8 Culture and developments in heuristics and biases from preschool through adolescence: challenges and implications for social development – The Development of Thinking and Reasoning

Culture and developments in heuristics and biases from preschool through adolescence

Challenges and implications for social development

Paul Klaczynski


The “heuristics and biases” research program arose from findings that adults’ responses on numerous tasks deviated from traditionally prescribed norms (e.g., Evans & Over, 1996; Kahneman & Tversky, 1972, 1996; Kahneman, Slovic, & Tversky, 1982; Stanovich & West, 1999). Repeated demonstrations of apparently irrational thinking led to questions about the adaptive value of heuristic shortcuts and sparked a plethora of theoretical and philosophical debates. Most polemics centered on rationality and its nature, but other controversies were spawned by analyses of “heuristics and biases” tasks, many of which are neither overwhelmingly complex nor computationally demanding. For example, in the “base rate neglect problem” below, most adults believe that Sarah is a Buddhist – despite the overwhelming likelihood that she is a Muslim:

Sarah is in a study of 95 Muslims and five Buddhists. Sarah likes philosophy, hates materialism, and wants to visit India. Is she more likely to be Buddhist or Muslim? (adapted from De Neys & Franssens, 2009)

The prevalence of non-normative responses extends to reasoning. A considerable evidential corpus indicates that performance on conditional and syllogistic reasoning problems is fraught with fallacies (e.g., Evans, 1972; Wason, 1966). A tie binding these findings to those in the heuristics and biases literature is the fact that reasoning errors are prevalent when problems cue responses based on beliefs, knowledge, or intuitions that conflict with responses based on logic. For instance, for the major and minor premises, “All mammals can walk” and “Whales are mammals,” adults often reject the logical conclusion, “Whales can walk” (Markovits & Nantel, 1989).1

The ubiquity of apparently irrational responses on reasoning, judgment, and decision-making tasks provided the foundation for “dual-process” theories (e.g., Chaiken, 1980; Evans, 1989; Wason & Evans, 1975). By the end of the 20th century, the influence of dual-process theories was readily apparent in social and cognitive psychology (Chaiken & Trope, 1999; Evans, 2008). Although dual-process theories have attracted considerably less attention from developmentalists, in the past decade interest in the potential dual-process theories for understanding developmental phenomena has begun blossoming (see Barrouillet, 2011a).2

The emergence of developmental dual-process theories

The increased prominence of dual-process theories in developmental research can be traced to at least four empirical and theoretical sources. First, repeated findings that reasoning competence is not always instantiated in performance led to a focus on variables that interfered with competence activation and raised questions about the nature and origins of responses that were not based on reasoning (O’Brien & Overton, 1982; Overton, 1985; Reyna, 1991). Second, these questions prompted theoretical proposals that integrated developments in reasoning, memory, processing speed, working memory, and various executive functions (e.g., Barrouillet, 2011b; Barrouillet & Lecas, 2002; Gauffroy & Barrouillet, 2009; Markovits & Barrouillet, 2002; Markovits & Potvin, 2001; Markovits & Vachon; 1989; Overton & Dick, 2007; Ricco & Overton, 2011). A tangible outcome of realizations that these developmental theories and adult dual-process theories focused on similar processes and shared several assumptions is the recent publication of a special issue of Developmental Review on developmental dual-process theories (Barrouillet, 2011a).

A third impetus for the emergence of developmental dual-process theories is fuzzy-trace theory (FTT). Fuzzy-trace theorists have long noted the inadequacy of unidirectional theories that characterize development as progressions toward increasingly complex thinking. By calling attention to evidence that development is more variable than traditionally assumed, explaining numerous phenomena that are not explicable by information processing, Piagetian, or neo-Piagetian theories, fuzzy-trace theory has been pivotal in directing researchers toward dual-process theories (see Brainerd & Reyna, 1990, 1995; Brainerd, Reyna, & Ceci, 2008; Reyna, 1991, 2005; Reyna & Brainerd, 2011).

Finally, counterintuitive age trends (developmental reversals) were found in several areas of developmental inquiry. Under some conditions, base rate neglect (Jacobs & Potenza, 1991), framing effects (Reyna & Ellis, 1994), the conjunction (Davison, 1995) and sunk cost fallacies (Krouse, 1986), conditional reasoning fallacies (Janveau-Brennan & Markovits, 1999), susceptibility to false memories (Brainerd & Reyna, 2005), and “non-logical” transitive inferences (Markovits & Dumas, 1999) increase with age. Findings that (a) variability characterizes everyday cognition and development (Jacobs & Klaczynski, 2002; Siegler, 1996), (b) gist processing preference increases with age (Reyna, 1991), (c) the effects of verbatim memory on reasoning are often minimal (Brainerd & Gordon, 1994), and (d) implicit and explicit memory develop independently (Schneider & Bjorklund, 1998) revealed additional inadequacies in traditional theories. Together with these data, the evidence accumulated for developmental reversals prompted searches for and attempts to construct theories that could explain age increases in normative and non-normative responses.

Chapter goals

Despite the momentum they currently enjoy, the potential of developmental dual-process theories may not be realized unless concerns expressed by critics (and advocates) of dual-process theories are addressed. A first goal of this contribution is therefore to elaborate and respond to several issues and potential controversies. A second goal emerged from an examination of developmental heuristics and biases (DHB) research indicating that most studies have focused primarily on constructs derived from adult reasoning and decision-making research. However, dual-process theories may be usefully extended to social developmental phenomena (e.g., aggression) and may benefit from investigating various social developments. The second goal was therefore to illustrate connections between DHB research and phenomena typically studied by social and cross-cultural developmentalists.

In the first major section, I summarize criticisms of dual-process theories, responses to these criticisms, and revisions to cognitive and developmental dual-process theories. Issues, emerging controversies in DHB research, and arguments that may clarify and help resolve these issues are the focus of the second section. The third section comprises a review of evidence that adds credence to these arguments, indicates the relevance of challenges facing DHB researchers to research with adults, and suggests possibilities for overcoming these challenges. In the final section, I propose increasing the scope of DHB research to phenomena that have garnered little attention from dual-process theorists, review studies demonstrating the utility of this proposal, and indicate an approach for bridging the gaps that separate DHB and social developmental research.

Dual-process theories: criticisms and revisions

Although various criticisms have recently been levied against adult dual-process theories (e.g., Kruglanski & Gigerenzer, 2011; Osman & Stavy, 2011), I limit my comments to criticisms most pertinent to developmental dual-process theories. Next, I outline revisions in Evans’ and Stanovich’s dual-process theories and terminological changes in the dual-process framework that guides my research.

Responses to criticisms of dual-process theories

Of the various responses to criticisms of dual-process theories (e.g., Evans, 2011, 2012; Evans & Over, 2010; Reyna & Brainerd, 2011; Stanovich, 2010; Stanovich, West, & Toplak, 2011), I extend and elaborate responses to two criticisms, and discuss an issue, that appears in most critiques of dual-process theories. For DHB researchers, a particularly relevant criticism involves the assertion that dual-process theories are based on “face value interpretations” (a specific type of “interpretation bias”; see Elqayam & Evans, in press). Face value interpretations equate conscious processing with normative responses and unconscious processing with non-normative responses. The fact that such overly zealous interpretations abound in psychology does not absolve dual-process researchers from drawing biased conclusions from weak data. The conclusion that dual-process researchers are “guilty as charged” should nonetheless be accompanied by the caveat that face value interpretations are considerably less common (with notable exceptions, discussed subsequently) now than ten years ago. A more cogent reply is that the criticism relies on erroneous assumptions. Dual-process theorists have long emphasized that normative responses can be based in predominantly conscious or unconscious processing and that non-normative responses can arise from predominantly conscious or unconscious processing (Klaczynski, 2001a; Evans & Over 1996; Moshman, 2000; Reyna & Brainerd, 1995; Reyna, Lloyd, & Brainerd, 2003; Stanovich, 2004; see Evans, 2012). Because they hinge on inaccurate premises, arguments supporting the criticism (e.g., Kruglanski & Gigerenzer, 2011; Marewski, Gaissmaier, & Gigerenzer, 2010) are not logically sound.

A second criticism is that dual-process theories are contradictory and ambiguous; for instance, theorists sometimes use different terms to describe the same processes and the same terms to describe different processes. Resulting from this “lack [of] conceptual clarity” (Keren & Schul, 2009, p. 534) are difficulties deriving specific predictions and determining whether dual-process theories can be falsified. Undermining attempts to identify the guilty theorists and, more generally, diminishing the value of Keren and Schul’s arguments are statements that obfuscate their goals. For instance, Keren and Schul’s implied intent was demonstrating conceptual difficulties with specific dual-process theories (“the examples we use should be viewed as illustrations [of] problems specific to the different models” p. 537). Seemingly antithetical to this intent are such statements as, “The present examination of two-system models is explicitly aimed at the generic level without going into the details [of] any particular framework” (p. 535), and “our critique does not address any specific two-system model” (p. 537).

Implied in the aforementioned quotes and explicit elsewhere (e.g., “Every [italics added] two-system model describes the two systems by a set of binary characteristics or dichotomies” p. 537) is a fallacy characteristic of virtually all attacks on dual-process theories. This fallacy, a derivative of the “received view” of dual-process theories (see Evans, 2011), implies that conceptual problems discovered in a dual-process theory extend to other dual-process theories. Specific dual-process theories can therefore be discussed as if those theories represented other dual-process theories. The “generic theory” fallacy is especially disconcerting because it has been committed by dual-process theorists (Reyna & Farley, 2006; Stanovich et al., 2011) and critics of developmental dual-process theories.

