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Article:
Field, M. orcid.org/0000-0002-7790-5559, Heather, N., Murphy, J.G. et al. (3 more authors)(2019) Recovery from addiction: Behavioral economics and value-based decision making. Psychology of Addictive Behaviors. ISSN 0893-164X
https://doi.org/10.1037/adb0000518
© American Psychological Association, 2019. This paper is not the copy of record and maynot exactly replicate the authoritative document published in the APA journal. Please do not copy or cite without author's permission. The final article is available, upon publication, at: https://doi.org/10.1037/adb0000518
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PsychologyofAddictiveBehaviors,inpress,acceptedforpublicationon30thAugust
2019
Recoveryfromaddiction:behavioraleconomicsandvalue-baseddecision-making
MattField
UniversityofSheffield
NickHeather
NorthumbriaUniversity
JamesG.Murphy
UniversityofMemphis
TomStafford
UniversityofSheffield
JalieA.Tucker
UniversityofFlorida
KatieWitkiewitz
UniversityofNewMexico
© 2019, American Psychological Association. This paper is not the copy of record and may not exactly replicate the final, authoritative version of the article. Please do not copy or cite without authors' permission. The final article will be available, upon publication, via its DOI: 10.1037/adb0000518
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Authorforcorrespondence:
MattField,DepartmentofPsychology,CathedralCourt,1VicarLane,Universityof
Sheffield,Sheffield,S12LT,UnitedKingdom.
Email:[email protected]
Telephone:+44(0)1142226510
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Authornote
MattFieldandTomStafford,DepartmentofPsychology,UniversityofSheffield,
Sheffield,UnitedKingdom.
NickHeather,DepartmentofPsychology,NorthumbriaUniversity,Newcastle-upon-
Tyne,UnitedKingdom.
JamesG.Murphy,DepartmentofPsychology,UniversityofMemphis,Memphis,TN,
UnitedStates.
JalieA.Tucker,DepartmentofHealthEducationandBehavior,CenterforBehavioral
EconomicHealthResearch,UniversityofFlorida,Gainesville,FL,UnitedStates.
KatieWitkiewitz,DepartmentofPsychology,CenteronAlcoholism,SubstanceAbuse
andAddictions,UniversityofNewMexico,Albuquerque,NM,UnitedStates.
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Abstract
Behavioraleconomicsprovidesageneral frameworktoexplaintheshift inbehavioral
allocation from substance use to substance-free activities that characterizes recovery
fromaddiction,but itdoesnotattempt toexplain the internalprocesses thatprompt
thosebehavioralchanges.Inthispaperweoutlineanovelanalysisofaddictionrecovery
based on computational work on value-based decision-making (VBDM), which can
explainhowpeoplewithaddictionareabletoovercomethereinforcementpathologies
anddecision-makingvulnerabilitiesthatcharacterizethedisorder.Thecentraltenetof
thisaccountisthatshiftsinmolarreinforcerpreferencesovertimefromsubstanceuse
tosubstance-freeactivitiescanbeattributedtochangesinevidenceaccumulationrates
andresponsethresholdsinthecontextofchoicesinvolvingsubstanceuseandsubstance-
freealternatives.Wediscusshow this account canbe reconciledwith theestablished
mechanismsofactionofpsychosocialinterventionsforaddiction,anddemonstratehow
ithasthepotentialtoempiricallyaddresslongstandingdebatesregardingthenatureof
impairmentstoself-controlinaddiction.Wealsohighlightanumberofconceptualand
methodological issues that require careful consideration in translating VBDM to
addictionandrecovery.
Keywords:Addiction;Behavioraleconomics;Decision-making;Recovery.
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Despiteinfluentialdepictionsofaddictionasachronicallyrelapsingbraindisease
thatrequireslifelongclinicalmanagement(Volkow,Koob,&McLellan,2016),itiswidely
recognized that recovery is common among treatment-seeking and non-treatment-
seekingpersonswithsubstanceusedisorders(Heyman,2013;Lewis,2017;Tucker&
Simpson, 2010). Behavioral economics has made important contributions to our
understandingofthenatureanddeterminantsofaddiction,itstreatment,andrecovery
(Murphy, MacKillop, Vuchinich, & Tucker, 2012). In the present paper we outline a
speculative theoretical account that builds on established behavioral economic and
cognitiveneurosciencefoundationstohighlightthepotentialimportanceofvalue-based
decision-making (VBDM) for recovery from addiction. This account yields new
hypothesesthatareamenabletoempiricalevaluation.Thecentraltenetisthatshiftsin
molarreinforcerpreferencesovertimefromsubstanceusetosubstance-freeactivities
canbeattributedtochangesinevidenceaccumulationratesandresponsethresholdsin
the context of choices involving substance use and substance-free alternatives. We
identifysomenoveltestablehypothesesaboutthemechanismsofactionofestablished
andemergingtreatmentsinthecontextofVBDM,anddescribeempiricalteststhatcan
resolvedisputesaboutthenatureofself-controlandrelatedconstructs.
BehavioralEconomicAccountsofAddictionandRecovery
In accordance with Herrnstein’s (1970) “matching law”, behavioral economic
explanations for substance use emphasize that, over extended periods of time, the
proportionofbehaviorallocatedtosubstanceusewillbeajointfunctionofreinforcement
gainedfromuseofthatsubstanceandthereinforcementgainedfromallothersources.
Inotherwords,thevalueof(ordemandfor)substanceuseisdeterminedbyitsbenefit/
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costratioinrelationtothebenefit/costratiosofallotheractivitiesthatapersonmight
engagein(Murphy,MacKillop,etal.,2012).
Thedevelopmentandpersistenceofaddictioncanbeattributedto‘reinforcement
pathologies’ (Ainslie, 2005; Bickel, Johnson, Koffarnus, MacKillop, & Murphy, 2014;
Heyman,1996;Lamb&Ginsburg,2018;Murphy,MacKillop,etal.,2012;Rachlin,1995;
Redish, Jensen, & Johnson, 2008), specifically distortion of valuation processes and
hyperbolicdiscountingofdelayedrewards.Asaddictionprogresses,substancesincrease
in value (hypervaluation) whereas alternative, substance-free reinforcers decrease in
value (hypovaluation) (Rachlin, 2000). Alongside this, hyperbolic discounting (the
phenomenonwherebyreinforcersreduceinvaluewithincreasingdelaytotheirreceipt)
increases (Ainslie, 1975; Madden & Bickel, 2010), thereby favoring more immediate
reinforcers suchas substanceuseover substance-freealternatives that typicallyhave
delayed and uncertain consequences. The combination of distorted valuations and
increased hyperbolic discounting leaves people with addiction vulnerable to abrupt
‘preferencereversals’:Substance-freeactivitiesmayhaveahighervaluethansubstance
usewhenbothareavailableafteradelay.However,whentheperson is facedwithan
imminentopportunitytousethesubstance,hyperbolicdiscountingmaximizesthevalue
ofthesubstance,leadingtoimmediateuse.
