TABLE OF CONTENTS
Serial Pattern Tracking
Studies have shown that certain blood glucose
levels are capable of enhancing memory, attention, and processing speed
in humans, and aid performance in mice and rats for certain tasks. The
majority of studies have focused on one aspect of memory, either a reference
or a working memory, largely measured by rote memory tasks. The following
experiment looks to examine the influence of glucose on a serial pattern-tracking
task involving both rule and rote memory simultaneously. Over a 26 day
period 16 naïve male CF1 mice received water reward for correct nose
poking responses in an octagonal operant chamber. Mice were divided into
two serial pattern conditions, one containing a violation element, and
upon successful completion of the 12 sequences of their pattern, received
either a saline or glucose injection (30mg/kg). Results showed that both
the perfect and violation patterns found chunk boundaries more difficult
evidenced by higher error rates, suggesting a difference in the mastering
of lower and higher order rules. The perfect pattern mice also made fewer
errors on other segments of the pattern sequence, suggesting that that
the presence of the violation element may have disrupted performance of
the mice in the violation condition.
The following study looks to examine the effects
of glucose on a serial pattern tracking task in mice. To facilitate the
readers comprehension of how the present hypothesis was determined, research
on the topics of glucose and serial pattern learning have been divided
and presented accordingly.
Glucose, a simple sugar from which all dietary sugars and other carbohydrates are ultimately broken down, is the brain’s soul energy source (Blaun, 1996). The brain uses glucose to perform its many functions among which are learning and memory formation. Recent studies have shown that specific levels of blood glucose may improve retention and performance on certain tasks in both humans and animals (Messier, & Destrade, 1988, and Kopf & Baratti, 1994). Improvement of Memory, attention and processing speed in humans has been associated with elevated glucose levels, while enhanced performance has been noted in mice and rats for appetitively-motivated tasks, inhibitory-avoidance tasks, habituation tasks and retention tasks (Lee, Graham, & Gold, 1988, Messier & Destrade, 1988, Kopf & Baratti, 1994, Benton, Owens & Parker, 1994, Owens & Benton, 1994, and Parker & Benton, 1995).
Drugs that improve attention and memory do so by increasing the availability and uptake of glucose either directly or indirectly (Benton, Owens & Parker, 1994). These glucose levels influence the activity level of, and are influenced by other neurotransmitters, including dopamine, seratonin, norepinephrine and acetylcholine. (Benton et al., 1994, Gold, Voght, & Hall, 1986, Messier & White, 1987, and Ragozzino, Wenk, & Gold, 1994). Benton et al. (1994) has shown that depending on the demands of the situation, high, low, rising and falling blood glucose levels have all been associated with better memory.
Exactly how glucose effects memory and performance is still debated. One theory is that the hormone epinephrine acts on memory by releasing hepatic glucose stores (Gold, 1986). Since glucose has central nervous system (CNS) access, it is possible that glucose acts directly on central processes to enhance memory (Lee, Graham, & Gold, 1988). White & Messier (1988) tested the hypothesis that glucose injections may act on the adrenal medulla, facilitating epinephrine (EP) release by testing retention in demedullated rats. Results showed that despite the removal of the adrenal medulla, glucose continued to improve memory. Lee et al. (1988) suggested that peripheral rather than direct epinephrine enhancement of memory storage may be mediated in part by an increase in circulating glucose levels subsequent to epinephrine release or injection, noting that while EP does not cross the Blood Brain Barrier (BBB), glucose does. It is possible that glucose injections cross the BBB and peripherally influence an increase in CNS glucose levels.
