jcn00953 1024..1038

Addiction History Associates with

the Propensity to Form Habits

Theresa H. McKim, Daniel J. Bauer, and Charlotte A. Boettiger

Abstract

■

Learned habitual response s to environmental stimul i allow

efficient interaction with the environment, freeing cognitive re-

sources for more demanding tasks. However, when the out-

come of such actions is no longer a desired goal, established

stimulus–response (S-R) associations or hab its must be over-

come. Among people with substance use disorders (SUDs), dif-

ficulty in overcoming habitual responses to stimuli associated

with their addic tio n in favor of new, goal-directed behaviors

contributes to relapse. Animal models of habit learning demon-

strate t hat chronic self-administration of drugs of abuse pro-

motes habitual responding beyond the domain of compulsive

dru g seeking. However, wh ether a similar propensity toward

domain-general habitual responding occurs in humans with

SUDs has remained unclear. To address this question, we used

a visuomotor S-R learning and relearning task, the Hidden

Association between Images Task, which employs abstract visual

stimuli an d ma nual respo nses. This task allows us to measure

new S-R association learning and well-learned S-R association

execution and includes a response contingency change manip-

ulation to quantify the degree to which responding is habit-

based, rather than goal-directed. We find that people with SUDs

learn new S-R associations as well as healthy control participants

do. Moreover, people with an SUD history slightly outperform

controls in S-R execution. In contras t, people w ith SUDs are

specifically impaired in overcoming well-learned S-R associa-

tions; those with SUDs make a significantly greater proportion

of perseverative errors during well-learned S-R replacement,

indicating the more habi tual nature of their resp onses. Thus,

with equivalent training and practice, people with SUDs appear

to show enhanced domain-general habit formation.

■

INTRODUCTION

Learned habitual responses to stimuli allow efficient

navigation of daily life by allocating cognitive resources

toward processes such as cognitive control, which enables

flexible behavioral. However, when the outcome of such

habitual actions is no longer a desirable goal, established

stimulus–response (S-R) associations must be overcome. A

definitive behavior of addiction is continued drug use

despite serious negative consequences of such use. In

essence, although the outc ome of dr ug seeking and/or

consumption is reduced in value from mostly positive to

mixed or largely negative, these actions persist and can be

potently triggered by drug-associated cues. As such, addic-

tion may be partially described as an initially goal-directed

behavior that becomes a habit-based process as a con-

sequence of reinforcement learning during repeated drug

use (Belin, Belin-Rauscent, Murray, & Everitt, 2013; Everitt

& Robbins, 2005, 2013; Balleine & O’Doherty, 2010; Belin,

Jonkm an, Dickinson, Robbins, & Everitt, 2009). Despite

the clinical importance of understanding the maladaptively

rigid behaviors that characterize substance use disorders

(SUDs), investigation of behavioral rigidit y in SU Ds has

been limited to date.

Data from animal models show that extended cocaine

(Za pata, Minney, & Shippenberg, 2010; Belin & Everitt,

2008) or alcohol use (Corbit, Nie, & Janak, 2012; Dickinson,

Wood, & Smith, 2002) promotes habitual behavior, sug-

gesting that chronic exposure to drugs of abuse potentiates

habitual responding more generally. In contrast to what is

known in animals, relatively little is known about habit

learning in humans, or whether addiction is associated with

altered general capacity to learn or replace S-R associations,

that is, to form or break habits. Either enhanced habit for-

mation or impaired ability to overcome habits could theo-

retically contribute to addiction.

Animal studies of S-R learning are typically limited to

simple one-to-one mapping of stimuli onto response op-

tions, and although such designs have been used with hu-

mans (Toni, Ramnani, Josephs, Ashburner, & Passingham,

2001; Deiber et al., 1997), people learn such associations

rapidly, limiting their use for examining learning over time

and measuring transitions between goal-directed and ha-

bitual response selection. Moreover, habitual responding

in animals is typically tested via outcome devaluation , with

continued responding for a devalued outcome taken to in-

dicate habitual responding (Dickinson, 1985). Outcome de-

valuation studies in humans replicate a nimal studies of

simple S-R learning tasks (de Wit, Corlett, Aitken, Dickinson,

& Fletcher, 2009 ; Tricomi, Balleine, & O’Doherty, 20 09;

Valentin, Dickinson, & O’Doherty, 2007), but such designs

have substantial methodological limitations in humans. In

part icular, it is very difficult to i dentify multiple primary

University of North Carolina

doi:10.1162/jocn_a_00953

reinforcers equated for value across individuals that may

then be devalued according to traditional animal paradigms.

This difficulty precludes their use with special populations,

which pose other recruitment challenges, and renders them

ill-suited to multisession tests of interventions to reduce

habitual re sponding. Furthermore, these paradigms lack

ecological validity for modeling stimulus-driven (i.e., habit-

based) actions in the “real world.” During daily life, it is less

often the case that the outcome of a no-longer adaptive

action loses value; rather, it is that the outcome itself

changes to one that is less (or un-) desirable. For example,

the cue of walking into a darkened room will often trigger

the automatic action of flipping the light switch. During a

power outage, this action will yield no positive outcome,

and yet this automatic action will persist despite its known

lack of utility. In this case, the former outcome of this action

(illumination of the darkened room) retains its value, but

that is simply no longer the outcome of flipping the light

switch. Likewise, in the case of maladaptive habit-based

actions, such as compulsive drug use during a binge, the

same action (e.g., lighting andsmokingfromacrackpipe)

will no longer yield the initial outcome, a euphoric “high,”

instead producing agitation and paranoid delusions. Again,

the former outcome of this action (a euphoric high state)

retains its value, but it is no longer the outcome of the

action. Most work in humans to date has overcome these

obstacles by instead employing probabilistic learning tasks

(Dolan & Dayan, 2013). However, although such paradigms

are useful in investigating the ability to fle x ibly adapt to

dynamic response contingencies, these paradigms cannot

promote enduring habitual responses to stimuli. Our task,

although simplistic, providesausefullaboratory-based

model of these sort of S-R outcome contingency changes

that are a natural part of human life.

Few studies to date have investigated the relationship

between habitual behavior and drugs of abuse in humans.

