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Affective decision-making predictive of Chinese adolescents drinking behaviors: a longitudinal study

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Affective decision-making predictive of Chinese adolescents drinking behaviors: a longitudinal study

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Content AFFECTIVE DECISION-MAKING PREDICTIVE OF CHINESE
ADOLESCENTS’ DRINKING BEHAVIORS: A LONGITUDINAL STUDY

by
Lin Xiao

A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(BIOSTATISTICS)

August 2008

Copyright 2008 Lin Xiao

ii
Acknowledgements

First and foremost, I would like to express my deepest gratitude to two of my advisors,
Dr. C. Anderson Johnson and Dr Antoine Bechara, for their exceptional guidance,
patience and support. I could not have succeeded in accomplishing this interdisciplinary
research without Dr. Johnson’s great expertise in prevention and Dr. Bechara’s
wonderful insight in neuroscience. They greatly encouraged me to develop independent
thinking and research skills. They are gifted academics, and I look forward to working
with them in the future.

I would also like to thank my committee members, Dr. W. Alan Stacy and Dr. Chih-
Ping Chou for their thoughtful suggestions and advice throughout the course of my
research and in the preparation of this manuscript. I am also extremely grateful for the
assistance, generosity, and advice I received from Jerry L. Grenard. His assistance made
it possible for me to collect and analyze the working memory data. I owe a special note
of gratitude to Huiyan Ma for her time and expertise in statistical analysis. I would also
like to extend my appreciation to many people in the intervention team in the institute
of health promotion and disease prevention (IPR), USC and center of disease control
(CDC) in Chengdu, China. They helped me to develop the questionnaire and conduct
the field work.

iii
Words can not express the heartfelt gratitude I offer my parents for their understanding
and support. I am grateful to my brothers for their encouragement and enthusiasm. I
would like to express my most sincere appreciation to my husband and best friend,
Baohua Liu, for his patience and for helping me keep my life in proper perspective and
balance. His knowledge of computers was also extremely helpful. Special appreciation
also goes to my friends, Lei Duan, Jie Yao, Xinxin Xu, Dalin Li for their accompany
and support throughout my life in United States.

iv
Table of Contents

Acknowledgements

ii
List of Tables

v
Abstract

vi
Introduction

1
Methods

8
Results

17
Figure 1: The original version of the IGT net scores ((C’+D’)-(A’+B’))
by drinkers with different drinking trajectories

25
Discussion

31
Bibliography

41

v
List of Tables

Table 1 Drinkers stratification 17

Table 2

Demographic characteristics of groups with different drinking
trajectories

19

Table 3

Two-year general number of drinks and drinking problems among
groups with different drinking trajectories

21

Table 4

Relationship between Time 1 drinkers who scored > 10 or ≤ 10 on
the IGT and Time2 drinkers

26

Table 5

Working memory capacity and academic performance scores
among groups with different drinking trajectories

27

Table 6

Partial correlations among drinking variables at Time 1 and Time 2,
affective decision-making, working memory, school academic
performance controlling for age, gender and school type

28

Table 7

Summary of linear regression analysis for variables predicting
general number of drinks at Time 2

29

Table 8

Summary of linear regression analysis for variables predicting
drinking problems at Time 2

30

vi
Abstract

To address whether affective decision-making could serve as a prospective
neuropsychological marker to predict alcohol use behaviors among adolescents, we
conducted a longitudinal study of 181 Chinese adolescents. In May 2006, we tested
these 10
th
grade adolescents’ affective decision-making ability using the Iowa Gambling
Task (IGT) and working memory capacity. Paper and pencil questionnaires were used
to assess drinking behaviors. The same questionnaires were completed again one year
later. Results indicated that a large proportion of adolescents who performed better on
the IGT at baseline remained abstinent or reduced their drinking levels one year later. In
comparison, a large proportion of those who performed poorly on the IGT at baseline
stayed at the same level or progressed to a higher level of drinking one year later.
Affective decision-making at baseline also significantly predicted general number of
drinks and drinking problems one year later. These findings suggest that those
adolescents with poor decision-making capabilities are more vulnerable to future
substance use, and those with better decision-making capabilities are more resistant.

1
Introduction

Although many previous studies have found impaired neuropsychological functions in
substance abusers, due to the cross-sectional design of those studies, the temporal causal
relationship between neuropsychological function and substance abuse remain unclear
(Bechara & Damasio, 2002; Bechara, Dolan, Denburg et al., 2001; Grant, 2000; Rogers,
Everitt, Baldacchino et al., 1999). That is, those studies could not determine whether
abnormalities in their neural systems are the consequence of long-term drug use, or
whether these abnormalities reflect a developmental predisposition that lead to substance
abuse. The primary goal of this longitudinal study was to address this issue directly by
exploring whether neuropsychological functions could serve as markers predictive of
addictive behaviors in adolescents (i.e., among those who have never been chronically
exposed to substances). That is, does poor decision-making and/or working memory
prospectively predict progression on alcohol use trajectories including poorly control
alcohol use? Specifically, we tested the hypothesis that poor decision-making, indicative
of neurologically-based deficits, as measured by the Iowa Gambling Task (IGT) at
baseline (Time 1) would predict increased levels of drinking one year later (Time 2). By
contrast, we hypothesized that individuals with better decision-making capabilities would
be more resistant.

2
The heightened vulnerability to alcohol and other substance use in adolescents seems to
be related to their protracted maturational changes in top-down Prefrontal Cortex (PFC)
systems relative to subcortical systems (Galvan, Hare, Parra et al., 2006; Giedd, 2004;
Gogtay, Giedd, Lusk et al., 2004; Spear, 2000; Toga, Thompson & Sowell, 2006). The
PFC has been involved in many executive functions (EF) such as impulse control,
planned behavior, working memory and decision-making. Although all adolescents
might still be undergoing developmental changes, the fact remains that not every
adolescent becomes a drug addict. Therefore, we investigated the individual variability
among adolescents at similar ages. We attempted to identify those who might be at
higher risk for making bad decisions, which might translate into poorly controlled risky
behavior in their real-life situations and, for some, progression to hazardous levels of
drinking and drinking problems. Understanding the role of neuropsychological
functions in individual vulnerability is potentially important, because it may contribute
to the ability to identify both those adolescents who are at greatest risk and those
problems which may be best targeted for behavioral or preventive intervention.

Despite the range of EF processes identified, most research on alcohol use among
adolescents has examined the role of the ‘cold’ aspects of EF, which have been linked
to the dorsolateral sector of the prefrontal cortex (DLPC) (Finn, Mazas, Justus et al.,
2002; Hartley, Elsabagh & File, 2004; Sher, 2006; Thush & Wiers, 2007; Zeigler, Wang,
Yoast et al., 2005; Zetteler, Stollery, Weinstein et al., 2006). Little research has

3
addressed the effects of “hot” aspects of EF, which have been linked more to the orbital/
ventromedial sector of the prefrontal cortex (OFC/VMPC) (Johnson, Xiao, Palmer et al.,
2007; Overman, Frassrand, Ansel et al., 2004). It is likely, however, that the ‘cold’ and
‘hot’ EF processes contribute differently to addictive behaviors such as alcohol abuse
among adolescents during development. Studies have indicated that the maturation of
the OFC/VMPC, and especially the frontal pole (e.g., Brodmann’s Area 10) might be a
developmentally distinct process from the maturation of other regions of the frontal lobe,
and ‘hot’ EF functions might mature later than do ‘cold’ EF functions (Crone & van der
Molen, 2004; Hooper, Luciana, Conklin et al., 2004; Steinberg, 2005). This lag in the
development of ‘hot’ EF processes might explain why some adolescents take excessive
risks with potentially negative consequences despite the fact that they are often capable
of understanding those consequences (Cauffman & Steinberg, 2000). It is important,
therefore, to investigate ‘hot’ EF functions among adolescents to understand their
reward-seeking behaviors such as alcohol consumption.

