Deciding how to decide: ventromedial frontal lobe damage affects information acquisition in multi-attribute decision making (original) (raw)

Abstract

Ventromedial frontal lobe (VMF) damage is associated with impaired decision making. Recent efforts to understand the functions of this brain region have focused on its role in tracking reward, punishment and risk. However, decision making is complex, and frontal lobe damage might be expected to affect it at other levels. This study used process-tracing techniques to explore the effect of VMF damage on multi-attribute decision making under certainty. Thirteen subjects with focal VMF damage were compared with 11 subjects with frontal damage that spared the VMF and 21 demographically matched healthy control subjects. Participants chose rental apartments in a standard information board task drawn from the literature on normal decision making. VMF subjects performed the decision making task in a way that differed markedly from all other groups, favouring an ‘alternative-based’ information acquisition strategy (i.e. they organized their information search around individual apartments). In contrast, both healthy control subjects and subjects with damage predominantly involving dorsal and/or lateral prefrontal cortex pursued primarily ‘attribute-based’ search strategies (in which information was acquired about categories such as rent and noise level across several apartments). This difference in the pattern of information acquisition argues for systematic differences in the underlying decision heuristics and strategies employed by subjects with VMF damage, which in turn may affect the quality of their choices. These findings suggest that the processes supported by ventral and medial prefrontal cortex need to be conceptualized more broadly, to account for changes in decision making under conditions of certainty, as well as uncertainty, following damage to these areas.

Introduction

Decision making, the process of choosing between options, is a fundamental human behaviour. In the last several years, cognitive neuroscience studies have begun to investigate the brain basis of decision making, with a particular focus on prefrontal cortex. This focus has been motivated, in part, by clinical reports that frontal damage [and particularly ventromedial frontal (VMF) damage] can be associated with strikingly poor decision making (Eslinger and Damasio, 1985; Harlow, 1999; Ackerly, 2000). The most influential laboratory studies of decision making after VMF damage have focused on risky decisions under uncertainty, as captured by a card-based gambling task (Bechara et al., 1994, 1997). Although this task does detect abnormal behaviour in subjects with VMF damage, there is ongoing controversy about these findings and their interpretation (Manes et al., 2002; Tomb et al., 2002; Sanfey et al., 2003; Maia and McClelland, 2004, 2005; Bechara et al., 2005; Fellows and Farah, 2005).

However, in some respects this controversy bypasses a larger issue. Decision making is a complex behaviour that clearly involves multiple component processes (Krawczyk, 2002; Fellows, 2004; Sugrue et al., 2005). In principle, the poor real-life decisions of VMF-damaged subjects could be the result of deficits in any one or more than one of these processes; there is no a priori reason to expect that the impaired decision making of such individuals rests solely, or even primarily, on a specific deficit in dealing with risk or uncertainty. It is particularly important to clarify the scope of decision-making impairments following frontal lobe damage, because such evidence can be very helpful in interpreting the data being generated in the burgeoning functional imaging and electrophysiology literatures examining the role of ventral and medial prefrontal cortex in various aspects of decision making (Critchley et al., 2001; Ernst et al., 2002; Gehring and Willoughby, 2002; Krawczyk, 2002; McClure et al., 2004_a_; Tanaka et al., 2004). The present study therefore represents an effort to move away from a narrow focus on risky decision making, and instead examine the effects of focal frontal lobe damage on a form of decision making under certainty: multi-attribute decision making.

Decision making does not have to involve risk to be hard. Anyone who has purchased a car, chosen a school to attend or hired an employee from amongst several applicants knows that weighing the pros and cons of options that differ across multiple, sometimes incommensurate, dimensions can be very difficult, even when all the information is known and the potential outcomes are certain. This kind of decision making has been examined in normal subjects, including studies of the information acquisition strategies such subjects use, the determinants of their choice of strategy and the quality of the decisions that result (Simon, 1955; Payne, 1976; Ford et al., 1989; Payne et al., 1993; Chu et al., 1999; Gigerenzer and Todd, 1999). This work has shown that individuals may use a variety of strategies and heuristics to cope with the potential complexity of such decisions (Payne et al., 1992, 1993; Gigerenzer and Todd, 1999). Indeed, in such settings ‘deciding how to decide’ can become the central problem. How many alternatives should be considered? How much information should be learned about each? Should one search for the best alternative or settle for one that is ‘good enough’? Importantly, different solutions to these pre-decisional problems often result in different final choices (Payne et al., 1992), and in different levels of satisfaction regarding these choices (Schwartz, 2004).

One view emerging from process-oriented studies of decision making is that these strategic pre-decisional processes are flexible and adaptive, often constructed ‘on the fly’ as subjects engage in information acquisition. External constraints, such as instructions, information complexity and time pressure, can influence such strategies. In addition, internal factors such as processing capacity and individual differences in decision goals (which may themselves be flexibly determined and updated as more information is acquired) also contribute (Ford et al., 1989; Payne et al., 1992; Gigerenzer and Todd, 1999; Simon et al., 2004).

