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The stoplight task: a procedure for assessing risk taking in humans.

Risk taking is a form of behavior that has an uncertain ratio of gain to loss as its consequence. Risk taking is commonly studied in nonhuman animals involving choices between alternatives that differ in probability and/or amount (Mazur, 1989; Mobini, Body, Ho, Bradshaw, Szabadi, Deakin, & Anderson, 2002), for example, when one alternative is smaller and certain (3 food pellets) and the other is larger and uncertain (15 pellets with a probability of .33). Under such conditions, risk taking is operationally defined as choosing the larger probabilistic alternative because on any trial, one could end up with nothing. In this example, choosing the larger, probabilistic outcome maximizes pellet gains over the long run (3 versus ~5 on average). Real-life gambling situations exemplify these contingencies, however they differ from the above example in that incurred loss will tend to increase with repeated trials. The risk the gambler faces is not just the potential gain from winnings but also the potential loss of money.

Various kinds of contingencies have been arranged to investigate risk taking in humans (Bechara, Damasio, Tranel, & Damasio, 1997; Lane & Cherek, 2000, 2001; Lejuez et al., 2002). The risk-taking task employed by Lane and Cherek (2000, 2001) involved discrete choices, such that the risky option offered a low probability of a large money reward or a high probability of a smaller money loss. The nonrisky option protects current earnings. Participants with a history of high-risk behavior (Lane & Cherek, 2000, 2001) chose the risky option more often, had lower overall earnings, and were more likely to persist in making risky responses following a single gain on the risky option. The Balloon Analog Risk-Taking task (Lejuez et al., 2002) contained a series of choice trials involving an analog balloon that can be "inflated" (to collect points), with the risk of popping the balloon (thereby losing points). These procedures have been useful to researchers investigating risk taking, however for the sake of generality and refining our conceptualizations of risk, it is important to explore other contingencies.

Brady, Bradford, and Heinz (1979) first described a procedure in which baboons were trained to respond under a fixed-ratio (FR) 100 schedule of food reinforcement when a green light was illuminated. At some point within the FR requirement, a yellow light replaced the green light. After 5 s, a red light replaced the yellow light. On different trials, the yellow light occurred either late (after 90 responses) or early (after 60 responses) in the FR sequence. The residual FR equals 100 minus the total responses emitted. A counter with 10 lights signaled progress toward completion of the FR 100; each FR 10 illuminated another light, sequentially. On some days, responding after the red light replaced the yellow light resulted in electric shock. On other days, no shocks occurred. High rates of response occurred during the yellow light with small residual FRs regardless of the shock contingency. With a large residual FR (when more responses were required to complete the FR 100), response rates during the yellow light were higher during the no shock condition than the shock condition.

In the present study we modified this stoplight task to assess risk taking in humans. One goal was to establish whether the procedure could generate orderly behavior in humans yet at the same time reveal individual differences in risk taking. A second goal was to determine if personality trait measures of risk taking and impulsivity were predictive of risk taking as measured with the task. As in Brady et al., (1979), the residual FR (the remaining response requirement at yellow light onset) and point loss probability (in lieu of electric shock) were manipulated. In the present study, reinforcement consisted of points exchangeable for money. Also, the yellow light duration (always 5 s in the Brady et al. study) was unpredictable; it varied randomly across trials from 1 s to 9 s (mean = 5 s).

The primary measure was the outcome on each trial: successful FR completion, failed attempt to complete FR, or cessation of responding. Completions and failed attempts were coded as 'go' responses, whereas response cessation was coded as a 'stop' response. A distinguishing feature of the present stoplight task, compared to other risk-taking behavioral procedures noted above is that, during each trial, a stream of operant responding is established. During go decisions, the participant persists in money-maintained responding whereas, during stop decisions, the participant must inhibit responding. We predicted that lower residual FRs and lower point loss probability would increase 'go' responses because there is less risk involved with the task at these values. We also predicted that high risk-taking, high impulsive participants (scored via personality inventories) would 'go' more often than low risk-taking, low impulsive participants. This latter prediction was based upon the assumption that these personality inventories are measuring some aspect of the participant's repertoire that is also captured by the behavioral task.



