Dissociation of memory systems: The story unfolds.

Behavioral Neuroscience 2013, Vol. 127, No. 6, 813– 834

© 2013 American Psychological Association 0735-7044/13/$12.00 DOI: 10.1037/a0034859

BEHAVIORAL NEUROSCIENCE AT 30

Dissociation of Memory Systems: The Story Unfolds Norman M. White

Mark G. Packard

McGill University

Texas A & M University

Robert J. McDonald This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

University of Lethbridge In this article we describe the ideas and circumstances that led to the experiment demonstrating a triple dissociation of memory systems. We then move on to discuss the results of 20 years of investigation of those ideas. First, evidence is described from animal studies consistent with the ideas that memory for different kinds of information is stored in different brain systems, and that the hippocampus, amygdala, and dorsal striatum are each central structures in one of the systems. We then focus on the 3 tasks used in the original triple dissociation: win–stay learning, conditioned cue preference, and win–shift learning. Each of these tasks is specific to behavior resulting from the type of information stored in one of the systems, but the use of other behavioral tests that are sensitive to the types of information stored in other systems has revealed that, in each case, other types of information are acquired in parallel. Next, evidence consistent with the idea that the outputs of the systems compete for control of behavior is discussed together with alternative forms of more direct interactions among the systems. Finally, some evidence that many of these ideas about multiple parallel memory systems may apply to humans is reviewed. Keywords: memory, parallel processing, amygdala, hippocampus, dorsal striatum

series of experiments on the posttraining, memory-modulating action of electrical self-stimulation of the brain (Major & White, 1978; White & Major, 1978a, 1978b). Two observations dissociated the rewarding effect of the stimulation from its modulating action. First, the posttraining stimulation strengthened both the performance of a rewarded behavior and the nonperformance of the same behavior when it was not rewarded. Second, not all self-stimulation sites had memory-modulating effects, suggesting that reward was not sufficient to produce such effects. We found evidence that the modulating effect was due to activation of nigrostriatal dopamine neurons. These findings led to a series of studies showing that dopamine activation in the dorsal striatum has memory-modulating effects (Carr & White, 1984; Viaud & White, 1987; White, 1988; White & Viaud, 1991). The second event occurred in 1975 when Dalbir Bindra, who had just become chair of the McGill Psychology Department, hired a research associate named Richard Hirsh. Hirsh’s (1974) nowfamous— but, at that time, unknown—article, “The Hippocampus and Contextual Retrieval: A Theory,” had just appeared. Hirsh proposed that the hippocampus functions to select the appropriate alternative from a range of possible behaviors by retrieving memories that are relevant to current contexts, such as motivational states. These behaviors are represented in a hypothetical construct he called “the performance line.” The performance line functions as a simple stimulus–response (S-R) system in which reinforcers act with the same strengthening effect on learned behaviors as dopamine agonists in the dorsal striatum did in our experiments. When Richard and I realized this, we decided to test the hypothesis that the dorsal striatum is the performance line. After several false starts, we eventually completed a series of experiments showing

The article that is the subject of this review (McDonald & White, 1993) described a triple dissociation of memory systems, presenting evidence for the idea that different kinds of information are processed and stored in different parts of the brain. The identification of brain structures with these functions had begun in a previous study (Packard, Hirsh, & White, 1989); the triple dissociation article was an extension of this earlier study. The selection of the brain structures to be lesioned and the tasks used to test the rats were based partly on theoretical considerations and previous empirical findings, and partly on the usual random circumstances and events that determine the behavior of researchers. Accordingly, we start by briefly describing the circumstances under which the triple dissociation experiment came to be done. We then move on to review the current state of research and theory about the notion that there are several parallel neural systems specialized to process and store different kinds of information.

Origins of the Experiment Four events leading to the triple dissociation experiment are described from the perspective of Norman White. The first was a

Norman M. White, Department of Psychology, McGill University, Montreal, QC, Canada; Mark G. Packard, Department of Psychology, Texas A & M University, College Station, Texas; Robert J. McDonald, Canadian Centre for Behavioural Neuroscience, Department of Neuroscience, University of Lethbridge, Lethbridge, AB, Canada. Correspondence concerning this article should be addressed to Norman M. White, Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, Montreal, QC H3A 1B1, Canada. E-mail: [email protected] 813

This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.

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WHITE, PACKARD, AND MCDONALD

that lesions of the hippocampus, but not the dorsal striatum, impaired the partial reinforcement extinction effect (Humphreys, 1939), a behavior that cannot be attributed to S-R learning but that must involve cognitive processing (Tolman, 1948), and that lesions of the dorsal striatum, but not the hippocampus, impaired the modulating effect of posttraining consumption of sucrose on a conditioned emotional response (Messier & White, 1984). Richard and I thought we had confirmed our hypothesis. However, the article we wrote describing these experiments, “Differential Roles of Hippocampus and Caudate Nucleus in Memory: Selective Mediation of Incentive and Memory Improving Properties of Reinforcement,” was rejected by three different journals and has never been published. The next event occurred when a new graduate student, Mark Packard, arrived in the lab. Mark had worked with an eight-arm radial maze in Aaron Ettenberg’s lab at University of California, Santa Barbara, where his reading of O’Keefe and Nadel’s (1978) “The Hippocampus as a Cognitive Map” (which he had on moreor-less permanent loan from the library) had introduced him to the idea that there is more than one memory system in the brain. Based on our article on dorsal striatum involvement in reinforced memory and the unpublished Hirsh and White experiments, Mark and I decided to have another try at testing the hippocampus-versusdorsal-striatum hypothesis using a radial maze. The standard win– shift paradigm (Olton, 1979; Olton & Papas, 1979) was an obvious choice for the hippocampal task. For the dorsal striatum task, a study by Winocur (1982), in which different visual cues in each arm slightly attenuated the win–shift impairment produced by hippocampus lesions, was an important influence. We eliminated both the working- and spatial-memory requirements of the task by putting the same stimulus, a light, at the entrance to each of the four arms that contained food, and made each rat return to each lit arm twice within a trial (hence, the name “win–stay”). Importantly, a different set of four arms was lit and baited on each trial, making the extramaze (spatial) cues and the local cues (the arm lights) independent of each other. Because we wanted to run the trials without interruption, the requirement that the rats obtain two pellets from each of the same four arms on each trial meant that the arms had to be rebaited while the trial was ongoing. To do this, we installed plastic tubes leading from a central, elevated location accessible to the experimenter to the ends of each arm. The experimenter dropped a pellet into the appropriate tube every time a rat ate the first pellet in each arm. This gravity-powered system required daily testing and maintenance to be sure the pellets reliably slid down the tubes and reached the ends of the arms. The dorsal striatum lesions were deliberately made very large and required two electrode placements on each side. These were done one side at a time with a 2-week recovery period after each side. These lesions impaired performance on the win–stay task but had little effect on win–shift behavior. In contrast, fimbria-fornix lesions impaired win–shift behavior, and, unexpectedly, the win– stay performance of the rats with these lesions was better than the performance of normal rats (Packard et al., 1989). The issue of the relative influences of the extramaze and local cues on win–stay learning raised the question of whether the task should be run with the maze open to the room cues or with curtains around the maze to attenuate those cues. Mark ran the experiment twice, with and without curtains. There was little difference in the rates of win–shift learning with and without the curtains, but the

interference with win–stay learning of exposure to the extramaze cues was clear (see Figure 1). The published article describing these experiments (Packard et al., 1989) reported data for only one curtain condition per task. Descriptions of the effects of the curtains around the maze were a casualty of our interaction with the reviewers in the course of three iterations of the manuscript. Those data, which have been entombed in Packard’s (1987) master’s thesis until now, are discussed later in this article. The final step was taken with Robert McDonald, who came to do his graduate work at McGill from Rob Sutherland’s lab at the University of Lethbridge, where he had worked on a series of experiments dissociating the functions of the hippocampus and amygdala in pattern separation and completion, spatial learning, fear conditioning, food neophobia, and spatial learning. He had also carried a copy of O’Keefe and Nadel in his backpack and read it several times. Rob read Packard’s article, and, based on an experiment in which another student in the lab had found that fimbria-fornix lesions did not affect the amphetamine conditioned cue preference (CCP; Hiroi & White, 1991), McDonald and White decided to test the idea that there are three parallel memory systems. A CCP task was designed in which the food-paired and unpaired arms were distinguished by a light in one of them. Using electrolytic lesions, Rob collected data for the 12 groups of rats required to show the triple dissociation. They were consistent with the hypothesis. He then replicated each of the lesion effects using neurotoxic lesions in an additional three groups, with three control groups. These findings constituted Rob’s master’s thesis (McDonald, 1992). The article we wrote for publication, “A Triple Dissociation of Memory Systems: Hippocampus, Amygdala and Dorsal Striatum,” was submitted to Behavioral Neuroscience and accepted after the usual minor revisions (McDonald & White, 1993).

Further Evidence for Independent Parallel Memory Systems Reinforcer Devaluation In an important series of experiments, Knowlton and her coworkers (Sage & Knowlton, 2000; Yin & Knowlton, 2002) used the reinforcer devaluation technique (Adams & Dickinson, 1981; Dickinson, Nicholas, & Adams, 1983) to test the idea that the food reinforcer has a different kind of action in each of the tasks used to demonstrate the triple dissociation. If the learned behavior observed in any of those tasks is based on information about the rewarding properties of the food reinforcer, then devaluing that reinforcer should impair performance of the task. In the CCP task, it was assumed that the preference for the food-paired arm is based on information about the relationship of the cues visible from the food-paired arm to the rewarding properties of the reinforcer. Therefore, devaluation should reduce or eliminate the preference for the food-paired arm. This is exactly what was found by Yin and Knowlton (2002). Using a procedure similar to that of McDonald and White (1993), they trained food-restricted rats on the radial maze CCP by pairing one arm with a sweet cereal as the reinforcer. Then the rats were given the cereal to eat in their home cages, followed by an illness-inducing injection of lithium chloride. When offered the