Affective decision-making is one of the most important ‘hot’ EF processes (Kerr &
Zelazo, 2004). It is essential for adequate functioning and is likely to have an effect on a
number of behaviors in which positive and negative affective consequences must be
acted on adaptively. From among several useful measures that assess ‘hot’ EF (Bechara,
Damasio, Damasio et al., 1994; Elliott, Friston & Dolan, 2000; Ernst, Nelson, McClure
et al., 2004; Rogers, Everitt, Baldacchino et al., 1999), in the current study we

4
employed one of the most widely researched measures of affective decision making—
the Iowa Gambling Test (IGT: Bechara, Damasio, Damasio et al., 1994).. Compared to
other tasks, which assess brain functions related to the calculation of probability or
expected value, the IGT requires participants to learn from their past experience (such
as rewards and punishments encountered during the task) in order to infer the probable
outcomes of the choices they are currently making (Bechara, 2004). Such learning
processes are strongly influenced by affective and emotional systems. The decision-
making of neurologically developed and intact participants is guided by an emotional
signal that assigns negative value for the disadvantageous choices and positive value for
advantageous choices, thereby leading behavior towards long-term favorable options.
The IGT has been shown to engage the OFC/VMPC-related brain regions (Fukui, Murai,
Fukuyama et al., 2005; Northoff, Grimm, Boeker et al., 2006). The IGT or IGT
analogous tasks have been used successfully to study ‘hot’ EF functions among
adolescents (Crone & van der Molen, 2004; Hooper, Luciana, Conklin et al., 2004;
Overman, 2004).

Since ‘hot’ and ‘cold’ EF processes diverge in their maturational trends and may have
different implications for alcohol use behaviors among adolescents, cognitive
performance associated with ‘cold’ cognition is also important to consider. One of the
specific and most well-researched ‘cold’ EF processes is working memory capacity.
Working memory capacity has been conceptualized as the control of executive attention

5
such that individuals with good working memory capacity are able to keep information
in an active state especially in the presence of competing demands on attention (Kane &
Engle, 2002). In order to assess working memory capacity, we administrated a
computerized version of the Self-ordered Pointing Test (SOPT) task (Peterson, Pihl,
Higgins et al., 2002). This task has been found to be effective in diverse adolescent
populations (Chaytor & Schmitter-Edgecombe, 2004; Johnson, Xiao, Palmer et al.,
2007; Thush, Wiers, Ames et al 2007) and in other studies of working memory (Chey,
Lee, Kim et al., 2002; Pukrop, Matuschek, Ruhrmann et al., 2003; Ward, Shum,
McKinlay et al., 2005). This task tests the capacity for transient online storage (Perry,
Heaton, Potterat et al., 2001) and for active monitoring and retrieval of increasing
amounts of information held in working memory (Petrides, 1995). The SOPT has been
linked to neural activity within the Dorsolateral Prefrontal Cortex (DLPC) (Petrides,
Alivisatos, Meyer et al., 1993). In addition to the SOPT, we asked participants to
indicate their academic performance at school as a potential correlate, because prior
studies have shown that working memory is highly related to general cognitive
functions such as reading, mathematics and reasoning (Colom, Rebello, Palacios et al.,
2004; Engle, Cantor & Carullo, 1992; Jarrold & Towse, 2006).

In this study, we address the question of whether neuropsychological functions could
serve as markers to predict adolescents’ hazardous alcohol consumption prospectively.
Our previous cross-sectional results indicated that affective decision-making was

6
associated with binge-drinking behavior among Chinese 10th grade adolescents
(Johnson, Xiao, Palmer et al., 2007). However, as with any cross-sectional design, such
study cannot offer evidence directly supporting a causal link between adolescent
substance use behaviors and affective decision-making. These findings again raise the
question of whether the difference in affective decision-making between adolescent
binge-drinkers and never-drinkers reflects alcohol neurotoxicity or pre-existing
developmental differences between adolescent binge-drinkers and never-drinkers.
Therefore, for the study reported here, we followed the same adolescents and measured
their drinking behaviors again one year later. Our primary hypothesis was that affective
decision-making would predict adolescents’ alcohol use behaviors one year later. We
expected that adolescents with poor decision-making capabilities would be more likely
to increase their levels of binge drinking or at least maintain hazardous levels of use,
and that those with better decision making would maintain abstinence or decrease their
alcohol consumption over time. Although ‘cold’ EF processes have been implicated in
adolescents’ alcohol use behaviors, one previous study among adolescents did not find
working memory impairment in poly-substance use behaviors (Overman, Frassrand,
Ansel et al., 2004). Our previous results also revealed that working memory and school
academic performance were not associated with binge-drinking behavior among the
Chinese 10th grade adolescents (Johnson, Xiao, Palmer et al., 2007). Therefore, our
second hypothesis regarding ‘cold’ EF processes and future alcohol behaviors among
adolescents remains open. It is possible that ‘cold’ EF such as working memory might

7
or might not affect drinking behaviors among the current sample of adolescents one
year later. Finally, we expect that the relationship between affective decision-making
and future drinking behaviors would not be affected by adjusting for demographics
variables, previous alcohol use, working memory, and school academic performance.

8
Methods

Participants

Data collection for this study was supported by the Pacific Rim Transdisciplinary
Tobacco and Alcohol Use Research Center, which is investigating social, environmental,
and biological determinants of tobacco and alcohol use and abuse among youth in
China. All research protocols and instruments were approved by the USC and Chengdu,
China CDC Institutional Review Boards. With the assistance of the Municipal
Education Committee and the Chengdu Center for Disease Control and Prevention
(CCDCP), in Chengdu City, Sichuan Province, four schools were recruited for the study.
To ensure maximum variability across the student sample, two academic high schools,
one of high- and one of low/middle academic status, and two vocational schools, one of
middle- and one of low academic status, were selected. School administrators and
teachers from the selected schools agreed to participate in the research after receiving a
thorough explanation of the project from the CCDCP staff. One 10th grade class from
each of the four schools was randomly selected, and a total of 223 students were invited
to participate. Students voluntarily took part in the study and were told that they could
discontinue their participation at any time. Out of that total, sixteen participants in the
May 2006 (Time 1) and twenty-six in the one year follow-up (Time 2) were excluded
from the data analysis due to computer malfunctions or failure to complete the survey

9
or follow instructions on the SOPT. The analytic data set included 207 participants
(92.8% of total participants) at baseline (Time 1) and 181 participants (81.2% of total
participants) at one year follow-up (Time 2).

