2019 Evaluating Disinhibition and Decision-making Using a ...

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2019

Evaluating Disinhibition andDecision-making Using a BART TaskRachel Bowns

Running head: DISINHIBITION AND DECISION-MAKING 1

THE FLORIDA STATE UNIVERSITY

COLLEGE OF ARTS AND SCIENCES

EVALUATING DISINHIBTION AND DECISION-MAKING USING A BART TASK

By

RACHEL BOWNS

A Thesis submitted to the Department of

Psychology

in partial fulfillment of the requirements for graduation with

Honors in the Major

Degree Awarded:

Spring, 2020

DISINHIBITION AND DECISION-MAKING 2

The members of the Defense Committee approve the thesis of Rachel Bowns defended on

November 6, 2019.

Dr. Christopher Patrick

Thesis Director

Dr. Jillian Turanovic

Outside Committee Member

Dr. Aaron Wilber

Committee Member

Signatures are on file with the Honors Program office.


Abstract

The present study assessed decision-making in a Balloon Analogue Risk Task as related

to Triarchic Psychopathy Measure disinhibition. This study used two versions of the BART task,

an automatic and a manual task. Paired samples t-tests were used to evaluate differences in

participant responses between-tasks, as well as within-tasks after win trials and after loss trials.

Repeated measures ANOVAs were used to assess effects of tasks and previous trial outcome on

number of pumps and inter-pump-interval (IPI) as well as interactions with TriPM disinhibition.

Further exploratory analyses were conducted to assess for similar interactions with TriPM

boldness. TriPM disinhibition was only found to impact the number of pumps or IPI in the

manual task. Both TriPM disinhibition and TriPM boldness were found to have unique impacts

on the number of pumps after win and after loss in the manual task. These results suggest that

TriPM disinhibition may impact behavior on tasks which require slowed, multi-step decision

making.

Keywords: Disinhibition, Balloon Analogue Risk Task, BART, Decision-making


Evaluating Disinhibition and Decision-making Using a BART Task

Trait Disinhibition

Disinhibition has been associated with risky decision-making; however, the mechanisms

underlying this dysfunction remain unclear. The types of risky decisions associated with

disinhibition are impulsive in nature as deficits in planning are a facet of disinhibition (Patrick,

Fowels, & Kreuger, 2009). Many behaviors associated with disinhibition are dangerous and

could have detrimental effects on public health and safety such as substance use, disregard for

safety measures such as seatbelts or helmets, risky sexual behavior, and crime and violence.

This trait has also been correlated with externalizing psychopathologies such as conduct

disorder, substance abuse, and antisocial personality (Venables, Foell, Yancy, Kane, Engle, &

Patrick, 2018) which each have additional public health and safety implications. Due to these

dangers, it is imperative researchers evaluate the link between disinhibition and risky decision-

making in order to influence these decisions before they are made. Disinhibition has many

indicators such as delay discounting, delaying gratification, (Ashenhurst, Bujarski, Jentsch, &

Ray, 2014; Reynolds, Patak & Penfold, 2008), and behavioral inhibitions, stopping a behavior

once it has started (Reynolds, Patak & Penfold, 2008). To avoid confusion regarding the

different conceptualizations of impulsivity, disinhibition specifically is defined as a general

tendency toward impulsivity, poor planning, and lack of restraint (Patrick, Fowels, & Kreuger

2009) and a general proneness to externalizing (Venables et al., 2018) in the current study. Being

that disinhibition has been correlated with risky decision-making, it is important that this

decision-making process is examined.