First, adult smokers demonstrate both goal-directed and

habitual res pond ing f or natural rewards and cigarettes,

dependent on age, smoking habit severity, and impulsive-

ness (Hogarth, Chase, & Baess, 2012; Hogarth & Chase,

2011). Second, a recent neuroimaging study in alcohol-

dependent patients, including t hose concurrently using

psychoactive medications for depression and anxiety disor-

ders, found preferential habit-based responding during

task performance at the expense of goal-directed behavior

(Sjoerds et al., 2013); however, confounding factors pre-

clude strong linkage between habitual responding and

alcohol use disorders.

Finally, no published work to date has investigated the

transition between goal-directed and habitual response

selection during S-R formation or the replacement of S-R

associations in people with SUDs. To address this knowl-

edge gap, we compared S-R association learning and re-

placement between healthy adults with no SUD history

and currently abstinent people with a lifetime SUD diag-

nosis (Table 1). We predicted that an SUD history would

associate with enhanced capacity to acquire new S-R asso-

ciations and impaired ability to replace established re-

sponses, with a specific increase in perseverative responding

when attempting to change established S-R associations.

To test these ideas, we employed the Hidden Association

between Images Task (HABIT; Figure 1), a visuomotor S-R

learning and relearning task with abstract vis ual stimuli

and manual responses. As the behavioral da ta were not

normally distribute d, we applied ge neralized linear

mixed-effects models (GLMMs) to characterize the change

in behavioral performance over time, evaluating whether

SUD status uniquely accounts for significant variability in

learning and/or relearning trajectories across individuals.

METHODS

Participants

A total of 62 participants were recruited from the University

of North Carolina at Chapel Hill (UNC) and the surround-

ing community via advertisements. Participants were re-

cruited into two groups, based on whether they did (n =

22 SUD) or did not (n = 40 control; Ctrl) meet DSM-IV cri-

teria for past drug or alcohol dependence in a structured

clinical interview (n = 7 alcohol, n = 4 opiates, n =11

stimulants, of which n = 13 were polysubstance abusers;

Sheehan et al., 1998). SUD participants self-reported a min-

imum of 2 weeks of abstinence at the time of recruitment

(M = 2 ± 2.5 years). All participants were healthy individ-

uals 18–40 years old with no known history of neurological

disorders, no current psychiatric diagnoses (n = 5 SUDs

met criteria for past depression) or psychoactive drug or

medication use (excluding nicotine and caffeine), and re-

ported an IQ within the normal range (≥80). Participants

were screened for psychoactive drug use (Biotechnostix,

Inc., Markham, Ontario), including alcohol (FC-10, Lifeloc,

Inc., Wheat Ridge, CO) in each session. Thirteen additional

participants were recruited but failed to complete the train-

ing session (see “Behavioral Task”), and another 12 partic-

ipants failed to return for or complete the testing session.

As expected, the SUD and control groups differed signifi-

cantly in terms of substance use , with higher scores on

all measures, including family history of alcohol abuse in

the SUD group (Table 1). The SUD and control groups

did not differ significantly in terms of education, socioeco-

nomic status, gender, or ethnicity but did differ in terms of

age and estimated IQ, with significantly lower average IQ

and higher average age for the SUD group relative to the

Ctrl group (Table 1); to control for these differences, age

and IQ were included as covariates in all analyses. Each par-

ticipant provided written informed consent as approved by

the UNC Office of Human Research Ethics.

General Procedure

Individuals participated in two sessions, with at least one

night’s sleep between the first and second sessions.

Pa rt i c i p a n ts were paid for their involvement, including

McKim, Bauer, and Boettiger 1025

Table 1. Sample Demographics and Psychometric Data

Ctrl (n = 40) SUD (n = 22) t(60) p

Demographics

Age (years) 24 ± 6 29 ± 6 −2.65 .01

SILS (calculated) IQ 105 ± 6 99 ± 6 4.32 <.001

Education (years) 15 ± 2 15 ± 2 0.40 .69

SES 46 ± 9 41 ± 12 1.51 .14

Gender (% female) 50 16 ns

Ethnicity (% non-White) 27 43 .28

Substance Use Related

AUDIT Total 4 ± 3 23 ± 10 −8.60 <.001

AUDIT Consumption 3 ± 2 8 ± 3 −7.28 <.001

AUDIT Dependence 0.08 ± 0.35 6 ± 5 −8.31 <.001

AUDIT Harm 0.78 ± 1.33 8 ± 6 −7.19 <.001

DAST 1 ± 1 17 ± 7 −10.79 <.001

DUSI-I (%) 0.10 ± 0.12 0.80 ± 0.18 −16.25 <.001

FTQ density (%) 0.16 ± 0.22 0.41 ± 0.23 −4.28 <.001

Psychometric

BDI 3 ± 4 6 ± 6 −1.69 .10

BIS Total 55 ± 8 68 ± 15 −3.61 .001

BIS Attention 14 ± 3 18 ± 5 −2.55 .01

BIS Motor 21 ± 3 25 ± 6 −2.73 .01

BIS Nonplanning 20 ± 4 25 ± 6 −4.31 <.001

LOC 10 ± 3 8 ± 4 1.99 .051

MMPI-Antisocial Practices Scale 6 ± 3 10 ± 5 −3.57 .001

STAI Total 60 ± 15 67 ± 15 −1.63 .11

STAI-State Anxiety 27 ± 7 29 ± 7 −0.96 .34

STAI-Trait Anxiety 33 ± 8 37 ± 9 −2.02 .048

TAF Total 17 ± 13 19 ± 14 −0.43 .67

TAF Moral 16 ± 11 15 ± 10 0.40 .69

TAF Self 1.2 ± 2.1 3.0 ± 3.6 −2.15 .03

TAF Others 0.6 ± 1.6 1.4 ± 3.1 −1.14 .26

Values are reported as mean ± standard deviation. Reported p values reflect the results of unpaired two-tailed comparison between groups. SUD =

history of SUD participant; Ctr l = control subject; IQ = intelligence quotient; SES = socioeconomic status; AUDI T = Alcohol Use Disorders

Identification Test; DAST = Drug Abuse Screening Test; DUSI-I = Drug Use Screening Inventory, Domain I; FTQ = Family Tree Questionnaire;

BDI = Beck Depression Index; BIS = Barratt Impulsivity Scale; LOC = Locus of Control; MMPI = Minnesota Multiphasic Personality Inventory;

SILS = Shipley Institute of Living Scale; STAI = State–Trait Anxiety Inventory; TAF = Thought Action Fusion Scale. Boldface indicates significant

values.

p value represents results of χ

test. ns: p > .05.

p value represents result of Fischer’s exact test.

p value represents results from Satterthwaite method for unequal variances.

