Imagine you are at a restaurant and the waiter offers you a choice of cheesecake or fruit salad for dessert. When making your choice it is likely that you will consider the features of these desserts, such as their taste, their sweetness or how healthy they are. However, when you decide which dessert to have, you will pick the one that you judge to have the highest value for you at that moment in time. In this sense, ‘value’ is a subjective concept that varies from person to person, while ‘features’ remain relatively static.

It is generally agreed that the orbitofrontal cortex (OFC) is involved in making these sorts of decisions, but its role is still a topic of debate. According to one theory the neurons in the OFC signal the subjective value of an outcome, whereas a rival theory suggests that they signal the features of the expected outcome. However, it has proved challenging to test these theories in experiments because it is difficult to say for certain that a given decision was clearly due to the value or a feature.

Now, McDannald et al. have devised an approach that can tell the difference between neurons signaling value and neurons signaling features. They trained thirsty rats to associate different odours with either an increase in the amount of milk they were given (a change in both value and a feature), or a change in the flavor of the milk (a change in a feature without a change in value). Extensive testing showed that the rats did not value one flavor over the other.

McDannald et al. then examined how the neurons in the OFC responded. If these neurons signal only value, they should only fire when the value of the outcome changes. On the other hand, if they signal features, they should fire when a feature changes, even if the value does not. It turned out that the neurons in the OFC responded whenever the features changed, irrespective of whether or not the value changed. These findings present a challenge to popular conceptions of the role of the neurons in the OFC.

The orbitofrontal cortex (OFC) is often described as signaling either an outcome expectancy, implying a knowledge of the features of the impending outcome (Schoenbaum et al., 1998; Delamater, 2007; Ostlund and Balleine, 2007; Steiner and Redish, 2012; Luk and Wallis, 2013), or a value that exists independent of those features (Padoa-Schioppa, 2011; Levy and Glimcher, 2012). Support for such pure or abstract value encoding comes largely from reports that single unit activity and the blood-oxygen level dependent (BOLD) response in the OFC tracks value, independent of outcome features such as identity or location or even the response required to obtain the outcome (Padoa-Schioppa and Assad, 2006; Plassmann et al., 2007; Levy and Glimcher, 2011).

Yet in some cases, outcome features could still be the underlying basis of apparent abstract value signals. This is conceivable even if the signal correlates with value across different outcomes, since some features or feature combinations might vary with value across the limited number of outcomes used in any particular session (but see Padoa-Schioppa and Assad, 2006, supplemental, and our correction notice). Further, any neural element (voxel or single unit) may participate in ensembles responding to more than one feature, so it is also possible that a particular element that appears not to distinguish specific features of different outcomes is in fact coding independent features that co-vary with each outcome's value.

So how can we address whether the OFC signals features of impending outcomes vs value independent of those features? One way is to strip away or ‘block’ the value portion of the outcome during learning, while leaving unblocked—free to enter into associations—the outcome's unique sensory and other features. This can be done by pairing a ‘target’ cue with a rewarding outcome in the presence of a cue that has been previously trained to predict a differently-flavored, but similarly-valued outcome. When this is done, the previously conditioned cue predicts the general value that is common to the two outcomes, but does not predict the unique features that distinguish the new outcome (note features are not limited to sensory properties, but might include the outcome timing, location, temperature, size, number, etc). As a result, the target cue acquires associations with the unique features of the new outcome but not with its general or common currency value (Rescorla, 1999; Burke et al., 2008). If OFC neurons represent only a general or common currency value, divorced from features, then they should respond no more to such a target cue than to a completely blocked cue (Kamin, 1969). However, if OFC neurons represent outcome features, independent of value, then they should respond to the target cue just as they do to a cue that has been explicitly unblocked by increasing the amount of the outcome delivered. Indeed, both pure value and outcome expectancy accounts of OFC function would predict neural activity to such an unblocked cue, but only an outcome expectancy account predicts encoding of the target cue signaling a valueless change in outcome flavor.

