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Abstract

The study of prosocial behavior, from the cognitive-evolutive approach, has indicated that moral reasoning is the basis of prosocial decisions. Advances in the study of decision-making have shown that there are other factors associated with rational capacity that affect prosocial decision-making. The role of rational decision-making is an aspect that has received little attention from this perspective and could be relevant to explaining prosocial behaviors. This study examines whether rational decision-making is a potential mediator between empathy and prosocial behavior in offenders adolescents. A total of 413 Colombian adolescent offenders aged 14 to 18 years (M_age = 16.67, SD = 1.05, 17.4% were girls) participated in the study. They belonged to Colombia’s four regions (Antioquia, Caldas, Cundinamarca, and Bogotá, the Capital District). Empathy, decision-making, and prosocial behavior were assessed with self-reports. Confirmatory factor analysis of the scales used, descriptive, correlational, predictive, and mediation analyses were performed. The results indicated positive relationships between the variables empathy, rational decision-making, and prosocial behavior. In the mediation analyses, empathic concern and personal distress were the background variables with the greatest effect on prosocial behavior when mediated by rational decision-making. The results indicate that having greater rational decision-making ability may help adolescent offenders display prosocial behaviors. Results, limitations, and practical implications for adolescent counseling are discussed, and future research suggestions are made.

Keywords

empathy rational decision-making prosocial behavior adolescent offenders simple mediation model

Introduction

This study examines whether rational decision-making is a potential mediator between empathy and prosocial behavior in adolescent offenders. Prosocial behavior is a multidimensional construct that has been analyzed from biological, social, cultural, and contextual perspectives, and its study is relevant because of its impact on individual and social development (Padilla-Walker & Carlo, 2014). Prosocial behavior has been defined as voluntary acts that benefit others (Eisenberg et al., 2015), and there is considerable evidence associating it with biological, environmental, socio-cognitive, and emotional factors (Padilla-Walker & Carlo, 2014). This knowledge has helped to understand the dynamics implicated, recognize associated factors, and develop prosocial behavior programs for specific groups (Stürmer & Snyder, 2010; Yeager, 2017).

Knowing how to promote prosocial behavior in offending adolescents is a topic of current interest because of the possibilities for individual development and the benefits for institutions and the community (Barroso-Hurtado & Bembibre-Serrano, 2018). These benefits have been observed when implementing prosocial behavior programs in normative adolescents (Mesurado et al., 2019). Prosociality is considered a desirable factor among adolescents because more prosocial individuals demonstrate greater self-regulation, empathy, improved interpersonal relationships, cooperation, and helping attitudes (Gaesser et al., 2018; Padilla-Walker & Carlo, 2014; Taylor et al., 2020).

Various theories have been proposed to explain prosocial behavior from the biological perspective, the cognitive-evolutive approach, learning theories, cultural and contextual theories, and the neuroeconomic theory of prosocial behavior (Declerck & Boone, 2016; Eisenberg et al., 2015; Padilla-Walker & Carlo, 2014). In this study, the cognitive-evolutive approach to prosocial behavior has been chosen as a framework because it situates it from a lifespan developmental perspective; moreover, it has delved into the relationships between empathy and prosocial behavior. The cognitive-evolutive approach has limitations; for example, it lacks explanations of decision-making, an advance that the neuroeconomic theory of prosocial behavior has achieved (Declerck & Boone, 2016). However, the cognitive-evolutive approach has been able to explain more variables that help the development of prosocial behavior in the life process (Eisenberg et al., 2015; Spinrad & Eisenberg, 2019).

The cognitive-evolutive approach is considered to provide the appropriate framework for understanding prosocial behavior because it links it to the individual’s cognitive and moral development, where the greater the development, the greater the likelihood of the emergence of prosocial behavior (Eisenberg et al., 1983; Kohlberg & Hersh, 1977; Piaget, 1984; Spinrad & Eisenberg, 2019). From this approach, prosocial decisions have been related to moral judgment development, including emotions and moral cognitions (Eisenberg et al., 2002; Spinrad & Eisenberg, 2019). It has also been indicated that prosocial behavior presents greater development if the educational emphasis is on moral emotions (Van der Graaff et al., 2018).

From the cognitive-evolutive approach, evidence indicates that prosocial behavior is associated with emotional development that favors moral feelings, sympathy, and compassion (Breithaupt, 2012). It is also related to cognitive development that facilitates, among other capacities, altruistic decision-making (Fromell et al., 2020). In this sense, it should be considered that cognitions and the decision-making process are also important in prosocial behavior. The contributions of studies that analyze prosocial decision-making, from a sociobiological perspective indicate that rationality helps prosocial behavior and provides the individual with analytical elements to decide (Declerck & Boone, 2016). This approach suggests that prosocial behavior is influenced by contextual factors (such as extrinsic incentives, threat cues, and trust), brain processes (cognitive control, social cognition, and the reward system), and manifestation in behavior (prosocial responding) (Declerck, 2021; Declerck et al., 2013). Among other conditions, prosocial behaviors and helping behaviors depend on cooperative incentives, social cues, relatedness to others, and trust (Declerck, 2021; Declerck & Boone, 2016). From the cognitive-evolutive approach, it has been established that empathy predicts prosocial behavior; in this study, we will analyze whether rational decision-making favors this relation.

Literature Review and Hypotheses

Factors Influencing Prosocial Behavior, Empathy, and Rational Decision-Making

Research has shown that different biological, cognitive, emotional, and social factors influence adolescents’ empathy, prosocial behavior, and rational decision-making. Available evidence suggests that prosocial behavior is inherent to the human condition and, in the evolutionary process, has contributed to species survival and social construction (Declerck & Boone, 2016). Prosocial behavior encompasses motivations, behaviors, and mechanisms such as sociomoral evaluation, preferences, affection, strategies to offer solace and comfort, helping, and cooperation that together represent important adaptive processes for social cohesion and cooperation (Decety & Steinbeis, 2020). Prosocial behavior is related to rational decision-making, and the decision to cooperate or not will depend on factors such as trust, motivation, resource availability, and incentives (Declerck et al., 2013).

To explain prosocial decisions Declerck and Boone (2016) suggest that the decision system “is supported by heuristics that are shaped by life experiences and are sensitive to environmental cues to resolve behavioral conflict derived from the brain’s motivational-emotional systems” (p. 28). If heuristics obey cost-benefit (economically rational) principles, extrinsic incentives are consistent with self-interest. If heuristics lead to disinterested decisions (socially rational), “decisional contents are influenced by reflection, trust, and affect” (p. 29). In the decisional process, at the biological level (neural) the decision is valued adaptively and moderated by external incentives, culturally established social norms, and social cues (Declerck, 2021; Declerck & Boone, 2016).

According to neuroeconomic theory, prosocial behavior is related to rational decision-making; it can be economically rational when the individual cooperates out of individual interest if his or her own goals coincide with those of the collective and can achieve rewards, synergies, accumulation of benefits, reciprocity, and high reputation. It is socially rational when the subject strives to achieve inclusion, reinforce belonging, and build social networks (Declerck & Boone, 2016). It has been observed that prosocial behavior seeks to favor social cohesion and improve the conditions of belonging groups, so there are different forms of expression in prosocial behavior that articulate with the conditions of the context (Hawley, 2015). Laboratory and cultural studies with children from diverse regions of the world show that there are common prosocial behaviors among people; for example, when assessing considerations of what is fair, it has been observed that humans naturally favor fair distributions and not equitable ones (Decety & Steinbeis, 2020; Huppert et al., 2019; Starmans et al., 2017).

Regarding gender, the studies consistently indicate that females manifest greater prosocial behavior than males; however, it has been noted that there may be differences in prosocial behaviors by gender depending on the prosocial behaviors assessed and the instruments used (Silke et al., 2018). It has also been suggested that prosocial behavior may be encouraged and that males who show high empathy may be as prosocial as females (Kamas & Preston, 2021). Prosocial behavior and empathy are closely related, and empathy is considered a predictor of prosocial behavior (Van der Graaff et al., 2018). Factors that influence prosocial behavior are personality traits, values, moral development, self-esteem, emotion management, social skills, relationships with parents and family members, peer relationships, education, and culture, in addition to the influence of the media and participation in sports, religious and cultural activities, among others (Padilla-Walker & Carlo, 2014; Silke et al., 2018). It has also been established that biological and genetic factors are associated with prosocial behavior (Padilla-Walker & Carlo, 2014). In addition, brain structures and hormones related to prosocial and cooperative decisions have been observed (Declerck, 2021; Declerck & Boone, 2018).

Evidence also shows that women are more empathic than men (Kamas & Preston, 2021). The development of empathy is influenced by, among others, early social and moral evaluations, personality traits, moral and social upbringing, cultural beliefs and practices, family environment, and exposure to aggression (Silke et al., 2018). Women’s higher empathy performance is related to biological, cognitive, emotional, educational, cultural, and social factors (Decety & Steinbeis, 2020; Moya Albiol et al., 2010).