Measures

Both baseline (Time 1) and one year follow-up (Time 2) measures included two kinds
of computer-assisted neuropsychological assessments and a paper-and-pencil self-
report questionnaire. The instructions for the neuropsychological tasks and the
questionnaires were translated into Mandarin Chinese (the only language used in the
surveys) and back-translated prior to use.

Measures at Time 1

Neuropsychological measures

Iowa Gambling Task (IGT): As described in previous studies (Bechara, Damasio,
Damasio et al., 1994; Bechara, Damasio, Damasio et al., 1999), the IGT is the
computerized gambling task with an automated and computerized method for collecting
data. In this task, four decks of cards labeled A’, B’, C’ and D’ are displayed on the
computer screen. The backs of the cards resemble real decks of cards. The participant

10
starts the task with a sum of make-believe money of $2,000in his or her account,
represented by a green bar that changes in length as the participants ‘wins’ or ‘loses’
money during the task. The subject is required to select one card at a time from one of
the four decks. When the subject selects a card, a message is displayed on the screen
indicating the amount of money the subject has won or lost. The pre-programmed
schedules of gain and loss are controlled by the computer. The goal of this task is to
win as much money as possible. Turning each card can bring an immediate reward of
$100 in Decks A’ and B’ and $50 in Decks C’ and D’. As the game progresses, there
are also unpredictable losses among the card selection. Total losses amount to $1250 in
every 10 cards in Decks A’ and B’ compared to $250 in Decks C’ and D’. Decks A’
and B’ are equivalent in terms of overall net loss, and Decks C’ and D’ are equivalent
in terms of overall net gain over the course of the trials. The difference is that in Decks
A’ and C’, the punishment is more frequent but of smaller magnitude. Whereas in
Decks B’ and D’, the punishment is less frequent but of greater magnitude. Thus, Decks
A’ and B’ are disadvantageous because they yield high immediate gains but a greater
losses in the long run (i.e. net loss of $250 for every 10 cards), and Decks C’ and D’ are
advantageous in that they yield lower immediate gains but a smaller losses in the long
run (i.e. net gain of $250 for every 10 cards). After the original version of the IGT is
completed, its net score is obtained by subtracting the total number of selections from
the disadvantageous decks (A’+B’) from the total number selections from the
advantageous decks (C’+D’).

11
Self-ordered Pointing Test (SOPT): We used a computerized version of the SOPT
(Peterson, Pihl, Higgins et al., 2002), which was based upon a task originally developed
by Petrides and Milner (Petrides & Milner, 1982). The SOPT has both verbal and non-
verbal components with 3 trials of each. In the verbal component, subjects view
pictures of concrete, nameable objects (clock, book, bus, etc.); whereas in the non-
verbal component, subjects view abstract designs that are difficult to name or encode
verbally. In each trial, 12 pages are presented sequentially, with each page depicting the
same 12 pictures but in a different spatial arrangement on each page. Subjects are
instructed to point to a different picture in each presentation. To effectively select a
different picture each time, subjects must retain pictures in working memory. The total
number of correct selections of different pictures represents the working memory score.
There is a maximum possible score of 12 on each trial and a total of 72 for all 6 trials.
In our study, the internal consistency across the 6 trials was 0.86.

Questionnaire Measures

Drinking behaviors. Ever drinking was assessed using the following item: “During
your life, on how many days have you had at least one drink of alcohol?” The response
options ranged from “0 day” to “100 or more days”. Past 30-day drinking was assessed
using the following item: “During the past 30 days, on how many days did you have at
least one drink of alcohol?” The response options ranged from “0 day” to “All 30 days”.

12
Binge drinking was assessed using the following item: “During the past 30 days, on
how many days did you have 4 or more drinks of alcohol in a row, that is, within a
couple of hours? ” The response options ranged from “0 day” to “20 or more days”.
Although 5 or more drinks for males and 4 or more drinks for females is typically taken
as the definition of binge drinking in western populations, we opted to define binge
drinking as 4 or more drinks for both males and females in this study because of the
generally lower body mass of Chinese youth. These three questions were classified into
four drinking categories of participants: (a) never-drinkers, who were defined as those
who reported never having had one drink of alcohol in their life, (b) ever-drinkers, who
were defined as those who had had at least one drink of alcohol in their life but not in
the past 30 days, (c) past 30-day drinkers, who were defined as those who had had at
least one drink of alcohol in the past 30 days but did not consume 4 or more drinks of
alcohol in a row in the past 30 days, and (d) binge-drinkers, who were defined as those
who had had 4 or more drinks of alcohol in a row on at least on one occasion in the past
30 days.

Other drinking behaviors: General number of drinks was assessed using the
following item: “When you drink alcohol, how many drinks do you usually have?” The
six response options range from “I don’t drink alcohol” to “5 or more”. The response “I
don’t drink alcohol” was assigned a score of ‘0’. Drinking problems were assessed
using the following item: “Indicate if any of the following things may have happened to

13
you because of drinking alcohol within the past one year (mark all that apply).” The
participants responded to the following 12 situations drawn from the Rutgers Alcohol
Problem Index (RAPI; White & Labouvie, 1989): ‘I was not able to do my homework
or study for a test’; ’I got into fights with other people (friends, relatives, strangers)’ ; ’I
went to school drunk’ ; ’I caused shame or embarrassment to someone’ ; ‘I neglected
my responsibilities’ ; ‘I was told by a friend, neighbor or relative to stop or cut down
drinking’ ; ‘I felt that I needed more alcohol than I used to in order to get the same
effect’ ; ‘I tried to control my drinking (for example: tried to drink only at certain times
of the day or in certain places)’ ; ‘I missed a day (or part of a day) of school’ ; ‘I
suddenly found myself in a place that I could not remember getting to’ ; ‘I passed out or
fainted suddenly’ ; ‘I kept drinking when I promised myself not to’. Each response was
assigned a score of ‘1’ or ‘0’, which represented the corresponding response had been
marked by participants or not, respectively. The score for drinking problems was the
sum of the 12 items.

School academic performance: Students self-reported their academic performance in
school by answering the following question: “What is your usual academic
performance at your current school or the last school where you received grades?” The
five response options ranged from: ‘Mostly A’s, or 90 or more points, or Superior’ to
‘Mostly F’s, or Below 60 points, or Failing’. A higher score represented a higher
academic performance.

14
Measures at Time 2

Questionnaire measures:

Substance use behaviors: The same questions at baseline were used to ask drinking
behaviors. Drinking problems were assessed using the same items at baseline. However,
the responses extended to 23 situations from The Rutgers Alcohol Problem Index
(RAPI; White & Labouvie, 1989).

Procedure

In May 2006 (Time 1), trained data collectors from the CCDCP and the University of
Southern California provided written and verbal instructions to the students and
administered the computer-based assessments and questionnaires in temporary
computer labs set up at each school. Students completed the questionnaire and the
computer-based assessments (the IGT and SOPT) during a one hour period. Students
were provided with earphones to muffle any potentially distracting noises in the
environment. In May 2007 (Time 2), students completed the follow-up questionnaire.