Thus, multi-attribute decision making involves a dynamic interplay between assessing the current relative value of alternatives, efficiently acquiring further information to optimize this assessment of value and adjusting overall decision goals in light of the available information. As such, this form of decision making would seem to call upon several capacities linked to prefrontal cortex, even in the absence of risk or uncertainty. Further, the study of this aspect of decision making may constitute a link between the literature examining prefrontal contributions to planning and problem solving in open-ended settings (Shallice and Burgess, 1991; Goel et al., 1997; Goel and Grafman, 2000) and recent work on the role of the VMF region in representing the relative value of stimuli in simple associative learning paradigms (reviewed in Rolls, 2000; Fellows, 2004; Sugrue et al., 2005).

This study used a simple process-tracing methodology adapted from studies of decision making in normal subjects to examine multi-attribute decision making in individuals with focal frontal lobe damage. The performance of those with VMF damage was contrasted with a lesioned control group with frontal damage that spared the VMF, and with healthy age-matched control subjects. The author asked whether damage to either frontal region is associated with systematic differences in the strategic, information-acquisition phase of such decision making.

Methods

Subjects

The study involved 13 subjects with damage to the VMF and 11 subjects with damage to dorsal and/or lateral frontal lobe (D/LF) (Fig. 1). These two groups differed in educational level, and so were compared to separate demographically matched healthy control groups [ventromedial control group (V-CTL), n = 11; dorsal/lateralcontrol group (D-CTL), n = 10]. Subjects with frontal damage were identified through the University of Pennsylvania Center for Cognitive Neuroscience patient database. VMF damage was due to rupture of anterior communicating aneurysm in 10 cases, and to ischaemic stroke in 3. D/LF damage followed ischaemic or haemorrhagic stroke in eight cases, and resection of low-grade glioma with local radiotherapy in three. Five VMF subjects and five D/LF subjects were taking psychoactive medications. These were most commonly anticonvulsants and/or antidepressants. One VMF subject was taking an acetylcholinesterase inhibitor, another both an acetylcholinesterase inhibitor and methylphenidate, and a third modafenil. Subjects were tested at least 6 months (mean, 4.2 years, range 0.5–12 years) after brain injury had occurred.

Fig. 1

Location and overlap of brain lesions. The top row shows the lesions of the 13 subjects with ventromedial frontal damage; the bottom row shows those of the 11 D/LF subjects. Lesions are projected on the same seven axial slices of the standard Montreal Neurological Institute brain for both groups, oriented according to radiological convention (right is left). Areas damaged in one subject are shown in purple; warmer shades denote the degree to which lesions involve the same structures in two or more individuals, as indicated in the key. Areas of damage common to both groups can be assessed by comparing each slice to the slice directly beneath it, and are described in more detail in the text.

Lesions were traced from the most recent CT or MR imaging available onto the standard Montreal Neurological Institute brain using MRIcro software (Rorden and Brett, 2000). Clinical imaging was used for this purpose and so pre-dated participation in this study by months to years, depending on the recency of the brain injury, and the intensity of clinical follow-up of individual patients. At the time of study participation, no subjects had clinical evidence (on history or neurological examination) of central nervous system injury beyond that associated with the index lesion.

Subjects were assigned a priori to the VMF group if the lesion principally affected medial orbitofrontal and/or the ventral aspect of the medial prefrontal cortex [following the boundaries laid out in Stuss and Levine (2002)]. Those with a lesion involving the frontal lobe anterior to the pre-central sulcus, but sparing VMF areas, were assigned to the D/LF group. As can be seen in Fig. 1, those in the D/LF group most commonly had damage affecting the inferior and middle frontal gyrus, and none had damage affecting either medial orbitofrontal cortex or that portion of medial prefrontal cortex ventral to the genu of the corpus callosum. Two VMF subjects had damage that extended into D/LF areas: one with involvement of the lateral aspect of the right inferior frontal gyrus and another with extensive bilateral frontopolar and dorsomedial prefrontal damage in addition to near-complete destruction of orbitofrontal and ventral medial prefrontal cortex.

Healthy control subjects were recruited by advertisement. Controls were not taking psychoactive medication and were free of significant current or past psychiatric or neurological illness as determined by history and screening neurological examination. Controls were excluded if they scored less than 28 out of 30 on the mini-mental status examination (Folstein et al., 1983). IQ was estimated by means of the American version of the National Adult Reading Test. All subjects provided written, informed consent prior to participation in the study, in accordance with the Declaration of Helsinki, and were paid a nominal fee for their time. The Institutional Review Board of the University of Pennsylvania approved the study protocol.