Several theories address decision-making; among them, normative theories explain how people are oriented to evaluate losses and benefits rationally (Fishburn, 1970). Other theories, prescriptive theories, indicate how people should decide (Pande et al., 2021), and descriptive theories, with more scientific evidence, assess how people actually decide (Kahneman, 2013; Reyna, 2018). All normative, prescriptive, and descriptive theories agree that rational decisions are indicated to achieve better results. Rational decision-making involves complex biological, neurological, cognitive, social, cultural, and emotional factors that facilitate adaptation and behavioral direction (Altman, 2017). Rational decision-making is useful for reducing uncertainty, minimizing risky behaviors, solving problems, planning, reducing adverse situations, and managing uncertainty and ambiguity (Minda, 2015). Evidence shows that adolescents tend to make more risky decisions, have less development of intuition, and have less foresight in the outcome of decisions (Reyna, 2018). In adolescents, rational decision-making is also affected by nurture, cognitive development, moral development, experience, heuristics, memory storage, and retrieval processes of information (Kahneman, 2013; Reyna, 2018; Reyna & Panagiotopoulos, 2020).

Relationship Between Prosocial Behavior, Empathy, and Rational Decisions

Social rationality in prosocial decisions (Declerck & Boone, 2016), is consistent with the cognitive-evolutionary approach in which empathy has been associated with prosocial behavior (Bailey et al., 2020; Batson, 2009; Davis, 2015; Van der Graaff et al., 2018). Empathy can be defined as a relational process (Yaseen & Foster, 2019) that involves knowing the internal state, thoughts, and feelings of other people, matching neural responses when observing another person, intuiting or projecting oneself into another’s situation, besides feeling distress and compassion for someone who is suffering (Batson, 2009). These empathy characteristics favor prosocial behaviors with voluntary actions that benefit others; these can be family members, friends, and even, strangers (Yang et al., 2018). In empathy, emotional experience is necessary to understand another person’s thoughts, feelings, and points of view (Batson, 2009; Singer & Tusche, 2015). Also, emotional experience facilitates understanding others’ emotions and situations and thus would play an important role in decision ethics (Higgs et al., 2020).

Recent theories in decision-making (e.g., prospect theory and fuzzy trace theory) propose dual cognitive processes with intuitive components that work by association and rational components with greater cognitive control and analytical processes (Kahneman, 2013; Reyna, 2018). It has also been indicated that the emotions experienced affect the decision’s outcome (Lerner et al., 2015; Phelps et al., 2014). Decision-making theories agree that rational decisions are the ones that best help individuals manage their activities and achieve their goals (Chick, 2019). Rational decisions are characterized by evaluating the different options, weighing the pros and cons, seeking information, and developing strategies to execute the decision (Cotrena et al., 2018; Janis & Mann, 1977; Mann et al., 1997). These rational processes in decision-making force the individual to use complex analysis processes in the cognitive system (Kahneman, 2013; Reyna, 2018).

Empathy in the rational decision process is not neutral; it is subjective, and it operates with available information and emotions. It is associated with social practices activated when necessary and makes the subject take sides, not necessarily in a rational or properly analyzed way, which means that being empathic does not guarantee the right or the better decision (Breithaupt, 2012; Hein et al., 2010). Empathy in humans is assisted by perceptual, linguistic, and executive function processes that promote altruism and prosocial behaviors (Decety, 2011). This condition suggests that prosocial behavior requires being empathic and, besides, rational decision-making processes that facilitate and enhance the level of success. In this sense, evidence indicates that when prosocial (altruistic) decisions are made, a conciliation between intuitive and rational analytical mechanisms may occur (Fromell et al., 2020).

The studies consulted indicate that multiple conditionsdetermine prosocial decision-making, but social values are the compass that guides behavior and are expressed in each individual’s contexts and reality (Declerck & Boone, 2016). For educational purposes, it has been proposed that decision-making is more accurate with a framework of principles and values (Blalock & Reyna, 2016; Reyna, 2018) and empathy must be developed and integrated with interpersonal emotional regulation (Zaki, 2020).

Consequently, prosocial decision-making is influenced by individual conditions and characteristics (Nash et al., 2014). Also by biological and neural factors, extrinsic incentives, threat cues, trust, cognitive control, social cognition, emotional processes, and affect (Boone et al., 2010; Declerck, 2021; Lambert et al., 2017).

Empathy, Prosocial Behavior, and Decision-Making in Offenders Adolescents

The manifestation of prosocial behavior is also affected by different degrees of development in socioemotional factors; for example, adolescents with delinquent behaviors show less empathy than their normative peers (Jolliffe & Farrington, 2004) as well as less prosocial behaviors (Llorca-Mestre et al., 2017). Besides, low empathy has been related to delinquent behavior (O’Neill, 2020). In adolescent offenders, decision-making has been observed to be influenced by the expectation of emotional and social outcomes (Fontaine et al., 2009). Also affecting adolescent offenders’ decisions are executive function difficulties, lack of self-control, lack of moral commitment, and rewards for offending (Altikriti, 2021). Offenders have been observed to use intuitive reasoning to judge the risks, costs, and rewards of crime (Pogarsky et al., 2018), and use rational decision strategies, such as cost/benefit, to decide whether to engage in assaults and robberies (Zhao et al., 2021). For intervention purposes, decision skills can help promote adolescent offenders’ reflection, analyze emotions and thoughts, and make better decisions in context (Andrews & Bonta, 2017; Heller et al., 2017; Pogarsky et al., 2018). It is also important to promote the socio-emotional development of adolescent offenders; studies with normative populations have shown that individuals with positive emotional states refrain from making decisions that lead to punishment and risk of incarceration (Pogarsky et al., 2018; Van Gelder & de Vries, 2014). Whereas experiencing negative emotional states such as anger, fear, stress, and impulsivity may favor criminal acts ( Van Gelder, 2017). In addition, offenders are less able to manage negative emotional states and ambiguity when making decisions (Jacobs & Cherbonneau, 2017).

Objective and Hypotheses

Moral, emotional, and cognitive components have been indicated to play an important role in individuals’ decision-making with criminal behavior ( Van Gelder et al., 2013). Studies indicate that moral, emotional, and cognitive development affect prosocial behavior (Breithaupt, 2012; de la Barrera et al., 2019; Fromell et al., 2020). Moreover, it has been suggested that understanding emotional mechanisms, including empathy, may aid in decision-making, and maybe the emotional aspects will be more critical for intervention than traditional rational deterrence to get the change (Tibbetts, 2014).

Previous evidence indicates that empathy is a predictor of prosocial behavior in adolescents (Van der Graaff et al., 2018) and that biological, emotional, cognitive, and contextual mechanisms influence rational helping or cost/benefit decision-making (Declerck & Boone, 2016). In adolescent offenders, it has been observed that they show less empathy, prosocial behavior, and decision-making ability (Altikriti, 2021; Jolliffe & Farrington, 2004; Llorca-Mestre et al., 2017).

It is suggested that adolescent offenders with greater empathy and rational decision-making skills could express greater prosocial behavior; thus, this study aimed to analyze whether rational decision-making is a potential mediator between empathy and prosocial behavior in adolescents with delinquent behavior. The results could be useful in the behavioral and socioemotional intervention processes of adolescent offenders.

The hypotheses posed are: (1) The variables prosocial behavior, empathy, and rational decision-making style are related; and (2) Rational decision-making style is a potential mediator between empathy variables and prosocial behavior in adolescents with delinquent behavior (Figure 1).

Figure 1.

The statistical plot of a simple mediation model for a study of prosocial behavior with three covariates.

Method

Participants

A total of 413 Colombian adolescent offenders aged 14 to 18 years (M_age = 16.67, SD = 1.05, 17.4% were girls) participated in the study. The adolescents were in judicial and socio-educational measures of the System of Criminal Responsibility for Adolescents (SRPA). They belonged to four regions of Colombia: Manizales, Caldas (22%, n = 91). Cajicá, Cundinamarca (18.7%, n = 77). Medellín, Antioquia (21.1%, n = 87) and Bogotá, Capital District (38.2%, n = 158). They were prosecuted for crimes against property 50.9% (n = 210); crimes against personal integrity (physical injury and attempted homicide) 8.3% (n = 34); crimes against public health (manufacture, trafficking, and sale of drugs) 20.1% (n = 83); sexual crimes 4.3% (n = 18); homicide 3.1% (n = 13), and other crimes 13.3% (n = 55). All were enrolled in secondary education (high school between sixth and eleventh grades in the Colombian school system).

Procedure

The procedures used for data collection were performed following the Declaration of Helsinki (World Medical Association, 2013). The research was approved by the Universitat de Valencia’s Ethics Committee (N0 1102812-07 /11/2019), and authorized by the National Headquarters of the Colombian Institute of Family Welfare-ICBF (SRPA) (SIM 17615328-37, 08/13/2019). The sample was obtained by availability and convenience because centers with many adolescent inmates were specifically chosen; the sample was not random and included adolescents who voluntarily wished to participate.

Written permissions and informed consents were requested from the adolescents, parents, lawyers, and legal guardians. The adolescents were informed about the research and participated voluntarily, anonymously, and free of charge. The investigators conducted the assessment, which lasted approximately 50 min, and paper questionnaires were used.