1026 Journal of Cognitive Neuroscience Volume 28, Number 7

performance bonuses in the second (testing) session. Dur-

ing Session 0, participants first underwent a structured clin-

ical interview, then completed a battery of standard

questionnaires (see “Behavioral Inventories”), followe d

by behavioral training on the computerized S-R learning

task (see “Behavioral Task”). Learning and habitual re-

sponding was then tested during Session 1.

Behavioral Inventories

We administered a number of standard questionnaires to

quantify factors that could impact our results. We quanti-

fied alcohol use behavior with the Alcohol Use and Dis-

orders Identification Test (Saunders, Aasland, Babor,

de la Fuente, & Grant, 1993) and substance use behavior

with the Drug Use Screening Inventory, Domain I (Tarter,

1990) and the Drug Abuse Screening Test (Skinner, 1982).

We calculated density of familial alcohol abuse using the

Family Tree Questionnaire (Mann, Sobell, Sobell, & Pavan,

1985). Neuropsychological questionnaires included the

Barratt Impulsivity Scale (Barratt, 1994), the Beck Depres-

sion Inventory (Beck & Steer, 1987), Rotter’s Locus of

Control Scale (Rotter, 1966), the State–Trait Anxiety Inven-

tory (Spielberger, 1985), the Thought Action Fusion Scale

(Shafran, Thordarson, & Rachman, 1996), and the Anti-

social Practices of the Minnesota Multiphasic Personality

Inve ntory 2 (Butche r, Graham, Williams, & Ben-P orath,

1990). Education and occupati on were quantified with

Figure 1. Diagram of HABIT paradigm structure. (A) Panel depicts training session (“Session 0”) and test session (“Session 1”), which occurs on a

subsequent day. Session 1 is divided into Part 1 (preresponse change; six runs) and Part 2 (postresponse change; six runs). (B) Task schematic

for Part 1 (left) and Part 2 (right) of the HABIT test session.

McKim, Bauer, and Boettiger 1027

the Hollingshead Socioeconomic Status score (Hollingshead,

1975). We estimated IQ with the Shipley Institute of Liv-

ing Scale (Zachary, 1991).

Behavioral Task

The HABIT is an S-R learning and relearning task im-

plemented in E-Prime 2.0 (PST, Inc., Pittsburgh, PA) con-

sisting of a HABIT Training Session and a two-part HABIT

Test Session, which occurs on a subsequent day (Figure 1).

The training and test session Part 1 have been described

in detail (Boettiger & D’Esposito, 2005). In brief, stimuli

were presented on a color LCD screen, and participants

used a four-button keypad for manual response selection

using the fingers of their dominant hand. Participants were

given instructions and a brief familiarization before com-

pleting the training phase of the task. Participants viewed

abstract visual stimuli displayed briefly (700 msec) on the

screen that they learned, through trial and error, to asso-

ciate with specific manual responses. During the first train-

ing sessi on, p articipa nts learned two sets of S-R rules

(Familiar) to a criterion of ≥90% accuracy. Participants

then returned after ≥1night’s sleep to complete the test

session. In the second testing session, participants first

demonstrated retention of the previously learned (Famil-

iar) associations, then the learning task (HABIT Test Part 1;

Figure 1) began. In the learning task, blocks of the two

Familiar sets were interspersed with blocks composed of

two new (Novel) stimulus sets to measure new S-R learn-

ing and blocks of a control condition, consisting of novel,

unrelated stimuli (No Rule); blocks consisted of 15 ran-

domly selected stimuli from the relevant set. Following

six “runs” of 15 blocks each (three per set type), par-

ticipants were informed that the correct responses for

two sets (one Familiar and one Novel set) had changed

(HABIT Test Part 2; Figure 1). As the previously correct

responses for the changed sets produce a negative rather

than positive outcome, one could construe this change in

response contingency as a response “devaluation,” al-

though this manipulation is quite different from the out-

come devaluation procedures traditionally used in studies

of habitual responding. Devaluing outcomes is methodo-

logically tricky in human studies, as primary rewards are

not universally palatable. Moreover, points (or other per-

formance metrics) or money tends to remain intrinsically

rewarding and is difficult to realistically devalue. Par-

ticipants then learned the new correct S-R associations

through trial and error. This “response devaluation” ma-

nipulation allows us to quantify habitual responding when

attempting to overcome both well-learned (Familiar) and

freshly learned (Novel) S-R associations, as the proportion

of perseverative er rors can be taken as an index of the

degree to which responses are outcome independent

(i.e., habit-based), as opposed to outcome driven (i.e.,

goal-directed). By introducing S-R changes for both Famil-

iar and Novel sets, at a point where performance is approx-

imately equivalent, we can rule out performance deficits

due to impaired response inhibition. Moreover, including

Familiar and Novel sets in which correct responses do not

change allows us to control for effects on performance of

time and of context change.

Data Analysis

Our main index of performance was number of correct

responses out of total responses across both epochs of

the task (six runs each, pre- and post-contingency

change). Our data structure is composed of 48 repeated

measures, consisting of four stimulus set types (2 Famil-

iar, 2 Novel) that are measured within person over the

12 time points. We also collected RT data in each trial

andwereabletocategorizeerror types (perseverative

button press, other incorrect button press) postcontin-

gency change to distinguish between habit-based and

goal-directed response strategies. Because of the non-

normal na ture of these data, ra ther than using a mixed

model repeated-measures ANOVA analytical approach,

we instead used a GLMM with a binomial distribution

and logit link function, which mode ls a linear rate of

learning and is ideally suited to account for the nonlinear

nature of learning rates in terms of probability. Our

GLMM approach is described in detail in the next section.

To test the significance of between-group comparisons

for demographic and psychological variables, we used

unpaired two-tailed t tests for continuous measures and

tests for categorical measures. Additionally, we used a

one-way ANOVA to test for statistically significant differ-

ences between groups in perseverative responding and

the nonparametric Kruskal–Wall is test to compare per-

severative responding among SUD subgroups. All analy-

ses included age and IQ as covariates. All data analyses

were performed within SAS (Cary, NC).