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Figure 1. A replotting of data from Packard (1987, p. 33, Figure 6), the thesis that first described the double dissociation reported in Packard et al. (1989). Groups of rats with a large dorsal striatum, fimbria-fornix, or sham lesions were tested on the win–stay task in two different conditions: open to extramaze cues and with the maze surrounded by heavy black curtains that blocked the rats’ view of those cues. Because the raw data are no longer available, the present reanalysis can only be described qualitatively. Top row: Comparing the learning curves for the sham and fimbria-fornix groups shows the retarding effect of extramaze cues (right graph) on acquisition of the win–stay task. The two graphs also show that fimbria-fornix lesions facilitate acquisition both in the presence and absence of the extramaze cues, suggesting that the facilitating effects of occluding extramaze cues and the effects of the lesions occurred for different reasons. Bottom row: These graphs show the retarding effects of exposure to the extramaze cues in the sham and fimbria-fornix groups, and the absence of this effect in the dorsal striatum group. The fact that dorsal striatum lesions eliminated the performance difference caused by the extramaze cues manipulation suggests that the extramaze cues may interfere with learning in the dorsal striatum system when it occurs. Further discussion of this may be found in the text.

cereal again in the home cage, the rats ate little or none of it, suggesting that the reinforcer had been devalued. When tested on the CCP with no food available in the maze arms, the injected rats spent less time in their food-paired than in their unpaired arms. A group of control rats that had not experienced devaluation of the reinforcer spent more time in their food-paired arms. This finding is consistent with the idea that the rats’ preferences were based on an association that involved the affective properties of the reinforcer. In contrast, if the reinforcer in a task acts solely to modulate neural representations of associations between events that a rat has experienced (McGaugh, 2004; Postman, 1947; Thorndike, 1933), the affective properties of the reinforcer should be irrelevant to the rat’s performance of the task. Our assumption that the tendency to enter lit arms in the win–stay task is based on this kind of modulated S-R association predicts that this behavior should not be affected by reinforcer devaluation. Sage and Knowlton (2000) reported this result. Rats were trained on the win–stay task and then subjected to devaluation of the food reinforcer as described. When put back on the maze, they continued to enter lit arms at the same rate as before the devaluation procedure. However, in contrast to their predevaluation behavior, they no longer ate the cereal pellets that were found at the ends of the lit arms. Latency to complete each trial was increased immediately after devaluation but returned to normal before the end of the experiment. The persistence of accurate performance after devaluation of the reinforcer strongly suggests that the affec-

tive properties of the reinforcer were not involved in producing this learned behavior. The win–stay behavior may have been based on an association between the light stimulus and the response of entering the arms. The reinforcer acted to strengthen that association, but this action did not involve learning anything about its affective properties. Sage and Knowlton (2000) also tested the effects of reinforcer devaluation on win–shift performance. They found that devaluation of the reinforcer in rats trained on the win–shift task had little effect on the accuracy of the rats’ performance, suggesting that information about the changing locations that contained food during a trial did not involve its affective properties. Rather than the experience of reward, win–shift behavior may be based on a prediction about the availability of reward (Balleine & Dickinson, 1992), which changes for each arm as the trial proceeds. Consumption of the food pellets found on the maze was significantly reduced by devaluation but still remained at about half of its predevaluation level, and there was a considerable increase in the time to complete each trial. These observations suggest that a memory of the devalued affective properties of the reinforcer also influenced the rats’ behavior, but that this influence was unrelated to the information required to win–shift.

Lesion Studies Water maze. The double dissociation produced by the hippocampal system and dorsal striatum lesions in the win–shift and


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win–stay radial maze tasks was replicated using two water maze tasks (Packard & McGaugh, 1992). In these tasks, similar to those introduced by Morris (1984), two rubber balls protruding above the surface of the water were used as cues. One ball (correct) was mounted on a platform and could be climbed to escape the water; the other ball (incorrect) was unstable and did not permit the rat to escape the water. The two balls also differed in appearance: One had vertical, and the other had horizontal, black and white stripes. In the cognitive/spatial version of the task, either the vertically or the horizontally striped ball could be attached to the platform and designated correct. The correct ball was in the same spatial location on every trial, so the rats had to ignore the patterns on the balls and respond to the spatial location. In the S-R/habit version of the task, either the vertically or horizontally striped ball was always correct, but it was in a different location on every trial, so the rats had to respond to the stimuli on the ball and ignore the spatial cues. As had previously been observed by Morris (1984), lesions of the fimbria-fornix impaired performance of the cognitive task. Dorsal striatum lesions had no effect on the cognitive task but did selectively impair performance of the habit task, which was not affected by fimbria-fornix lesions (Packard & McGaugh, 1992). These findings replicated the double dissociation of the same lesions on cognitive/spatial and S-R/habit tasks on the radial maze. Another water maze study (McDonald & White, 1994) provided further evidence for this dissociation. In this experiment, rats were trained over 4-day cycles. On the first 3 days of each cycle, they learned to swim to a visible (cued) escape platform that was in the same location on every trial. On the fourth day of each cycle, the platform was in the same location but was invisible because its top was just below the surface of the water. Normal rats found the invisible platform easily after visible platform training, but rats with fimbria-fornix lesions, which learned to swim to the visible platform normally, were unable to learn to find the invisible platform even after three training cycles. This showed that the lesioned rats could use a single local cue to locate the platform but were unable to use spatial cues consisting of relationships among a constellation of visible cues. On a final probe trial, there were two platforms. An invisible platform was located in the same location as it had been during training, and a visible platform was placed in a new location on the other side of the pool. This arrangement placed two behavioral tendencies acquired during training in competition with each other: swimming to the spatial location (cognitive/spatial memory) and swimming to the cue provided by the visible platform (S-R/habit memory). On this trial, half of a group of normal rats swam to the old spatial location of the platform, indicating preferential influence of cognitive/spatial learning, whereas the other half of the rats swam to the visible platform in its new location, indicating a preferential influence of S-R/habit learning. Almost all of a group of rats with fimbria-fornix lesions swam to the visible platform in its new location, indicating a reduction in the influence of cognitive/spatial memory, which favored expression of S-R/habit memory. In contrast, all rats in a group with dorsal striatum lesions swam to the old spatial location, indicating a reduced influence of S-R/habit memory, allowing expression of cognitive/spatial memory (McDonald & White, 1994). Thus, consistent with the earlier lesion experiments involving the win–shift and win–stay radial maze tasks, studies conducted in the water maze support the hypothesis that the hippocampal sys-

tem selectively mediates cognitive/relational memory, whereas the dorsal striatum selectively mediates S-R/habit memory. The water maze findings also indicate that the double dissociation produced by lesions to these two brain regions generalizes to aversively motivated tasks, suggesting that the motivational (appetitive vs. aversive) aspect of specific learning tasks is not a central feature that distinguishes among memory systems. Plus-maze. Packard and McGaugh (1996) used the plus-maze apparatus, originally introduced by Tolman and colleagues (Blodgett & McCutchan, 1948; Tolman, Ritchie, & Kalish, 1946), and temporary inactivation of localized neural activity with intracerebral injections of lidocaine, a sodium channel blocker, to demonstrate some important features of the dorsal striatum/hippocampus dissociation. The plus-maze is arranged so that a goal box (e.g., east or west) can be approached from one of two start boxes (e.g., north or south). Rats were trained from one start box (south) to traverse the maze and obtain a food reward from a consistently baited goal box (west). This required a left turn at the choice point. After a number of training trials, a probe trial was given in which the rats were started from the opposite start box (north). If the rats had acquired hippocampus-based cognitive/ spatial information about the spatial location of the food in the west goal box, this learned expectancy should lead them to turn right at the choice point. Rats that do this are called place learners. In contrast, if the rats acquired a dorsal-striatum-based S-R association that leads them to turn right at the choice point, they should make the same response on the probe trial and turn away from the food. Rats that do this are called response learners. Extensive investigation of this paradigm (Restle, 1957) showed that although approximately equal numbers of rats have innate place or response tendencies, most can exhibit either type of learning, depending on the design of the experiment. External variables that influence the response made on the probe trial include the number of extramaze cues (many cues favor place learning) and the number of training trials. Early in training, rats tend to exhibit place learning, but after extended training, they show response learning (Ritchie, Aeschliman, & Peirce, 1950; Thompson, Guilford, & Hicks, 1980). The hypothesis that place and response learning are mediated by independent memory systems was tested by producing a reversible lesion of either the hippocampus or dorsolateral striatum just prior to the probe trial (Packard & McGaugh, 1996). On a probe trial given following a week of training, rats that were given injections of a saline solution into the dorsolateral striatum or dorsal hippocampus predominantly displayed place learning. The same behavior was observed in a group that were given injections of lidocaine into the dorsal striatum, but rats given intra-hippocampal lidocaine did not exhibit place learning. This was a single dissociation between the roles of these two brain structures in the retrieval of information guiding spatial/place learning. The rats were then trained for an additional week and the probe trial with lidocaine injections was repeated. This time, vehicleinjected rats predominantly displayed response learning, a shift from the place learning they had shown earlier. Intra-hippocampal injections of lidocaine had no effect on this behavior. However, injection of lidocaine into the dorsolateral striatum caused almost all the rats tested to exhibit place learning. These observations have two implications. First, taken together, the results of the two probe trials demonstrate a double dissociation of the roles of the hippocampus and dorsolateral striatum in place and response



learning. Moreover, they are consistent with previous studies in suggesting that this is a dual-solution task, in which learning in both memory systems can produce behavior that leads to the correct response during the training trials. The probe trials suggest that hippocampus-based learning controlled behavior early in training and that dorsal-striatum-based learning tended to control behavior later in training. This switch may have occurred as the strength of the S-R association in the dorsal striatum increased with repeated reinforced trials (Hull, 1943). The second implication of the findings is that the hippocampus and dorsal striatum systems operated independently. When dorsalstriatum-based habit learning had assumed control of the rats’ behavior and was temporarily impaired with lidocaine, the rats exhibited place learning. This shows that the place information was not erased or forgotten. Rather, it remained intact in the hippocampus system and resumed control over the rats’ behavior when the dominant influence of the dorsal-striatum-based habit learning was impaired.

