Understanding subprocesses of working memory through the lens of model-based cognitive neuroscience

Understanding

subprocesses

of

working

memory

through

the

lens

of

model-based

cognitive

neuroscience

Anne

C

Trutti

,

Sam

Verschooren

,

Birte

U

Forstmann

and

Russell

J

Boag

Workingmemory(WM)referstoasetofprocessesthat makestask-relevantinformationaccessibletohigher-level cognitiveprocesses.RecentworksuggestsWMis

supportedbyavarietyofinformationgating,updating,and removalprocesses,whichensureonlytask-relevant informationoccupiesWM.Currentneurocomputational theorysuggestsWMgatingisaccomplishedvia‘go/no-go’ signallinginbasalganglia-thalamus-prefrontalcortex pathways,butislessclearaboutothersubprocessesand brain structuresknownto playaroleinWM.Wereview recenteffortsto identifytheneuralbasisofWM

subprocessesusingtherecentlydevelopedreference-back taskasabenchmarkmeasureofWMsubprocesses.Targets forfutureresearchusingthemethodsofmodel-based cognitiveneuroscienceandnovelextensionstothe reference-backtaskaresuggested.

Addresses

1_{DepartmentofPsychology,UniversityofAmsterdam,Amsterdam,The}

Netherlands

2_{InstituteofPsychology,LeidenUniversity,Leiden,TheNetherlands} 3_{DepartmentofExperimentalClinicalandHealthPsychology,Ghent}

University,Ghent,Belgium Correspondingauthor:

Trutti,AnneC(r.j.boag@uva.nl),Boag,RussellJ(r.j.boag@uva.nl)

CurrentOpinioninBehavioralSciences2020,38:57–65

ThisreviewcomesfromathemedissueonComputationalcognitive neuroscience

EditedbyGeoffSchoenbaumandAngelaJLangdon

https://doi.org/10.1016/j.cobeha.2020.10.002

2352-1546/_ã2020TheAuthor(s).PublishedbyElsevierLtd.Thisisan openaccessarticleundertheCCBYlicense(http://creativecommons. org/licenses/by/4.0/).

Working

memory

and

its

subprocesses

Working memory (WM) refers to a set of processes

that makes task-relevant information accessible to

higher-level cognitive processes such as learning,

decision making, reasoning, and reading

comprehen-sion [1–3]. Working memory is extremely

capacity-limited, withcurrent researchsuggesting that between

oneandfouritems4canbemaintainedinanactivated

stateinWMatatime[4–7].Thisstrictlimitdemandsa

highdegreeofcontroloverWMcontent,suchthatWM

must strikea balancebetween stability(i.e. protecting

thecurrentcontentsofWMfromirrelevantor

distract-inginformation)andflexibility(i.e.keepingWM

up-to-date with new relevant information and removing

outdatedinformation).Thistrade-offbetweenstability

and flexibility [8–11] is a core feature of executive

controlprocesses(e.g.cognitivecontrol,conflict

moni-toring/resolution, task switching; [12]) and managing

thetrade-offstronglydependsonthebrain’sdopamine

systems [13,14].

Prominent computational theories suggest that WM

resolves thestability-flexibilitytrade-offbyoperating

in two modes: An updating (gate-open) mode, which

allowsnewinformationtoenterWM,andamaintenance

(gate-closed) mode, which prevents irrelevant and

distracting information frominterfering with the

cur-rentcontentsofWM[15–22].Inthegate-openmode,

updating is further supported by two main

subpro-cesses: Item removal and item substitution, which

togetherensurethatonlyrelevantinformationiskept

active in WM [23,24]. Together, these processes

allow WM to alternate modes between flexible(when

new information is encountered) and stable (when

distractors are encountered). This enables successful

performance in dynamic environments in which

dis-tractionsarecommonandtherelevanceofinformation

frequently changes.

