Psychological Services 2014, Vol. 11, No. 2, 171–178
© 2014 American Psychological Association 1541-1559/14/$12.00 DOI: 10.1037/a0035004
Temporal Discounting and Criminal Thinking: Understanding Cognitive Processes to Align Services Femina P. Varghese, Shawn R. Charlton, Mara Wood, and Emily Trower
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University of Central Arkansas Temporal discounting is an indicator of impulsivity that has consistently been found to be associated with risky behaviors such as substance abuse and compulsive gambling. Yet, although criminal acts are clearly risky choice behaviors, no study has examined temporal discounting in the criminal attitudes and behaviors of adult offenders. Yet, such investigations have potential to understand the cognitive processes that underlie various criminal patterns of thinking and may help distinguish between high and low risk offenders. Therefore, the current study endeavored to fill this gap in the literature using 146 male inmates within 5 months of release. Results found that temporal discounting is correlated with reactive criminal thinking but was not correlated with proactive criminal thinking. In addition, inmates with higher rates of incarceration were also more likely to have higher rates of temporal discounting. Results shed light on the different cognitive processes that may underlie different styles of criminal thinking as well as potential differences in the discounting rates depending on history of incarcerations. This finding has implications for service delivery in criminal justice settings as those with reactive criminal thinking may benefit from specialized treatments for temporal discounting. Keywords: temporal discounting, offender, incarcerations, criminal thinking
decision-making, imagine a man has a savings bond worth $100 when it matures 6 months from now. Would he be willing to sell it for money right now? If so, what is the least that the he would accept for the bond? If he would be willing to sell the bond for anything less than its full future value, he is experiencing temporal discounting, because temporal discounting is devaluing of future outcomes, even after being presented with both choices and its consequences. The difference between the full value of the bond and the smallest amount he would be willing to accept for it right now can be viewed as an estimate of how much he discounts the future. If there is relatively little difference between full future value and his selling price then not much discounting has occurred. On the other hand, if he would part with his savings bond for a relatively small amount, then the discount rate is quite high. Temporal discounting refers specifically to the value of future outcomes. In this way, discounting is a process involved in impulsive behavior, but unique from other aspects such as consideration of future outcomes (Joireman, Balliet, Sportt, Spangenberg, & Schultz, 2008) and the ability to inhibit responses (Cheng, Lu, Han, Gonzalez-Vallejo, & Sui, 2012). Considering that high discounting of future outcomes may have ramifications for continued criminal behavior, temporal discounting could be a valuable tool for informing treatments on decisionmaking in offenders. Indeed, temporal discounting is strongly tied to risk-taking behaviors. Persons who value future outcomes less than controls are more likely to abuse substances like alcohol, cocaine, heroin, nicotine, and opiates (e.g., Bickel, Odum, & Madden, 1999; Coffey, Gudleski, Saladin, & Brady, 2003; Kirby, Petry, & Bickel, 1999). Further, those with high temporal discounting are more likely to gamble pathologically and show patterns of risky decision-making behavior (e.g., Chesson et al., 2006; Odum, Madden, Badger, & Bickel, 2000). Thus, elevated temporal discounting may be a predictor of long-term behavioral problems
Offenders have often been described as impulsive (Carroll et al., 2006) with difficulty setting long-term goals (McMurran & Ward, 2004). A long-standing explanation for their impulsive behavior is due to a tendency to choose the immediate positive reward of a criminal act while discounting the potential long-term punishment of the crime (Walters, 2006a; Wilson & Herrnstein, 1985). This explanation can be viewed as temporal discounting: the tendency for the perceived value of future outcomes to be viewed as less than if they were immediately available (Critchfield & Kollins, 2001; Green & Myerson, 2004). Wilson and Herrnstein (1985) first suggested that persons with short time horizons are prone to criminal behavior because of decreased value of the future punishments; this includes the idea of how far into the future an individual considers consequences. Yet, in temporal discounting future consequences are considered, but devalued that they no longer control behavior. Temporal discounting is an element of impulsivity unique from simply lacking self-control, the latter being associated with lacking awareness of future consequences or not being able to inhibit oneself from choosing the immediate over the long-term (Marcus, 2004). In temporal discounting, awareness of immediate and future consequences of choices is present but does not hold its value (Madden & Bickel, 2010). To appreciate the role of discounting in
This article was published Online First March 17, 2014. Femina P. Varghese, Shawn R. Charlton, Mara Wood, and Emily Trower, Department of Psychology and Counseling, University of Central Arkansas. Correspondence concerning this article should be addressed to Femina P. Varghese, Department of Psychology and Counseling, University of Central Arkansas, 201 Donaghey Avenue, 213 Mashburn Hall, Conway, AR 72035-0001. E-mail: [email protected] 171
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(Bickel & Johnson, 2003). Criminal behavior also shares characteristics associated with temporal discounting. Crime involves choices for immediate benefits (e.g., money, drugs) but may result in larger, delayed negative outcomes (e.g., incarceration). Discounting may play a role in criminal behavior, independent of substance abuse. A recent study comparing the discount rates between nonoffenders and offenders, controlling for substance abuse, found greater discount rates in offenders (Arantes, Berg, Lawlor, & Grace, 2013), suggesting discounting’s role in criminal behavior, independent of substance abuse. Despite these findings, temporal discounting as it relates to offenders has not received adequate attention in the literature. A recent literature search in PsycINFO, PsychARTICLES, and SocIndexFulltext with the key terms for offender (i.e., offender, criminal) and temporal discounting (i.e., temporal discounting, delay discounting) resulted in just eight total peer-reviewed articles. Of these, only one (Hanoch, Rolison, & Gummerum, 2013) conducted an actual study with offender participants; they found offenders discounted more. None of the eight articles examined temporal discounting with two significant risk factors for crime: criminal attitudes and criminal history (see Andrews & Bonta, 2010), particularly in terms of history of incarcerations. Yet, such investigations may foster effective interventions to reduce recidivism. Criminal thinking (i.e., criminal attitudes) is one of the strongest risk factors for criminal behavior (Andrews, Bonta, & Hoge, 1990; Gendreau, Little, & Goggin, 1996). Yet, the literature on criminal thinking focuses on listing the content of criminal thoughts (Andrews & Bonta, 2010), whereas the process of criminal thinking in criminal behavior has not been adequately explored. Likely as a result of the content focus, the majority of treatment approaches emphasize changing thought content (see Andrews & Bonta, 2010). Therefore, examining the relationship between temporal discounting and criminal thinking would be a first step in helping shift the focus from content of criminal thoughts to examining the underlying processes of these thoughts. Further, there is still a significant need for research that distinguishes different types of criminal thinking (Taxman, Rhodes, & Dumenci, 2011). Such research could help tailor treatments to be responsive to offenders’ individual needs, an important component in treatment effectiveness (Andrews & Bonta, 2010; Taxman et al., 2011). Considering this, we explored temporal discounting with different criminal attitudes. Altough several measures of criminal thinking exist, the Psychological Inventory of Criminal Thinking Styles (PICTS, Walters, 1995) is among the few that have undergone several validation studies with both incarcerated and nonincarcerated offenders (Taxman et al., 2011). The PICTS has eight different scales measuring various criminal thinking styles (see Walters, 1995). Further, two composite scales termed reactive and proactive criminal thinking have been developed, which comprise several criminal thinking scales (Walters, 2006b). Proactive criminal thinking incorporates “calculated” (p. 25) criminal styles of thinking and reactive criminal thinking “reflects spontaneity, rashness, and impulsivity” (Walters, 2006b, p. 25). The PICTS is organized hierarchically with general criminal thinking at the top, then proactive and reactive, then individual criminal thinking style scale below that (see Walters, Hagman, & Cohn, 2011). Examination of the composite scales has been noted to hold implications for the development of effective interventions for offenders (Walters,
2006b). Given the association between temporal discounting and risky and impulsive behavior, and considering that reactive criminal thinking is associated with little consideration for the future, it is hypothesized that higher scores on temporal discounting will be most associated with higher scores on the reactive criminal thinking scale, but not the proactive criminal thinking scale. Career criminals are “those persistent offenders who make repeated transits of [the] criminal justice system” (Petersilia & Honig, 1980, p. v). Such high-risk offenders should be the focus of treatment (Andrews & Bonta, 2010). Yet, little empirical research has examined repeat offenders in terms of cognitive processes such as temporal discounting. Such examinations may assist in developing intervention methods specifically targeting the repeat or high-risk offender. Thus, this article examines the relationship between history of incarcerations and temporal discounting. Given this, this current study aimed to discover in what way temporal discounting is associated with criminal thinking. On a lesser note, we also wanted to explore temporal discounting’s association with history of incarcerations. We hypothesized that higher scores on temporal discounting will be most associated with higher scores on the reactive criminal thinking scale, but not with the proactive criminal scale. In addition, we hypothesized that higher rates of incarceration are associated with higher rates of temporal discounting.