Seventeen participants ranging from 21 to 35 years old were recruited using newspaper ads. Nine participants were male (5 white, 4 African-American) and 8 were female (4 white, 3 African-American, 1 Hispanic). Average ([+ or -] SD) age was 26.5 [+ or -] 4.4 years. Educational level ranged from 12 to 16 years. Participants were healthy and free of prescribed medications, DSM-IV psychopathology, and cognitive impairment. Participants also tested negative for cocaine, benzodiazepines, barbiturates, and opioids via a urine sample and alcohol via a breath sample.


In this study, we used a mixed-model design, with three within-subject factors and one between-subject factor. Within-subject factors were point loss probability (PLP: 2 levels: 12.5 vs. 100%), residual FR (8 levels: size 15, 20, 25, 30, 35, 40, 45, and 50), and repetition (2 levels). Participants were exposed to each PLP condition twice (repetition), with levels of residual FR nested within PLP conditions. The between-subject factor was an individual difference measure (2 levels: high vs. low) determined by median split on questionnaire scores.

Personality inventories. Participants first completed the Eysenck Personality Inventory (EPI I-7 Form; Eysenck & Eysenck, 1978), which has three dimensions labeled impulsivity (24 items), venturesomeness (17 items), and empathy (20 items). Seven items from the I-7 Form load on a risk-taking factor from a previous EPI version (I-5 Form; Eysenck & Eysenck, 1977): "Do you long for excitement?" "Do you quite enjoy taking risks?" "Would you enjoy parachute jumping?" "Would you prefer a job involving change, travel and variety, even though it might be insecure?" "When the odds are against you, do you still usually think it worth taking a chance?" "Do you get bored more easily than most people, doing the same old things?" and "Would life with no danger in it be too dull for you?" Because in this study we attempted to validate a behavioral measure of risk taking, these seven items constituted another scale. Participants next completed the Barratt Impulsivity Scale (BIS-10; Barratt, 1965), which has three dimensions labeled lack of planning (12 items), cognitive speed (11 items), and motor activity (11 items).

Stoplight task (SLT). Participants completed four sessions of the SLT on 1 day. For each session, participants worked at a computer keyboard for 48 trials, which took around 20-30 min depending on response rate. On each trial, completing 100 'X-Y' key presses (FR 100) added 25 points to a counter that was continuously displayed on the monitor. Each point represented one cent. The counter reflected earnings and was updated after each trial. Total points earned were added across sessions and exchanged for money after the fourth session. Participants earned about $10 on average on the SLT. Each session began with 100 points on the counter. Before the first session, participants sat at the computer and read the instructions in Table 1.

The monitor displayed a simulated traffic light with green, yellow, and red signals. Participants had to respond during the green light on each trial to advance a response counter. When the yellow light replaced the green light--which happened randomly between 50 to 89 responses toward FR 100 completion--participants could either attempt to complete the sequence before the red light appeared or to quit responding. The response counter provided visual feedback (one dash) for each 10 X-Y responses completed. Based on pilot work (Greenwald, Schuster, & Johanson, 1999), the distribution of yellow-light onsets in each session was divided into eight bins, with six onset times per bin (hence, 48 trials/session). Yellow-light onset bins of 50, 55, 60, 65, 70, 75, 80, and 85 corresponded to residual FR values (i.e., 100 minus yellow onset) of 50, 45, 40, 35, 30, 25, 20, and 15. In each block of eight trials, each residual FR level was presented once. A random order of yellow-light durations (1, 3, 5, 7, and 9 s; mean = 5 s) was constructed for each residual FR variable such that there was an equal probability of shorter and longer yellow-light durations in combination with shorter and longer residual ratios.

There were three possible outcomes on each trial. The participant could stop responding during the yellow light, yielding no change in earnings. The participant could earn 25 points by completing the FR 100 before the red light appeared. Finally, the participant could continue responding during the yellow light and fail to complete the FR 100 before the red light appeared (either the 'X' or 'Y' keys had to be depressed at the time of red-light onset, thus the actual number of "go" choices would be underestimated to the extent that red-light onset occurred during an interkeypress interval of "go" choices). This last outcome resulted in no change in earnings or a loss of 25 points, depending on the condition (see below). There was a 3-s time-out (all lights off) after each trial before the green light was illuminated again. There was a 30-s time-out between each block of eight trials. Responding during the time-out delayed onset of the next trial.