The fallacy is premised on the assumption of a single, unified dual-process theory. No such theory exists, however. Stanovich’s (2004) list of over 20 dual-process theories testifies to diversity among dual-process theories, strongly implies the improbability of a “unified” theory, and provides a compelling clue that dual-process theories cannot be reduced to a single theory. Attacks on a “generic” dual-process theory (or “at the generic level”) ignore theoretical differences and reify an illusory entity. Publications outlining various assumptions shared by dual-process theories simply indicate that these theories belong to the same family (Evans, 2011; see also Reese & Overton, 1970), but may have unintentionally implied that the information was intended for the purpose of abstracting a generic theory (Stanovich et al., 2011). However, within-family resemblances imply neither that dual-process theories are identical nor that different theories can be accurately represented by a single (“generic”) abstraction.

At a minimum, scholars interested in or critical of developmental dual-process theories should recognize differences in the origins of these theories and the effects of those differences on DHB research. For example, whereas some developmental dual-process theories represent modifications of theories originally intended to explain adult behavior, other theories originated in developmental psychology (e.g., Reyna & Brainerd, 2011; Ricco & Overton, 2011). Although developmental dual-process theories have in common several foundational tenets, failures to recognize theoretical differences have led to criticisms similar to criticisms of adult theories. For example, developmental dual-process theories have been considered contradictory, vague, and underspecified. Were they applicable to all (or most) developmental dual-process theories or clearly intended to highlight shortcomings in specific theories, such criticisms might have merit, stimulate theoretical and empirical advances, bring to light to difficulties in data interpretation, and indicate misconstrued theoretical assumptions. For instance, the weaknesses Kuhn (2006) observed in developmental dual-process theories were based primarily on a review of my research and theoretical perspective. Kuhn’s concerns were articulated clearly, well justified, and critically, neither implicitly nor explicitly extended to other dual-process theories (e.g., FTT).3

It is nonetheless worth noting that, because several developmental dual-process theories were derived from cognitive theories and only recently modified for examinations of development (exceptions include FTT and Overton’s competence « performance relational systems theory), neither vague/underspecified descriptions of the mechanisms underlying change nor disagreements over descriptions of the developmental trajectories should be surprising. This disclaimer does not detract from the value of well-founded challenges. Positive steps forward include recognizing and clarifying differences among theories, determining whether differences can be reconciled, reducing theoretical ambiguities, and increasing theoretical precision to ensure that specific hypotheses can be derived and tested.

Brief theoretical and terminological notes

The essence of dual-process theories is that operations in two processing systems underlie judgments, decisions, inferences, and behaviors. To provide a background for the following sections, theoretical and terminological revisions in the dual-process perspective that guides my research are outlined below. Although I do not present the complete theory, I would be remiss if I failed to acknowledge that my views have been influenced by numerous scholars, particularly Evans (e.g., 2012), Overton (e.g., 1985, 1991, 2010; Overton & Dick, 2007), Stanovich (2004; Stanovich & West, 1998, 2000), Reyna and Brainerd (Brainerd & Reyna, 2001; Reyna & Brainerd, 2008), and Jan Jacobs. Indeed, the theoretical and terminological amendations in the framework I have been constructing were partially fueled by theoretical proposals and clarifications made and/or implied by Stanovich (2004), Evans (2012), and Reyna and Brainerd (2011; Reyna et al., 2003).

My theoretical orientation is not, however, entirely consistent with the theories of Evans and Stanovich. Consequently, I have not adopted the Type I/Type II processing terminology those theorists employ (see Evans, 2008; Stanovich et al., 2011). Instead, I refer to analytic and autonomous processing (adapted from Stanovich, 2004). Although I do not equate autonomous and Type I processing, autonomous processing is similar to Type I processing. Neither do I equate analytic processing with Type II processing, although analytic processing and Type II processing have similar characteristics. Finally, many of my theoretical musings are broadly consistent with the theories of Stanovich and Evans, but my perspective also reflects influences and concepts (e.g., “degrees of rationality”) from fuzzy-trace theory (Reyna & Farley, 2006) and competence « performance systems theory (Ricco & Overton, 2011). Consequently, considerable caution is warranted in interpreting the data I present as supporting or contravening hypotheses from those theories.4

According to Stanovich and Stanovich (2010), “The defining feature of Type 1 processing is its autonomy – the execution of Type 1 processes is mandatory when their triggering stimuli are encountered, and they are not dependent on input from high-level control systems” (p. 104). More generally, “autonomous processing” is rapid, capacity-independent, capable of parallel operations, and is not consciously controlled. However, to varying degrees individuals are aware of the products of autonomous processing; this momentary availability (in working memory) affords opportunities for considering these products and determining whether more deliberative processing is needed (i.e., metacognitive intercession; “overriding” autonomous processing with analytic processing). Relatively slow, serial, deliberative, reflective, and conscious analytic processing is sometimes limited by working memory capacity, underdeveloped or misapplied metacognitive abilities (e.g., monitoring abilities, inhibitory skills; see Kuhn, 2000; Kuhn & Pearsall, 1998; Moshman, 1990, 2005), the absence of relevant competencies, and failures to activate or apply correctly relevant competencies (Moshman, 1998; Reyna et al., 2003; Ricco & Overton, 2011). Although they may arise in autonomous or analytic processing, individual differences in analytic processing are likely larger and more variable than in autonomous processing.5

The terminological shift from the “experiential processing” (Klaczynski, 2009) to “autonomous processing” is not simply cosmetic but instead reflects an important theoretical amendment. Similar to problems with the term “heuristic processing” (Klaczynski, 2001a), the term “experiential processing” is problematic because it implies processing limited to automated procedures (i.e., highly practiced, over-learned rules, skills, etc.; Stanovich et al., 2011). Because “experiential processing” requires automatized skills, knowledge, and procedures, it is a type of autonomous processing. Autonomous processing also includes processes required for implicit learning and implicit memory and “built in” processes that appear “online” at different points in development that may lead to biases early in life (a focus of the final section). Subsuming experiential processing to autonomous processing does not diminish its importance. For instance, because practice can transform analytic processes to experiential processes, unlike other autonomous processes, domain-specific developments in experiential processing are possible throughout much of life.

Despite the importance of further elaborating these theoretical speculations, a more pressing concern is that the progress of DHB theory and research may be hindered unless several issues and potential controversies are addressed. Therefore, I describe these issues in the next section and later summarize findings and arguments that may resolve, or at least clarify, some controversial developmental proposals.

Issues and potential controversies in developmental heuristics and biases research

As discussed previously, facilitating the progress of DHB research requires explicitly recognizing and acknowledging similarities and differences among developmental dual-process theories. However, the continued momentum of DHB research and theory depends on confronting challenges involving (1) face value interpretations, (2) “knowledge,” age, and developmental reversals, and (3) developments in autonomous processing beyond childhood.

Face value interpretations

No necessary connections exist between analytic processing and normative responses or between autonomous processing and non-normative responses (Evans, 2011; Stanovich et al., 2011). Analytic processing sometimes leads to non-normative responses and autonomous processing sometimes results in normative responses (Evans, 2009; Klasczynski, 2001a; Moshman, 2000). Because they reflect untested assumptions that processes can be inferred from normative and non-normative responses, the difficulties created by face value interpretations are analogous to the problems that arise when the competence/performance distinction is downplayed or ignored (see Overton 1985, 1990). Despite philosophical and research traditions emphasizing the need to distinguish performance from competence (Overton & Newman, 1982; Overton, 2006), clear attempts to establish that performance adequately indexes competence are sparse in contemporary cognitive developmental research. Similarly, despite multiple precautions against this interpretative fallacy (Klaczynski, 2001a, Table 5; Moshman, 2000; Reyna et al., 2003; Reyna & Farley, 2006; Stanovich et al., 2011) and despite considerably less evidence of face value interpretations in adult research, face value interpretations remain problematic for DHB research and theory.

The gist of the issue is that, in part because DHB research is in its infancy, face value interpretations have hindered theoretical and empirical progress. For instance, this interpretive fallacy has provided support for extending the “normative = analytic processing/non-normative = autonomous processing” criticism to developmental dual-process theories. In addition, face value interpretations have led to misguided theoretical assertions, resulted in exaggerated and misconstrued theoretical differences, provoked needless controversies (see Stanovich et al., 2011), and contributed to the issues discussed below.

Knowledge and age increases in heuristic use

“Knowledge” is construed here to include widely accepted, empirical “facts” (Kuhn, 2000; Moshman, 2008, this volume), beliefs (Kuhn, 1989; Kuhn & Pearsall, 1998), and stereotype content (Kokis, MacPherson, Toplak, West, & Stanovich, 2002). The effects of age and individual differences in knowledge (e.g., beliefs, factual knowledge) on children’s and adults’ reasoning have been demonstrated repeatedly (e.g., Evans, Barston, & Pollard, 1983; Gauffroy & Barrouillet, 2009; Markovits & Barrouillet, 2002; Reyna, Nelson, Han, & Dieckmann, 2009; Ward & Overton, 1990). Only recently, however, has the possibility that knowledge may impact children’s responses on heuristics and biases tasks gained widespread recognition (e.g., Reyna & Brainerd, 2011; Stanovich, Toplak, & West, 2008). The “knowledge” issue has since become somewhat controversial by, for instance, provoking reactions that may indicate fundamental differences (discussed subsequently) in the developmental assumptions of several dual-process theorists.

The issue is based on the premises that knowledge increases with age, certain heuristics require stereotype knowledge, and age and knowledge are typically confounded. Following from these premises is the “knowledge argument”: age increases in stereotype knowledge can account for age increases in heuristics (e.g., Davidson, 1995; Jacobs & Potenza, 1991). For instance, Jacobs and Potenza provided children base rates (10 girls want to be cheerleaders, 20 want to join the band) that conflicted with stereotype-relevant “individuating” information (i.e., a description implying that a person is or is not representative of a stereotyped group; e.g., Juanita is popular, attractive, etc.). Children then judged story characters’ group membership (e.g., does Juanita want to be a cheerleader or band member?). Older children and adults made more judgments based on representativeness than younger children. A “knowledge” explanation of such developmental reversals assumes that young children have less stereotype-relevant knowledge than older children. For example, if younger children do not associate sociability, popularity, and attractiveness with cheerleaders, their judgments cannot be based on representativeness. For young children, the only information relevant to their judgments is the base rates. Further, because the individuating information does not activate a heuristic that conflicts with the base rates, the problems are less complex and more straightforward for younger than for older children. Developmental reversals are outcomes of “decks” that are “stacked” against older children.