Methods Participants Data were gathered from inmates within 5 months of release from a state prison within the southern part of the United States. Inmates completed all measures in groups of 15–30 persons, and the researcher saw approximately two groups during four separate visits. Among the 242 offenders who were available and could read, 197 male inmates agreed to participate (81%). PICTS (Walters, 1995) was used to identify participants who provided various invalid responses; as a result, data from 40 participants were removed due to these invalid responses including one that did not complete the measures at all. Five more cases were deleted due to univariate outliers (z ⬎ ⫹ 3.29; Tabachnick & Fidell, 2007) on two temporal discounting subscales (three total cases) and outliers on number of incarcerations (two total cases). In addition, six more cases did not have usable discount scores on the discounting measure (the Monetary Choice Questionnaire, MCQ; Kirby et al., 1999), because of a failure to answer the majority of the items on the scale. Thus, 146 cases remained for the analyses. The vast majority of these offenders indicated being at a minimal security level (67.1%); 30.1% indicated medium security level; and 1.4% reported being at a maximum security level, and finally 1.4% (two total participants) reported “Other” with one stating “none” and the other writing “A,” an unclear response. Information provided by the community corrections department (taken from court records) indicated offenders’ diverse criminal backgrounds ranging from violent (e.g., murder, sexual offenses) to nonviolent (e.g., drug offenses, theft).
Materials Participants completed a survey packet consisting of several scales including a demographic survey, a temporal discounting
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TEMPORAL DISCOUNTING AND CRIMINAL THINKING
scale (MCQ; Kirby et al., 1999), and the PICTS (Walters, 1995). The demographics section asked several questions, including age, race, socioeconomic status, education, and number of times incarcerated, including the present incarceration. The temporal discounting scale used in the current study was modeled after the commonly used Monetary Choice Questionnaire (MCQ, developed by Kirby et al., 1999) that has been used frequently in discounting studies of the relation between discounting and real-world behaviors such as substance abuse (Duckworth & Seligman, 2005; Kirby & Finch, 2010; Liu, Vassileva, Gonzalez, & Martin, 2012; MacKillop et al., 2010). The questions were the same as the MCQ, although the instructions were not exact to the original but similar to what was provided in the original article and is exactly as was used in Charlton et al. (2013). The MCQ is a paper-and-pencil measure that allows for the quick estimation of a discount rate based on Mazur’s (1987) discounting equation: V ⫽ A ⁄ 共1 ⫹ kD兲 , where the perceived value (V) of an outcome is a function of its absolute value (A) divided by the delay (D) to its occurrence and the discount rate (k). The discount rate (k) determines the slope of the discounting function. As the value of k increases, the discounting curve steepens and the value of future outcomes decreases more rapidly. As larger k values are associated with future outcomes being viewed as less valuable, larger k values are typically associated with problem behaviors such as substance abuse, gambling, and other forms of risky behavior. For example, if the perceived value of a $100 prize available in 30 days is $75, then the approximate discount rate would be .011 (75 ⫽ 100/(1 ⫹ .011 ⴱ 30). However, if the perceived value of the $100 prize were $25, then the approximate discount rate (k) would be .10 (25 ⫽ 100/ (1 ⫹ .10 ⴱ 30). Studies of temporal discounting typically use the discount rate, k, as the dependent variable (for more detail of how discount values are estimated using the MCQ, see Kirby et al., 1999). Discount rates were estimated for each participant in the study using the MCQ (see Kirby et al., 1999, or Charlton et al., 2013, for detailed instructions regarding the scoring of the MCQ). Each of the 27 items on the MCQ (Cronbach’s alpha of .91 in the current study) asks participants to choose between either an immediately available option or a larger, delayed option. For example, the first question on the MCQ asks the respondent to choose between “$54 right now” or “$55 in 186 days.” While completing the MCQ, participants indicate their preference on each choice pair by circling either the immediate option or the delayed option for each question. The pattern of responses on the MCQ is analyzed to determine an estimate of the participants’ discount rate (k). The MCQ includes three sets of questions representing small ($25–$35), medium ($55–$60), and large ($75–$85) delayed outcomes. These three subscales serve as a manipulation check, as previous literature indicates that discount rates tend to decrease as the magnitude of the outcome increases. Discount rates for each of the three magnitudes (i.e., small, medium, and large) are estimated by finding the point at which participants switch from a preference for the delayed outcome to the more immediate outcome allowing for a general estimate of the participant’s k value for that magnitude. For the exact questions and a detailed explanation of how discount rates are estimated, see Charlton and colleagues (2013; Kirby et al., 1999). In addition to estimating the discount rate at
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each of the three magnitudes, an overall discount score was calculated by totaling the number of times the immediate, smaller option was selected on the 27 items, which was indicated as an “F total” score (Ftotal; range of 0 to 27). The Ftotal measure was used here to provide a general estimate of overall discounting pooled across the three magnitudes. Analyses of discounting were conducted mainly by the second author. The PICTS (Walters, 1995) was also given to the participants. The PICTS contains 80 questions and is comprised of several scales including three validity scales, eight thinking-style scales, four factor scales, and two general content scales. Participants respond to the question on the PICTS using a 4-point scale ranging from 4 (strongly agree) to 1 (disagree). For validity purposes, four cases were eliminated due to not completing the surveys; 27 additional cases were deleted for high scores on the confusion scale (above Confusion T score of 70); nine more cases were deleted because of high defensiveness (above Defensiveness T score of 65; see Walters, 2001). PICTS scales were then scored and prorated following Walters (2001, 2006b, 2011a). Consistent with our hypothesis that temporal discounting will be associated with reactive criminal thinking versus proactive criminal thinking, the methods will focus on the Proactive and Reactive scales and the scales that comprise them. The Proactive and Reactive Criminal Thinking Scales on the PICTS are considered composite scales and the most reliable among the PICTS scales with Proactive having test-retest reliabilities between .78 –.88 and Reactive at .70 –.73 (Walters, 2002, as cited in Walters, 2011b). The Proactive scale consists of historical criminal thinking (associated with criminal past), self-assertion (pressing will over others), and entitlement (sense of esteem and privilege the respondent takes in their criminal activity; Walters, 2006b). The Reactive Criminal Thinking scale consists of current criminal thinking (the best predictor of recidivism among the PICTS scales); cutoff (impulsivity and tendency to brush common deterrents to crime off); and problem avoidance (escapes challenges through crime; Walters, 2006b). In the current study, the Cronbach’s alphas for the scales are the following: current criminal thinking ⫽ .84; cutoff ⫽ .74; problem avoidance ⫽ .81; historical ⫽ .79; entitlement ⫽ .50; self-assertion ⫽ .77. These numbers are consistent with past research with the PICTS that shows internal consistencies ranging from .55 to .88 for males (Walters, 2001). Walters (2001) further indicates that test-retest stability at 2 weeks exceeds .70. The composite scales were scored following Walters (2006b, 2011a).
Results Data for 146 cases were analyzed in this study. Of these participants, 43.2% were African American, 1.4% American Indian, 46.6% White, 2.7% Hispanic/Latino, 2.7% biracial, 2.7% Other, and .7% (1 person) indicated both American Indian and Other. Average age was 36.74 (SD ⫽ 9.61; age range, 20 – 65) and marital status included single (56.2%), married (14.2%), partnered (11%), and divorced (18.5%). In regard to socioeconomic status, 43.8% reported being a member of the working class and not another class. The mean number of incarcerations (including current incarceration) per participant was 2.63 (SD ⫽ 1.81), with a range of one to eight incarcerations. For number of incarcerations, 35.6% reported the current incarceration as the only incarceration, 19.2%
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reported completing second incarceration, 41.8% reported three or more incarcerations, and 3.4% did not provide this information.