Posttraining Intra-Cerebral Injections The phenomenon of memory modulation (McGaugh, 1966; Roozendaal & McGaugh, 2011) has also been used to investigate the involvement of brain areas in the processing and storage of different kinds of information. Unlike the lesion technique—which can only give an indication that a part of the brain is necessary for some aspect (representation, storage, retrieval, performance) of how a particular type of information influences behavior—techniques that demonstrate memory modulation actually implicate a brain area in the storage of new information. The memory modulation concept is based on evidence that memory for new information is in a labile state for a short time following initial exposure (C. P. Duncan, 1949; McGaugh & Herz, 1972; Muller & Pilzecker, 1900; Zubin & Barrera, 1941), and can therefore be either strengthened or weakened by various treatments given immediately after training, but not by the same treatments if they are given some time after training (Breen & McGaugh, 1961; McGaugh, 1966; White & Milner, 1992). This pattern of effects shows that a treatment interacts with time-limited posttraining storage processes initiated by exposure to the information, but has no direct influence on the animal’s performance of the task (McGaugh, 1989). The first study to compare the effects of a posttraining manipulation on memory consolidation processes in the hippocampus and dorsal striatum investigated the dopaminergic system and used the win–shift and win–stay radial maze tasks (Packard & White, 1991). In the win–shift task, four randomly selected arms on the eight-arm radial maze were blocked and the rats were allowed to obtain food from the four open arms. Following a delay (5 to 15 min), they were returned to the maze for a retention test in which all maze arms were open, but only the four arms not previously visited contained food. After rats learned to make at least 80% correct responses on the retention test, a single drug trial was administered. Rats were removed from the maze after the first four choices and received an immediate intra-cerebral posttraining injection. They were given the retention test 18 hr later, a delay found to result in poor performance by control rats. Posttraining injections of the dopamine D1 receptor agonist SKF 38393, or the dopamine D2 receptor agonist quinpirole, into the hippocampus

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enhanced memory on this test, but injections of the same substances into the dorsal striatum had no effect. Intra-hippocampus injections delayed for 2 hr after the initial four choices had no effect. On the win–stay task, rats obtained food rewards by visiting four randomly selected and illuminated maze arms twice each on training days (the normal procedure) and received posttraining intracerebral injections of vehicle or the dopamine agonists immediately after training on Day 5. They were returned to the maze for a retention test 24 hr later. Injections of the dopamine agonists into the dorsal striatum enhanced performance on the win–stay task, whereas intra-hippocampal injections had no effect. Injections into the dorsal striatum 2 hr after the Day 5 training trial had no effect. For other examples of posttraining effects of dopaminergic drugs on dorsal-striatum-dependent memory, see Carr and White (1984), Viaud and White (1987), White and Viaud (1991), and White (1988). These findings provide a further demonstration of the different roles of the hippocampus and dorsal striatum in cognitive and habit memory, respectively, and indicate a task-dependent role for dopaminergic function in these two brain structures. Packard, Cahill, and McGaugh (1994) reported an additional demonstration of the dissociation, together with evidence that the amygdala modulates memory in both systems. Task-dependent effects of posttraining manipulations of glutamate neurotransmission in the hippocampus and dorsal striatum have also been reported (Packard & Teather, 1997). Posttraining intra-hippocampal injections of the glutamatergic N-methyl-D-aspartate (NMDA) receptor antagonist 2-amino-5phosphonopentanoic acid (AP5) selectively impaired memory for a water maze task in which rats were trained to swim to a hidden escape platform that remained in the same spatial location on all trials. Similar injections of AP5 into the dorsal striatum selectively impaired memory for a cued-platform water maze task in which rats were trained to swim to a visible escape platform in a different spatial location on each trial. Moreover, intra-hippocampal posttraining injections of glutamate selectively enhanced memory for the hidden platform task, whereas glutamate injections into the dorsal striatum selectively enhanced memory for the visible platform task (Packard & Teather, 1997). It was also found that, in the dual-solution plus-maze task described previously, the transition from hippocampus-dependent place learning to dorsolateral striatum-dependent response learning can be influenced by posttraining intra-cerebral injections of glutamate (Packard, 1999). Posttraining intra-dorsal-striatum injections of glutamate early in training accelerated the switch to response learning on a probe trial given when control rats were still exhibiting place learning. In contrast, posttraining intrahippocampal injections of glutamate early in training prevented the transition from place to response learning that is normally produced by extended training. It has also been shown that infusions of the NMDA receptor antagonist AP5 into the dorsolateral striatum impaired acquisition of response learning in a plus-maze (Palencia & Ragozzino, 2005). Cholinergic mechanisms in the hippocampus and dorsal striatum have also been implicated in the use of place and response learning strategies in the plus-maze. In a series of experiments that used intra-cerebral microdialysis to measure acetylcholine release during plus-maze training (McIntyre, Marriott, & Gold, 2003; for


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a review, see Gold, 2003), rats that displayed response learning on a probe trial had a higher level of acetylcholine release in the dorsolateral striatum than in the hippocampus. The opposite pattern of acetylcholine release was observed in rats that exhibited place learning. Other findings indicate a role for gonadal hormones in the adoption of place and response learning strategies in the dual-solution plus-maze task in female rats (see Korol, 2004, for a review). In summary, as is the case with permanent and temporary brain lesions, posttraining pharmacological manipulations indicate a double dissociation of hippocampal and dorsal striatal involvement in cognitive/relational and S-R/habit memory, respectively. Furthermore, although they do not prove that the memories are stored in these structures, these studies directly implicate them in some aspect of the storage in memory of different types of information.

What Kinds of Learning Actually Occur in Each of the Triple Dissociation Tasks? Win–Stay Task The win–stay radial maze task was developed as a parallel to the win–shift task, but differs from it in two critical ways. First, the spatial learning component of the win–shift task was eliminated by putting lights at the arm entrances to indicate the location of the food. Second, because entering a lit arm was always followed by the food reinforcer, there was a consistent relationship between the stimulus that led to food and the presence of food. This arrangement also eliminated the working memory component of the win–shift task: Because the location of the food was always indicated by the lit arms, the rats did not have to remember which arms they had already visited to find food. The requirement that the rats visit each arm twice during each trial was intended to allow the rats to make the same number of reinforced arm entries as on the win–shift task. However, as discussed later this may have introduced a factor that affected learning in the hippocampus system. Furthermore, although the effects of dorsal striatum lesions on this task were consistent with our assumptions, further investigation of the task and its anatomical basis has revealed that these assumptions were much too simple. Medial and lateral dorsal striatum. In the initial experiment on the win–stay task, the dorsal striatum lesions were made as large as possible in order to maximize the chances of getting an effect. However, research on the dorsal striatum both before (Divac & Oberg, 1979; Oberg & Divac, 1979) and since our experiment (see White, 2009, for review) shows that the structure is both anatomically and functionally heterogeneous. Both the pattern of corticostriatal projections (Alexander & Crutcher, 1990; Alexander, DeLong, & Strick, 1986; McGeorge & Faull, 1989) and the patch-matrix organization within the structure (Gerfen, 1985; Gerfen, Herkenham, & Thibault, 1987) suggest the existence of a major functional difference between the medial and lateral parts of the dorsal striatum. A recent extensive review (Devan, Hong, & McDonald, 2011) suggests that the medial and lateral parts of the dorsal striatum are parts of two parallel circuits mediating cognitively based flexible control of behavior and relatively inflexible S-R habits, respec-

tively. An early series of studies in the water maze (Devan, McDonald, & White, 1999; Devan & White, 1999) dissociated the medial and lateral striatum along these lines and provided evidence that the dorsomedial striatum and the hippocampus are functionally linked via the fimbria-fornix. Two other series of studies provided further evidence of the functional difference between dorsomedial and dorsolateral striatum using operant learning situations. Featherstone and McDonald (2004a, 2004b, 2005a, 2005b) found that dorsolateral, but not dorsomedial, striatum lesions, made before or after training, impaired rats’ ability to learn simultaneously to make one instrumental response (bar press or chain pull) in the presence of a tone and the other response in the presence of a light, for the same reinforcer. The lesions did not affect learning to make either response alone or CCP learning. The authors concluded that the dorsolateral striatum is critical for S-R learning without involvement of the reward value of the reinforcer. In the second series (Yin & Knowlton, 2004, 2006; Yin, Knowlton, & Balleine, 2005, 2006; Yin, Ostlund, Knowlton, & Balleine, 2005), the reinforcer devaluation technique was used to show that the dorsolateral and dorsomedial striatum support the same instrumental response by processing different kinds of information about the learning situation. Responding supported by the dorsomedial striatum includes information about the reward properties of the reinforcer; responding supported by the dorsolateral striatum does not include this information and consists solely of S-R associations. More recently, it was reported (Moussa, Poucet, Amalric, & Sargolini, 2011) that lesions of dorsomedial striatum impaired reinforced spatial alternation, whereas dorsolateral lesions improved alternation performance, a pattern of effects that is consistent with the cognitive-relational and S-R/habit functions attributed to the dorsomedial and dorsolateral striatum, respectively. The demonstration that win–stay learning is impaired by lesions confined to the lateral part of the dorsal striatum (McDonald & Hong, 2004) is also consistent with the idea that performance of this task depends on S-R associations. Devan, Hong, and McDonald (2011) speculate that relatively large lesions of the dorsolateral striatum may be required to impair performance of tasks based on S-R associations because the cortical projections to the matrix compartment in that part of the structure are dispersed over a wide area (L. L. Brown & Sharp, 1995). In contrast, projections to the dorsomedial striatum tend to cluster in hypothesized functional units called matrisomes (Graybiel, 1991, 2005), so smaller lesions affecting these units may be sufficient to impair the more cognitive functions of that part of the dorsal striatum. Facilitation of win–stay learning. The initial observation that fimbria-fornix lesions (Figure 1, top row) facilitated win–stay learning was unexpected. It was explained by assuming that the hippocampus system learns the spatial location of food in parallel with the acquisition of the S-R association by the dorsal striatum system. Because the lit, food-containing arms were moved on each trial, much of this information about previous food locations was wrong on any given trial, leading to errors. Fimbria-fornix lesions eliminated the errors caused by this hippocampus-based spatial learning, resulting in the improved performance that was observed. A related finding in the Packard et al. (1989) study was that placing curtains around the maze to occlude the extramaze cues that are the basis of spatial learning also improved win–stay performance (Figure 1, bottom row). In parallel with the effect of



