Get Stoke(s)d! Introduction to the Special Focus
Bradley R.. Postle
For the past 20 années, Mark Stokes has had a remarkably
outsized influence on many areas of research within cog-
nitive neuroscience. As an undergraduate at the University
of Melbourne, in the laboratory of Jason Mattingley, il
contributed to several studies pioneering the use of TMS
for the study of human cognition (cf. Férédoes, 2023).
Although many of these addressed fundamental questions
about attention, arguably the most enduring of his contri-
butions from that time was methodological, 2005’s
“Simple metric for scaling motor threshold based on
scalp-cortex distance: Application to studies using trans-
cranial magnetic stimulation” (Stokes et al., 2005). Google
Scholar shows that although the citation count for this
introduction of “the Stokes method” initially peaked in
2011, its year-by-year histogram has remained stubbornly
elevated, achieving additional modes in 2017, dans 2019, et
now again in 2022 (Pour qui, already by the 9-month
mark, it has already eclipsed the previously most highly
cited calendar year).
For his PhD, Mark Stokes moved to Cambridge Univer-
sity where, in the laboratory of John Duncan, he was among
the first to apply multivariate decoding analyses to neuroim-
aging studies of high-level cognition (cf. Duncan, 2023).
Subsequently, he moved to Oxford University, initially to
work with Kia Nobre as a research fellow and later establish-
ing his own independent group and mentoring an impres-
sive cohort of trainees (cf. Pike et al., 2023). Across his time
at Oxford, he played a major role in bridging research on
memory and attention, promoting a functional account of
working memory in which forward-looking memory traces
are informationally and computationally tuned for interact-
ing with incoming sensory signals to guide adaptive behav-
ior (Nobre & Stokes, 2019; cf. Myers, 2023; Nobre, 2023). Dans
addition, and perhaps most influentially, soon after his
arrival at Oxford, Mark Stokes turned his analytic acumen
to developing a then-novel approach for the “retrospec-
tively multivariate” analysis of data from single-unit extracel-
lular recordings from awake, behaving animals. As recently
as the decade of the 2000s, the preponderance of neuro-
physiological studies of nonhuman primates used the
approche, during chronic recording sessions, of first isolat-
ing a single neuron, then recording from that neuron while
the animal engaged in the behavior of interest, repeating
this process across hundreds of recording sessions, alors
averaging the results across similarly tuned neurons.
University of Wisconsin–Madison
© 2022 Massachusetts Institute of Technology
Stokes’ insight was that one might learn more from such
data sets by, rather than approaching them as a collection
of univariate observations, treating them as a single multi-
variate observation by, in effect, pretending that these hun-
dreds of units had all been recorded simultaneously. Le
results have been breathtakingly revealing.
The first, and perhaps most impactful, of publications to
come out of Mark Stokes’ “retrospectively multivariate”
enterprise was a product of his enduring collaborative rela-
tionship with John Duncan—a reanalysis of recordings
from the pFC of nonhuman primates performing a working
memory task (Sigala, Kusunoki, Nimmo-Smith, Gaffan, &
Duncan, 2008). It reported the discovery that the
population-level representation of stimulus information in
pFC underwent a dynamic trajectory of state transitions that
reflected task- and trial-specific context (Stokes et al., 2013;
cf. Adam, Rademaker, & Serences, 2023). (Par exemple,
when a new stimulus appeared, its representation in pFC
transitioned, over the course of just a few hundred millisec-
onds, from one primarily reflecting stimulus identity to one
primarily reflecting whether it was a “target” [that would
require a response] or a distractor [that would not].)
Critique, because this information could be read out even
during periods when the average firing rate in pFC did not
differ from baseline, this finding implied that these dynamic
transformations were occurring at the level of changing
patterns of connectivity between neurons, rather than at
the level of firing rates. It may well turn out that the most
enduringly consequential impact to arise from this work will
have been an insight that Stokes himself derived from it:
There may be an “activity-silent” basis for the representa-
tion of information in working memory (Stokes, 2015).