Demographic information is summarized in Table 1. The frontal groups did not differ significantly from their respective control groups in age, education or estimated IQ (_t_-test, all P > 0.05). The D/LF group had significantly more education than the VMF group (P < 0.05), but did not differ significantly in estimated IQ or in volume of damaged tissue (all _P_ > 0.05). The D/LF group scored significantly higher on the Beck Depression Inventory than the D-CTL group (P < 0.01), as did the VMF compared with the V-CTL group (_P_ < 0.05), but the two frontal groups did not differ significantly on this measure (_P_ > 0.05).

Table 1

Subject characteristics; see text for details [mean (SD)]

Table 1

Subject characteristics; see text for details [mean (SD)]

Subjects with frontal damage were administered a short neuropsychological battery for screening purposes. Results from the tasks with potential sensitivity to frontal damage, as well as a verbal memory task (recall of a list of five words after a 1 min delay), are provided in Table 2 (not all subjects completed all tests). The groups differed significantly only in their performance on the Trails B task, with VMF subjects making more errors (Mann–Whitney _U_-test, P < 0.01). The control groups also completed the animal and ‘F’ verbal fluency tasks. The D/LF group was significantly impaired on both fluency tasks compared with the D-CTL group (P < 0.01), whereas the VMF group did not differ from the V-CTL group on either measure.

Table 2

Results of selected neuropsychological screening tests [mean (SD)]

Table 2

Results of selected neuropsychological screening tests [mean (SD)]

Decision task

The pattern of information search in multi-attribute decision making was assessed using a standard ‘information board’ paradigm (Payne, 1976). Subjects made hypothetical choices between one-bedroom apartments. Information about the apartments was displayed in table format on a computer screen. Initially, only the apartment labels (e.g. ‘apartment A’, as column headings) and the attributes for which information was available (e.g. ‘neighborhood’, as row headings) were shown (Fig. 2). The information itself was masked by white rectangles. Subjects indicated which information they wanted by clicking the mouse on the relevant white rectangle. The rectangle was replaced by the information, which then remained visible until a final decision was made, to eliminate any memory requirement. Subjects were instructed to seek as much information as they felt they needed, and in the order that made sense to them, to make a decision as efficiently as possible, proceeding ‘just as they would in real life’. They were further told that it was not necessary to examine all the information and that they could decide on an apartment whenever they felt ready. They were also told that there was no correct answer and that they should choose the apartment that was right for them. There was no time limit. In cases where subjects were uncomfortable using a mouse, they instead pointed to the rectangle they wished to open, and the experimenter moved the mouse for them. Subjects were also encouraged to ‘think aloud’, and their comments were tape recorded, in keeping with the original method of Payne (1976). However, participants with frontal lobe damage made few or no informative comments, so these data were not analysed further.

Fig. 2

Example of information board layout, showing a partial view of the 4 option-6 attribute (4 × 6) task, with some of the information revealed.

All subjects first performed a practice decision problem, choosing between two cars, provided with information about two attributes. They then selected an apartment from information boards of increasing complexity: 2 apartments-4 attributes (2 × 4), 4 apartments-6 attributes (4 × 6), and 6 apartments-7 attributes (6 × 7). The tasks were administered in the same order for all subjects. As is typical for such tasks, there was no obviously best (or worst) choice. The pattern of information acquisition was the main dependent variable. This was summarized using the scoring system of Payne (1976), according to the formula: (movements across − movements down)/(movements across + movements down). This results in a score that ranges from −1 to +1, capturing the degree to which the board was searched ‘down’ (by alternatives), or ‘across’ (by attributes), regardless of how much information was acquired. An entirely alternative (i.e. apartment)-based search strategy would yield a score of −1, whereas an entirely attribute-based strategy would yield a score of +1. The percentage of the available information examined and the final choice were also recorded.

Statistical analysis

Subjects with frontal lobe damage were classified a priori into two groups: VMF and D/LF. These two groups were similar in age, but differed significantly in level of education, necessitating two healthy control groups matched with the respective frontal-damaged group on these two variables. Comparison of each frontal group with the appropriate control group was carried out using _t_-tests where the data conformed to a normal distribution, and Mann–Whitney _U_-tests where they did not. Significance was set at P < 0.05, two-tailed.

Results

Information search patterns

Subjects with VMF damage acquired information in a very different way than did either those with D/LF damage or either healthy control group. As shown in Fig. 3, VMF subjects predominantly pursued an alternative-based information search strategy, acquiring information regarding multiple attributes of one alternative (e.g. apartment A), then moving to another alternative. In contrast, the other three groups of subjects pursued a primarily attribute-based strategy, comparing several alternatives across one attribute (e.g. rent), then moving to another attribute. The total score of VMF subjects differed significantly from that of the V-CTL group [mean score (SD) summed across all three tasks: VMF −1.3 (2.3), V-CTL 2.1 (1.2), Mann–Whitney U = 21, P < 0.01], whereas the scores of D/LF and D-CTL groups did not (U = 49.5, P = 0.7). The same pattern was evident in all three boards when analysed individually (Fig. 3).