Specification of GLMMs

Performance data in this S-R learning task were the num-

ber of correct responses within each block, a nonnor-

mally distributed outcome heavily skewed toward the

top end of possible values; hence, performance accuracy

was characterized by fitting GLMMs with a binomial dis-

tribution and logit link function. GLMMs provide a statis-

tically efficient way to independently account for variance

at different levels within nested data. In the present in-

stance, repeated measures (performance within six runs

each during the learning and relearning epochs) are

nested within persons, and thus, GLMMs could be spec-

ified to account for both within- and between-person var-

iability in performance accuracy. Our data structure is

composed of 48 repeated measures, consisting of four

stimulus set types (2 Familiar, 2 Novel) that are measured

within person over the 12 time points. The measurement

of accuracy at multiple time po ints both pre- and post-

contingency change yields increased power to detect

between-subject differences in within-subject change, with

1028 Journal of Cognitive Neuroscience Volume 28, Number 7

particular emphasis on the ability to compare pre-

and postcontingency change trajectories and to capture

changes in perform ance over time. For each set type,

we modeled the time course of performance over each

epoch (pre- and post-contingency change) independently

and also included additional unique variables capturing

thechangeinperformancefollowingtheS-Rcontingency

change manipulation. Our analytic approach involved first

fitting a baseline model (Model 1) to represent changes in

performance accuracy during the learning and relearning

epochs as a function of set type and changed response

contingencies, controlling for age and IQ. Next, we added

SUD status as a predictor of performance (Model 2) and

conducted a likelihood ratio test to evaluate improvement

in model fit. Models were estimated using maximum like-

lihood in the GLIMMIX procedure of SAS 9.3, imple-

mented using adaptive quadrature with nine quadrature

points per dimension of integration.

Defining π

to be the probability that person j will pro-

duce an accurate response to a stimulus given during run

i, Model 1 was specified as

logit π



¼ β

0ij

þ β

1ij

Trend1

þ β

2ij

Dropof f

þ β

3ij

ChangeTrend

þ β

Age

þ β

þ u

Trend1

þ u

Dropof f

þ u

ChangeTrend

(1)

where fixed effects are designated by β and the first four

fixed effects, which capture changes in performance

accuracy over time and across conditions, are decom-

posed as follows:

0ij

¼ β

þ β

Set

1ij

¼ β

þ β

Set

2ij

¼ β

þ β

Set

þ β

NewResponse

þβ

Set

 NewResponse

3ij

¼ β

þ β

Set

þ β

NewResponse

þ β

Set

 NewResponse

(2)

Last, the random effects are designated b y u and as-

sumed to be normally distributed with a full covariance

matrix G.

The variables within the model were coded to enhance

interpretation of the parameter estimates. The covariates

Age and IQ were mean-centered so that all fixed effects

could be interpreted to represent effects for a participant

of typical Age and IQ. Trend1 was coded −5, −4, …,6

for the 12 runs, Dropoff was coded 0 for runs occurring

during the learning epoch and 1 for runs during the re-

learning epoch, and ChangeTrend was coded 0 for runs

occurring during the learni ng epoch and 1, 2, …, 6 for

runs during the relearning epoch. Given this coding,

0ij

represents performance at the final run of the learn-

ing epoch, β

1ij

represents the increase in accuracy over

the learning epoch, β

2ij

represents the drop off in accuracy

between the last run of the learning epoch and the first run

of the relearning epoch due to changes in S-R contingen-

cies, and β

3ij

indicates the difference in rate of improve-

ment in accuracy in the relearning epoch relative to the

learning epoch. Equation 2 shows that the values of these

four coefficients were a function of set type (Set; coded 1

for Familiar, 0 for Novel) and whether the response was

devalued in the relearning epoch (NewResponse;coded1

for devalued sets and 0 for nondevalued sets). Addition-

ally, the random effects in Equation 1 allowed for person-to-

person variability in the four components of the performance

accuracy trajectories.

Model 2 retains Equation 1 but includes SUD status as

a predictor such that Equation 2 is elaborated as follows:

0ij

¼ β

þ β

Set

þ β

SUD

þ β

Set

 SUD

1ij

¼ β

þ β

Set

þ β

SUD

þ β

Set

 SUD

2ij

¼ β

þ β

Set

þ β

NewResponse

þ β

Set

 NewResponse

þ β

SUD

þ β

Set

 SUD

þ β

NewResponse

 SUD

þ β

Set

 NewResponse

 SUD

3ij

¼ β

þ β

Set

þ β

NewResponse

þ β

Set

 NewResponse

þ β

SUD

þ β

Set

 SUD

þ β

NewResponse

 SUD

þ β

Set

 NewResponse

 SUD

(3)

Models 1 and 2 are nested in their fixed effects, permit-

ting a likelihood ratio test of t he overall effect of SUD

status on performance accuracy trajectories (Table 2).

RESULTS

Participants learned two sets of (Familiar) S-R associa-

tions during an initial H ABIT training session, then re-

turned for a HABIT testing session broken into two

epochs: an initial learning epoch in which participants

both executed the previously learned (Familiar) S-R asso-

ciations and learned two new (Novel) sets of S-R associ-

ations, and a subsequent “relear ning ” e poch, in which

the established S-R contingencies for one of the Familiar

S-R sets and one of the Novel S-R sets changed (Figure 1).

During the re learning epoch, the previously correct re-

sponse to the stimuli in the changed sets is met with a

punishment instead of a reward, reducing the value of

selecting the previously learned action in response to

those s timuli; this change in response contingency al-

lowed us to quantify perseverative errors as an index of

habitual responding. The learning and relearning epochs

were each divided in to six segments, each in turn con-

sisted of three randomly ordered blocks of S-R set types

(18 trials per block). Thus, at the onset of the relearning

epoch, participants have completed 324 trials for each of

the Novel S-R sets and approximately three times as many

trials for the Familiar sets (average: 10 38 trials; 95% CI

[952, 1124]).

McKim, Bauer, and Boettiger 1029

Behavioral Performance during Training Session

During the initial training session, participants were re-

quired to reach a performance criterion of 90% accuracy

for each S-R set. These sets are designated as Familiar in

the subsequent learning and relearning epochs. Set order

was counterbalanced across participants, and set order

did not differ betwee n groups, χ

(1)

= 0.40, p = .53.

Training to criterion took an average of ∼25 min, with

no significant difference between groups in the number of

blocks to criterion (Ctrl: 11 blocks; SUD: 9 blocks; 40 trials

per block; F(3, 56) = 0.67, p = .57). Learning the associa-

tive rules for the second S-R set was always more rapid and

also did not differ significantly between groups (Ctrl: 4 blocks;

SUD: 4 blocks; F(3, 56) = 0.39, p = .76). Thus, before return-

ing for the testing session, training performance between

groups was equivalent. Moreover, the time between the

training and testing sessions did not differ significantly

between groups (Ctrl: 10 days; SUD: 8 days; t(60) = 1.09,

p =.28).