We recorded single–unit activity in the OFC in six rats during an odor-based unblocking task (Figure 1A). Prior to implantation with electrodes rats were trained to sample an odor in a central port following house light illumination and then respond to a reward well below for two drops of Nestlé's flavored milk (chocolate or vanilla, counterbalanced). This training was meant to establish the initial odor as a reliable predictor of a specific flavor and number of drops of milk. Each rat had extensive experience with both flavors, thus neither flavor was novel. Following initial training rats were implanted with microelectrodes in the OFC. When recovered from surgery, rats were retrained on the initial odor; after retraining, each rat underwent 7–9 rounds of unblocking.

Figure 1 with 1 supplement see all

Download asset Open asset

Each round of unblocking began with 2 days of training and consisted of four trial types. One type was a reminder; the initially trained odor was followed by the expected outcome. On the other three trial types (blocked, number, flavor), rats were presented with the initially trained odor, followed immediately by one of three novel odors. On ‘blocked’ trials, the novel odor was followed by the expected two drops of the same flavor used in initial training. This outcome is fully predicted by the initial odor, thus the novel odor should be blocked from acquiring associative significance (Kamin, 1969). On ‘flavor’ trials, the novel odor was followed by two drops of the flavor not used in initial training (i.e., chocolate or vanilla). Here the value is unchanged, since the two flavors are equally preferred and the same amount is delivered, but the features of the outcome are different. Thus the novel target odor should enter into associations with the unique features of the outcome (Rescorla, 1999; Burke et al., 2008). On ‘number’ trials, the novel odor was followed by an additional drop of the flavor used in the initial training. Since the initial odor does not predict anything after the second drop, the novel odor should enter into associations with both the features and the additional value of the outcome presented in the third drop (Holland, 1984).

To ensure that learning in the flavor condition did not result from an explicit shift in value, two types of preference tests were administered in conjunction with the unblocking training procedures. In one test, given on days separate from unblocking, preference for each flavor over water was assessed. Both flavors were highly preferred to water (Figure 1B), demonstrating both are highly palatable. The more important preference test came just following unblocking sessions, from which the critical neural data came. In these tests, the two milk flavors were pitted directly against one another. This test is critical to demonstrate that specific satiety to one flavor did not develop over the course of the unblocking session, a finding that would strongly suggest different valuation of the two flavors. Indeed, we found no evidence of a preference in these tests (Figure 1C). In both types of tests the locations of the bottles were swapped every 20–30 s, meaning that rats were required to switch locations if the solution did in fact differ in value. This pattern was present when either milk flavor was compared to water but was absent when the two flavors were directly compared. The consumption data demonstrate that both flavors were highly palatable yet of equivalent value. Finally, to ensure the two flavors were discriminable we subjected another set of rats to a selective conditioned flavor aversion procedure. After initial exposure to both milk flavors, consumption of one flavor (fully counterbalanced) was devalued by pairing with LiCl-induced nausea while consumption of the other was paired with saline injection that is of minimal consequence. At no point did rats show a preference for the chocolate or vanilla flavor but all rats selectively reduced consumption of the devalued flavor. This was apparent both in conditioning (Figure 1—figure supplement 1) and in the final choice test (Figure 1D). Thus extensive consumption testing indicated that the flavors used were of equivalent value but readily discriminable.

In the unblocking sessions rats were sensitive to presentation of the novel odors, exhibiting longer latencies to respond at the reward well following odor sampling on these three trial types. Longer latencies to the novel odors were most apparent on the very first trial of each session, particularly on day 1. In support, ANOVA revealed a main effect of trial (F_1,47 > 2, p's < 0.01) and a trial × day interaction (F_1,47 = 34.73, p < 0.01). However the rats also learned that the two novel odors that predicted changes in the outcome were meaningful. This was evident in the extinction probe test in which they initially spent more time in the fluid well following sampling of the flavor and number odors than following the blocked odor (Figure 1E).

