal detection theory. In addition, the student will learn how to design, conduct and analyse electrophysiological experiments using EEG. Acquisition of generic skills such as team-working, time-management and communication skills amongst many others will also be an important part of the students training.

Funding Notes

This project is eligible for the EASTBIO Doctoral Training Partnership: View Website

This opportunity is only open to UK nationals (or EU students who have been resident in the UK for 3+ years immediately prior to the programme start date) due to restrictions imposed by the funding body.

Apply by 5.00pm on the 14th December 2015 following the instructions on how to apply at: View Website

Informal enquiries to the primary supervisor are very strongly encouraged.

References

O’Connor, A.R. & Moulin, C.J.A. (2010). Recognition without identification, erroneous familiarity, and déjà vu. Current Psychiatry Reports, 12(3), 165-173.

Strozyk, J.V. & Jentzsch, I. (2012). Weaker error signals do not reduce the effectiveness of post-error adjustments: Comparing error processing in young and middle-aged adults. Brain Research, 460, 41-49

Taiminen, T. & Jääskeläinen, S.K. (2001). Intense and recurrent déjà vu experiences related to amantadine and phenylpropanolamine in a healthy male. Journal of Clinical Neuroscience, 8, 460-462.

Finding needle in haystack
Finding needle in haystack (Photo credit: Bindaas Madhavi)

A former colleague of mine at an institution I no longer work at has admitted to being a science fraudster.*

I participated in their experiments, I read their papers, I respected their work. I felt a very personal outrage when I heard what they had done with their data. But the revelation went some way to answering questions I ask myself when reading about those who engage in scientific misconduct. What are they like? How would I spot a science fraudster?

Here are the qualities of the fraudster that stick with me.

  • relatively well-dressed.
  • OK (not great, not awful) at presenting their data.
  • doing well (but not spectacularly so) at an early stage of their career.
  • socially awkward but with a somewhat overwhelming projection of self-confidence.

And that’s the problem. I satisfy three of the four criteria above. So do most of my colleagues. If you were to start suspecting every socially awkward academic of fabricating or manipulating their data, that wouldn’t leave you with many people to trust. Conversations with those who worked much more closely with the fraudster reveal more telling signs that something wasn’t right with their approach, but again, the vast majority of the people with similar character flaws don’t fudge their data. It’s only once you formally track every single operation that has been carried out on their original data that you can know for sure whether or not someone has perpetrated scientific misconduct. And that’s exactly how this individual’s misconduct was discovered – an eagle-eyed researcher working with the fraudster noticed some discrepancies in the data after one stage of the workflow. Is it all in the data?

Let’s move beyond the few bad apples argument. A more open scientific process (e.g. the inclusion of original data with the journal submission) would have flagged some of the misconduct being perpetrated here, but only after someone had gone to the (considerable) trouble of replicating the analyses in question.  Most worryingly, it would also have missed the misconduct that took place at an earlier stage of the workflow. It’s easy to modify original data files, especially if you have coded the script that writes them in the first place. It’s also easy to change ‘Date modified’ and ‘Date created’ timestamps within the data files.

Failed replication would have helped, but the file drawer problem, combined with the pressure on scientists to publish or perish typically stops this sort of endeavor (though there are notable exceptions such as the “Replications of Important Results in Cognition”special issue of Frontiers in Cognition ). I also worry that the publication process, in its current form, does nothing more constructive than start an unhelpful rumour-mill that never moves beyond gossip and hearsay. The pressure to publish or perish is also cited as motivation for scientists to cook their data. In this fraudster’s case, they weren’t at a stage of their career typically thought of as being under this sort of pressure (though that’s probably a weak argument when applied to anyone without a permanent position). All of which sends us back to trying to spot the fraudster and not the dodgy data. It’s a circular path that’s no more helpful than uncharitable whispers in conference centre corridors.

So how do we identify scientific misconduct? Certainly not with a personality assessment, and only partially with an open science revolution. If someone wants to diddle their data, they will. Like any form of misconduct, if they do it enough, they will probably get caught. Sadly, that’s probably the most reliable way of spotting it. Wait until they become comfortable enough that they get sloppy. It’s just a crying shame it wastes so much of everyone’s time, energy and trust in the meantime.

 

*I won’t mention their name in this post for two reasons: 1) to minimise collateral damage that this is having on the fraudster’s former collaborators,  former institution and their former (I hope) field; and 2) because this must be a horrible time for them, and whatever their reason for the fraud, it’s not going to help them rehabilitate themselves in ANY career if a Google search on their name returns a tonne of condemnation.