To-date, themostdetailedneurocomputationalaccount

of the gating mechanism controlling the trade-off

between updating and maintenance is the prefrontal

4

Weusetheterm‘item’torefertoanindividualrepresentationheld inWM.‘Item’ isthussynonymouswith‘chunk’ [97]and‘cognitive object’[98,7]whichdenotethesameconcept.Thereisongoingdebate aboutwhetheritemsinWMarerepresentedindiscreteslots(itemsheld withhighprecisioninanumberofdiscretememorylocations),allocation of continuous resources (items allocated limited resources in inverse proportiontothetotalnumberofitemsinWM),orsomehybridof thetwoframeworks(e.g.Refs.[78,74,79,80]).Themodelsandgeneral approach thatwediscussin thispaperare notcommittedto either architecturebutcouldbeusedtotestbetweenthecompetingaccounts (seeSection‘Currentdirections’below).

cortex-basal ganglia WM (PBWM) model (Figure 1;

[25–27]).Inthismodel,gatingisimplementedviabasal

ganglia (BG)-thalamus-prefrontal cortex (PFC) circuits

that control ‘go/no-go’ signalling. As illustrated in

Figure 1, gate opening is controlled by a striatal ‘go’

signal which inhibits substantia nigra pars reticulata

(SNr) and disinhibits thalamus, which in turn excites

PFC.ThisallowsinformationtoenterWMandupdating

tooccur.Gateclosing5iscontrolled byastriatal ‘no-go’

signal which inhibits external globus pallidus (GPe),

disinhibits SNr, and inhibits thalamus. This in turn

inhibits PFC,which preventsWM from beingupdated

(Figure 1;[26]).In short,the‘go’ signalpassesthrough

two inhibitory connections (striatum-SNr-thalamus),

which excites PFC, while the ‘no-go’ signal passes

through three inhibitory connections

(striatum-GPe-SNr-thalamus),whichinhibitsPFC.Thesecircuitshave

alsobeenimplicatedinupdatingvaluerepresentationsin

reinforcementlearningandvalue-baseddecisionmaking,

suggestingageneralneuralmechanismforaccomplishing

informationgating([16,28,29,26,20,30,22]).

The components of the PBWM model have received

broadsupportfromfunctionalmagneticresonance

imag-ing (fMRI) studies ([19,28,31–33,22,34]). For example,

activation in striatum and dorsolateral PFC has been

widelyreportedintasksbroadlyinvolvingWMupdating

(e.g.Refs.[31–34]),whileotherworkhaslocalized

activ-ity specifically related to the updating and gating

pro-cessesrather than other WMprocesses. Roth et al. [22]

identifiedafrontoparietalnetworkspecificallyinvolvedin

updating,whileMurtyetal.[19]foundselective

engage-mentofSN/ventraltegmentalarea(VTA),caudate,

dor-solateralPFC,andsomeareasofparietalcortexrelatedto

theupdatingbutnotmaintenancemodeofWM.Striatal

dopamine-receptor expressing neurons and

dopamine-producingmidbrainstructureshavealsobeenimplicated

inWM updating [19,28,33],anddynamic causal

model-ling suggests that BG plays a central role in gating

informationtoPFC[35].Moreover,anumberofcortical

areas(e.g.dorsolateralPFC,medialPFC,posterior

pari-etalcortex)havebeenlinkedtothemaintenancemodeof

WM but not updating ([22,36–38]). This is consistent

withtheideathattonicdopamineactivityinPFCcontrols

thestabilityofWMrepresentationswhereasphasic

dopa-minereleaseinthestriatumtrainstheBGwhentoopen

thegate(viadisinhibitionofthalamusandPFC)toallow

informationintoWM6[27].

Overall,thesefindings showthatWMupdatingengages

cortico-striatalcircuitryinvolvingBG,midbrain,andPFC

structures broadly in line with the neurocomputational

mechanisms of the PBWM model [39,26] and more

general accounts of cognitive control (e.g. Ref. [40]).

However, as will be discussed, recent work highlights

thatWMalsodependsonseveralimportantsubprocesses

notaccountedforinthePBWM,andonneuralsubstrates

outside of the PBWM’s BG-thalamus-PFC pathways.

Modellingtheseprocessesandtheirneuralbasisis

nec-essarytoachieveacompleteneurocomputational

under-standingofWM.

This review discusses recent progress toward this goal.