We recorded 240 single units during the first day of unblocking and 220 units on the second unblocking day in 48 rounds of training across all six rats (Figure 1F). To address our hypothesis, units from both unblocking days were screened for phasic responses to one of the four odors using a t test, which compared firing rates during the ITI and novel odor period (significance level = p < 0.0125; Bonferroni correction). This screen found 135 units (Day 1 = 79, Day 2 = 56) that showed a significant increase in firing to at least one of the odor cues. The majority–98/135 or 73% of the neurons within this population–exhibited activity that fell into one of two categories (see Figure 2—figure supplement 1 for analysis of other neurons). We will consider each category in turn below.

The first major category, not directly anticipated by our hypothesis, consisted of neurons (55/135, Figure 2A) that showed a significant phasic response to each of the four odor cues. Since these neurons fired to all of the odors, even the blocked odor, their firing cannot be easily explained as signaling information about the predicted outcomes. However they might be signaling information about the cues themselves, such as their shared sensory features or intrinsic salience. Unlike shared sensory features, salience should be higher for the novel cues than for the pre-trained, initial odor in our design, and this pattern should be most noticeable early in training when the novel cues were first presented. As a population, these neurons did show greater activity to the blocked, number and flavor odors than to the more familiar, initial odor (Figure 2B). This effect was present despite the fact that we did not select based on this criterion. Further analyses of the individual units showed that many exhibited significantly higher firing to the novel odors than to the initial odor (Figure 2C), and few exhibited differences in firing among the three novel odors (Figure 2D,E). Moreover, when we examined the firing of neurons in this population on the first 10 trials of unblocking (31/79 odor-responsive neurons; Figure 2—figure supplement 2), analyzing the difference in firing between the novel and initial odor cues in a sliding, 300-ms window across each trial, we found that activity in this population was maximal at the onset of the novel odors and on the first exposure and then declined rapidly on subsequent trials (Figure 2F). This same pattern held when each novel odor was analyzed separately (Figure 2—figure supplement 3). Further, this pattern was only seen on the first day of unblocking (Figure 2—figure supplement 4). This pattern of activity is consistent with signaling of the salience of these cues.

Figure 2 with 4 supplements see all

Download asset Open asset

The second major category, of greater relevance to our hypothesis, consisted of neurons (43/135, Figure 3A–C) that showed a significant phasic response the flavor and/or number odors (but did not fire to all four odors). Activity across this population was greater in response to the two predictive odor cues than to either the blocked or initial odors (Figure 3D), and an analysis of individual units showed that nearly all of these neurons (38/43) fired more to the predictive odors than to the blocked one (Figure 3E). This result marks these neurons as candidates for encoding of associative information or meaning, since this is the primary feature that distinguishes the two odor cues from the blocked odor.

Figure 3 with 1 supplement see all

Download asset Open asset

Interestingly, these neurons did not appear to distinguish, at least as a population, between the flavor and number odors. For example, they showed similar levels of activity in response to both the flavor and the number odor (Figure 3D,F), and when we examined the firing of neurons in this population on the first 10 trials of unblocking (25/79 odor-responsive neurons, Figure 3—figure supplement 1), we found that differential firing to each cue developed at a similar rate during training (Figure 3G–J). The acquisition of differential firing to the flavor and number odors demonstrates that selective odor encoding was not driven by physical properties of the odors. If neurons were encoding the odor itself, independent of its outcome signaling, this would have been apparent on the very first trial. Thus, as a population, these neurons responded more strongly to the unblocked ‘flavor’ and ‘number’ odors than to the ‘blocked’ odor. Even more striking, the population responded similarly to a cue signaling a valueless change in the outcome flavor as they did to a cue that signaling that more of the outcome would be delivered.

Similar numbers of neurons fired to the flavor and number cues (flavor: 31, number: 37; χ² = 0.3, p = 0.47). In our design, firing to the flavor cue cannot be readily explained as signaling general or common value. Thus these data confirm that many OFC neurons signal associative meaning independent of at least a general value. In support of this, odor firing in the flavor population, as well as the number population, was positively correlated with firing to the actual outcome delivered on each of these trials (Figure 4). This relationship supports the idea that cue-evoked activity in the flavor population is signaling features of the new outcome.