15
Data Analysis

One-way ANOVA tests were used to test for differences in means of age and Chi-
square tests were used to test for differences in frequency distributions by gender and
school type among drinkers with different drinking trajectories. Paired t-tests were used
to analyze the difference of general number of drinks or drinking problems between
Time 1 and Time 2 among drinkers. For analyzing the profile of the IGT performance
among drinkers, we conducted between-within ANOVA tests with ‘Block’ as the
within-subject factor. The relationships between Time 1 drinkers who scored > 10 or ≤
10 on the IGT and Time 2 drinkers were analyzed using Chi-square tests separately for
different levels of Time 1 drinking. Differences of means on working memory and
school academic performance among drinkers with different drinking trajectories were
calculated using the One-way ANOVA test. Associations among drinking variables,
affective decision-making, working memory and school academic performance were
calculated as partial correlations (controlling for age, gender and school type). Since our
sample size (N=181) was relatively large, and since the residuals from the methods
satisfied the normality and homoscedasticity assumptions, the general number of drinks
or drinking problems were treated as continuous without any transformation. To reveal
potential predictors of general number of drinks or drinking problems at Time 2, linear
regression models were used with general number of drinks (Time 2) or drinking
problems (Time 2) as the dependent variable and affective decision-making as the

16
independent variable, conditioning on Time 1 drinking variables, demographic
characteristics, working memory, and academic performance.

17
Results

Demographic characteristics and drinking variables

In order to demonstrate the behavioral performance of the IGT in participants with
different drinking behavioral trajectories, we categorized the participants according to
their drinking behaviors in the two years that we tested as shown in Table 1. Those who
were never drinkers at both Time 1 and Time 2 were grouped as never to never. Those
who were never or ever drinkers at Time 1 and became ever drinkers at Time 2 were
grouped as never/ever to ever. Similarly, other groups included never/ever to past 30-
day, never/ever to binge, past 30-day to ever, past 30-day to past 30-day, past 30-day to
binge, binge to ever and binge to binge. Since there were only 2 persons who were
binge-drinkers Time 1 and became past 30-day drinkers at Time 2, we did not
categorize them into any group.

Table 1. Drinkers stratification
Drinkers % (N)
Time 1 (May 2006) Time 2 (May 2007)
Groups with Different
Drinking Trajectories
Never Drinkers Never Drinkers 19.4 (46) Never to Never

Never/Ever Drinkers Ever Drinkers 24.5 (58) Never/Ever to Ever
Past 30-day Drinkers 5.5 (13) Never/Ever to Past 30-day
Binge Drinkers 2.1 (5) Never/Ever to Binge

Past 30-day Drinkers Ever Drinkers 8.0 (19) Past 30-day to Ever
Past 30-day Drinkers 5.5 (13) Past 30-day to Past 30-day
Binge Drinkers 3.0 (7) Past 30-day to Binge

18

Table 1. Continued.
Drinkers % (N)
Time 1 (May 2006) Time 2 (May 2007)
Groups with Different
Drinking Trajectories
Binge Drinkers Ever Drinkers 3.8 (9) Binge to Ever
Past 30-day Drinkers 0.8 (2)
Binge Drinkers 3.8 (9) Binge to Binge
Total 100 (181)

Table 2 shows the demographic characteristics of those groups. Most participants were
16 years old at Time 1, except that the past 30-day to binge group were significantly
older than past 30-day to ever and past 30-day to past 30-day groups (F
(2,36)
=6.0,
P<0.05). Compared to females, there were marginally significantly more males in the
past 30-day to binge group and significantly more males in the binge to binge group
( χ
2
(2)
=5.3, P=0.07; χ
2
(1)
=5.5, P<0.05; respectively). Compared to the academic school,
there were marginally significantly more vocational school students in the binge to
binge group ( χ
2
(1)
=3.6, P=0.06).

19

Table 2. Demographic characteristics of groups with different drinking trajectories
Age Gender School type
Groups with Different
Drinking Trajectories

(Time 1)
Mean±
S.D.

Female
% (n)
Male
% (n)

Academic
% (n)
Vocational
% (n)

Never to Never 16.07±0.46 F
(3,118)
=0.5 42.4 (28) 32.1 (18) χ
2

(3)
=2.5 40.0 (28) 34.6 (18) χ
2

(3)
=3.9
Never/Ever to Ever 16.17±0.57 P=0.7 47.0 (31) 48.2 (27) P=0.5 50.0 (35) 44.2 (23) P=0.3
Never/Ever to Past 30-day 16.23±0.60 7.6 (5) 14.3 (8) 8.6 (6) 13.5 (7)
Never/Ever to Binge 16.00±0.00 3.0 (2) 5.4 (3) 1.4 (1) 7.7 (4)
Total 100 (66) 100 (56) 100 (70) 100 (52)

Past 30-day to Ever 16.53±0.69 F
(2,36)
=6.0 60 (9) 41.7 (10) χ
2
(2)
=5.3 55.0 (11) 42.1 (8) χ
2

(2)
=1.8

Past 30-day to Past 30-day 16.15±0.38 P<0.05 40 (6) 29.2 (7) P=0.07 35.0 (7) 31.6 (6) P=0.4
Past 30-day to Binge 17.14±0.69 0(0) 29.2 (7) 10.0 (2) 26.3 (5)
Total 100 (15) 100 (25) 100 (20) 100 (19)

Binge to Ever 16±0.5 F
(1,16)
=0.2 77.78 (7) 22.22 (2) χ
2

(1)
=5.5 75.0 (6) 30.0 (3) χ
2

(1)
=3.6
Binge to Binge 16.21±0.58 P=0.7 22.22 (2) 77.78 (7) P<0.05 25.0 (2) 70.0 (7) P=0.06
Total 100 (9) 100 (9) 100 (8) 100 (10)

19

20
Table 3 reports the general number of drinks and drinking problems at Time 1 and Time
2. Paired t-tests show that never/ever to past 30-day group significantly increased the
general number of drinks and marginally significantly increased the drinking problems
from Time 1 to Time 2 (t
(12)
=4.78, P<0.001; t
(12)
=1.95; P=0.08; respectively). Past 30-
day to Ever group marginally significantly reduced the general number of drinks from
Time 1 to Time 2 (t
(18)
=-1.71; P=0.10). Binge to Ever group significantly reduced the
general number of drinks from Time 1 to Time 2 (t
(8)
=-2.60; P<0.05).