Table 1 Intercorrelations of Estimates of Temporal Discounting Estimate
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Temporal Discounting As the MCQ had not been used with an incarcerated population, we first investigated the results of the MCQ alone. As is typical for MCQ data, the estimated discount rates were not normally distributed. The mean discount rates (k, described above) for the small, medium, and large magnitudes were: .08 (SD ⫽ .09), .07 (SD ⫽ .09), and .06 (SD ⫽ .09), respectively. These discount rates suggest that, on average, the delays required for outcomes to lose half their value (the half-life; Kirby, 2009) for the small, medium, and large outcomes were 12.5 days, 14.28 days, and 16.67 days, respectively. Because of the nonnormal distribution of discount rates, a Friedman’s test (a nonparametric anlaysis of variance) was used to test for differences across the three magnitudes of outcomes tested in the MCQ. Consistent with previous literature on the MCQ and discounting (Green, Myerson, & McFadden, 1997; Kirby et al., 1999), discount rates were highest in the small magnitude condition and decreased as the magnitude increased, Friedman test of overall effect: 2(146, 2) ⫽ 62.19, p ⬍ .001. Differences between magnitudes were significant according to follow-up Wilcoxon signed-ranks test: small versus medium, z ⫽ ⫺5.10, p ⬍ .001; small versus large, z ⫽ ⫺6.09, p ⬍ .001; medium versus large, z ⫽ ⫺2.91, p ⫽ .004. The differences between magnitudes, the order of such differences, and the high internal consistency indicate that the MCQ produced valid data for incarcerated offenders. The relative ranking of discount rates between participants should also be consistent, such that participants with high discount rates in the small magnitude condition should also have relatively higher rates in the medium and large magnitude conditions as well. That is, although magnitude of outcome is associated with different discount rates, the relative rate of discounting should stay constant. We tested this prediction using a series of Spearman’s rho correlations, due to the nonnormal distribution of discount rates. As anticipated, despite differences in discount rate across the three magnitudes, the Spearman’s rho correlations found strong relative consistency within participants across the magnitudes: small to medium, r ⫽ .83, small to large, r ⫽ .79, and medium to large, r ⫽ .88. All correlations were significant at p ⬍ .001. These correlations importantly indicate a general consistency in the delay discounting process across the magnitudes within participants. The total number of times the immediate option was selected on the 27 items (the Ftotal variable) was calculated as an overall estimate of discounting, independent of magnitude. The mean Ftotal score for all participants was 18.14 (SD ⫽ 5.16). To verify Ftotal’s similarity to the three estimated discount rates, Spearman’s rho correlations (due to nonnormal distribution of Ftotal scores and k values) were used to correlate Ftotal and the small, medium, and large magnitude outcomes (see Table 1). Strength of correlations suggests Ftotal as a general estimator of temporal discounting.
Temporal Discounting and Criminal Thinking We tested the hypothesis that temporal discounting would be associated with reactive criminal thinking versus proactive crimi-
1. 2. 3. 4.
Ftotal Klarge Kmedium Ksmall
1
2
3
4
— .92ⴱⴱ .94ⴱⴱ .91ⴱⴱ
— .88ⴱⴱ .79ⴱⴱ
— .83ⴱⴱ
—
Note. Ftotal ⫽ Total number of immediate choices on the 27 items of the Monetary Choice Questionnaire (MCQ); Klarge ⫽ observed discount rate for the nine large magnitude questions on the MCQ; Kmedium ⫽ observed discount rate for the nine medium magnitude questions on the MCQ; Ksmall ⫽ observed discount rate for the nine small magnitude questions on the MCQ. ⴱⴱ All correlations were significant at .01 level (two-tailed).
nal thinking. Results indicated that various magnitudes (i.e., small, medium, and large) of temporal discounting scores were found to correlate with Reactive Criminal Thinking Scale and the scales that compose the Reactive Criminal Thinking Scale (see Table 2). Spearman’s rho correlations indicated a positive relation between the reactive scale and Ftotal (r ⫽ .19, p ⬍ .05), the medium (r ⫽ .23, p ⬍ .01), and the large (r ⫽ .17, p ⬍ .05) magnitude discount rates. The relation between the cutoff subscale and discounting with Spearman rho correlations of Ftotal was: r ⫽ .24 (p ⬍ .01), small magnitude: r ⫽ .20 (p ⬍ .05), medium magnitude: r ⫽ .27 (p ⬍ .01), and large magnitude: r ⫽ .25 (p ⬍ .01). These correlations were consistent with hypotheses. Similarly, and consistent with our hypotheses, medium magnitude discounting was also related to the two additional scales that compose the reactive composite scale, the current criminal thinking scale, r ⫽ .17 (p ⬍ .05), and the problem avoidance scale, r ⫽ .17 (p ⬍ .05). As hypothesized, temporal discounting was not correlated with the proactive criminal thinking scale or any of the scales that compose the proactive scale: entitlement, historical, and self-assertion criminal thinking styles (all correlations, r ⱕ.09; p ⬎.05; see Table 2). To rule out confound by age, as further detailed in the next section, age was not correlated with temporal discounting (p ⬎ .05 for all magnitudes). Using Spearman’s Rho, age was also not correlated with reactive criminal thinking (r ⫽ ⫺.07; p ⫽ .39) and only slightly, but negatively correlated with proactive criminal thinking (r ⫽ ⫺.18; p ⫽ .03). Thus age was unlikely to be a factor in the significant correlations between reactive criminal thinking and temporal discounting.
Temporal Discounting and Incarcerations Age and number of incarcerations. A primary question of interest in the current study is the relation between discount rates and number of incarcerations. A positive correlation was observed between the number of times an offender was incarcerated and the total times the immediate outcome was selected on the MCQ (Ftotal, r ⫽ .19, p ⫽ .03), and the k values small, r ⫽ .17 (p ⫽ .04); medium, r ⫽ .18 (p ⫽ .04); and large, r ⫽ .17 (p ⫽ .04), discount rates. However, a possible confound in this analysis is that number of incarcerations is positively correlated with age. Previous published research has indicated a negative relation between age and discount rates where discounting decreases as age of participant increases (e.g., Green, Fry, & Myerson, 1994). Spearman Rho
TEMPORAL DISCOUNTING AND CRIMINAL THINKING
Table 2 Correlations of Criminal Thinking Subscales and Incarcerations With Estimates of Temporal Discounting
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Estimate of temporal discounting Estimate criminal thinking
Ftotal
Klarge
Kmedium
Ksmall
PICTS subscales Reactive Proactive Cutoff Entitlement Current Historical Problem-avoidance Self-assertion Number of Incarcerations
.19ⴱ .05 .24ⴱⴱ .08 .14 .04 .12 .00 .19ⴱ
.17ⴱ .05 .25ⴱⴱ .09 .10 .03 .09 .02 .17
.23ⴱⴱ .06 .27ⴱⴱ .06 .17ⴱ .05 .17ⴱ .03 .18ⴱ
.15 .03 .20ⴱⴱ .09 .13 .01 .11 ⫺.02 .17ⴱ
Note. PICTS ⫽ Psychological Inventory of Criminal Thinking Styles. ⴱ Correlations significant at the 0.05 level (two-tailed). ⴱⴱ Correlations significant at the 0.01 level (two-tailed).
correlations between age and discount rates at each of the three magnitudes failed to find any significant correlations: small, r ⫽ .09 (p ⫽ .30); medium, r ⫽ .09 (p ⫽ .29); and large, r ⫽ .16 (p ⫽ .06), nor was the correlation between age and Ftotal significant: total immediate, r ⫽ .11 (p ⫽ .21). These findings suggest that although age and number of incarcerations are positively correlated, age of the participant is not a significant covariate in the relationship between temporal discounting and number of incarcerations. Number of incarcerations and “career” versus “early career” offenders. Spearman rho correlations indicated a weak correlation between discount rates and number of incarcerations (including the present incarceration); Ftotal: r ⫽ .19, p ⫽ .03; k values: large: r ⫽ .17, p ⫽ .05; medium: r ⫽ .18, p ⫽ .04; small: r ⫽ .17, p ⫽ .04. To explore this relation, an approximate mediansplit of the sample was used to divide the distribution into early career (those in their first or second incarceration, for a total of one to two incarcerations) and career (three or more incarcerations) groups. This allowed for the most equal groups for this comparison and was appropriate as confirmed with independent samples t tests that failed to find any significant differences between either the mean Ftotal scores (one incarceration: M ⫽ 16.94, SD ⫽ 5.17, two incarcerations: M ⫽ 17.36, SD ⫽ 5.19; t(78) ⫽ ⫺.34, p ⫽ .73) or between log-transformed discount rates from the MCQ for the small (one incarceration: M ⫽ ⫺1.55, SD ⫽ .61, two incarcerations: M ⫽ ⫺1.54, SD ⫽ .64; t(78) ⫽ ⫺.07, p ⫽ .95), medium (one incarceration: M ⫽ ⫺1.70, SD ⫽ .72, two incarcerations: M ⫽ ⫺1.63, SD ⫽ .69; t(78) ⫽ ⫺.46, p ⫽ .65), and large (one incarceration: M ⫽ ⫺1.85, SD ⫽ .68, two incarcerations: M ⫽ ⫺1.82, SD ⫽ .67; t(78) ⫽ ⫺.22, p ⫽ .83). Independent t tests were also made for discount rates between two and three incarceration participants. Overall, the mean differences in discount rates for those with two compared to three incarcerations were greater (but not significant) than between those with one and two incarcerations (Ftotal—two incarcerations: M ⫽ 17.36, SD ⫽ 5.19, three incarcerations: M ⫽ 19.36, SD ⫽ 4.47; t(51) ⫽ ⫺1.50, p ⫽ .14; log-k small—two incarceration: M ⫽ ⫺1.54, SD ⫽ .64, three incarcerations: M ⫽ ⫺1.32, SD ⫽ .62; t(51) ⫽ ⫺1.27, p ⫽ .21; log-k medium—two incarcerations: M ⫽ ⫺1.63, SD ⫽ .69, three