Another possibility is that certain glucose levels stimulate acetylcholine (ACH) synthesis, which could mediate the impact of glucose on memory and reaction times (Owens & Benton, 1994). Glucose is the principle source of Acetyl-Co A, a precurser of ACH. This is important since studies have associated learning with high demands of ACH (Benton, & Sargeant, 1992, and Smith, Kendrick, & Maben, 1992). Ragozzino et al. (1994) found that glucose administration ameliorates memory deficits produced by morphine treatment, which decreases ACH output. Glucose when given concurrently with morphine attenuates the reduction in ACH output. These findings raise the possibility that behavioral impairments related to morphine treatment may be related to a decrease in cholinergic neuron activity. Further research by Kopf et al. (1991) also supported the notion that glucose may be directly acting on the brain by regulating rates of ACH synthesis. Their findings suggested that ACH synthesis or release may in turn modulate the activity of muscarinic cholinergic mechanisms that are critically involved in memory storage. The fact that the effects of glucose are both dose and time dependent (Kopf et al., 1994, and Lee et al., 1988) suggests an action on mechanisms involved in memory storage. This idea that the muscarinic cholinergic mechanisms are the ones being influenced by glucose, is supported by that fact that the effects of glucose were prevented by the central acting muscarinic cholinergic antagonist atropine (Stone, Croul & Gold, 1988, and Stone, Walser, Gold & Gold, 1991). Micheau, Messier & Jaffard (1995) found a positive correlation between plasma blood glucose levels and hippocampal ACH uptake, also suggesting that glucose can attenuate ACH synthesis.
Messier & White (1984) tested glucose analogs including fructose and sucrose and found that fructose, which acts peripherally on the brain, also increased performance, which suggests that improvements may be dependent on a membrane of glucose transport mechanism.
Since an increased glucose supply results in more consistent functioning near the physiological limit (Owens & Benton, 1994), studies have begun to examine how we can optimize our glucose levels, and the possible impact on performance of those who are unable to do so. Glucose stores in the brain will be consumed in five to ten minutes if not constantly replenished (Parker & Benton, 1995). This important information may have great implications for diabetics, the elderly and Alzhiemer’s Disease (AD) patients. Demanding tasks may be performed slower during periods of hypoglycemia (Holmes, Koepke, & Thompson, 1986, and Langan, Deary, Hepburn, & Frier, 1991). The fluctuations in blood glucose levels of meal skippers, dieters and anorexics may cause periods of slower or less than optimal functioning (Owens & Benton, 1994, and Elliman, & Rogers, 1997). In elderly patients, persistent impaired glucose tolerance is associated with mildly impaired cognitive function, and there is also an association between insulin levels and Alzhiemer’s Disease (Vanhanen & Hanninen, 1998). Dementia of the Alzhiemer’s Type (DAT) is related to disruption of glucose regulation and utilization, and studies have shown that increasing glucose availability by raising plasma glucose improves DAT patient’s memories (Craft, Newcomer, Kanne, Dagogo-Jack, Cryer, Sheline, Luby, Dagogo-Jack, & Alderson 1996). Glucose must be obtained through systemic circulation and transported across the BBB, however DAT patients have been found to have decreased numbers of glucose transporters (Kalaria & Harik, 1989, and Simpson, Koteswara, Davies-Hill, Honer, & Davies, 1994). Interestingly, one area of the brain highly susceptible to changes in glucose availability is the hippocampus, which is an area largely involved in memory formation (Craft et al., 1996). Although these findings offer us a piece of the AD puzzle, they are modest and should be considered possibilities for therapy or further research rather than a cure (Messier & Gragnon, 1996).
Glucose has clearly been established through
research as a memory and performance enhancer, however the exact types
of memory enhanced have not been as clearly examined. Memory is composed
of primary, or working memory, and long term or reference memory (Fuster,
1995). Working memory is a type of short term memory in which information
is retained for as long as 30 seconds unless it is rehearsed or repeated
to oneself so that it may be retained longer (Stantrok, 1997). It is the
temporary storage that deals with what we are immediately attending to
(Kalat, 1996). Reference memory is what is used when we are referring back
to knowledge of general principles (Kalat, 1996). Glucose would be expected
to improve both working and reference memory, however few studies have
examined it in such a way. We are capable of learning and incorporating
information into long term storage by abstracting rules (a rule based learning
method) and by repetition (a rote learning method). Serial pattern learning
tasks are unique in that they allow for working and reference memory to
be examined simultaneously while incorporating both rule and rote memory
storage tasks. A serial pattern-tracking task requires a mouse to use reference
memory to recall the pattern sequence, and working memory to know where
he is in the sequence. The perfect pattern and parts of the violation pattern
may be learned through rule abstraction, while the violation element must
be memorized with rote techniques.