Behavioral Performance during Testing Session—

Model 1: Baseline Model without SUD Status

We found no significant main effects of age or IQ in the

baseline GLMM fit to the performance data (Table 2). Dur-

ing the learning epoch, we observed a significant interac-

tio n between set type and time before S-R contingency

changes ( p < .001; “Set × Trend1,” Table 2), indicating

that, as expected, performance improved more over time

Table 2. Fixed Effect Estimates (Top) and Variance–Covariance

Estimates (Bottom) for Models of the Predictors of Learning

Behavior

Parameter Model 1 Model 2

Fixed Effects

Intercept 0.95** (0.08) 0.91** (0.10)

Set 0.33** (0.03) 0.24** (0.04)

Trend1 0.23** (0.02) 0.23** (0.02)

Set × Trend1 −0.12** (0.01) −0.12** (0.01)

Dropoff −0.53** (0.07) −0.48** (0.08)

Set × Dropoff −0.01 (0.06) 0.02 (0.07)

Dropoff ×

NewResponse

−0.66** (0.04) −0.69** (0.05)

Set × Dropoff ×

NewResponse

0.33** (0.06) 0.39** (0.07)

ChangeTrend −0.13** (0.02) −0.15** (0.02)

Set × ChangeTrend 0.07** (0.02) 0.10** (0.02)

NewResponse ×

ChangeTrend

0.11** (0.01) 0.15** (0.02)

Set × NewResponse ×

ChangeTrend

−0.01 (0.02) −0.03 (0.03)

Age (centered) 0.01 (0.01) 0.01 (0.01)

IQ (centered) 0.002 (0.01) 0.01 (0.01)

SUD 0.13 (0.18)

Set × SUD 0.28** (0.07)

Trend1 × SUD 0.02 (0.03)

Set × Trend1 × SUD 0.01 (0.02)

Dropoff × SUD −0.14 (0.13)

Set × Dropoff × SUD −0.11 (0.13)

Dropoff × NewResponse

× SUD

0.10 (0.09)

Set × Dropoff ×

NewResponse × SUD

−0.17 (0.13)

ChangeTrend × SUD 0.04 (0.04)

Set × ChangeTrend ×

SUD

−0.09* (0.04)

NewResponse ×

ChangeTrend × SUD

−0.09* (0.03)

Set × NewResponse ×

ChangeTrend × SUD

0.08 (0.04)

Variance of Random Effects

Intercept 0.40 0.38

Trend1 0.01 0.01

Changetrend 0.01 0.01

Dropoff 0.16 0.15

Table 2. (continued )

Parameter Model 1 Model 2

Correlations between Random Effects

Trend1/intercept 0.05 0.05

Changetrend/intercept −0.04 −0.04

Changetrend/trend1 −0.01 −0.01

Dropoff/intercept −0.14 −0.13

Dropoff/trend1 −0.03 −0.02

Dropoff/changetrend 0.01 0.01

−2*log-likelihood 23,279.02 23,207.43**

Standard errors are in parentheses. Set denotes the familiar versus novel

set type variable, with novel set type as the reference category. Trend1

indicates the slope of performance during precontingency change time

points. Dropoff signifies the difference in perfor mance pre- and post-

contingency change. New Response indicates a change in th e corr ect

response as a result of devaluation. Changetrend is the variable denoting

the change in postchange perf ormance relative to pre-change perfor-

mance. The random parameters represent the variance and covariance

estimates generated from inclusion of random effects in the model. The

−2log-likelihood demonstrates the value for model fit.

*p < .05.

**p < .001.

1030 Journal of Cognitive Neuroscience Volume 28, Number 7

in the Novel S-R sets relative to the Famil iar S-R set s.

Somewhat surpris ingly, t he performance drop- off effect

after contingency change was greater for Novel S-R sets

with changed responses contingencies relative to Familiar

S-R sets with changed responses contingencies ( p < .001;

“Set × Dropoff × NewResponse,” Table 2).

During the relearning epochs the difference in learn-

ing rate interacted with S-R set type, with unchanged

Novel S-R sets showing a shallower rate of improvement

( p < .0 01; “ChangeTrend,” Table 2), which was less

pronounced for unchanged Familiar S-R sets ( p < .001;

“Set × ChangeTrend,” Table 2). This difference in learn-

ing rate between epochs also differed between S-R sets

with changed versus unchanged response contingencies

( p < . 001; “NewResponse × ChangeTrend,” Table 2).

For changed S-R sets, the rate of relearning was steeper

than that observed during the learning epoch, in contrast

to the shallower relearning rate for unchanged sets.

Model 2: Including SUD Status

Across both epochs, a model including group as a perfor-

mance predictor (Model 2, Table 2) fit the data signifi-

cantly better than did an identical model excluding

group as a predictor (Model 1, Table 2; p < .001). This

result indicates that presence or absence of an SUD his-

tory accounted for significant variability in HABIT perfor-

mance across individuals. As described below, this result

does not reflect a performance deficit in the SUD group.

To further dissect HABIT performance, we first evaluated

the initial learning epoch. Task performance improved

over the course of the epoch, with greater improvement

for the Novel S-R sets (Figure 2; p <.001,“Set × Trend1,”

Table 2). As shown in Figure 2, participants executed Famil-

iar S-R sets more accurately than Novel S-R sets, a distinc-

tion that was heightened in the SUD group. The groups did

not differ in terms of performance improvement during

the initial learning epoch ( p = .50; “Set × Trend1 ×

SUD,” Table 2), but an SUD history predicted more accu-

rate execution of Familiar S-R sets (Figure 2, magenta lines;

p <.001;“Set × SUD ,” Table 2). Thus, an SUD history

predicts intact ability to form new S-R associations and a

somewhat heightened ability to accurately execute estab-

lished S-R associations.

At the outset of the relearning epoch, performance im-

mediately declined for all sets in both groups as shown in

Figure 3 (right). As in Model 1, the changed–unchanged

S-R contingency contrast was more pronounced in the

Novel S-R sets (yellow) relative to the Familiar S-R sets

(magenta; p <.001;“Set × Dropoff × NewResponse,”

Table 2); SUD status did not significantly interact with

these parameters (Tabl e 2). This finding indicates tha t

both groups show evidence of overtraining in the Familiar

S-R sets relative to the Novel S-R sets, which is reported to

facilitate reversal learning for S-R tasks (McLar en et al.,

2014). This is consistent with the fact that participants

completed two to three times as many trials for the Famil-

iar S-R sets relative to the Novel S-R sets (n = 324 trials

per set). As is evident in Figure 3, performance improved

over the course of the relearning epoch, with shallower

rates of increase relative to the initial learning epoch

for unchanged, Novel sets ( p < .001; “ChangeTre nd,”

Table 2), an effect that did not differ by group ( p = .35;

“ChangeTrend × SUD,” Table 2). For Novel sets with chan-

ged S-R contingencies, control participants demonstrated

steeper rates of performance improvement postchange

Figure 2. Mean accuracy

values during prechange

task performance. Solid lines

represent the control group

(Ctrl), and dashed lines

represent the SUD history

group. Mixed models

demonstrated that group status

significantly predicts accuracy

(Table 2). Familiar (magenta)

performance starts high and

remains high as performance

progresses, with SUD history

predicting more accurate

execution of S-R associations

(Model 2, Table 2). Novel

(yellow) set performance

improves over time as S-R

associations are learned, with

no difference between groups

in rate of learning (Table 2).

(A) Data plots depict raw accuracy

values adjusted for Age and IQ;

error bars represent within-subject

SEM.(B)Correspondingmodel

predicted values.

McKim, Bauer, and Boettiger 1031

relative to prechange ( p < .001; “NewResponse × Change

Tre nd,” Table 2). In contrast, th e SUD group showe d a

shallower rate of improving performance for Novel

changed sets postchange relative to prechange. For Famil-

iar unchanged sets, the control group demonstrated a

steeper rate of improvement in the relearning epoch rela-

tive to the prechange epoch ( p < .001; “Set × Change

Trend,” Table 2). During relearning, the SUD group

showed significantly shallower rates of performance im-

provement for unchanged Familiar S-R sets ( p < .05;

Figure 3. Mean accuracy values during pre- and postchange task performance. Solid lines represent the control group and dashed lines represent

the SUD history group. Overall, a dropoff in performance occurred in blocks with changed response contingencies and performance improved

over the course of relearning (“Post” panels; Table 2); the rate of relearning compared with the initial (“Pre”) learning rate was dependent on group

status, set type, and change status (Table 2). (A) Panels depict performance in Familiar sets (magenta) during pre- and postchange. Dark blue lines

in the right panel (“Post”) indicate performance in the set with unchanged response contingencies during the relearning phase; performance in

the response-changed set shown in magenta. (B) Performance in Novel sets (yellow) during pre- and postchange. Black lines in the right panel

(“Post”) indicate performance in the set with unchanged response contingencies during relearning; performance in the response-changed set shown

in yellow. Performance dropped more dramatically after response change for Novel sets relative to Familiar sets (Table 2). Additionally, the

control group showed steeper learning rates for Novel changed sets relative to the SUD group. Corresponding model predicted values are shown

in panels C and D.

1032 Journal of Cognitive Neuroscience Volume 28, Number 7

“Set × ChangeTrend × SUD,” Table 2), whereas the inter-

action between SUD status, set type, contingency change,

and the change in learning ra te in the re learning epoch

was not significant ( p = .08; “Set × NewResponse ×

ChangeTrend × SUD,” Table 2). To summarize, S-R contin-

gency ch ange did not reveal a global impairment in re-

sponse flexibility or inhibitory control among people with

an SUD history. In fact, for the changed Novel S-R set, the

SUD group’s performance was less impaired than that of

the control group immediately following contingency

change, resulting in a more rapid performance recovery

for the SUD group (Figure 3, yellow dashed line).

Responding to a stimulus with an action that is no lon-

ger valued (i.e., no longer positively reinforced) is taken

as an indicator of habit-based, rather than goal-directed,

responding. Thus, to quantify the habitual nature of re-

sponding after response contingency change, we eval-

uated the percentage of perseverative errors during the

relearning epoch. A one-way ANOVA between group for

each set type indicated significant differences between

groups for the overall percentage of perseverative errors

for the Familiar set type ( p = .004), but not for the Novel

set type ( p = .43). These results reflect the fact that

when trying to replace the well-established Familiar S-R

associations, errors made by the SUD group were more

apt to be perseverative errors ( p = .002; Figure 4). No

such group difference was observed for replacement of

more recently established Novel S-R associations ( p =

.146; Figure 4). These findings indicate the more habitual

nature of responding in the Familiar S-R sets among SUD

participants.

To evaluate the contribution of abused substance type

to perseverative responding during S-R relearning, we

stratified SUD participants into two categories: history

of stimulant dependence (n = 11) or no history of stim-

ulant dependence (n = 11). We found a significant

difference in perseverative errors during Familiar S-R re-

learning (Kruskal–Wallis test, p = .009; Figure 5, pink bars),

but not during Novel S-R relearning ( p = .182; Figure 5,

yellow b ars). Post hoc tests (Bonferroni corrected, p <

.025) demonstrated that participants with or without a his-

tory of stimulant dependence made significantly more per-

severative errors during relearning of Familiar S-R sets

relative to controls (stimulant history, p =.007;nostimu-

lant history, p = .009). However, only the stimulant depen-

dence group showed a trend toward more perseverative

responding during Novel S-R relearning relative to controls

(stimulants; p = .038; no stimulants; p = .342). The results

in the Novel condit ion suggest that stimulant addiction

may be associated with an even more rapid transition to

habitual responding.

DISCUSSION

We demonstrate that people with SUDs learn new S-R

associations as well as cont rol participants do and can

flexibly adapt newly learned S-R associations but are spe-

cifically impaired in overcoming well-learned S-R associa-

tions. Notably, those with SUDs differ from controls only

in terms of perseverative errors committed during well-

established S-R replacement, indicating the more habit-

based nature of their responses. These findings suggest

that people with a history of an SUD more rapidly acquire

habitual responding outside the drug-taking domain.

Prior Studies Linking Habit and Addiction

Despite extensive investigation of dr ugs of abuse and

habit in animal mod els, modest translation of these ex-

perimental paradigms to human studies has occurred

to date. Young, light-smoking adults will pursue both cig-

arette and chocolate rewards via goal-directed strategies

(Hogarth & Chase, 2011). Nicotine dependence was low

inthissample;however,Hogarth,Chase,etal.(2012)

made similar findings in a sample of daily and nondaily

smokers, in addition to finding a positive correlation be-

tween motor impulsiveness and habitual responding.

These studies suggest that habitual drug consumption

may associate with personality factors that predispose

individuals toward habit-based responding.

Hogarth and colleagues have also found that acute

alcohol intake renders the selection strategy for both

water and chocolate rewards habitual (Hogarth, Attwood,

Figure 4. Postchange percentage of perseverative errors by group for

S-R sets. The percentage of perseverative errors in the Familiar set

(magenta) with changed response contingencies significantly differed

by group, with SUD history participants making incorrect responses

that were perseverative in nature, F(3, 58) = 4.88, p = .004. In contrast,

the percentage of perseverative errors in the Novel set (yellow) with

changed response contingencies did not differ between groups, F(3,

58) = 0.93, p = .43. Error bars represent SEM.

McKim, Bauer, and Boettiger 1033

Bate,&Munafo,2012),whichisconsistentwithdata

showing that exposure to alcohol potentiates habitual re-

sponding in rats (Corbit et al., 2012). A recent fMRI study

of alcohol-dependent patients in an instrumental choice

task (de Wit, Niry, Wariyar, Aitken, & Dickinson, 200 7)

found evidence of preferential S-R based responding,

rather than goal-directed acti ons, in alcohol-dependent

individuals relative to controls (Sjoerds et al., 2013). Fur-

thermore, relative to contro ls, the alcohol-dependent

group increased activation of the posterior putam en

and reduced vmPFC activation during instrumental choice.

Although these data are consistent with the animal litera-

ture associating chronic exposure to drugs of abuse with

an overreliance on S-R response strategies, many of the

alcohol-dependent patients in Sjoerds et al.’s study were

concurrently using psychoactive medications for depres-

sion and anxiety disorders, precluding unequivocal attribu-

tion of group differences solely to alcohol dependence.

Regardless, these results point to neural correlates within

frontostriatal circuits for enhanced reliance on S-R versus

goal-directed actions. Such findings enable strong predic-

tions about expected differences between people with

SUDs and healthy controls in terms of neural activation

associated with response selection in the HABIT.

Neurobiology of Habit in Humans

Although the neural bases of behavioral differences in S-R

learning among people with SUDs is largely unexplored,

the SUD neuroimaging literature suggests that alterations

in frontostriatal circuit recruitment u nderlie atypical

behavior in SUDs (Ersche et al., 2012; Konova et al.,

2012; Goldstein & Volkow, 2011; Koob & Volkow, 2010;

Park et al., 2010; Kal ivas, 2 008; O laus son et a l., 2007 ).

Although this work has not investigated habits per se, it

logically follows that impaired frontal control of striatal

output signals could yield overrelia nce on striatal habit

circuits. Drugs of abuse may alter frontostriatal circuitry

(Izquierdo & Jentsch, 2012) such that frontal input to

the striatum can no longer effectively act as a “switch”

to regulate the contribution of br ain signals for auto-

matic, habitual responding versus goal-directed action

(Smith & Graybiel, 2013; Smith, Virkud, Deisseroth, &

Graybiel, 2012).

A unique aspect of the HABIT paradigm is the ability to

measure behavior during attempts to overcome habitual

responding; using perseverative errors as an index of

habit-based responding and continuing task conditions

that require goal-directed responding allows us to mea-

sure the flexibility of behavior over an extended period

after response contingency change. Essentially, we are

able to measure the ability to “break” habits that have

been formed within this task. The ability to ch ange or

“break” habitual behaviors has not direct ly been tested

in humans to our kn owledge, but converging evidence

from both recent animal and human studies relate drugs

of abuse and fro ntostriatal circuitry to the regulation of

behavioral change. In primates, prolonged cocaine intake

profoundly impairs S-R relearning (Jentsch, Olausson,

De la Garza, & T aylor, 2002). These data suggest that

chronic drug exposure potentiates habitual response se-

lection and further supports a role for extended sub -

stance abuse in altering the circuits underlying S-R

learning and replacement. In rodents, optoge netic per-

turbation of the infralimbic portion of the mPFC results

in a switch from a previously to recently learned behavior

and thus facilitates the replacement of habitual behaviors

(Smith et al., 2012). Computational modeling of human

choice behavior in which prefrontal brain regions “arbi-

trate” between habit-based or goal-directed responses

further supports these animal findings (Lee, Shimojo, &

O’Doherty, 2014). TMS applied to the DLPFC shifts the

balance between goal-directed versus habit-based re-

sponse s election strategies (Smittenaar, FitzGerald,

Romei, Wright, & Dolan, 2013; Knoch, Brugger, & Regard,

Figure 5. Postchange percentage of perseverative errors by abused

substance type group for S-R sets. We categorized participants according

to substance dependence history as follows: no history (control), no

stimulant dependence (alcohol or opiate dependence; “No Stims”), or

stimulant and alcohol dependence (stimulants; “Stims”). Overall

nonparametric comparison of the three groups indicated a significant

difference in the percentage of perseverative errors for the Familiar set

(magenta), χ

(3) = 11.67, p = .009. The groups did not differ in terms

of the percentage of perseverative errors for the Novel set (yellow),

(3) = 4.86, p = .182. Post hoc tests corrected for multiple comparisons

( p = .025) demonstrated that, compared with controls, participants with

a history of stimulant dependence committed a higher percentage of

perseverative errors for both the Familiar set (z =2.69,p = .007) and the

Novel set (z =2.07,p = .038). In contrast, participants with no history of

stimulant dependence committed a higher percentage of perseverative

errors when compared with controls only in the Familiar set (z =2.60,

p = .009), not in the Novel set (z =2.69,p =.342).

1034 Journal of Cognitive Neuroscience Volume 28, Number 7

2005). Taken together, these studies provide compelling

evidence for the regulation of behavioral control via fron-

tostriatal circuitry. An important future direction is to deter-

mine whether abnormal functioning of these same

frontostriatal ci rcuits underlies the atypical S-R learning

and replacement we find in people with a history of

addiction.