Mechanisms/Models of Decision-Making

Decision-making is a process in which solutions to a current problem are considered and

rejected until a preferred option is chosen by using available information (Wang, Wang, Patel, &

Patel, 2004; Wang & Ruhe, 2007; Wilson & Keil, 2001). It is proposed to be made of three basic

components: the givens (the available information), goals (the desired outcomes), and operations

(the potential solutions to attain the desired outcomes; Ormrod, 1999; Polya, 1954; Wang, &

Chiew, 2010). There are two “systems” that have been proposed as guiding decision making: a

“hot” system, that is quick and through a reliance on heuristic information and habits, and a

“cold” system that is slower and more calculated. Cold decision-making refers to using available

information to make a calculated choice; hot decision-making is more emotionally and

instinctively driven (Buelow & Blaine, 2015; Damasio, 1994; Seguin, Arseneault, & Tremblay,

2007; Shafir, Simonson, & Tversky, 1993). Alternatively, the different methods of decision-

making have been termed system 1 and system 2, system 1 referring to hot decision-making, and

system 2 referring to cold decision-making (Evans, 2008; Kahneman, 2003; Mukherjee, 2010;

Pleskac, & Wershable, 2014; Reyna, 2004; Sloman, 1996; Stanovich & West, 2000). The

Balloon Analogue Risk Task (BART; Lejuez et al. 2002) is one proposed way of measuring the

above-outlined different systems in decision-making (Buelow & Blaine, 2015).

The BART is a computer-based task designed to measure risk-taking. The task features a

cartoon balloon that the participant is instructed to “blow up” by determining how many pumps

of air should be added to the balloon. Each balloon has the ability to “pop” at any time. For each

pump of air that the balloon does not pop, the participant receives money, but if the balloon pops

on a particular trial, all money for that trial is lost. The BART has been shown to correlate with

real-world risk-taking behaviors such as smoking (Lejuez, Aklin, Jones, Strong, Richards,


Kahler, & Read, 2003), unprotected sex (Lejuez, Simmons, Aklin, Daughters, & Dvir, 2004),

illicit drug use, alcohol consumption, stealing, gambling, fighting, and refraining from seatbelt or

helmet use (Aklin, Lejuez, Zvolensky, Kahler, & Gwadz, 2005; Lejuez, Aklin, Zvolensky,

Pedulla, 2003; Pleskac, 2008). The task comes in several forms of varying lengths and response

methods. The current study will utilize two different versions of the BART. One version requires

the participant to type a number into a box on the screen to decide how many pumps to add to the

balloon (henceforth automatic version), the other version requires the participant to click a button

for each pump they want to add to the balloon (henceforth manual version).

The task is generally scored using a mean number of pumps across all trials or trials that

the balloon did not burst as a measure of risk-taking or risky decision-making (Lejuez et al.

2002; Schmitz, Manske, Preckel, & Wilhelm, 2016); however, there are other proposed methods

of scoring and evaluating participants on the BART. Schmitz, et al. (2016) describe several

alternative scoring methods. An alternative measure of risk-taking is the number of balloons

burst as this would be considered the consequence of a risk. In order to measure success in the

participant’s objective, Schmitz, et al. (2016) suggests using the total money earned as a scoring

method, higher gains would indicate making a more optimal decision. On a manual version,

researchers may evaluate the number of repetitive button-presses as they are recorded and the

balloon continues to fill with air until every press is accounted for, having a high number of

repetitive presses may indicated urgency. The mean response time (RT) may indicate the

impulsivity or instinctual nature of the decision on a text-entry version of the task. While RT

may be confounded by individual differences in cognitive processing speed for clinical

evaluations (Schmitz, et al. 2016), mean RT for multiple participants may carry implications for

between-group differences in an experimental setting. RTs can be measured either between


button presses, or when the number of pumps is saved for a trial. Change in post-loss pumps or

bursts, or between trial effects, may carry implications for level of caution, impulsivity and risk-

taking, and integration of available information (Schmitz, et al. 2016).

Current Study

The current study aims to evaluate mechanisms of decision-making associated with

disinhibition. Specifically, this proposal seeks to differentiate aspects of hot and cold decision

making through examination of the BART. Mean inter-pump-interval (IPI) vs post-loss IPI,

response differences after loss, and response differences in automatic and manual version will be

evaluated. It is hypothesized that higher disinhibition will be associated with more risky

decision-making on the BART. Higher disinhibition will be associated with quicker responses on

the BART which will implicate that the decision-making process in more disinhibited individuals

finish searching the problem space sooner. Higher disinhibition individuals will be less affected

by prior trial failures. More disinhibited individuals will have a larger difference in BART

responses using a manual task than a automatic BART task.