Figure 4

Download asset Open asset

Neural signals in the OFC are often described as representing either outcome expectancies or abstract value. Although many studies have argued for one or the other, few have used behavioral designs that clearly dissociate predictions of these two hypotheses. Here we tried to address this question by using an unblocking procedure to strip away or ‘block’ the abstract value of the outcome during learning, while leaving unblocked—free to enter into associations—the outcome's sensory and other unique features. This approach revealed two distinct populations of OFC neurons.

One population consisted of neurons that fired immediately on initial presentation of all three target cues, perhaps reflecting these cues' novelty or salience. While unexpected and not directly relevant to the question motivating this study, this finding is consistent with reports of neural correlates of salience in OFC (Kahnt and Tobler, 2013; Ogawa et al., 2013) and with studies implicating the OFC in phenomena such as latent inhibition, set formation, and even auto-shaping (Chudasama et al., 2003; Schiller and Weiner, 2004; Chase et al., 2012) which depend in part on the appropriate attribution of salience to cues. Together these results point to a largely unappreciated role for this area in the modulation of attention for the purposes of learning (Esber et al., 2012). Of course novelty is just one instance of ‘salience’. Modern learning theories describe salience as a function of both intrinsic and acquired properties. Within this framework, this population was correlated with intrinsic salience.

The second population, of more direct relevance to our hypothesis, consisted of neurons that fired preferentially to the target cues that predicted changes in the outcome flavor. Nearly all of these neurons fired more to these cues than to the similarly trained but fully blocked cue, and this activity was acquired with learning, a pattern consistent with signaling of the associative significance or meaning of these cues (or possibly their acquired salience). Importantly, the acquired neural activity was observed to both the explicitly unblocked ‘number’ cue as well as to the ‘flavor’ cue, which was unblocked by shifting the features of the expected outcome while holding the value constant. This was accomplished by using two differently-flavored but similarly-preferred outcomes (Figure 1C,D). The lack of any flavor preference makes it unlikely that the cue added prior to this manipulation acquired what might be termed a general or cached value. Indeed, in prior work we have found that responding to this target cue in the probe test is dissociable from even the smallest animal-by-animal differences in conditioned or unconditioned responding to the two outcomes used (Schoenbaum et al., 2011), indicating that it is not driven by any sort of shift in value that might accrue to the cue.

Instead conditioned responding to a target cue unblocked by shifting the identity of the outcome seems to be particularly dependent upon the unexpected outcome's unique features, at least in comparison to an explicitly unblocked cue. As evidence of this, it has been shown that cues trained in this manner support behavior that is more sensitive to (in fact completely dependent upon) the features of the predicted outcome—or value inferred through those features—than similar behaviors supported by cues directly paired with reward in isolation. For example, conditioned reinforcement supported by a normally trained cue is insensitive to devaluation of the predicted outcome (Parkinson et al., 2005); however if the cue is trained like the ‘flavor’ cue in the current experiment, then devaluation of the predicted outcome completely abolishes the ability of the cue to serve as a conditioned reinforcer (Burke et al., 2008). This result indicates that a cue trained in this manner has little or no intrinsic, cached or acquired value except what can be inferred through knowledge of the features of the outcome. This cue's special link to the sensory features of the outcome is also apparent in that such cues retain the ability to support Pavlovian-to-instrumental transfer when that transfer is specific to the outcome (Rescorla, 1999). Interestingly outcome-specific transfer is both insensitive to devaluation (Holland, 2004) and disrupted by OFC lesions (Ostlund and Balleine, 2007), results which are difficult to reconcile with the view that the OFC is directly involved in the representation of value.

