{"doi":"10.1162/jocn_e_01938","title":"Get Stoke(s)d! Introduction to the Special Focus","abstract":"For the past 20 years, Mark Stokes has had a remarkably outsized influence on many areas of research within cognitive neuroscience. As an undergraduate at the University of Melbourne, in the laboratory of Jason Mattingley, he contributed to several studies pioneering the use of TMS for the study of human cognition (cf. Feredoes, 2023). Although many of these addressed fundamental questions about attention, arguably the most enduring of his contributions from that time was methodological, 2005's “Simple metric for scaling motor threshold based on scalp-cortex distance: Application to studies using transcranial 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, in 2019, and now again in 2022 (for which, 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 University where, in the laboratory of John Duncan, he was among the first to apply multivariate decoding analyses to neuroimaging 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 establishing his own independent group and mentoring an impressive 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 interacting with incoming sensory signals to guide adaptive behavior (Nobre & Stokes, 2019; cf. Myers, 2023; Nobre, 2023). In 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 “retrospectively multivariate” analysis of data from single-unit extracellular recordings from awake, behaving animals. As recently as the decade of the 2000s, the preponderance of neurophysiological studies of nonhuman primates used the approach, during chronic recording sessions, of first isolating 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, then averaging the results across similarly tuned neurons. 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 multivariate observation by, in effect, pretending that these hundreds of units had all been recorded simultaneously. The 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 relationship 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). (For example, when a new stimulus appeared, its representation in pFC transitioned, over the course of just a few hundred milliseconds, 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].) Critically, 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 cha","journal":"Journal of Cognitive Neuroscience","year":2022,"id":312636,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9616,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":258180,"name":"Bradley R. Postle","orcid":"0000-0001-8555-0148","position":0,"is_corresponding":true}],"reference_count":15,"raw_metadata":null,"created_at":"2026-07-19T00:33:40.460845Z","pmid":"36306255","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}