21
Table 3. Two-year general number of drinks and drinking problems among groups with different drinking trajectories
Difference between Difference between
Groups with Different
Drinking Trajectories
General number of Drinks
(Mean±S.D.)
Time1 and Time 2
Drinking Problems
(Mean±S.D)
Time1 and Time 2
Time 1 Time 2 Time 1 Time 2
Never to Never 0. 0 0 0
Never/Ever to Ever 0.9±1.0 1.0±1.1 t
(57)
=0.78; P=0.44 0.3±0.8 0.5±1.2 t
(57)
=1.27; P=0.20
Never/Ever to Past 30-day 0.8±0.9 2.2±1.2 t
(12)
=4.78; P<0.001 0.2±0.4 1.2±2.2 t
(12)
=1.95; P=0.08
Never/Ever to Binge 1.0±0.7 2.0±0.7 t
(4)
=1.83; P=0.14 0.2±0.4 1.8±1.8 t
(4)
=1.73; P=0.16

Past 30-day to Ever 1.7±1.1 1.2±1.0 t
(18)
=-1.71; P=0.10 0.5±0.5 1.5±5.1 t
(18)
=0.66; P=0.52
Past 30-day to Past 30-day 2.0±0.9 2.2±1.2 t
(12)
=0.52; P=0.61 0.7±0.8 1.7±2.1 t
(12)
=1.33; P=0.21
Past 30-day to Binge 1.7±1.0 2.7±1.6 t
(6)
=1.45; P=0.20 1.1±1.1 2.3±2.9 t
(6)
=1.11; P=0.31

Binge to Ever 3.0±1.9 1.6±1.9 t
(8)
=-2.60; P<0.05 0.3±1.4 0.9±1.5 t
(8)
=1.21; P=0.26
Binge to Binge 3.1±1.4 2.9±1.3 t
(8)
=-0.48; P=0.65 1.4±1.2 6.3±11.1 t
(8)
=0.84; P=0.21

21

22
Behavioral performance on the IGT by drinkers with different drinking trajectories

We subdivided the 100 card selections into five blocks of 20 cards each in the IGT. For
each block, we counted the number of selections from Decks A’ and B’
(disadvantageous) and the number of selections from Decks C’ and D’ (advantageous),
and then derived a net score for that block ((C’+D)-(A’+B’)). A net score above zero
implied that the participants were selecting cards advantageously, and a net score below
zero implied disadvantageous selection. Figure1 presents the net scores as a function of
groups with different drinking trajectories and blocks. The left plot in Figure 1 shows
that, as the task progressed, never to never, never/ever to ever, never/ever to past 30-day
and never/ever to binge groups showed similar learning curves. They gradually
switched their preferences toward the advantageous decks (C’ and D’) and away from
the disadvantageous decks (A’ and B’), as reflected by increasingly positive net scores.
A 4 (group)×5 (IGT block) ANOVA did not reveal any significant difference in groups
(F
3, 118
=0.33; P=0.80) or interaction between groups and blocks (F
7.9, 308.9
=0.77;
P=0.69). A block effect (F
2. 6, 308.9
=4.67; P<0.001) was significant after the Greenhouse-
Geisser adjustment.

Previous results have revealed that past 30-day drinkers as a group performed normally
compared to never drinkers (Johnson, Xiao, Palmer et al., 2007). However, the past 30-
day drinkers with different drinking trajectories at Time 2 demonstrated different

23
learning curves (see Figure 1: middle plot). Those past 30-day drinkers who quit past
30-day drinking (past 30-day to ever) selected more and more cards from the
advantageous decks (C’ and D’) than the disadvantageous decks (A’ and B’) as the task
progressed. In contrast, those past 30-day drinkers who progressed to a higher level of
drinking (past 30-day to binge) failed to demonstrate a shift in behavior on the IGT;
they tended to select more cards from the disadvantageous decks. The past 30-day to
past 30-day group performed at a level between the past 30-day to ever and past 30-day
to binge groups. A 3 (group)×5 (IGT block) ANOVA revealed a significant main
effect of group (F
2, 36
=3.30; P<0.05). The block effect and interaction between groups
and blocks were not significant (F
2. 3, 81.98
=2.08, P=0.13; F
4.6, 81.98
=1.23, P=0.31;
respectively). Post hoc Tukey tests confirmed that past 30-day to binge group had
significantly lower scores than the past 30-day to ever group (P<0.05), and marginally
significantly lower scores than the past 30-day to past 30-day group (P=0.07). These
results demonstrate that those adolescents who progressed to heavier drinking behavior
(past 30-day to binge) performed worse on the IGT than the adolescents who quit past
30-day drinking (past 30-day to ever) or stayed their drinking behaviors at the similar
level (past 30-day to past 30-day).

Although binge-drinkers at Time 1 performed worse on the IGT compared to never
drinkers (Johnson, Xiao, Palmer et al., 2007), those who quit binge drinking (binge to
ever) at Time 2 revealed a different IGT learning curve from those who continued binge

24
drinking (binge to binge). As shown in the right-hand plot of Figure 1, the binge to ever
and binge to binge groups made similar choices in the first 2 blocks. After that, the
binge to ever group selected more cards from the good decks (decks with more
favorable payoffs). The binge to binge group, however, selected more cards from the
bad decks. A 2 (group)×5 (IGT block) ANOVA revealed a significant interaction
between groups and blocks (F
4, 64
=5.09; P<0.001). The block effect and group effect
were not significant (F
4, 64
=1.58, P=0.19; F
1, 16
=0.95, P=0.34; respectively). These
results demonstrate that those adolescents who continued binge-drinking (binge to binge)
performed worse than those adolescents who quit binge-drinking (binge to ever), as
reflected by deficits in the IGT learning curve.

25

Figure 1. The original version of the IGT net scores ((C’+D’)-(A’+B’)) by drinkers with different
drinking trajectories across five blocks of 20 cards expressed as mean+S.E. Positive net scores reflect
advantageous (non-impaired performance) while negative net scores reflect disadvantageous (impaired)
performance.

Bechara and Damasio (2002) found that 37% of normal adult controls performed
disadvantageously on the IGT, and that their performance was within the range of
patients with lesions in the VMPC (i.e. an overall net score of no more than 10).
Therefore, we used an IGT net score 10 as a cut-off point. Based on this criterion, as
shown in Table 4, 43.2% of never/ever drinkers who performed better than 10 on the
IGT at Time 1 were drinkers (ever, past 30-day or binge) at Time 2. However, a
significantly larger proportion (70.6%) of never/ever drinkers who performed poorly
(less than 10) on the IGT at Time 1 were drinkers (ever, past 30-day or binge) at Time 2
( χ
2
(3
)=8.24, P<0.05). A significantly higher proportion (81.8%) of past 30-day drinkers

26
who performed better on the IGT at Time 1quit past 30-day drinking at Time 2, whereas
only 35.7% of the past 30-day drinkers who performed poorly on the IGT at Time 1 quit
past 30-day drinking at Time 2 ( χ
2
(2)
=7.25, P<0.03). Similarly, a higher proportion
(75.0%) of binge drinkers who performed better at Time 1 quit binge-drinking at Time
2, whereas only 37.5% of binge-drinkers who performed poorly at Time 1 quit drinking
at Time 2. However, these differences for binge drinkers were not significant probably
due to the small sample size in this group ( χ
2
(2)
=1.94, P=0.37). These results
demonstrate that, regardless of their drinking behaviors at Time 1, a large proportion of
those adolescents who performed better on the IGT remained abstinent or reduced their
drinking level at Time 2. A large proportion of those adolescents who performed poorly
on the IGT, however, kept the same level or progressed to a higher level of drinking at
Time 2.