Although persuasive, there are plausible alternatives to the knowledge argument. Base rate neglect problems like those in Jacobs and Potenza (1991) and De Neys and Vanderputte (2011) involve two stereotypes (i.e., cheerleaders, band members) with few overlapping attributes (similar to the “engineer/lawyer” problem; Kahneman & Tversky, 1973). Even if young children do not know the “cheerleader” stereotype, they could “know” the band stereotype (not pretty, not sociable, not popular) and could base their judgments on a “not representative” heuristic or on base rates. This alternative is simply a variation of the knowledge argument that predicts developmental reversals if young children are insufficiently familiar with either stereotype. No variant of representativeness has been acquired; therefore, the base rates provide the only compelling basis for judgments. Reyna and Brainerd (2011) concurred that age differences in knowledge could account for some developmental reversals, but countered that compelling evidence for developmental reversals in framing effects, sunk cost decisions, false memories, and reasoning-knowledge dissociations could not be explained by knowledge differences.

Nonetheless, the possibility that knowledge differences explain some developmental reversals has positively affected DHB research. In the next section, I return to the knowledge argument, review this research, indicate that the issue is more complicated than thus far implied, and propose extending the argument to adult research. In the paragraphs below, however, I discuss potentially controversial proposals about developments in autonomous processing. The question central to following discussion is: “Are developments in autonomous processing completed by the end of childhood or by early adolescence?”

Developmental dual-process theories: issues in predicting changes in heuristics and biases during adolescence

The impact and progress of DHB research likely depends on providing researchers more detailed, precise, and testable theories. This issue was raised by Kuhn (2006). Although Kuhn commented positively on the potential of dual-process theories, she added that current developmental dual-process theories are too ambiguous and imprecise to derive testable developmental hypotheses. To illustrate the concerns expressed by Kuhn and others (e.g., Stanovich et al., 2011), consider Klaczynski and Cottrell’s (2004) proposal, “Adolescents and adults … have access to more heuristics [than children]. Because these heuristics have a longer history of use in adults than in children, it is very possible that heuristics become increasingly easy to activate with age” (pp. 521–533). We then accompanied our proposal that “metacognitive intercession” requires specific metacognitive abilities (e.g., executive function abilities) and thinking dispositions (e.g., reflect on and determine whether responses are appropriate to specific contexts) with two arguments. First, metacognitive abilities, other intellectual skills (e.g., reasoning abilities; i.e., “mindware”; Stanovich, 2009), and intellectual dispositions increase with age. Second, intercession skills are therefore likely to improve with age. In effect, we proposed two developmental paths. The first hypothesized trajectory invites the prediction that age and heuristic availability should correlate positively. By contrast, the second hypothesized trajectory is consistent with the prediction that age and heuristic judgments should correlate negatively.

The availability/judgment distinction (i.e., numbers, automaticity of heuristics vs. actual judgments) does provide a basis for empirical investigations of both trajectories. For Kuhn (2006), the essential question was: “Did the ‘theory’ provide a basis for predicting age differences in heuristics?” I concur with Kuhn’s implied response: the “theory” was too imprecise to provide a clear basis for expecting age increases, decreases, or stability in heuristic responses (e.g., improved intercession skills could nullify age increases in heuristic availability).

One of the many reasons (see Stanovich et al., 2011) that predicting developmental trends in heuristics and bias is difficult is the absence of research on age-related differences in “metacognitive intercession” and thinking dispositions. Metacognitive abilities – including, but not limited to, executive function abilities (e.g., inhibition), awareness of potential autonomous processing influences on responses, and selecting appropriate procedures (see Kuhn, 2000; Kuhn, Iordanou, Pease, & Wirkala, 2008; Moshman, 2008, this volume) – and intellectual dispositions (e.g., open-mindedness) are prerequisites for effective metacognitive intercession (Amsel, Close, Sadler, & Klaczynski, 2009; Klaczynski, 2004). Yet, with the exception of executive function abilities (e.g., inhibition), little is known of the developmental trajectories of these prerequisites. Specifically, investigations of developments in thinking dispositions, individual differences in the acquisition of thinking dispositions (e.g., open-mindedness, need for cognition), and the domain-specificity/generality of thinking dispositions are almost non-existent (Kuhn, 2009; Stanovich et al., 2011).

This paucity of research has contributed to “the devilish complexities involved in drawing developmental predictions from many dual-process models” (Stanovich et al., 2011, p. 115). Adding to these complexities are findings that, although adolescents and adults have the requisite analytic abilities, metacognitive intercession is not always successful (Evans, 2008; Klaczynski & Gordon, 1996; Klaczynski & Narasimham, 1998b; Reyna et al., 2003). Further complicating matters are conjectures of multiple autonomous processing systems with different functions, origins, products, and effects on analytic processing (Evans, 2006, 2008, 2010; Stanovich, 2004, 2010). The idea that autonomous processing comprises several processing systems (Stanovich, 2004, 2009; e.g., implicit learning/implicit memory processes, domain-specific processing, experiential processing, etc.) implies that “various kinds of Type 1 processing … develop through childhood and adolescence” (Evans, 2011, p. 100) and that autonomous processing developments follow non-parallel trajectories. These implications are clearly at odds with domain-general hypotheses of developments in autonomous processing.

Note that the foregoing argument does not preclude hypothesizing age differences in specific heuristics and biases, as the research presented subsequently testifies. My position is simply that domain-general hypotheses and sweeping theoretical claims concerning autonomous processing developments are inadvisable. The multifarious nature of autonomous processing, together with the cautionary notes of Stanovich, Evans, and Reyna et al. (e.g., face value interpretations), should make theorists pause when predicting general developments in autonomous processing.

Nonetheless, several general proclamations concerning developments in autonomous processing have been issued. For instance, according to Ricco and Overton (2011, p. 134), “Heuristic-based responding from late childhood through adolescence is generally found to decrease or remain stable … age-related increases in heuristic-based responding appear limited to research with children from the early to late elementary years rather than adolescence.” Similarly, Gauffroy and Barrouillet (2009) argued, “though a tendency exists for a developmental increase in heuristic responding in reasoning tasks, it is mainly observed in childhood and on tasks requiring the retrieval and integration of social knowledge … analytic processes develop through childhood but also during adolescence and until adulthood” (p. 255). And, according to Barrouillet (2011b, p. 156):

significant changes in heuristic use have more often been described during childhood than during adolescence, suggesting that if there is a development of System 1, this system reaches its maturity level earlier than System 2, and most of the reported developmental trends in System 1 seem to rely on social knowledge and stereotypes as they are more often related to social than non-social contents.

These assertions were not made without qualifications. Barrouillet (2011b) recognized that some age-related increases in biases (e.g., framing effects; Reyna & Ellis, 1994) are not easily attributed to differences in social knowledge, yet retained the basic argument that age increases in heuristics and biases did not continue beyond childhood. Ricco and Overton cited evidence for age increases in adolescents’ use of a specific heuristic (e.g., Klaczynski, Daniel, & Keller, 2009), but considered such findings “exceptions” or “select findings of increased use of specific social heuristics [that] might be learned at a particular point in development” (p. 134). Referring to Jacobs and Potenza (1991), Davidson (1995), and Klaczynski (2001a), Kuhn (2006) expressed similar sentiments, “Despite two earlier reports of increased susceptibility to certain decision-making fallacies during adolescence, Klaczynski did not replicate these findings and found no evidence that reliance on experiential processing increases with age on any task” (pp. 63–64).6

Such arguments are problematic, first, because they rely on face value interpretations and, second, because the paucity of developmental research on heuristics and biases, particularly during adolescence, provides an insufficient basis for inferring developmental trajectories (Stanovich et al., 2008; Strough, Karns, & Schlosnagle, 2011). A third issue is that the various “adolescent-autonomous processing” arguments (e.g., Gauffroy & Barrouillet, 2009; Kuhn, 2006; Ricco & Overton, 2011) may confuse two different questions. The explicit question is: Does autonomous processing development conclude by early adolescence? The implicit question, addressed below, is: Do developments in heuristics and biases conclude by early adolescence?

Emerging evidence, the age-knowledge issue, and heuristics during adolescence

Because these questions are related to the “knowledge issue,” I revisit the issue by reviewing research indicating that age differences in knowledge cannot fully explain developmental reversals. This review is accompanied by arguments that the “knowledge” issue is more complex than it appears and that differences in knowledge may create problems interpreting research with adults. Support for the latter proposition, demonstrating effects of knowledge on belief biases in adults’ reasoning, is then presented. The section concludes with a summary of research addressing the question: “Do developments in heuristic and biases conclude by early adolescence?”

The age-knowledge issue and individual differences in knowledge

Scientific progress is endangered by criticisms based on erroneous premises and misleading interpretations of theory and evidence. By contrast, criticisms in the form of carefully articulated evaluative reviews are essential to scientific advance and often point to the necessity of theoretical clarifications (e.g., Kuhn, 2006; Moshman, 2004). For instance, Kokis et al. (2002) contended that age-related increases in the conjunction fallacy (Davidson, 1995) and representativeness judgments (Jacobs & Potenza, 1991) could have arisen from age differences in stereotype knowledge. The implication that some developmental reversals are methodological artifacts led to provocative theoretical proposals (e.g., Gauffroy & Barrouillet, 2009; Reyna & Brainerd, 2011; Reyna et al., 2009; Ricco & Overton, 2011) and instigated several recent DHB investigations.