Evidenceisbroadlysupportiveoftheseclaims,asreviewedelsewhere(Bickelet
al., 2014; Madden & Bickel, 2010; Murphy, MacKillop, et al., 2012). For example,
substanceuseissensitivetoitscost,andindividualdifferencesinpricesensitivityare
associatedwithindividualdifferencesinsubstanceuse(MacKillopetal.,2010;Murphy,
MacKillop, Skidmore, & Pederson, 2009; Tucker, Roth, Vignolo, & Westfall, 2009).
Addiction isassociatedwith increasedvalueofsubstanceusecomparedtocompeting
reinforcers (Hogarth & Hardy, 2018), and chronic substance use is characterised by
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diminishedrewardresponsetonon-substancerewards(Lubmanetal.,2009;Meshesha
et al., 2017). A meta-analysis confirmed that addiction is robustly associated with
elevated hyperbolic discounting (MacKillop et al., 2011). Individual differences in the
valueofsubstanceuseandinhyperbolicdiscountingareassociatedwiththeinitiationof
substance use (e.g., Audrain-McGovern et al., 2009; Fernie et al., 2013) and they are
predictive of substance use outcomes after treatment (MacKillop & Kahler, 2009;
MacKillop&Murphy,2007)andnaturalrecoveryattempts(Tuckeretal.,2016;Tucker
etal.,2009).
Recoveryfromaddictioncanalsobeunderstoodthroughthelensofbehavioral
economics. Although ‘recovery’ is often equated with complete abstinence, here we
includecontrolled substanceusewithoutproblems if that is in linewith theperson’s
goals (Witkiewitz, 2013). Broadly speaking, people recover from addiction when the
availability of substance-free rewarding activities increases (Tucker, Vuchinich, &
Gladsjo,1994;Tucker,Vuchinich,&Pukish,1995;Tucker,Vuchinich,&Rippens,2002)
andasthecostsoftheiraddictiononphysicalandmentalhealth,aswellasinterpersonal
relationships, become more salient (McIntosh & McKeganey, 2000; Prins, 2008).
Comparable findings have been demonstrated in animal models of addiction and
recovery(seeLambetal.,2016;Lamb&Ginsburg,2018).Theefficacyofpsychosocial
treatmentsmaybepartiallydependentontheextenttowhichtheycanfacilitatethese
changes (e.g., Dennhardt, Yurasek, & Murphy, 2015; McKay, 2017). Contingency
management(CM),anefficaciousaddictiontreatmentinwhichparticipantsreceivesmall
financial rewards for verified abstinence or other desirable behaviors (e.g., seeking
employment),andrelatedinterventionssuchasemployment-basedreinforcement(‘the
therapeutic workplace’; Silverman et al., 2012) were directly informed by behavioral
economicapproachestoaddiction(Petry,Martin,Cooney,&Kranzler,2000;Petryetal.,
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2005;Petryetal.,2004;Stitzer&Petry,2006).Recoveryisalsoassociatedwithadoption
of situational and intrapersonal strategies that offer protection against preference
reversals(e.g.,Monterosso&Ainslie,2007;Snoek,Levy,&Kennett,2016)andbypost-
recovery changes in life circumstances that reinforce sobriety (King & Tucker 1998;
Tuckeretal.,1994,1995,2002).
Behavioral economic approaches explain substance use and addiction from a
molarratherthanamolecularperspective:theyareconcernedwithpatternsofbehavior
over time rather than individual acts (Rachlin, 1995), and with how proportional
reinforcement fromsubstanceuseversus competingactivities changesoverextended
time periods (Murphy, MacKillop, et al., 2012). In the next section we describe
computational neuroscience accounts of value-based decision-making that model the
internalprocessesthatcontributetodiscretechoices.Wespeculatethattheseresearch
methodsmightbeadaptedtoexplainindividualinstancesofsubstanceuse,i.e.individual
acts (cf., Rachlin, 1995). Most importantly, we tentatively suggest that this approach
couldbeextendedtomodel the internalprocessesthatdetermineshiftingpatternsof
behavioralallocationovertime,andbyextensionrecoveryfromaddiction.
Value-BasedDecision-Making(VBDM)andItsPotentialRoleinMotivated
BehaviorandAddiction
VBDMprovidesaframeworkandsetofexperimentaltoolstoexplaintheinternal
processesthatunderliediscretechoices.InatypicalVBDMtask(e.g.,Polanía,Krajbich,
Grueschow,&Ruff,2014)participantsfirstmakevaluejudgmentsaboutasetofpictorial
stimuli(forexample,differenttypesoffood)sothatthestimulussetcanberankordered
frommostvaluedtoleastvalued.Inasubsequentforcedchoicetask,oneachtrialtwo
stimuliarepresentedside-by-sideonacomputerscreen,andparticipantsareinstructed
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toselecttheirpreferreditemasquicklyaspossible.‘Errors’areinferredifchoicesare
inconsistentwithvalue judgments thatwereexpressedbefore the forcedchoice task.
VBDMassumesthatparticipants’reactiontimeanderrordataarise fromaprocess in
which internal evidence for each possible decision accumulates over time, with the
additionofrandomnoiserepresentinguncertainty,untiltheaccumulatedevidencefor
onedecisioncrossesathresholdor‘decisionboundary’.Weakerinternalevidenceresults
inslowerevidenceaccumulation,andthereforelongerresponsetimes.Theaccumulation
of random noise along with evidence signals causes both occasional errors and the
characteristic distribution of response times. This basic ‘accumulation to threshold’
conceptisattheheartofafamilyofso-called‘sequentialsamplingmodels’ofdecision-
making(Busemeyer,Gluth,Rieskamp,&Turner,2019;Ratcliff,Smith,Brown,&McKoon,
2016).
Byfittingthesedecisionmodelstobehavioraldata,theVBDMapproachpermits
the description of parameters that are hypothesized to underlie value-based choice.