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incarcerations: M ⫽ ⫺1.40, SD ⫽ .55; t(51) ⫽ ⫺1.31, p ⫽ .20; log-k large—two incarcerations: M ⫽ ⫺1.82, SD ⫽ .67, three incarcerations: M ⫽ ⫺1.52, SD ⫽ .63; t(51) ⫽ ⫺1.65, p ⫽ .10). This pattern of results supports grouping one and two incarceration participants together as early career. The separation into career and early career is complicated by the correlation between age and number of incarcerations (Spearman’s rho: r ⫽ .23; p ⬍ .01). An independent samples t test found that the career group was older (M ⫽ 39.40, SD ⫽ 8.95) than the early career group (M ⫽ 34.75, SD ⫽ 9.46), t(137) ⫽ ⫺4.65 (p ⬍ .01). To account for possible effect of age, age was a covariate in the following analyses. To get a general idea of the relation between discount rates and the career versus early career distinction, a one-way analysis of covariance (ANCOVA) was conducted using type of offender (career vs. early career) as the independent variable, total number of immediate choices on the MCQ (Ftotal) as the dependent variable, and age as a covariate. Results found a nonsignificant effect of the covariate age, F(1, 136) ⫽ 1.48, p ⫽ .23, and a significant difference between the number of immediate choices on the MCQ for career (M ⫽ 19.28, SD ⫽ 4.81) compared to early career offenders (M ⫽ 17.11, SD ⫽ 5.18), F(1, 136) ⫽ 4.65, p ⬍ .05, 2 ⫽ .03. Results suggests that, controlling for age, career criminals (those with three or more incarcerations), discount the future more strongly than early career criminals (those with one to two incarcerations, including current). A second test was conducted using a 2 (Early Career, Career) ⫻ 3 (Small, Medium, and Large Discounting Magnitudes) repeatedmeasures ANCOVA to test for differences in discount rates across the three magnitudes for the two classifications of offenders. This repeated-measures test used type of offender as a between-subjects independent variable, magnitude of outcome type as a withinsubject repeated dependent variable, and age as a covariate. Prior to running the repeated measures test, the discount rates were log-transformed to correct for strong positive skew in the distribution. The log-transformed discount rates for each of the three models were then used as a repeated measures outcome variable. The result of this test found a main effect of career status with career criminals (3 or more incarcerations), showing higher rates of discounting (M ⫽ ⫺1.67, SEM ⫽ .07) compared to early career criminals (one or two incarcerations: M ⫽ ⫺1.44, SEM ⫽ .08; F(1, 136) ⫽ 4.32, p ⫽ .04; 2 ⫽ .03; see Figure 1). A repeatedmeasures main effect of magnitude was observed, F(2, 272) ⫽ 8.43, p ⬍ .001, 2 ⫽ .06. However, no significant differences were observed for the covariate age, F(1, 136) ⫽ 2.25, p ⫽ .14, 2 ⫽ .02; the age by magnitude interaction, F(2, 272) ⫽ 2.558, p ⫽ .08, 2 ⫽ .02; or the career by magnitude interaction, F(2, 272) ⫽ .04, p ⫽ .96, 2 ⫽ .00. In sum, those with one to two incarcerations discounted future outcomes less than those with more than two incarcerations.
Discussion This study explored the relationship between temporal discounting and criminal risk factors, specifically criminal thinking and history of incarcerations. Temporal discounting was found to be associated with reactive criminal thinking and the scales comprising reactive criminal thinking, but not proactive criminal thinking or any of the scales comprising proactive criminal thinking. Fur-
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Figure 1.
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Log transformed discount rates by number of incarcerations.
ther, the cutoff criminal thinking style showed particularly strong correlations with temporal discounting. This suggests that different cognitive processes may be involved in different types of criminal thinking. This finding is among the first to begin filling a gap in a needed area of scholarship: understanding the distinguishable features between criminal thinking styles (Taxman et al., 2011). As such, it also has promise in informing the development of effective interventions for offenders who endorse different styles of criminal thinking. For example, those with a reactive criminal thinking style may benefit from interventions targeting temporal discounting than those with a proactive criminal thinking style. Such interventions may include cognitive skills training programs that target improvements in temporal discounting. Studies exist that suggest cognitive interventions as useful in treating temporal discounting and risk behaviors (e.g., Bickel, Yi, Landes, Hill, & Baxter, 2011; Houben, Wilers, & Jansen, 2011). For example, helping clients in developing working memory capacity produced significant decreases in temporal discount rates (Bickel et al., 2011). Working memory training has also been shown to be effective in reducing the amount of alcohol consumed by problem drinkers (Houben et al., 2011). Future research may benefit in understanding working memory training on recidivism, as evidence indicates that problems in working memory do exist among offenders (see Syngelaki, Moore, Savage, Fairchild, & Van Goozen, 2009). Further, the research above suggests that computer-based cognitive training (as used in the studies cited above) may present a novel treatment approach for use in rehabilitation. As part of their daily routine, inmates could be asked to engage in computer-based working memory training aimed at decreasing their rate of temporal discounting. Further, current findings suggest that those with higher rates of incarcerations are more likely to choose an immediate reward and less likely to value long-term rewards or consequences. A consistent finding was that the medium magnitude was the most strongly associated with the reactive scale and consistently correlated with each of the subscales composing it. One might infer that a situation that involves monetary values of a medium amount is less clear compared to situations with a small or a large monetary amount, and therefore poses ambiguous consequences. This ambiguity in
consequences may be related to reactive criminal thinking, and may be the result of low tolerance for frustration that may be activated in a medium magnitude situation. More research is needed, but might speak to the need for anger management training. Indeed, Walters (2009) found anger management intervention was effective in reducing reactive criminal thinking, but not proactive criminal thinking. The existing discounting literature demonstrates that the devaluing of future outcomes can occur for both positive outcomes, such as the monetary gains used in this study, as well as for future negative consequences, such as money lost or negative health impacts (Odum, Madden, & Bickel, 2002). Of critical importance here is that education regarding both the future positive outcomes of abstaining from criminal behavior (i.e., positive family and social interactions) and the future negative consequences (i.e., imprisonment) of these actions is of decreasing value as the discounting rate of the client increases. An implication of this is that treatment for persons with high discount rates needs a greater emphasis on the immediate positive and negative outcomes associated with the behavior. This implication affirms Andrews and Bonta (2010) assertion for intense interventions for this type of offender, which includes interventions lasting at least 3 months, but typically not more than 9 months and use at least 40% of the offender’s time (Gendreau, 1996 as cited in Varghese, 2013). The current findings with offenders expand upon recent findings (e.g., Arantes et al., 2013) showing increased temporal discount rates in offenders as compared to nonoffenders. Current results showed that temporal discounting is related to two specific criminal risk factors in offenders, criminal thinking and criminal history (i.e., history of incarcerations). These findings may shed light on the nature of recidivism, that it may be related to cognitive processes such as temporal discounting. It is hoped that future research will build in this area to examine temporal discounting’s utility in predicting recidivism. As with all research, this study had limitations. The current study examined a limited range of offenders, those incarcerated for the first time to those with up to eight incarcerations, after taking out outliers which included two cases of offenders with 12 and 14 incarcerations, respectively. Correlations between number of incarcerations and temporal discounting were stronger when the outliers were included. Therefore, future research needs to examine temporal discounting and the career criminal, particularly those with extraordinary high amount of incarceration, arrests, and offenses (e.g., 100 plus, Walters, 1990). DeLisi (2005) indicates that such offenders, while accounting for a small percentage of the offender population, commit the vast majority of serious offenses. In addition, the results of the current study are not predictive but descriptive. Longitudinal studies that predict future criminal behavior from current temporal discounting would be helpful in establishing a predictive relationship. Despite these limitations, this study highlights the relationship between temporal discounting and criminal thinking and behavior and provides important implications for service delivery. Research with temporal discounting and offenders has the potential to shed light on individual differences of offenders and has implications in developing focused, targeted interventions that can better serve this population. Andrews and Bonta (2010) have indicated that effective treatments for offenders that reduce recidivism are responsive and tailored to the individual needs of the offender.
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Assessing and addressing the temporal-discounting inclinations of offenders may be one way to tailor interventions. Thus, this study attempted to understand at least some of the microlevel differences in cognitive processes and decision-making with criminal justice populations so that new interventions may be developed. Future studies need to confirm findings and develop treatments for offenders reduce recidivism.
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Walters, G. D. (2009). Anger management training in incarcerated male offenders: Differential impact on proactive and reactive criminal thinking. The International Journal of Forensic Mental Health, 8, 214 –217. doi:10.1080/14999010903358995 Walters, G. D. (2011a). Predicting recidivism with the Psychological Inventory of Criminal Thinking Styles and Level of Service InventoryRevised: Screening version. Law and Human Behavior, 35, 211–220. doi:10.1007/s10979-010-9231-7 Walters, G. D. (2011b). Taking the next step: Combining incrementally valid indicators to improve recidivism prediction. Assessment, 18, 227– 233. doi:10.1177/1073191110397484 Walters, G. D., Hagman, B. T., & Cohn, A. M. (2011). Toward a hierarchical model of criminal thinking: Evidence from item response theory and confirmatory factor analysis. Psychological Assessment, 23, 925– 936. doi:10.1037/a0024017 Wilson, J. Q., & Herrnstein, R. J. (1985). Crime & human nature. New York, NY: Simon & Schuster.