fimbria-fornix lesions, this was explained as the result of attenuated learning about the spatial location of the food, which eliminated errors caused by memory of previous food locations. A reconsideration of the data, however, suggests other explanations for the effects of fimbria-fornix lesions and maze curtains on win–stay learning. Timberlake and White (1990) found that hungry rats win–shift early in training on an eight-arm radial maze, even when there is no food on the maze. This behavior was seen as a special case of rats’ normal tendency to explore (Barnett & Cowan, 1976; Berlyne, 1966; Corey, 1978; Galani, Weiss, Cassel, & Kelche, 1998; Pisula & Siegel, 2005) and spontaneously alternate among available choices (Dember & Fowler, 1958), and, in particular, to avoid returning to a location in which food was recently obtained (Gaffan & Eacott, 1986; Olton & Samuelson, 1976; Roberts, 1991). In order to avoid a location in which food has just been found, or to alternate among two or more choices, a rat has to remember the locations it has recently visited and avoid them. This means that the unlearned hippocampus-based tendency to win–shift also requires working memory. Lesions of the hippocampus system have repeatedly been shown to impair working memory in various situations (see Lalonde, 2002, for a review). Although performing the win–stay task does not require working memory, in normal rats, the hippocampus-system would still promote a tendency to avoid entering previously visited arms that does depend on working memory. Because the rats had to enter each lit arm twice in the standard version of the task, this tendency would promote errors, and hippocampus-system lesions would eliminate those errors. Some support for this idea comes from comparing two studies by McDonald and coworkers (McDonald, Jones, Richards, & Hong, 2006; McDonald, Ko, & Hong, 2002) in which the win–stay procedure was altered slightly. In these experiments, rats were placed on the maze with food at the ends of four lit arms and removed when the food had been eaten. In the 2002 study, the same four lit arms were immediately rebaited, and the rats were replaced on the maze and allowed to find and eat the new pellets. During the second part of this trial, a hippocampus-based tendency to shift responses would have caused errors because the rats had to reenter previously visited arms to obtain the food pellets. Consistent with this explanation, hippocampus lesions improved acquisition of the win–stay task in this study. In contrast, in the 2006 study the rats were given only the first part of the trial, in which they ate four pellets at the ends of four lit arms, eliminating the need for them to return to recently visited arms. In this situation, no interference from the hippocampusbased tendency to shift responding would be predicted. Consistent with this prediction, hippocampus lesions did not facilitate win– stay learning in this study. Interference effects due to memory for the locations of food on previous trials do not depend on repeated arm entries within a trial. If such memories were the source of the facilitation produced by hippocampus-system lesions, performance of the group with those lesions in the 2006 study, in which no arm reentries were required, should have improved. The fact that there was no improvement suggests that the original explanation of improved win–stay performance based on memory for previous food locations may have been incorrect.

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It must be noted that the hippocampus lesions in the two studies discussed were not the same. The lesions in both studies were made with NMDA, but in the 2006 study, the dorsal and ventral parts of the hippocampus were lesioned in separate groups of rats. Neither lesion facilitated win–stay acquisition, although the dorsal lesion impaired a tactual discrimination. In the 2002 study, the entire hippocampus was lesioned and the facilitation was observed. The possibility that this difference contributed to the difference in the facilitation effects cannot be ruled out. The facilitation of win–stay learning produced by surrounding the maze with curtains may also be due to a general hippocampusbased tendency to avoid arms previously visited within a trial, rather than to memory for previous spatial locations of food. Figure 1 (bottom row) shows the effects of curtains surrounding the maze on win–stay performance (Packard, 1987). Comparing the graphs for the groups with sham and with fimbria-fornix lesions shows that the extramaze cues had the same retarding effect whether or not the fimbria-fornix was lesioned, and that the performance of both lesioned groups was better than the performance of their corresponding sham groups. This suggests that the effects of eliminating extramaze cues and fimbria-fornix lesions may have been independent. Furthermore, the absence of an effect of extramaze cues in the group with dorsal striatum lesions, in which there was little or no learning, suggests that, rather than acting through the hippocampus system, the extramaze cues may have their effect by interfering with dorsal-striatum-based learning, when it occurs. Such an effect could be understood as competition between the extramaze cues and the intramaze lights for a limited pool of attentional processing (Kaye & Pearce, 1984; Pearce & Hall, 1992). This form of competition would not be between systems, but may occur at the cortical level, which is thought to provide information to all systems (White, 2004). Interestingly, this interpretation of the effects of surrounding the maze with curtains may also provide an explanation for the failure of the hippocampus-system to learn the win–stay task in rats with dorsal striatum lesions. Assuming that the same cortical input reaches both the hippocampus and dorsal striatum, the competition for attentional processing that may attenuate dorsal-striatum-based win–stay learning when the maze is open to extramaze cues may also apply to the hippocampus, preventing it from receiving information about the individual light cues. This would explain why there is little learning about these cues in the sham-lesion or dorsal-striatum-lesion groups tested in the open condition shown in Figure 1 (bottom row, left and right). Amygdala and hippocampus-based learning in the win–stay task. A central idea about the interactions among multiple parallel memory systems is that all systems are learning all the time, resulting in the acquisition of multiple representations of the world and the individual’s relation to it. Because each system is specialized to represent a different type of information, the representation in each one consists of different information pertaining to the same external situation. In any given situation, some combination of these representations comes to control behavior. In some situations, such as those used in the triple dissociation experiment, one system at a time may predominate, but that does not mean that the others are inactive. An extensive series of experiments from the McDonald lab has elegantly demonstrated that this is the case for the win–stay visual


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discrimination task. This behavior is controlled by the dorsal striatum system, which is thought to form S-R representations of the world, but these experiments demonstrate that both the amygdala and the hippocampus also acquire their own types of information that can be shown to influence behavior with the appropriate tests. Amygdala-based stimulus–reward learning. The demonstration that the amygdala acquires stimulus–reward associations during win–stay training was straightforward (McDonald, Foong, & Hong, 2004; McDonald & Hong, 2004). Rats were trained on the win–stay task, attained criterion performance, and were then given a preference test. On this test, the rats were placed on the maze with four randomly selected lit arms and the other four arms dark. During a 10-min test, the rats spent significantly more time in the lit arms than in the dark arms, a CCP. Rats with lesions of the basolateral amygdala learned the win–stay task normally, but did not show a preference for the lit arms. Rats with lesions of the dorsolateral striatum failed to acquire the win–stay task after extensive training, but showed a preference for the lit arms on the CCP test. This double dissociation suggests that although the amygdala system and the stimulus–reward associations it acquires were not necessary for the rats to learn win–stay task, the system still acquired these associations: Their existence was demonstrated by the preference test. In contrast, the dorsolateral striatum was not involved in acquiring the stimulus–reward association that produced the preference, but it was critical for performing the win– stay task. Hippocampus-based stimulus-stimulus (S-S) learning. S-S associations represented in the hippocampus system include relationships among large numbers of stimuli; it is this feature that allows this system to discriminate among so-called contexts. In contrast, S-R associations represented in the dorsal striatum are specific to individual stimuli or cues. A stimulus represented in this type of association elicits its associated response regardless of what other stimuli are or are not present; that is, behaviors due to S-R associations occur independently of the context. Based on this difference, it was predicted that well-learned win–stay behavior, which is thought to be based on S-R associations, would not be affected by a change in context. Operationally, this meant training a group of rats in one room and then testing them on an identical maze in a different room (McDonald, King, & Hong, 2001). The initial training required 54 daily trials for the rats to reach criterion performance (85% correct over 2 days). On the first few trials after they were switched to the new room, the time taken to complete the daily test increased as the rats explored the new environment. Their accuracy at selecting lit arms was also impaired, but returned to the criterion level of performance after four to ten trials, suggesting that the disruption was due to their response to the change in context and that the S-R representation did not have to be relearned in the new context. The next step was to examine incidental learning in the hippocampus system, which was expected to be context dependent. Specifically, it was hypothesized that, in parallel with the reinforced S-R association involving the light cue acquired by the dorsolateral striatum, a series of S-S associations representing the context, which included the prediction of no food in the dark arms, was acquired by the hippocampus system. The latter representation is inhibitory in the sense that it involves learning not to enter the

dark arms. Because this inhibitory S-S association is part of the context, behavior based on it should be affected by changing the context. To test this hypothesis, rats were trained to criterion on the win–stay task and then given reversal training, in which the dark arms contained food and the lit arms were empty (see Figure 2). Since the rats were now reinforced for entering dark arms, they should start to acquire an S-R association involving the dark cue, resulting in a tendency to enter the dark arms. However, their previously acquired tendency to avoid those arms during the initial training should have interfered with the newly acquired reinforced tendency to enter those arms, and learning the reversal task should be retarded (see Figure 2). The situation should be different when the reversal trials are given in a different room from the original training. Because the tendency to avoid dark arms is context specific, it should not function in the new room. Eliminating this source of interference with entering the reinforced dark arms should result in faster reversal learning in the different context than in the same context. These predictions were confirmed by the experimental results (see Figure 2). The rats required 50 trials to reach criterion on the initial win–stay training with the lit arms reinforced. On reversal training with the dark arms reinforced, the rats trained in the same room failed to reach criterion after 50 trials, but the rats trained on reversal in the different room reached criterion after 24 trials. In a final phase of the experiment, the rats that were reversed in the different context were transferred back to the original context and tested with no food in either the lit or dark arms. These rats should have acquired a reinforced S-R tendency to enter dark arms that was independent of the context, but during their original training in the original context, they acquired a tendency to avoid the dark arms. The competition between these two tendencies should have resulted in chance performance, and this is what was observed (see Figure 2). Previous evidence that a functional hippocampus is required for context learning (Anagnostaras, Maren, & Fanselow, 1999; Fendt & Fanselow, 1999; Sutherland & McDonald, 1990) led to the hypothesis that the effects of contextual learning to avoid dark arms should be absent in rats with hippocampus lesions. To test this idea, the experiments described in the previous paragraphs were repeated using rats with neurotoxic lesions of the dorsal or ventral hippocampus (McDonald et al., 2006). Neither of these lesions affected initial learning of the win–stay task with the lit arms reinforced. Dorsal hippocampus lesions had no effect on reversal learning in either the same or the different context. However, rats with ventral hippocampus lesions learned the samecontext reversal faster than rats with sham or dorsal striatum lesions. Because learning the new reinforced S-R association involving the dark arms is thought to be retarded by previous context-dependent learning to avoid those arms, this finding is consistent with the hypothesis that the ventral (but not the dorsal) hippocampus is a critical structure for the mediation of contextspecific inhibitory learning (i.e., learning not to enter the dark arms). Ventral hippocampus lesions had no effect on reversal learning in the different context. This finding is consistent with the hypothesis that the context-dependent inhibitory learning does not act in the different context, and so did not affect the sham-lesioned rats.



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Summary. The original triple dissociation experiment showed that performance of the win–stay task is primarily due to dorsalstriatum-based S-R learning and that any learning that may occur in other systems is not necessary for accurate performance of the task. Nevertheless, the experiments reviewed here show that these systems do acquire information during win–stay training and that the effect of this information can be revealed by tasks that are capable of detecting the behavioral effects of the kinds of information they acquire. Thus, the standard CCP task was used to detect amygdala-based stimulus–reward associations, and a task designed to exploit the context specificity of hippocampus-based inhibitory learning was used to detect the acquisition of that kind of information.