Table 4. Relationship between Time 1 drinkers who scored > 10 or ≤ 10 on the IGT and Time2 drinkers
Time 1 Time 2
Drinkers

IGT Net score

Never
% (n)
Ever
% (n)
Past 30-day
% (n)
Binge
% (n)

Never/Ever >10 56.8(21) 32.4 (12) 8.1 (3) 2.7 (1)
χ
2
(3)
=8.24
a
P<0.05
≤10 29.4(25) 51.4(46) 11.8(10) 4.7 (4)

Past 30-day >10 81.8 (9) 18.2 (2)
χ
2
(2)
=7.25
b
P<0.05
≤ 10 35.7 (10) 39.3 (11) 25.0 (7)

Binge >10 75.0 (3) 25.0 (1)
χ
2
(2)
=1.94
c
P=0.37
≤ 10 37.5 (6) 12.5 (2) 50.0 (8)
Note:
a
At Time1 Never/Ever drinkers, IGT Net score>10 Vs. IGT Net score ≤ 10
b
At Time1 Past 30-day drinkers, IGT Net score>10 Vs. IGT Net score ≤ 10

c
At Time1 Binge-drinkers, IGT Net score>10 Vs. IGT Net score ≤ 10

27
Working memory and school academic performance

Table 5 shows the mean scores of working memory and school academic performance
by groups with different drinking trajectories. One-way ANOVA tests revealed no
differences in working memory capacity or school academic performance among the
groups of drinkers (P>0.1).

Table 5. Working memory capacity and academic performance scores among groups with
different drinking trajectories
Working Memory

School Academic
Performance
Groups with Different Drinking
Trajectories
Mean±S.D. Mean±S.D.
Never to Never 61.1±7.9 3.6±0.9
Never/Ever to Ever 61.8±7.1 3.5±1.1
Never/Ever to Past 30-day 63.4±5.2 3.6±1.0
Never/Ever to Binge 62.2±3.1 2.8±1.3

Past 30-day to Ever 62.6±7.7 3.9±1.1
Past 30-day to Past 30-day 61.67±5.5 3.4±1.1
Past 30-day to Binge 59.7±4.9 3.7±1.1

Binge to Ever 62.6±5.4 3.0±1.3
Binge to Binge 60.3±6.2 3.4±0.9

Partial correlations among drinking variables, affective decision-making, working
memory and school academic performance

Table 6 reports partial correlations among two-year drinking variables, affective
decision-making, working memory and school academic performance (adjusting for age,

28
gender and school type). As expected, the general number of drinks (Time 1 and Time 2)
and drinking problems (Time 1 and Time 2) were significantly correlated with one and
another (P<0.0001). Working memory was significantly correlated with school
academic performance (r=0.19, P<0.05). However, neither working memory nor school
academic performance was significantly correlated with affective decision-making or
any of the drinking variables (r ≤0.1). Affective decision-making was significantly
negatively correlated with general number of drinks (Time 1 and Time 2) and drinking
problems (Time 2) (P<0.05).

Table 6. Partial correlations among drinking variables at Time 1 and Time 2, affective decision-making,
working memory, school academic performance controlling for age, gender and school type (N=181)
Measures 1 2 3 4 5 6 7
1. Affective Decision-making － 0.01 0.04 -0.15
*
-0.21
**
-0.03 -0.22
**
2. Working Memory － 0.19
**
-0.01 0.08 0.04 -0.04
3. School Academic Performance － -0.03 0.01 0.10 -0.04
4. General number of Drinks
(Time 1)
－ 0.46
***
0.37
***
0.24
***
5. General number of Drinks
(Time 2)
－ 0.40
***
0.31
***
6. Drinking Problems (Time 1) － 0.20
***
7. Drinking Problems (Time 2) －
Note: Results of two-tailed significance tests are denoted by superscripts. ***P<0.001, **P<0.01, * P<0.05,
IGT=Iowa Gambling Task.

Variables predicting general number of drinks/drinking problems at Time 2

Linear regressions were performed to predict general number of drinks at Time 2.
Results are presented in Table 7.The dependent variable was general number of drinks
at Time 2, and the independent variables included the general number of drinks (Time
1), affective decision-making, demographic variables (age, gender, school type),

29
working memory and academic performance. As expected, general number of drinks at
Time 1 produced a significant increase in the predictive power of the model (P <0.05,
B=0.46, 95%CI=0.32, 0.60). Working memory and school academic performance were
not significant predictors. After adjusting for general number of drinks (Time 1),
demographic variables, working memory and academic performance, the IGT score was
a significant predictor of general number of drinks at Time 2 (P <0.05, B=-0.01,
95%CI=-0.02, -0.01). Better IGT performance predicted fewer generic drinks at Time 2.

Table 7. Summary of linear regression analysis for variables predicting general number of
drinks at Time 2
Model 1 B S.E. Sig. 95% CI for B
Lower Upper
Age 0.04 0.16 0.81 -0.28 0.37
Gender (0=female, 1=male) 0.14 0.19 0.46 -0.23 0.52
School type (0=academic, 1=vocational) 0.45 0.24 0.06 -0.01 0.92
General number of drinks (Time 1) 0.46 0.07 0.00 0.32 0.60
Affective Decision-making -0.01 0.01 0.04 -0.02 -0.01
Working Memory 0.02 0.02 0.23 -0.02 0.05
School Academic Performance 0.01 0.11 0.93 -0.20 0.22
Note:

B=regression unstandardized beta weights; S.E.=Standard Error; CI=confidence
interval; In bold are the P-values statistically significant at the 5% or marginally
significantly at 10% level.

Linear regressions were performed to predict drinking problems at Time 2. Results are
presented in Table 8. The dependent variable was drinking problems at Time 2, and the
independent variables included drinking problems (Time 1), affective decision-making,
demographic variables (age, gender, school type), working memory and academic
performance. Results are presented in Table 8. As expected, drinking problems at Time
1 significantly predicted drinking problems at Time 2 (P <0.05, B=1.06, 95%CI=0.30,

30
1.83). Age, gender, school type, working memory and school academic performance did
not significantly predict drinking problems at Time 2. After adjusting for demographic
variables, working memory and academic performance, the IGT score was a significant
predictor of drinking problems at Time 2 (P <0.05, B=-0.04, 95%CI=-0.07, -0.01).
Better IGT performance predicted fewer drinking problems at Time 2.

Table 8. Summary of linear regression analysis for variables predicting drinking problems
at Time 2
Model 2 B S.E. P-value. 95% CI for B
Lower Upper
Age 0.14 0.50 0.78 -0.85 1.13
Gender (0=female, 1=male) 0.70 0.58 0.23 -0.44 1.84
School type (0=academic, 1=vocational) -0.30 0.70 0.67 -1.67 1.08
Drinking problems (Time 1) 1.06 0.39 0.01 0.30 1.83
Affective Decision-making -0.04 0.01 0.01 -0.07 -0.01
Working Memory -0.02 0.05 0.68 -0.11 0.07
School Academic Performance -0.09 0.31 0.77 -0.71 0.53
Note:

B=regression unstandardized beta weights; S.E.=Standard Error; CI=confidence
interval; In bold are the P-values statistically significant at the 5% or marginally
significantly at 10% level.