Instruments

Empathy was assessed with the Interpersonal Reactivity Index-IRI (Davis, 1980; Mestre-Escrivá et al., 2004). This scale assesses cognitive and emotional factors of empathy and is composed of 28 items distributed in four subscales, each with seven items: Perspective Taking, Fantasy, Empathic Concern, and Personal Distress. Perspective taking is understood as the subject’s cognitions when he/she tries to put in the other’s place without necessarily experiencing an affective response. It presents elements such as, “I often find it difficult to see things from another person’s point of view.”Fantasy refers to the subject’s tendency to identify with and take the place of fictional characters in movies, television, or books through imaginative ability (e.g., “After watching a movie or a play, I feel as if I had been one of the characters”). Empathic concern refers to the subject’s response with feelings of compassion, appreciation, pity, and affection toward others, especially when they are in difficulty (e.g., “I often feel tenderness and concern for people less fortunate than myself”). Personal distress assesses the feelings of anxiety and emotional discomfort that the subject experiences when faced with adverse situations that happen to other people (e.g., “In emergencies I feel apprehensive and uncomfortable”) (Carrasco Ortiz et al., 2011). It presents five response options: does not describe me well (1); describes me a few (2); describes me more or less (3); describes me well (4); describes me very well (5). Higher scores indicate a higher degree of empathy. Reliability for the total scale and subscales with the current sample was adequate (α = .88), Perspective taking (α = .66), Fantasy (α = .67), Empathic concern (α = .68), and Personal distress (α = .70), similar reliability values have been observed in other studies with adolescents (Mestre-Escrivá et al., 2004).

The vigilance subscale of the Melbourne Decision Making Questionnaire-MDMQ (Mann et al., 1997) was used to assess rational decision-making. It consists of six items and has three response options: “Very true for me” (score 2), “Somewhat true for me” (score 1), and “Not at all true for me” (score 0). In this scale, the rational decision-making style implies a careful, impartial, exhaustive and rational evaluation of alternatives, search for information and assess pros and cons. Some items are: “I try to be clear about my objectives before making a decision,”“When making decisions, I like to gather a good amount of information,” and “I like to consider all alternatives when making a decision” (Cotrena et al., 2018). The reliability for the subscale was α = .70.

The Prosocial Behavior Scale (PBS) was used to assess prosocial behavior (Caprara & Pastorelli, 1993; Del Barrio et al., 2001). It consists of 15 items with three response alternatives: never (1), sometimes (2), and often (3). The items refer to helping, sympathetic, and trusting behaviors (e.g., “I try to help others,”“I trust others,” and “I am kind”). Higher scores indicate a greater degree of prosocial behavior. The reliability of the scale was α = .71.

Data Analysis

Descriptive analyses were performed, identifying the mean and standard deviation and reliability analysis of the scales for the current sample using the Statistical Package for the Social Sciences SPSS V.25 program. Confirmatory factor analysis (CFA) of the current sample scales was also performed using the AMOS v.24 program. The estimation was developed using the Maximum Likelihood method (ML).

The variables did not have a normal distribution. The rank of each of the factors was compared according to the gender variable to evaluate possible differences. Spearman correlations and hierarchical regression were also performed to examine the relationships between the empathy variables, rational decision-making, and prosocial behavior. Bootstrapped mediation analyses (10,000) were conducted using the SPSS PROCESS V3.0 macro with 95% confidence intervals (CI) to examine the effects of rational decision-making between empathy and prosocial behavior. According to Rucker et al. (2011), mediations occur and are significant when the CIs do not cross zero. The effect size of mediation is reported through the beta value (Wen & Fan, 2015). The use of bias-corrected bootstrapping is considered a robust method and allows an estimate of indirect effects to be made regardless of the presence of significant total or direct effects and can be successfully applied even if the data do not have a normal distribution (Williams & MacKinnon, 2008).

The simple mediation model in PROCESS, uses ordinary least squares (OLS) regression-based paths to analyze “how the effect of an antecedent variable (X) acts on a consequent or final variable (Y), generating two paths of influence, direct and indirect” (Hayes, 2018, p. 85). There is a risk of realizing an epiphenomenal association in mediation models that constitutes a severe threat to the validity of causal inference. According to Hayes (2018), “epiphenomenal association occurs when X affects some other variable that is not in the model, and that other variable affects Y, but because M correlates with that other variable, it appears that M is the variable through which the effect of X on Y occurs” (p. 121). To control for the epiphenomenal association, one can either randomize the values of X or introduce covariates. In this study, we include three covariates: age, gender, and schooling. If the indirect effect persists, despite controlling for covariates, the causal claim remains viable.

Results

Factorial Validity of the Measures

Confirmatory factor analyses (CFA) were performed to verify the scales’ factor structure for the current sample (n = 413). Goodness-of-fit was based on the following indicators: the general goodness-of-fit index (GFI), the comparative fit index (CFI), the incremental fit index (IFI), and the Tucker-Lewis index (TLI); values above 0.90 indicate a good fit (Brown, 2015). The root mean square error of approximation (RMSEA) was calculated to analyze the discrepancy between the hypothesized model and the covariance matrix of the data. Values below 0.05 indicate a good fit and between 0.05 and 0.08, a moderate fit (Harrington, 2009).

The results of the CFA for the IRI did not show satisfactory indices (Table 1). However, the observed values are congruent with previous CFAs of the scale (Carrasco Ortiz et al., 2011; Pérez-Albéniz et al., 2003; Pineda et al., 2013). Previous research on the scale’s psychometric properties has indicated that it does not achieve optimal fit indices in many CFAs. However, it has been recommended to use Davis’ (1980) four-factor structure for empathy assessments (Chrysikou & Thompson, 2016). The results of the AFC with the current sample for the prosocial behavior scale (PBS) and decision-making (MDMQ) showed satisfactory indices (Table 1).

Table 1.

Fit Index of the Confirmatory Factor Analysis of the Measures Used in This Study.

Instrument	Model	χ²	df	GFI	CFI	IFI	TLI	RMSEA [95% CI]
Interpersonal Reactive Index-IRI	First-order model of 28 items, 4 factors.	941.355	337	0.84	0.80	0.80	0.78	0.06 [0.061, −0.071]
Melbourne Decision Making Questionnaire-MDMQ	First-order model of 22-items, 4-factors.	328.188	200	0.95	0.92	0.92	0.90	0.04 [0.032, −0.047]
Melbourne Decision Making Questionnaire-MDMQ	Rational Decision-Making (Vigilance) subscale of the MDMQ, 6 items	8.074	9	0.99	1.00	0.100		0.00 [0.000, 0.051]
Prosocial Behavior Scale PBS	First-order model, 10-items, 5 control items, 1 factor.	74.454	34	0.96	0.93	0.93	0.91	0.06 [0.040, −0.074]

Note. χ² = Chi-square; df = degree of freedom; GFI = good fit index; CFI = comparative fit index; TLI = Tucker-Lewis index; IFI = incremental fit index; RMSEA = root mean squared error of approximation.

Descriptive Results

The mean of the study variables was compared according to gender. Comparisons were made using the Mann-Whitney U test due to the difference in sample size between boys and girls and the non-normality of the variables. The difference was only observed in prosocial behavior, indicating that girls (Rank 239. 92) showed greater prosociality than boys (Rank 200.05) (U_{(72. 341)=}9,905.500, Z = −2.586, p = .01, d = 0.30).

Descriptive and correlation analyses were performed. The results show positive and statistically significant correlations between rational decisions, prosocial behavior, and empathy variables (Table 2).

Table 2.

Descriptive Statistics and Correlations of Spearman for Study Variables (n = 413).

Variable	Rank	M	SD	1	2	3	4	5	6
1. Perspective taking	7–35	20.70	5.00	—
2. Fantasy	7–35	19.68	5.62	.54**	—
3. Empathic concern	7–35	19.61	5.34	.62**	.56**	—
4. Personal distress	7–35	18.80	5.34	.56**	.56**	.67**	—
5. Rational decision-making	0–12	7.23	2.65	.42**	.24**	.29**	.25**	—
6. Prosocial behavior	10–30	22.71	3.34	.48**	.37**	.35**	.29**	.23**	—

p < .01.

A hierarchical regression model was performed to evaluate the effect of empathy variables and rational decision-making style on prosocial behavior (Table 3). The model was evaluated in three steps. In the first step, sociodemographic variables were introduced: gender, age, and schooling. In the second step, empathy variables, and in the third step, rational decision-making.

Table 3.

Hierarchical Regression Results for Prosocial Behavior.

Variable	B	95% CI for B	SE B
Variable	B	[LL, UL]	SE B	β	R ²	ΔR²
Sample (n = 413)
Step 1					.02	.02*
Constant	20.180	[15.06, 25.29]	2.60
Gender	0.99	[0.15, 1.84]	0.43	.11*
Age	0.06	[−0.26, 0.38]	0.09	.02
School grade	0.16	[−0.03, 0.35]	0.09	.08
Step 2					.27	.25***
Constant	15.57***	[11.07, 20.07]	2.29
Perspective taking	0.27***	[0.19, 0.35]	0.04	.40***
Fantasy	0.08**	[0.02, 0.15]	0.03	.15**
Empathic concern	0.03	[−0.04, 0.11]	0.04	.06
Personal distress	−0.04	[−0.11, 0.04]	0.04	−.06
Step 3					.27	.00
Constant	15.49***	[10.97, 20.02]	2.30
Rational decision-making	0.02	[−0.09, 0.14]	0.05	.02

Note. CI = confidence interval; LL = lower limit; UL = upper limit.

p < .05. **p < .01. ***p < .001.