Specifically, it enables the quantification of the subjective value of different stimuli
independently of other properties of the decision-maker, such as their response
thresholds. Importantly, the decision process is hypothesized to be noisy and
probabilistic.Thismeansthatanymomentary change in favorofone choiceoption is
determinedbybothrandomnoiseandthesubjectivevalueofthatoption.Computational
models treat value signals as evidence fororagainst a particular choice. These value
signalsaccumulateovertime(hence,‘evidenceaccumulation’(EA)signals)untiloneof
them crosses its response threshold, at which point the appropriate choice option is
selected (see Berkman, Hutcherson, Livingston, Kahn, & Inzlicht, 2017). Decision
modellinghasbeenappliedtodelineatedecision-makingdeficitsandabnormalities in
other psychological disorders (e.g., Moustafa et al., 2015; Pirrone, Dickinson, Gomez,
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Stafford,&Milne,2017),andVBDMhasbeenappliedtothestudyofcognitiveregulation
offoodchoice(Tusche&Henderson,2018).
The schematic in Figure1 illustrateshow VBDM could be applied to explain a
person’sdecision-makingwhentheyarefacedwithanopportunitytodrinkalcohol(or
not),orwhentheyfaceachoicebetweendrinkingalcoholandpursuinganalternative,
substance-freeactivity.,ForthesakeofconceptualclarityandincommonwithBerkman
etal.,(2017),theFiguredepictsthediscretechoicebetweendrinkingalcoholversusan
alternativesubstance-freeactivityasa‘race’toasingleresponsethresholdalthoughwe
notethat,inreality,eachresponseoptionwouldhaveitsownresponsethreshold.
WespeculativelysuggestthatifalcoholconsumersweretocompleteaVBDMtask
that requires them to make value judgments about alcohol use and alcohol-free
alternatives, this should permit extraction of individual differences in the VBDM
parameters(EAratesandresponsethresholds)thatmaypredictlong-termpatternsof
behavioralallocation, irrespectiveofoccasionalactsorinstancesthatcontradict those
long-term patterns (Ainslie, 2005; Rachlin, 1995). Substance-related VBDM in people
with addictionhasonly recently been studied (see Lawn et al., 2019 for a study that
investigated the neural substrates of smoking-related VBDM in tobacco smokers).
However,thepredictivevalidityofindividualdifferencesinVBDMforsubstanceuseand
substance-freealternativeshasnotyetbeeninvestigated..
It is important toacknowledgeother forcedchoice tasks inwhichparticipants
choosebetweenimagesthatdepictsubstance-relatedcuesversuscompetingreinforcers,
datafromwhichhavemadeimportantcontributionstoourunderstandingofaddiction
(e.g.,Hardy,Parker,Hartley,&Hogarth,2018;Moelleretal.,2018).Thereisanimportant
distinctionbetweenconventionalforcedchoicetasksandtheVBDMtasksdescribedhere:
both types of tasks can measure the overall proportion of (hypothetical) substance-
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related versus substance-free choice, and they are both considered measures of
substance demandor value. However, only the decisionmodelling that is inherent to
VBDMtasksisabletocapturetheinternalprocessesthatcontributetochoice.Werevisit
thisimportantdistinctionlater.
ContemporaryaccountsofVBDMposit thatEA foragivenchoiceoption is the
resultof avalue integrationprocess that incorporatesdiverse sourcesof information
about theoverallutilityof thatresponseoption, including itsanticipatedpositiveand
negativeconsequences,financialandopportunitycost,effort,andsoon(Berkman,2018;
Berkman et al., 2017; Levy & Glimcher, 2012; Rangel, Camerer, & Montague, 2008),
although this is contentious (e.g., Busemeyer et al., 2019). According to one account
(Berkman et al., 2017), delay to receipt of the outcome(s) of a response option is
incorporated intothisvalue integrationprocess,suchthatevidenceaccumulatesmost
rapidly foroutcomesthatareavailable immediately. Thisaccountofself-controlasa
formofvalue-basedchoicethereforeprovidesacomputationalaccountfortheeffectsof
hyperbolicdiscountingonchoiceandpreferencereversals.
OursuggestionthatVBDMcouldbeappliedtochoicesthatinvolvesubstanceuse
canbereconciledwithmanyexistingtheoriesofaddiction.Forexample,Redishetal.,
(2008)proposeaunifiedframeworkforaddictionthatdescribesvarious‘vulnerabilities
inthedecision-process’,themajorityofwhichleadtoadistortionofvaluationprocesses
during decision-making that can be directly equated with alterations toEA rates and
response thresholds in the context of VBDM. For example, sensitization of dopamine
neuronsasaconsequenceofchronicdruguseincreasesthe‘incentivesalience’ofdrugs
and drug-related cues (Robinson & Berridge, 1993) which should correspond to an
amplification of EA for substance use. Chronic substance use also leads to
neuroadaptations that result in anhedonia (Koob & Le Moal, 1997) that should
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correspondtoasuppressionofEAforsubstance-freealternatives.DuringVBDM,whena
person faces multiple choice options, selective attention plays an important role:
attentionalallocationtostimuliisinfluencedbythedegreetowhichthosestimuliwere
previouslyassociatedwithreward(DellaLibera&Chelazzi,2009),andtheamountof
attentiondirectedateachresponseoptionamplifiestheEArateforthatresponseoption,
makingitmorelikelytobechosen(Krajbich,Armel,&Rangel,2010;Krajbich&Rangel,
2011).Thismayexplainthedevelopmentofattentionalbiasesforsubstancecuesandthe
influence of attentional biases on substance use (Field et al., 2016; Rose, Brown,
MacKillop,Field,&Hogarth,2018).
InthissectionandinFigure1wehavesketchedoutaspeculativeaccountofthe
roleof VBDM in substance use and addiction.Although this account awaits empirical
testing,itcomplementsmuchofwhatisalreadyknownaboutthebehavioralchangesthat
characterizethedevelopmentandpersistenceofthedisorder,andtheneurobiological
underpinningsofthosechanges.However,aspreviouslynoted,completerecoveryfrom
addiction is a commonoutcome (Heyman, 2013) and most theories of addiction that
emphasizetheimportanceofcompulsionandhabitstruggletoexplainwhythisisso(see
Heather,2017).Assuch,webelievethatthemostimportantcontributionofthepresent
account may be its potential to explain how people overcome the ‘reinforcement
pathologies’ (Bickel et al., 2014) and diverse ‘vulnerabilities in the decision process’
(Redishetal.,2008)astheyrecoverfromaddiction.Thisisthefocusofthenextsection.
Inthesubsequentsectionweconsiderchallengestothisapproachandhowitmightbe
reconciledwithothertheoreticalperspectives.Inthefinalsectionofthepaperweoutline
theclinicalimplicationsofourapproachandoffersomesuggestionsforfutureresearch.