Wefocus on recent effortsto linkbrain measurements

with behaviour on the reference-back task (Figure 2;

[24,21]),a WM-based decision-makingtask that

pro-videsseparatebehaviouralmeasuresofgateopeningand

closing,aswellasupdatingandsubstitutionprocessesnot

accountedforinthePBWM.Indoingso,wesuggestthat

Figure1

Posterior Cortex Frontal Cortex

STN GPe SNr NoGo Go thalamus VA,VL,MD excitatory inhibitory dorsal striatum D1 D2

Current Opinion in Behavioral Sciences

IllustrationofthePBWMmodel.Gateopeningiscontrolledbya striatal‘go’signalthatinhibitsSNranddisinhibitsthalamusandPFC, enablingupdatingtooccur.Gateclosingiscontrolledbyastriatal ‘no-go’signalthatinhibitsGPe,disinhibitsSNr,whichinhibitsthalamus andPFC,preventingupdating.Extendingthismodeltoinclude additionalstructuresimplicatedinWMandcognitivecontrol(e.g. hippocampus,ventraltegmentalarea,anteriorcingulatecortex)and theirroleinWMsubprocessesbeyondgateopening/closingisakey targetformodel-basedcognitiveneuroscience.AdaptedfromHazy etal.[26]withpermission.

5

The PBWM model assumes that WM sits in the ‘gate-closed’/ maintenancemodebydefault.Wenotethatthisassumptionislikely toostrong,sinceitimpliesthatgateopeningmustalwaysaccompany updating.UnderthisassumptionthePBWMwouldfailtopredictthe differentgatingcoststoWMupdatingthatoccurinbehaviouraldata(e. g.Refs.[24,21]).

6

ThePBWMmodelsuggestsaphasicdopaminergicsignalfromthe midbraindopaminestructuresonlyintheearlyphasesofaWMtask whentheBGmustlearnwhentoupdate.OnceWMupdatingrulesare learned,BGnucleinolongerrelyonaphasicdopaminergicresponsebut control WM gating via the non-dopaminergic SNr. Any additional dopaminergicinputreflects eitherreward associationsora feedback-based response which evaluates theupdating process based on the rewardpredictionerrorcodedbythesameneurons[84].Thisresponse, intheformof burstsanddips indopaminergic releaseontostriatal neurons,isthoughttoreinforce‘go’and‘no-go’activation,respectively.

furtherprogresscanbemadebyapplyingthemethodsof

model-basedcognitiveneuroscience[41,42],whichlinksbrain

activityto behaviour viadetailedcomputationalmodels

of cognitive and neuralprocesses[43–45].Model-based

cognitive neuroscience generates detailed quantitative

theories that span multiple levels of abstraction (e.g.

behavioural, cognitive, neural). This provides greater

constraint on theory and leads to more robust and

detailed inferences. In particular, combining

model-based approacheswith developmentsin ultra-high field

fMRI enables testing neurocomputational theories of

WM (such as thePBWM)with greaterspatial and

psy-chometric precision than has previously been possible.

Applyingthesemethodstothereference-backtask

pro-misesamoredetailedneurocomputationalunderstanding

of WMthaniscurrently available.

Measuring

WM

subprocesses

with

the

reference-back

paradigm

Most laboratory tasks used to study WM (e.g. n-back,

delayed-match-to-sample)aredesignedtoinvestigatethe

capacityandtemporalpropertiesofWMbutareunableto

differentiate the contribution of WM subprocesses to

observedbehaviour([24,46,47,48,21,22]).Arecently

developed exception isthe reference-backtask[24,21],

which provides dissociable measures of core WM

sub-processes(gateopening,gateclosing,updating,

substitu-tion)frombehaviouralchoice-response time(RT)data.