Serial Pattern Tracking
Learning and memory, although not unitary processes, are closely related (Fountain, 1986). Learning, the process of acquiring new information, is made possible through memory, which is the persistence of learning in a state which may be revealed at a later time (Squire, 1987). Learning is in effect the process of acquiring new memory. However, information is not only stored for later reference, it can also be manipulated and adapted to apply to new situations (Fuster, 1995). Learning and memory do not occur in isolation from other cognitive processes. The details of exactly what and how much of an experience is retained in memory is dependant on such factors as the nature of events occurring right before or after the event has taken place (punishment or reward), and the alertness level at the time of learning (information is deemed trivial or important) (Squire, 1987).
What exactly is occurring when a behavior is performed and learned? Behaviors that are routinely performed are actually composed of many different parts that must be executed in a specific order to complete the desired behavior (Krauchunas, 1988). First, the individual components of the behavior must be learned, and then their order of importance must be learned. This rule based learning system is sensitive to the structure of the pattern and enables one to learn the overall pattern of the behavior as opposed to memorizing each specific component. For example, when a series of events (letters, numbers, etc.) do not occur randomly (have a pattern) and this results in an overall meaning (structure), the sequence is easier to learn rather than having to memorize individual items in a specific order. The structure implies a relationship between the items. For example, consider the following number sequence:
The rote learning system is important for memorizing items that are not consistent or in keeping with a pattern. These can be described as violations of the pattern structure. For example:
1212 5555 2323 5555 1214
They must simply be memorized as is. Consider also the following group of numbers:
Were it necessary to know this number sequence it would simply have to be memorized as is, using rote memory. This is because there is no rule to help us learn the number sequence. The numbers are unrelated, and therefore lack any pattern structure. Humans and animals both have the capacity to learn serial patterns by rules and to master their violations through rote memory (Fountain & Hulse, 1981, Fountain, Henne & Hulse, 1984).
The ‘memory load’ hypothesis by Hulse (1980) posits that organisms will naturally utilize the mechanism that produces the lowest load on memory to learn a pattern. Concerning patterned serial sequences, rote memory is viewed as being a less efficient method for learning, and therefore would be used only if absolutely necessary.
When a pattern is highly structured, humans and animals are capable of mastering the sequence by developing an abstract representation of the pattern and using strategies or rules to govern the behavior (Fountain & Annau, 1984, Fountain, Krauchunas & Rowan, in press, Fountain & Hulse, 1981). When this takes place, humans and animals are able to anticipate the next correct response in the pattern because they are familiar with its structure (Fountain & Hulse, 1981, Fountain & Annau, 1984, Fountain, 1990, Fountain & Rowan, 1995).
This idea of rules is conceptualized in the rule learning theory which assumes that organisms are sensitive to the abstract relationships between the individual items of the pattern, and that it uses this relationship to develop rules which explain the structure of the pattern (Fountain & Hulse, 1981). If the order of these items corresponds to the meaning, rules that explain the relationship are established and the human / animal is capable of learning the pattern and predicting responses.
On the other hand, a series of randomly sequenced items that lack overall structure is more difficult to learn because the between-item relationships are unclear. In this case, subjects are unable to determine a rule and subsequently cannot predict future responses (Krauchunas, 1988).
Highly structured patterns can be described by a few repeating rules that are easily learned. Fountain and Rowan (1995) found that for both humans and rats, pattern structure predicted pattern learning difficulty and also the nature and frequency of errors (Fountain, Krauchunas & Rowan, in press). Both pattern length and pattern complexity interact to define pattern difficulty (Fountain, Evensen & Hulse, 1983).