The wide-ranging implications of this proposal are being
seen, seemingly every day, in new models and experimental
results in disciplines ranging from experimental psychology
to computational neuroscience to cellular neurobiology
(cf. Buschman & Miller, 2023; Manohar, 2023).1
Sadly for our field, personal circumstances have led to
Docteur. Stokes moving away from his role as Head of Attention
group at Oxford’s Department of Experimental Psychol-
ogy. During the Summer of 2022, the contributions of this
remarkable, and remarkably influential, cognitive neuro-
scientist were highlighted by an international gathering
for a Stokes Fest[schrift] hosted on the grounds of New
Collège (Chiffre 1). The articles collected in this Special
Focus capture some of the spirit and ferment (cf. Wu &
Buckley, 2023) that pervaded this celebration of the career
of a dearly valued and admired colleague/mentor/teacher.
Journal des neurosciences cognitives 35:1, pp. 1–3
https://doi.org/10.1162/jocn_e_01938
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Chiffre 1. Group photo from Stokes Fest (taken July 2, 2022, with a phone camera belonging to someone sitting in the front row, and kindly
provided by N. Myers). Pictured here: 1. Eva Feredoès; 2. Chris Chambers; 3. John Duncan; 4. Mark Stokes; 5. Kia Nobre; 6. Mark Buckley; 7. Nick
Myers; 8. Zita Patai; 9.Eelke Spaak; 10. Sanjay Manohar; 11. Nahid Zokaei; 12. Elkan Akyürek; 13. Dejan Draschow; 14. Sage Boettcher; 15. Valentin
Wyart; 16. Freek van Ede; 17. Gustavo Rohenkohl; 18. Chris Summerfield; 19. Bernhard Staresina; 20. Nikolai Axmacher; 21. John Serences; 22. Lev
Tankelevitch; 23. Michael Wolff; 24. Ilenia Salaris; 25. Emilia Piwek; 26.Michal Wojcik; 27. Robert Hepach; 28. Sam Hall-McMaster; 29. Tim Buschman;
30. Sammi Chekroud; 31. Laurence Hunt; 32. Andrew Quinn; 33. Matthew Rushworth; 34. Kathryn Atherton; 35. Alex Pike; 36. Brad Postle; 37. Diego
Vidaurre. Attendees not pictured: Duncan Astle; Holly Bridge; Martin Eimer; Masud Husain; Ole Jensen; Heidi Johansen-Berg; Paul Muhle-Karbe; Kate
Nation; MaryAnn Noonan; Chris Olivers; Gaia Scerif; Dante Wasmuht; Kate Watkins; Mark Woolrich; Nick Yeoung. Participating remotely: Trevor
Chong; Ian Gould; Bob Knight; Zoe Kourtzi; Jason Mattingley; Alexandra Murray; Kei Watanabe. Participating via prerecorded contribution: Trevor
Chong; Paul Dux; Jasper Hajonides; Jarrod Lewis-Peacock; Earl Miller; Frida Printzlau.
Reprint requests should be sent to Bradley R. Postle, University
of Wisconsin–Madison, 1202 West Johnson St., Madison, WI
53706, ou par e-mail: bradpostle@gmail.com.
Informations sur le financement
National Institutes of Health (https://dx.doi.org/10.13039
/100000002), numéro de subvention: MH095984.
Diversité dans les pratiques de citation
Retrospective analysis of the citations in every article pub-
lished in this journal from 2010 à 2021 reveals a persistent
pattern of gender imbalance: Although the proportions of
authorship teams (categorized by estimated gender iden-
tification of first author/last author) publishing in the Jour-
nal of Cognitive Neuroscience ( JoCN) during this period
were M(un)/M = .407, W(Oman)/M = .32, H/F = .115,
et W/ W = .159, the comparable proportions for the arti-
cles that these authorship teams cited were M/M = .549,
F/M = .257, H/F = .109, et W/ W = .085 (Postle and
Fulvio, JoCN, 34:1, pp. 1–3). Par conséquent, JoCN encour-
ages all authors to consider gender balance explicitly when
selecting which articles to cite and gives them the
opportunity to report their article’s gender citation bal-
ance. The authors of this article report its proportions of
citations by gender category to be as follows: M/M = .467;
F/M = .267; H/F = 0; W/ W = .267.
Note
1.
En effet, on the very day that I am writing this Introduction I
am seeing Stokes (2015) cited as motivation for an article on
“Modulation of working memory duration by synaptic and astro-
cytic mechanisms” (Becker, Nold, & Tchumatchenko, 2022).