Fig. 3

Median decision board scores for each group. Individual bars show the score for each decision board problem, as indicated by the key (above). More positive scores indicate a more ‘attribute-based’ search strategy, whereas more negative scores indicate an ‘alternative-based’ search strategy.

Despite the differences in search strategies, all four groups accessed a similar proportion of the available information overall (Table 3). In keeping with the literature (Payne, 1976; Kerstholt, 1992), as the information content of the decision boards increased, participants acquired more information in absolute terms. However, they became relatively more selective, examining a progressively smaller proportion of the available information. This pattern did not differ across groups [ANOVA (analysis of variance), effect of group F(3,41) = 1.7, P > 0.05, effect of task complexity F(2,42) = 30.6, P < 0.0001, task complexity × group, _F_(6,82) = 1.2, _P_ > 0.05)]. In contrast, the pattern of information acquisition (search strategy) was not systematically affected by increasing complexity.

Table 3

Amount of information acquired and number of attributes examined summed across all three decision boards and expressed as a percentage [mean (SD)].

The modal choice in each decision board for each group is shown in the third column. Groups did not differ significantly in the amount of information, nor in the number of attributes they examined.

Table 3

Amount of information acquired and number of attributes examined summed across all three decision boards and expressed as a percentage [mean (SD)].

The modal choice in each decision board for each group is shown in the third column. Groups did not differ significantly in the amount of information, nor in the number of attributes they examined.

Although there have been both anecdotal (Eslinger and Damasio, 1985) and experimental (Rogers et al., 1999) reports that VMF damage can lead to prolonged decision times, this was not evident in the present experiment: The time to complete each task increased with task complexity (ANOVA, F(2,42) = 48.5, P < 0.0001), but there was no significant effect of group, nor interaction (all _P_ > 0.5; Table 3).

The decision boards have no obvious right or wrong answers and therefore provide no absolute measure of choice quality, as is typical for such process-oriented tasks. Nevertheless, some apartments were more popular than others amongst the control subjects. Seventeen out of 21 control subjects chose apartment B in the first problem, 12 out of 21 chose apartment B in the second problem, and 11 out of 21 chose apartment C in the third problem (Table 3). These were also the modal choices for the D/LF group. In contrast, the VMF group tended to choose apartments that were not the modal choice overall more often than the other groups. This tendency was only statistically significant in the 4 × 6 task, where fewer VMF subjects chose the option that was the modal choice of the group as a whole than did the V-CTL subjects (Fisher's exact test, P < 0.05). This suggests that the markedly different search strategy of the VMF group influenced decision outcomes, as has been reported in normal subjects (Payne et al., 1992).

Thus, subjects with VMF damage acquired information in a systematically different way, and tended to make different final choices. Although there was no statistically significant difference in the amount of information gathered across all four groups, there was considerable variability in this measure. Clinical lore [and some systematic experimental work (e.g. Berlin et al., 2004)] indicates that VMF damage can lead to heightened impulsivity. Can the choices of those with VMF damage in the present experiment be considered ‘impulsive’, in some sense of this variously defined term (Evenden, 1999)? The lack of any group-wise differences in the amount of information examined or in the time taken to complete the tasks argues against this possibility. However, individual patterns of behaviour in the simplest decision task provide some support for this view: in the 2 × 4 task, in which subjects chose between just 2 apartments, all control and all D/LF subjects accessed at least some information about both alternatives, with 29 out of 32 examining all of the eight pieces of information available. Strikingly, 6 of 13 VMF subjects did not examine all the information, and 4 of 13 only examined information about one of the alternatives before choosing it. There were no evident differences in lesion characteristics or demographic variables distinguishing these subjects from the VMF group as a whole. This behaviour could be interpreted as impulsive, in the sense that subjects are making a decision based on information that is incomplete compared with that considered by the control group. An alternative explanation is that VMF subjects had different decision goals: they were not attempting to determine the relative merits of the different alternatives, but were instead comparing a given alternative to some absolute standard of acceptability, an often effective decision-making strategy known as ‘satisficing’ (Schwartz et al., 2002).

The majority of the VMF group had at least some degree of bi-hemispheric damage, precluding analysis of laterality effects. The D/LF group included five individuals with left hemisphere damage and six with right hemisphere damage. There did not appear to be any systematic difference in search strategy or amount of information examined in these two subgroups. [Median total decision board score left D/LF +2.1, right D/LF +1.8; mean (SD) proportion of information examined left D/LF 0.67 (0.22), right D/LF 0.66 (0.24)]. Within the D/LF and VMF groups, lesion aetiology did not appear to reliably predict search strategy, although such patterns would be difficult to detect given the small sample size.