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Recovery,Relapse,andMechanismsofTreatmentAction
In thissectionweoutlineanovel analysisof recovery fromaddiction thatwas
inspiredbyBerkmanandcolleagues’(2017)accountofself-controlasvalue-basedchoice
andthatyieldsnewhypothesesthatareamenabletoempiricaltesting.Ourcentralclaim
isthatrecoverycanbeattributedtoanyofthefollowingchangesinVBDMparameters,
eitheraloneorincombination:(1)suppressionofEAforthesubstance,suchthatitisless
likelytobefirsttocrosstheresponsethreshold;(2)augmentationofEAforsubstance-
freeactivities,suchthattheyaremorelikelytobefirsttocrosstheresponsethreshold,
and(3)agradualupwardsshiftintheresponsethresholdwhenfacedwithachoiceset
that includes substance use. These hypothesized changes in EA rates and response
thresholdsaredepictedinFigure2.
According to this account,people remainvulnerable to lapsesduring theearly
stagesofrecoverybecauseEAratesarenoisyandprobabilistic,soitisalwayspossible
thatEAforsubstanceusewillcrosstheresponsethresholdfirst(cf.the‘cuspcatastrophe’
model of relapse and recovery; Hufford, Witkiewitz, Shields, Kodya, & Caruso, 2003;
Witkiewitz,VanDerMaas,Hufford,&Marlatt,2007).AsdepictedinFigure2,lapsesmay
bemorelikelytooccurwhenresponsethresholdsarelow.However,assumingthatthe
‘directionoftravel’ofVBDMparametersoverextendedperiodsoftimefavorsrecovery
fromaddiction(asdepictedinFigure2),thelikelihoodoflapseshoulddecline,eventually
approachingzero.
This tentative account of the changes in VBDM that underlie recovery from
addictionawaitsempiricaltesting.Thiscouldinitiallybeachievedbyconductingcross-
sectionalcomparisonsofpeoplewhohaveachievedstablerecoveryversusthosewho
havenotyetsoughttoreducetheirsubstanceuseandrelatedproblems.Intheremainder
of this section, we demonstrate how this account can be reconciledwith the broader
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literature on recovery and the mechanisms of action of established treatments. We
suggest that treatments will be effective to the extent that they directly or indirectly
supportthehypothesizedchangesinVBDMparametersthataredepictedinFigure2.
Recoveryfromabehavioraleconomicperspective:Theeffectivenessofcontingency
managementdemonstratesthatdirectlyincreasingthemonetaryvalueofabstinenceand
other desirable behaviors and outcomes is an effective treatment for addiction, even
whenthemonetaryvalueofthereinforcersofferedisverylow(Petryetal.,2000;Petry
etal.,2005;Petryetal.,2004;Stitzer&Petry,2006).Inprinciple,theeffectivenessofCM
andrelatedinterventions(e.g.,Silvermanetal.,2012)shouldbedependentontheextent
towhichtheyareabletorebalancetherelativevalueofsubstanceuseversussubstance-
freebehaviours.Consistentwith this claim,Goelzet al (2014) found thatpeoplewho
successfullyquitsmokingshowedincreasesinsubstance-freereinforcementeightweeks
following their quit attempt, whereas Rogers et al. (2008) found CM that included
vouchersredeemableforsubstance-freeitems/activitiesincreasedbothabstinenceand
engagement in substance-free activities relative to abstinent-contingent
pharmacotherapy,inasampleofcocaineandheroinusers(seealsoHigginsetal2003).
TheproposedVDBMaccountsuggeststhattheeffectivenessofCMonsubstance-
useoutcomesmaybemediatedbyasuppressionofEAforsubstanceusecombinedwith
augmentationofEAforsubstance-freealternatives.Analternativeexplanation for the
clinical effectiveness of CM suggests that it “engages deliberative processes … and
improves the ability of those deliberative processes to attend to non-drug options”
(Regier&Redish,2015).Thisprovidesanalternativemechanismofaction:anincrease
in the response threshold combined with potentiation of EA for substance-free
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alternativesthatismediatedbychangesinselectiveattention(cf.,Krajbichetal.,2010;
Krajbich&Rangel,2011).
Qualitativeresearchindirectlyimplicatestheimportanceofchangesinvaluations
for recovery from addiction: people attribute their recovery to regaining interest in
competing rewards that used to hold value, such as spending time with family,
satisfaction at work, and so on (Klingemann, Sobell, & Sobell, 2010; McIntosh &
McKeganey,2000;Prins,2008),andapersonmightbeconsideredtohave ‘recovered’
whentheseevaluativeshiftscementintoastablechangeinidentity(Bestetal.,2016).
These findingsarecomplementedbystudiesofnon-treatmentseekingheavydrinkers
thatusedbehavioral economic simulations (suchashypotheticalpurchase tasks)and
demonstrated that receipt of brief motivational intervention or pharmacotherapy
promptedchangesindrugdemand,whichinturnpredictedthelikelihoodofsustained
behaviorchange(Bujarski,MacKillop,&Ray,2012;Dennhardtetal.,2015).Inarecent
study,changesinproportionatereinforcementfromsubstance-relatedtosubstance-free
activitiespartiallymediatedtheeffectsofabriefmotivationalinterventiononalcoholuse
andproblems(Murphyetal.,2019).
ThesefindingsraiseanumberofpossibilitiesabouttheroleofchangesinVBDM
duringthetransitiontorecovery.WespeculatethattheinitialshiftinVBDMparameters
asdepictedinFigure2mayinitiallyarisefromchanginglifecircumstancesorreceiptof
treatment,includingbriefintervention.SubsequentlytheseshiftingtrajectoriesinVBDM
parameters that support recovery are potentiated each time the person resists an
opportunitytousethesubstanceandengagesinanalternativesubstance-freeactivity.
These predictions could be tested in longitudinal studies in which life experiences,
treatment completion, episodes of substanceuse, and shifts in VBDM parameters are
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repeatedlymeasuredfromtheearlystagesofrecoveryforwardintime,inordertomodel
thetemporalrelationshipsbetweenthesevariables.