Toperformthereference-back, participantsholdoneof

two stimuli (e.g. an ‘X’ or ‘O’) in WM while deciding

whetheraseriesofprobesmatchthecurrentiteminWM

(Figure 2). On reference trials (indicated by ared frame

around thestimulus), theparticipant must updateWM

with the currently displayed stimulus. On comparison

trials(indicatedbyablueframe),theparticipantsimply

comparesthecurrentstimulustotheoneheldinWM(the

one appearing in the most recent red frame) without

updating WM. Both reference and comparison trials

requireasame/differentdecisionbutonlyreferencetrials

require updating. Comparingperformanceon reference

and comparison trials thus provides abehavioural

mea-sure of the cost of updating. By similar logic, switching

fromcomparisontoreferencetrialsrequiresopeningthe

WM gate (to allow for updating), whileswitching from

reference to comparison trials requires closing theWM

gate (to maintain the current contents). Gate opening is

measured by comparing trials on which participants

switchtowardsareferencetrialtothosewherereference

trialsarerepeated. Likewise,gateclosingismeasured by

comparingtrials onwhich participantsswitchtowards a

comparison trial to those where comparison trials are

repeated.Finally,substitutionismeasuredviathe

interac-tioneffectoftrialtype(reference/comparison)andmatch

type(same/different)andrepresentsthecostofupdating

anewitem intoWM.

Thebenchmarkbehaviouralfindingfromthe

reference-backtaskisthattrialsrequiringadditionalWMprocesses

tend to have slower RTs and/or more frequent errors

than trials that do not require such processes

[24,47,49,21,50,51,52]. These costs are typically

interpreted asreflectingacombinationoftime required

for additional subprocesses to run outside of the same/

Figure2 Trial type: Switching: Matching: Response:

X

O

O

X

O

X

X

reference reference reference reference

mismatch mismatch mismatch mismatch match match ‘different’ ‘different’ ‘different’ ‘different’ ‘same’ ‘same’

comparison comparison comparison

no-switch no-switch -switch (gate closing) switch (gate closing) switch (gate opening) switch (gate opening)

Current Opinion in Behavioral Sciences

Illustrationofthereference-backtask.Oneachtrial,participantsindicatewhetherthepresentedletterissameordifferentfromtheletterinthe mostrecentredframe.Onreference(redframe)trials,participantsmustalsoupdateWMwiththecurrentlydisplayedletter.Oncomparison(blue frame)trials,participantsmakethesame/differentdecisionbutdonotupdateWM.Comparingbehaviouraloutcomes(e.g.responsetime,error rate)betweendifferenttrialtypesmeasuresthecostofgateopening,gateclosing,updating,anditemsubstitutionprocesses(seetextfordetails). Explainingthesebehaviouralphenomenaviacomputationalcognitivemodelsandestablishingfurtherlinkstoneuraldataisakeygoalofcurrent WMresearch.AdaptedfromRac-LubashevskyandKessler[21]withpermission.

different decision stage, and subprocesses interfering

withtheprimary task(e.g. creatingnoisierWM

repre-sentationsduetodrawingattention/capacityawayfrom

the decision process) [53]. However, distinguishing

these accounts requires detailed choice-RT models

of the latent cognitive processes underlying

memory-based decision making(e.g. the highly successful

evi-denceaccumulationframework,[54,55]),whichareyetto

be applied to the reference-back paradigm. Before

discussingapproachestomodellingthereference-back

task,wefirstreviewrecenteffortstoidentifytheneural

substrates of WM subprocesses by correlating brain

activity with behavioural measures derived from the

reference-back.

Neural

correlates

of

the

reference-back

task

Asoutlinedabove,thereisbroadconsensusfrom

neuro-imagingsupportingtheroleofBG,thalamus,andPFCin

WMgatingasinstantiatedinthePBWM[27].However,

theneural basis of several coreWM subprocesses (e.g.

gateclosing,updating,substitution)islessclear.Recent

workhasbeguntoaddressthisgapbylinkingbehavioural

measures derived from the reference-back with

neuro-physiological measures such as EEG and fMRI

[47,49,51,52].

Two initial studies investigated EEG correlates of the

reference-backtask.Rac-LubashevskyandKessler[51]

found that gate closing was associated with increased

theta power, a neural signature of cognitive control

[56–58],whilegateopeningandupdatingwereassociated

withincreaseddeltapower,asignatureofreactive

(event-driven)controlandactionselectionprocessesthatengage

in response to reward prediction errors [59–61]. This

suggests afunctional role for deltaand theta signals in

thecontrolofWMconsistentwith‘go/no-go’signallingin

thePBWMmodel[25,39,26].Afollow-upstudyexplored

theroleoftheP3bEEGsignal(apositiveevent-related

potential that signals task-relevant events and peaks

300ms after stimulus onset) in gating and updating

[52].P3b amplitudespikeddepending onwhetherthe

stimulus matched the WM reference item, implicating

P3b in stimulus comparison/categorisation processes

rather than updating per se. Greater negative activity

(in an N2-like ERP component unrelated to the P3b)