Discussion

This was an exploratory study of the effects of frontal lobe damage on the processes underlying multi-attribute decision making. The intent was to more comprehensively delineate the stages of decision making that are affected by such damage. In contrast to other paradigms used in recent studies of decision making in patients with frontal damage, multi-attribute decision making does not involve risk, uncertainty or learning.

Subjects with VMF damage successfully performed the decision tasks: they acquired a similar amount of information as the other groups and made decisions within a comparable period of time (although their choices were somewhat different than the norm). However, the VMF group differed strikingly from all other groups in how they acquired information. Importantly, this difference was systematic. Such patients did not acquire information in a disorganized way, but rather consistently applied an information acquisition strategy that differed from that of other participants.

Process-oriented studies of multi-attribute decision making in normal subjects have argued that it is possible to draw inferences about a subject's underlying decision strategy by measuring how much information is acquired, and the pattern of this information search (e.g. Payne et al., 1993). VMF subjects acquired information following an alternative-based (sometimes called inter-dimensional) pattern, in contrast to the largely attribute-based (intra-dimensional) pattern followed by all other groups in the context of this task. That is, VMF subjects tended to organize their information acquisition around individual apartments, whereas other groups compared information about specific attributes, such as rent, across several apartments. This finding suggests that VMF damage leads to systematic differences in the strategies and heuristics called upon in this form of decision making under certainty.

How might VMF damage result in such strategy differences? Two possibilities are suggested by separate lines of evidence: first, that such damage leads to difficulties in managing information in minimally structured environments, similar to the impairments seen in ill-defined problem-solving tasks following frontal injury. Second, (perhaps relatedly) that it results in an impaired ability to determine the relative value of alternatives.

There is clearly an overlap between the ‘pre-decisional’, information acquisition phase of multi-attribute decision making and some forms of problem solving, both conceptually and in their experimental instantiations. Indeed, the process-oriented approach to studying decision making used here was originally adapted from studies of problem solving (Newell and Simon, 1972). Broadly similar methods have previously been used to study the effects of frontal lobe damage on ill-structured problem solving. One such study relied on a detailed analysis of ‘think aloud’ data generated while subjects performed a hypothetical financial planning task (Goel et al., 1997). Although frontal patients were comparable with healthy controls on a number of process measures, they had more difficulty structuring the multi-part problem, and made poor judgements about how satisfactory their proposed solutions were. Subjects with frontal damage tended to stop before they had developed an objectively adequate plan. Similar difficulties have been noted following frontal lobe damage in another ill-structured, real-world task, the Multiple Errand test of Shallice and Burgess (1991). Such studies have typically not examined D/LF and VMF damage separately, although case reports suggest that (relatively) isolated right D/LF/frontopolar (Goel and Grafman, 2000), or right orbitofrontal cortex damage (Satish et al., 1999), can be sufficient to cause difficulties in ill-structured problem-solving tasks.

It may be that the performance of VMF subjects in the present study is simply an expression of similar difficulties in the context of a decision-making task. However, there are several reasons to think that there is a more specific basis for these findings. VMF subjects pursued a consistent strategy (albeit one that differed from the other groups), spent as much time and gathered as much information as other participants. Further, the demands of the multi-attribute decision tasks administered here differed in important ways from those of the ill-defined problem-solving tasks reviewed above: the decision tasks were brief, the possible behaviours were simple and relatively constrained and information was supplied in a form intended to minimize working memory requirements. These factors may explain why subjects with D/LF damage performed normally in the present experiment, in contrast to the reported effects of such damage on ill-structured problem solving. They also make it less likely that the performance of the VMF group can be explained by poor planning or disorganization, at least in the most general senses of these terms.

Multi-attribute decision tasks share with ill-structured problem solving the requirement to flexibly develop a strategic, orderly sequence of behaviours in a setting in which the end-state is not made explicit. Participants were instructed to make a choice, but had to specify for themselves their decision-making goal, and judge when it had been met. There is growing evidence that VMF damage impairs the ability to evaluate options. One possibility is that an underlying deficit in the ability to compare the relative value of options led the subjects studied here to adopt qualitatively different decision-making goals.

Decision making in relatively open-ended contexts can be viewed in terms of a fundamental dichotomy between two possible decision-making goals: seeking the best alternative (maximizing) or seeking an acceptable alternative (satisficing). These two goals typically involve different information search strategies. Empirical data suggest that maximizing can be carried out with a variety of search strategies, either alternative- or attribute-based, whereas satisficing typically involves alternative-based information acquisition, the pattern followed by the VMF group in the present study.

Interestingly, studies of normal individuals suggest that maximizing decision strategies are often adopted to avoid the emotional state of regret. That is, individuals are motivated to continue to search the decision space after acceptable, or even excellent, options are identified, to avoid the possibility of missing even better choices (Schwartz et al., 2002; Schwartz, 2004).