Method

Participants

Data was collected from 66 participants 18 – 24 years of age, mean age = 19.5, SD = 1.46

years. 65.6% of participants were female, 24.4% male.

Measures

The BART is a computer-based task designed to measure risk taking. Participants are

shown a balloon on a screen with the objective of pumping the balloon with air without letting

the balloon explode. For each pump the balloon does not explode, the participant wins money,

but if the balloon pops, the participant does not win any money for that trial. Participants are


instructed that 64 is the optimal number of pumps to use to prevent the balloon from popping as

frequently as possible in order to curb learning effects. The BART was administered in two

versions. One version of the task, an automatic version, requires participants to type-in the

number of pumps they would like to add to the balloon. The second version of the task, a manual

version, requires the participant to click a button for each pump they would like to add to the

balloon. For a manual BART task, adjusted average pumps did not differ over a two week period

(t(1,38) = .85, ns; White, Wit, & Lejuez, 2008).

The Triarchic Psychopathy Measure (TriPM; Patrick, 2010) is a self-report questionnaire

which assess boldness, meanness, and disinhibition as conceptualized by the Triarchic Model of

Psychopathy (Patrick, Fowels, & Kreuger, 2009). This study will only use disinhibition (TriPM

disinhibition) and boldness (TriPM boldness) scales to index participant disinhibition and

boldness.

Procedure

This study will use data collected for a previous study in 2017.

Planned Analyses

This study assessed between task effects for the BART task using paired samples t-tests.

We assessed differences in number of pumps, total money earned, and number of explosions in

the automatic versus manual task. We also assessed differences within tasks, between trials using

paired sample t-test. Paired sample t-tests were used to determine significant differences, if any,

between number of pumps after win versus after loss in the automatic task, and number of pumps

after win versus after loss in the manual task. Within task, between trial differences in IPI were

also assessed using paired samples t-tests after win versus after loss in the automatic task and

after win versus after loss in the manual task.


Repeated measures ANOVAs were used to assess for task and previous trial outcome

(after win versus after loss) effects on number of pumps between the automatic and manual

tasks.

Repeated measures ANOVAs were used to examine individual difference effects. TriPM

disinhibition was entered as a covariate into a repeated measures ANOVA assessing number of

pumps after win versus after loss between automatic and manual tasks to test for task and

previous trial outcome effects with TriPM disinhibition. TriPM boldness was entered as a

covariate in a separate repeated measures ANOVA assessing number of pumps after win versus

after loss between the two tasks to test for task effects, previous trial outcome effects, and TriPM

boldness interaction. Repeated measures ANOVAs were used to test for IPI differences between

after win versus after loss with the automatic and manual tasks, respectively. Each of these

analyses were run with TriPM disinhibition as a covariate in order to assess for three-way

interactions. As an exploratory aim, each task was also evaluated in relation to TriPM boldness.

Results

Experimental Task Effects

Paired samples t-tests were used to investigate differences between BART automatic and

manual tasks, and between-trial effects post-win and post-loss. This showed a significant

difference in total number of pumps for the automatic and manual versions of the BART task

(t(65) = 16.94; p <.001) with individuals using more pumps in the automatic version (Mpumps =

1183.02, SD = 233.47) compared to the manual version (Mpumps = 826.91, SD = 187.45).

However, they did not show a significant difference in the total number of explosions (t(65) =

1.23; p = 0.22) between the automatic (Mexplosions = 9.41, SD = 2.08) and manual (Mexplosions =

9.03, SD = 2.81) task. Paired samples t-test did show a significant difference in the amount of


money earned between the two tasks (t(65) = 2.94; p = 0.005) with participants earning more

money in the automatic (M = 270.65¢, SD = 47.50¢) version than the manual (M = 249.24¢, SD

= 47.11¢). Paired samples t-test did not show a significant difference in pumps following a win

versus following a loss in the automatic task (t(66) = 1.25; p = .21; Mwin = 58.25; SD = 13.30;

Mloss = 60.78; SD = 15.71), or in the manual task (t(66) = -.78; p = .44; Mwin = 41.29; SD =

10.40; Mloss = 40.20; SD = 12.32).