CCP Task

Figure 2. Interaction of dorsal-striatum- and hippocampus-based learning in the win–stay radial maze task. (A) Initial training. Rats were trained normally with four different randomly selected lit and baited arms on each daily test. (Note that the lit/baited arms in all diagrams are the same, but in the experiment, they changed on every trial.) Correct performance required acquisition of a dorsal-striatum-based, S-R tendency to enter lit arms (red arrows). At the same time, the hippocampus system acquired a tendency to avoid the dark arms (green arrows). The rats required 50 trials to reach criterion. (B) Reversal learning in the same room. Early in training, the rats’ previously learned tendency to enter lit arms (dashed red arrows) produced lots of errors, but this tendency eventually extinguished due to nonreinforcement. Because the rats were now reinforced for entering dark arms, they should start to acquire an S-R tendency to enter those arms (solid red arrows). However, because they previously learned to avoid dark arms (dashed green arrows), this behavior competes with the new learning to enter those arms, preventing expression of the new S-R association. The rats in this group failed to reach criterion after 50 trials. (C) Reversal learning in a different room. In this situation, errors early in training result from the previously learned S-R tendency to enter lit arms (dotted red arrows), which then extinguishes. The new reinforced S-R tendency to enter dark arms (solid red arrows) is gradually expressed. Note that because the tendency to avoid dark arms is context dependent, it does not affect performance in the different context. The rats required only 24 trials to reach criterion in this context. (D) Rats reversed in different room brought back to original room. No reinforcers were present on these trials, so there was no new learning. The most recently learned S-R tendency to enter dark arms is expressed in this situation (dotted red arrows), which should result in a large number of errors. However, the originally acquired tendency to avoid dark arms in this context (dotted green arrows) competes with the tendency to enter those arms, resulting in no net tendency to enter either type of arm. This is expressed as chance performance.

Therefore, elimination of this form of learning by the lesion should not be detectable in the different context. The findings therefore suggest that the ventral, but not dorsal, hippocampus mediated context-specific learning in these experiments.

The CCP task (aka conditioned place preference) was originally developed (Mucha, van der Kooy, O’Shaughnessy, & Bucenieks, 1982; Rossi & Reid, 1976; van der Kooy, Mucha, O’Shaughnessy, & Bucenieks, 1982) to study the (conditioned) affective properties of addictive drugs in drug-free animals, avoiding a major confound inherent in the self-administration procedure. In the triple dissociation experiment, we used the CCP with a food reinforcer as an example of a task in which no overt behavior is reinforced, because the rats have no control over the occurrence of the reinforcer. This is a central feature of Pavlovian learning. We assumed that the preference we observed for the food-paired arm was due to the expression of a learned association between the light stimulus in that arm and the rewarding properties of the food that was consumed in that arm. The elimination of the CCP by reinforcer devaluation, described previously, is consistent with this assumption. Spatial CCP. In a follow-up experiment, White and McDonald (1993) showed that rats acquire a CCP in the radial-arm maze when the maze is open to extramaze cues, and the food-paired and unpaired arms are on opposite sides of the maze and contain no distinguishing internal cues. To ensure that only extramaze cues were used to discriminate between the arms, the entire maze was rotated by one arm position to the left before each daily trial so that a different arm occupied each spatial location. As in the triple dissociation study, the CCP in this situation was impaired by lesions of the lateral amygdala, suggesting that it was also due to an association between the stimuli visible from the food-paired arm and the affective consequences of the food that was eaten there. Dorsal striatum lesions had no effect on the spatial CCP and, like the cue-based CCP in the triple dissociation experiment, its acquisition was facilitated by lesions of the fimbria-fornix (discussed later, under the heading of latent inhibition). The effect of hippocampus lesions on the spatial CCP is not as clear. Ferbinteanu and McDonald (2001) found that neurotoxic lesions of the total hippocampus impaired acquisition of the CCP with one unreinforced preexposure session and four training trials, but White and Wallet (2000) found that similar lesions had no effect on the CCP with no preexposure and two training trials. Evidence that the CCPs produced by two and four training trials are due to different kinds of learning may provide an explanation for this difference in the effect of hippocampus lesions. Naeem and White (2011) found that acquisition of the CCP produced by no preexposures and two training trials was impaired by lesions of the


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central nucleus of the amygdala (CNA) but not by lesions of the basolateral nucleus (BLA). In contrast, the CCP produced by one preexposure and four training trials was impaired by BLA but not CNA lesions. These findings suggest that although similar preferences are acquired with these two sets of parameters, they are, in fact, due to different kinds of learning with different anatomical substrates. Evidence for this idea comes from a study by Holland and coworkers (Holland, Han, & Winfield, 2002), who found that BLA and CNA lesions impaired different types of conditioned responses in a Pavlovian conditioning paradigm. These workers exposed rats to pairings of light and food, and found that the main effect of BLA lesions was to impair the rats’ tendency to respond to the food magazine, an apparent example of what Boakes (1977) called goal tracking. Lesions of the CNA, however, mainly impaired the rats’ tendency to approach the light conditional stimulus (CS), a type of response sometimes called sign tracking (Hearst & Jenkins, 1974) or autoshaping (P. L. Brown & Jenkins, 1968). Goal tracking and sign tracking are CRs acquired simultaneously during Pavlovian conditioning. Because they both involve approach responses, they can be differentiated only when the CS and the source of the unconditioned stimulus (US) are spatially separated (Zener, 1937). In the radial maze, both CRs could produce a preference, but they would be indistinguishable because a rat approaching the source of the US (the end of the arm in which the food was located on the training trial) would also be approaching the CS (the spatial stimuli that are visible from the end of the arm). However, the impairment of the two-training-trial CCP by CNA lesions suggests that it may have been due to a sign tracking CR, whereas the impairment of the four-training-trial CCP by BLA lesions suggests that it may have been due to a goal-tracking CR (Naeem & White, 2011). Goal tracking is due to an acquired CS–US association (stimulus substitution; Pavlov, 1927), as a result of which the CS evokes a representation of the US that includes its affective properties (Holland, 1984; Holland & Straub, 1979), leading the rat to approach the source of the US. This conditioned response is thought to be mediated by a circuit that includes the BLA, orbitofrontal cortex, and hippocampus, among other structures (Corbit & Balleine, 2005; Pickens et al., 2003; Vafaei & Rashidy-Pour, 2004). In contrast, sign tracking may be due to a CS–CR association, which causes the CS to elicit an approach response that does not depend on the affective properties of the CS and is mediated in circuits that do not appear to involve the hippocampus (Balleine & Killcross, 2006; Holland & Gallagher, 1999; Parkinson, Robbins, & Everitt, 2000). The possibility that the CCPs learned with two and four training trials are due to sign- and goal-tracking responses, respectively, together with the possibility that the goal-tracking response is mediated by a circuit that includes the hippocampus, and that the sign-tracking circuit does not involve that structure, could provide an explanation for the impairment of the four-training-trial CCP by hippocampal lesions (Ferbinteanu & McDonald, 2001), and for the lack of effect of similar lesions on the two-training-trial CCP (White & Wallet, 2000). This speculative discussion shows that much remains to be learned about the role of the hippocampus in CCP learning. Latent inhibition of CCP. White and McDonald (1993) found that when rats were given a single session of unreinforced

preexposure to the maze, a minimum of four training trials was required for them to express a preference for the food-paired maze arm. However, in the same conditions, rats with fimbria-fornix lesions expressed a preference after only one or two training trials. Ferbinteanu and McDonald (2001) also found that fimbria-fornix lesions facilitated CCP learning in rats given a preexposure trial followed by three training trials. McDonald and White (1995a) found that fimbria-fornix lesions had this facilitating effect only when made before the preexposure session, and not when made 24 hr after preexposure or on the day after the last training trial. This finding suggested that the fimbria-fornix lesions might interact with the learning that occurs during the preexposure session. When the preexposure session was omitted, rats learned the CCP with only two training trials (McDonald & White, 1995a). A CCP was also acquired after two training trials when preexposure consisted of exploring a similar radial maze located in a different room (with different extramaze cues) from the maze used for the training and test trials. These findings showed that during preexposure, specific information is acquired about the spatial cues that will later be paired with food, and that the presence of this information retards acquisition of the association that produces the CCP. On the basis of these results, we suggested that the impaired tendency for the extramaze cues to form associations with the food reinforcer caused by unreinforced preexposure to those cues is a case of latent inhibition (Lubow, Alek, & Arzy, 1975; Lubow & Moore, 1959). Although an intact fimbria-fornix is required for learning during unreinforced preexposure, the effect of hippocampus lesions in this situation is still not clearly understood. Neither neurotoxic lesions of the entire hippocampus (White & Wallet, 2000) nor temporary inactivation of its dorsal part (Gaskin, Chai, & White, 2005) eliminated the retarding effect of unreinforced preexposure on subsequent CCP learning with two training trials. These findings suggest that the hippocampus is not involved in latent inhibition in this learning situation. In other learning situations, hippocampal lesions have varying effects on latent inhibition. There are several reports that various hippocampus lesions fail to affect latent inhibition in conditioned taste-aversion learning (Gallo & Candido, 1995; Purves, Bonardi, & Hall, 1995; Reilly, Harley, & Revusky, 1993), or in a conditioned emotional response (CER) paradigm (Clark, Feldon, & Rawlins, 1992), although there is at least one report that electrolytic lesions of the dorsal hippocampus do impair latent inhibition of a CER (Kaye & Pearce, 1987). Injection of the NMDA receptor antagonist MK-801 into the dorsal hippocampus also failed to affect latent inhibition of a conditioned emotional response (Zhang, Bast, & Feldon, 2000), and hippocampus lesions did not impair latent inhibition of a conditioned eyeblink response in rabbits (Shohamy, Allen, & Gluck, 2000). On the other hand, there are several reports that neurotoxic lesions of the entire hippocampus impair conditioned orienting and approach responses (Han, Gallagher, & Holland, 1995; Kaye & Pearce, 1987; Oswald et al., 2002); these effects could lead to misinterpretation of the effects of hippocampus lesions in some latent inhibition paradigms. Obviously, this issue requires further research. In a related series of experiments the ability of rats to acquire a CCP for adjacent radial maze arms was studied (see summary in Figure 3). In this configuration, it was found that a minimum of three unreinforced preexposure sessions (in the same maze in the same room used for training and testing), followed by four training


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White, 2007) showing that latent learning in this situation is mediated by a circuit that includes the fimbria-fornix and dorsal entorhinal cortex, but not the hippocampus. The fimbria-fornix/ entorhinal circuit may participate in the acquisition of pure spatial information, possibly constituting a simplified cognitive map the existence of which may retard or facilitate subsequent learning involving the hippocampus and other structures.