Received February 23, 2013 Revision received September 5, 2013 Accepted September 26, 2013 䡲
© 2014 American Psychological Association 1541-1559/14/$12.00 DOI: 10.1037/a0035004
Temporal Discounting and Criminal Thinking: Understanding Cognitive Processes to Align Services Femina P. Varghese, Shawn R. Charlton, Mara Wood, and Emily Trower
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University of Central Arkansas Temporal discounting is an indicator of impulsivity that has consistently been found to be associated with risky behaviors such as substance abuse and compulsive gambling. Yet, although criminal acts are clearly risky choice behaviors, no study has examined temporal discounting in the criminal attitudes and behaviors of adult offenders. Yet, such investigations have potential to understand the cognitive processes that underlie various criminal patterns of thinking and may help distinguish between high and low risk offenders. Therefore, the current study endeavored to fill this gap in the literature using 146 male inmates within 5 months of release. Results found that temporal discounting is correlated with reactive criminal thinking but was not correlated with proactive criminal thinking. In addition, inmates with higher rates of incarceration were also more likely to have higher rates of temporal discounting. Results shed light on the different cognitive processes that may underlie different styles of criminal thinking as well as potential differences in the discounting rates depending on history of incarcerations. This finding has implications for service delivery in criminal justice settings as those with reactive criminal thinking may benefit from specialized treatments for temporal discounting. Keywords: temporal discounting, offender, incarcerations, criminal thinking
decision-making, imagine a man has a savings bond worth $100 when it matures 6 months from now. Would he be willing to sell it for money right now? If so, what is the least that the he would accept for the bond? If he would be willing to sell the bond for anything less than its full future value, he is experiencing temporal discounting, because temporal discounting is devaluing of future outcomes, even after being presented with both choices and its consequences. The difference between the full value of the bond and the smallest amount he would be willing to accept for it right now can be viewed as an estimate of how much he discounts the future. If there is relatively little difference between full future value and his selling price then not much discounting has occurred. On the other hand, if he would part with his savings bond for a relatively small amount, then the discount rate is quite high. Temporal discounting refers specifically to the value of future outcomes. In this way, discounting is a process involved in impulsive behavior, but unique from other aspects such as consideration of future outcomes (Joireman, Balliet, Sportt, Spangenberg, & Schultz, 2008) and the ability to inhibit responses (Cheng, Lu, Han, Gonzalez-Vallejo, & Sui, 2012). Considering that high discounting of future outcomes may have ramifications for continued criminal behavior, temporal discounting could be a valuable tool for informing treatments on decisionmaking in offenders. Indeed, temporal discounting is strongly tied to risk-taking behaviors. Persons who value future outcomes less than controls are more likely to abuse substances like alcohol, cocaine, heroin, nicotine, and opiates (e.g., Bickel, Odum, & Madden, 1999; Coffey, Gudleski, Saladin, & Brady, 2003; Kirby, Petry, & Bickel, 1999). Further, those with high temporal discounting are more likely to gamble pathologically and show patterns of risky decision-making behavior (e.g., Chesson et al., 2006; Odum, Madden, Badger, & Bickel, 2000). Thus, elevated temporal discounting may be a predictor of long-term behavioral problems
Offenders have often been described as impulsive (Carroll et al., 2006) with difficulty setting long-term goals (McMurran & Ward, 2004). A long-standing explanation for their impulsive behavior is due to a tendency to choose the immediate positive reward of a criminal act while discounting the potential long-term punishment of the crime (Walters, 2006a; Wilson & Herrnstein, 1985). This explanation can be viewed as temporal discounting: the tendency for the perceived value of future outcomes to be viewed as less than if they were immediately available (Critchfield & Kollins, 2001; Green & Myerson, 2004). Wilson and Herrnstein (1985) first suggested that persons with short time horizons are prone to criminal behavior because of decreased value of the future punishments; this includes the idea of how far into the future an individual considers consequences. Yet, in temporal discounting future consequences are considered, but devalued that they no longer control behavior. Temporal discounting is an element of impulsivity unique from simply lacking self-control, the latter being associated with lacking awareness of future consequences or not being able to inhibit oneself from choosing the immediate over the long-term (Marcus, 2004). In temporal discounting, awareness of immediate and future consequences of choices is present but does not hold its value (Madden & Bickel, 2010). To appreciate the role of discounting in
This article was published Online First March 17, 2014. Femina P. Varghese, Shawn R. Charlton, Mara Wood, and Emily Trower, Department of Psychology and Counseling, University of Central Arkansas. Correspondence concerning this article should be addressed to Femina P. Varghese, Department of Psychology and Counseling, University of Central Arkansas, 201 Donaghey Avenue, 213 Mashburn Hall, Conway, AR 72035-0001. E-mail: [email protected] 171
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(Bickel & Johnson, 2003). Criminal behavior also shares characteristics associated with temporal discounting. Crime involves choices for immediate benefits (e.g., money, drugs) but may result in larger, delayed negative outcomes (e.g., incarceration). Discounting may play a role in criminal behavior, independent of substance abuse. A recent study comparing the discount rates between nonoffenders and offenders, controlling for substance abuse, found greater discount rates in offenders (Arantes, Berg, Lawlor, & Grace, 2013), suggesting discounting’s role in criminal behavior, independent of substance abuse. Despite these findings, temporal discounting as it relates to offenders has not received adequate attention in the literature. A recent literature search in PsycINFO, PsychARTICLES, and SocIndexFulltext with the key terms for offender (i.e., offender, criminal) and temporal discounting (i.e., temporal discounting, delay discounting) resulted in just eight total peer-reviewed articles. Of these, only one (Hanoch, Rolison, & Gummerum, 2013) conducted an actual study with offender participants; they found offenders discounted more. None of the eight articles examined temporal discounting with two significant risk factors for crime: criminal attitudes and criminal history (see Andrews & Bonta, 2010), particularly in terms of history of incarcerations. Yet, such investigations may foster effective interventions to reduce recidivism. Criminal thinking (i.e., criminal attitudes) is one of the strongest risk factors for criminal behavior (Andrews, Bonta, & Hoge, 1990; Gendreau, Little, & Goggin, 1996). Yet, the literature on criminal thinking focuses on listing the content of criminal thoughts (Andrews & Bonta, 2010), whereas the process of criminal thinking in criminal behavior has not been adequately explored. Likely as a result of the content focus, the majority of treatment approaches emphasize changing thought content (see Andrews & Bonta, 2010). Therefore, examining the relationship between temporal discounting and criminal thinking would be a first step in helping shift the focus from content of criminal thoughts to examining the underlying processes of these thoughts. Further, there is still a significant need for research that distinguishes different types of criminal thinking (Taxman, Rhodes, & Dumenci, 2011). Such research could help tailor treatments to be responsive to offenders’ individual needs, an important component in treatment effectiveness (Andrews & Bonta, 2010; Taxman et al., 2011). Considering this, we explored temporal discounting with different criminal attitudes. Altough several measures of criminal thinking exist, the Psychological Inventory of Criminal Thinking Styles (PICTS, Walters, 1995) is among the few that have undergone several validation studies with both incarcerated and nonincarcerated offenders (Taxman et al., 2011). The PICTS has eight different scales measuring various criminal thinking styles (see Walters, 1995). Further, two composite scales termed reactive and proactive criminal thinking have been developed, which comprise several criminal thinking scales (Walters, 2006b). Proactive criminal thinking incorporates “calculated” (p. 25) criminal styles of thinking and reactive criminal thinking “reflects spontaneity, rashness, and impulsivity” (Walters, 2006b, p. 25). The PICTS is organized hierarchically with general criminal thinking at the top, then proactive and reactive, then individual criminal thinking style scale below that (see Walters, Hagman, & Cohn, 2011). Examination of the composite scales has been noted to hold implications for the development of effective interventions for offenders (Walters,
2006b). Given the association between temporal discounting and risky and impulsive behavior, and considering that reactive criminal thinking is associated with little consideration for the future, it is hypothesized that higher scores on temporal discounting will be most associated with higher scores on the reactive criminal thinking scale, but not the proactive criminal thinking scale. Career criminals are “those persistent offenders who make repeated transits of [the] criminal justice system” (Petersilia & Honig, 1980, p. v). Such high-risk offenders should be the focus of treatment (Andrews & Bonta, 2010). Yet, little empirical research has examined repeat offenders in terms of cognitive processes such as temporal discounting. Such examinations may assist in developing intervention methods specifically targeting the repeat or high-risk offender. Thus, this article examines the relationship between history of incarcerations and temporal discounting. Given this, this current study aimed to discover in what way temporal discounting is associated with criminal thinking. On a lesser note, we also wanted to explore temporal discounting’s association with history of incarcerations. We hypothesized that higher scores on temporal discounting will be most associated with higher scores on the reactive criminal thinking scale, but not with the proactive criminal scale. In addition, we hypothesized that higher rates of incarceration are associated with higher rates of temporal discounting.
