Conscious Learningsubject To Conscious And Unconscious Recall

A. Declarative Learning

Human declarative learning is what we typically think of when we think of "learning." This is the conscious acquisition of new facts, or the formation of memories for events that occur in our lives, that are available for subsequent recall at will. The extent to which you remember what you read in this book will depend upon the processes of conscious declarative learning. Of course, the extent to which you remember this book will also depend on a large number of other factors, such as motivation, attention, and level of arousal. Thus, human declarative learning, and likely most analogous forms of learning in animals, is subject to a wide variety of modulatory factors.

For example, particularly robust memory for single events is typically referred to as "flashbulb" memory in humans. Most Americans have shared several examples of this type of memory, the most recent example being the terrorist destruction of the Twin Towers in New York City. Like most people, I remember vividly how I learned of the attacks, and I am sure I will never forget seeing live on television the second tower collapse. Flashbulb memories are usually associated with a high state of arousal or a high level of emotional valence—an example of the strong modulatory influences to which learning is subject to.

As are the other types of conscious learning we will discuss in this section, declarative learning is dependent upon the hippocampus. In later sections of the book we will return to the importance of modulatory influences on hippocampus-dependent learning and discuss some likely molecular mechanisms underlying this effect.

It is difficult to model declarative learning in nonhuman animals because the behavioral output for these types of memories is actually quite subtle. In most cases, it is not even clear what the relevant types of learning might be in lower animals. In part for this reason, most of what we know about declarative learning comes from human studies, in particular studies of patients with hippocampal lesions.

Because I am largely focusing on studies in rodents in this book, I am not going to go into much detail on these important and interesting human studies. Moreover, there have been many excellent reviews of these types of studies and their relationship to learning and memory theory (see references 1, 2, 4, 12, and 13). Suffice it to say for our purposes that a number of classic studies of humans with hip-pocampal lesions led to the dissociation of declarative from nondeclarative forms of memory in humans.

Declarative memory is that type of memory that is lost when a human suffers hippocampal damage—this includes the capacity to form memories for facts, names, places, and personal experiences (Figure 3). Hippocampal damage results in anterograde amnesia for these types of memories; that is, there is a loss of the capacity to form new memories. Old memories (> about 1 year) are largely spared (i.e., there is relatively little retrograde amnesia). Nondeclarative forms of memory such as sensitization, motor learning, and delay classical conditioning are spared in humans with hip-pocampal lesions.

It is difficult to imagine a rodent model for declarative memory, but there is one potentially parallel type of learning in rodents that I will mention briefly. Because toxic plants and other poisonous foodstuffs coexist with most animals, conditioned taste aversion evolved to protect animals from poisoning themselves out of the gene pool. However, avoidance of something that is toxic is not possible if it has been ingested in lethal quantities, so a supplementary behavior has also evolved to protect animals from toxic foods. Neophobia is the characteristic fear of novel foods, and ensures that animals ingest only small quantities, as if to sample the food to determine if it is safe to eat. If the animal develops illness, a conditioned taste aversion results, and this foodstuff will be avoided on future encounters. If no illness results, and assuming the food is reasonably palatable, animals will increase their intakes on subsequent exposures. This is readily demonstrated in the laboratory. When rats or mice are presented with highly palatable solutions of novel tastes such saccharin or sucrose, they will consume small amounts on the first exposure; on subsequent exposures, the animals consume more (Figure 13). This attenuation of neophobia is a behavioral measure of memory for the novel taste and is part of a process of familiarization to the formerly novel taste. There is a fairly clear consensus that the insular cortex is the primary site of learning and memory for novel tastes, so this form of learning is clearly not strictly analogous to human declarative learning.

However, it does depend on the cerebral cortex as its storage site, as is likely in human declarative memory. Furthermore, it is reasonably analogous to a human learning a "fact," in this case what something tastes like, and having that information available for conscious recall.

Finally, declarative learning is generally associative, although not in the sense of classical associative conditioning where a cause-and-effect relationship is learned. Most declarative learning does not take place in a cognitive vacuum, but items are typically learned in the context of other related facts or objects. A good example is learning someone's name. Learning a name is a declarative learning event certainly, and you can list off the names of all the people you know well as a reiteration of a list of "facts." However, each name also serves as a descriptor of an individual and is associated with that person, their face, their house, and so on. This type of multiple association for learned facts (i.e., declarative learning) is the rule rather than the exception. It is likely that most declarative learning occurs as learning something within a variety of contexts (i.e., other facts or places with which the fact is associated). I stress this point because it is important to keep it in mind as we begin to explore the molecular basis for declarative learning. Certainly, many of the molecular mechanisms that are discovered as subserving what we have defined as associative conditioning may translate directly as mechanisms for declarative learning. Stated more strongly, at this point, it is appropriate to hypothesize that associative molecular mechanisms will be part of the molecular infrastructure of declarative learning.