For instance, De Neys and Vanderputte’s (2011) investigation of age and base rate neglect was designed to determine whether stereotype familiarity accounted for age increases in representativeness (e.g., Jacobs & Potenza, 1991). The problems De Neys and Vanderputte constructed contained stereotypes with which young children were familiar (e.g., weight-food preferences, gender-toy preferences) or unfamiliar (e.g., stereotypical food preferences of Dutch and Italians). On each problem, five- and eight-year-olds were presented with base rate data (e.g., cards picturing nine boys and one girl) and told that stereotype-consistent and inconsistent qualities (e.g., trucks, dolls) were on the other (not visible) side of the cards. The experimenter placed the cards in a bag, shuffled them, removed a card, and showed each child the side with a stereotype-relevant quality (e.g., a doll or truck). Children indicated the group membership of the “child” on the hidden side. For example, if shown a truck, children indicated whether a boy or girl was on the other side of the card. This example is a “familiar/conflict” problem because the base rates (nine girls, one boy) indicated that “girl” was most probable but the stereotype implied “boy” (the problem would have been “familiar/no-conflict” if a doll’s picture was shown because the base rates and stereotype would have supported the same inference [i.e., “girl”]).

Responses on “familiar/conflict” problems are critical because the stereotypes pulled for non-normative (representativeness) judgments and the base rates pulled for normative responses. Specifically, if five-year-olds made more base rate judgments than eight-year-olds on these problems, the difference could be attributed to neither five-year-olds’ superior understanding of base rates nor less stereotype knowledge. This is precisely what De Neys and Vanderputte (2011) found: on the familiar/conflict problems, five-year-olds made more base rate consistent judgments than eight-year-olds (e.g., “girl” on the familiar/conflict example). Although they were not more knowledgeable than five-year-olds, eight-year-olds’ judgments were more often stereotype-consistent, a finding inconsistent with the “knowledge argument.” That is, age differences in stereotype knowledge cannot account for this developmental reversal. The De Neys and Vanderputte findings are important in their own right and because they strengthen conclusions that differences in knowledge cannot account for developmental reversals in framing effects, false memories, risk preferences, or reasoning-memory dissociations (Chapman, Gamino, & Mudar, 2012; Chick & Reyna, 2012; Reyna & Brainerd, 2011).

Nonetheless, De Neys and Vanderputte (2011) suggested a cautious interpretation of their findings: “the familiar problems will not yet be as familiar [to five-year-olds] as they are for eight-year-olds. Hence, on familiar conflict problems, older reasoners will still experience more heuristic bias than the younger ones” (p. 9). Together with the authors’ call for additional investigations of the age–knowledge– heuristic relationship and more fine-grained assessments of knowledge, this cautionary note hints at the complexity of the “knowledge” issue. For instance, even if age groups are equally knowledgeable, they may differ in the ease and speed with which problems activate stereotypes (see also Stanovich et al., 2008). Even if age differences in explicit familiarity are eliminated, stereotype automaticity or “implicit stereotypes” may increase with age. Even if both levels of familiarity are equated, ages may differ in stereotype endorsement (i.e., beliefs that specific attributes actually typify a stereotyped group), perceptions of stereotyped group homogeneity, and beliefs that attributes associated with one stereotyped group may be similarly characteristic of other social groups.

Belief-biases and individual differences in adults’ knowledge

Both developmental and adult studies of reasoning are often designed to determine the influence of knowledge on inferences. In conditional reasoning, knowledge of alternative antecedents facilitates recognition that conclusions cannot be drawn with certainty from premises that affirm the consequent (AC; pq and q is true). By contrast, knowledge of “disabling conditions” (i.e., p¹q, if z is true) often invites judgments of indeterminacy on modus ponens (MP; pq and p is true) problems (Cummins, 1995; Markovits, Lortie-Forgues, & Brunet, 2010). However, although knowledge should influence reasoning and judgments on other reasoning and decision making problems, whether theorists have recognized the implications differences in knowledge could have on adults’ responses is unclear.

Most college students know that mammals have hair, that dogs are mammals, and that dogs have hair. However, do these students understand that all mammals have hair? Consider porpoises: How many people know that porpoises are mammals and have hair? Among people who know that porpoises have hair, are there differences in the confidence they have in their knowledge? Do differences in knowledge and knowledge confidence affect reasoning?

In two studies, my students and I explored the relationships among knowledge, confidence, and belief biases in reasoning. Although we had several reasons for examining belief-biases in reasoning, perhaps the most intriguing is the importance that some dual-process theorists have attributed to evidence for belief biases. For instance, Evans (2008, p. 164) claimed, “The paradigm case for dual processes in reasoning is belief bias” (p. 164). Similarly, De Neys (2006) wrote, “Paradigmatic support for the dual-process framework has come from individual differences studies on belief bias” (p. 428). If belief bias is as critical as these authors indicated, then determining whether belief biases sometimes arise because of knowledge differences is of paramount importance.

In each study, participants evaluated the logical validity of MP and AC conclusions on “conflict” and “no-conflict” problems. In the first study, the results of which are described below, after completing “natural fact knowledge” and “knowledge confidence” tests, participants rated the degree that conclusions were logically valid (e.g., Markovits, Saelen, & Forgues, 2009). For instance, on conflict problems with the major premise, “All mammals have hair,” the minor premises and conclusions were:

Modus ponens Affirmation of the consequent
Porpoises are mammals Porpoises have hair
Therefore, porpoises have hair Therefore, porpoises are mammals

The MP conclusion is logically necessary, but conflicts with the intuition that fish-like animals are hairless. How would adults who are highly confident that porpoises are not mammals respond? How would adults who believe that porpoises are mammals, but have little confidence that their knowledge is correct, respond? Their inferences would likely differ from those of adults who know with certainty that porpoises are mammals and that all mammals have hair. For the latter group (correct knowledge, high confidence), the MP syllogism may cause little (or no) conflict. For the same group, the AC syllogism brings empirical knowledge into conflict with logic: knowing, with a high degree of certainty, that porpoises have hair (e.g., by virtue of being mammals) invites the logically fallacious conclusion. By contrast, individuals uncertain that porpoises are mammals, and individuals confident that porpoises do not have hair, would likely infer (correctly) that the conclusion is erroneous. Inaccurate knowledge could therefore support logically valid inferences.

High ratings on MP and AC problem implied that participants believed that the conclusions were valid. However, because the AC conclusions were not valid – and high ratings therefore implied the acceptance of logically invalid conclusions – we reverse scored AC ratings so that higher ratings indicated rejecting these conclusions (on both problem types, high ratings therefore indexed reasoning consistent with formal logic). Figure 8.1 presents the most critical findings.

Figure 8.1 Ratings of modus ponens (upper panel) and affirmation of the consequent (lower panel) conflict (CN) and no-conflict (NC) conclusions by knowledge and confidence (adjusted for thinking disposition, SAT, and verbal ability scores).

On MP problems, knowledge and ratings were related positively: the highest rating were given by those highly confident in their knowledge and the lowest ratings by adults confident that their inaccurate knowledge was correct. Could confidence in inaccurate knowledge have been associated with less conflict? Do “incorrect knowledge/high confidence” participants’ ratings indicate poor reasoning? A different pattern was evident on the AC problems: the more people knew, the more prone they were to accept (logically invalid) conclusions. Those who knew the least, but were confident that their “knowledge” was accurate, gave the highest ratings; thus, confidence in inaccurate knowledge facilitated logical inferences. In the second study, the response options for the MP and AC syllogisms were categorical, but the findings were similar. It appears that, under some conditions, knowledge and confidence support MP inferences and interfere with AC inferences.

What, if any, implications do these findings have for research on belief biases? In a related study, Markovits et al. (2009, Exp. 1) found that MP conclusions were rejected less often for implausible (e.g., “If something is a plant, then it is made of stone”) than plausible premises. A plausible premise (with a valid conclusion) was: “If something is an insect, then it has six legs. Spiders are insects. Conclusion: Spiders have six legs.”

All insects have six legs, but spiders are not insects. Markovits et al. (2009) reasoned that more correct inferences were made on implausible problems because the need to inhibit empirical knowledge was more obvious. An alternative, but not necessarily incompatible, interpretation rests on individual differences in knowledge and confidence. Participants confident that spiders are insects and all insects do not have six legs (incorrect, but “plausible,” counterexamples include caterpillars, centipedes, millipedes) would have more difficultly accepting the conclusion than participants confident in their (incorrect) knowledge that, by virtue of being insects, spiders must have six legs. A knowledge-difference explanation could also account for Markovits et al.’s other findings and findings from other belief-bias studies. For instance, the following problem is used frequently in this literature: “All mammals can walk; whales are mammals; therefore, whales can walk” (Markovits & Nantel, 1989). Conflict is assumed because people know that whales are mammals and that whales cannot walk and, logically, that if p is true (“Whales are mammals”), q (“Whales can walk”) must also be true. However, for college students who do not know that whales are mammals, this is not an MP problem. No conclusion necessarily follows and (logically) the MP conclusion must be rejected. Conflict may arise, but not for the intended reason. The minor premise, “Whales can walk,” and the AC conclusion, “Therefore, whales are mammals,” may cause little conflict because it clearly should be rejected – it neither follows from the premises nor is empirically correct.

If responses on belief-bias problems are affected by differences in knowledge and knowledge confidence, then it is likely that knowledge differences also influence responses on heuristics and biases problems. Knowledge (e.g., of stereotype content) is relevant to a number of frequently studied heuristics and biases (e.g., availability, the conjunction fallacy, representativeness, the “recognition” heuristic, etc.). It is clear that (a) individual differences exist in adults’ knowledge and it is quite likely that (b) these differences affect responses on various heuristics and biases tasks. An obvious question arising from these statements is: If the knowledge that supports certain reasoning faux pas and heuristics is lacking, should responses be considered biased or non-normative? If knowledge affects responses on heuristics and biases problems, another important question is: Can individual differences in knowledge explain variance typically attributed to, or above and beyond that attributed to, intellectual dispositions and capacity?

Although the next section centers on age differences in heuristics and biases during adolescence, the data also illustrate the importance of distinguishing between stereotype knowledge and endorsement. However, to demonstrate that developmental reversals are not limited to heuristics, age-related increases in conditional reasoning errors are first discussed.