Methods Participants Data were gathered from inmates within 5 months of release from a state prison within the southern part of the United States. Inmates completed all measures in groups of 15–30 persons, and the researcher saw approximately two groups during four separate visits. Among the 242 offenders who were available and could read, 197 male inmates agreed to participate (81%). PICTS (Walters, 1995) was used to identify participants who provided various invalid responses; as a result, data from 40 participants were removed due to these invalid responses including one that did not complete the measures at all. Five more cases were deleted due to univariate outliers (z ⬎ ⫹ 3.29; Tabachnick & Fidell, 2007) on two temporal discounting subscales (three total cases) and outliers on number of incarcerations (two total cases). In addition, six more cases did not have usable discount scores on the discounting measure (the Monetary Choice Questionnaire, MCQ; Kirby et al., 1999), because of a failure to answer the majority of the items on the scale. Thus, 146 cases remained for the analyses. The vast majority of these offenders indicated being at a minimal security level (67.1%); 30.1% indicated medium security level; and 1.4% reported being at a maximum security level, and finally 1.4% (two total participants) reported “Other” with one stating “none” and the other writing “A,” an unclear response. Information provided by the community corrections department (taken from court records) indicated offenders’ diverse criminal backgrounds ranging from violent (e.g., murder, sexual offenses) to nonviolent (e.g., drug offenses, theft).
Materials Participants completed a survey packet consisting of several scales including a demographic survey, a temporal discounting
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TEMPORAL DISCOUNTING AND CRIMINAL THINKING
scale (MCQ; Kirby et al., 1999), and the PICTS (Walters, 1995). The demographics section asked several questions, including age, race, socioeconomic status, education, and number of times incarcerated, including the present incarceration. The temporal discounting scale used in the current study was modeled after the commonly used Monetary Choice Questionnaire (MCQ, developed by Kirby et al., 1999) that has been used frequently in discounting studies of the relation between discounting and real-world behaviors such as substance abuse (Duckworth & Seligman, 2005; Kirby & Finch, 2010; Liu, Vassileva, Gonzalez, & Martin, 2012; MacKillop et al., 2010). The questions were the same as the MCQ, although the instructions were not exact to the original but similar to what was provided in the original article and is exactly as was used in Charlton et al. (2013). The MCQ is a paper-and-pencil measure that allows for the quick estimation of a discount rate based on Mazur’s (1987) discounting equation: V ⫽ A ⁄ 共1 ⫹ kD兲 , where the perceived value (V) of an outcome is a function of its absolute value (A) divided by the delay (D) to its occurrence and the discount rate (k). The discount rate (k) determines the slope of the discounting function. As the value of k increases, the discounting curve steepens and the value of future outcomes decreases more rapidly. As larger k values are associated with future outcomes being viewed as less valuable, larger k values are typically associated with problem behaviors such as substance abuse, gambling, and other forms of risky behavior. For example, if the perceived value of a $100 prize available in 30 days is $75, then the approximate discount rate would be .011 (75 ⫽ 100/(1 ⫹ .011 ⴱ 30). However, if the perceived value of the $100 prize were $25, then the approximate discount rate (k) would be .10 (25 ⫽ 100/ (1 ⫹ .10 ⴱ 30). Studies of temporal discounting typically use the discount rate, k, as the dependent variable (for more detail of how discount values are estimated using the MCQ, see Kirby et al., 1999). Discount rates were estimated for each participant in the study using the MCQ (see Kirby et al., 1999, or Charlton et al., 2013, for detailed instructions regarding the scoring of the MCQ). Each of the 27 items on the MCQ (Cronbach’s alpha of .91 in the current study) asks participants to choose between either an immediately available option or a larger, delayed option. For example, the first question on the MCQ asks the respondent to choose between “$54 right now” or “$55 in 186 days.” While completing the MCQ, participants indicate their preference on each choice pair by circling either the immediate option or the delayed option for each question. The pattern of responses on the MCQ is analyzed to determine an estimate of the participants’ discount rate (k). The MCQ includes three sets of questions representing small ($25–$35), medium ($55–$60), and large ($75–$85) delayed outcomes. These three subscales serve as a manipulation check, as previous literature indicates that discount rates tend to decrease as the magnitude of the outcome increases. Discount rates for each of the three magnitudes (i.e., small, medium, and large) are estimated by finding the point at which participants switch from a preference for the delayed outcome to the more immediate outcome allowing for a general estimate of the participant’s k value for that magnitude. For the exact questions and a detailed explanation of how discount rates are estimated, see Charlton and colleagues (2013; Kirby et al., 1999). In addition to estimating the discount rate at
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each of the three magnitudes, an overall discount score was calculated by totaling the number of times the immediate, smaller option was selected on the 27 items, which was indicated as an “F total” score (Ftotal; range of 0 to 27). The Ftotal measure was used here to provide a general estimate of overall discounting pooled across the three magnitudes. Analyses of discounting were conducted mainly by the second author. The PICTS (Walters, 1995) was also given to the participants. The PICTS contains 80 questions and is comprised of several scales including three validity scales, eight thinking-style scales, four factor scales, and two general content scales. Participants respond to the question on the PICTS using a 4-point scale ranging from 4 (strongly agree) to 1 (disagree). For validity purposes, four cases were eliminated due to not completing the surveys; 27 additional cases were deleted for high scores on the confusion scale (above Confusion T score of 70); nine more cases were deleted because of high defensiveness (above Defensiveness T score of 65; see Walters, 2001). PICTS scales were then scored and prorated following Walters (2001, 2006b, 2011a). Consistent with our hypothesis that temporal discounting will be associated with reactive criminal thinking versus proactive criminal thinking, the methods will focus on the Proactive and Reactive scales and the scales that comprise them. The Proactive and Reactive Criminal Thinking Scales on the PICTS are considered composite scales and the most reliable among the PICTS scales with Proactive having test-retest reliabilities between .78 –.88 and Reactive at .70 –.73 (Walters, 2002, as cited in Walters, 2011b). The Proactive scale consists of historical criminal thinking (associated with criminal past), self-assertion (pressing will over others), and entitlement (sense of esteem and privilege the respondent takes in their criminal activity; Walters, 2006b). The Reactive Criminal Thinking scale consists of current criminal thinking (the best predictor of recidivism among the PICTS scales); cutoff (impulsivity and tendency to brush common deterrents to crime off); and problem avoidance (escapes challenges through crime; Walters, 2006b). In the current study, the Cronbach’s alphas for the scales are the following: current criminal thinking ⫽ .84; cutoff ⫽ .74; problem avoidance ⫽ .81; historical ⫽ .79; entitlement ⫽ .50; self-assertion ⫽ .77. These numbers are consistent with past research with the PICTS that shows internal consistencies ranging from .55 to .88 for males (Walters, 2001). Walters (2001) further indicates that test-retest stability at 2 weeks exceeds .70. The composite scales were scored following Walters (2006b, 2011a).
Results Data for 146 cases were analyzed in this study. Of these participants, 43.2% were African American, 1.4% American Indian, 46.6% White, 2.7% Hispanic/Latino, 2.7% biracial, 2.7% Other, and .7% (1 person) indicated both American Indian and Other. Average age was 36.74 (SD ⫽ 9.61; age range, 20 – 65) and marital status included single (56.2%), married (14.2%), partnered (11%), and divorced (18.5%). In regard to socioeconomic status, 43.8% reported being a member of the working class and not another class. The mean number of incarcerations (including current incarceration) per participant was 2.63 (SD ⫽ 1.81), with a range of one to eight incarcerations. For number of incarcerations, 35.6% reported the current incarceration as the only incarceration, 19.2%
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reported completing second incarceration, 41.8% reported three or more incarcerations, and 3.4% did not provide this information.