That OFC neurons developed robust responses to such a valueless cue indicates that many OFC neurons—more than half of the population responsive to the acquired significance of the cues—represent associative features that must be, strictly speaking, independent of general or common value. Notably this population included neurons that fired only to the flavor cue as well as neurons that participated in both the flavor and number ensembles. Such dual encoding would be expected if, as suggested earlier, individual neurons are not labeled lines but participate in ensembles coding more than one outcome feature (flavor, number, temperature, location, timing, etc).

Of course some neurons fired preferentially to the valued, number cue. The firing of these neurons could reflect the general value that accrued to this cue during training. However such firing could equally well reflect associations with features of the additional outcome delivered on these trials, known to develop when additional rewards are delivered during unblocking (Holland, 1984). While it is impossible to say for sure, the similarities in the overall level of neural activity to the flavor and number cues, their similar rates of development with learning, and the finding that neurons do develop activity to a valueless cue make the latter explanation the most parsimonious.

If OFC neurons signal associations between cues and specific outcome features, this would accord well with results showing that the OFC is not necessary when behavior—or learning—can be accomplished using general value alone. For example, the OFC is not required for simple Pavlovian or instrumental conditioning (Gallagher et al., 1999; Izquierdo et al., 2004; Ostlund and Balleine, 2007; Gremel and Costa, 2013) discrimination learning (Schoenbaum et al., 2002; Izquierdo et al., 2004; McDannald et al., 2005; Walton et al., 2010), extinction by reward omission (Takahashi et al., 2009), transfer (Ostlund and Balleine, 2007), and even perhaps reversal learning (Rudebeck et al., 2013), all of which can be accomplished without reference to specific information about predicted outcomes. Similarly both blocking and unblocking—when it can be accounted for by value—do not require the OFC (Burke et al., 2008; McDannald et al., 2011). While the preserved function in these studies could reflect compensation by other areas, it must at least call into question the idea that OFC, writ large, is fundamental to all behavior that reflects value, instead highlighting suggestions that common value representation may at least be limited to the medial subregion (Noonan et al., 2010, 2012). Indeed recent work in humans has shown that OFC represents specific outcome features and that more lateral orbital areas represent those outcomes in a way that is dependent upon prior cues (Klein-Flugge et al., 2013). Thus far from signaling general value about outcomes without regard to their features and attendant events, this work shows that the OFC maintains highly specific representations.

Such highly specific representations are consistent with observations that the OFC is necessary for superficially similar behaviors (Pavlovian or instrumental responding, discriminations, even learning) when they require knowledge of the outcome features in order to recognize errors or to derive or infer a value (Gallagher et al., 1999; Izquierdo et al., 2004; Ostlund and Balleine, 2007; McDannald et al., 2011; Gremel and Costa, 2013). This is even true in the current paradigm, where we have shown that the OFC is required for the development of conditioned responding to the target cue paired with a shift in outcome identity but not to the target cue paired with additional outcome (McDannald et al., 2011). Our present finding of robust encoding of a valueless Pavlovian cue that exceeds that of a blocked cue, and is equivalent to a cue paired with additional outcome, provides further support for outcome expectancy theories of OFC function.

1. 1. Burke KA
   2. Franz TM
   3. Miller DN
   4. Schoenbaum G

   (2008) The role of the orbitofrontal cortex in the pursuit of happiness and more specific rewards

   Nature 454:340–344.

   https://doi.org/10.1038/nature06993

   • Google Scholar
2. 1. Chase EA
   2. Tait DS
   3. Brown VJ

   (2012) Lesions of the orbital prefrontal cortex impair the formation of attentional set in rats

   The European Journal of Neuroscience 36:2368–2375.

   https://doi.org/10.1111/j.1460-9568.2012.08141.x

   • Google Scholar
3. 1. Chudasama Y
   2. Passetti F
   3. Rhodes SE
   4. Lopian D
   5. Desai A
   6. Robbins TW

   (2003) Dissociable aspects of performance on the 5-choice serial reaction time task following lesions of the dorsal anterior cingulate, infralimbic and orbitofrontal cortex in the rat: differential effects on selectivity, impulsivity and compulsivity