Recoveryfromaddictionisoftendifficultbecause,asaresultofeconomicorsocial
deprivationortheeffectsofchronicsubstanceuse,peoplemayinitiallyhavenoviable
alternativesourcesofreinforcementintheirlivesotherthansubstanceuse.Anumberof
novel treatment interventions such as behavioral activation (Daughters et al., 2018;
Martínez-Vispo, Martínez, López-Durán, Fernández del Río, & Becoña, 2018) and
substance-freeactivitysessions(Murphyetal.,2019;Murphy,Dennhardt,etal.,2012;
Yurasek,Dennhardt,&Murphy,2015)aimtorestructuretheenvironment inorderto
provide alternative sources of reinforcement. There is emerging evidence for the
effectivenessoftheseinterventionswhichmaybemediatedbytheextenttowhichthey
increasereinforcementfromsubstance-freeactivities(Fazzinoetal.,2019;Murphyetal.,
2019).InthecontextofourVBDMaccount,wehypothesizethatpotentiationofEAfor
substance-freeactivitieswillmediatetheeffectsoftheseinterventionsonsubstanceuse
outcomes.
Reconciliationwithmechanismsofactionof establishedpsychosocial treatments:
The psychological mechanisms of action of efficacious treatments, such as cognitive-
behavior therapy (CBT),motivational interviewing (MI)ormotivational enhancement
therapy, andAlcoholics’Anonymous (AA)and related therapies, are increasinglywell
understood.Forexample,improvementsin‘copingskills’inparticipantswhoreceiveCBT
mayberelatedtopost-treatmentdrinkingoutcomes(Roos,Maisto,&Witkiewitz,2017),
whereasMImaypromptrecoverybecause“clientstalkthemselvesintochanging”(Magill
et al., 2014; Magill & Hallgren, 2019). The effects of AA attendance on recovery are
mediated by a number of factors, including facilitation of adaptive social network
17
changes, increasing abstinence self-efficacy and coping skills, and helping people to
maintain their recovery motivation over time (Kelly, 2017). Alongside these
demonstrations about how specific treatments exert their therapeutic effects are
examples of how some therapist behaviors (Gaume, Heather, Tober, & McCambridge,
2018;Magilletal.,2016)andpsychologicalchangesinclients(forexample,self-reported
motivation to change; Cook, Heather, & McCambridge, 2015) are observed across
treatments and are associated with treatment outcome, regardless of the type of
treatmentthatwasprovided.
Animportantquestionforfutureresearchistoclarifytherelationshipsbetween
theaforementionedmechanismsofbehaviorchangeandtheVBDMparametersthatare
positedtorepresent the finalpathwaytobehaviorbecausetheydeterminewhethera
person will prefer a substance or an alternative substance-free activity at any given
choice point (Berkman et al., 2017; Rangel et al., 2008). For example, negative social
networkchanges(inwhichpeopledropheavydrinkersfromtheirsocialnetworks)might
be associated with a suppression of EA for alcohol, whereas positive social network
changes (in which people in stable recovery are added to the social network) (Kelly,
2017) might be associated with a potentiation of EA for substance-free activities.
Generatingpredictionsaboutothermechanismsofbehaviorchangeismorecomplicated.
Forexample,‘copingskills’and‘copingrepertoire’aremultifacetedandincludespecific
skills,someofwhichmightplausiblyberelatedtosuppressionofEAforalcohol(e.g.,
thinkingabouthowdrinkingishurtingothers,andactivelyavoidingdrinkingsituations),
whereastheroleofothercopingskills(e.g.counterconditioning)maybemoreclosely
related to augmentation of EA for substance-free alternatives (Roos et al., 2017). A
further possibility is that acquisition of a broad coping repertoire raises response
thresholdswhenthepersonhasasubstanceuseopportunity.
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Mindfulness-based relapse prevention and related approaches may exert their
beneficialeffectsonsubstanceuseviatheirinfluenceonVBDM.Specifically,thefocuson
“acceptance of uncomfortable states or challenging situations without reacting
automatically” (Witkiewitz, Lustyk, & Bowen, 2013) can be understood as providing
participantswith the skillsneeded to raise the response thresholdwhen they facean
opportunitytouseasubstance.Alternatively,mindfulnesstechniquesthattrainpeople
to ‘savor’ positive, substance-free options in their lives (Garland, Roberts-Lewis,
Tronnier,Graves,&Kelley,2016)mightamplifyEAforsubstance-freeactivities.
Assuming that relationships exist between VBDM parameters and the
psychologicalandsocialchangesreferredtoabove(copingskills,motivationtochange,
changesinsocialnetworks),acrucialtaskwillbetodelineatethetemporalandcausal
relationshipsbetweentheseconstructs.Forexample,self-reportedmotivationtochange
and‘changetalk’mightreflectparticipants’awarenessofshiftingEAforalcoholversus
valued substance-free alternatives, resulting from treatment related changes to their
social/environmental context or via pharmacotherapy, with the implication that the
subjective reports would not play a meaningful causal role in treatment outcome. An
alternativepossibilityisthatthesesubjectivechangesariseinresponsetotreatmentand
theyplayacriticalroleinrecoverybutexerttheirbeneficialeffectsonsubstanceuseby
modulatingEArateswhenthepersonisfacedwithanopportunitytousethesubstance.
Inotherwords,ifrecoveryultimatelyarisesbecausepeoplelearntomodulateEArates
andresponsethresholdswhentheyhaveanopportunitytousethesubstanceorengage
in a substance-free activity, established mechanisms of behavior changemay provide
essentialscaffoldingthatsupportsthesechanges.
19
Emergingtreatments:Interventionssuchasworkingmemorytraining(Bickel,Yi,
Landes,Hill,&Baxter,2011;Rassetal.,2015)andnon-invasivebrainstimulation(Song,
Zilverstand, Gui, Li, & Zhou, 2019) that are intended to partially compensate for
neurocognitivedeficitsarisingfromchronicsubstanceuseshouldexerttheirbeneficial
effects by raising response thresholds. Similarly, experimental interventions that are
intended to mitigate hyperbolic discounting processes, including reward bundling
(Ainslie & Monterosso, 2003; Hofmeyr, Ainslie, Charlton, & Ross, 2011) and episodic
futurethinking(Rung&Madden,2018;Steinetal.,2016),mightinitiallyraiseresponse
thresholdsbeforeamplifyingtheEAsignal forsubstance-freeactivities.Cognitivebias
modification (CBM; see Boffo et al., 2019) might influence substance use through a
number of mechanisms. For instance, given that selective attention to choice options
amplifiesvaluesignals(Krajbichetal.,2010;Krajbich&Rangel,2011),attenuationof
attentional-biasesforsubstance-relatedcuesafterattentionalbiasmodificationwouldbe
expectedtosuppressEAforsubstanceuse(Fieldetal.,2016).Bycontrast,approachbias
modification(e.g.,Rinck,Wiers,Becker,&Lindenmeyer,2018),whichreversesautomatic
approachtendenciesevokedbysubstance-relatedcues,couldsuppressthesubstance-
relatedEAsignalmoredirectly.