In the first step, the sociodemographic variables model was significant (R² = .02; F = 2.816; p = .04), but only the gender variable contributed to the variance explained (β = .11; t = 2.315, p = .02). In the second step, the model is significant when adding the IRI dimensions (R² = .27; F = 21.619; p < .001); perspective taking and fantasy contributed significantly to the explained variance (perspective taking: β = .40; t = 6.832, p < 001; Fantasy: β = .15; t = 2.686, p = .008). In step 3, the regression remained significant (R² = .27; F = 18.896; p < .001); however, the rational decision-making variable was not statistically significant (β = −.02; t = 0.394, p = .694).

Direct and Indirect Effects of Empathy, Rational Decision-Making, and Prosocial Behavior Variables

Four mediation models were evaluated to establish whether rational decision-making is a potential mediator between empathy variables and prosocial behavior; gender, age, and school grade were included as covariates. Simple mediations are found in Figures 2 to 5 and summarized in Table 4.

Figure 2.

Simple mediation model of rational decision-making between perspective taking and prosocial behavior.

Figure 3.

Simple mediation model of rational decision-making between fantasy and prosocial behavior.

Figure 4.

Simple mediation model: Rational decision-making mediating between empathic concern and prosocial behavior.

Figure 5.

Simple mediation model: Rational decision-making mediating between Personal distress and prosocial behavior.

Table 4.

Coefficients of the Simple Mediation Model for Prosocial Behavior With Three Covariates.

Antecedent		Consequent
		M (Rational decision-making)				Y (Prosocial behavior)
		Coef.	SE	p	c′	Coef.	SE	p
X (perspective taking)	a	.219	0.02	<.001		.320	0.03	<.001
M (rational decision-making)		——	——	——	b	.251	0.06	.674
C ₁ (age)	f ₁	−.135	0.11	.256	g ₁	−.030	0.14	.831
C ₂ (gender)	f ₂	−.571	0.31	.067	g ₂	.935	0.38	.013
C ₃ (school grade)	f ₃	−.166	0.07	.021	g ₃	.087	0.09	.320
Constant	i_M	3.414	1.91	.074	i_Y	19.498	2.318	<.001
		R² = .292F_(4,408)= 24.238, p < .001				R² = .257F_(5,407)= 27.953, p < .001
Fantasy		Coef.	SE	p	c′	Coef.	SE	p
X (fantasy)	a	.106	0.02	<.001		.201	0.03	<.001
M (rational decision-making)		——	——	——	b	.180	0.06	.002
C ₁ (age)	f ₁	−.130	0.13	.310	g ₁	−.037	0.15	.806
C ₂ (gender)	f ₂	.543	0.33	.104	g ₂	.774	0.40	.052
C ₃ (school grade)	f ₃	.195	0.08	.011	g ₃	.090	0.09	.327
Constant	i_M	5.531	2.026	.006	i_Y	17.555	0.24	<.001
		R² = .073F_(4,408)= 8.075, p < .001				R² = .174F_(5,407)= 17.155, p < .001
Empathic concern		Coef.	SE	p	c′	Coef.	SE	p
X (empathic concern)	a	.146	0.02	<.001		.199	0.03	<.001
M (rational decision-making)		——	——	——	B	.159	0.06	.009
C ₁ (age)	f ₁	−.136	0.12	.278	g ₁	−.016	0.10	.914
C ₂ (gender)	f ₂	.425	0.33	.196	g ₂	.657	0.40	.103
C ₃ (school grade)	f ₃	.199	0.08	.008	g ₃	.109	0.09	.240
Constant	i_M	4.844	1.995	.015	i_Y	16.876	0.24	<.001
		R² = .108F_(4,408)= 12.394, p < .001				R² = .159F_(5,407)= 15.374, p < .001
Personal distress		Coef.	SE	p	c′	Coef.	SE	p
X (Personal distress)	a	.125	0.02	<.001		.169	0.03	<.001
M (Rational decision-making)		——	——	——	b	.194	0.03	.001
C ₁ (age)	f₁	−.128	0.3	.311	g ₁	.002	0.15	.986
C ₂ (gender)	f₂	.436	0.33	.191	g ₂	.655	0.41	.110
C ₃ (school grade)	f₃	.215	0.08	.004	g ₃	.124	0.09	.189
Constant	i_M	5.073	2.022	.012	i_Y	16.999	2.496	<.001
		R² = .084F_(4,408)= 9.429, p < .001				R² = .132F_(5,407)= 12.420, p < .001

Note. X = antecedent variable; M = mediating variable; Y = consequent variable; a = effect of X on M; b = effect of M on Y; c′ = direct effect; C_1,2,3 = covariates; f_1,2,3 = effect of covariates on a; g_1,2,3 = effect of covariates on b; R² = explained variance.

The Perspective Taking

The effect of perspective taking on rational decision-making proved to be significant (β = .219, p < .001), but not the effect of rational decision-making on prosocial behavior (β = .025, p = .06). The direct effect of perspective taking (β = .320, p < .001) is explained as the estimated difference between the degree of prosocial behavior of adolescents experiencing the same level of rational decision-making but differing in reported perspective taking. The coefficient is positive, meaning that adolescents who show greater perspective taking, and at the same time, exhibit greater rational decision-making, are estimated to have a higher level of prosocial behavior at 0.320. This effect is statistically different from zero, t₍₄₁₃₎ = 10.142, p < . 001, 95% CI [0.258, 0.382].

The total effect of the perspective taking on prosocial behavior, which is explained because of the sum of direct and indirect effects, is significant (β = .325, p < .001). Adolescents with higher perspective taking are estimated to differ by 0.325 degrees in their reported prosocial behavior. The positive sign indicates that adolescents reporting greater perspective-taking have greater prosocial behavior. This effect is statistically different from zero, t₍₄₁₃₎ = 11.348, p < .001, 95% CI [0.269, 0.382].

The indirect effect (β = .005) was not statistically different from zero 95% CI [−0.021, 0.034]. No indirect effect was observed due to the mediation of rational decision-making between perspective taking and prosocial behavior. Regarding covariates, gender was significant for prosocial behavior (t₍₄₁₃₎ = 2.523, p = <.01, 95% CI [0.20, 1.68], indicating greater prosocial behavior in girls (M_girls 23.51; M_boys 22.50). Figure 2. shows the statistical plot of the simple mediation model of rational decision-making between perspective taking and prosocial behavior.

Fantasy

The effect of fantasy on rational decision-making was found to be significant (β = .106, p < .001); similarly there was a positive effect of rational decision-making on prosocial behavior (β = .180, p = .002). The direct effect of fantasy (β = .201, p < .001) indicates that adolescents who show greater fantasy, and at the same time present greater rational decision-making, are estimated at 0.201 with a higher degree of prosocial behavior (t₍₄₁₃₎ = 7.260, p < .001, 95% CI [0.146, 0.255]).

The total effect of fantasy on prosocial behavior is significant (β = .220, p < .001). Adolescents with higher fantasy are estimated to differ by 0.220 degrees in their reported prosocial behavior. Adolescents reporting higher fantasy show a higher degree of prosocial behavior (t₍₄₁₃₎ = 8.074, p < .001, 95% CI [0.166, 0.237]). Regarding covariates, in the total effect, gender was significant for prosocial behavior (t (413) = 2.176, p = .03, 95% CI [0.08, 1.66], indicating greater prosocial behavior in girls (M_girls 23.51; M_boys 22.50).

The indirect effect (β = .019) resulted different from zero and is statistically significant at 95% CI [0.006, 0.038]. It can then be estimated that adolescents differ to a degree of 0.019 due to the mediation of rational decision-making between fantasy and prosocial behavior. Figure 3. shows the statistical plot of the simple mediation model of rational decision-making between fantasy and prosocial behavior.

Empathic Concern

The effect of empathic concern on rational decision-making was found to be significant (β = .146, p < .001); likewise there was a positive effect of rational decision-making on prosocial behavior (β = .159, p = .009). The direct effect of fantasy (β = .199, p < .001) indicates that adolescents who show greater empathic concern, and at the same time present greater rational decision-making, are estimated at 0.199 with a higher degree of prosocial behavior (t₍₄₁₃₎ = 6.649, p < .001, 95% CI [0.140, 0.258]).

The total effect of empathic concern on prosocial behavior (β = .222, p < .001) shows that adolescents with higher empathic concern differ by 0.222 degrees in their reported prosocial behavior. Adolescents reporting higher empathic concern show a higher degree of prosocial behavior (t₍₄₁₃₎ = 8.074, p < .001, 95% CI [0.165, 0.279]).

The indirect effect (β = .023) resulted different from zero and is statistically significant at 95% CI [0.005, 0.046]. It can then be estimated that adolescents differ to a degree of 0.023 because of the mediation of rational decision-making between fantasy and prosocial behavior. None of the covariates were significant. Figure 4. shows the statistical plot of the simple mediation model of rational decision-making between empathic concern and prosocial behavior.