Paired samples t-tests revealed a significant difference in IPI following a win (MIPI =

2.11, SD = .35) versus following a loss (MIPI = 2.42, SD = .59) in the automatic task, with

participants responding more quickly following a loss with the automatic task (t(66) = 4.17; p <

.001). Paired samples t-tests showed no significant difference in IPI with the manual task (t(66) =

-1.33; p = .19) after win (MIPI = .13, SD = .05) and after loss trials (MIPI = .12, SD = .06).

Paired samples t-test showed a significant difference in pumps following a win between

the two task (t(65) = 11.41; p < .001; Mautomatic = 58.23; SD = 13.40; Mmanual = 41.00; SD =

10.18) and following a loss between the tasks (t(65) = 13.08; p < .001; Mautomatic = 60.76; SD =

15.83; Mmanual = 40.00; SD = 12.30) with individuals pumping more in the automatic task for

both previous trial outcomes.

The repeated measures ANOVA used to test for significant effects of task and previous

trial outcome on number of pumps showed a significant task effect on the number of pumps after

win versus after loss (F(1,65) = 317.60; p < .001; Ș2 = 0.35), but no significant previous trial

outcome effect (F(1,65) = .32; p = .57) or previous trial by task interaction (F(1,65) = 2.46; p =

.12 (see Figure 1).


Individual Difference Effects

TriPM disinhibition did not correlate significantly with total number of pumps on the

automatic (ȕ = .13; p = .32) or manual (ȕ = .07; p = .61) task. A repeated measures ANOVA with

TriPM disinhibition entered as a covariate showed a significant task effect for the number of

pumps after win versus after loss (F(1,60) = 59.79; p < .001; Ș2 = .101), but no significant

previous outcome effect (F(1,60) = 3.56; p = .55; Ș2 = .012). However, it did reveal a previous

outcome by task interaction (F(1,60) = 7.12; p = .010, Ș2 = .012), as well as a significant three-

way interaction with TriPM disinhibition (F(1,60) = 4.66; p = .035; Ș2 = .008).

This three-way interaction was probed by running two separate repeated measures

ANOVAs for each task, automatic and manual, with TriPM disinhibition. The automatic task

ANOVA did not show significant previous trial outcome (F(1,61) = 1.10; p = .30; Ș2 = .006) or a

significant previous trial outcome by TriPM disinhibition interaction (F(1,61) = .25; p = .62; Ș2 =

.001). The manual task ANOVA showed a significant previous trial outcome effect (F(1,61) =

6.99; p = .01; Ș2 = .03) and significant previous trial outcome by TriPM disinhibition effect

(F(1,61) = 6.69; p = .01; Ș2 = .03) only in the manual task. A difference score of pumps after win

and after loss in the manual task correlated significantly with TriPM disinhibition (t = -2.59; p =

.01; see Figure 2) such that while individuals overall tended to pump more after win, more

disinhibited individuals had a smaller difference in pumps after win and pumps after loss.

Boldness correlated significantly with total number of pumps only on the automatic

version of the BART task (ȕ = .33; p = .02), while correlation with the manual task was not

significant (ȕ = .23; p = .09). Repeated measures ANOVA with TriPM boldness showed a

significant task effect (F(1,52) = 8.24; Ș2 = .017; p = .006) and previous trial outcome by task


effect (F(1,52) = 4.46; p = .040; Ș2 = .010) on pumps after win versus after loss, but no

significant three-way interaction with TriPM boldness (F(1,52) = 2.90; p = .09; Ș2 = .007).