   Behavioural Brain Research 146:105–119.

   https://doi.org/10.1016/j.bbr.2003.09.020

   • Google Scholar
4. 1. Delamater AR

   (2007) The role of the orbitofrontal cortex in sensory-specific encoding of associations in pavlovian and instrumental conditioning

   Annals of the New York Academy of Sciences 1121:152–173.

   https://doi.org/10.1196/annals.1401.030

   • Google Scholar
5. 1. Esber GR
   2. Roesch MR
   3. Bali S
   4. Trageser J
   5. Bissonette GB
   6. Puche AC
   7. Holland PC
   8. Schoenbaum G

   (2012) Attention-related Pearce-Kaye-Hall signals in basolateral amygdala require the midbrain dopaminergic system

   Biological Psychiatry 72:1012–1019.

   https://doi.org/10.1016/j.biopsych.2012.05.023

   • Google Scholar
6. 1. Gallagher M
   2. McMahan RW
   3. Schoenbaum G

   (1999)

   Orbitofrontal cortex and representation of incentive value in associative learning

   The Journal of Neuroscience 19:6610–6614.

   • Google Scholar
7. 1. Gremel CM
   2. Costa RM

   (2013) Orbitofrontal and striatal circuits dynamically encode the shift between goal-directed and habitual actions

   Nature Communications 4:2264.

   https://doi.org/10.1038/ncomms3264

   • Google Scholar
8. 1. Holland PC

   (1984) Unblocking in Pavlovian appetitive conditioning

   Journal of Experimental Psychology: Animal Behavior Processes 10:476–497.

   https://doi.org/10.1037/0097-7403.10.4.476

   • Google Scholar
9. 1. Holland PC

   (2004) Relations between Pavlovian-instrumental transfer and reinforcer devaluation

   Journal of Experimental Psychology Animal Behavior Processes 30:104–117.

   https://doi.org/10.1037/0097-7403.30.2.104

   • Google Scholar
10. 1. Izquierdo A
    2. Suda RK
    3. Murray EA

    (2004) Bilateral orbital prefrontal cortex lesions in rhesus monkeys disrupt choices guided by both reward value and reward contingency

    Journal of Neuroscience 24:7540–7548.

    https://doi.org/10.1523/JNEUROSCI.1921-04.2004

    • Google Scholar
11. 1. Kahnt T
    2. Tobler PN

    (2013) Salience signals in the right temporoparietal junction facilitate value-based decisions

    The Journal of Neuroscience 33:863–869.

    https://doi.org/10.1523/JNEUROSCI.3531-12.2013

    • Google Scholar
12. Book

    1. Kamin LJ

    (1969)

    Predictability, surprise, attention, and conditioning

    In: Campbell BA, Church RM, editors. Punishment and aversive behavior. New York: Appleton-Century-Crofts. pp. 279–296.

    • Google Scholar
13. 1. Klein-Flugge MC
    2. Barron HC
    3. Brodersen KH
    4. Dolan RJ
    5. Behrens TE

    (2013) Segregated encoding of reward-identity and stimulus-reward associations in human orbitofrontal cortex

    Journal of Neuroscience 33:3202–3211.

    https://doi.org/10.1523/JNEUROSCI.2532-12.2013

    • Google Scholar
14. 1. Levy DJ
    2. Glimcher PW

    (2011) Comparing apples and oranges: using reward-specific and reward-general subjective value representation in the brain

    Journal of Neuroscience 31:14693–14707.

    https://doi.org/10.1523/JNEUROSCI.2218-11.2011

    • Google Scholar
15. 1. Levy DJ
    2. Glimcher PW

    (2012) The root of all value: a neural common currency for choice

    Current Opinion in Neurobiology 22:1027–1038.

    https://doi.org/10.1016/j.conb.2012.06.001

    • Google Scholar
16. 1. Luk CH
    2. Wallis JD

    (2013) Choice coding in frontal cortex during stimulus-guided or action-guided decision-making

    The Journal of Neuroscience 33:1864–1871.