Personal Distress

The effect of personal distress on rational decision-making was found to be significant (β = .125, p < .001), likewise there was a positive effect of rational decision-making on prosocial behavior (β = .194, p < .001). The direct effect of personal distress (β = .169, p < .001) indicates that adolescents who show greater personal distress, and at the same time present greater rational decision-making, are estimated at 0.169 with a higher degree of prosocial behavior (t₍₄₁₃₎ = 5.517, p < .001, 95% CI [0.108, 0.228]).

The total effect of personal distress on prosocial behavior is significant (β = .193, p < .001). Adolescents with greater personal distress are estimated to differ by 0.193 degrees in their reported prosocial behavior. Adolescents reporting greater personal distress show greater degrees of prosocial behavior (t₍₄₁₃₎ = 6.443, p < .001, 95% CI [0.134, 0.251]).

The indirect effect (β = .024) resulted different from zero and is statistically significant at 95% CI [0.008, 0.045]. It can then be estimated that adolescents differ to a degree of 0.024 because of the mediation of rational decision-making between personal distress and prosocial behavior. None of the covariates were significant. Figure 5. shows the statistical plot of the simple mediation model of rational decision-making between personal distress and prosocial behavior.

Discussion

Prosocial behavior is a multidimensional construct that can hardly be explained from a single theory. There is evidence for related biological, environmental, socio-cognitive, and emotional factors that contribute to explaining prosocial behaviors (Padilla-Walker & Carlo, 2014). However, some theories, such as the cognitive-evolutionary one, have made essential advances useful for understanding prosocial development. This approach has shown the importance of empathy, moral emotion, and moral cognition in prosocial behavior (Eisenberg et al., 1983; Kohlberg & Hersh, 1977; Piaget, 1984; Spinrad & Eisenberg, 2019). Empathy is a predictor of prosocial behavior, and considering that recent research on prosocial decision-making has highlighted the importance of rationality in helping and cooperative behaviors (Declerck, 2021; Declerck & Boone, 2016). This study aimed primarily to analyze whether rational decision-making is a potential mediator between empathy variables and prosocial behavior in adolescents with delinquent behavior. It is fundamental to consider that adolescent offenders manifest less prosocial behaviors, empathy, and greater difficulty in making reasoned decisions (Altikriti, 2021; Jolliffe & Farrington, 2004; Llorca-Mestre et al., 2017). This study showed positive and significant relationships between the variables, and it was observed that rational decision-making could mediate the relationship between empathy variables and prosocial behavior in adolescent offenders.

Correlation Between Prosocial Behavior, Empathy, and Rational Decision-Making

The first hypothesis was confirmed. A positive relationship was observed between empathy variables, prosocial behavior, and rational decision-making. The relationship between prosocial behavior and empathy has been widely documented (Bailey et al., 2020; Batson, 2009; Davis, 2015; Van der Graaff et al., 2018). However, the role of empathy in prosocial decision processes has been controversial due to the bias and subjectivity of the empathic person that may lead to erratic decisions (Breithaupt, 2012; Hein et al., 2010). Therefore, some social-biological theoretical approaches have been proposed to analyze the role of rationality in prosocial decisions (Declerck, 2021; Declerck & Boone, 2016).

This study’s results seem to indicate that the cognitive and emotional empathy factors of IRI (Davis, 1980; Mestre-Escrivá et al., 2004) influence the prosocial behavior of adolescent offenders. Empathic concern and personal distress were enhanced and positively affected prosocial behavior when they were mediated by rational decision-making. Concerning perspective taking and fantasy, the neuroscientific model (Preston & de Waal, 2002), has provided evidence indicating that using imagination and fantasy can intuit the experience, emotions, and even pain of others (Singer & Tusche, 2015). In this sense, Gaesser et al. (2018), observed that when people imagine future episodes helping people in need, there is an increase in actual prosocial behavior. This can be used in intervention with adolescent offenders through strategies and methods that use imagination and fantasy with prosocial scenes.

The Mediating Role of Rational Decision-Making Between Empathy and Prosocial Behavior

The second hypothesis was also confirmed. In all the mediation models tested, the empathy variables showed positive effects on rational decision-making; the same was true between rational decision-making and prosocial behavior, except in the model with the perspective-taking variable as antecedent. This model was also the only one in which indirect effects were not significant. We observed statistically significant direct, total, and indirect effects in the remaining three models. The first implication of these results is that adolescents who exhibit criminal behaviors show different prosocial behaviors, and it is essential to know what contextual factors, biological processes, and culturally mediated responses may affect prosocial responding (Declerck et al., 2013). Similarly, it may be essential to analyze social conditions, heuristics, incentives, relatedness, and trust for their possible effects on the development of prosocial behavior (Declerck, 2021; Declerck & Boone, 2016).

The second implication is the need to explore how to foster empathy in adolescents with delinquent behavior. Evidence suggests that empathy is primarily manifested with emotionally close people (Singer & Tusche, 2015) and those belonging to affiliative groups (Hein et al., 2010). These observations on the development of empathy with emotionally close people and affiliative groups indicate that empathy could be fostered through intervention with educational groups and with peers in correctional settings, family members, and other emotionally close people. These possibilities are important because they may contribute to adherence to recovery and change processes, strengthening support networks, improving prosocial decision-making, and social adjustment. Empathy could transcend its effect beyond affiliative groups; for example, it has been observed that by developing it, people can show prosocial behaviors and attitudes even with ideologically diverse groups (Taylor et al., 2020).

The third implication is the need to favor socioemotional competencies, empathy, and decision-making in adolescents with delinquent behavior. Previous evidence indicates that adolescents in correctional systems show lower performances in empathy (Jolliffe & Farrington, 2004), and prosocial behavior (Llorca-Mestre et al., 2017). They also show difficulties in executive function, lack of self-control, lack of moral commitment, and decisions associated with cost/benefit in rewards associated with crime (Altikriti, 2021). It is advisable to intervene in these aspects in adolescents with delinquent behaviors, and favoring socioemotional competencies and empathy can help optimal adaptation and well-being as has been observed in the normative population (de la Barrera et al., 2019; Mesurado et al., 2019).

Intervention in the socio-emotional domain is important because of the benefits observed in prosociality, for example, greater self-regulation and empathy, improved interpersonal relationships, cooperation, and helping attitudes (Gaesser et al., 2018; Padilla-Walker & Carlo, 2014; Taylor et al., 2020). Evidence from studies with normative populations indicates that people with positive emotional states, for example, greater self-control and satisfaction, and those who show greater morality and risk perception, are likely to refrain from offending because they judge differently the heuristics associated with the likelihood of incarceration and severity of punishment (Pogarsky et al., 2018;Van Gelder & de Vries, 2014). The opposite is true for negative emotional states, for example, anger, fear, stress, and impulsivity that may favor criminal acts ( Van Gelder, 2017). Studies with offender populations show that offenders are less adept at using strategies to manage emotional agitation, ambiguity, and negative thoughts and emotions, which disfavors social adjustment and decision-making (Jacobs & Cherbonneau, 2017).

The fourth implication is the possibility of helping adolescent offenders and potential offenders, to use deliberative skills characteristic of rational decision-making (Kahneman, 2013) that help “to analyze the negative consequences of offending” (Pogarsky et al., 2018, p. 399) and to value the advantages of prosocial behavior (Taylor et al., 2020). Evidence has been shown in favor of decreased delinquency when decision speed is reduced, reflection on automatic thoughts and behaviors is increased, and contextual decisions are guided (Heller et al., 2017).

Predictors of Prosocial Behavior, Empathy, and Rational Decision-Making in Offending Adolescents

Studies consistently state that empathy is a predictor of prosocial behavior (Van der Graaff et al., 2018), and in this study, we ratify this influence. Furthermore, we observed that rational decision-making favorably mediates the influence of cognitive and emotional empathy on adolescent offenders’ prosocial behavior.

It is suggested that it is important to promote the development of empathy, prosocial behavior, and rational decision-making in adolescents. It has been indicated that humans have biological resources to respond cooperatively or competitively in an adaptive manner depending, among others, on contextual conditions (Declerck, 2021; Declerck & Boone, 2016). Enhancing cooperative responses is necessary to improve self-regulation, interpersonal relationships, social adjustment, sense of belonging, and prosocial behavior (Gaesser et al., 2018; Padilla-Walker & Carlo, 2014; Taylor et al., 2020). We observed in this study that adolescent offenders can benefit from rational decision-making to improve prosocial responding; this is important due to the interest in improving social adjustment with the prison population (Barroso-Hurtado & Bembibre-Serrano, 2018).

Regarding gender, the results of this study show that girl offenders perform better in prosocial behavior and empathy than men; similar results have been observed in the normative population (Kamas & Preston, 2021; Moya Albiol et al., 2010; Silke et al., 2018). This study showed that gender is a covariate affecting the mediation between cognitive empathy (perspective taking and fantasy) with prosocial behavior performance in favor of girls. These results indicate that girls perform better on the prosocial behavior assessed, and their performance positively affects the mediation exerted by rational decisions. In addition, some studies have indicated that prosocial behavior may be encouraged and that males who show high empathy may be as prosocial as females (Kamas & Preston, 2021). However, it has been suggested that it is important to establish gender-differentiated measures of prosocial behavior (Silke et al., 2018).