Repeated measures ANOVA for the automatic task and TriPM disinhibition showed no

significant previous trial outcome effect on IPI (F(1,61) = 2.80; p = .10; Ș2 = .02) and no

significant previous trial outcome by TriPM disinhibition interaction (F(1,61) = .21; p = .65; Ș2

= .001). Repeated measures ANOVA with the automatic task and TriPM boldness also showed

no significant previous trial outcome effect (F(1,53) = 2.10; p = .15; Ș2 = .01) or previous trial

outcome by TriPM boldness interaction (F(1,53) = .28; p = .60; Ș2 = .002) on IPI.

The repeated measures ANOVA for the manual task and TriPM disinhibition showed no

significant previous trial effect (F(1,61) = 1.86; p = .18; Ș2 = .008), but did show a significant

previous trial outcome by TriPM disinhibition interaction (F(1,61) = 5.48; p = .022; Ș2 = .024)

on IPI. A repeated measures ANOVA for the manual task with TriPM boldness showed no

significant previous trial outcome effect (F(1,53) = 1.16; p = .27; Ș2 = .006) or previous trial

outcome by TriPM boldness interaction (F(1,53) = 2.01; p = .16; Ș2 = .01) on IPI.

Repeated measures ANOVA for the automatic task showed no previous trial outcome

(F(1,61) = 2.80; p = .10; Ș2 = .02) or previous trial outcome by TriPM disinhibition interaction

(F(1,61) = .21; p = .65; Ș2 =.001) on IPI. Additional previous measures ANOVA for the

automatic task showed no previous trial outcome (F(1,53) = 2.10; p = .15; Ș2 = .01) or previous

trial outcome by TriPM boldness interaction on IPI (F(1,53) = .28; p = .60; Ș2 = .002).

A linear regression for the difference in pumps after win and after loss in the manual task

showed independent effects of TriPM disinhibition (ȕ = .27; p = .04) and TriPM boldness (ȕ =

.23 p = .09) on the number of pumps after win and after loss.


Discussion

This study was seeking to show risky decision-making differences in a BART task in

relation to TriPM disinhibition. Two versions of a BART task, an automatic and a manual, were

used to assess between-task effects. Within-task, between-trial effects were assessed to examine

the differences in decision making post-gain and post-loss. We found significant differences in

the number of pumps between the two versions of the task, and the amount of money gained, but

no significant difference in the number of explosions between the tasks. Individuals pumped

more overall in the automatic version and earned more money; being that the number of

explosions was approximately the same in both tasks, it stands to reason that individuals would

make more money in the automatic task.

In both tasks, there was no significant difference in the number of pumps after win versus

after loss. This was an unexpected result because it may be expected that individuals would make

less risky decisions, i.e. fewer pumps, after a loss, and similar or riskier decisions after a win.

However, while there was not an overall difference, the manual task did show a difference in the

number of pumps when TriPM disinhibition was introduced as a covariate. In only the manual

task, which requires more time to make the final decision to cash out or continue pumping, there

was a between-trial difference dependent on TriPM disinhibition. In the automatic task, when

individuals are able to make one quick decision for the number of pumps to add to the balloon,

disinhibition did not make a difference in the decision individuals made after a loss or after a

win. It appears that the necessity of time, and multi-step decision making in a task is implicated

in the effects of trait disinhibition in decision-making. A similar result was seen for IPI.

Individuals’ IPI on the manual task, but not the automatic task, was impacted by the previous

trial outcome depending upon the individual’s TriPM disinhibition.


In the manual task, more disinhibited individuals were less affected by the previous trial

outcome indicating these individuals may not be using the previous trial feedback in their

decision making for the next trial; less disinhibited individuals tended to pump more following a

win to a larger magnitude than more disinhibited individuals. Ashtenhurst, Bujarski, Jentsch, &

Ray (2014) discuss the “near-miss” effect, which is generally applied to problem gambling, with

respect to the BART task. This phenomenon shows individuals choosing to continue gambling

after what they perceive to be an instance in which they almost won (Côté, Caron, Aubert,

Desrochers, & Ladouceur, 2003). Ashtenhurst, et al. proposes that this effect could also occur in

the BART task, when an individual believes they only barely lost on the previous trial, they may

choose the same amount or more pumps in the following trial. More disinhibited individuals

have a smaller difference in pumps after win and pumps after loss, and so these individuals may

be more affected by the near-miss effect. While disinhibition is not the only trait implicated in

problem gambling, it is a factor and may help to explain why more disinhibited individuals

appear to be more affected by near misses.