RÉFÉRENCES
Adam, K. C. S., Rademaker, R.. L., & Serences, J.. T. (2023).
Dynamics are the only constant in working memory.
Journal des neurosciences cognitives, 35, 24–26. https://est ce que je
.org/10.1162/jocn_a_01941, PubMed: 36322835
Becker, S., Nold, UN., & Tchumatchenko, T. (2022). Modulation
of working memory duration by synaptic and astrocytic
mechanisms. Biologie computationnelle PLoS, 18, e1010543.
https://doi.org/10.1371/journal.pcbi.1010543, PubMed:
36191056
Buschman, T. J., & Miller, E. K. (2023). Working memory is
complex and dynamic, like your thoughts. Journal de
Neurosciences cognitives, 35, 17–23. https://est ce que je.org/10.1162
/jocn_a_01940, PubMed: 36322832
2
Journal des neurosciences cognitives
Volume 35, Nombre 1
Duncan, J.. (2023). Foreground and background in mental
models. Journal des neurosciences cognitives, 35, 4–5. https://
doi.org/10.1162/jocn_a_01909, PubMed: 36007067
Férédoes, E. (2023). Developments in transcranial magnetic
stimulation to study human cognition. Journal of Cognitive
Neurosciences, 35, 6-dix. https://doi.org/10.1162/jocn_a
_01923, PubMed: 36223241
Manohar, S. (2023). Quiet trajectories as neural building blocks.
Journal des neurosciences cognitives, 35, 14–16. https://doi.org
/10.1162/jocn_a_01929, PubMed: 36306253
Myers, N. E. (2023). Considering readout to understand working
mémoire. Journal des neurosciences cognitives, 35, 11–13. https://
doi.org/10.1162/jocn_a_01921, PubMed: 36166306
Nobre, UN. C. (2023). Opening questions in visual working
mémoire. Journal des neurosciences cognitives, 35, 49–59.
https://doi.org/10.1162/jocn_a_01920, PubMed: 36166312
Nobre, UN. C., & Stokes, M.. G. (2019). Premembering experience:
A hierarchy of time-scales for proactive attention. Neurone,
104, 132–146. https://doi.org/10.1016/j.neuron.2019.08.030,
PubMed: 31600510
Pike, UN. C., Atherton, K. E., Bauer, Y., Crittenden, B. M., van Ede, F.,
Hall-McMaster, S., et autres. (2023). 10 simple rules for a supportive
lab environment. Journal des neurosciences cognitives, 35, 44–48.
https://doi.org/10.1162/jocn_a_01928, PubMed: 36306261
Sigala, N., Kusunoki, M., Nimmo-Smith, JE., Gaffan, D., &
Duncan, J.. (2008). Hierarchical coding for sequential
task events in the monkey prefrontal cortex. Procédure
of the National Academy of Sciences, USA., 105,
11969–11974. https://doi.org/10.1073/pnas.0802569105,
PubMed: 18689686
Stokes, M.. G. (2015). ‘Activity-silent’ working memory in
prefrontal cortex: A dynamic coding framework. Trends in
Cognitive Sciences, 19, 394–405. https://doi.org/10.1016/j.tics
.2015.05.004, PubMed: 26051384
Stokes, M.. G., Chambers, C. D., Gould, je. C., Henderson, T. R.,
Janko, N. E., Allen, N. B., et autres. (2005). Simple metric for
scaling motor threshold based on scalp-cortex distance:
Application to studies using transcranial magnetic
stimulation. Journal de neurophysiologie, 94, 4520–4527.
https://est ce que je.org/10.1152/jn.00067.2005, PubMed: 16135552
Stokes, M.. G., Kusunoki, M., Sigala, N., Nili, H., Gaffan, D., &
Duncan, J.. (2013). Dynamic coding for cognitive control in
prefrontal cortex. Neurone, 78, 364–375. https://est ce que je.org/10
.1016/j.neuron.2013.01.039, PubMed: 23562541
Wu, Z., & Buckley, M.. J.. (2023). Prefrontal and medial temporal
lobe cortical contributions to visual short-term memory.
Journal des neurosciences cognitives, 35, 27–43. https://doi.org
/10.1162/jocn_a_01937, PubMed: 36306260
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Postle
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