Heuristics and biases during adolescence

Far from being baseless, the previously presented proposals that developments in autonomous processing end before or during early adolescence were based on extensive literature reviews. In brief, the vast majority of that literature indicates that adolescents outperform children and older adolescents outperform younger adolescents on most tasks. Why, then, question contentions that age increases in non-normative responding during adolescence are exceptions to a general rule (Ricco & Overton, 2011) and that heuristic processing does not increase during adolescence (Barrouillet, 2011b) when impressive support for these contentions is available?

The four cornerstones on which I founded responses to these questions were: (1) the literature on which these claims are referenced; (2) conceptual issues arising from interpretations of extant data; (3) the conditional reasoning literature; and (4) recent data on developments in specific domains. To discuss the empirical literature taken as support for “adolescent heuristic development” claims, a first question is: How extensive is the adolescent heuristic and biases literature? The answer: extremely limited. In their reviews of DHB research, Strough et al. (2011) and Stanovich et al. (2008) noted the dearth of adolescent-heuristic development research and warned against drawing developmental conclusions from this sparse literature. A second question is: How was evidence that supported conclusions about autonomous processing development interpreted? By and large, sweeping claims that developments in autonomous processing end in early adolescence, or proceed more slowly during adolescence, relied on evidence that normative responses on heuristics and biases tasks increased with age. In other words, these conclusions were, at least partially, based on face value interpretations: normative responses were considered indicative of analytic processing and non-normative responses were construed as evidence for autonomous processing. At present (if I am to avoid the same fallacy), extant data prohibit direct responses to the question: Do developments in autonomous processing continue into adolescence?”

However, ample data exist to answer the question implicit in recent commentaries: Are developments in heuristics and biases concluded by early adolescence? There is also sufficient evidence to answer a subordinate question: Are age increases in heuristics and biases limited to “select findings of increased use of specific social heuristics [that] might be learned at a particular point in development” (Ricco & Overton, 2011, p. 134)?

To address the latter question, I refer to reports of developmental reversals in conditional reasoning. Wildman and Fletcher (1977) presented 14-, 16-, 18-, and 21-year-olds arbitrary conditional statements (e.g., “If it is a square, then the color is brown”), information affirming the antecedent (i.e., p; MP; e.g., “it is a square”), denying the antecedent (i.e., ˜p), affirming the consequent (i.e., q), or denying the consequent (i.e., ˜q; modus tollens [MT]). On MT problems, a development reversal was found: older adolescents rejected valid conclusions (i.e., ˜p; e.g., “it is not a square”) more than younger adolescents. More recently, Janveau-Brennan and Markovits (1999) found that, when conditionals had few alternative antecedents, adolescents made fewer valid inferences on MP and MT problems than children (see also De Neys & Everaerts, 2008). Simoneau and Markovits (2003) similarly found that, after generating alternative antecedents, middle adolescents made fewer valid MP and MT inferences than early adolescents (see also O’Brien, 1972). Daniel and Klaczynski (2006) reported that 16- and 13-year-olds made fewer valid MP and inferences than ten-year-olds when conditionals had few alternatives, logic instructions were “weak,” and alternatives were experimentally provided. Finally, Klaczynski and Narasimham (1998a) observed developmental reversals on two indeterminate logical forms: affirmation of the consequent (AC) and denial of the antecedent (DA). On three reasoning tasks and on conditionals with few alternative antecedents, 17-year-olds committed the AC and DA “fallacies” more often than 14-year-olds who, in turn, committed these fallacies more frequently than 11-year-olds.

Although developmental reversals are not the norm in adolescent reasoning, the number of reversals – found on different logical forms and different tasks and contents under different instructional conditions – indicates that they should not be dismissed. Although predicting adolescent reasoning reversals requires additional research, several clues can be gleaned from extant data. For instance, conditional reasoning reversals seem to be found most often when instructions to reason logically are vague, few alternative antecedents are available, and/or antecedents activate disabling conditions. However, this review was not intended to explain conditional reasoning reversals but was, instead, to show that adolescent reversals are not limited to “specific social heuristics” (Ricco & Overton, 2011, p. 134) and demonstrate that, under some conditions, the competence/performance gap increases with age. How can age-related increases in competence and performance discrepancies in reasoning be reconciled with (a) considerable evidence that competence clearly increases with age and (b) findings that, on some heuristics and biases tasks, the competence/performance gap also increases with age?

The latter part of this question may raise some eyebrows. Kuhn (2006), Ricco and Overton (2011), and Gauffroy and Barrouillet (2009) observed that few studies indicate age-related increases in heuristic use during adolescence. Evidence that representativeness (De Neys & Vandeputte, 2011; Jacobs & Potenza, 1991), framing effects (Reyna & Ellis, 1994), and the “if-only” (Morsanyi & Handley, 2008), sunk cost (Krouse, 1986), and conjunction (Davidson, 1995; Morsanyi & Handley, 2008) fallacies increase during childhood cannot be extended to adolescence. Although scarce, studies of adolescents have typically revealed positive associations between age and normative responses on sunk cost, “if-only,” and conjunction problems (Klaczynski, 2001b) and a variety of other heuristics and biases (Klaczynski, 2001a). Although age increases have been found on some indexes of belief-biased or “motivated” reasoning (e.g., Klaczynski, 1997, 2000), Ricco and Overton (2011) correctly observed that age and indexes of belief-biased reasoning are unrelated (e.g., Klaczynski & Fauth, 1997; Klaczynski & Lavallee, 2005) or (slightly) negatively related (e.g., Kuhn, 1991).

These findings neither support nor refute claims concerning adolescence and developments in autonomous processing. With the possible exceptions of experimental manipulations of motivation (e.g., Klaczynski & Gordon, 1996; Klaczynski & Narasimham, 1998b), the data from studies of belief-biased reasoning provide little basis for inferring age differences in awareness that beliefs affect reasoning. This research, together with findings from other studies of adolescent heuristics, may provide an answer to the question: Are adolescents more or less biased (or more or less reliant on heuristics) than children? Research on reversals in conditional reasoning and the scarcity of research on age and adolescents’ heuristic use implies that sweeping conclusions would be premature (Stanovich et al. 2008; Strough et al., 2011). A final reason for guarding against the domain-general conclusions about heuristics and adolescent development are new findings that refute proposals that heuristic development concludes before, or at the beginning of, adolescence.

Prior to reviewing these findings, a critical difference between this research and earlier work was that the approach we recently adopted enabled a priori predictions of age increases in biases and heuristics judgments. The tacit assumption that age differences in heuristics are domain-general seems to underlie previous research. As a consequence, decisions about task content were not based on the adolescent development literature. Recent research was based on extensive reviews of findings in several disciplines and led us to suspect domain-specific increases in reasoning biases and heuristic judgments.

Despite some unanticipated findings, the age increases in heuristics and reasoning biases we found in three studies support our general hypotheses and a message central to this contribution. The essence of that message is that reasoning biases, heuristics judgments, and decisions biased by social heuristics do increase during adolescence; however, these age increases may be found only in domains and with beliefs that increase in salience from childhood through adolescence.

In two investigations, the domain of interest was obesity, a domain selected in part because we could determine whether variance associated with stereotype knowledge and stereotype endorsement was independent. More important to this choice was evidence that body type, “thin idealization,” and physical appearance become increasingly salient with age (Klaczynski et al., 2009). In the first study, Hispanic and Caucasian adolescents were asked to help an alien learn about Earth’s children by describing specific children (i.e., “targets”) to it. Each problem included a drawing of the “target” (an obese or average-weight child) and a description indicating that the “target” had a negative characteristic. Participants indicated how generalizable these qualities were to other children who “looked like” the target. For instance, “most students’ grades on a test were ‘A’ and ‘B+.’ Juan’s grade was only a ‘C-.’ If other kids who look like Juan took the same test, how many would also get bad grades?” Klaczynski et al. (2009) found age increases in generalizations from obese targets and age decreases in generalizations from average-weight targets were found, but did not control for stereotype endorsement. A reanalysis indicated that age increases in anti-obesity generalization biases (generalizations from obese targets – generalizations from average targets) remained significant after controlling for variance associated with stereotype endorsement (see Figure 8.2).

In a second study (Klaczynski, 2012a), Hispanic and Caucasian 11-, 14-, 17-year-olds were presented vignettes that differed the obesity stereotype consistency of individuating information (consistent, inconsistent) and the stereotype consistency of base rate information. This design resulted in two “conflict” problems and two “no-conflict” problems. The “no-conflict” problems were: (1a) individuating-consistent/base rates-consistent and (1b) individuating-inconsistent/base rates-inconsistent. The conflict problems were: (2a) individuating-inconsistent/base rates-consistent and (2b) individuating-consistent/base rates-inconsistent. On each problem, participants indicated (on a 1–4 scale) the likelihood that the person in the vignettes was obese. Participants should have judged that “peers” were obese on noconflict, individuating-consistent/base rates-consistent problems (i.e., the response implied by both types of evidence) and that peers were not obese on no-conflict,

Figure 8.2 Age differences in generalization ratings (range = 1–5) from obese and average-weight targets, controlling for endorsement of obese and average-weight stereotypes (based on Klaczynski et al., 2009).

individuating-inconsistent/base rates-inconsistent problems (neither type of evidence suggested “obese” judgments). Abbreviated examples of no-conflict and conflict problems are presented below:

  • No-conflict: individuating-inconsistent/base rate-inconsistent. Jennifer’s teachers think she is active (lazy) and …. Other students really like (avoid) Jennifer; she is popular (lonely) and has some very good friends (no real friends). One student likes (dislikes) Jennifer because, “she’s happy (sad) and makes me feel happy (like gagging).” In Jennifer’s lunch period, 18 (62) girls are obese and 62 (18) girls are average weight or thin. How likely is it that Jennifer is obese? (In parentheses: information for the no-conflict, individuating-consistent/base rate-consistent information version.)
  • Conflict: individuating-consistent/base rate-inconsistent. Kevin really likes (doesn’t like) video games, but hates going to gym class (loves working out in gym class). Once after gym, some girls whispered, “He smells like he’s decaying,” “gross, needs ultra-powerful deodorant” (“smells so clean,” “really manly”). Other students said Kevin, “Couldn’t get a date with a dog (could get a date with anyone).” In Kevin’s English class, 14 (44) students are obese and 44 (14) students are average weight. How likely is it that Kevin is obese? (In parentheses, information for the conflict: individuating-inconsistent/base rate-consistent version.)