The probability of losing points (point loss probability or PLP) for attempting but failing to complete the FR 100 before the red light onset was constant for each session but varied between sessions. The PLP was either 12.5% (money loss on 1/8 trials) or 100% (loss on all trials) and was displayed continuously on the monitor. Each participant was exposed to each PLP condition twice, and the order was counterbalanced across participants.

Data analysis. Response rates during green and yellow lights, attempts to complete the remaining FR 100 requirement after yellow light onset ('go' choice, regardless of outcome) and points earned were collected on each trial. These measures were subjected to PLP x Repetition x Residual FR (repeated measures) x Group (2 levels: at or above questionnaire median split vs. below) analyses of variance (ANOVAs). Huynh-Feldt corrected significance levels were used for repeated measures terms. Pearson correlation coefficients between questionnaire scores were calculated to examine the independence of these individual difference dimensions.


Figure 1 presents 'go' choices as a function of PLP, Repetition, and Residual FR factors. There were significant effects of PLP F(1, 16) = 9.98, Residual Ratio F(7, 112) = 33.06, and their interaction F(7, 112) = 5.49, all ps < .01. 'Go' responding in the low-PLP (12.5%) condition was enhanced at higher (> FR 25) residual ratios. Although these effects were slightly greater during the second exposure (left vs. right panels, Figure 1), this interaction was not significant, PLP x Repetition x Residual Ratio, F(7, 112) = 1.90, p < .09.


Figure 2 (left panel) illustrates that mean response rates during the green light were consistent, ranging from 3.75 to 4.00/s across conditions. Figure 2 (left panel) illustrates that mean response rates during the yellow light paralleled the choice data (Figure 1) and were lower than response rates during the green light. At short residual ratios, response rates were high (3.5/s), but as the residual ratio increased to FR 50, response rates decreased monotonically, F(7, 112) = 24.35, p < .0001. This decrease was greater in the 100% than 12.5% condition, PLP F(1, 16) = 7.84, p < .02, and PLP x Residual ratio F(7, 112) = 4.62, p < .005.

Figure 2 (right panel) illustrates average money earned in each test condition. As residual FR increased, earnings from the fewer 'go' decisions decreased, Residual Ratio, F(7, 112) = 19.51, p < .0001. This effect was more pronounced in the 100% than 12.5% PLP condition, F(1, 16) = 57.83, p < .0001. The PLP x Residual Ratio, F(7, 112) = 7.48, p < .0001. Participants lost money overall in the 100% PLP condition, especially at higher ratios (> FR 20), whereas participants gained money in the 12.5% PLP condition except for the highest ratios ([greater than or equal to] FR 40).


Eysenck Personality Inventory. Figure 3 shows individual differences in 'go' choices based on scores from EPI dimensions of risk-taking, venturesomeness, and impulsivity. High risk-taking (median split of 4 on the 1-7 scale) compared to low risk-taking participants made more 'go' choices overall (Ms = 52.4% vs. 21.5%), Group F(1, 15) = 16.70, p < .001. This effect was enhanced at shorter residual FRs, Group x Residual Ratio, F(7, 105) = 6.79, p < .002 (upper left, Fig. 3), and when point loss was less likely, Group x PLP, F(1, 15) = 5.50, p < .04 (upper right, Fig. 3).

High-venturesome participants (median split of 11 on the 1-17 scale) made more 'go' choices than low-venturesome participants overall (Ms = 53.2% versus 24.4%), Group F(1, 15) = 13.19, p < .003. Similar to the risk-taking scale, this effect was enhanced at shorter residual FRs, Group x Residual Ratio, F(7, 105) = 3.47, p < .04 (lower left, Fig. 3). High-venturesome participants tended to make more 'go' choices than low-venturesome participants when PLP was low, although (unlike the risk-taking scale) this effect was not significant, Group x PLP, F(1, 15) = 3.77, p < .08.


High-impulsive participants (median split of 6 on the 1-24 scale) made more 'go' choices than low-impulsive participants when PLP was low, Group x PLP, F(1, 15) = 7.33, p < .02 (lower right, Fig. 3). This effect was greater during the second than first exposure, Group x PLP x Repetition, F(1, 15) = 5.66, p < .04 (data not shown).

There were no significant effects for the EPI empathy dimension with respect to SLT performance.