In this section wehave outlined a tentative account that describes how VBDM
could be applied to explain how people recover from addiction, and how addiction
treatmentsmightexerttheirbeneficialeffectsonsubstanceusebychangingtheVBDM
parametersthatinfluencebehaviorwhenthepersonfacesasubstanceuseopportunity.
Ouraccountisofferedasaheuristictoguidenewresearchquestionsthatmayinforma
more comprehensive account of recovery than is provided by extant theories of
20
addiction.Inanticipationofconceptualandmethodologicalobjections,inthefinalsection
weconsiderthistentativeaccountinthebroadercontext.
ChallengesandReconciliationwithOtherPerspectives
Doesthisaccountofferanythingthatconventionalbehavioraleconomicaccountsdo
not?Behavioraleconomicaccountsattributerecoveryfromaddictiontoareductionin
thevalueorutility(benefit/costratio)ofsubstanceusethatmaybecombinedwithan
increase in the value or utility of competing substance-free activities (see Murphy,
MacKillop,etal.,2012).Thenovelaccountoutlinedheredevelopsthoseconstructsand
articulatestheminthelanguageoftheinternalprocessesthatcontributetoVBDM,and
putativechangesinthoseprocessesovertimeasapersonrecoversfromaddiction.
Thisfocuscouldbeinformativeregardingthatperson’slikelihoodofrecovering
from addiction before any change in overt substance use is observed. Consider the
schematic in Figure 2: In a person who is currently receiving treatment, their EA for
substance-free activities might shift upwards and to the left over the course of a
treatmentprogram,whichwouldbeabeneficial ‘directionoftravel’forthatparticular
parameter. Unfortunately, this person’s response threshold might be low, and EA for
substanceusemaynotbesuppressed.Asaconsequence,thepersonmightconsistently
favorsubstanceuseoveralternativesubstance-freeactivities,bothintermsoftheirovert
behaviorbutalsointermsoftheirrespondingonaconventionalforcedchoicetask(e.g.,
Hardyetal.,2018;Moelleretal.,2018).Thepotentialadvantageofthecurrentapproach
isitsabilitytoidentifywhentheinternalprocessesthatsupportdecision-makinginfavor
ofrecoveryfromaddictionaremovingintherightdirection,evenintheabsenceofovert
behaviorchange.
21
Isthisamolaroramolecularaccountofbehaviorandbehaviorchange?Although
thereisincreasingenthusiasmforcomputationalapproachestopsychologicaldisorders
and their treatment (Huys,Maia,&Frank,2016), the literatureonVBDM isprimarily
concernedwiththeinternalprocessesthatcontributetodiscretechoices(Berkmanetal.,
2017;Rangeletal.,2008).Assuch,thefocusofVBDMonindividual‘acts’isincompatible
withthefocusonlonger-termpatternsofbehaviorasfavoredbytraditionalbehavioral
economics (e.g. Rachlin, 1995; for a critique of attempts to explain individual acts in
isolationfrombroaderpatternsofbehavior,seeTucker&Vuchinich,2015).
ItmaybepossibletousetheVBDMtasksdescribedheretopredictthelikelihood
of substanceuse in thenear futureand,byextension, asan ‘earlywarningsystem’ to
predicttheriskoflapsesduringoraftertreatment.(cf.Marhe,Waters,VanDeWetering,
&Franken,2013).However,measuresobtainedfromasingleassessmentofVBDMmay
betoo‘noisy’tohavereliablepredictivevalidityforindividualacts(e.g.alapseinthenear
future), although this is an empirical question that is worthy of investigation. More
importantly,becausewebelievethatitisimportanttoviewrecoveryfromaddictionfrom
a molar perspective, we suggest that the primary application of this account to
understanding recovery from addiction may be its ability to track changes in VBDM
parameters (EA for substance use, EA for substance-free activities, and response
thresholds for those particular choice sets) over time. We speculate that repeated
administration of VBDM tasks as people progress through treatment and into stable
recovery(orrelapse)willpermitmonitoringoftheinternalprocessesthatcontributeto
molarbehavioralallocation.AdoptionofVBDMmethodsmayfacilitateunderstandingof
how changes in those internal processes over extended periods of time precede and
determinechangesinobservablebehavior,includingsubstanceuseandengagementin
22
substance-freeactivities.Itisalsoimportanttodeterminethecorrespondencebetween
VBDM task parameters and behavioral economic measures of delay discounting and
demand, which may also change dynamically in response to internal processes and
predictsubsequentchangesinbehavioralpatterns(Murphyetal.,2015;Rung&Madden,
2018).Additionally,itisimportanttoconsidertheroleoftheavailabilityofsubstance-
free rewards inan individual’s environment,whichcanexertsubstantial influenceon
substanceuseindependentofanydecisionmakingprocess(Higginsetal.,2004).
Can this accountmodel patterns, or only acts? Figures 1 and 2depict a choice
betweentwospecificacts:drinkingalcohol,orspendingtimewithone’schildren.This
illustrativechoicesetisourattempttomirrorthetypicalVBDMexperimentalsetup,in
which participants choose between two alternative reinforcers (e.g. chocolate versus
peanuts; see Polanía et al., 2014). However, molar behavioral economic perspectives
emphasize that this discrete choice (between two mutually exclusive acts) must be
understoodinthecontextofbroaderpatternsofbehavior(Rachlin,1995).Therelative
valuationofparentingvs.drinkingcanbedeterminedbyobservingthedistributionof
these behaviors over time. Thus, a pattern of increasing engagement in parenting
behavior by a person early in recovery may reflect increasing reinforcement from
parenting (perhaps due to improving parenting skills or increased identificationwith
one’s role/identityasaparent),whichover time,will reduce therelativevaluationof
substanceuse.EmpiricaltestingofourVBDMaccountreliesontheassumptionthat,if
participants were to complete a VBDM task as depicted in Figures 1 and 2, the two
response options are either representative of the broader pattern (rather than the
specificactthatisdepicted),or,attheveryleast,thatEAforthespecificchoiceoptionsis
determinedbytheunderlyingpatterns,andthereforeitcancapturevariationinthose
23
patterns.Modificationstotheexperimentalprocedure,suchasprimingparticipantswith
theiridentityasaconscientiousparentbeforetheycompleteaVBDMtask(cf.Tusche&
Hutcherson,2018)mightberequiredinordertoachievethisgoal.