In adolescent offenders, risk factors that may affect the development of empathy, prosocial behavior, and decision-making are increased. Risk factors associated with delinquent behavior affect adolescent offenders’ well-being, physical and mental health, and social adjustment (Andrews & Bonta, 2017). It is important to determine how factors predictive of prosocial behavior have been affected in the life cycle of adolescent offenders and to seek strategies to intervene them; among them, psychological, social, and cultural factors such as personality traits, values, moral development, self-esteem, emotion management, social skills, relationships with parents and family members, peer relationships, education and culture (Padilla-Walker & Carlo, 2014; Silke et al., 2018). Similarly, it is important to address factors that affect empathy development and prosocial development, including early social and moral evaluations, personality traits, moral and social upbringing, cultural beliefs and practices, family environment, and exposure to aggression (Silke et al., 2018). Similarly, it is important to analyze gender differences in factors affecting empathy among adolescent offenders (Decety & Steinbeis, 2020).

Adolescent offenders are more likely to make risky choices that affect their developmental and social adjustment, health, and well-being outcomes. Therefore, it is important to address decision-making and help manage the negative effects of risky decisions; this involves intervening in aspects of decision-making that have been affected across the lifespan, including cognitive, moral, and socioemotional development, experiences, exposure to risk factors, heuristics associated with antisocial behavior, and psychological processes such as memory and perception (Andrews & Bonta, 2017; Kahneman, 2013; Reyna, 2018; Reyna & Panagiotopoulos, 2020).

Practical Implication

Given the above discussion and analysis, this study suggests that empathy, prosocial behavior, and decision-making are important for adolescents’ social adjustment. Furthermore, the development of rational decision-making skills may favor the relationship between empathy and prosocial behavior in adolescents with criminal behavior. Therefore, we propose the following recommendations for the care of adolescents in penitentiary establishments:

First, it is important to carry out interventions that favor empathy, prosocial behavior, and decision-making. It has been observed that a lack of empathy favors aggression and the violation of the rights of others; in contrast, a higher degree of empathy helps to understand the point of view of others, their thoughts, feelings, and emotions, decreases aggression, and increases prosocial behavior (Batson, 2009; Singer & Tusche, 2015; Yang et al., 2018). Given that lack of empathy is one of the most critical factors favoring antisocial behavior, it is important to develop intervention processes that lead adolescents to respect people, social boundaries, and the rights of others. It is relevant to address errors in social and moral evaluations, favor moral education, cultural beliefs and practices, deficiencies in family education, and the consequences of exposure to aggression that are predictors in the development of empathy (Silke et al., 2018).

Prosocial behavior should also be favored in intervention processes. It is recommended to identify various prosocial and altruistic behaviors that adolescents can practice in institutions, with family and others in their environment, demonstrate the benefits of prosocial behavior, and intervene in educational deficiencies and factors that affect the development of prosocial behavior. Evidence shows that implementing programs that incentivize prosocial behavior improves helping behavior and altruism in adolescents (Barroso-Hurtado & Bembibre-Serrano, 2018; Mesurado et al., 2019). Prosocial behavior can be intervened by developing social skills, guidance in relationship management, and participation in cultural, recreational, and psychoeducational activities (Padilla-Walker & Carlo, 2014; Silke et al., 2018).

It is also important to educate adolescents in decision-making strategies. Evidence shows that false premises, individual experiences, and educational factors, among others, guide people’s decision-making (Kahneman, 2013). Adolescents with behavioral problems present higher cognitive distortion, negative experiences, and greater difficulty making rational decisions. Hence, they should be offered guidance, and for this purpose, it has been recommended to guide them toward the construction of objectives adjusted to the adolescent’s reality and formulate goals focused on ethical, personal, and social values (Reyna, 2018).

Secondly, it is important to guide adolescents in prosocial decision-making. According to the results of this study, rational decision-making can mediate the relationship between empathy and prosocial behavior; these observations are evidence that rationality can contribute to social adjustment and interpersonal relationships. Rational decisions are not always prosocial and may be oriented only to self-benefit and may even aim to affect others; therefore, it is important to educate adolescent offenders toward decision-making that contributes to positive development, helpfulness, and altruism (Declerck & Boone, 2016; Eisenberg et al., 2015; Padilla-Walker & Carlo, 2014).

Limitations and Future Directions

This study has some limitations that should be mentioned. First, it constitutes a cross-sectional exploratory study that does not guarantee verification of the stability of the data with several measurements. Also, its cross-sectional nature does not allow causality to be inferred. Second, self-reports were used, and the data may be biased by factors such as social desirability. The participants in this study comprise a group of adolescents with delinquent behavior; therefore, the results may not be generalizable to other populations. Boys in the sample are overrepresented, and gender-related results may be affected. Future research may develop longitudinal measures that can include other factors associated with prosocial decision-making, such as the role of the peer group, trust, incentives, heuristics, cognitive distortions, emotions, and specific prosocial behaviors expected of adolescents with delinquent behavior. In line with recent studies, mediation analyses can be developed within the prospect theory framework (Kahneman, 2013), and fuzzy trace decision theory that addresses adolescent decision-making (Reyna, 2018).

Conclusion

In this study, we observed that empathy, rational decision-making, and prosocial behavior are related variables, and rational decision-making is a potential mediator between empathy and prosocial behavior. Empathy is not a relationally neutral process (Breithaupt, 2012; Hein et al., 2010), and the degree of development to make rational decisions could affect prosocial behaviors. Therefore, empathy and prosocial behavior may be aided by rational decision-making, not only to execute the prosocial activity but also to detect the heuristics, biases, and prejudices involved. In line with recent theories on decision-making and empathy, it is crucial to teach decision-making by developing a framework of principles and values (Blalock & Reyna, 2016; Reyna, 2018), and integrating empathy with interpersonal emotional regulation (Zaki, 2020).

Footnotes

Acknowledgements

The authors are immensely grateful to the Instituto Colombiano de Bienestar Familiar for giving its endorsement and permission for the study, the authorities and legal guardians who signed the consent, the institutions that provided their support when applying the instruments, and above all, to the participating adolescents who donated their time and provided the information.

Data Availability Statement

The data that support the findings of this study are available with request to the corresponding author Inmaculada Montoya-Castilla. The data are not publicly available due to restrictions of Instituto Colombiano de Bienestar Familiar, the containing information could compromise the privacy of research participants (Share upon Request).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by grants from the Spanish Ministry of Science, Innovation, and Universities, the State Agency of Research, and the European Regional Development Fund (FEDER) from the European Union (PSI2017-84005-R).

Ethics Declarations

The procedures were carried out following the guidelines of the Declaration of Helsinki (World Medical Association, 2013). The study was approved by the Ethics Committee of the University of Valencia (N0 1102812-07/11/2019), and the permission of the Dirección de Planeación y Control de la Gestión, Subdirección de Monitoreo de Evaluación Pública del Instituto Colombiano de Bienestar Familiar (ICBF), Sistema de Responsabilidad Penal para Adolescentes (SRPA) (SIM 17615328-37, 08/13/2019).

ORCID iD

Arcadio de Jesús Cardona-Isaza

References

Altikriti

(2021). Toward integrated processual theories of crime: Assessing the developmental effects of executive function, self-control, and decision-making on offending. Criminal Justice and Behavior, 48(2), 215–233. https://doi.org/10.1177/0093854820942280

Altman

(2017). Handbook of behavioural economics and smart decision-making: Rational decision-making within the bounds of reason. Edward Elgar Publishing Limited.

Andrews

D. A.

Bonta

(Eds.). (2017). The psychology of criminal conduct (6th ed.). Routledge.

Bailey

P. E.

Brady

Ebner

N. C.

Ruffman

(2020). Effects of age on emotion regulation, emotional empathy, and prosocial behavior. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 75(4), 802–810. https://doi.org/10.1093/geronb/gby084

Barroso-Hurtado

Bembibre-Serrano

(2018). Revisión de los factores de éxito en la promoción de comportamientos prosociales como estrategia preventiva en la justicia juvenil en España. Revista Complutense de Educación, 30(1), 75–91. https://doi.org/10.5209/rced.55509

Batson

C. D.

(2009). These things called empathy: Eight related but distinct phenomena. In Decety

Ickes

(Eds.), The social neuroscience of empathy (pp. 3–16). The MIT Press.

Blalock

S. J.

Reyna

V. F.

(2016). Using Fuzzy-trace theory to understand and improve health judgments, decisions, and behaviors: A literature review. Psychology and Health, 35(8), 781–792. https://doi.org/10.1037/hea0000384

Boone

Declerck

Kiyonari

(2010). Inducing cooperative behavior among proselfs versus prosocials: The moderating role of incentives and trust. Journal of Conflict Resolution, 54(5), 799–824. https://doi.org/10.1177/0022002710372329

Breithaupt

(2012). Author reply: Empathy does provide rational support for decisions. But is it the right decision? Emotion Review, 4(1), 96–97. https://doi.org/10.1177/1754073911421394

10.

Brown

T. A.

(2015). Confirmatory factor analysis for applied research (2nd ed.). The Guilford Press.

11.

Caprara

G. V.

Pastorelli

(1993). Early emotional instability, prosocial behaviour, and aggression: Some methodological aspects. European Journal of Personality, 7(1), 19–36. https://doi.org/10.1002/per.2410070103

12.