It is tempting to speculate that the alternative-based information acquisition of VMF subjects in the present study may reflect a tendency to satisfice, to judge an alternative in absolute terms as ‘good enough’ rather than ‘seeking the best’. This possibility requires further study, but two existing lines of evidence are at least consistent with this hypothesis. First, recent work using a simple gambling paradigm found that subjects with VMF damage felt less regret after making what turned out to be a sub-optimal choice (Camille et al., 2004). A diminished capacity to experience regret might make such patients less inclined to adopt a maximizing strategy.

Second, a variety of lines of evidence suggest that regions within VMF are involved in flexibly representing the context-specific value of stimuli more generally. Lesions of OFC in several species, including humans, have been shown to impair performance on tasks that require the flexible updating of stimulus-reinforcement associations, such as reversal learn-ing (Jones and Mishkin, 1972; Dias et al., 1996; Schoenbaum et al., 2002; Fellows and Farah, 2003; Clark et al., 2004; Hornak et al., 2004; Izquierdo et al., 2004). Single unit recordings in monkeys have identified OFC neurons that respond selectively to the most highly valued stimulus offered to the animal, and that rapidly cease responding when circumstances change in a way that reduces the value of that stimulus (Rolls, 1999, 2000; Tremblay and Schultz, 1999; Wallis and Miller, 2003). The somatic marker hypothesis, a prominent theory of the role of VMF in risky decision making (Bechara et al., 1997), is also fundamentally consistent with the view that this region is important in tracking the value of options, at least in certain circumstances.

There is some evidence that VMF is involved even in simple evaluative judgements under conditions of certainty: VMF lesions in macaques lead to abnormal food preferences (Baylis and Gaffan, 1991), and in humans to inconsistent choices in a simple pairwise preference task (Fellows and Farah, 2004). In addition, functional imaging studies of preference judgements in healthy human subjects have implicated regions of OFC and medial prefrontal cortex in evaluation (Zysset et al., 2002; Arana et al., 2003; Cunningham et al., 2003; Paulus and Frank, 2003; McClure et al., 2004_b_).

If VMF damage leads to difficulty in determining the relative value of simple stimuli, comparing the relative value of multi-attribute choices is presumably even more difficult. Maximizing requires determining the relative value of all options under consideration, whereas satisficing involves judging only whether an option's value meets some minimum standard. As such, the tendency of VMF subjects to acquire information in an alternative-based pattern in the present experiment may reflect a more basic impairment in making determinations of relative value, with a compensatory reliance on a satisficing strategy.

The tasks used here do not provide information about decision-making quality, so no conclusions can be drawn about whether VMF subjects are effective in their alternative-based decision making. Future work examining this question could shed light on whether VMF is implicated in judgements of both absolute (good–bad) and relative (better–worse) value, or is important only in the latter.

In summary, this process-oriented study found that VMF damage systematically affected information acquisition in multi-attribute decision-making problems. These findings argue that such damage can fundamentally affect how decisions are made, even in the absence of uncertainty, risk or the need to consider future outcomes. Process-oriented approaches to the study of decision making would seem to hold promise for understanding the intersection between strategic, ‘pre-decisional’ information acquisition and decision making per se. This study suggests that the VMFs play an important role at that intersection.

The author wishes to thank Hilary Gerstein and Alisa Padon for technical assistance, Marianna Stark for her help in patient recruitment, and Martha Farah and Barry Schwartz for helpful discussion. This work was supported by NIH R21 NS045074, CIHR MOP-77583, and by a CIHR Clinician-Scientist award.

References

Ackerly S. Prefrontal lobes and social development. 1950.

Yale J Biol Med

2000

;

73

:

211

–9.

Arana FS, Parkinson JA, Hinton E, Holland AJ, Owen AM, Roberts AC. Dissociable contributions of the human amygdala and orbitofrontal cortex to incentive motivation and goal selection.

J Neurosci

2003

;

23

:

9632

–8.

Baylis LL, Gaffan D. Amygdalectomy and ventromedial prefrontal ablation produce similar deficits in food choice and in simple object discrimination learning for an unseen reward.

Exp Brain Res

1991

;

86

:

617

–22.

Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to future consequences following damage to human prefrontal cortex.

Cognition

1994

;

50

:

7

–15.

Bechara A, Damasio H, Tranel D, Damasio AR. Deciding advantageously before knowing the advantageous strategy.

Science

1997

;

275

:

1293

–5.

Bechara A, Damasio H, Tranel D, Damasio AR. The Iowa Gambling Task and the somatic marker hypothesis: some questions and answers.

Trends Cogn Sci

2005

;

9

:

159

–62; discussion

162–4

.

Berlin HA, Rolls ET, Kischka U. Impulsivity, time perception, emotion and reinforcement sensitivity in patients with orbitofrontal cortex lesions.