Win–Shift Task

Figure 3. Effects of various treatments on radial maze CCP learning with separated and adjacent food and no-food arms. Unreinforced preexposure to the maze retards CCP learning with separated arms (latent inhibition [LI]) and facilitates CCP learning with adjacent arms (latent learning [LL[). Fimbria-fornix lesions made before, but not after, preexposure block both of these effects. Dorsal hippocampus lesions made before or after preexposure had no effect on LI; these lesions were not tested on LL because an intact hippocampus is required to learn the adjacent arms CCP. Temporary inactivation of the dorsal hippocampus during preexposure had no effect on either LI or LL. Temporary inactivation of the dorsal entorhinal cortex, or disconnection of the dorsal entorhinal and the fimbria-fornix blocked LL in the adjacent arms configuration. References and addition discussion may be found in the text.

trials, was required to learn the CCP (Chai & White, 2004), an apparent case of latent learning (Blodgett, 1929). As in the case of the separated arms CCP, fimbria-fornix lesions made before, but not after, the preexposure sessions impaired learning the adjacent arms task (Chai & White, 2004). Hippocampus lesions impaired adjacent arms CCP learning when made either before or after preexposure (Chai & White, 2004), and temporary inactivation of the dorsal hippocampus during either the training trials or the test trial blocked adjacent arms CCP learning (White & Gaskin, 2006). However, temporary inactivation of the hippocampus during the preexposure sessions had no effect on either latent learning or latent inhibition (Gaskin et al., 2005), suggesting that the hippocampus is not involved in the unreinforced spatial learning that occurs during preexposure. Our finding that inactivation of the hippocampus during preexposure has no effect on latent learning in the adjacent arms CCP task is consistent with a previous demonstration that large electrolytic lesions of the dorsal hippocampus do not affect the latent learning that occurs when rats explore a maze in which they subsequently have to learn the location of food (Kimble & BreMiller, 1981; Kimble, Jordan, & BreMiller, 1982). The finding that fimbria-fornix lesions made before, but not after, preexposure impair adjacent arms CCP learning led to another study (Gaskin &

The win–shift task depends on a memory for the location of food. Location information is at least partly obtained from reference to visual cues in the environment around the maze (Mazmanian & Roberts, 1983; Olton & Collison, 1979), and, in some cases, this may involve relational or spatial computations using this information (Suzuki, Augerinos, & Black, 1980). Rats are also capable of orienting and locating themselves on the maze using egocentric vestibular information (Rossier, Grobety, & Schenk, 2000; Schenk, Grobety, & Gafner, 1997; Sharp, Blair, Etkin, & Tzanetos, 1995). Regardless of which of these sources of spatial information is used to discriminate the arm locations, the locations that contain food change with each arm entry on this task, so a working memory function is also critical (Beatty & Shavalia, 1980; Knowlton, Shapiro, & Olton, 1989; Olton & Papas, 1979). It was appreciated that the win–shift task was the most complex of the three used in the dissociation, requiring several different kinds of learning. In order for a rat to enter each arm only once, it has to be able to discriminate among the arms. Because there are no consistent local cues, they do this by associating each arm with features of the surrounding environment. Because every environmental feature can be seen from more than one arm, no single stimulus is associated with any individual arm. The use of these ambiguous cues to discriminate among the arms requires a spatial map in which each place (arm location) is identified by its proximity to a unique combination of cues. McDonald and White (1995b) examined these ideas by training rats to discriminate arms on a radial maze using a discrete trial paradigm. On each trial, a rat was placed on the center platform of the maze and allowed to forage for food in the open arms. Food was always placed in the same arm, which was open on every trial. A second arm, which never contained food, was always open either to the right or left of the food arm. When the two open arms were adjacent to each other, the cues available to discriminate between them were ambiguous. However, when the two open arms were on opposite sides of the maze, different sets of cues unambiguously identified each arm. Normal rats learned both discriminations without difficulty. Rats with fimbria-fornix lesions were unable to learn the adjacent arms discrimination, but acquired the separated arms discrimination normally, showing that cue ambiguity is a factor that makes correct performance of the discrimination dependent on the unique representational capacity of the hippocampus. Interestingly, neither fimbria-fornix nor dorsal striatum lesions impaired learning the separated arms discrimination; only lesions of both structures prevented learning this task (McDonald & White, 1995b). This finding suggests that, like the plus-maze (Packard & McGaugh, 1996), the separated arms discrimination is a dual solution task. Both the hippocampus and dorsal striatum systems were able to represent this learning situation, as a spatial


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map and as an S-R association, respectively. Both representations were able to produce correct behavior, so both had to be disabled to prevent correct performance of the task.


Interactions Among Systems Perhaps the most frequently asked question about the multiple memory systems hypothesis is, “What determines which system controls behavior?” The answer to this question in previous articles that reviewed evidence for the theory (White & McDonald, 2002; White, 2008) was based on the working hypothesis that each system functions independently of the others, with no direct communication among them. Selective control is due to differences in the innate capacity of each system to represent a specific type of information, and to the nature of the information required to produce correct behavior in any situation. The system that forms the best, most coherent representation of the information required for correct performance is the one that controls behavior. There is now evidence suggesting that the notion of total independence of the systems may be wrong, and that direct transfer of information from one system to another probably occurs. In this section, the original idea of total independence will be reviewed. The evidence for direct transfer of information will be described, and the changes to the original idea that are required will be discussed.

Independent Parallel Systems The independent model is perhaps most clearly supported by the Packard and McGaugh (1996) plus-maze study, in which the nature of the information controlling correct responding in a plusmaze was assessed with a probe trial. Early in training, most rats expressed place learning, and temporary inactivation of the dorsal striatum impaired this tendency, resulting in random behavior. Later in training, most rats expressed response learning, and inactivation of the dorsal striatum impaired that behavior. But in this case, the resulting behavior was not random; instead, most rats switched back to expressing place learning. The behavioral switch that occurred with extended training had no effect on the hippocampal representation that produced the place choice. That representation remained intact and reassumed behavioral control when the dorsal striatum system was disabled. These findings are exactly what would be predicted by the notions of coherence and output competition, which will now be explained in detail. Coherence. As already suggested, a basic assumption of multiple-memory systems theory is that each system has the capacity to form representations of a specific type of associative relationship (e.g., S-R, S-S, stimulus-outcome (S-O), response outcome (S-R). Any learning situation that includes one of these associative relationships will cause that relationship to be represented in the system with the matching capacity. The notion of coherence describes the degree to which a system’s representative capacity is engaged by a particular situation. If the associative elements of a situation are closely matched by a system’s representative capacity, a highly coherent representation will be formed. If the match is less than perfect, less of the system will be engaged, resulting in a less coherent representation. An implication of the coherence notion is that all systems are continually forming their unique representations of the relationship

between the individual and the world (Yeshenko, Guazzelli, & Mizumori, 2004). In some situations, there may be no information that is compatible with the capacity of a system, so the system will have no influence on behavior in that situation. Others may result in multiple highly coherent representations that are capable of influencing behavior. These representations may all be activated when the individual is in the situation they represent, leading to several possible results. In this model, the coherence of a representation in a system determines the strength of the output from the system when the representation is activated by external input. Because the systems do not communicate directly in this scheme, the only way they interact is when their outputs converge, most likely at more than one place in the brain. Two kinds of output interactions are possible: Cooperative interactions occur if the behaviors promoted by two or more systems are the same; competitive interactions occur when two systems acquire coherent representations that lead to different behaviors. If two systems promote the same behavior, one or both of them may dominate in a situation. In this case, impairing the function of either system may have little or no effect on performance, but the conclusion that the impaired system is not involved in controlling the behavior would be wrong. The dual-solution tasks, such as the plus-maze (Packard & McGaugh, 1996) and the separated arms discrimination in the discrete trial task on the radial maze (McDonald & White, 1995b), are examples of this situation. A different form of cooperation can occur if two or more different kinds of information represented in different systems contribute different behaviors that result in correct performance. The two-process theory of active avoidance (Mowrer, 1956; Mowrer & Lamoreaux, 1946) is an early example of such a situation. If two systems promote different behaviors, two kinds of results can occur, depending on whether the situation allows the behaviors to be expressed. In the win–stay task, both dorsal-striatum- and hippocampus-based behaviors can be expressed so that, as described previously, the outputs of these systems can either cooperate (as in normal win–stay learning) or compete (as in reversal training). The win–stay situation does not permit expression of the representation of the situation acquired by the amygdala, so there is normally no evidence for the existence of this representation. However, its existence can be detected when the situation is changed into a preference test, which allows expression of the amygdala-based CCP. Evidence compatible with coherence. Several lines of evidence are consistent with the notion of coherence. Chang and Gold (2003) gave rats 100 training trials on the plus-maze task with a probe test after every 20 trials. On the first probe test, five out of six rats made the place choice; on the second probe, only two of the six made the place choice—the rest had switched to making the response choice. Each rat had dialysis probes in the hippocampus and the dorsolateral striatum. Samples were collected during the training trials and analyzed for acetylcholine (ACh) content. On the first few trials, ACh in the hippocampal samples increased by about 60% compared to baseline and remained at that level throughout the rest of the experiment, even after the rats had switched to the response choice. No increase in ACh levels was seen in the striatal samples during the early trials, but a gradual increase began after a few trials and eventually reached a level of