Additionally, contrary to these results, Ashtenhurst, et al. (2014) found that individuals

with alcohol use disorder made less risky decisions and were more affected by previous trial

outcome. Being that alcohol use disorder implicates multiple traits, not just disinhibition, these

other traits likely explain this contradiction. Lauriola, Panno, Levin, & Lejuez (2013) describe

sensation seeking, which is also implicated in alcohol use disorder, as potentially being a better

predictor of BART performance than impulsivity because the BART task has an element of

arousal that is present in sensation seeking. Another TriPM trait, boldness, has an element of

sensation seeking as well as low stress reactivity, and social dominance (Benning, Patrick, Hicks,


Blonigen, & Krueger, 2003; Benning, Patrick, Blonigen, Hicks, & Iacono, 2005; Patrick, Fowels,

& Kreuger, 2009).

With boldness entered as a covariate, there was a task effect and task by previous

outcome interaction on number of pumps; however, there was no three-way interaction. This

study did use a small sample size, so it may have lacked the statistical power to detect an

interaction; with a larger sample, a three-way interaction with task, previous trial outcome, and

TriPM boldness on pumps could become significant.

Both TriPM disinhibition and TriPM boldness showed effects on the number of pumps in

the manual task, but the effects were unique such that both traits impact decision making, but in

different ways.

These results were similar to those in Snowden, Smith, and Gray (2017) which found that

TriPM disinhibition did not correlate significantly with adjusted pumps (average number of

pumps in non-explosion trials; r = -03; p > .01 ) or explosions (r = .01; p > .01) on a manual

BART, but TriPM boldness correlated significantly with both explosions (r = .35; p < .001) and

adjusted pumps (r = .30; p < .001). Snowden and colleagues suggest TriPM boldness may affect

decision-making in the manual BART task because the “punishment” of the balloon exploding

and losing the accrued winnings may not impact individuals with higher TriPM boldness to the

same extent. This study also found that TriPM disinhibition did not correlate significantly with

overall number of pumps in the BART task. However, this study only found a significant

correlation with TriPM boldness for the automatic version of the task, however it should be

noted that this study utilized a small sample size which may have played a role in this analyses

being non-significant.


The present study was limited by a small sample size which impacted the statistical

power of the analyses. Some effects may be less reliable due to this sampling limitation.

Ashtenhurst et al. (2014) found that individuals made slightly less risky decisions in latter

trials, but this finding was in contrast to Mata, Hua, Papassotiropoulos, & Hertwig (2013) which

used fewer trials and did not find this effect. Similar effects should be evaluated for in IPI, as

well as differences in pumps in later trials between the automatic and manual tasks.

Conclusion

This study assessed differences in BART responses as related to task effects, previous

trial outcome effects, and TriPM disinhibition effects. BART responses were significantly

different in the number of overall pumps between the automatic and manual tasks. Only the

manual task had significant effects of previous trial outcome and previous trial outcome by

TriPM disinhibition on the number of pumps and IPI after win and after loss trials. More

disinhibited individuals experienced a smaller difference in pumps after win and after loss

indicating that more disinhibited individuals may be less susceptible to external factors in

decision-making.


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Figure 1.

Fig. 1 Repeated measures ANOVA shows significant task effect on number pumps after win

versus after loss but no significant previous trial outcome. Participants pump more overall in the

automatic task, but pump about the same amount after win trials and after loss trials within task.


Figure 2.

Fig. 2 Higher disinhibition is associated with lower difference in pumps after win and pumps

after loss in the manual task.

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