Barratt Impulsivity Scale. Upon initial task exposure, participants scoring low on BIS planning (i.e., more impulsive) made more 'go' choices at higher residual ratios (> FR 30) than participants scoring high on BIS planning. This group difference diminished with repeated exposure and was independent of PLP, Group x Residual Ratio x Repetition, F(7, 105) = 3.14, p < .01. A similar effect was observed on another BIS scale: High cognitive-speed (i.e., more impulsive) participants made more 'go' choices at higher residual ratios (> FR 30) upon initial exposure, Group x Residual Ratio x Repetition, F(7, 105) = 2.78, p < .02. There were no significant effects for the BIS motor speed dimension.

Gender. Males and females did not significantly differ in SLT performance. The only questionnaire scale significantly related to gender was EPI empathy, with females exhibiting higher mean scores than males.

Interscale correlations. Table 2 indicates that EPI risk-taking and venturesomeness scales were significantly correlated. Risk-taking and venturesomeness scores were not significantly related to EPI impulsivity. BIS cognitive speed and motor speed were significantly related. BIS cognitive and motor speed scores were also significantly associated with EPI empathy.


A modified stoplight procedure, based on the baboon study by Brady et al. (1979), was developed and employed to assess risk taking in humans. Consistent with their findings, the remaining response requirement and probability of point loss modulated the likelihood of risk-taking "go" behavior. As hypothesized, risk taking was inversely related to residual fixed ratio (FR) and point-loss probability (PLP). Thus, greater residual response requirements and certainty of money loss deterred 'go' responding. When the residual FR was small (< 25), 'go' responding was highest (75% of trials at FR 15), and there was no significant effect of point-loss probability. At larger residual FRs ([greater than or equal to] 30), 'go' responses markedly decreased (25% of trials at FR [greater than or equal to] 40) and more so when point loss was certain. The green light maintained higher response rates than yellow despite the fact that participants were told there was no advantage to responding quickly during the green light; only 'go' choices during the yellow light required rapid responding. Furthermore, there were no group differences in green light response rates. Thus, the group differences in risk taking were unrelated to baseline psychomotor speed. As with stop/go responses, yellow light response rates similarly were modulated by the residual response requirement and PLP.

Monetary outcomes paralleled stop/go decisions. Participants earned more money overall in the low PLP condition, but the difference diminished as the residual ratio increased ([greater than or equal to] FR 45). Participants lost modest amounts of money in the 100% PLP condition, except when response requirement was small ([less than or equal to] FR 20). The fact that participants did not lose money overall--but instead made modest gains--contrasts with other decision-making tasks. In gambling (Vuchinich & Simpson, 1998) and risk-taking tasks (Lane & Cherek, 2000), 'riskier' decisions resulted in modest short-term gains but larger long-term losses. Nevertheless, behavior of participants in the present study was sensitive to the contingencies, and we can conclude that point loss functioned as a punisher. Further research will have to determine whether net loss for risky behavior would produce different findings. It is conceivable that increasing the relative ratio of money loss to gain on each trial would affect the probability of go/no go responses.

The present study also sought to determine the relationship between performance under the stoplight task and questionnaire-based personality dimensions such as risk taking and impulsivity. These characteristics accounted for significant variance in 'go' choices. Under conditions that generally promote greater payoffs (i.e., smaller residual FRs and 12.5% PLP), individuals scoring higher on Eysenck risk-taking, venturesomeness, and impulsivity scales made more 'go' choices than individuals low on these dimensions. Because risk-taking and venturesomeness questionnaire scores were highly correlated, the SLT behavioral data might be related to both risk-taking and impulsivity constructs. The present results must be interpreted cautiously, however, due to the small sample size and use of post hoc statistical tests. A more compelling test would involve preselecting individuals high vs. low on these trait dimensions and replicating these effects in a larger sample. It would be useful to determine whether this task is not only sensitive, but specific, to individual differences in decision making. Nevertheless, these personality/risk-taking behavior associations are remarkably consistent with results from Lejuez et al. (2002), who found that risk-taking behavior on the Balloon Analog Risk Taking task was positively correlated with questionnaire measures of sensation seeking, impulsivity, and negatively correlated with behavioral constraint.