Table 1 Intercorrelations of Estimates of Temporal Discounting Estimate
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Temporal Discounting As the MCQ had not been used with an incarcerated population, we first investigated the results of the MCQ alone. As is typical for MCQ data, the estimated discount rates were not normally distributed. The mean discount rates (k, described above) for the small, medium, and large magnitudes were: .08 (SD ⫽ .09), .07 (SD ⫽ .09), and .06 (SD ⫽ .09), respectively. These discount rates suggest that, on average, the delays required for outcomes to lose half their value (the half-life; Kirby, 2009) for the small, medium, and large outcomes were 12.5 days, 14.28 days, and 16.67 days, respectively. Because of the nonnormal distribution of discount rates, a Friedman’s test (a nonparametric anlaysis of variance) was used to test for differences across the three magnitudes of outcomes tested in the MCQ. Consistent with previous literature on the MCQ and discounting (Green, Myerson, & McFadden, 1997; Kirby et al., 1999), discount rates were highest in the small magnitude condition and decreased as the magnitude increased, Friedman test of overall effect: 2(146, 2) ⫽ 62.19, p ⬍ .001. Differences between magnitudes were significant according to follow-up Wilcoxon signed-ranks test: small versus medium, z ⫽ ⫺5.10, p ⬍ .001; small versus large, z ⫽ ⫺6.09, p ⬍ .001; medium versus large, z ⫽ ⫺2.91, p ⫽ .004. The differences between magnitudes, the order of such differences, and the high internal consistency indicate that the MCQ produced valid data for incarcerated offenders. The relative ranking of discount rates between participants should also be consistent, such that participants with high discount rates in the small magnitude condition should also have relatively higher rates in the medium and large magnitude conditions as well. That is, although magnitude of outcome is associated with different discount rates, the relative rate of discounting should stay constant. We tested this prediction using a series of Spearman’s rho correlations, due to the nonnormal distribution of discount rates. As anticipated, despite differences in discount rate across the three magnitudes, the Spearman’s rho correlations found strong relative consistency within participants across the magnitudes: small to medium, r ⫽ .83, small to large, r ⫽ .79, and medium to large, r ⫽ .88. All correlations were significant at p ⬍ .001. These correlations importantly indicate a general consistency in the delay discounting process across the magnitudes within participants. The total number of times the immediate option was selected on the 27 items (the Ftotal variable) was calculated as an overall estimate of discounting, independent of magnitude. The mean Ftotal score for all participants was 18.14 (SD ⫽ 5.16). To verify Ftotal’s similarity to the three estimated discount rates, Spearman’s rho correlations (due to nonnormal distribution of Ftotal scores and k values) were used to correlate Ftotal and the small, medium, and large magnitude outcomes (see Table 1). Strength of correlations suggests Ftotal as a general estimator of temporal discounting.
Temporal Discounting and Criminal Thinking We tested the hypothesis that temporal discounting would be associated with reactive criminal thinking versus proactive crimi-
1. 2. 3. 4.
Ftotal Klarge Kmedium Ksmall
1
2
3
4
— .92ⴱⴱ .94ⴱⴱ .91ⴱⴱ
— .88ⴱⴱ .79ⴱⴱ
— .83ⴱⴱ
—
Note. Ftotal ⫽ Total number of immediate choices on the 27 items of the Monetary Choice Questionnaire (MCQ); Klarge ⫽ observed discount rate for the nine large magnitude questions on the MCQ; Kmedium ⫽ observed discount rate for the nine medium magnitude questions on the MCQ; Ksmall ⫽ observed discount rate for the nine small magnitude questions on the MCQ. ⴱⴱ All correlations were significant at .01 level (two-tailed).
nal thinking. Results indicated that various magnitudes (i.e., small, medium, and large) of temporal discounting scores were found to correlate with Reactive Criminal Thinking Scale and the scales that compose the Reactive Criminal Thinking Scale (see Table 2). Spearman’s rho correlations indicated a positive relation between the reactive scale and Ftotal (r ⫽ .19, p ⬍ .05), the medium (r ⫽ .23, p ⬍ .01), and the large (r ⫽ .17, p ⬍ .05) magnitude discount rates. The relation between the cutoff subscale and discounting with Spearman rho correlations of Ftotal was: r ⫽ .24 (p ⬍ .01), small magnitude: r ⫽ .20 (p ⬍ .05), medium magnitude: r ⫽ .27 (p ⬍ .01), and large magnitude: r ⫽ .25 (p ⬍ .01). These correlations were consistent with hypotheses. Similarly, and consistent with our hypotheses, medium magnitude discounting was also related to the two additional scales that compose the reactive composite scale, the current criminal thinking scale, r ⫽ .17 (p ⬍ .05), and the problem avoidance scale, r ⫽ .17 (p ⬍ .05). As hypothesized, temporal discounting was not correlated with the proactive criminal thinking scale or any of the scales that compose the proactive scale: entitlement, historical, and self-assertion criminal thinking styles (all correlations, r ⱕ.09; p ⬎.05; see Table 2). To rule out confound by age, as further detailed in the next section, age was not correlated with temporal discounting (p ⬎ .05 for all magnitudes). Using Spearman’s Rho, age was also not correlated with reactive criminal thinking (r ⫽ ⫺.07; p ⫽ .39) and only slightly, but negatively correlated with proactive criminal thinking (r ⫽ ⫺.18; p ⫽ .03). Thus age was unlikely to be a factor in the significant correlations between reactive criminal thinking and temporal discounting.
Temporal Discounting and Incarcerations Age and number of incarcerations. A primary question of interest in the current study is the relation between discount rates and number of incarcerations. A positive correlation was observed between the number of times an offender was incarcerated and the total times the immediate outcome was selected on the MCQ (Ftotal, r ⫽ .19, p ⫽ .03), and the k values small, r ⫽ .17 (p ⫽ .04); medium, r ⫽ .18 (p ⫽ .04); and large, r ⫽ .17 (p ⫽ .04), discount rates. However, a possible confound in this analysis is that number of incarcerations is positively correlated with age. Previous published research has indicated a negative relation between age and discount rates where discounting decreases as age of participant increases (e.g., Green, Fry, & Myerson, 1994). Spearman Rho
TEMPORAL DISCOUNTING AND CRIMINAL THINKING
Table 2 Correlations of Criminal Thinking Subscales and Incarcerations With Estimates of Temporal Discounting
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Estimate of temporal discounting Estimate criminal thinking
Ftotal
Klarge
Kmedium
Ksmall
PICTS subscales Reactive Proactive Cutoff Entitlement Current Historical Problem-avoidance Self-assertion Number of Incarcerations
.19ⴱ .05 .24ⴱⴱ .08 .14 .04 .12 .00 .19ⴱ
.17ⴱ .05 .25ⴱⴱ .09 .10 .03 .09 .02 .17
.23ⴱⴱ .06 .27ⴱⴱ .06 .17ⴱ .05 .17ⴱ .03 .18ⴱ
.15 .03 .20ⴱⴱ .09 .13 .01 .11 ⫺.02 .17ⴱ
Note. PICTS ⫽ Psychological Inventory of Criminal Thinking Styles. ⴱ Correlations significant at the 0.05 level (two-tailed). ⴱⴱ Correlations significant at the 0.01 level (two-tailed).