about 150% of baseline. This increase roughly corresponded to the shift from place to response choices on the probe tests. On the assumption that ACh release is a marker for neural system activation, these results can be interpreted to mean that the ACh release reflects the acquisition of a coherent representation of a learning situation. The rapid increase of ACh release in the hippocampus reflects rapid acquisition of a coherent S-S representation, and the slow increase of ACh release in the dorsal striatum represents the incremental acquisition of an S-R representation over a large number of reinforced trials. When the latter becomes sufficiently coherent, the output from the dorsal striatum system starts to win the competition with the output from the hippocampus, resulting in the switch from the place to the response choice on the probe trials. These findings and others relating to the role of ACh in memory systems (e.g., Colombo & Gallagher, 1998) have been reviewed by Gold (2003). Colombo and coworkers (Colombo, Brightwell, & Countryman, 2003) trained rats on the plus-maze task and tested them with a probe trial. Immediately or 1 hr after the probe, the rats were injected with a ketamine–xylazine solution and their brains prepared for examination of phosphorylated cAMP response elementbinding protein (pCREB) and c-Fos. In rats injected immediately after the probe trial, there were no differences in levels of these substances in the hippocampus and dorsal striatum, regardless of which response they had made on the probe trial. However, in rats killed 1 hr after the probe trial, those that made the place choice had higher levels of pCREB in the hippocampus than in the striatum. The opposite was true for rats that made the response choice. These observations are consistent with several of the assumptions about the coherence of learned information represented in the brain. First, the lack of a relationship between the response made on the probe trial and pCREB activation in the hippocampus and dorsal striatum immediately after training is consistent with the idea that all systems are actively learning at all times. The probe trial was a new experience for these rats, and both the systems examined would therefore be expected to actively consolidate new information during the period immediately following the trial. Second, the correlation between the system exhibiting persistent pCREB activation and the type of information associated with the response made on the probe trial is consistent with the idea that each system is specialized to represent a particular type of information. Thus, the representation that led to the place choice was active in the hippocampus, and the representation that led to the response choice was active in the dorsal striatum. These findings and others related to the relationship between transcription factors and the nature of the information represented in different brain areas have been reviewed by Colombo (2004). Finally, Boucard, Mons, Micheau, and Noguès (2009) attempted to observe the development of coherent representations in the hippocampus, amygdala, and dorsal striatum when these systems were activated by the appropriate learning situations. Two tasks were run in a swimming pool using a four-arm maze. In a cued task, mice had to swim from a start arm to a goal arm marked by cues covering the maze walls; in a place task, the mice had to enter the arm in a particular location regardless of which one contained the cues. Performance of the cue task was impaired by amygdala, but not hippocampus, lesions; performance of the spatial task was impaired by hippocampus, but not amygdala, lesions. Mice were

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given 5 days of training on either task and then killed, and their brains prepared and examined for activation of the immediate early gene, zif-268, in 22 brain areas. Following training on the place task, the correlations of zif-268 activation in most areas were significantly higher with hippocampal zif-268 levels than with amygdala zif-268 levels. Following training on the cue task, the correlation of zif-268 activation in most areas was significantly higher with both amygdala and dorsal striatum zif-268 levels than with hippocampal zif-268 levels. Thus, training on different tasks created clusters of zif-268 activation focused on the central structure of the system thought to represent the information required to perform each task. Interestingly, the fact that training on the cue task increased the correlations with both the dorsal striatum and the amygdala is consistent with the acquisition of both dorsal-striatum-based win– stay and amygdala-based CCP behaviors during win–stay training (McDonald & Hong, 2004). These findings are consistent with the idea that the memory systems are composed of central structures that are the basis of the unique representational capacity of each system. Most of the rest of each system includes structures that are not unique to that system, but have connections with at least two, and usually all, of the central structures. The data of Boucard et al. (2009) suggest that these structures can participate in forming representations of information with each of the central structures. Connectivity is dynamically shifted to the central structure that is activated by ongoing experience. This allows systems that are partly composed of the same structures to function in parallel. These findings also emphasize the idea that memories (or representations of information) are not stored in individual brain areas, but are distributed across a large number of different structures. There may be a very small number of structures that are unique to each type of representation. Output competition. Prefrontal cortex. Previously (White & McDonald, 2002), it was suggested that the prefrontal cortex could be one location for competitive interactions among the outputs of memory systems. Parts of prefrontal cortex are implicated in cognitive functions that support flexible responding to achieve goals. These processes have been referred to collectively as executive function (Chudasama & Robbins, 2006; Dalley, Cardinal, & Robbins, 2004; J. Duncan & Owen, 2000; Kolb, 1984; Moscovitch, 1992; Wise, Murray, & Gerfen, 1996) although decision making now seems to be the preferred term (e.g., Bechara & Van der Linden, 2005; Kennerley & Walton, 2011; Rushworth, Noonan, Boorman, Walton, & Behrens, 2011; Weller, Levin, Shiv, & Bechara, 2007; Wunderlich, Rangel, & O’Doherty, 2009). It is also possible to understand these functions in terms of competition for control of behavior among the outputs of neural systems that process and store different kinds of information. The prefrontal cortex receives major inputs from the hippocampus, amygdala, and dorsal striatum (Groenewegen, Wright, & Uylings, 1997), the central structures of the memory systems described here. Major prefrontal outputs engage brain areas involved in motivation and attention, such as the nucleus accumbens and dorsomedial striatum (Ongür & Price, 2000), as well as voluntary behavior via projections to the motor cortex (Kolb, 1984; Reep, 1984). These connections allow the prefrontal cortex to integrate information recalled from memory in the different


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systems with information about external context and motivational state, a process that results in allowing one (or more) types of information stored in or currently being processed in the memory systems to assume control over behavior (McDonald, King, Foong, Rizos, & Hong, 2008). Several studies have shown that neurotoxic lesions of the medial prefrontal cortex impair the ability of rats to switch from a hippocampus-based place-learning response to a dorsolateralstriatum-based egocentric response and vice versa (de Bruin, Swinkels, & de Brabander, 1997; Ragozzino, Detrick, & Kesner, 1999). Using a plus-maze, Rich and Shapiro (2007) trained rats to use either place or response strategies on different test days. Inactivation of prelimbic/infralimbic cortex impaired the ability of the rats to remember the last valid strategy, suggesting that a function of the inactivated area is mediation of competition between outputs of the dorsal striatum and hippocampus systems. This interpretation was supported by a second study (Rich & Shapiro, 2009) showing that prefrontal cortex neurons encode and predict the strategy a rat will use on each trial. The fact that the strategies originate in different systems suggests that the prefrontal cortex is involved in mediating the competition between them. Another series of studies with similar implications used instrumental behavior that, like behavior on the plus-maze, switches from cognitive, flexible, goal-directed responding to habitual, fixed responding with extended training. Temporary inactivation of the prelimbic area caused the appearance of habitual responding without the initial phase of goal-directed behavior (Killcross & Coutureau, 2003), and inactivation of the infralimbic portions of the medial prefrontal cortex caused rats to switch from habitual responding back to goal-directed responding after extended training (Coutureau & Killcross, 2003). In another study (McDonald, Foong, Ray, Rizos, & Hong, 2007), rats with medial prefrontal cortex lesions were reinforced for entering lit arms in the win–stay task and then tested on reversal learning (reinforced for entering dark arms) on the same maze. The lesioned rats acquired the reversal in fewer trials than controls, suggesting that the influence of ventral-hippocampusbased learning to avoid the dark arms was reduced in favor of the newly reinforced dorsal-striatum-based learning to enter those arms. Other evidence (Tait & Brown, 2007) suggests that other parts of prefrontal cortex, such as the orbital-frontal portion, are also involved in influencing memory system control during reversal learning. Striatal output. Another mechanism for competition among the outputs of memory systems was recently suggested by Gruber and McDonald (2012). These authors propose that competitive interactions can occur via inhibition among circuits that flow through the striatum. According to this idea, gamma aminobutyric acid (GABA) mediated inhibition within striatal circuits modulates the response of medium-sized spiny projection neurons in the striatum. These neurons receive a wide range of input from learning and memory systems (e.g., the hippocampus and amygdala) and related cortical and subcortical sites (Bennett & Bolam, 1994; Kita, 1993; Pennartz & Kitai, 1991). The theory suggests that GABAergic fast-spiking interneurons inhibit specific learning and memory system loops depending on which system is activated at the time of training or testing. GABAergic inhibition of one neural circuit mediating the output of a learning and memory system by the output of another system could also occur in striatal output

sites such as the globus pallidus and substantia nigra (Bevan, Booth, Eaton, & Bolam, 1998; Millhouse, 1986; Parent et al., 2000). Another possible mechanism of interaction between learning and memory systems might involve a loop in which outputs of striatal medium-sized spiny neurons project to the dopaminergic inputs to the so-called direct and indirect outputs of the striatum (Gruber & McDonald, 2012). According to this idea, activation of the direct pathway promotes behavioral output, whereas activation of the indirect pathway results in suppression of learned behavior. The direct pathway predominantly contains D1 receptors, whereas the indirect pathway contains D2 receptors. Thus, the balance between the outputs can be modulated by activation of different dopamine receptors, which could result in the inhibition or activation of particular learned responses.

Information Transfer Between Systems Fear conditioning. In their analysis of fear conditioning, Rudy and colleagues (Rudy, Huff, & Matus-Amat, 2004) argue that the amygdala is a central structure for fear conditioning, whether the CS is a temporally discrete stimulus or a context composed of a constellation of permanent stimuli. In the latter case, the hippocampus system is also normally involved in fear conditioning (see Antoniadis & McDonald, 2000, for review), probably because it is the system that is capable of forming a coherent representation of complex stimulus situations (contexts), called configural representations by Rudy et al. However, Rudy and colleagues review evidence that the hippocampus-based configural representation is not the only representation of the context formed during contextual fear conditioning. There is a second nonconfigural representation formed outside the hippocampus, consisting of a series of discrete stimuli. They suggest that these two representations can form associations with the fear information in the amygdala, that they normally compete to form this association, and that when the hippocampus is functioning normally, it always wins the competition. Biedenkapp and Rudy (2008) showed that this competition could occur in the basolateral amygdala (see also Maren & Hobin, 2007). These findings exemplify one possible form of cooperative interaction among memory systems, in which the hippocampus provides information about the external world to the amygdala, which associates this information with an emotional state, leading to a wide array of autonomic and skeletal responses. As a result of this cooperative process, the external stimuli alone come to elicit the responses in the absence of the original fear-producing event. Pavlovian-to-instrumental transfer (PIT). In PIT (Estes, 1943; Lovibond, 1983), rats are first trained to make an instrumental response; then the reinforcer is paired with a neutral stimulus in a Pavlovian conditioning paradigm. Subsequently, the CS potentiates the instrumental response. Using two instrumental responses with different reinforcers, and two CSs, Corbit and colleagues (Corbit & Balleine, 2005; Corbit & Janak, 2007) have shown that this effect is specific to the reinforcer associated with each response, and that the basolateral amygdala and dorsolateral striatum are critical for this effect. Although detailed evidence is not yet available, it is possible that this is a case of a cooperative interaction between the amygdala and dorsal striatum systems in which specific information transferred from one system to another


selectively potentiates specific behaviors mediated by the latter system.