Brain

2004

;

127

:

1108

–26.

Camille N, Coricelli G, Sallet J, Pradat-Diehl P, Duhamel JR, Sirigu A. The involvement of the orbitofrontal cortex in the experience of regret.

Science

2004

;

304

:

1167

–70.

Chu PC, Spires EE, Sueyoshi T. Cross-cultural differences in choice behavior and use of decision aids: a comparison of Japan and the United States.

Organ Behav Hum Decis Process

1999

;

77

:

147

–70.

Clark L, Cools R, Robbins TW. The neuropsychology of ventral prefrontal cortex: decision-making and reversal learning.

Brain Cogn

2004

;

55

:

41

–53.

Critchley HD, Mathias CJ, Dolan RJ. Neural activity in the human brain relating to uncertainty and arousal during anticipation.

Neuron

2001

;

29

:

537

–45.

Cunningham WA, Johnson MK, Gatenby JC, Gore JC, Banaji MR. Neural components of social evaluation.

J Pers Soc Psychol

2003

;

85

:

639

–49.

Dias R, Robbins TW, Roberts AC. Dissociation in prefrontal cortex of affective and attentional shifts.

Nature

1996

;

380

:

69

–72.

Ernst M, Bolla K, Mouratidis M, Contoreggi C, Matochik JA, Kurian V, et al. Decision-making in a risk-taking task: a PET study.

Neuropsychopharmacology

2002

;

26

:

682

–91.

Eslinger PJ, Damasio AR. Severe disturbance of higher cognition after bilateral frontal lobe ablation: patient EVR.

Neurology

1985

;

35

:

1731

–41.

Evenden JL. Varieties of impulsivity.

Psychopharmacology (Berl)

1999

;

146

:

348

–61.

Fellows LK. The cognitive neuroscience of decision making: a review and conceptual framework.

Behav Cog Neurosci Rev

2004

;

3

:

159

–72.

Fellows LK, Farah MJ. Ventromedial frontal cortex mediates affective shifting in humans: evidence from a reversal learning paradigm.

Brain

2003

;

126

:

1830

–7.

Fellows LK, Farah MJ. Damage to ventromedial prefrontal cortex impairs simple preference judgments in humans. San Diego, CA: Society for Neuroscience;,

2004

.

Fellows LK, Farah MJ. Different underlying impairments in decision-making following ventromedial and dorsolateral frontal lobe damage in humans.

Cereb Cortex

2005

;

15

:

58

–63.

Folstein MF, Robins LN, Helzer JE. The mini-mental state examination.

Arch Gen Psychiatry

1983

;

40

:

812

.

Ford JK, Schmitt N, Schechtman SL, Hults BM, Doherty ML. Process tracing methods: contributions, problems, and neglected research questions.

Org Behav Hum Decision Proc

1989

;

43

:

75

–117.

Gehring WJ, Willoughby AR. The medial frontal cortex and the rapid processing of monetary gains and losses.

Science

2002

;

295

:

2279

–82.

Gigerenzer G, Todd PM. Simple heuristics that make us smart. New York: Oxford University Press,

1999

.

Goel V, Grafman J. Role of the right prefrontal cortex in ill-structured planning.

Cogn Neuropsychol

2000

;

17

:

415

–36.

Goel V, Grafman J, Tajik J, Gana S, Danto D. A study of the performance of patients with frontal lobe lesions in a financial planning task.

Brain

1997

;

120

:

1805

–22.

Harlow JM. Passage of an iron rod through the head.

J Neuropsychiatry Clin Neurosci

1999

;

11

:

281

–3.

Hornak J, O'Doherty J, Bramham J, Rolls ET, Morris RG, Bullock PR, et al. Reward-related reversal learning after surgical excisions in orbito-frontal or dorsolateral prefrontal cortex in humans.

J Cogn Neurosci

2004

;

16

:

463

–78.

Izquierdo A, Suda RK, Murray EA. Bilateral orbital prefrontal cortex lesions in rhesus monkeys disrupt choices guided by both reward value and reward contingency.

J Neurosci

2004

;

24

:

7540

–8.

Jones B, Mishkin M. Limbic lesions and the problem of stimulus—reinforcement associations.

Exp Neurol

1972

;

36

:

362

–77.

Kerstholt JH. Information search and choice accuracy as a function of task complexity and task structure.

Acta Psychol

1992

;

80

:

185

–97.

Krawczyk DC. Contributions of the prefrontal cortex to the neural basis of human decision making.

Neurosci Biobehav Rev

2002

;

26

:

631

–64.

Maia TV, McClelland JL. A reexamination of the evidence for the somatic marker hypothesis: what participants really know in the Iowa gambling task.

Proc Natl Acad Sci USA

2004

;

101

:

16075

–80.

Maia TV, McClelland JL. The somatic marker hypothesis: still many questions but no answers.