correlations between age and discount rates at each of the three magnitudes failed to find any significant correlations: small, r ⫽ .09 (p ⫽ .30); medium, r ⫽ .09 (p ⫽ .29); and large, r ⫽ .16 (p ⫽ .06), nor was the correlation between age and Ftotal significant: total immediate, r ⫽ .11 (p ⫽ .21). These findings suggest that although age and number of incarcerations are positively correlated, age of the participant is not a significant covariate in the relationship between temporal discounting and number of incarcerations. Number of incarcerations and “career” versus “early career” offenders. Spearman rho correlations indicated a weak correlation between discount rates and number of incarcerations (including the present incarceration); Ftotal: r ⫽ .19, p ⫽ .03; k values: large: r ⫽ .17, p ⫽ .05; medium: r ⫽ .18, p ⫽ .04; small: r ⫽ .17, p ⫽ .04. To explore this relation, an approximate mediansplit of the sample was used to divide the distribution into early career (those in their first or second incarceration, for a total of one to two incarcerations) and career (three or more incarcerations) groups. This allowed for the most equal groups for this comparison and was appropriate as confirmed with independent samples t tests that failed to find any significant differences between either the mean Ftotal scores (one incarceration: M ⫽ 16.94, SD ⫽ 5.17, two incarcerations: M ⫽ 17.36, SD ⫽ 5.19; t(78) ⫽ ⫺.34, p ⫽ .73) or between log-transformed discount rates from the MCQ for the small (one incarceration: M ⫽ ⫺1.55, SD ⫽ .61, two incarcerations: M ⫽ ⫺1.54, SD ⫽ .64; t(78) ⫽ ⫺.07, p ⫽ .95), medium (one incarceration: M ⫽ ⫺1.70, SD ⫽ .72, two incarcerations: M ⫽ ⫺1.63, SD ⫽ .69; t(78) ⫽ ⫺.46, p ⫽ .65), and large (one incarceration: M ⫽ ⫺1.85, SD ⫽ .68, two incarcerations: M ⫽ ⫺1.82, SD ⫽ .67; t(78) ⫽ ⫺.22, p ⫽ .83). Independent t tests were also made for discount rates between two and three incarceration participants. Overall, the mean differences in discount rates for those with two compared to three incarcerations were greater (but not significant) than between those with one and two incarcerations (Ftotal—two incarcerations: M ⫽ 17.36, SD ⫽ 5.19, three incarcerations: M ⫽ 19.36, SD ⫽ 4.47; t(51) ⫽ ⫺1.50, p ⫽ .14; log-k small—two incarceration: M ⫽ ⫺1.54, SD ⫽ .64, three incarcerations: M ⫽ ⫺1.32, SD ⫽ .62; t(51) ⫽ ⫺1.27, p ⫽ .21; log-k medium—two incarcerations: M ⫽ ⫺1.63, SD ⫽ .69, three
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incarcerations: M ⫽ ⫺1.40, SD ⫽ .55; t(51) ⫽ ⫺1.31, p ⫽ .20; log-k large—two incarcerations: M ⫽ ⫺1.82, SD ⫽ .67, three incarcerations: M ⫽ ⫺1.52, SD ⫽ .63; t(51) ⫽ ⫺1.65, p ⫽ .10). This pattern of results supports grouping one and two incarceration participants together as early career. The separation into career and early career is complicated by the correlation between age and number of incarcerations (Spearman’s rho: r ⫽ .23; p ⬍ .01). An independent samples t test found that the career group was older (M ⫽ 39.40, SD ⫽ 8.95) than the early career group (M ⫽ 34.75, SD ⫽ 9.46), t(137) ⫽ ⫺4.65 (p ⬍ .01). To account for possible effect of age, age was a covariate in the following analyses. To get a general idea of the relation between discount rates and the career versus early career distinction, a one-way analysis of covariance (ANCOVA) was conducted using type of offender (career vs. early career) as the independent variable, total number of immediate choices on the MCQ (Ftotal) as the dependent variable, and age as a covariate. Results found a nonsignificant effect of the covariate age, F(1, 136) ⫽ 1.48, p ⫽ .23, and a significant difference between the number of immediate choices on the MCQ for career (M ⫽ 19.28, SD ⫽ 4.81) compared to early career offenders (M ⫽ 17.11, SD ⫽ 5.18), F(1, 136) ⫽ 4.65, p ⬍ .05, 2 ⫽ .03. Results suggests that, controlling for age, career criminals (those with three or more incarcerations), discount the future more strongly than early career criminals (those with one to two incarcerations, including current). A second test was conducted using a 2 (Early Career, Career) ⫻ 3 (Small, Medium, and Large Discounting Magnitudes) repeatedmeasures ANCOVA to test for differences in discount rates across the three magnitudes for the two classifications of offenders. This repeated-measures test used type of offender as a between-subjects independent variable, magnitude of outcome type as a withinsubject repeated dependent variable, and age as a covariate. Prior to running the repeated measures test, the discount rates were log-transformed to correct for strong positive skew in the distribution. The log-transformed discount rates for each of the three models were then used as a repeated measures outcome variable. The result of this test found a main effect of career status with career criminals (3 or more incarcerations), showing higher rates of discounting (M ⫽ ⫺1.67, SEM ⫽ .07) compared to early career criminals (one or two incarcerations: M ⫽ ⫺1.44, SEM ⫽ .08; F(1, 136) ⫽ 4.32, p ⫽ .04; 2 ⫽ .03; see Figure 1). A repeatedmeasures main effect of magnitude was observed, F(2, 272) ⫽ 8.43, p ⬍ .001, 2 ⫽ .06. However, no significant differences were observed for the covariate age, F(1, 136) ⫽ 2.25, p ⫽ .14, 2 ⫽ .02; the age by magnitude interaction, F(2, 272) ⫽ 2.558, p ⫽ .08, 2 ⫽ .02; or the career by magnitude interaction, F(2, 272) ⫽ .04, p ⫽ .96, 2 ⫽ .00. In sum, those with one to two incarcerations discounted future outcomes less than those with more than two incarcerations.
Discussion This study explored the relationship between temporal discounting and criminal risk factors, specifically criminal thinking and history of incarcerations. Temporal discounting was found to be associated with reactive criminal thinking and the scales comprising reactive criminal thinking, but not proactive criminal thinking or any of the scales comprising proactive criminal thinking. Fur-
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Figure 1.
VARGHESE, CHARLTON, WOOD, AND TROWER
Log transformed discount rates by number of incarcerations.
ther, the cutoff criminal thinking style showed particularly strong correlations with temporal discounting. This suggests that different cognitive processes may be involved in different types of criminal thinking. This finding is among the first to begin filling a gap in a needed area of scholarship: understanding the distinguishable features between criminal thinking styles (Taxman et al., 2011). As such, it also has promise in informing the development of effective interventions for offenders who endorse different styles of criminal thinking. For example, those with a reactive criminal thinking style may benefit from interventions targeting temporal discounting than those with a proactive criminal thinking style. Such interventions may include cognitive skills training programs that target improvements in temporal discounting. Studies exist that suggest cognitive interventions as useful in treating temporal discounting and risk behaviors (e.g., Bickel, Yi, Landes, Hill, & Baxter, 2011; Houben, Wilers, & Jansen, 2011). For example, helping clients in developing working memory capacity produced significant decreases in temporal discount rates (Bickel et al., 2011). Working memory training has also been shown to be effective in reducing the amount of alcohol consumed by problem drinkers (Houben et al., 2011). Future research may benefit in understanding working memory training on recidivism, as evidence indicates that problems in working memory do exist among offenders (see Syngelaki, Moore, Savage, Fairchild, & Van Goozen, 2009). Further, the research above suggests that computer-based cognitive training (as used in the studies cited above) may present a novel treatment approach for use in rehabilitation. As part of their daily routine, inmates could be asked to engage in computer-based working memory training aimed at decreasing their rate of temporal discounting. Further, current findings suggest that those with higher rates of incarcerations are more likely to choose an immediate reward and less likely to value long-term rewards or consequences. A consistent finding was that the medium magnitude was the most strongly associated with the reactive scale and consistently correlated with each of the subscales composing it. One might infer that a situation that involves monetary values of a medium amount is less clear compared to situations with a small or a large monetary amount, and therefore poses ambiguous consequences. This ambiguity in
consequences may be related to reactive criminal thinking, and may be the result of low tolerance for frustration that may be activated in a medium magnitude situation. More research is needed, but might speak to the need for anger management training. Indeed, Walters (2009) found anger management intervention was effective in reducing reactive criminal thinking, but not proactive criminal thinking. The existing discounting literature demonstrates that the devaluing of future outcomes can occur for both positive outcomes, such as the monetary gains used in this study, as well as for future negative consequences, such as money lost or negative health impacts (Odum, Madden, & Bickel, 2002). Of critical importance here is that education regarding both the future positive outcomes of abstaining from criminal behavior (i.e., positive family and social interactions) and the future negative consequences (i.e., imprisonment) of these actions is of decreasing value as the discounting rate of the client increases. An implication of this is that treatment for persons with high discount rates needs a greater emphasis on the immediate positive and negative outcomes associated with the behavior. This implication affirms Andrews and Bonta (2010) assertion for intense interventions for this type of offender, which includes interventions lasting at least 3 months, but typically not more than 9 months and use at least 40% of the offender’s time (Gendreau, 1996 as cited in Varghese, 2013). The current findings with offenders expand upon recent findings (e.g., Arantes et al., 2013) showing increased temporal discount rates in offenders as compared to nonoffenders. Current results showed that temporal discounting is related to two specific criminal risk factors in offenders, criminal thinking and criminal history (i.e., history of incarcerations). These findings may shed light on the nature of recidivism, that it may be related to cognitive processes such as temporal discounting. It is hoped that future research will build in this area to examine temporal discounting’s utility in predicting recidivism. As with all research, this study had limitations. The current study examined a limited range of offenders, those incarcerated for the first time to those with up to eight incarcerations, after taking out outliers which included two cases of offenders with 12 and 14 incarcerations, respectively. Correlations between number of incarcerations and temporal discounting were stronger when the outliers were included. Therefore, future research needs to examine temporal discounting and the career criminal, particularly those with extraordinary high amount of incarceration, arrests, and offenses (e.g., 100 plus, Walters, 1990). DeLisi (2005) indicates that such offenders, while accounting for a small percentage of the offender population, commit the vast majority of serious offenses. In addition, the results of the current study are not predictive but descriptive. Longitudinal studies that predict future criminal behavior from current temporal discounting would be helpful in establishing a predictive relationship. Despite these limitations, this study highlights the relationship between temporal discounting and criminal thinking and behavior and provides important implications for service delivery. Research with temporal discounting and offenders has the potential to shed light on individual differences of offenders and has implications in developing focused, targeted interventions that can better serve this population. Andrews and Bonta (2010) have indicated that effective treatments for offenders that reduce recidivism are responsive and tailored to the individual needs of the offender.
TEMPORAL DISCOUNTING AND CRIMINAL THINKING
Assessing and addressing the temporal-discounting inclinations of offenders may be one way to tailor interventions. Thus, this study attempted to understand at least some of the microlevel differences in cognitive processes and decision-making with criminal justice populations so that new interventions may be developed. Future studies need to confirm findings and develop treatments for offenders reduce recidivism.
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Received February 23, 2013 Revision received September 5, 2013 Accepted September 26, 2013 䡲