Input Competition White (2004) suggested that because all three proposed memory systems receive input from the same cortical areas, representations of the external world formed in the cerebral cortex provide a major source of input to the systems (see White & McDonald, 2002, for anatomical details). Furthermore, each system also has the anatomical connections to influence the cortex, suggesting the possibility that output based on the representations formed in each system can modify cortical representations (Wise et al., 1996). Subsequently, these modified cortical representations would condition the information passing through the cortex to each of the systems influencing their representations. As suggested earlier, it is possible to imagine a mechanism of this kind as the basis for the Pearce–Hall theory of competition for attentional processing (Mackintosh, 1977; Pearce & Hall, 1992). One possible implication of this idea is that when the hippocampus forms a representation of a situation, it could actively inhibit the acquisition of representations by other systems (Driscoll, Howard, Prusky, Rudy, & Sutherland, 2005). The data described in the section on contextual fear conditioning could also be an example of this process, with the acquisition of a configural representation of the context by the hippocampus impeding the formation of a representation of the individual cues in the context by an extrahippocampal system. When the hippocampus has acquired a relational representation of an episode based on the cortical circuit activated during an event, the hippocampus can reactivate this representation (pattern completion), resulting in a memory for the episode. As already suggested, the same cortical circuit may also be part of the representations of the same situation formed in other systems. Hippocampal reactivation might strengthen the hippocampal connectivity for subsequent control by this circuit, and this process might weaken or interfere with the cortical contribution to the representations in the other systems. Future research is required to test this idea.

Human Experiments Although we were of course aware of the descriptions by Milner and colleagues (Milner, 1958, 1959; Milner, Corkin, & Teuber, 1968; Milner & Penfield, 1955–1956; Scoville & Milner, 1957) Scoville’s patient HM’s memory deficits, suggesting the existence of independent memory systems that store different kinds of information, these were not central to our original thinking about multiple memory systems. Furthermore, although we imagined that there might be a very general correspondence between our findings with rats and what might eventually be discovered in humans, we did not anticipate the close correspondence that has been revealed as the investigation of human memory using both brain-damaged patients and imaging techniques in normal participants has developed over the past 20 years. We do not attempt a complete review of this literature here, but just mention a few highlights that illustrate the correspondence between the human and animal findings.

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Brain-Damaged Participants Knowlton, Mangels, and Squire (1996) used a probabilistic classification learning task in which each of four individual visual cues predicted one outcome (sunshine) 25%, 43%, 57%, or 75% of the time, and a different outcome (rain) 75%, 5%7, 43%, or 25% of the time. On each trial one, two, or three of these cues were displayed on a computer screen and subjects pressed a key to predict the weather. Feedback (i.e., correct or incorrect prediction) was provided. The study replicated previous research (Knowlton, Squire, & Gluck, 1994) showing that probabilistic learning in this task is nearly normal in patients with bilateral damage to the hippocampus or to the diencephalic midline, suggesting that performing the task does not require the use of cognitive/declarative memory, a conclusion supported by the fact that the amnesic participants who learned the task were later unable to describe any of the events that had occurred during the test session. Participants with Parkinson’s disease failed to acquire the probabilistic classification task, performing at approximately 50% to 55% correct after 50 trials, whereas age- and education-matched control subjects performed at approximately 70% correct (Knowlton, Mangels, & Squire, 1996). The same Parkinson’s patients displayed normal cognitive/declarative memory for the training session. This double dissociation closely parallels the dissociations between hippocampus and dorsal striatum function in rats, as described here. Bechara et al. (1995) studied three patients, one with bilateral amygdala damage, one with hippocampal damage, and one with damage to both structures. The participants were presented with a series of solid colors on a screen, one of which was paired with a startling noise. Electrodermal responses to the noise and to the colors were measured. The noise elicited a response in all participants, and, after a number of trials, the color associated with the noise elicited a response in the in the participant with hippocampal damage, but not in those with impaired amygdala function. Following the test, the participants were asked a series of questions about the experimental situation. The amygdala patient was able to answer the questions correctly, but the patients with hippocampal damage had very poor recall for the situation. This dissociation of amygdala-based memory for a Pavlovian-type association and hippocampus-based episodic memory for recent events is consistent with the dissociation between the information-processing functions of the hippocampus and amygdala demonstrated in rats. In another dissociation involving the amygdala, Johnsrude and coworkers (Johnsrude, Owen, Zhao, & White, 1999) adapted the rat CCP task for humans. Participants were presented with three black squares on a computer screen and told to guess which one contained a red ball. Touching a square randomly revealed either the red or a black ball. The red ball was followed by a small food reward; the black ball was not. Each ball was presented against a background of one of six abstract patterns. All patterns were presented on an equal number of trials; two were paired with the reward 90% of the trials on which they appeared, two were paired on 50%, and two on 10% of the trials on which they appeared. As a distractor task, participants were told to count and remember how many times the red ball appeared in each of the three black squares. All participants were able to remember accurately the number of times the red ball appeared in each black square. When asked to


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choose between pairs of background patterns, participants preferred the patterns that had been paired with food 90% of the time over the patterns that had been paired 10% of the time. When asked to explain their preferences, they gave various confabulated reasons, but none indicated that they were aware of the reward contingencies, suggesting that their preference may have been due to an unconscious conditioned response. In a second study, Johnsrude and coworkers (Johnsrude, Owen, White, Zhao, & Bohbot, 2000) found that patients with unilateral amygdala damage failed to show a preference for the patterns paired with reward 90% of the time, but were able to remember accurately the number of times the red ball appeared in each black square. In contrast, patients with unilateral frontal cortex damage exhibited normal preferences, but were unable to report accurately the number of times the red ball had appeared in each black square. This dissociation between preference conditioning and a cognitive task is consistent with dissociations in rats described here.

Imaging in Normal Participants The first imaging study with a design based on findings with rats (Iaria, Petrides, Dagher, Pike, & Bohbot, 2003) used two virtual reality eight-arm radial maze tasks, one that could be acquired using cognitive/spatial learning and one that favored a habit/ egocentric learning strategy. Functional magnetic resonance imaging (fMRI) was used to image brain activity during learning. Following acquisition, participants were asked how they had solved the tasks. Based on their responses, they were categorized as having used a cognitive strategy (i.e., use of extramaze landmarks to guide spatial choice behavior) or a habit strategy (i.e., use of an egocentric turning response from a single maze starting point to guide choice behavior). These categories were used to group the fMRI results. Participants using a spatial strategy showed increased activation in the hippocampus; participants using a nonspatial strategy showed increases in activity within the caudate nucleus (dorsal striatum), a dissociation that is consistent with those shown in rats. A subgroup of participants used a spatial strategy at the start of training but switched to an egocentric strategy at some point during training. The imaging results for these participants showed that brain activation tracked the strategy switch, shifting from hippocampus to caudate nucleus during training. This finding is consistent with the switch from hippocampus to dorsal striatum control of behavior over trials shown in several rat studies discussed here. Another demonstration in humans of the shift in behavioral control that occurs with repeated performance was based on the rat studies by Dickinson and colleagues (Adams & Dickinson, 1981; Dickinson, Nicholas, & Adams, 1983), described earlier, which used reinforcer devaluation to show that bar pressing for food shifted from cognitive control that is sensitive to devaluation to habitual control that is not sensitive to reinforcer devaluation. Tricomi, Balleine, and O’Doherty (2009) trained human participants to press buttons for food rewards. One group was given a small number of training trials; another was overtrained. One of the foods was then devalued by selective satiation (i.e., overeating of the food), and the participants were then retested in an extinction condition. The rate of responding for the devalued food decreased in the group given few trials, but was unaffected in the group that was overtrained. fMRI scanning during acquisition of

the button-pressing task showed gradually increasing activity in the posterior dorsolateral striatum in response to the onset of task-related stimuli as training progressed. These findings are consistent with conclusions about the neural control of cognitive and habit learning in rats from a number of different lines of evidence discussed here, and as recently reviewed by Balleine and O’Doherty (2010). Another fMRI neuroimaging study (Hartley, Maguire, Spiers, & Burgess, 2003) examined the functions of the hippocampus and caudate nucleus in navigational behavior in large-scale virtualreality towns. In one condition, participants could learn about a town using free exploration; in a second condition, they learned to move between points in a different town by following a fixed route. The participants were then tested on their ability to devise novel short routes between points (wayfinding) and to follow specific routes (route following) in the towns while in an fMRI scanner. Accurate wayfinding was associated with activation of the right anterior hippocampus, whereas route following was correlated with activation of the head of the caudate nucleus, another case in which findings with human participants corresponds to findings with rats. Finally, several neuroimaging studies by Poldrack and colleagues (for reviews, see Poldrack & Foerde, 2008; Poldrack & Packard, 2003) have used the basal-ganglia-dependent probabilistic classification task previously described (Knowlton, Squire, & Gluck, 1994) to compare activation patterns in the medial temporal lobe and the basal ganglia during task acquisition. A meta-analysis of data from four fMRI studies of this task revealed consistent activation of corticostriatal circuitry during learning. As already discussed, this is consistent with findings in rats. The additional observation that midbrain dopaminergic regions are activated during probabilistic classification learning in humans is also consistent with rat studies indicating a role for dopaminergic function in S-R learning (e.g., Packard & White, 1991; White & Viaud, 1991). Poldrack et al. (2001) found that as acquisition of the probabilistic classification task progresses and caudate nucleus activation increases, medial temporal lobe regions (including the hippocampus) become significantly less active when compared with baseline, and activity in the two structures is negatively correlated. This correlation is consistent with the possibility discussed earlier that some form of direct mutual inhibition of memory systems may exist in some learning situations.

Conclusions In this article, we have reviewed how the thinking that led to the original triple dissociation of memory systems experiment have developed and changed over the past 20 years. Among the more important points reviewed are • New evidence from both rats and humans is consistent with the basic idea that different kinds of information are processed and stored in different brain systems. • This evidence is also consistent with the ideas that the hippocampus, amygdala, and dorsal striatum are central structures in these systems. • Evidence that all three systems acquire their unique types of new information (i.e., learn) more or less continuously has been described. Moreover, these basic processes appear to occur independently in each system.


• Competition for control of behavior by the outputs of the systems is one way in which they interact, but other forms of direct interaction among the systems may also occur. As the title of the article suggests, the multiple parallel memory systems story is ongoing. Although some progress has been made, there is still a long way to go before we understand exactly what kinds of information are used by the brain to represent the world and the individual’s relationship to it, how this information is organized in the brain, and how it is used to control thought and behavior.


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Received July 18, 2013 Revision received October 1, 2013 Accepted October 1, 2013 䡲

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