Akrasia:Accordingtoouraccount,whenaperson inrecovery is facedwiththe
choicebetweensubstanceuseandasubstance-freeactivity,EAinfavorofthesubstance
free option should consistently cross the response threshold before EA in favor of
substanceuse.Anotherimplicationisthat,whenapersoninrecoveryexperiencesalapse,
theydosobecause,atthetimethatthedecisionwasmade,themomentaryvaluationof
substanceusewashigherthanthemomentaryvaluationofnotusingthesubstance.They
mayregretthatdecisioninhindsight,butthisdoesnotimplythatthedecisionwasnot
basedonahighervaluationat thetimethat itwasmade.Thisviewisconsistentwith
conventionalbehavioral economicaccountsof the ‘preference reversals’ thatunderlie
lossofcontrol(Ainslie,2005;Bickel&Marsch,2001),andotheraccountsofshort-lived
changesinvaluationsasdeterminantsofapparent‘lossofcontrol’(Berkmanetal.,2017;
Dill&Holton,2014;Levy,2018;Yaffe,2014).
However,thisviewisrejectedbymanyonphilosophicalgrounds(seeLevy,2014).
Inparticular,thephenomenonofakrasia–inwhichaperson“actsintentionallycounter
to his own best judgment” (Heather & Segal, 2013) – highlights some conceptual
problemswithanyattempttoimplicateovertbehaviorasadirectoutcomeofvaluation
processes. For example, Kennett and Smith (1996) attributed self-control failures to
‘failuresoforthonomy’inwhichmomentarydesires(forsubstanceuse)overwhelm“all
thingsconsidered”judgmentsaboutwhatisthemostvaluedoption.Indeed,themajority
ofmodelsofaddictionfromneuroscience(Redishetal.,2008)philosophy(Dill&Holton,
2014) and psychology (Stacy & Wiers, 2010) emphasize that habitual or automatic
24
processescaneffectivelybypassvaluationprocesses,therebyexplaininglossofcontrol.
Evenwithoutappeal tohabitualorautomaticprocesses, self-control canbe seenasa
decision not to choose the behavioral option that is most highly valued, implying the
existenceofanadditionalprocessbeyondVBDMthatdeterminesovertchoice(Holton,
2009).
By contrast, in common with conventional behavioral economic accounts, the
VBDMaccountofrecoverythatwehaveoutlinedyieldsthestraightforwardhypothesis
thatrecoveryariseswhenpeopleconsistentlyvaluesubstanceuselessthansubstance-
free activities, and lapses occur when that general pattern is disrupted such that
valuationsfavorsubstanceuseoveralternatives,evenifthatreversalisonlytemporary
andsubsequently regretted (cf.Berkmanet al., 2017).This issuehasbeendifficult to
resolveempiricallybecauseitisverydifficulttoknowwhatpeoplewerethinkingatthe
moment they relapsed: retrospective claims that people relapsed to substance use
against their better judgment might reflect self-serving justifications rather than an
accurate account of why the person acted the way that they did (Davies, 1997).
Alternatively,ifpreferencereversalsareextremelybrief(beforerevertingbacktolong-
termpatterns), thepersonmightsincerelybelievethatwhentheyusedthesubstance
theywereactingagainsttheirownbetterjudgment.
Fortunately, theaccountproposedhereoffersaway toempiricallydistinguish
thesecompetingaccountsaboutwhypeopleintreatmentorstablerecoverysometimes
experience lapses to substance use, and indeed in the broader sense why people are
pronetofailuresofself-control.Althoughwebelievethattheprimaryadvantageofour
account is its ability to capture the internal processes that predict molar behavioral
allocationoverextendedperiodsoftime,italsoyieldssometestablehypothesesabout
thepsychologicalprecursorsoflapsestosubstanceuse(thatdonottypicallyderailthat
25
person’srecoveryinthelonger-term,becauselapsesareacommonpartoftherecovery
process;Witkiewitz&Masyn,2008).Specifically,ifoneweretotakeasampleofpeople
in treatment or stable recovery and repeatedly administer a VBDM task using EMA
methods(e.g.,Marheetal.,2013),thenoneshouldexpectlapsestosubstanceusetobe
precededbypredictablechangesinVBDMparameters.Comparedtolonger-termtrends
forthatperson,wewouldexpecttoseeincreasedEAforsubstanceuse,suppressionof
EAforsubstance-freealternatives,oraloweredresponsethreshold,asprecursorsofa
lapse.Ifsuchapatternwasnotidentifiedsoonbeforealapse,thiswoulddemonstrate
thatsubstanceuseoccurredcontrarytowhatonewouldexpectonthebasisofVBDM,
whichwoulddisconfirmtheaccountproposedhere.
Whatistheoutcomeofvalue-baseddecision-making?InthecontextofmostVBDM
studies,themomentthatEAinfavorofoneresponseoptioncrossesaresponsethreshold,
thatresponseisimmediatelyenacted(seeRangeletal.,2008).Aninfluentialviewisthat
diverse influences on behavior, including weighting of short-term versus longer-term
goals,exercisingself-control,andsoon,allworktoeitheramplifyorsuppressaunified
valuesignal(seeHare,Camerer,&Rangel,2009;Levy&Glimcher,2012),aviewthathas
beenexplicitlyendorsedinrecentattemptstoapplyVBDMtohealthbehavior(Berkman,
2018)andself-controlchoices(Berkmanetal.,2017).Howeversomeempiricaldata(e.g.
(Tusche&Hutcherson,2018)andalternativetheoreticalaccountsquestionthisview.For
example,theoutcomeofvaluationsmaydeterminetheformationofintentionswhich,in
turn,determineactions.Addictionmayprimarilyinvolveweaknessesorsourcesofbias
in the latter (intention formation and action implementation) (Dill & Holton, 2014;
Redishetal.,2008;Verdejo-Garcia,Chong,Stout,Yücel,&London,2018).Iftheselatter
accountsarecorrect,self-regulationorexecutivefunctioningabilitymaybeanimportant
26
moderatoroftheassociationbetweenVBDMparametersandrelapseandrecoveryafter
addictiontreatment.Thesecompetingpredictionscouldbetestedinfutureresearch.