Trends Cogn Sci

2005

;

9

:

162

–4.

Manes F, Sahakian B, Clark L, Rogers R, Antoun N, Aitken M, et al. Decision-making processes following damage to the prefrontal cortex.

Brain

2002

;

125

:

624

–39.

McClure SM, Laibson DI, Loewenstein G, Cohen JD. Separate neural systems value immediate and delayed monetary rewards.

Science

2004

;

306

:

503

–7.

McClure SM, Li J, Tomlin D, Cypert KS, Montague LM, Montague PR. Neural correlates of behavioral preference for culturally familiar drinks.

Neuron

2004

;

44

:

379

–87.

Newell A, Simon HA. Human problem solving. Englewood Cliffs, NJ: Prentice-Hall,

1972

.

Paulus MP, Frank LR. Ventromedial prefrontal cortex activation is critical for preference judgments.

Neuroreport

2003

;

14

:

1311

–5.

Payne JW. Task complexity and contingent processing in decision making: An information search and protocol analysis.

Organ Behav Hum Perform

1976

;

16

:

366

–87.

Payne JW, Bettman JR, Johnson EJ. The adaptive decision maker. New York: Cambridge University Press,

1993

.

Payne JW, Bettman JR, Coupey E, Johnson EJ. A constructive process view of decision making: multiple strategies in judgment and choice.

Acta Psychol

1992

;

80

:

107

–41.

Rogers RD, Everitt BJ, Baldacchino A, Blackshaw AJ, Swainson R, Wynne K, et al. Dissociable deficits in the decision-making cognition of chronic amphetamine abusers, opiate abusers, patients with focal damage to prefrontal cortex, and tryptophan-depleted normal volunteers: evidence for monoaminergic mechanisms.

Neuropsychopharmacology

1999

;

20

:

322

–39.

Rolls ET. The brain and emotion. Oxford: Oxford University Press,

1999

.

Rolls ET. The orbitofrontal cortex and reward.

Cereb Cortex

2000

;

10

:

284

–94.

Rorden C, Brett M. Stereotaxic display of brain lesions.

Behav Neurol

2000

;

12

:

191

–200.

Sanfey AG, Hastie R, Colvin MK, Grafman J. Phineas gauged: decision-making and the human prefrontal cortex.

Neuropsychologia

2003

;

41

:

1218

–29.

Satish U, Streufert S, Eslinger PJ. Complex decision making after orbitofrontal damage: neuropsychological and strategic management simulation assessment.

Neurocase

1999

;

5

:

355

–64.

Schoenbaum G, Nugent SL, Saddoris MP, Setlow B. Orbitofrontal lesions in rats impair reversal but not acquisition of go, no-go odor discriminations.

Neuroreport

2002

;

13

:

885

–90.

Schwartz B. The tyranny of choice.

Sci Am

2004

;

290

:

70

–5.

Schwartz B, Ward A, Monterosso J, Lyubomirsky S, White K, Lehman DR. Maximizing versus satisficing: happiness is a matter of choice.

J Pers Soc Psychol

2002

;

83

:

1178

–97.

Shallice T, Burgess PW. Deficits in strategy application following frontal lobe damage in man.

Brain

1991

;

114

:

727

–41.

Simon D, Krawczyk DC, Holyoak KJ. Construction of preferences by constraint satisfaction.

Psychol Sci

2004

;

15

:

331

–6.

Simon HA. A behavioral model of rational choice.

Q J Econ

1955

;

69

:

99

–118.

Stuss DT, Levine B. Adult clinical neuropsychology: lessons from studies of the frontal lobes.

Annu Rev Psychol

2002

;

53

:

401

–33.

Sugrue LP, Corrado GS, Newsome WT. Choosing the greater of two goods: neural currencies for valuation and decision making.

Nat Rev Neurosci

2005

;

6

:

363

–75.

Tanaka SC, Doya K, Okada G, Ueda K, Okamoto Y, Yamawaki S. Prediction of immediate and future rewards differentially recruits cortico-basal ganglia loops.

Nat Neurosci

2004

;

7

:

887

–93.

Tomb I, Hauser M, Deldin P, Caramazza A. Do somatic markers mediate decisions on the gambling task?

Nat Neurosci

2002

;

5

:

1103

–4; author reply

1104

.

Tremblay L, Schultz W. Relative reward preference in primate orbitofrontal cortex.

Nature

1999

;

398

:

704

–8.

Wallis JD, Miller EK. Neuronal activity in primate dorsolateral and orbital prefrontal cortex during performance of a reward preference task.

Eur J Neurosci

2003

;

18

:

2069

–81.

Zysset S, Huber O, Ferstl E, von Cramon DY. The anterior frontomedian cortex and evaluative judgment: an fMRI study.

Neuroimage

2002

;

15

:

983

–91.

© The Author (2006). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org