In the stoplight task, point-loss probability and response requirement contingencies were signaled. The probability of point loss was continuously displayed, and the completed FR 10 units were shown and updated after every 10 responses. Including the signals depends on the experimental question(s). The function of these signals awaits experimental analysis, however we predict that removing the signals would decrease risk taking, increase response variability, and delay the acquisition of stable performance. The signals were included to attenuate the magnitude of those potential effects.

One way in which the stoplight task differs from existing human behavioral choice procedures such as intertemporal choice is that a point-loss contingency is included. The intertemporal choice procedure used to characterize self-control arranges choices between small, immediate reinforcers and larger, delayed ones; the element of risk (i.e., punishment) is absent in these procedures. Contingencies of punishment should be investigated under the context of risk taking and impulsivity because these contingencies are often present under the naturalistic counterparts of our paradigms. The added component of risk/loss makes the stoplight procedure similar to many real-life situations and increases its external validity. A second unique and important feature of the stoplight procedure is that operant responding is established during the green light (trial baseline) period. This produces a prepotent tendency to continue responding ('go' bias) during the yellow light, which the participant must inhibit when making a 'stop' decision. Requiring the participant to monitor signals and interrupt real-time behavior based on momentary information distinguishes this task from extant discrete-trial choice procedures. Finally, the stoplight task (as well as the risk-taking task of Lane & Cherek, 2000, 2001) is well suited to repeated measures evaluation, whereas several other related tasks are limited to single exposures.

The stoplight task has potential applications. On laboratory tasks of self-control, substance abusers (Kirby, Petry, & Bickel, 1999; Madden, Petry, Badger, & Bickel, 1997; Vuchinich & Simpson, 1998) have been shown more impulsive than controls. Perhaps the stoplight task could be similarly employed to assess behavioral differences in substance abusers vs. controls. The stoplight task might also be suitable for investigating choice behavior among children, particularly with populations that have response inhibition deficits (e.g., ADHD, conduct disorder).

Brady et al. (1979) provided another application when they introduced the stoplight task; the effects of drugs on risk taking. They showed that diazepam dose dependently and selectively increased response rates at a higher residual FR in the absence of electric shock; that is, diazepam did not alter response rates at a high residual FR with certain shock or on lower residual FR trials. These data raise two issues. First, benzodiazepines can disrupt acquisition of complex human behavior (Desjardins, Moerschbaecher, Thompson, & Thomas, 1982; Hinrichs, Mewaldt, Ghoneim, & Berie, 1982; Kelly, Foltin, King, & Fischman, 1992). Thus, drug effects on risky choice may be greatest when the discriminative stimulus control of behavior is weakest (during acquisition) or when the task is unsignaled. Second, because risky choice may be inversely related to anxiety (Gasper & Clore, 1998), it would be useful to determine whether anxiolytics selectively promote 'go' choices under conditions where this has been suppressed (i.e., cause 'disinhibition'). Similar effects have been shown in animals using conflict paradigms (Geller & Seifter, 1960; McMillan, 1975) and humans using a variable reinforcement-variable loss schedule with monetary consequences (Carlton, Siegel, Murphree, & Cook, 1981).

We should note that making a 'high-risk' choice is not inherently maladaptive because values associated with certain choices may produce personal or prosocial gains, for example, investing in a new business or rescuing an accident victim. Because of the personal, social, and public health impact of choice behavior, it is important to study effects of contingencies and individual differences that modify risky decision making. Understanding the factors that govern risky and impulsive choice has important implications for many societal issues (e.g., gambling, substance use). Developing better paradigms such as the stoplight task with high internal and external validity could improve our understanding of behavioral mechanisms underlying decision making and choice in psychopathological conditions, with potential clinical applications (e.g., whether treatment alters risky decision making).


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Central Michigan University


Wayne State University School of Medicine

This research was supported by grants from NIH/NIDA DA10239 and DA00485. We thank Ed Bunker, who implemented the software code for the stoplight task, and Joseph Brady, whose work inspired this human experimental model.

Please address correspondence about this article to Mark P. Reilly, Department of Psychology, 2176 Health Professions Bldg., Central Michigan University, Mt Pleasant, MI 48859. (E-mail:
Table 1 Task Instructions

During the task, you will work at this keyboard to earn points that will
be exchanged for money. The amount of money you earn will depend on the
decisions that you make and how rapidly you respond. On each trial,
three things can happen: You can earn money, lose money, or have no
change in earnings. Now I'd like you to press the space bar to make the
traffic light appear on the screen.