Carrasco Ortiz

M. A.

Delgado-Egido

Barbero-García

M. I.

Holgado-Tello

F. P.

del Barrio-Gándara

M. V

. (2011). Propiedades psicométricas del Interpersonal Reactivity Index (IRI) en población infantil y adolescente española. Psicothema, 23(4), 824–831.

13.

Chick

C. F.

(2019). Cooperative versus competitive influences of emotion and cognition on decision making: a primer for psychiatry research. Psychiatry Research, 273, 493–500. https://doi.org/10.1016/j.psychres.2019.01.048

14.

Chrysikou

E. G.

Thompson

W. J.

(2016). Assessing cognitive and affective empathy through the interpersonal reactivity index: An argument against a two-factor model. Assessment, 23(6), 769–777. https://doi.org/10.1177/1073191115599055

15.

Cotrena

Branco

L. D.

Fonseca

R. P.

(2018). Adaptation and validation of the Melbourne Decision Making Questionnaire to Brazilian Portuguese. Trends in Psychiatry and Psychotherapy, 40, 29–37. https://doi.org/10.1590/2237-6089-2017-0062

16.

Davis

M. H.

(1980). A mulitdimensional approach to individual differences in empathy. Catalog of Selected Documents in Psychology, 10(85), 1–17.

17.

Davis

M. H.

(2015). Empathy and prosocial behavior. In Schroeder

D. A.

Graziano

W. G.

Davis

M. H.

(Eds.), The Oxford Handbook of prosocial behavior (pp. 282–306). Oxford University Press.

18.

Decety

(2011). The neuroevolution of empathy. Annals of the New York Academy of Sciences, 1231(1), 35–45. https://doi.org/10.1111/j.1749-6632.2011.06027.x

19.

Decety

Steinbeis

(2020). Multiple mechanisms of prosocial development. In Decety

(Ed.), The social brain: A developmental perspective (pp. 219–247). The MIT Press.

20.

Declerck

C. H.

(2021). Neuroeconomics of cooperation heuristics: The role of incentives, social cues, and hormones. In Harbecke

Herrmann-Pillath

(Eds.), Social neuroeconomics: Mechanistic integration of the neurosciences and the social science (pp. 77–98). Routledge.

21.

Declerck

C. H.

Boone

(2016). Neuroeconomics of prosocial behavior: The compassionate egoist. Academic Press.

22.

Declerck

C. H.

Boone

(2018). The neuroeconomics of cooperation. Nature Human Behaviour, 2(7), 438–440. https://doi.org/10.1038/s41562-018-0387-3

23.

Declerck

C. H.

Boone

Emonds

(2013). When do people cooperate? The neuroeconomics of prosocial decision making. Brain and Cognition, 81(1), 95–117. https://doi.org/10.1016/j.bandc.2012.09.009

24.

de la Barrera

Schoeps

Gil-Gómez

J. A.

Montoya-Castilla

(2019). Predicting adolescent adjustment and well-being: The interplay between socio-emotional and personal factors. International Journal of Environmental Research and Public Health, 16(23), 4650. https://doi.org/10.3390/ijerph16234650

25.

Del Barrio

Moreno

López

. (2001). Evaluación de la agresión y la inestabilidad emocional en niños españoles: Su relación con la depresión. Clínica y Salud, 12(1), 33–50.

26.

Eisenberg

Guthrie

I. K.

Cumberland

Murphy

B. C.

Shepard

S. A.

Zhou

Carlo

(2002). Prosocial development in early adulthood: A longitudinal study. Journal of Personality and Social Psychology, 82(6), 993–1006. https://doi.org/10.1037/0022-3514.82.6.993

27.

Eisenberg

Lennon

Roth

(1983). Prosocial development: A longitudinal study. Developmental Psychology, 19(6), 846–855. https://doi.org/10.1037/0012-1649.19.6.846

28.

Eisenberg

Spinrad

T. L.

Knafo-Noam

(2015). Prosocial development. In Lerner

R. M.

(Ed.), Handbook of child psychology and developmental science: Socioemotional processes (vol.3, 7th ed., pp. 610–656). John Wiley & Sons.

29.

Fishburn

P. C.

(1970). Utility theory for decision making. Wiley & Sons.

30.

Fontaine

R. G.

Yang

Dodge

K. A.

Pettit

G. S.

Bates

J. E.

(2009). Development of response evaluation and decision (RED) and antisocial behavior in childhood and adolescence. Developmental Psychology, 45(2), 447–459. https://doi.org/10.1037/a0014142

31.

Fromell

Nosenzo

Owens

(2020). Altruism, fast and slow? Evidence from a meta-analysis and a new experiment. Experimental Economics, 23(4), 979–1001. https://doi.org/10.1007/s10683-020-09645-z

32.

Gaesser

Keeler

Young

(2018). Moral imagination: Facilitating prosocial decision-making through scene imagery and theory of mind. Cognition, 171, 180–193. https://doi.org/10.1016/j.cognition.2017.11.004

33.

Harrington

(2009). Confirmatory factor analysis. Oxford University Press.

34.

Hawley

P. H.

(2015). Evolution, prosocial behavior, and altruism: A roadmap for understanding where the proximate meets the ultimate. In Padilla-Walker

L. M.

Carlo

(Eds.), Prosocial development: A multidimensional approach (pp. 43–69). Oxford University Press.

35.

Hayes

A. F.

(2018). Introduction to mediation, moderation, and conditional process analysis: A Regressión-based approach (2nd ed.). The Guilford Press.

36.

Hein

Silani

Preuschoff

Batson

C. D.

Singer

(2010). Neural responses to ingroup and outgroup members’ suffering predict individual differences in costly helping. Neuron, 68(1), 149–160. https://doi.org/10.1016/j.neuron.2010.09.003

37.

Heller

S. B.

Shah

A. K.

Guryan

Ludwig

Mullainathan

Pollack

H. A.

(2017). Thinking, fast and slow? Some field experiments to reduce crime and dropout in Chicago. The Quarterly Journal of Economics, 132(1), 1–54. https://doi.org/10.1093/qje/qjw033

38.

Higgs

McIntosh

Connelly

Mumford

(2020). Self-focused emotions and ethical decision-making: Comparing the effects of regulated and unregulated guilt, shame, and embarrassment. Science and Engineering Ethics, 26(1), 27–63. https://doi.org/10.1007/s11948-018-00082-z

39.

Huppert

Cowell

J. M.

Cheng

Contreras-Ibáñez

Gomez-Sicard

Gonzalez-Gadea

M. L.

Huepe

Ibanez

Lee

Mahasneh

Malcolm-Smith

Salas

Selcuk

Tungodden

Wong

Zhou

Decety

(2019). The development of children’s preferences for equality and equity across 13 individualistic and collectivist cultures. Developmental Science, 22(2), e12729. https://doi.org/10.1111/desc.12729

40.

Jacobs

B. A.

Cherbonneau

(2017). Nerve management and crime accomplishment. Journal of Research in Crime and Delinquency, 54(5), 617–638. https://doi.org/10.1177/0022427817693037

41.

Janis

I. L.

Mann

(1977). Decision making: A psychological analysis of conflict, choice, and commitment. The Free Press.

42.

Jolliffe

Farrington

D. P.

(2004). Empathy and offending: A systematic review and meta-analysis. Aggression and Violent Behavior, 9(5), 441–476. https://doi.org/10.1016/j.avb.2003.03.001

43.

Kahneman

(2013). Thinking, fast and slow. Farrar, Straus and Giroux.

44.

Kamas

Preston

(2021). Empathy, gender, and prosocial behavior. Journal of Behavioral and Experimental Economics, 92, 101654. https://doi.org/10.1016/j.socec.2020.101654

45.

Kohlberg

Hersh

R. H.

(1977). Moral development: A review of the theory. Theory Into Practice, 16(2), 53–59. https://doi.org/10.1080/00405847709542675

46.

Lambert

Declerck

C. H.

Boone

Parizel

P. M.

(2017). A functional MRI study on how oxytocin affects decision making in social dilemmas: cooperate as long as it pays off, aggress only when you think you can win. Hormones and Behavior, 94, 145–152. https://doi.org/10.1016/j.yhbeh.2017.06.011

47.

Lerner

J. S.

Valdesolo

Kassam

K. S.

(2015). Emotion and decision making. Annual Review of Psychology, 66, 799–823. https://doi.org/10.1146/annurev-psych-010213-115043

48.

Llorca-Mestre

Malonda-Vidal

Samper-García

(2017). Prosocial reasoning and emotions in young offenders and non-offenders. The European Journal of Psychology Applied to Legal Context, 9(2), 65–73. https://doi.org/10.1016/j.ejpal.2017.01.001

49.

Mann

Burnett

Radford

Ford

(1997). The Melbourne Decision Making Questionnaire: An instrument for measuring patterns for coping with decisional conflict. Journal of Behavioral Decision Making, 10, 1–19. https://doi.org/10.1002/(sici)1099-0771(199703)10:1<1::aid-bdm242>3.0.co;2-x

50.

Mestre-Escrivá

Frías-Navarro

M. D.

Samper García

(2004). La medida de la empatía: Análisis del interpersonal reactivity index. Psicothema, 16(2), 255–260.

51.