B. Spatial Learning

Another example of hippocampus-dependent learning in both humans and lower animals is spatial learning. Obviously animals must learn to navigate their environment and learn to associate particular places with particular items or events. This type of learning has been the classically defined learning system in which the hippocampus is involved. A wide variety of different studies have shown that molecular or anatomical lesions of the hippocampus lead to spatial learning deficits in both humans and lower animals. Also, direct measurements of a wide variety of molecular and physiologic changes have been shown to correlate with spatial learning. Much of this behavioral literature for humans and lower animals has been nicely covered in recent texts by Squire and Kandel (1) and Eichenbaum and Cohen (4), so I will not reiterate the details here.

In the next chapter, I will delve in more detail into rodent learning paradigms that probe spatial learning, such as the Morris water maze and contextual learning. In fact, much of this book deals with hippocampus-dependent learning, synaptic plasticity, and molecular regulation. This book will be strongly biased toward hippocampal synaptic plasticity and hippocampus-dependent learning and memory for several reasons. We have the most detailed understanding of the pertinent molecular mechanisms of hip-pocampal synaptic plasticity, and, of course, this book is focused on trying to understand memory at that level. Even though it may seem conterintuitive, I am also focusing on hippocampus-dependent memory formation because these memory processes are the least understood at the cellular and circuit levels.

Compared to the amygdala and cerebellum, for example, our understanding of the hippocampal neuronal circuit in mediating behavior is rudimentary. For me this is not a negative, but rather the hippocampus becomes a great frontier to be explored. The hippocampus and associated cortices are involved in conscious learning and memory. This is in contrast to the better-understood amygdala and cerebellum, where the systems operate in simpler and in most cases unconscious learning. Understanding the hippocampus, including its associated circuitry and molecular and cellular function, is likely to give us the greatest insights into higher-level cognitive function. This is the most appealing aspect of studying the hippocampus and a principal reason for the focus of this book on hip-pocampal processes.


Finally, a comparison and contrast of behavioral learning studies with biochemical learning studies is in order. The advent of new technologies has made it possible to measure distinct biochemical changes in particular brain regions in the CNS in response to environmental stimuli. The behavioral parts of these studies typically are based on behavioral protocols established over time to study learning and memory. The general design of the experiments is to use an established behavioral paradigm such as associative conditioning to measure some biochemical change in the brain. This approach has great appeal of course because one can do parallel experiments to control for the training protocol eliciting learning and memory formation. One also can perform additional parallel experiments where, for example, one can show that blocking the molecular change with an inhibitory drug leads to a disruption of the learning or memory.

Doing these types of studies based on the long-standing behavioral literature leads to an interesting problem, however. These types of behavioral studies have well-established behavioral control experiments that go along with them. For example, consider cued fear conditioning. In this paradigm, you give the animal a pairing of noise cue preceding foot shock. The animal of course learns that the noise (conditioned stimulus, CS) predicts the foot shock (unconditioned stimulus, US). The foot shock elicits a behavioral response, freezing (unconditioned response, UR), and after training the noise (CS) then elicits the same response (conditioned response, CR). One type of behavioral control experiment that you typically do along with this conditioning protocol is "backward pairing," where you give CS and US identically except with a reverse order. In this case, re-presentation of the CS (noise) does not cause the conditioned response (freezing) behaviorally—the animal has not learned that the noise predicts the foot shock because it in fact does not. Thus, with this experiment, you demonstrate that the conditioned response is specifically elicited only when the animal receives paired training in the correct order and can eliminate possible alternative explanations such as a general arousal effect of sensitization.

But now consider the same experiment where one is measuring a learning-associated molecular change in the nervous system. You pair noise and foot shock in that order and observe some specific molecular change in the brain—in this thought experiment, the animal uses this molecular mechanism to store the information that noise predicts foot shock. You do the "backward pairing" control where you give foot shock and then noise and observe the same molecular change. Does this mean that the animal is not using this mechanism for information storage? No. Perhaps the animal is using this same molecular mechanism to store the information that foot shock predicts noise.