Figure 8.3 shows ratings above the scale midpoint (i.e., 2.5), indicating judgments that the “peers” in the vignettes were probably obese, on the no-conflict individuating-consistent/base rates-consistent problems. On individuating-inconsistent/base rate-inconsistent problems, ratings decreased with age and, with the exception of the youngest age group, were below the scale midpoint. Both “no-conflict” findings indicate that the evidence constrained adolescents’ judgments, implying a certain “level of rationality” in their responses (Reyna et al., 2003; see also Klaczynski & Aneja, 2002). When both types of evidence supported obese judgments, adolescents may have felt little choice but to give high ratings – particularly if they were concerned with appearing rational. When no evidence supported “obese” judgments, adolescents likely felt limited to low ratings. On the one hand, these constraining effects led to ratings consistent with the statistical evidence and thus could be considered normative. On the other hand, even if their responses were normative, the age-related decline on individuating-inconsistent/base rate-inconsistent problems also indicates an age increase in obesity biases. On these problems (e.g., the “Jennifer” problem), “not obese” judgments supported beliefs that the obese do not have the positive (i.e., stereotype-inconsistent) qualities described in these vignettes. Note, however, that straightforward interpretations of the no-conflict findings are problematic because the base rate and individuating information “pulled” for the same judgments. It therefore is not clear whether judgments reflected use of base rates, individuating information, or both.

The conflict problems provided clear evidence for age-related increases (a) in judgments based on representativeness and (b) judgments biased against obese peers;

Figure 8.3. Age differences in obesity representativeness judgments on no-conflict (upper graph) and conflict (lower graph) problems, controlling for stereotype knowledge and endorsement, ability, and thinking dispositions. High scores indicate judgments that “peers” are representative of obesity.

means supporting these conclusions are presented in Figure 8.3. On individuating-consistent/base rate-inconsistent problems, the individuating evidence supported “obese” judgments and the base rates supported “not obese” judgments. Reliance on representativeness (i.e., high ratings) increased with age on these problems, thereby revealing age increases in obesity biases (i.e., judgments that “peers” with stereotype-consistent characteristics were obese). For instance, on the “Kevin” problem, despite the low obesity (14/58) base rate, older adolescents judged Kevin more likely to be obese than younger adolescents.

On the individuating-inconsistent/base rate-consistent problems, age was related to lower ratings. Recall that, on these problems, low ratings indicate judgments that the vignettes were not describing an obese peer and that reliance on base rates would have led to high ratings and obese peer judgments (e.g., 44/58 students were obese). By contrast, the individuating stereotype-inconsistent information (e.g., “Kevin loves working out”) indicated that “peers” were not representative of obesity. The age-related decline in ratings thus reflected “not representative of obesity” judgments (or “representative of average, popular, etc.” judgments). In sum, the key findings were: (1) despite statistical evidence supporting “not obese” judgments, obesity judgments increased with age when individuating evidence was stereotype-consistent (e.g., Kevin hates gym class); (2) despite statistical evidence “pulling” for obese judgments, obesity ratings decreased with age when the individuating evidence was stereotype-inconsistent (e.g., Kevin could date anyone). Each trend illustrates age increases in anti-obesity biases, neglect of relevant base rates, and representativeness judgments; notably, each age trend was significant after controlling for thinking dispositions, intellectual ability, stereotype endorsement, and stereotype knowledge.

Because this was the first study showing that beliefs lead to biases in judgments based on heuristics, we extended this research to a different domain. Klaczynski and Felmban (2012) explored the relationships among age, juvenile justice system beliefs, biases in reasoning, biases in heuristic decisions, and “offender” ethnicity. Again, the domain we selected and hypotheses we developed were based on reviews of specific literatures. For instance, positive links between age and exposure to media portrayals of teen violence, cynicism toward juvenile justice, and the perceived illegitimacy of legal authorities and the justice system have been reported (e.g., Fagan & Tyler, 2005; Tang, Nunez, & Bourgeois, 2009; Woolard, Harvell, & Graham, 2008; see Klaczynski, 2011). We also re-examined the belief-biased reasoning literature. In general, the correlations between age and belief-biased reasoning have been modestly negative (Kuhn, 1991; Kuhn, Amsel, & O’Loughlin, 1988) or null (e.g., Klaczynski, 2000). Although age and belief strength have been unrelated in most of this research (e.g., Klaczynski, 2000; Klaczynski & Narasimham, 1998b), when age-related increases in belief strength have been found, so have age increases in biases (Klaczynski, 1997; Klaczynski et al., 2009). Integrating these literatures led us to hypothesize age increases in anti-juvenile justice beliefs, anti-juvenile justice reasoning biases, and biased “waiver” decisions (i.e., decisions to try teen suspects in adult courts).

In a first session, we assessed self-reported juvenile justice beliefs, thinking dispositions, inhibition, and impulsiveness of 10-, 13-, 16-, and 19-year-olds. Participants later evaluated “research” describing Hispanic or Caucasian juveniles found guilty of felonies, sentenced to and eventually released from either juvenile or adult criminal facilities. The evidence (e.g., probability of recidivism) led to conclusions that juvenile detention centers were either more or less effective than adult prisons. However, careful analyses and critical reasoning could lead to the detection of flaws (e.g., confounds, selection) built into each “experiment” and to justifiable rejection of conclusions. In the third session, individuating information depicting alleged Caucasian or Hispanic offenders as “prototypical good citizens” or “prototypical delinquents” was pitted against base rates showing that juvenile justice was ineffective or effective. On a rating scale, participants “recommended” whether alleged offenders should be tried as juveniles or adults. High ratings reflected use of representativeness; low ratings indicated decisions that relied on base rates.

Reasoning biases were defined by differences in justification quality and ratings of “anti-juvenile” and “pro-juvenile” evidence; positive scores revealed biases favoring the efficacy of adult prisons over juvenile detention centers. Representativeness biases were defined by differences in ratings of “prototypical delinquent” and “prototypical good citizen” problems; positive scores indicated biases favoring adult trials. We expected age increases in both biases on “Caucasian” and “Hispanic” problems, but that the slope of these increases would be steeper for Hispanic problems. Figure 8.4 reveals support for this expectation, but only for Hispanic problems. Both “anti-juvenile” reasoning biases and “anti-juvenile” trail decision biases increased with age when problems involved Hispanic “ex-cons” and alleged offenders. In contrast to expectations, we found the opposite “Caucasian” problems: both anti-juvenile reasoning biases and representativeness biases decreased with age. Stated differently, research evaluation and trial recommendation biases favoring the juvenile justice system increased with age on “Caucasian” problems. Despite this unexpected finding, we replicated and extended the obesity studies: beliefs led to biases in reasoning and biases in reliance on heuristics; each type of bias increased with age, although the form (supporting the adult or juvenile justice system) of these increases differed on the Caucasian and Hispanic problems.

In sum, heuristics and biases increased with age during adolescence. On four bias indexes (i.e., generalizations, heuristic judgments, juvenile justice reasoning and decisions), age was related positively to biases. Because these studies employed within-subjects designs, the findings show that the gap between the competence and performance increased with age – at least in these domains. The conclusions I draw reflect the seriousness with which I take various precautions (e.g., Evans, 2010; Moshman, 2000; Reyna et al., 2003; Stanovich et al., 2011) to guard against face value interpretations. Consequently, although relevant to claims that autonomous processing is complete by adolescence (e.g., Kuhn, 2006; Ricco & Overton, 2011), our findings do not directly implicate the processes underlying the observed age differences. Nonetheless, the data do imply that arguments that heuristic development concludes late in childhood or early in adolescence are incorrect.

The data presented next permit stronger inferences about autonomous processing, although conclusions must be limited to young children. Three questions are addressed below: (1) Do decision biases develop earlier than some researchers have implied and as early as some theories anticipate? (2) Are biases manifested similarly

Figure 8.4 Anti-juvenile justice biases in reasoning (upper graph) and representativeness-based decisions (lower graph) by age and alleged offender ethnicity.

and do they emerge at similar ages in children from different cultures? (3) How is cross-cultural data with young children relevant to the tenets central to some, if not all, dual-process theories?

Culture and early indications of heuristics and biases

The importance of cross-cultural research with young children

There are at least two compelling reasoning for extending DHB research and theory to young children in different cultures. First, little DHB research involves children younger than five years. Indeed, discussions of age differences and similarities in early manifestations of heuristics and biases and the potential for age-related variability in the emergence of heuristics are notably absent from reviews of DHB research. A complete account of heuristic and bias development, however, requires explanations for age variations in the appearance of different heuristics and biases, the biological and/or cultural origins of heuristics, and precursors of heuristics. Yet, the sparse attention paid to “emergence questions” can be undermined by seemingly innocuous claims. For instance, I consider De Neys and Vanderputte’s (2011) findings on age, representativeness, and stereotype knowledge among the most important in recent years. Nonetheless, those authors wrote, “The present study has demonstrated the impact of stereotype familiarity in the youngest age range” (p. 10, italics added). If accurate, at least with respect to stereotype familiarity, then attempts to determine whether stereotypes influence younger children’s judgments will prove fruitless. This statement is somewhat mystifying for at least two reasons. First, consider the responses of the youngest children (five-year-olds) De Neys and Vanderputte studied. On familiar conflict problems, over 60% of five-year-olds’ responses indicate reliance on stereotypes and neglect of base rates. Because they typically weighed stereotypical evidence more heavily than base rates, some five-year-olds must have acquired the relevant stereotype-based heuristics (e.g., representativeness) before the study and, in all likelihood, before the age of five. A second reason for questioning the statement is based on indirect evidence that younger children’s judgments are affected by representativeness. For instance, three-year-olds make inferences based on gender and obesity stereotypes (Cramer, & Steinwert, 1998; Harriger, Calogero, Witherington, & Smith, 2010; Martin & Ruble, 2004; Schuneman & Klaczynski, 2003; Klaczynski, 2012a). Other heuristics and biases, such as framing effects (Levin & Hart, 2003; Moreira, Matsushita, Da Silva, 2010) and hindsight biases (Birch & Bernstein, 2007) – judgment tendencies less obviously based on experience than representativeness – have been observed in three-year-olds. These findings, and evidence that stereotypes influence three-year-olds’ inferences, imply strongly that some heuristics and biases are used by toddlers.

With several notable exceptions (e.g., Birch & Bernstein, 2007; see also Bernstein, Erdfelder, Meltzoff, Peria, & Loftus, 2010; Klaczynski, 2008; Levin, Hart, Weller, & Harshman, 2007; Morrongiello, Lasenby-Lessard, & Corbett, 2009; Reyna & Ellis, 1994), DHB research has been limited to “social” heuristics and biases. A second reason for DHB research with young children is based on (a) the paucity of “non-social” heuristic research and (b) the importance of such data to the proposals of several dual-process theorists. Specifically, many dual-process theorists assume that autonomous processing evolved before analytic processing (e.g., Epstein, 1994; Evans, 2008; Stanovich, 1999, 2009). To illustrate, Stanovich’s characterization of autonomy of processing (e.g., Stanovich & Stanovich, 2010; Stanovich et al., 2011) is reminiscent of Heyes’ (2003) definition, guided by a cognitive evolutionary framework, of “non-conscious mechanisms”: “non-cognitive mechanisms differ from cognitive mechanisms in being automatic or bottom up. They are controlled by stimulation rather than by knowledge or expectancies, are relatively immune to interference, and do not necessarily give rise to conscious awareness” (p. 717). Autonomous processing includes modular or innate, domain-specific processes, shaped by evolutionary pressures, that come “online” at different points in development. Critically, Stanovich and others do not limit autonomous processing to “built-in” processes and note that the products of autonomous processing are not necessarily adaptive, particularly in modern societies, and can be overridden by analytic processing.7

Despite arguments to the contrary and despite the heuristic value they sometimes have, testing evolutionary hypotheses is problematic and controversial. Perhaps, by restating the proposal, problems inherent to testing the evolution-autonomous processing proposition can be circumvented. An example, in the form of a general hypothesis, is that some autonomous processes influence the behaviors of very young children (see also Boyer & Bergstrom, 2011; Medin & Atran, 2004). Deriving more precise (and more testable) hypotheses requires answering such questions as: Which processes should be evident early in life? What behaviors indicate autonomous processing influences? Empirical tests will necessarily entail cross-cultural research with young children.

Illustrations of the value of cross-cultural research

The following paragraphs are devoted to DHB research on judgment biases in Chinese and American children. This program was originally motivated by findings that theoretically relevant variables (e.g., ethnicity, body esteem, “thin idealization”) do not entirely mediate the relationship between age and obesity stereotypes (Klaczynski, 2008; Klaczynski et al., 2009). Evidence that obesity shares features with certain contagious illnesses (e.g., the flu) led to changes in this motivation and the underlying theoretical framework. Also driving these rather dramatic changes was the belief that similar hypotheses could be derived from social psychological theories of stigmatization (Kurzban & Leary, 2001), theories of magical contagion (Nemeroff & Rozin, 1989, 2000), and dual-process theories. The overarching empirical goal of this research program is now to determine the origins of and early developments in social stigmas. The accompanying theoretical goal is to test and refine a developmental dual-process theory of stigmatization.

Prior to summarizing our research on culture, early development, and stigmatization, I first define and discuss the difference between two constructs, stereotypes and stigmas, that too often are treated as identical. In brief, stigmas are physical attributes (e.g., mephitic smelling, physical abnormalities) and verbal markers (e.g., convict, addict, “untouchable”) of physical or psychological defects signifying that individuals should be avoided, shunned, and socially devalued and eliciting associated emotions (e.g., disgust) (Corrigan, 2004; Hinshaw, 2005; Rozin, Lowery, & Ebert, 1994). Stereotypes are implicit or explicit beliefs about the characteristics of different social groups. These beliefs are sometimes used to infer an individual’s characteristics, infer an individual’s social group, or infer group characteristics from an individual’s appearance and behavior. Although rough, this sketch implies that stigmas and stereotypes have different cognitive, behavioral, and emotional effects.

An effect specific to stigmas, known as “stigma by association,” “stigma by proxy,” and “courtesy stigma” (Goffman, 1963; Neuberg, Smith, Hoffman, & Russell, 1994; Pryor, Reeder, Yeadon, & Hesson-McInnis, 2004) is especially pertinent to the present discussion. For instance, parents of children with emotional and cognitive disorders (e.g., ADHD, depression) are often victims of social exclusion (Corrigan, 2004; Hinshaw, 2005). Stigma by association is not limited to real associates (e.g., friends, relatives) of the stigmatized, but extends to people and objects contacted by, or in the physical proximity of (i.e., the “mere proximity” effect; Hebl & Mannix, 2003), the stigmatized. People are frequently unaware of their stigmatizing behaviors and, when informed, find contriving rational explanations difficult (see Rozin, Markwith, & Nemeroff, 1992; Rozin & Nemeroff, 2002). These findings are consistent with proposals that some stigmas are byproducts of evolutionary adaptations (e.g., Kurzban & Leary, 2001; Schaller & Duncan, 2007) and have fueled speculations that autonomous processing often underlies stigmatization and that some stigmas develop before, and serve as foundations for, stereotypes (Klaczynski, 2008, 2010).

Specifically, a derivative of theoretical propositions that evolution equipped humans with preconscious threat detection systems (e.g., Boyer & Bergstrom, 2011; Kurzban & Leary, 2001) is “pathogen detection theory” (Park, Schaller, & Crandall, 2007; Schaller & Duncan, 2007). In theory, perceptual heuristics activate an evolved pathogen detection system that triggers disgust and interpersonal avoidance. Although derived from “magical” contagion theories (Rozin et al., 1992; Rozin & Nemeroff, 2002), fuzzy-trace theory, and evidence that children overgeneralize and misapply “theories” of illness (Kalish, 1997, 2002; Notaro, Gelman, & Zimmerman, 2002), “illness overgeneralization” theory (Klaczynski, 2008) shares several predictions with pathogen avoidance theory. Both approaches feature the hypothesis that deviations from species-typical appearance norms lead to “false positives” such that healthy individuals are perceived as if they were pathogen carriers. Such “pseudo-rational” reactions (“errors on the side of caution”) result in avoidance of and discrimination toward those who, by virtue of behavioral and appearance cues, are treated as though mere proximity could lead to contamination.

Despite support from investigations of adults’ reactions to obesity and physical disabilities (Park, Faulkner, & Schaller, 2003; Park et al., 2007; Schaller & Duncan, 2007), stringent tests of the hypothesis require examinations of young children from different cultures (see Medin & Atran, 2004). Providing these more stringent tests was the goal of several recent investigations of Chinese and American children. In three studies, a “taste test” was developed to examine reactions to drinks purportedly “created” by children in different appearance categories. Although color drink varied randomly, each drink was identically flavored. To indicate the association between “child creators” and the drinks, each “creator’s” picture was on the label of his or her drink. In Klaczynski (2008), drinks “creators” varied by ethnicity (Asian, Caucasian), gender, and body category (obese, average-weight). Chinese and American seven- and ten-year-olds, primed by either ingestion→illness or ingestion→contagious illness stories, tasted drinks “created” by average-weight and obese children. Klaczynski (2012b) replicated the original investigation and extended it by including child amputees as a third “drink creator category” and Chinese and American five- to eight-year-olds.

After tasting each drink, children rated its flavor and the chances of getting sick from ingesting “a lot” of the drink. Across ages and cultures, children rated “obese-created” drinks lower than “amputee-created” drinks and “amputee-created” drinks lower than “average-weight” created drinks. In the contagious illness condition, biases (differences in ratings from average-created drinks) against obese- and amputee-created drinks were particularly pronounced and increased with age – regardless of culture – as did biases that favored amputee-created drinks over obese-created drinks (see Figure 8.5).

In a more rigorous test, three- to five-year-olds tasted drinks created by average-weight children, obese children, child amputees, and children whose faces indicated they had contagious illnesses (Klaczynski, 2012c). Of six indexes of biases, five increased with age: taste rating biases against obese and diseased “creators” and illness rating biases against obese, diseased, and amputee creators (Figure 8.6). On only one measure were between-country differences significant (the ageamputee illness biases correlation was stronger for Chinese children). More important were the following findings: (1) biases against obese- and disease-created drinks did not differ significantly; (2) the youngest children were as biased against amputee creators as they were against obese and diseased creators; (3) gist memories (i.e., creator category memories) for the worst tasting drink, but not verbatim memories for specific drink creators, were more accurate for obese- and diseased-creators than for average-weight creators; (4) when children’s memories were inaccurate, they misremembered the drink they indicated as worst tasting as being created by obese or diseased children.

Most recently, we studied the “mere proximity” effect by presenting Chinese and American preschoolers a series of drawings, each of which depicted a pair of “children” (e.g., an obese child with an average-weight child; a child amputee with an average-weight child, etc.). The critical dependent measures were preschoolers’ ratings of average-weight children when they were pictured next to obese children, child amputees, extremely thin children, and other average-weight children. Preschoolers in both countries believed that average-weight children were less likeable, less liked by teachers, and had fewer friends when pictured close to obese children or physically disabled children than when they were pictured near thin or other average-weight children (see Figure 8.7).