31
Discussion

To our knowledge, this is the first longitudinal study to investigate the relative role of
two specific neuropsychological factors, affective decision-making and working
memory capacity, in the development of alcohol use behaviors among adolescents. First,
the key findings of this study support our primary hypothesis that those adolescents
with poor affective decision-making capacities are more likely to progress to higher
levels of drinking, while those with better affective decision-making capacities are less
likely to do so. Moreover, regardless of their drinking behaviors at Time 1, a large
proportion of those adolescents who performed better on the IGT remained abstinent or
reduced their drinking level at Time 2. On the other hand, a large proportion of those
adolescents who performed poorly on the IGT remained at the same level or progressed
to higher levels of drinking at Time 2.

The second key finding was that adolescents with different drinking trajectories did not
show a significant difference in their performance on the working memory task or in
their school academic performance. These results show that the ‘cold’ dorsolaterally
mediated EF measured as working memory capacity did not affect these adolescents’
drinking behaviors in this one-year-long prospective study. Finally, after controlling for
the demographic variables, drinking behaviors at Time 1, working memory, and school
academic performance, affective decision-making significantly predicted the general

32
number of drinks (Time 2) and drinking problems (Time 2). These findings support our
third hypothesis that affective decision-making would be predictive of future drinking
behaviors even after adjusting for previous drinking variables, demographic variables,
working memory, and school academic performance. It also indicates that an affective
decision-making impairment can be separated from general cognitive intelligence
impairment, consistent with an OFC/VMPC dysfunction but not a DLPC dysfunction.

Although few studies have focused on affective decision-making and alcohol use
among adolescents, the notion that poor decision-making might render some
adolescents more vulnerable to heavy drinking is consistent with two previous cross-
sectional studies. Overman (2004) found that poly-substance use is negatively
correlated with performance on the IGT analogous task among adolescents (Overman,
Frassrand, Ansel et al., 2004). Recently, Goudriaan (2007) examined the relations
between decision-making measured by the IGT and longitudinal binge-drinking patters
in young adults. They found that stable high binge-drinkers performed poorly on the
IGT compared to the consistent low-binge-drinkers (Groudriaan, Brekin, Sher, 2007).
However, in Goudriaan’s study, the IGT was only administrated after the longitudinal
heavy-drinking patterns had occurred. Therefore, these studies could not provide the
casual information between binge-drinking and affective decision-making. Poor
decision-making could either be the consequences of prolonged binge-drinking or could
be present before the onset of binge-drinking. By using a prospective design in our

33
study, we found that affective decision-making at Time 1 significantly predicted the
development of alcohol behaviors from Time 1 to Time 2, and a large proportion of
those adolescents who performed better on the IGT remained abstinent or reduced their
drinking levels while a large proportion who performed poorly on the IGT stayed the
same level or progressed to higher levels of drinking at Time 2. These findings in our
study thus directly support the hypothesis that a poor capacity for affective decision-
making might be a causal factor in the progression toward habitual and abusive levels of
alcohol use.

Results from this study also suggest that better affective decision-making capacities
might protect adolescents from habitual drinking. Better affective decision-making in
this study predicted fewer drinks and drinking problems at the one year follow-up. A
previous study indicated that the neural systems subserving affective decision-making
and the neural systems that support emotional intelligence (EI) overlapped (Bar-On,
Tranel, Denburg et al., 2003). In addition, a recent functional magnetic resonance
imaging (fMRI) study confirmed that adolescents with high EI responded to
emotionally provocative stimuli with less but more focal brain activation in the brain
regions that are critical for affective decision-making than did their peers with poorly-
developed EI (Killgore & Yurgelun-Todd, 2007). Another recent fMRI study reported
that individuals with high resistance to peer influence showed highly-coordinated brain
activity in the neural systems underlying the perception of action and affective decision-

34
making compared to early adolescents with low resistance (Grosbras, Jansen, Leonard
et al., 2007). Taken together, these studies suggest that adolescents with well-developed
affective decision-making may possess more efficient and effective functioning neural
circuitry than do adolescents with poorly-developed affective decision-making. They
might drink recreationally, but their better affective decision-making capacity would
protect them from habitual or abusive drinking.

Although immature prefrontal cortex functions have been implicated in many models to
explain the heightened vulnerability to substance use in adolescents compared to adults
(Crews, He & Hodge, 2007; Spear, 2000), our results demonstrated that not all EF of
the prefrontal cortex contribute equally to substance use behaviors. Consistent with our
previous study, which showed that those who did not drink and those who did drink (at
all levels) at Time 1 performed equally well on the working memory task and reported
similar academic performance (Johnson, Xiao, Palmer et al., 2007), this longitudinal
study also demonstrated that adolescents with different drinking trajectories from Time
1 to Time 2 performed equally well on the working memory task and did not show a
significant difference in their academic performance. After controlling for the
demographic variables, working memory, school academic performance and drinking
behaviors at Time 1, affective decision-making significantly predicted the number of
drinks and drinking problems at Time 2. These results suggest that affective decision-
making impairment can be independent from the type of general cognitive intelligence

35
impairment most likely revealed by tests of working memory capacity (i.e., fluid
intelligence).

In this study, the different contributions of affective decision-making and working
memory to adolescents’ drinking behaviors can be explained by two lines of research.
First, researchers have found an asymmetrical relationship between affective decision-
making and working memory (Bechara & Martin, 2004). Working memory is not
influenced by deficits in affective decision making, whereas affective decision-making
is adversely affected by deficits in working memory. This finding is consistent with
neurological evidence that the OFC/VMPC plays a critical role in coupling of the meso-
limbic reward system, which is associated with ‘hot’ affective processing of emotional
experiences related to reward and punishment, and the DLPC, which is associated with
‘cold’ cognitive systems (Anderson, Barrash, Bechara et al., 2006; Beer, John, Scabini
et al., 2006; Fellows & Farah, 2005; O'Doherty, Kringelbach, Rolls et al., 2001; Oya,
Adolphs, Kawasaki et al., 2005). It is possible, therefore, that adolescents may have
deficits in affective decision-making without deficits in working memory, as found in
the current study.

Second, developmental and behavioral studies suggest that ‘cold’ EF might mature
earlier than ‘hot’ EF. One study reported a significant and steady improvement on the
IGT analogous task in participants ages 11 to 23, while working memory was not

36
correlated with the age of the participant (Overman, Frassrand, Ansel et al., 2004).
Although some studies have cautioned that the content and complexity of the tasks used
in developmental research might influence the results (Booth, Burman, Meyer et al.,
2003; Durston & Casey, 2006; Jacobs J.E. & Klaczynski P.A., Byrnes J.P., 2005;
Munoz, Broughton, Goldring et al., 1998), the SOPT used in this study appeared to be
of the appropriate difficulty for the target age group because no ceiling or floor effects
were observed. Moreover, one study has demonstrated that, compared to the brain
processes assessed by other working memory tasks, the process taxed by an SOPT-
similar task develops later in adolescence (Luciana, Conklin, Hooper et al., 2005).
Other evidence also shows dissociation between laboratory-based and real-life measures
of intellectual functioning (Steinberg, 2004). For example, while similar cognitive
abilities have been revealed among adolescents compared to adults when measured in
‘cold’ laboratory or non-stressful conditions, they are more likely to be impaired in
stressful situations (Leslie, Loughlin, Wang et al., 2004) or in emotionally-aroused
conditions (Arnsten & Shansky, 2004). In addition, adolescents make risky choices
even when they are consciously aware of the dangers involved in certain activities
(Cauffman & Steinberg, 2000). Taken as a whole, the results from this study and
previous studies support one hypothesis which proposes that adolescents 16 and older
share the same logical competencies with those of adults, but those adolescents might
still have age variance in their affective decision-making ability due to different age-
related developmental, social and emotional factors (Steinberg, 2005).