    https://doi.org/10.1523/JNEUROSCI.4920-12.2013

    • Google Scholar
17. 1. McDannald MA
    2. Saddoris MP
    3. Gallagher M
    4. Holland PC

    (2005) Lesions of orbitofrontal cortex impair rats' differential outcome expectancy learning but not conditioned stimulus-potentiated feeding

    Journal of Neuroscience 25:4626–4632.

    https://doi.org/10.1523/JNEUROSCI.5301-04.2005

    • Google Scholar
18. 1. McDannald MA
    2. Lucantonio F
    3. Burke KA
    4. Niv Y
    5. Schoenbaum G

    (2011) Ventral striatum and orbitofrontal cortex are both required for model-based, but not model-free, reinforcement learning

    Journal of Neuroscience 31:2700–2705.

    https://doi.org/10.1523/JNEUROSCI.5499-10.2011

    • Google Scholar
19. 1. Noonan MP
    2. Kolling N
    3. Walton ME
    4. Rushworth MF

    (2012) Re-evaluating the role of the orbitofrontal cortex in reward and reinforcement

    European Journal of Neuroscience 35:997–1010.

    https://doi.org/10.1111/j.1460-9568.2012.08023.x

    • Google Scholar
20. 1. Noonan MP
    2. Walton ME
    3. Behrens TE
    4. Sallet J
    5. Buckley MJ
    6. Rushworth MF

    (2010) Separate value comparison and learning mechanisms in macaque medial and lateral orbitofrontal cortex

    Proceedings of the National Academy of Sciences of USA 107:20547–20552.

    https://doi.org/10.1073/pnas.1012246107

    • Google Scholar
21. 1. Ogawa M
    2. van der Meer MA
    3. Esber GR
    4. Cerri DH
    5. Stalnaker TA
    6. Schoenbaum G

    (2013) Risk-responsive orbitofrontal neurons track acquired salience

    Neuron 77:251–258.

    https://doi.org/10.1016/j.neuron.2012.11.006

    • Google Scholar
22. 1. Ostlund SB
    2. Balleine BW

    (2007) Orbitofrontal cortex mediates outcome encoding in Pavlovian but not instrumental conditioning

    The Journal of Neuroscience 27:4819–4825.

    https://doi.org/10.1523/JNEUROSCI.5443-06.2007

    • Google Scholar
23. 1. Padoa-Schioppa C

    (2011) Neurobiology of economic choice: a good-based model

    Annual Review of Neuroscience 34:333–359.

    https://doi.org/10.1146/annurev-neuro-061010-113648

    • Google Scholar
24. 1. Padoa-Schioppa C
    2. Assad JA

    (2006) Neurons in the orbitofrontal cortex encode economic value

    Nature 441:223–226.

    https://doi.org/10.1038/nature04676

    • Google Scholar
25. 1. Parkinson JA
    2. Roberts AC
    3. Everitt BJ
    4. Di Ciano P

    (2005) Acquisition of instrumental conditioned reinforcement is resistant to the devaluation of the unconditioned stimulus

    The Quarterly Journal of Experimental Psychology B 58:19–30.

    https://doi.org/10.1080/02724990444000023

    • Google Scholar
26. 1. Plassmann H
    2. O'Doherty J
    3. Rangel A

    (2007) Orbitofrontal cortex encodes willingness to pay in everyday economic transactions

    The Journal of Neuroscience 27:9984–9988.

    https://doi.org/10.1523/JNEUROSCI.2131-07.2007

    • Google Scholar
27. 1. Rescorla RA

    (1999) Learning about qualitatively different outcomes during a blocking procedure

    Animal Learning & Behavior 27:140–151.

    https://doi.org/10.3758/BF03199671

    • Google Scholar
28. 1. Rudebeck PH
    2. Saunders RC
    3. Prescott AT
    4. Chau LS
    5. Murray EA

    (2013) Prefrontal mechanisms of behavioral flexibility, emotion regulation and value updating