wasfoundinanteriorcorticalregionsonreferenceversus

comparison trials. This signal has been implicated in

controlled inhibition and action selection [62] and, in

thecontext of thereference-back task, likely reflectsa

gate-opening or updating signal, consistent with the

PBWM’s assumption that reference trials trigger an

update or ‘go’ signal to allow new information into

WM. This initial work demonstrates that neural

signa-turesofspecificupdatingandgatingprocessesare

detect-ableinEEGoscillatorysignalsthatshowactivitybroadly

consistent with ‘go/no-go’ signalling in

BG-thalamus-PFCpathwaysinvolvedinWMgating[25,26].However,

thepoor spatial resolution of EEG limits our ability to

drawconclusionsaboutthespecificstructuresassociated

witheachWMsubprocess.

Extending this work, Nir-Cohen et al. [47] used 3T

fMRItoidentifyneuralsubstratesofWMsubprocesses

using a modified reference-back with more complex

face-morph stimuli. BG, frontoparietal cortex, and

task-relevant sensory areas such as visual cortex were

involvedingateopening.Gateclosingactivatedparietal

cortexand substitution elicitedactivationinleft

dorso-lateralPFC and inferior parietal lobule. Awhole-brain

conjunction analysis revealed shared activity in the

supplementarymotorareaforupdatingandsubstitution,

while updating and gatingboth activated the posterior

parietal cortex. These results broadly agree with the

PBWM model [26] and support the role of BG and

PFC in controlling the flow of information into WM

andreplacingoldwithnewinformation.However,

pari-etalcortexactivationduringgateclosingisnotpredicted

by the PBWM. This suggests that additional brain

structuresareinvolvedin controllingWMsubprocesses

and points to an opportunity to extend the PBWM to

explain the neural basis of WM subprocesses beyond

gateopening.

Jongkees[49] provided furtherevidence for the

dopa-minergicbasisofWMgatingandupdating processesby

administering dopamine precursor L-tyrosine to young

adults and comparing reference-back performance to a

placebo-control group. The L-tyrosine group had less

variablegate opening times thanplacebo controls,

sug-gestingthatthedrugimprovedWMperformanceforpoor

performersbut impairedhighperformers.Therewasno

effectonupdatingorgateclosing,consistentwiththerole

ofstriataldopaminesignalsinopeningthegatetoWMin

linewiththePBWM[25,26].Furtherindirectsupportfor

striataldopamineinvolvementcomesfromastudy

link-ingevent-basedeye-blinkrate(aproxymeasureofstriatal

dopamine) to WM updating in the reference-backtask

[50].However,follow-upworkcombiningthisapproach

with ultra-high field fMRI is needed to identify how

activity in small subcortical structures as well as layers

incortex(e.g.striatum,GP,thalamus,PFC)ismodulated

bydopamine.

Current

directions

The work reviewed above has taken important first

stepstowardidentifyingtheneuralsubstratesofWM

subprocesses beyond the BG-thalamus-PFC

‘go/no-go’gatingmechanismofthePBWM[39,26].However,

existingworkhassofarbeenlimitedtorelatingbrain

activity directly to the reference-back’s behavioural

measures rather than the latent cognitive processes

that give rise to behaviour. Model-based approaches

that link brain and behaviour via computational

traditional statistical analyses of mean RT and error

rateinunderstandingthecognitiveandneuralbasisof

WM. For example, applying evidence accumulation

modelsofchoice-RT(e.g.Refs.[54,55])to

reference-back data would reveal whether performance costs

occur because WMsubprocesses add time outside of

thedecisionstage(longernondecisiontime),interfere

with the decision process itself (reduced or noisier

processingrate; [53]),orinducestrategic adjustments

engaging top-down cognitive control (increased

response caution). Decomposing behavioural effects

(e.g.gating, updatingcosts)into asetoflatent

cogni-tive processes (e.g. accumulation rate, nondecision

time, cognitive control of thresholds) rather than

coarse behavioural-level summary statistics enables

exploringtheneuralsubstratesofWMingreaterdetail

thanispossiblewithtraditionalmethods[63,64].This

places stronger constraints on theory and ultimately

produces more robust and detailed inferences about

thelatentprocessesthatgeneratebehaviour.Applying

cognitive models to the reference-back holds great

promise in this regard.