37

In real life, decisions are often made under uncertainty. The decision maker must learn
to infer future outcome probabilities from past experience (Busemeyer & Townsend,
1993). Both experienced and anticipated emotions, therefore, influence the decision-
making process (Damasio, 1994; Damasio, 1996). In addition, peer influence is a strong
predictor of adolescent substance use across different cultures (Unger, Yan, Shakib et
al., 2002). One study found that adolescents are more likely to engage in risky
behaviors in the presence of friends than they are when alone (Gardner & Steinberg
2005). One explanation might be that adolescents in such situations are unlikely to
evaluate logically the pros and cons of their behaviors due to the complex feelings
experienced at the moment of decision-making, such as the fear of being teased or
rejected by peers, the excitement of taking risks, and the desire to impress friends (Dahl,
2003). Therefore, in spite of apparently normal ‘cold’ EF, decrements in ‘hot’ EF in
adolescents might lead to risky behaviors such as substance use as shown in this study.

The prevalence of drinking behaviors in this Chinese sample is similar to that of one
large scale study in China (Xing, Ji & Zhang, 2006). In our sample, only approximately
4 % of the participants at Time 1 and 6 % of the participants at Time 2 reported they
had had drunk more than 10 days in the past month. Even in the binge to binge group,
the average number of drinks is about 3, and the average number of drinking problems
is about 6 (from a list of 23). Therefore, our study captured an early stage in progression

38
across abuse trajectories. Other studies have revealed working memory impairment and
memory-related brain structure alteration among adolescents with alcohol use disorders
(Caldwell, Schweinsburg, Nagel et al., 2005; De Bellis, Clark, Beers et al., 2000; Sher,
2006). Whereas adolescent alcohol abuse might lead to some neural function alterations
(such as DLPC-hippocampus circuitry related-memory functions), the current study
shows that other neural characteristics of high-risk youth (such as OFC/VMPC-
amygdala circuitry-related emotions and decision-making) may predate alcohol use and
may reflect risk factors for, rather than the consequence of, adolescent alcohol abuse.
This study thus supports one hypothesis that before one gets to the stage where a certain
pattern of substance use can cause changes to the brain, there is a decision by the person
to use, or not to use the substance. This mechanism protects most individuals who have
used the substance from losing control or succumbing to addiction. Individuals whose
affective decision-making mechanism is relatively weak are more vulnerable to
addiction (Bechara, 2005).

Although the current study extended prior research in many important ways, it had
several limitations. First, in our study, the sample sizes of adolescents with substance
use are relatively small compared to the Western adolescents in the literature. However,
the prevalence of substance use in our sample was very similar to that of other large-
scale population studies of students in China (Grenard, Guo, Jasuja et al., 2006;
Johnson, Palmer, Chou et al., 2006). The statistical significance on the decision task

39
indicates that the effects are robust, and population representativeness of the sample,
bolstered by inclusion of students from both major types of Chinese high schools,
suggests that the findings are widely generalizable to Chinese youth. However, future
studies are needed to establish replicability to other cultural/environmental settings.

Second, because self-report questionnaires were used to examine the substance use
behaviors among adolescents, and the adolescents could have answered the questions in
a socially desirable way, levels of drinking may be underestimated in our study.
However, we hope that our emphasis on confidentiality (our asking students not to
provide their names on questionnaires, our promise of not sharing their responses with
their parents, teachers, and peer students), along with our request for honest answers
reduced such bias to a minimal level.

Third, because the longitudinal design of the present research covered one year, we do
not know if the heavy substance users in our study would eventually become addicts.
However, previous research shows substance use behaviors among adolescents
significantly predict their subsequent substance use in adulthood. For example, those
who have used substances such as alcohol are far more likely to progress to other illegal
substances such as marijuana, cocaine, heroin, crack in their later life compared to non-
substance users (Johnson, Boles & Kleber, 2000).

40
Despite these limitations, the current study addressed some of the important omissions
of earlier works. To our knowledge, this is the first longitudinal study which has
demonstrated that affective neuropsychological functions could serve as markers to
predict alcohol use among adolescents. These results suggest that good affective
decision-making capacity protects adolescents from substance use, while diminished-
affective decision-making capacity may be a causal factor in the progression toward
addiction. Further research, using imaging methods, should address differences
between brain circuitry underlying affective decision-making in those adolescents who
progress toward greater substance abuse and those who do not.. It would also be
interesting to investigate whether these differences result from genetic, environmental,
or the interaction of genetic and environmental sources. Furthermore, it remains to be
determined whether interventions that enhance affective decision-making among high-
risk adolescents might improve their real-life decision-making, and thus avert potential
alcohol and substance abuse (Bechara, Damasio & Bar-On, 2006). Transdisciplinary
research on decision-making, which integrates neurocognitive and intervention sciences,
holds promise for improving adolescents’ well-being and reducing the risks for
substance use and abuse.

41
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Abstract (if available)

Abstract To address whether affective decision-making could serve as a prospective neuropsychological marker to predict alcohol use behaviors among adolescents, we conducted a longitudinal study of 181 Chinese adolescents. In May 2006, we tested these 10th grade adolescents ' affective decision-making ability using the Iowa Gambling Task (IGT) and working memory capacity. Paper and pencil questionnaires were used to assess drinking behaviors. The same questionnaires were completed again one year later. Results indicated that a large proportion of adolescents who performed better on the IGT at baseline remained abstinent or reduced their drinking levels one year later. In comparison, a large proportion of those who performed poorly on the IGT at baseline stayed at the same level or progressed to a higher level of drinking one year later. Affective decision-making at baseline also significantly predicted general number of drinks and drinking problems one year later. These findings suggest that those adolescents with poor decision-making capabilities are more vulnerable to future substance use, and those with better decision-making capabilities are more resistant.

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Asset Metadata

Creator Xiao, Lin (author)

Core Title Affective decision-making predictive of Chinese adolescents drinking behaviors: a longitudinal study

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Publication Date 07/29/2008

Defense Date 06/11/2008

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Adolescent,affective decision-making,Drinking,OAI-PMH Harvest,substance use,working memory

Language English

Advisor Johnson, Carl Anderson (committee chair), Chou, Chih-Ping (committee member), Stacy, W. Alan (committee member)

Creator Email linxiao@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m1437

Unique identifier UC1447954

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Legacy Identifier etd-Xiao-20080729.pdf

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Document Type Thesis

Rights Xiao, Lin

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu