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Neural bases of judgment and decision making Oshin Vartanian and David R. Mandel
Introduction Humans are naturally inclined to seek explanations of behavior. Due to recent technical and theoretical advances, neuroscience has begun to elucidate the material causes of human behavior, including judgment and decision making. Material-cause explanations focus on the substrates that comprise or give rise to a phenomenon (Aristotle, 1929; Killeen, 2001), and in the present chapter we focus on recent studies examining the neural substrates of human judgment and decision making. Specifically, we review evidence from brain imaging literature to assess the contribution of neuroscience to understanding judgment and decision making in four areas: (a) decision making under risk; (b) base-rate neglect in probability judgment under conditions of uncertainty; (c) belief bias – namely, the biasing effect of personal knowledge on epistemic judgments; and (d) decision making in the social context of two-party economic exchange games. Within each area we will focus on studies in which this methodology has been applied to address theoretically important questions, enabling us to evaluate the contribution of findings to our understanding of the causal origins of judgment and decision making. Generally speaking, neuroscientific explanations of psychological phenomena have taken the form of investigating the involvement of various brain structures that underlie behaviours of interest. However, cognitive and social neuroscientists are less interested in human brain mapping per se than they are in understanding the underlying processes and mechanisms that are represented by the involvement of the activated brain structures (Poldrack, 2006). This is because when knowledge about brain function is combined with standard forms of behavioral evidence, inferences about underlying psychological processes and mechanisms may be augmented. The extent to which satisfactory explanations can be provided is in turn a function of the ability of the available techniques (and related theories) to pinpoint with specificity the processes and mechanisms associated with the regions of interest. Although specificity can have 29
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multiple connotations in the context of neuroimaging, here we will focus on functional specificity, and address its effect on generating accounts of behavior. By functional specificity (i.e. selectivity) we refer to the ability to infer specific function (e.g. memory) based on the activation of a specific brain region or structure. From an evolutionary perspective it is unlikely that any given neural tissue evolved to partake in only one specific function (e.g. only to engage when the organism detects a red umbrella). It is far more likely that neural tissue evolved to serve multiple functions (e.g. to engage when the organism experiences pleasant or unpleasant emotion, views an extremely attractive or extremely unattractive face, etc.). Of course, an evolved design feature that is economically advantageous is not necessarily ideal for making specific functional inferences. This highlights the necessity to have a clear understanding of the inferential processes (and associated problems) used to evaluate neuroscientific findings, which we will focus on next. The key issue concerns the appropriate rules of logic for using findings of differential brain activation as tests of cognitive theories. Currently, this is often done through “reverse inference,” whereby the engagement of a particular cognitive process is inferred from the activation of a particular brain region (Poldrack, ibid.). For example, given considerable neuropsychological evidence demonstrating that Broca’s Area is involved in linguistic processing (in the form of speech production), a researcher would engage in reverse inference by concluding that a task manipulation involved linguistic processing because it activated Broca’s Area. Such inferences are logically invalid, because the mapping of theoretical postulates to observations is usually a many-to-one enterprise if the putative theories are well specified (i.e. more than one theory will make the same prediction) and a many-to-many enterprise if they are not (i.e. each theory makes multiple mutually exclusive predictions) (Bub, 2000). Poldrack (2006) has shown that this type of reasoning would be valid if and only if Broca’s Area were activated in speech production. Because such functional exclusivity is usually lacking, reverse inference is often invalid. However, what if the focus were shifted from the logical validity of the argument to the degree of belief in the plausibility of the conclusion? Under this condition, belief in the reverse inference would increase as a function of (a) the selectivity (i.e. specificity) of the neural response (i.e. the ratio of process-specific activation to the overall likelihood of activation in that area across all tasks) as well as (b) prior belief in the engagement of a specific cognitive process given the task manipulation of interest (ibid.). How can this increase be achieved? The degree of the selectivity of a region is outside of the experimenter’s control. However, there is an
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inverse relationship between the size of a region and its selectivity. For example, whereas a large region of the brain known as the occipital lobe is involved in vision, various layers of cortex within it are selectively attuned in relation to specific visual functions (e.g. Area V4 to colour, Area V5 to motion, etc.). This suggests that to the extent possible, researchers should aim to formulate hypotheses about smaller regions of interest that will be involved in fewer functions. However, what is under the researcher’s control is determining the extent to which a cognitive process of interest is engaged, given a task manipulation. This can be achieved through proper task analysis prior to data collection in the scanner. Hence, the combination of a well-designed experimental manipulation conducted in relation to hypothesized activation in a well-defined (i.e. small) region of interest (ROI) can increase our degree of belief in the plausibility of a reverse inference. While not deductively valid, this form of abductive reasoning has proven to be an essential tool for discovery in science (Polya, 1954). Neuroscience and the logic and prospect of explanation Although neuroscience today provides the primary scientific basis for explaining the material causes of behaviour, we agree with Killeen (2001) that a complete explanation of a phenomenon requires understanding all four types of Aristotle’s (1929) (be)causes: Efficient causes, capturing the contemporary meaning of cause, represent the triggers sufficient to generate or prevent an effect against its causal background (e.g. Mackie, 1974; Mandel, 2005b). Final causes are functional explanations that address purposive questions, such as “Why does it do that?” and “What is it supposed to do?” (Minsky, 1967). Formal causes are models that specify the transition from efficient causes to final causes, whose explanatory value is measured in terms of correspondence between attributes of the model and attributes of the phenomenon that the model is meant to describe. Material causes are explanations of the substrates that comprise a phenomenon or give rise to it, an exclusive focus which is known as reductionism. We believe that the most exciting promise of neuroscience for understanding the origins of judgment and decision making in humans is not in fleshing out explanations of material causality, but rather in the potential of such findings to test and refine explanations of the formal and final causes of human behaviour (see also Camerer, Loewenstein & Prelec, 2005). In this sense, an important contribution neuroscience can make
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involves placing empirical constraints on formal models (Goel, 2005; see Newell, 1990; Pylyshyn, 1984). This contribution is critical, because the mental computations that are described in formal models must be realizable in the physical substrate of the brain, and knowledge gained through neuroscience about brain structure and function can in turn help us discriminate between competing formal models. Next, we turn to a discussion of the contribution of neuroscience to the resolution of theoretical problems in four areas of judgment and decision making. Most studies discussed in this chapter involve functional Magnetic Resonance Imaging (fMRI). For a brief overview of some of the technical features of this method, refer to the Appendix. Decision making under risk We begin our exploration of the neuroscientific literature on judgment and decision making by focusing on an important topic in behavioral decision-making research: decision making under risk. Specifically, we will examine the extent to which knowledge related to the neural processing of risk has contributed to the resolution of theoretical debates related to risky choice. The literature on the neural processing of risk can be partitioned in relation to three questions. First, to what extent is risky choice influenced by emotions? Second, to what extent does making risky choices recruit cognitive and/or emotional processes over and above what is recruited in mere anticipation of risky outcomes? Third, is risk processed differently when individuals are faced with the prospect of gains versus losses? Although each of these questions has been studied by a large number of researchers, here we will focus our discussion on a recent metaanalysis of the literature on the neural processing of risk (Mohr, Biele & Heekeren, 2010). In the process we will also touch on specific studies to clarify outstanding issues, as well as pointing out shortcomings that remain to be addressed in future neural studies on decision making under risk. Risky choice: role of emotion The literature on the neuroscience of judgment and decision making has been heavily concerned with the role of emotion in choice under risk and uncertainty (see Bechara, 2011; Sanfey, 2007; Sanfey et al., 2006). This focus is perhaps not surprising, given the centrality of this question to behavioral decision theorists (e.g. Loewenstein et al., 2001; Slovic et al., 2004). Furthermore, methodologically, investigating this problem at the brain level has been made possible due to the continued development of
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increasingly sophisticated cortical maps of emotion processing. Essentially, researchers have investigated whether (a) any part of the brain’s extended emotion system is activated more when participants process risk compared to a baseline condition, or (b) activation in any part of this system is correlated with experimentally manipulated variations in the level of risk. Mohr, Biele and Heekeren (2010) employed the activation likelihood estimation method (ALE) to conduct a metaanalysis to address this issue. Unlike meta-analyses in the behavioral sciences that are conducted to estimate true effect sizes for specific manipulations of interest, ALE is conducted to pinpoint specific brain structures and/or networks that are reliably activated in relation to specific experimental manipulations. Following the localization of the relevant structures, interpretations of the functions of those structures and/or networks is required to elucidate their contribution to the effects of interest. As noted earlier, the success of this undertaking will depend on (a) the functional selectivity of the structures and/or networks, and (b) proper task analysis; in this case, the choice of studies to be included in the metaanalysis. Mohr, Biele & Heekeren (ibid.) relied on the following six criteria to select studies for their meta-analysis: (1) fMRI studies involving healthy young adult humans, to allow generalizability to the population of neurologically healthy normal persons; (2) imaging data acquired over the whole brain, so as not to limit analyses to selected ROIs; (3) availability of peak activation coordinates from group activation maps; (4) tasks with outcomes that were at least partly uncertain; (5) information about outcome probabilities was available to the participants; and (6) outcomes of the task were independent of the behavior of others, as would be the case, for example, in two-person economic exchange games (see the section on Decision making in economic games in this chapter). These criteria resulted in the selection of 30 fMRI studies for their meta-analysis. The results demonstrated that risky choice activated a distributed network including key components of the brain’s emotion circuitry, particularly the bilateral anterior insula, the thalamus, and the dorsomedial prefrontal cortex (DMPFC). The insula plays a general and wellestablished role in interoception (Craig, 2002), as well as experiences of distress, pain, anger, and disgust (Adolphs, 2002; Ohara, Vit & Jasmin, 2005). Furthermore, lesions to the insula cause disturbances in emotional processing. Located above the brainstem, the thalamus is frequently referred to as the brain’s “relay station.” This is because it receives sensory information from the body, which it sends to the cerebral cortex; and it receives information from the cerebral cortex, which it sends to the body. Given its transmission of visceral sensory information, it forms a core part
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of the emotion system (Barrett et al., 2007). Finally, the DMPFC is involved in the more cognitive aspects of emotion processing, such as the formation of strategic preferences in the face of conflict (Venkatraman et al., 2009a,b). In summary, the results of Mohr, Biele and Heekeren’s (2010) meta-analysis showed that risky choice does activate wellestablished components of the brain’s emotion circuitry, offering confirmatory evidence to support the role of emotion in judgment and decision making (Loewenstein et al., 2001; Slovic et al., 2004). Risky choice: action versus anticipation It is possible to distinguish between risk processing that occurs during or before choice (decision risk), and risk processing that occurs after or without a choice (anticipation risk). This is an important distinction because, whereas risk information may be used to guide choices in the context of decision risk, this will not be the case in the context of anticipation risk (Mohr, Biele and Heekeren, 2010). Correspondingly, the neural representation of risk may differ between decision risk and anticipation risk. Note that, in this case, the meta-analysis was not geared toward showing activation in any specific area in decision or anticipation risk, but simply to show whether a greater network was activated in the former condition. The decision–anticipation contrast revealed relatively greater activation in right anterior insula, the DMPFC, dorsal lateral prefrontal cortex (DLPFC), parietal cortex, striatum, and the occipital cortex. The network activated by decision risk shows much overlap with the distributed network involved more generally in decision making (Sanfey, 2007). In contrast, the anticipation–decision contrast only revealed relatively greater activation in left anterior insula and left superior temporal gyrus. The results of this analysis demonstrated that decision risk recruits cognitive and/or emotional processes over and above what is recruited in mere anticipation of risky outcomes. Risky choice: gains versus losses Interestingly, the existing neural studies on risk have either investigated risky choice in the context of gains, or risky choice in the context of gains and losses. In other words, no study thus far has studied risky choice in the context of losses exclusively. Therefore, Mohr, Biele and Heekeren’s (2010) meta-analysis contrasted risky choice in the context of gains with risky choice in the context of gains and losses, with the proviso that this contrast does not allow for a direct look at neural processes that underlie risky choice in the context of losses per se. The results demonstrated that left
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anterior insula was activated more when losses and gains were possible, compared to risky choice in the context of gains alone. This activation is consistent with the well-established role of the insula in the experience of negative psychological states such as distress (Ohara, Vit & Jasmin, 2005), as would be expected in the context of potential losses as well as gains. Neural bases of risky choice: conclusions The results of Mohr, Biele and Heekeren’s (2010) meta-analysis of neural studies of risky choice demonstrate that neuroscientific findings can be used to shed light on theoretically important questions in behavioral decision making. Specifically, the results have shown that (a) risky choice recruits emotion, (b) making risky choices recruits more cognitive and/or emotional processes than the mere anticipation of possible outcomes that might arise from a risky choice, and (c) risk is processed differently when individuals are faced with the prospect of losses and gains, as opposed to the prospect of gains exclusively. Although the interpretation of the findings is based on reverse inference, confidence in the functional inferences is bolstered by the selectivity of the activated structures, as well as the proper choice of studies to be included in the ALE meta-analysis. Furthermore, given that fMRI results are by definition correlational, inferences about the direction of the observed effects are made possible by confirmatory evidence gleaned from neuropsychological studies of patient populations. Two important caveats remain in extending the findings of Mohr, Biele and Heekeren’s (2010) meta-analysis to all instances of risky choice. First, as noted above, the meta-analysis did not include any fMRI study that had investigated risk perception in the context of losses alone. This is a shortcoming that can be easily addressed in future studies. Second, whereas all the studies included in this meta-analysis involved risky choice in the financial domain, recent evidence suggests that contextual factors can influence various aspects of the neural systems underlying choice (Mandel & Vartanian, 2011). Specifically, hypothetical monetary decisions activated a dissociable neural system than formally identical hypothetical decisions involving lives (Vartanian, Mandel & Duncan, 2011). Thus, caution must be exercised in extending the results of this metaanalysis to non-financial domains. Probability judgment and base-rate neglect We continue our exploration of the neuroscientific literature on judgment and decision making by focusing on a central question: are people who
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make judgments that deviate from normative benchmarks aware of the discrepancy? Neuroscience has contributed to this question in two ways. First, there is evidence demonstrating that people can make advantageous decisions in the absence of conscious awareness. Specifically, emotions expressed as unconscious “hunches” can affect decision making (Bechara et al., 1994, 1997, Bechara, Damasio & Damasio, 2000). Furthermore, the ventral medial prefrontal cortex (VMPFC) – which forms part of the neural system for emotion (Barrett et al., 2007) – may play a critical role in this process by linking emotions to cognition. Extending this line of reasoning, it is of interest to ask whether there are brain structures or neural systems that respond to deviations between norms and behavior, and whether sensitivity to such deviations might in turn influence behavior. Second, perhaps one of the most intensely studied problems in neuroscience involves elucidating the neural system that underlies detecting and overriding errors in the service of task goals (i.e. error monitoring and cognitive control). There is now a great deal of convergent evidence linking the anterior cingulate cortex (ACC) to error monitoring and the lateral prefrontal cortex (PFC) to cognitive control (Ridderinkhof et al., 2004; van Veen & Carter, 2006). Extending this line of work, one might expect that awareness of norm violations would involve the ACC due to its role in error monitoring, whereas overriding such errors in the service of normative responding would involve the lateral PFC. A classic example of a normative violation in judgment under uncertainty is base-rate neglect (Kahneman & Tversky, 1973). In some studies of base-rate neglect, subjects are asked to make relative probability judgments in which stereotype-relevant individuating information cues are a salient but normatively inappropriate response. Subjects are usually first presented with information about the composition of a sample (e.g. 70 lawyers and 30 engineers), and then presented with a short personality description of a randomly drawn person from that sample. Given these base rates, it is probable that a person drawn randomly from the sample will be a lawyer. However, Kahneman and Tversky (ibid.) observed that when the individuating information was more typical or representative of an engineer, most subjects neglected the base-rate information and judged the target individual as more likely to be an engineer than a lawyer. There are different schools of thought regarding the nature of this error. Some theorists have proposed that subjects neglect the base rates because they may be unaware of a discrepancy between their judgments and the indicative nature of the information provided (Evans, 2003). To them, base-rate neglect is an example of what Kahneman and Tversky (1982) called errors of comprehension – namely, errors that stem from failing to
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understand a relevant normative principle (such as the fact that the probability of random draws from n categories ought to take into account the base rates of those categories). Other theorists have proposed that subjects may be aware of the discrepancy, but they are unable to inhibit the intuitive response in favor of the response indicated by the distributional base-rate information (Epstein, 1994; Sloman, 1996). To them, base-rate neglect is an example of what Kahneman and Tversky (1982) called errors of application – namely, errors that arise when one knows the relevant principle but fails to apply it. De Neys, Vartanian and Goel (2008) conducted an fMRI experiment in an attempt to shed light on this issue. They based their predictions on a model of conflict detection and resolution according to which the ACC responds to the detection of conflict, error and expectancy violation, and the lateral PFC is recruited in response selection by inhibiting or overriding inappropriate responses (Ridderinkhof et al., 2004; van Veen & Carter, 2006). De Neys, Vartanian and Goel (2008) presented their subjects in the fMRI scanner with various types of base-rate problems. Before entering the scanner, subjects were given practice problems for familiarization with the concept of random sampling. Subjects were instructed to think like statisticians, given that previous research had demonstrated that doing so would increase the likelihood that they would consider probability information (Schwarz et al., 1991). We will focus here only on the “incongruent” trials in which there was a discrepancy between base-rate information and the response cued by the individuating information. An example of an incongruent trial follows: (Base rate) 5 engineers and 995 lawyers (Individuating information) Jack is 45 and has four children. He shows no interest in political and social issues and is generally conservative. He likes sailing and mathematical puzzles. What statement is most likely to be true? (a) Jack is an engineer (b) Jack is a lawyer
We refer to judgments on such trials that were congruent with the baserate information as correct and those that were incongruent with the baserate information as incorrect. Replicating earlier studies, most subjects (55%) made incorrect responses on incongruent trials, judging the stereotype-consistent, low base-rate category as more probable. Furthermore, as expected, correct judgments on incongruent trials required more cognitive processing as measured by reaction time compared to incorrect judgments (see also De Neys & Glumicic 2008). In line with earlier work by Ridderinkhof et al.
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(2004) and van Veen and Carter (2006), De Neys, Vartanian and Goel (2008) found that lateral PFC, given its role in inhibiting or overriding inappropriate responses, was recruited more when subjects judged incongruent trials correctly than when they did not. A key prediction of De Neys, Vartanian and Goel (ibid.) was that if subjects were unaware of a conflict between their choices and probability, then ACC activation on incorrect incongruent trials should not be observed. In contrast, if subjects were aware of the conflict but were unable to inhibit the intuitive response in favour of the response driven by the base-rate information, then ACC activation should be observed. The results supported the latter prediction, suggesting that subjects were aware of the conflict but were unable to overcome it. An analysis of the cognitive processes underlying base-rate neglect also supports this interpretation (De Neys & Glumicic, 2008). The findings of De Neys, Vartanian and Goel (2008) indicate that the source of base-rate neglect in probability judgment may not be failure to comprehend the relevance of base-rate information, but rather insufficient involvement of brain systems required to inhibit or override inappropriate responses, in particular the lateral PFC. Furthermore, the findings offer support for the recruitment of this system (ACC and lateral PFC) in tasks that require error detection and subsequent inhibition for successful performance. These findings exemplify how neural data may be used to shed light on the cognitive bases of judgment processes. First, the researchers predicted the recruitment of specific structures (ACC and lateral PFC) based on the engagement of specific cognitive processes. Second, although the ACC and lateral PFC are engaged by multiple cognitive processes, there is much convergent evidence to suggest that they jointly form a system that underlies the detection and override of conflict. Third, on the critical conflict trials, the neural and behavioral data pointed to the same direction, adding another level of explanation to the causal model. The neural findings converge with recent behavioral research examining the basis for error in probability judgment in showing that systematic biases are often attributable to errors of application. For example, Mandel (2005a) showed that the coherence of probability assessments of binary complements was greater when the complements were judged consecutively rather than spaced apart by an unrelated task. If incoherence had stemmed from an error of comprehension, then the presentation of test items should make little difference. The fact that presentation format did make a difference indicates that people lose track of the relevant normative principle (in those studies, the additivity property) when salient cues to their relevance are not present. Like De Neys, Vartanian and Goel (2008),
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these findings indicate the importance of errors of application in judgment under uncertainty. The triangulation of such sources of evidence could be used to constrain formal-cause explanations of judgment and decision making. Epistemic judgments and belief bias In taking up our third area of investigation, we move from the realm of judgment under uncertainty to a case of judgment under conditions of certainty: namely, deductive reasoning. Neuroimaging and neuropsychological studies have made substantial contributions to our understanding of the cognitive architecture of reasoning (reviewed in Goel, 2007). Two main conclusions can be derived from these studies. First, there is no single brain module for reasoning. Rather, the act of reasoning activates a distributed network in the brain that engages different structures as a function of task characteristics. Second, patterns of activation can be predicted reliably because this network involves the dynamic configuration of relevant component subsystems. Overall, the main contribution of neuroscience to understanding reasoning has been the verification at the neural level of a hierarchical and context-dependent reasoning anatomy. Here, we focus on deductive reasoning research that has examined how people make judgments about what is implied by – that is, what must follow from – a given set of true statements. For example, given the information that “all mammals are animals” and “all humans are mammals,” we are logically compelled to draw the conclusion that “all humans are animals.” One critical feature of such reasoning is that the validity of the syllogistic argument is a function of its logical form rather than its content. Thus, given the premises “all A are B” and “all C are A” are regarded as true, we are compelled to draw the correct logical conclusion that “all C are B,” even though we have no knowledge of the entities A, B, and C. Indeed, we would be logically bound to state that “all C are B” is true under circumstances in which we knew on the basis of prior knowledge that at least one of the premises was false, but were instructed to assume that the premises were true regardless of what our prior knowledge might indicate. Researchers have long been interested in the effect of such prior knowledge on reasoning, particularly when there is a conflict between what is known to be true or false in fact and what would necessarily be true or false if one were to temporarily suspend belief and assume that the premises for that proposition were true. A robust finding, known as belief bias, is that subjects perform more accurately on reasoning tasks when the logical conclusion is congruent with their beliefs compared to when it is
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incongruent (Byrne, Evans & Newstead, 1993; Evans, Barston & Pollard, 1983; Wilkins, 1928). In syllogistic reasoning tasks, belief-congruent trials are those with either valid and believable or invalid and unbelievable conclusions, whereas belief-incongruent trials are those with either valid and unbelievable or invalid and believable conclusions. To illustrate this bias, consider the following syllogistic argument (i.e. two premises followed by a conclusion): No cigarettes are inexpensive. Some addictive things are inexpensive. ∴ Some addictive things are not cigarettes.
When asked to decide whether or not the conclusion must follow from the premises, 96 percent of subjects respond affirmatively to this valid beliefcongruent syllogism (Evans, Barston & Pollard, 1983). Now, consider the following argument: No addictive things are inexpensive. Some cigarettes are inexpensive. ∴ Some cigarettes are not addictive.
This valid but belief-incongruent syllogism is accepted as valid by only 46 percent of subjects (ibid.). This performance deficit has been explained in terms of the inability of subjects to inhibit their personal beliefs in the service of logical assessments. Given that inhibition requires drawing on limited cognitive resources, one might expect to see a relationship between individual differences in inhibitory ability and vulnerability to this bias. This explanation is supported by studies showing that subjects who score higher in tests of general intellectual ability are less susceptible to this bias (Stanovich & West, 2000), and by direct evidence from dual-task paradigms demonstrating that taxing working memory leads to higher susceptibility to it (De Neys, 2006). Furthermore, De Neys and Van Gelder (2008) have shown that across the lifespan there is a curvilinear relationship between one’s ability to reason about arguments where logic and beliefs conflict, such that superior performance is observed when inhibitory ability is at its peak in young adulthood. Thus, correlational, experimental, and developmental evidence converge on inhibition as a likely mechanism underlying susceptibility to this bias. Recently, neuroscience has begun to shed light on the neural bases of the susceptibility of epistemic judgments to belief. Goel and Dolan (2003) conducted a study to investigate the brain in the course of reasoning about arguments with and without believable content. They presented subjects with syllogisms in the fMRI scanner and asked them to judge
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whether the conclusions were valid (i.e. whether they necessarily followed from their premises). As expected, belief bias was evident: accuracy was significantly higher for belief-congruent trials compared to beliefincongruent trials. Two neural results emerged. First, errors on beliefincongruent trials activated VMPFC. Second, when subjects made the correct judgment on belief-incongruent trials, activation was observed in the right lateral PFC. According to the somatic marker hypothesis, the VMPFC is the structure critical for forming associations between cognitions and emotions (Bechara et al., 1994, 1997; Bechara, Damasio & Damasio, 2000). This hypothesis is supported by much neuropsychological evidence showing that patients with lesions to the VMPFC are unable to use emotional cues in decision making. Goel and Dolan’s (2003) experiment exemplifies how neuroscientific data may contribute to important theoretical debates. Specifically, the extent to which beliefs are driven by emotions is one of the oldest questions in psychology (James, 1890). McDougall (1921) went so far as to argue that beliefs can be viewed as “derived emotions.” Nevertheless, only recently have investigators begun to study the influence of emotions on beliefs in the context of deductive reasoning directly (e.g. Goel & Vartanian, 2011). The results by Goel and Dolan (2003) suggest that to understand the influence of beliefs on reasoning, our models would do well by incorporating emotions. Furthermore, one would expect the involvement of a neural system that underlies inhibition (and cognitive control) to be active when subjects make correct judgment on belief-incongruent trials. Indeed, a review of the neuroimaging literature has shown that the primary function of the right inferior PFC is inhibition (Aron, Robbins & Poldrack, 2004). This is consistent with its role within the context of reasoning (Goel et al., 2000), as well as its association with cognitive control – as was apparent in De Neys, Vartanian and Goel’s study discussed above. The results highlight the role of inhibition in overcoming the influence of beliefs during reasoning, pointing yet again to inhibitory processes as an important factor in the expression of normative behavior. As such, these results shed light not only on the material causes of belief bias, but also on its formal causes. Although the results were interpreted in part through association logic (i.e. reverse inference), the interpretations were consistent with prior behavioral evidence that paved the way for the task analysis and the predictions that followed. Decision making in economic games In our final area of investigation, we shift our focus from judgment in intrapersonal contexts to decision making in interpersonal domains. A
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guiding assumption of standard economic models is that humans act purely based on self-regarding preferences (Camerer & Fehr, 2006). Preferences are self-regarding if a person does not take into consideration other people’s behavior or outcomes associated with those behaviors insofar as they do not affect his or her economic well-being. The major contribution of neuroscience to this research program has been to highlight that notions of fairness – and the emotions that accompany them – have a major impact on choice. Given that games have proven to be a useful arena for studying the descriptive validity of the self-regarding preference assumption, we turn next to a description of the research involving some well-known economic exchange games. Consider the Ultimatum Game (UG) (Guth, Schmittberger & Schwarze, 1982), which involves two players, the “proposer” and the “responder.” The proposer is given a sum of money and must decide how to allocate it between himself or herself and the responder. The split is proposed to the responder, who can accept or reject the offer. If the responder accepts the offer, the sum of money is divided as proposed. If the offer is rejected, neither player receives anything. Either way, this game does not involve negotiation, because the game ends when the responder decides. According to standard economic theory, two types of behavior are rational: first, the proposer should offer the minimum sum to the responder, because having the most money for oneself is optimal. Second, the responder should accept any sum offered, because having some money trumps having none. Contrary to these assumptions, however, most offers range between 40% and 50%, and offers markedly below this range are rejected by about half the responders (Henrich et al., 2001). Why is this pattern observed? Sanfey et al. (2003) posited that part of the problem with the standard economic solution to the UG is that it ignores the responder’s emotional reaction to offers that are perceived as unfair. This suggestion is consistent with evidence from behavioral studies demonstrating that subjects experience anger in relation to unfair offers or outcomes (Pillutla & Murnighan, 1996). For instance, in one field study, Dhami, Mandel and Souza (2005) found that prisoners’ perceived unfairness of their trial and sentencing decisions were directly related to their current anger. In the UG context, perceived unfairness may trump economic considerations, resulting in seemingly irrational choices in which the responder opts for the dominated option. Sanfey et al. tested their prediction in an fMRI study. Before entering the scanner, subjects were introduced to ten people who would be their partners in ten separate UG rounds. Subjects were told that they would play each partner only once. The findings revealed that subjects accepted all fair offers (i.e. 50–50 splits), and that the likelihood of offer acceptance was directly related to degree of fairness.
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If a sense of unfairness were accompanied by the experience of negative emotion, one would expect to observe activation specifically in structures that underlie negative emotion under such conditions. In fact, the neural findings revealed an interesting interplay between the insula and dorsal lateral PFC when subjects were confronted with unfair offers. Activation in insula is associated with the experience of distress and pain (Ohara, Vit & Jasmin, 2005), as well as the emotions of anger and disgust (Adolphs, 2002). In turn, activation in dorsal lateral PFC has been linked to cognitive control (Petrides, 2005). Interestingly, unfair offers that were rejected resulted in relatively higher activation in insula than dorsal lateral PFC, whereas unfair offers that were accepted resulted in relatively higher activation in dorsal lateral PFC than insula. Notwithstanding difficulties in comparing the magnitude of the fMRI signal across two different brain regions, these findings indicate that rejection of unfair offers is driven by an emotional response to unfairness, whereas acceptance of unfair offers is driven by the exercise of cognitive control, perhaps attenuating the emotional response in favour of economic gain. The findings show that decision making does not operate purely based on self-regarding preferences, but may be the outcome of competing processes – in this case, emotion in response to a sense of unfairness and cognitive control. People care about the extent to which others’ behavior corresponds to a fairness norm and are willing to forego economic gains to uphold such norms. Moreover, the brain circuitry that underlies responses to unfairness has emotional and cognitive components, the differential activation of which would seem to offer a preliminary material account of people’s responses to unfairness. Another intriguing question concerns how far people would go to enforce fairness norms. Punishment of norm violations predates the establishment of modern penal institutions, and knowledge of punishment deters cheating (Fehr & Gächter, 2002). In addition, economic models of choice that incorporate elements of fairness and cooperation predict economic behavior better than models based purely on selfinterest motives (Rabin, 1993). To address the punishment issue, de Quervain et al. (2004) studied an economic exchange game that allowed a player whose trust was violated to punish the violator. The researchers posed two questions: first, they asked whether trust violation would be punished even when such punishment bore a direct economic cost for the punisher (i.e. altruistic punishment). Second, they asked whether altruistic punishment would be enjoyable to the punisher. Both questions are important to consider because, as social animals, people often have to decide whether to compete or cooperate with others for the acquisition of scarce resources. Which strategy we adopt depends in part on whom we
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trust and how we choose to maintain trust-enhancing reciprocity norms. De Quervain et al.’s experiment addressed whether the motivation to enforce norms through punishment is strong enough to override the short-term monetary disadvantage in terms of cost incurred to the punisher. In this experiment, subjects played an allocation game with seven different counterparts. At the beginning of the game, the two players, A and B, were both endowed with 10 monetary units. Subjects were always in the A role and had two options. One option was to keep all 10 monetary units. If this option was selected, the game ended, and A and B each earned 10 monetary units. If A trusted B, the second option was for A to send all 10 monetary units to B, in which case the experimenter quadrupled the amount transferred from A to B (i.e. 4 × 10 monetary units) such that B would now have 50 monetary units in total. At this point, B had the choice of keeping all 50 monetary units or sending half to A. If B decided to keep all the money, A was given 1 minute during which he could decide whether to punish B for non-cooperation with up to 20 monetary units. Subject A was scanned using positron emission tomography (PET) only during this 1-minute interval leading up to the decision (to reduce exposure to radioactivity) and only when B opted not to cooperate (i.e. when B kept all the money). Like fMRI, PET is a technique to measure brain activity indirectly, but in this case through monitoring blood flow tagged with radioactive isotopes. In the span of that minute, subjects also rated the extent to which B’s behavior was unfair, and their desire to punish B. As can be seen, this game is designed such that if A trusts B, and B is trustworthy and reciprocates A’s cooperative behavior, both players can earn a much larger monetary sum. De Quervain et al. (2004) hypothesized that if A trusts B and offers 10 monetary units, but B does not reciprocate, A will interpret this as a norm violation, consider the behavior unfair, and as a result have a desire to punish B. For our purposes here, we focus on cases of altruistic punishment in which punishing B also costs A monetary units. The findings showed that when B opted to keep all the money, the vast majority of subjects (85%) opted for altruistic punishment. The neural findings from this study indicated that the caudate nucleus and the thalamus were recruited when subjects opted to punish B. The caudate nucleus has been linked strongly to goal-directed action to increase reward (Delgado, Stenger & Fiez, 2004; O’Doherty et al., 2004), as has the thalamus to processing reward-related information in monetary contexts (Delgado et al., 2003). This suggests that activation in the caudate nucleus and the thalamus reflects important components of the neural bases of A’s desire to punish B, as indicated by self-report data
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collected in the scanner. In fact, the association between reward-related activity in the brain and punishment may have been amplified by the explicit instruction to self-report on “desire to punish” in the period leading up to the decision. In addition, rate of punishment was correlated positively with rate of blood flow in the caudate nucleus. The findings show not only that people are prone to punishing violations of fairness norms, but also that anticipating the punishment of norm violators in the period leading up to behavior activates reward-processing centers of the brain. The combined results of Sanfey et al. (2003) and de Quervain et al. (2004) suggest that in the context of economic exchange, people are sensitive to notions of fairness above and beyond mere economic gain. Furthermore, people pay close attention to others’ behavior (see Camerer & Fehr, 2006), and are willing to enforce reciprocity norms even when doing so involves accruing a monetary cost. The neural data suggests that responding to a sense of unfairness is emotionally driven, and that punishing a norm violator is enjoyable at least partly because of the anticipation of its hedonic reward. In other words, revenge is sweet because we savor it. Of course, both studies share the limitation that each subject interacts with another agent only once. This type of interaction arguably lacks external validity, since everyday interactions are often repeated in nature and governed by relational norms (Mandel, 2006; Mills & Clark, 1982). Norm-based decision making may thus have its origins in the contribution it makes to reputation building and the development of an intention to trust. Whereas normative considerations may promote reciprocity as a fair strategy among agents who do not have an enduring relationship (e.g. mere acquaintances), recent research suggests that generosity may be an even more important normative factor in personal relationships (e.g. friendships) that extend over time (Mandel, 2006; Van Lange, Ouverkerk & Tazelaar, 2002). King-Casas et al. (2005) conducted an fMRI experiment to examine normative considerations in the context of a repeated (i.e. 10 round) economic exchange game. In this game the “investor” was provided with $20 and decided how much of it to offer to the “trustee.” After this decision the investment tripled, and the trustee decided how much of the appreciated amount to repay to the investor. King-Casas et al. defined reciprocity as “a fractional change in money sent across rounds by one player in response to fractional change in money sent by their partner” (pp. 78–9). They further distinguished between benevolent reciprocity – cases in which investors are being generous (i.e. sending more money in response to a defection by the trustee) – and malevolent reciprocity – cases
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in which investors are being selfish (i.e. sending less money in response to the trustee’s generosity). Analysis of the behavior of investor-trustee pairs identified reciprocity to be the strongest predictor of subsequent increases or decreases in trust. This is analogous to saying that subjects followed a tit-for-tat strategy throughout the rounds. The neural findings revealed that the caudate nucleus was activated more in benevolent (than malevolent) reciprocity, and that the time course of its activation was phase advanced by 14 seconds as player reputations developed. This phase advancement in the caudate may represent the neural signature of the “intention to trust” as players built mental representations of their counterparts’ reputation in the course of successive interactions, which in turn predicted behavior. However, this inference remains to be tested directly. The results of the three experiments discussed in this section indicate that human decisions are not purely self-regarding (Camerer & Fehr, 2006). Humans appear to be acutely responsive to the extent to which the behavior of others deviates from various social norms, and they use such deviations to compute mental representations of others (e.g. trustworthy, unfair, etc.) that in turn influence choice behavior. Furthermore, behavior appears to be influenced by the interaction of several neural systems that drive it under specific conditions (see Sanfey, 2007; Sanfey et al., 2006). A clear example of this phenomenon is the reciprocal modulation of insula and dorsal lateral PFC that drives acceptance or rejection of unfair offers in the UG (Sanfey et al., 2003). Not only do these results contribute to a better understanding of the material causes of judgments and decisions in the context of economic exchange games, they also refine our understanding of the formal and final causes of behavior. For example, all three studies suggest that emotion must be given a prominent role in models of behavior in the context of economic exchange games. In addition, the goal of adhering to trust-enhancing relational norms appears to be a strong predictor of behavior, thereby shedding light on our understanding of the final causes or functional bases of behavioral choice in economic exchange contexts. General conclusion In this chapter we discussed neuroscientific studies in four research areas to show that this new line of evidence is transforming our understanding of the causal origins of judgment and decision making. Our discussion of the contribution of neuroscience to understanding the causal origins of judgment and decision making can be underscored by four themes. The first consists of the confirmation of factors that influence decision making under risk, specifically emotions, the nature of prospects (losses and gains
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versus gains alone), and task conditions (decision versus anticipation). Second, for probability judgment, the neural findings converge with recent behavioral research to show that systematic biases are often attributable to errors of application rather than errors of comprehension (see Mandel, 2005a). Third, for epistemic judgment, the neural findings have confirmed the role of inhibition in belief bias, and highlighted the involvement of emotions in belief contemplation. This is an important contribution to the historically important problem regarding the interaction between emotions and beliefs (James, 1890; McDougall, 1921). Fourth, neural findings have highlighted that notions of fairness have a major impact on choice in economic exchange. These results have contributed to the revision of the classical assumption of standard economic models that viewed humans as purely self-regarding actors (Camerer & Fehr, 2006). In addition, they have contributed to emerging consensus that observed behavior may be the result of multiple and sometimes competing cognitive and emotional processes at the brain level (Sanfey, 2007; Sanfey et al., 2006). We believe that these and other neuroscientific findings have begun to add value to our understanding of the material, final, and formal causes of judgment and decision making by offering new insights and perspectives on old problems.
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Appendix fMRI: a few basic facts While a detailed discussion of the features of this technology is beyond the scope of this chapter, we present a condensed description of its key features here. First, functional Magnetic Resonance Imaging (fMRI) is not a direct measure of brain activity. Rather, it is a measure of the hemodynamic response (i.e. change in blood flow) in relation to neural activity in the brain. When neurons are active, they increase their consumption of oxygen. The local response to increased consumption of oxygen is increased blood flow to the region, accompanied by changes in local blood volume and flow. Oxygen consumption changes the local concentration of oxyhemoglobin and deoxyhemoglobin molecules. Most current fMRI studies rely on the blood-oxygen-level dependent (BOLD) response to quantify this hemodynamic response by measuring local oxyhemoglobin and deoxyhemoglobin concentrations. The BOLD response can be used as a proxy measure for brain activity. The temporal dynamics are such that the BOLD response lags neuronal activity by a few seconds (lag time can vary as a function of physical and biological characteristics of the region). This lag is taken into consideration during the analysis of fMRI data. Second, while electrophysiological recordings with monkeys enable researchers to record from individual neurons in the brain, fMRI is a measure of neuronal mass activity, making inferences about the involvement of specific processes driven by specific classes of neurons problematic. Consider that in humans there are about 90,000–100,000 neurons under 1 mm2 of cortical surface (Logothetis, 2008). When reconstructing two-dimensional brain scans into three-dimensional brain volumes, researchers can partition the volume of the brain into (≈ 100,000) threedimensional pixels of equal size – known as voxels. In turn, it is possible to determine statistically the degree of correlation between activation in any given voxel and the behavior of interest. Thus, while at the level of fMRI data the voxel constitutes the smallest unit of analysis, each voxel in turn houses a large number of potentially heterogeneous types of neurons. This means that the observed activation maps are necessarily maps of mass
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action of groups of neurons. This is important to note, because to the extent that neurons tied to different processes are housed within the same voxel, the activation in any given voxel will be an average activation of different types of input. Going back to Poldrack (2006), this makes it all the more critical that researchers test hypotheses focused on specific and anatomically well-defined regions of interest (ROI), defined as structures of a priori theoretical interest in brain mapping. Third, when a subtractive contrast indicates that area Z was activated more in condition A compared to condition B, this contrast reveals a relative rather than an absolute difference in activation. In other words, this difference could be due to deactivation or inhibition of brain activity in Z under condition B, activation or excitation of brain activity in Z under condition A, or some combination of both. This explains why fMRI hypotheses are typically stated in terms of relative differences. This is not a problem for the researcher if testing a hypothesis relies on testing for relative differences, but it does become an issue if the presence/ absence of a process – inferred from the “involvement” of a region – must be ascertained. Furthermore, the high level of noise intrinsic to the data acquisition process means that the difference between activated and non-activated areas represents approximately a 5 percent difference in the signal (Desco et al., 2001). This dictates that researchers conduct proper power analyses to select hypotheses that can be tested given the low signalto-noise ratio.