It is important to note that elevated perseverative er-

rors in the changed Familiar S-R sets in the SUD group

is unlikely to reflect impaired response inhibition, as in-

hibitory impairments should have manifest in the Novel

condition as well as the Familiar condition based on nearly

equal performance in the Novel condition before the con-

tingency change, particularly among the SUD group. How-

ever, we only observed this deficit in the highly practiced

Fam iliar condition in whi ch the SUD group appears to

have transitioned to a more automatic S-R strategy. One

could make the case that suppressing a more automatized

action requires greater response inhibition and that only

under this higher “inhibitory load” condition did a deficit

in the SUD group emerge; however, this argument merely

lends support to our interpretation of a more rapid transi-

tion to an automatic response strategy in people with an

SUD history.

Possible Role of Stress in the SUD Group Findings

Although we make the case above that atypical frontos-

triatal function likely underlies th e apparent earlier

switch to dominance of habit-b ased respon ding in the

SUD group, a growing body of literature shows that stress

can potentiate habit- based responding in both humans

(Schwabe & Wolf, 2010, 2011, 2013; Schwabe et al.,

2007) and animal models (Dias-Ferreira et al., 2009). This

tendency for stress to shift the balance of behavior from

goal-directed to habitual has been investigated phar ma-

cologically in humans, with evidence indicating roles

for both elevated cortisol levels and increased noradren-

ergic activity (Schwabe, Tegenthoff, Hoffken, & Wolf,

2012; Schwabe, Hoffken, Tegenthoff, & Wolf, 2011).

Moreover, neuroimaging research has found that partici-

pants subject to chronic psychosocial stress fail to change

responses after outcome devaluation, indicative of habit-

based act ions (Soa res et al., 201 2). The stressed par-

ticipants in that study showed greater activation of the

putamen during response selection after devaluation, rel-

ative to nonstressed controls, consistent with prior links

between the pu tamen and habitual respond ing (de Wit

et al., 2009; Tricomi et al., 2009). Notably, the behavioral

and neural effects of stress in the Soares study were re-

versible, declining after the st ressful period ended; this

demonstrates the plasticity of the neural systems regulat-

ing habitual actions and holds promise for interventions

to facilitate behavioral change of ingrained behaviors.

The evidence that stress can promote habit-based re-

sponding, together with evidence of dysregulated hypo-

thalamic-pituitary axis function in individuals with SUDs

(Lijffijt, Hu, & Swann, 2014; Porcu, O’Buckley, Leslie

Morrow, & Adinoff, 2008; Kreek, Nielsen, Butelman, &

LaForge, 2005; King et al., 2 002; Anthene lli, Maxwell,

Geracio ti, & Hauger, 2001), suggest that the behavioral

differences that we observed in the SUD group could re-

flect greater stress levels in the SUD group. We did not

collect physiological or subjective report measures of

stress for this study, although we did collect measures

of anxiety; the groups did not differ in terms of state

anxiety, but the SUD group did report slightly higher

levels of trait anxiety (Table 1), suggesting possibly high-

er levels of ch ronic stress in the SUD group com pared

with controls. Stress is well known to precipitate relapse

(Sinha, 2012), and although the underlying mechanisms

are not well understood, it is tempting to speculate that a

contributing factor could be stress-induced promotion of

habitual responding in people with SUDs. This question

can be addressed with the HABIT paradigm, which may

ultimately identify new therapeutic approaches to relapse

prevention.

Study Limitations

The observation of more habit-based responding in the

SUD group could be a consequence of chronic drug ex-

posure or a predisposing trait that contributes to SUD

vulnerability; these alternatives cannot be disentangled

by the current study. If this heightened propensity to es-

tablish habits in people with SUDs predates the SUD, it

would represent a promising, unexplored intermediate

phenotype for SUDs. A further limitation is the SUD

sample studied. Participants were recruited based on

any lifetime history of an SUD (including an alcohol use

disorder), which yielded a heterogeneous population.

Given the distinct effects of differing abused substances

on neurotransmitter systems, it is rather unlikely that the

behavioral effects we observed reflect c ommon ne ural

dysfunction caused by chronic substance abuse. How-

ever, biological predispositions play a large role in SUDs

and that heritability is not necessarily substance-specific

(Hicks, Iacono, & McGue, 2012). As such, one would ex-

pect to find common neural substrates across different

substance abuse categories underlying shared behavioral

deficits that represent preexisting vulnerability factors.

Our finding here of propensity to more rapidly transition

to habit-based responding could theoretically contribute

to establishing and/or maintaining compulsive, habitual

substance use and, as such, could theoretically play a role

as a preexisting risk trait. Be that as it may, this heteroge-

neity, coupled with our small sample size, precludes

drawing conclusions regarding specific substances or

polysubstance use. The range of disease severity was also

limited in our sample, with all participants falling at the

severe end (range = 6–11; Hasin et al., 2013); thus, we

were unable to assess whether SUD severity correlates

with greater propensity for habitual responding. Another

limitation was our exclusion of individuals currently using

McKim, Bauer, and Boettiger 1035

any psychoactive medication s or with comorbid mental

health disorders, neurological conditions, or below nor-

mal IQ. The advantage of this “clean” sample is our confi-

dence in attributing group differences in behavior to SUD

history, but among the SUD population at large, comor-

bidities and psychoactive medication use is common. The-

se exclusion criteria also likely and substantially increased

our power to detect group effects, as different comorbid

conditions may have either amplified or compensated for

excess habitual responding; psychoactive medications may

have similarly increased variance. Finally, our participants

with SUDs were abstinent from s ubstance use, and as

such, their engagement of motivational circuitry and the

ability to form habitual associations might be substantially

different from people in the active phase of an SUD. These

limitations point to key future avenues of research that will

expand the scope of our understanding of habit-based

responding in addiction.

Acknowledgments

We thank L. Babwah, C. Baldner, S. Dove, E. Pelehach, A. Roy,

and C. Whitsett for assistance with data collection, L. Andrews

for assistance with behavioral data analysis, and D. Robinson for

valuable comments and discussion. Ms. McKim’s work has been

funded by the NIH and the UNC Department of Psychology &

Neuroscience. Dr. Bauer’s work has been funded by the NIH.

Dr. Boettiger’s work has been funded by the NIH and by The

Foundation for Alcohol Research/ABMRF. She has also con-

sulted for Blackthorn Therapeutics and received compensation.

Reprint requests should be sent to Charlotte A. Boettiger, De-

partment of Psychology and Neuroscience, Davie Hall, CB

#3270, University of Nort h Carolina, Chapel Hill, NC 27599-

3270, or via e-mail: [email protected].

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