Initsstandardform,thereference-backparadigmignores

several important additional WM processes. These

includemechanismsthatoperateoninformationalready

active in WM [65–67], such as object selection and

retrieval [7], item-specific removal ([23]; but see Ref.

[68], for evidence of removal in the reference-back),

and groupingand reorganizationoperations(e.g. sorting

itemsintoalphabeticalor chronologicalorder,chunking

or grouping items together to form a single accessible

representation, changing the serial position of items;

[69–72]). These mechanisms support effective

remem-beringbyrestructuringinformationintomorememorable

formats andensuringonlyrelevant informationis

main-tainedandretrievedfromWM.Thestandard

reference-backalsoignoresphenomenaassociatedwithWM’s

lim-ited capacity (e.g. WM load/set-size effects; [73–75,7])

and thetemporaldegradation(e.g.bydecayor

interfer-ence)ofWMrepresentations(forareview,seeRef.[76]).

Analyses that do not account for these processes risk

misattributing theireffects to other processes, resulting

in biasedinferences.

Simpleextensionstothereference-backtask(e.g.using

multiple-item WM sets, inserting delays between the

updatecue andstimuluspresentation),however,enable

testing such effects alongside the gating and updating

processes of the standardreference-back. For example,

Verschooren etal. [77]developed amodified

reference-back paradigmwhereone among severalitems in

long-termmemoryorperceptionisgatedintoWM.Thisallows

forcomparinggatingdynamicsforperceptualversus

long-termmemoryinformation.Similarmultiple-item

modifi-cations can be used to investigate some of the WM

phenomena described above, including informing the

ongoing debate about whether items in WM are held

inasmallnumberofdiscretehigh-precisionslots[74]or

allocated capacity from a limited pool of continuous

resources [78–80]. In discrete slots models, the fidelity

ofitemsinWMonlydegradesonceallmemoryslotsare

full(e.g.whenn>4).Incontinuousresourcemodels,an

item’sfidelityisdeterminedbyitsshareoftheavailable

resourcesandthusshoulddegradeininverseproportion

tothetotalnumberofitemsinWM7.Evidence

accumu-lation models are well suited to test between these

competing accounts(e.g. viaaccumulation rate

parame-ters) as they can beused to assess the fidelity of WM

representations and measure capacity-sharing effects;

[74,81]).Varyingsetsizeinthereference-backand

asses-singtheeffectsondecision-makingandWMprocesses(as

measuredbycognitivemodels)couldtestbetweenslots

andresourcearchitectures.Similarly,combininga

multi-ple-itemreference-backtaskwithreinforcementlearning

(e.g.byreinforcingsomeitemsbutnotothers)couldshed

light on the interplay between WM and learning (e.g.

Refs. [73,75]) and the role of expected value in

WM-baseddecisions.Overall,webelievethatdetailed

choice-RT modelling will play an important role in resolving

these important questions and in explaining additional

WM phenomenacaptured byvariants ofthe

reference-backtask.

Combiningcomputationalapproacheswithrecent

devel-opments in ultra-high field fMRI (7T and higher)(e.g.

increased resolution and better signal- and

contrast-to-noiseratios)holdsgreatpromiseforidentifyingactivityin

small subcortical structures (e.g. GP, SN, subthalamic

nucleus,VTA;[82,83])andgainingadeeper

understand-ing of their functional role in WM than is currently

available.Forexample,thiswouldenableastrongertest

of the so-called ‘third phase’ response of the PBWM

model [27], which evaluates the updating process via

dopaminergicmidbrainneuronsthatcodereward

predic-tion errors[84].UnderthePBWM, midbraindopamine

responses thattrain the BG whento update should no

longeroccuronceupdating-relatedtaskruleshavebeen

learned. This mechanism has proven difficult to verify

with low fieldstrength fMRI[85,86],however,imaging

reference-back performance with ultra-high field fMRI

and linking neural measurements to cognitive model

parameters would enable identifying these anatomical

andfunctionalmechanismsingreaterdetailandprovide

additional constraint oncognitive modelsof WM.