    Nature Neuroscience 16:1140–1145.

    https://doi.org/10.1038/nn.3440

    • Google Scholar
29. 1. Schiller D
    2. Weiner I

    (2004) Lesions to the basolateral amygdala and the orbitofrontal cortex but not to the medial prefrontal cortex produce an abnormally persistent latent inhibition in rats

    Neuroscience 128:15–25.

    https://doi.org/10.1016/j.neuroscience.2004.06.020

    • Google Scholar
30. 1. Schoenbaum G
    2. Chiba AA
    3. Gallagher M

    (1998) Orbitofrontal cortex and basolateral amygdala encode expected outcomes during learning

    Nature Neuroscience 1:155–159.

    https://doi.org/10.1038/407

    • Google Scholar
31. 1. Schoenbaum G
    2. Nugent SL
    3. Saddoris MP
    4. Setlow B

    (2002) Orbitofrontal lesions in rats impair reversal but not acquisition of go, no-go odor discriminations

    Neuroreport 13:885–890.

    https://doi.org/10.1097/00001756-200205070-00030

    • Google Scholar
32. 1. Schoenbaum G
    2. Takahashi Y
    3. Liu TL
    4. McDannald MA

    (2011) Does the orbitofrontal cortex signal value?

    Annals of the New York Academy of Sciences 1239:87–99.

    https://doi.org/10.1111/j.1749-6632.2011.06210.x

    • Google Scholar
33. 1. Steiner AP
    2. Redish AD

    (2012) The road not taken: neural correlates of decision making in orbitofrontal cortex

    Frontiers in Neuroscience 6:131.

    https://doi.org/10.3389/fnins.2012.00131

    • Google Scholar
34. 1. Takahashi YK
    2. Roesch MR
    3. Stalnaker TA
    4. Haney RZ
    5. Calu DJ
    6. Taylor AR
    7. Burke KA
    8. Schoenbaum G

    (2009) The orbitofrontal cortex and ventral tegmental area are necessary for learning from unexpected outcomes

    Neuron 62:269–280.

    https://doi.org/10.1016/j.neuron.2009.03.005

    • Google Scholar
35. 1. Walton ME
    2. Behrens TE
    3. Buckley MJ
    4. Rudebeck PH
    5. Rushworth MF

    (2010) Separable learning systems in the macaque brain and the role of orbitofrontal cortex in contingent learning

    Neuron 65:927–939.

    https://doi.org/10.1016/j.neuron.2010.02.027

    • Google Scholar

Author details

1. Michael A McDannald

   Intramural Research Program, National Institute on Drug Abuse, Baltimore, United States

   Contribution

   MAM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   mcdannal@bc.edu

   Competing interests

   The authors declare that no competing interests exist.

2. Guillem R Esber

   Intramural Research Program, National Institute on Drug Abuse, Baltimore, United States

   Contribution

   GRE, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Meredyth A Wegener

   Center for Neuroscience Graduate Program, University of Pittsburg, Pittsburg, United States

   Contribution

   MAW, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-0699-7748

4. Heather M Wied

   Program in Neuroscience, University of Maryland School of Medicine, Baltimore, United States

   Contribution

   HMW, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Tzu-Lan Liu

   Department of Psychology, National Taiwan University, Taipei, Taiwan

   Contribution

   T-LL, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Thomas A Stalnaker

   Intramural Research Program, National Institute on Drug Abuse, Baltimore, United States

   Contribution

   TAS, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Joshua L Jones

   Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, United States

   Contribution

   JLJ, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Jason Trageser

   Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, United States

   Contribution

   JT, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Geoffrey Schoenbaum

   1. Intramural Research Program, National Institute on Drug Abuse, Baltimore, United States
   2. Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, United States
   3. Department of Neuroscience, Johns Hopkins University, Baltimore, United States

   Contribution

   GS, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   geoffrey.schoenbaum@nih.gov

   Competing interests

   The authors declare that no competing interests exist.

• 2,030

  views

• 197

  downloads

• 55

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.