On every trial, a "traffic light" will appear on the computer screen,
just as it appears here. A single trial consists of a green light, which
is then replaced by a yellow light, which is then replaced by a red
light. The green light signals the start of a trial. When the green
light appears, you should start typing the "X" and "Y" keys--always in
that order--at the keyboard. Your goal on each trial is to type 100 XY
combinations before the red light appears. After you type the first 10
XY responses, a dash [-] and a dollar sign [$] will appear on the screen
to show you how far towards the 100-response goal you have gone. Right
now, I want you to type the XY keys until 4 dashes appear [----$], then
stop typing.

You must repeat this XY sequence 100 times to earn points on that trial.
Therefore, ten dashes (or 10 moves of the dollar sign to the right
[----------$]) on each trial means you have earned points. If you type
other keys besides X or Y, nothing will happen and you will hurt your
chances of earning money. At some point while you're responding and the
green light is on, the yellow light will replace the green light, and
then the red light will replace the yellow light. Right now, I want you
to type the XY keys until the yellow light appears, then stop typing.
(Participant and experimenter watch as the red light appears and
terminates the trial.)

The moment at which the yellow light replaces the green light is when
you should make your "quick decision." If you have not completed all 100
responses when the yellow light disappears and is replaced by the red
light, you might lose 25 points. If you do complete the 100 responses
before the yellow light disappears and the red light comes on, you will
always win 25 points. You can also stop responding during the yellow
light before the red light comes on. If so, you will neither gain nor
lose points for that trial. This is what I just had you do in our first
example trial.

In different trial blocks, you will see displayed on the screen either
12.5% or 100% (experimenter points to the percentage below the green
light). This means there is either a small (12.5%) or certain (100%)
chance that you'll lose points if you are still responding when the red
light appears and you haven't completed the 100 responses. A 12.5%
chance of losing points means that you will lose on 1 out of every 8
trials and a 100% chance means that you will lose every time, if you are
still responding when the red light appears.

Two important things about the yellow light will make it harder for you
to decide whether to complete the 100-response requirement or not.
First, the yellow light will replace the green light at different times
during the 100-response sequence, which is randomly determined by the
computer. Second, the yellow light will stay on for different amounts of
time, which is randomly determined by the computer. Since you can't be
certain whether you'll complete the 100 responses before the red light
comes on, you'll have to decide whether you want to "go for it" on that
trial--that is, try to earn points even if it means you could lose. Or
you may decide to "play it safe"--that is, stop responding and protect
your points. There is no right or wrong decision; it's entirely up to

Finally, it is important to understand that there is no advantage in
responding quickly when the green light is on. It is only once the
yellow light appears that you should decide whether you want to respond
quickly to finish the 100 responses or not.

Table 2 Correlations (1) Among Individual Difference Measures

Individual Difference BIS low BIS cog. BIS motor EPI
Measure planning speed speed risk-taking

BIS low planning --
BIS cognitive speed 0.403 --
BIS motor speed 0.129 0.696# --
EPI risk-taking 0.161 -0.102 -0.110 --
EPI impulsivity 0.450 0.393 0.578# 0.187
EPI venturesomeness 0.037 -0.144 -0.078 0.744#
EPI empathy 0.299 0.558 0.566# 0.262
Sex (0=fem., 1=male) 0.128 -0.056 -0.211 -0.039

Individual Difference EPI EPI EPI
Measure impulsivity venturesome empathy

BIS low planning
BIS cognitive speed
BIS motor speed
EPI risk-taking
EPI impulsivity --
EPI venturesomeness 0.192 --
EPI empathy 0.476 0.167 --
Sex (0=fem., 1=male) -0.200 -0.015 -0.584#

(1) Pearson correlations in bold face numbers are statistically
significant (two-tailed p < .05 with df = 15 requires r
[greater than or equal to] 0.482).

Note: Pearson correlations indicated with # are statistically
significant (two-tailed p < .05 with df = 15 requires r
[greater than or equal to] 0.482).
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Author:Reilly, Mark P.; Greenwald, Mark K.; Johanson, Chris-Ellyn
Publication:The Psychological Record
Geographic Code:1USA
Date:Mar 22, 2006
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