FutureDirectionsandImplicationsforTreatment
Some methodological issues should be considered before incorporating VBDM
methods into addiction research and treatment. For example, it will be important to
assesswhetherparticipantscan‘fake’respondingonthesetasksandifso,iffakingiseasy
todetectandquantify.Itwillalsobeimportanttoassessparticipants’perspectivesand
whether participants find it aversive or helpful to complete VBDM tasks during
treatment. Inadditiontopredictionofrelapseandrecovery,VBDMcouldbeexplicitly
incorporated into the treatment of addiction via traditional methods and via mobile
platforms.Forexample,ifapersonintreatmentrepeatedlycompletedaVBDMtaskat
thestartofeachtreatmentsession,thenthetreatmentprovidercouldusethesedatato
predictlikelihoodoflapsesinbetweensessions.Forapersonwhoisatahigherriskof
lapsewemightexpectincreasedEAforsubstanceuse,suppressionofEAforalternatives,
oraloweredresponsethreshold.Treatmentproviderscouldusethesedatatodiscuss
upcomingopportunities for substanceuse (addressing response thresholdandEA for
substance use), alongside commitment to change and availability of substance-free
alternatives(decreasingthesuppressionofEAforalternatives).
VBDMcouldalsobeusedtoinformandimproverecoverysupportdeliveredvia
smartphone applications. For example, VBDM tasks could be incorporated in the
Addiction-Comprehensive Health Enhancement Support System (A-CHESS) mobile
healthrecoverysupportsystem(Gustafsonetal.,2014)topredictinnearreal-timethe
EAforsubstanceuseandsubstance-freeactivities,aswellastheresponsethreshold.This
27
couldbeusedtopredictprobabilityoflapsingandtheparametersthataremostlikelyto
increaseriskoflapse.Updatedprobabilitiescouldbeshowntothepersonandalsosent
toasupportivesignificantotherortreatmentprovider.
SummaryandConclusions
Based on the diverse ‘reinforcement pathologies’ (Bickel et al., 2014) and
‘vulnerabilitiesinthedecisionprocess’(Redishetal.,2008)thatcharacterizeaddiction,
itwouldbereasonabletoexpectrecoveryfromaddictiontobeuncommon.Yet,recovery
isacommonoutcome(Heyman,2013),andacoherenttheoreticalaccountoftheinternal
processes that are involved when a person transitions from being addicted to being
recoveredislacking.Behavioraleconomicscanexplaintheexternalfactorsthatfacilitate
recovery,butdoesnotattempttomodeltheinternalprocessesthatpredictchangesin
overtbehavior.InthepresentarticlewearguedthatrecentworkonVBDMmightbeable
tofillthatgap,andwehavespecifiedhowchangesinvaluationofsubstanceuseversus
substance-freeactivities,andresponsethresholdswhenpeopleare facedwithchoices
involving those options, might be important outcomes of established and emerging
psychosocialtreatments.Ouraccountisnecessarilytentativeandprovisional,andthere
areanumberofmethodologicalandconceptualchallengesahead.However,wesuggest
that this account generates a number of hypotheses that should be tested in future
empiricalresearch.Theresultsof thisresearchwillenhanceourunderstandingof the
internalprocessesthatsupportrecoveryfromaddiction,andeitherconfirmorfalsifythe
centraltenetsofthisaccount.
Figure1:Schematic illustrationofvalue-baseddecisionmaking(VBDM)parameters thatmay
determine the behavior of alcohol consumers when faced with the choice between drinking
alcohol(dashedlines)oranalternative,substance-freeactivitythatisincompatiblewithdrinking
28
alcohol,suchasspendingtimewithone’schildren(solidlines).PanelAdepictsafrequentdrinker
whoisnotalcohol-dependent.Duringtheearlystagesofdeliberation(leftsideofgraph),therate
of evidence accumulation (EA) is roughly comparable for alcohol versus the substance-free
alternative,soiftheresponsethresholdislow(forexample,ifthedecisionismadeundertime
pressure),eithercouldcrossthethresholdfirst,althoughinthisexampleEAforalcoholisfirstto
cross the response threshold. However, if the response threshold were higher, EA for the
substance-free alternative would cross the threshold first. Note that EA rates are noisy and
probabilisticandresponsethresholdsarelikelytovaryacrosssituations,thereforeevenminor
variationsinanyoftheseparameterscouldresultinadifferent‘winner’andthereforeadifferent
behaviorbeingenacted.PanelBdepictsapersonwhoisalcohol-dependent:whenfacedwiththis
choiceset,theresponsethresholdistypicallylow,EAforalcoholisaugmented(shiftedupwards
andtotheleft)whereasEAforthesubstance-freealternativeissuppressed(shifteddownwards
and to the right), making it probable that EA for alcohol will be first to cross the response
threshold.SchematicsareadaptedfromthoseinBerkmanetal.(2017),imagesarereproduced
fromUnsplash.com.
https://unsplash.com/photos/M44ppvVbnEQ
,https://unsplash.com/photos/dmkmrNptMpw
A.Frequentbutnon-dependentdrinker
29
B.Dependentdrinker
Figure2:SchematicillustrationofthechangesinVBDMparametersthatmayunderlierecovery
fromaddictionandlapsesduringtherecoveryprocess.Aspeopleprogressthroughrecoverythey
30
acquire skills that enable them to take more time to consider their options when faced with
opportunitiestodrinkalcohol,sothetypicalresponsethresholdincreases(thetransitionfrom
thelowertotheupperhorizontalresponsethresholdline).Furthermore,EAforalcoholshifts
downwardsandtotheright(thetransitionfromthedashedblacklinetothedashedgreyline),
whereasEAforsubstance-freealternativesshiftsupwardsandtotheleft(thetransitionfromthe
solidblacklinetothesolidgreyline).Eachofthesechanges(oranyindividualchangeinisolation)
increasetheprobabilitythatEAforthesubstance-freeactivitywillbefirsttocrosstheresponse
threshold as recovery stabilizes. However, these changes are fragile: lapses could occur in
responsetoadownwardsshift intheresponsethreshold(ifthepersonisrequiredtomakea
decision quickly) or because of the noisy and probabilistic nature of EA rates which make it
possiblethateithercouldcrosstheresponsethresholdfirst.However,asrecoverystabilizes,the
likelihoodof(re)lapseapproacheszerobecausethetrajectoriesofEAratescontinuetoseparate,
peopleareabletoadoptstrategiestoamplifythesechanges,andtheyareabletoadoptdifferent
strategiesthatraisetheresponsethresholdwhentheyarefacedwithanopportunitytousethe
substance. See text fordetails. Schematics are adapted from those inBerkmanet al. (2017),
imagesarereproducedfromUnsplash.com.
https://unsplash.com/photos/M44ppvVbnEQ
,https://unsplash.com/photos/dmkmrNptMpw
31
32
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