I use this example to illustrate that it is not straightforward to map control experiments developed for behavioral studies measuring a behavioral output onto behavioral studies measuring a molecular output in the brain. In measuring behavioral outputs, we use the animal itself as a filter— can nicely design my experiment to measure a behavior that is selectively expressed in response to one environmental stimulus and not another. We do not have this luxury in looking at molecular events triggered in the nervous system. Anytime learning of any sort occurs, it will be reflected as a set of molecular changes in the animal's nervous system.

Moreover, the molecular mechanisms for learning one type of contingency are likely to be the same ones used for learning any other type of contingency. Most of the types of control experiments done in learning studies using a behavioral index of learning are likely to lead to the animal learning a variety of different things, it's just that none of them lead to the behavioral output being measured. Nevertheless, all those many things the animal is learning are being encoded in meaningful molecular changes in their nervous system.

I raise these issues to make several points. First, molecular studies of learning and memory using the behaving animal are at a very early stage—in fact only relatively few such studies have been published (at least in mammalian systems). Advancing into this area, and similarly for studies using real-time brain imaging, will require some rethinking of how to design appropriate controls when there is experimentally such an all-encompassing read-out. We will not necessarily be able to map the tried-and-tested behavioral control paradigms directly onto these new systems. Second, this may necessitate some reevaluation of learning and memory terminology in general, with molecular aspects taken into consideration. For example, it is difficult to apply the terms CS, US, CR, and UR in a molecular study (measuring molecular changes in the brain in response to environmental stimuli) in a way that is strictly analogous to their application to behavioral studies. Even though a CS that gives no UR behaviorally can be selected, it is likely that every CS gives an UR at the molecular level in the nervous system. In the limit, this rethinking may have to extend to the definitions of learning and memory as well. We started the chapter by defining learning and memory in behavioral terms. It is certainly premature at this point, but in the future it may be necessary to refine the definitions to take molecules into consideration.


1. Squire, L. R., and Kandel, E. R. (1999). Memory: from mind to molecules. New York: Scientific American Library: Distributed by W. H. Freeman and Co.

2. Eichenbaum, H. (2001). "The hippocampus and declarative memory: cognitive mechanisms and neural codes." Behav. Brain Res. 127:199-207.

3. Kandel, E. R., and Squire, L. R. (2000). "Neuroscience: breaking down scientific barriers to the study of brain and mind." Science 290:1113-1120.

4. Eichenbaum, H., and Cohen, N. J. (2001). From conditioning to conscious recollection : memory systems of the brain. New York: Oxford University Press.

5. Vianna, M. R., Szapiro, G., McGaugh, J. L., Medina, J. H., and Izquierdo, I. (2001). "Retrieval of memory for fear-motivated training initiates extinction requiring protein synthesis in the rat hippocampus." Proc. Natl. Acad. Sci. USA 98:12251-12254.

6. Nader, K., Schafe, G. E., and LeDoux, J. E. (2000). "The labile nature of consolidation theory." Nat. Rev. Neurosci. 1:216-219.

7. Clark, R. E., and Squire, L. R. (1998). "Classical conditioning and brain systems: the role of awareness." Science 280:77-81.

8. LeDoux, J. E. (2001). Synaptic self : how our brains become who we are. New York: Viking.

9. Quirk, G. J., Repa, C., and LeDoux, J.E. (1995). "Fear conditioning enhances short-latency auditory responses of lateral amygdala neurons: parallel recordings in the freely behaving rat." Neuron 15:1029-1039.

10. Berman, D. E., and Dudai, Y. (2001). "Memory extinction, learning anew, and learning the new: dissociations in the molecular machinery of learning in cortex." Science 291:2417-2419.

11. Swank, M. W., and Sweatt, J. D. (2001). "Increased histone acetyltransferase and lysine acetyltrans-ferase activity and biphasic activation of the ERK/RSK cascade in insular cortex during novel taste learning." J. Neurosci. 21:3383-3391.

12. Eichenbaum, H. (1999). "The hippocampus and mechanisms of declarative memory." Behav. Brain Res. 103:123-133.

13. Milner, B., Squire, L. R., and Kandel, E. R. (1998). "Cognitive neuroscience and the study of memory." Neuron 20:445-468.

14. Kandel, E. R. (2001). "The molecular biology of memory storage: a dialogue between genes and synapses." Science 294:1030-1038.

15. Walters, E. T., and Erickson, M. T. (1986). "Directional control and the functional organization of defensive responses in Aplysia." J. Comp. Physiol. A. 159:339-351.

Lashley Maze J. David Sweatt, Acrylic on canvas, 2GG2

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