Spe-cifically,whenmodellingtwoormoresourcesofdata(e.g.

fMRI and choice-RT) simultaneously, the power to

detect joint effects (e.g. correlations between BOLD

7

Note,however,thatcontinuousresourcemodelscanmimicdiscrete slotsmodels.Forexample,ifaresourcepoolhascapacityto accommo-date fouritems, thenitem fidelitymay onlybegintodegrade once demands exceed capacity (i.e. when n>4), thus producing similar predictions toadiscreteslotsmodel.Carefulexperimental designis neededinordertocorrectlyattributeeffectstocapacitylimitations[99].

signalandcognitivemodelparameters)isdeterminedby

thesignal-to-noise ratiosofeachdatasource.Increasing

thesignal-to-noiseratioofneuraldata(e.g.via7TfMRI;

[82])reducesuncertaintythroughoutthemodel,asdoes

including data from additional modalities (e.g.

EEG+fMRI+behavioural; [87])8. A further benefit is

thatconnectingneuralsignalstocognitivemodel

param-etersallowsforselectingbetweencognitivemodelsthat

makeidenticalpredictionsatthelevelofchoice-RTbut

differ in their internal dynamics [45,64,88,89].That is,

differentinternalmechanismscanbetitratedby

evaluat-ingwhichismostconsistentwiththeadditionalstructure

providedbytheneuraldata.Combiningsuchapproaches

withthereference-backtaskhaspotentialtoshedlighton

other structures known to be involved in WM (e.g.

hippocampus;[90,91,92]),dopaminergicresponse

evalu-ation (e.g. VTA; [93,94]), and cognitive control (e.g.

anterior cingulate cortex; [95]), which are not yet

accounted for in existing neurocomputational models.

Linking state-of-the-art fMRI to the latent cognitive

processes engaged by the reference-back would offer

particular insight into the function of small

dopamine-producing midbrain structures, with implications for

understanding WM impairments in a range of clinical

disorders involving abnormal dopamine function [96].

Overall,webelievethatviewingthereference-backtask

throughthelensof model-basedcognitiveneuroscience

promises a more detailedunderstanding of the

subpro-cessesthatsupportWMandtheirneuralsubstrates.

Concluding

remarks

Thisreviewdiscussedrecenteffortstoidentifytheneural

basis of subprocesses that support WM in therecently

developed reference-back task. Current empirical work

supportstheideathatWMgatingiscontrolledbystriatal

‘go/no-go’ signalling in BG-thalamus-PFC pathways.

However, the neural substrates of several additional

WMsubprocesses areyetto beestablished,pointing to

aneedfor ultra-highfield functionalimagingcombined

withdetailedcomputationalcognitivemodelling.Targets

forfutureresearchincludeextendingthereference-back

task to account for additional WM subprocesses (e.g.

removal, selection, and reorganization operations) and

effects of WM load and capacity (e.g. longer retrieval

times, noisier WM representations), as ignoring such

processesleads to mis-specifiedmodels and potentially

biasedinferences.Applyingthemethodsofmodel-based

cognitiveneurosciencetothereference-backtaskwould

provideamajoradvanceinunderstandingWMatneural,

cognitive, and behavioural levels. A comprehensive

understandingofWMsubprocessesandtheirneuralbasis

iswithinreach,withimplicationsforbothcognitiveand

clinicalneuroscience.

Author

contributions

RJBandACTconductedtheliteraturereviewandledthe

writingofthemanuscript.Allauthorsprovidedfeedback

at different stages, reviewed, edited, and revised the

manuscript.

Conflict

of

interest

statement

Nothingdeclared.

Acknowledgements

WethankDrYoavKesslerformakingseveralsuggestionsthatimprovedan earlierversionofthismanuscript.Thisworkwassupportedbyagrantfrom theNetherlandsOrganisationforScientificResearch(NWO;grantnumber 016.Vici.185.052;BUF).

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