Mesurado

Distefano

M. J.

Robiolo

Richaud

M. C.

(2019). The Hero program: Development and initial validation of an intervention program to promote prosocial behavior in adolescents. Journal of Social and Personal Relationships, 36(8), 2566–2584. https://doi.org/10.1177/0265407518793224

52.

Minda

J. P.

(2015). The psychology of thinking: Reasoning, decision-making & solving-problem. Sage.

53.

Moya Albiol

Herrero Sebastián

Bernal Santacreu

M. C

. (2010). Bases neuronales de la empatía. Revista de Neurología, 50(02), 89–100. https://doi.org/10.33588/rn.5002.2009111

54.

Nash

Gianotti

L. R.

Knoch

(2014). A neural trait approach to exploring individual differences in social preferences. Frontiers in Behavioral Neuroscience, 8, 458. https://doi.org/10.3389/fnbeh.2014.00458

55.

O’Neill

K. K.

(2020). Adolescence, empathy, and the gender gap in delinquency. Feminist Criminology, 15(4), 410–437. https://doi.org/10.1177/1557085120908332

56.

Padilla-Walker

L. M.

Carlo

(2014). The study of prosocial behavior: Past, present, and future. In Padilla-Walker Laura

Carlo

(Eds.), Prosocial development: A multidimensional approach (pp. 3–16). Oxford University Press.

57.

Pande

S. M.

Papamichail

K. N.

Kawalek

(2021). Compatibility effects in the prescriptive application of psychological heuristics: Inhibition, integration and selection. European Journal of Operational Research, 295(3), 982–995. https://doi.org/10.1016/j.ejor.2021.03.046

58.

Pérez-Albéniz

De Paúl

Etxeberría

Montes

M. P.

Torres

(2003). Adaptación de interpersonal reactivity index (IRI) al español. Psicothema, 15(2), 267–272. https://reunido.uniovi.es/index.php/PST/article/view/8118

59.

Phelps

E. A.

Lempert

K. M.

Sokol-Hessner

(2014). Emotion and decision making: Multiple modulatory neural circuits. Annual Review of Neuroscience, 37, 263–287. https://doi.org/10.1146/annurev-neuro-071013-014119

60.

Piaget

(1984). El criterio moral en el niño. Martiänez Roca, D.L.

61.

Pineda

D. A.

Aguirre-Acevedo

D. C.

Trujillo

Valencia

A. M.

Pareja

Á.

Tobón

Velilla

Ibáñez

(2013). Dimensiones de la empatía en excombatientes del conflicto armado Colombiano utilizando una escala estandarizada. Revista Colombiana de Psiquiatría, 42(1), 9–28. https://doi.org/10.1016/s0034-7450(14)60084-6

62.

Pogarsky

Roche

S. P.

Pickett

J. T.

(2018). Offender decision-making in criminology: Contributions from behavioral economics. Annual Review of Criminology, 1, 379–400. https://doi.org/10.1146/annurev-criminol-032317-092036

63.

Preston

S. D.

de Waal

F. B.

(2002). Empathy: Its ultimate and proximate bases. The Behavioral and Brain Sciences, 25(1), 1–20; discussion 20. https://doi.org/10.1017/s0140525x02000018

64.

Reyna

(2018). When irrational biases are smart: A fuzzy-trace theory of complex decision making. Journal of Intelligence, 6(2), 29. https://doi.org/10.3390/jintelligence6020029

65.

Reyna

V. F.

Panagiotopoulos

(2020). Morals, money, and risk-taking from childhood to adulthood: The neurodevelopmental framework of fuzzy trace theory. In Decety

(Ed.), The social brain: A developmental perspective (pp. 385–405). The MIT Press.

66.

Rucker

D. D.

Preacher

K. J.

Tormala

Z. L.

Petty

R. E.

(2011). Mediation analysis in social psychology: Current practices and new recommendations. Social and Personality Psychology Compass, 5(6), 359–371. https://doi.org/10.1111/j.1751-9004.2011.00355.x

67.

Silke

Brady

Boylan

Dolan

(2018). Factors influencing the development of empathy and pro-social behaviour among adolescents: A systematic review. Children and Youth Services Review, 94, 421–436. https://doi.org/10.1016/j.childyouth.2018.07.027

68.

Singer

Tusche

(2015). Understanding others: Brain mechanisms of theory of mind and empathy. In Glimcher

P. W.

Fehr

(Eds.), Neuroeconomics: Decision making and the brain (2nd ed., pp. 513–531). Academic Press.

69.

Spinrad

T. L.

Eisenberg

(2019). Socialization of moral emotions and behavior. In Laible

D. J.

Carlo

Padilla-Walker

L. M.

(Eds.), The Oxford handbook of parenting and moral development (pp. 57–72). Oxford University Press.

70.

Starmans

Sheskin

Bloom

(2017). Why people prefer unequal societies. Nature Human Behaviour, 1(4), 0082. https://doi.org/10.1038/s41562-017-0082

71.

Stürmer

Snyder

(Eds.). (2010). The psychology of prosocial behavior: Group processes, intergroup relations, and helping. Wiley-Blackwell.

72.

Taylor

L. K.

O’Driscoll

Dautel

J. B.

McKeown

(2020). Empathy to action: Child and adolescent out-group attitudes and prosocial behaviors in a setting of intergroup conflict. Social Development, 29(2), 461–477. https://doi.org/10.1111/sode.12421

73.

Tibbetts

S. G.

(2014). Traits and states of selfconscious emotions in criminal decision making. In Van-Gelder

J.-L.

Elffers

Reynald

Nagin

(Eds.), Affect and cognition in criminal decision making (pp. 121–238). Routledge.

74.

Van der Graaff

Carlo

Crocetti

Koot

H. M.

Branje

. (2018). Prosocial behavior in adolescence: Gender differences in development and links with empathy. Journal of Youth and Adolescence, 47(5), 1086–1099. https://doi.org/10.1007/s10964-017-0786-1

75.

Van Gelder

J. L

. (2017). Dual-process models of criminal decision making. In Bernasco

Elffers

Van Gelder

J.-L.

(Eds.), The Oxford handbook of offender decision making (pp. 166–180). Oxford University Press.

76.

Van Gelder

J. L.

de Vries

R. E

. (2014). Rational Misbehavior? Evaluating an integrated dual-process model of criminal decision making. Journal of Quantitative Criminology, 30(1), 1–27. https://doi.org/10.1007/s10940-012-9192-8

77.

Van Gelder

J.-L.

Elffers

Reynald

Nagin

D. S

. (2013). Affect and cognition in criminal decision making. Routledge.

78.

Wen

Fan

(2015). Monotonicity of effect sizes: Questioning kappa-squared as mediation effect size measure. Psychological Methods, 20(2), 193–203. https://doi.org/10.1037/met0000029

79.

Williams

MacKinnon

D. P.

(2008). Resampling and distribution of the product methods for testing indirect effects in complex models. Structural Equation Modeling A Multidisciplinary Journal, 15(1), 23–51. https://doi.org/10.1080/10705510701758166

80.

World Medical Association. (2013). World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. Journal of American Medical Association, 310, 2191–2194. https://doi.org/10.1001/jama.2013.281053

81.

Yang

(2018). Longitudinal relations between adolescents’ materialism and prosocial behavior toward family, friends, and strangers. Journal of Adolescence, 62, 162–170. https://doi.org/10.1016/j.adolescence.2017.11.013

82.

Yaseen

Z. S.

Foster

A. E.

(2019). Teaching empathy in healthcare: Building a new core competency. Springer.

83.

Yeager

D. S.

(2017). Social and emotional learning programs for adolescents. The Future of Children, 27(1), 73–94.

84.

Zaki

(2020). Integrating empathy and interpersonal emotion regulation. Annual Review of Psychology, 71, 517–540. https://doi.org/10.1146/annurev-psych-010419-050830

85.

Zhao

Wang

Zhang

Zhao

(2021). Rational choice theory applied to an explanation of juvenile offender decision making in the Chinese setting. International Journal of Offender Therapy and Comparative Criminology, 65(4), 434–457. https://doi.org/10.1177/0306624x20931429

Empathy and Prosocial Behavior in Adolescent Offenders: The Mediating Role of Rational Decisions

Abstract

Keywords

Introduction

Literature Review and Hypotheses

Factors Influencing Prosocial Behavior, Empathy, and Rational Decision-Making

Relationship Between Prosocial Behavior, Empathy, and Rational Decisions

Empathy, Prosocial Behavior, and Decision-Making in Offenders Adolescents

Objective and Hypotheses

Method

Participants

Procedure

Instruments

Data Analysis

Results

Factorial Validity of the Measures

Descriptive Results

Direct and Indirect Effects of Empathy, Rational Decision-Making, and Prosocial Behavior Variables

The Perspective Taking

Fantasy

Empathic Concern

Personal Distress

Discussion

Correlation Between Prosocial Behavior, Empathy, and Rational Decision-Making

The Mediating Role of Rational Decision-Making Between Empathy and Prosocial Behavior

Predictors of Prosocial Behavior, Empathy, and Rational Decision-Making in Offending Adolescents

Practical Implication

Limitations and Future Directions

Conclusion

Footnotes

Acknowledgements

Data Availability Statement

Declaration of Conflicting Interests

Funding

Ethics Declarations

ORCID iD

References