In four studies of children in two different cultures, we found several forms of appearance biases; in two of these researches, these biases were evident by early preschoolers. Three-year-olds displayed biases in their (a) test and illness ratings of drinks associated with amputee, obese, and “diseased” children, (b) memories that the worst tasting drinks were “created” by obese and “diseased” children, and (c) dislike for average-weight children pictured near amputee and obese children. These stigmatizing behaviors, particularly the similar responses obese and “diseased” children, support the illness overgeneralization and pathogen detection

Figure 8.5 Collapsed over country, American and Chinese children’s average-obese biases (positive scores indicate biases favoring average drink creators), average-amputee biases (positive scores favor average creators), and amputee-obese-creator biases (positive scores favor amputee creators) in the “illness only” (upper graph) and “contagious illness” (lower graph) conditions.

theories. Physical deviations from species-typical appearance, particularly when these deviations parallel characteristics associated with pathogen carriers (e.g., profuse sweating), may key feelings of wariness toward amputees and obese children. Wariness, possibly coupled with disgust, serves to warn children against contact (Klaczynski, 2008, 2010; Kurzban & Leary, 2001; Schaller & Duncan, 2007; Park et al., 2007).

At a broader level, the data are consistent with dual-process speculations that specific autonomous processes are operational early in life and sometimes produce

Figure 8.6 Preschoolers’ drink rating biases (taste biases, upper graph; illness biases, lower graph), collapsed over country. Positive scores indicate biases favoring average-weight drink creators.

biases. The memory data support the fuzzy-trace contentions that (a) salient verbal and perceptual information guides the types of representations children use in making judgments and decisions, (b) gist representations affect children to a greater extent than verbatim representations, and (c) biased preliminary reactions influence gist abstraction and lead to false or misleading verbatim memories (Klaczynski, 2008).

Regardless of theoretical orientation, this research is relevant to social, cognitive, and social-cognitive developmentalists. Although our hypotheses and the theory from which they were derived were supported, I am not arguing against explanations of Chinese and American preschoolers’ biases that include influences from

Figure 8.7 Chinese and American preschoolers’ “likeability” ratings of average-weight children in the proximity of other average-weight, very thin, physically disabled, and obese children.

relatively universal socialization practices. Neither am I arguing that the observed developmental trends are unrelated to cultural practices. Although formulating plausible accounts without reference to cognitive-affective autonomous processes will prove difficult, explanations that exclude social-contextual influences are also likely to fall short. Consequently, I hope these data will increase interest among social developmentalists in dual-process theories, motivate DHB researchers to investigate similar phenomena, and tie their work to that of social developmentalists.


Much more can be said of the issues addressed in this chapter: the theoretical and empirical challenges for DHB research are complex, the processes underlying age increases in reasoning fallacies, heuristic judgments, and adolescent reasoning biases require additional exploration, and the origins and specificity of the processes responsible for early stigmatizing reactions have been little explored. In these final paragraphs, my intent is not to reiterate these issues or summarize the data presented previously. Instead, I highlight the potential developmental dual-process theories have for research on social development. Hints of this potential are apparent in DHB research on early developments in biases and stigmas and in research on age and heuristic development in domains (e.g., obesity, ethnicity, delinquency) also researched by social developmentalists.

Others have implied that DHB research should be tied to, for instance, research on childhood aggression. In Crick and Dodge’s (1994) social information processing theory, biases play central roles. Despite the vast literature on aggression and other adjustment difficulties that their theory has generated, critical hypotheses and assumptions remain untested. Specifically, Crick and Dodge argued that aggressive children probably use heuristics that covertly bias them toward hostile attributions and highlighted the importance of examining differences in preconscious information processing in children with and without adjustment difficulties. In discussing their theory, Crick and Dodge provided a foundation for connecting dual-process theories to social development. Despite the frequency with which the paper is cited, fourteen years later Nummenmaa, Peets, and Salmivalli (2008) noted that virtually no research had tested Crick and Dodge’s “automaticity hypothesis.”

Of the challenges facing developmental dual-process theorists, perhaps extending research to social developmental phenomena should not be at the forefront. Nonetheless, illustrating the utility of dual-process theories by, for instance, examining whether isolated and rejected adolescents engage in subtle behaviors to trigger preconscious “warnings” to avoid engagement, would enrich both research on social development and perhaps lead to more innovative attempts to increase the precision and clarity of developmental dual-process theories.


1 Throughout this chapter, the terms “normative” and “non-normative” refer to responses consistent or inconsistent with traditional standards for logical reasoning, statistical judgments, and critical thinking. This usage does not entail equating normative responses with adaptive responses or non-normative responses with maladaptive responses. Under different conditions, purportedly normative responses may be maladaptive and non-normative responses may be adaptive. Likewise, I do not use “normative responses” to infer predominantly analytic processing or “non-normative” responses as indexing autonomous processing predominance. Finally, references to “age increases” (and decreases) should not be interpreted as age-related changes because most of the research I reviewed was cross-sectional.

2 Freud’s (1965, 1923; Brenner, 1982) theory of developments driven by Id-Ego-Superego conflicts was likely the first dual-process theory in psychology. With few exceptions (e.g., Epstein, 1994), Freud’s impact on contemporary dual-process theories has been negligible.

3 I refer readers to Evans (2010, 2011), Stanovich (2010; Stanovich et al., 2011), and Reyna and Brainerd (2011; Brainerd & Reyna, 2001; Reyna & Rivers, 2008; Reyna & Farley, 2006) for accounts of their theories, discussion of theoretical differences (see also Ricco & Overton, 2011), and responses to critics’ arguments. For dual-process theories specific to reasoning, I suggest Barrouillet (this volume; Barrouillet, 2011b), Overton (this volume; Overton & Dick, 2007), Markovits, Forgues, and Brunet (2012), and Verschueren, Schaeken, and d’Ydewalle (2005).

4 Stanovich (2009, 2010) and Evans (2008, 2011) explain the terminological shift from “System I” and “System II” to “Type I” and “Type II” processing. I neither reiterate their arguments for this change nor refer, except for comparative purposes, to “Type I” and “Type II” processing. I have not adopted the Type I/Type II distinction and instead refer to analytic and autonomous processing because my dual-process perspective is not entirely consistent with those of Stanovich and Evans. In discussing processing systems that do not map clearly onto Type I/Type II processing, I suggest other dual-process theories use different labels. This approach would nullify attempts to reduce individual theories to a “generic” theory and maintain theoretical differences. This imperfect solution could lead to some confusion, much of which can be dealt with by briefly explaining theoretical differences. With sufficiently detailed theories, competing hypotheses could be tested empirically, eventually resulting in fewer dual-process theories.

5 Some dual-process theorists have argued that, because autonomous processing is largely capacity independent, few (if any) individual differences exist in autonomous processing. This strong position does not, however, appear particularly tenable. First, arguments from evolutionary theories (e.g., evidence for neural plasticity, within-specifies differences that increase with age/development) cast doubt on the plausibility of such arguments. Second, autonomous processing is tied to speed of processing; speed of processing increases with age and differs between individuals. Third, of the different types of autonomous processing, the type I now refer to “experiential processing” is highly dependent on thoughts and behaviors practiced to automaticity. Consequently, within-domain differences in autonomous processing may emerge during most developmental periods.

6 When base rates and stereotypical evidence conflicted, Jacobs and Potenza (1991) found age increases in representativeness judgments. Only De Neys and Vanderputte (2011) have conducted an “approximate” replication. Using different tasks and fewer (and younger) age groups than Jacobs and Potenza, De Neys and Vanderputte also found age increases in representativeness when familiar stereotypes and base rate evidence conflicted. However, several authors have cited Klaczynski (2001a) and Kokis et al. (2002) as failures to replicate Jacobs and Potenza. These claims are inaccurate because both Klaczynski and Kokis et al. studied different ages from Jacobs and Potenza and because neither Klaczynski nor Kokis et al. used problems involving stereotype-base rate conflict. Neither study could have replicated, or failed to replicate, the Jacobs and Potenza “age-representativeness” finding. Nonetheless, despite studying older children, Klaczynski and Kokis et al. did extend (vs. “replicate”) an infrequently cited finding from Jacobs and Potenza: that the Klaczynski and Kokis et al. “statistical reasoning” problems, which pitted large evidential samples against smaller samples of personal evidence, were analogous to Jacobs and Potenza’s “individuating object” problems. In all three studies, age was related positively to normative judgments. Making these findings interesting is evidence that adults often neglect base rates when pitted against vivid, small evidential sample, arguments (Fong, Krantz, & Nisbett, 1986; Nisbett, Krantz, Jepson, & Kunda, 1983). Several questions arise: Why did older children (in Jacobs and Potenza and Kokis et al.) perform better than adults in the Nisbett group’s research? Given adults’ poor performance: Why were age-related increases in normative responses observed in these three studies? Were the small sample arguments were less convincing or the questions less difficult in the developmental studies? To date, these questions have received little attention.

7 “Social” heuristics (e.g., representativeness) require knowledge or experience. The defining characteristics of “non-social” (e.g., framing effects, the sunk cost fallacies, the if-only fallacy) heuristics are not clear, but if they are less dependent on specific knowledge, these heuristics may be more domain-general than social heuristics. Whether the sunk cost and if-only fallacies are “social” or “non-social” is an empirical question because the underlying heuristics (e.g., “waste not, want not”) may originate from specific experiences/socialization practices. Even if they have “social” origins, these heuristics may become domain-general (see Arkes & Ayton, 1999) or may remain domain-specific. This is another unresolved empirical question. Some evidence shows that the “sunk cost fallacy” is domain-specific and experience-dependent; other evidence indicates age decreases in “waste not” decisions from childhood through late life across domains (Strough et al., 2011). The temptation to discuss heuristics as domain-general could explain tendencies to (a) collapse across problems with different contents (without reporting inter-problem correlations) and (b) use problems from specific domains.


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