The process of chunking, in which segments of the sequence that represent groups of items are related by a common rule, increases short term memory capacity, thus facilitating learning (Fountain, Henne & Hulse, 1984). The natural break that occurs between each chunk is referred to as the chunk boundary, and is representative of transition points in the pattern structure. Fountain (1990) found that rats, just like humans, make more errors and response omissions at boundaries of chunks than within chunks. These errors often reflect anticipation of the next chunk or over-extension of the preceding chunk. These errors made at chunk boundaries are evident of the use of rules for they would not occur if rote memory were being used.
Lower-order rules describe the relationship between individual items in each chunk, while the higher-order rules describe the relationships between three chunk elements that must be applied to the chunk boundary (Fountain, 1986). The rules in higher levels can be used to describe the organization of chunks created by other, lower-level rules. Rules in higher levels can relate rules and lower-levels, creating a ‘nested organization’ referred to as a hierarchical ‘tree structure’ (Fountain & Rowan, 1995).
There are many rules that may be extracted to describe the relationship between elements and they are relatively simple, for example:
1-2-3-4 = the plus one rule (one to the right)
5-5-5-5= the repeat or stay rule
3-4-3-4-3-4-3-4= the alternate or ‘trill’ rule
4-3-2-1= the minus one rule (one to the left).
In examining rule governed behavior it would seem logical to find that a pattern in which each element was consistent with its overall formal structure, and which can be described by a few rules (a perfect pattern) would be easier to learn than one that contained an element that was not consistent with the rest of the structure (a violation pattern) (Fountain, Krauchunas & Rowan in press). This is because once a violation is imposed on a rule pattern, the location and proper response must be memorized with respect to the rest of the pattern, thus increasing the memory load as described by Hulse. Fountain and Rowan (1995) found that rats subjected to run and trill patterns had difficulty learning the violations and made errors conforming to the structure of the pattern. This data was also supported by Fountain et al. (Fountain, Krauchunas & Rowan in press).
As these previous studies have shown, humans, rats and mice share the ability to abstract generalizations for pattern sequencing and use higher-order rules to reduce short-term memory load and enhance learning in rule governed behavior, evidenced through these serial pattern learning tasks. This is made clear through the difficulty in learning violation elements and with errors made at chunk boundaries, such as over-extension and anticipatory errors.
The following study will look to examine any possible links between the memory enhancing, and thus performance-facilitating effects of glucose on a rule governed behavior task in mice. It is predicted that glucose will enable the mice to perform better. This will be evidenced by fewer mistakes at chunk boundaries and at the violation element, combined with an overall shorter task completion time.
Subjects. For this experiment subjects were 16 naïve male CF1 mice, approximately six months of age during testing. Mice were each housed in individual plastic cages. All animals had free access to food, and were maintained on a 12:12 hour light-dark schedule and tested during the light cycle. Mice were restricted from water in their cages and received approximately 2.0ml of water each day from the operant chamber. Mice were monitored daily for signs of dehydration indicated by a hump on the back, closed eyes, shivers and lethargy. In the event that a single animal displayed these signs, all mice were given a 2-4 ml water supplement.
Apparatus. A single shaping chamber (11
x 7.6 x 15.3 cm) equipped with a single nose poke receptacle, was employed
to shape the nose poke response for water reward. The nose poke receptacle
was a 2.54cm diameter tube, 3 cm deep, centered on one wall, 1.5 cm above
the floor. The chamber itself was constructed of clear Plexi-glass with
a wire mesh floor. A single 1 watt light bulb was located in the back of
the receptacle. An infrared emitter and detector were located approximately
1.5 cm inside the receptacle. To activate the nose poke mechanism, the
subject was required to break the infrared beam to trigger the solenoid
and receive a drop of water (approximately 0.0045ml) as a reward. The water
drop formed on the floor of the receptacle approximately 1.5 cm inside
The test chamber was octagonal shape (walls 7.6 cm wide and 15.3 cm tall and approximately 18 cm separated parallel walls) and also constructed of clear Plexiglas walls and a wire mesh floor (see Appendix). Centered on each of the eight walls was a single nose poke receptacle identical in shape and design as the receptacle in the shaping chamber. The octagonal operant chamber was placed inside a larger wooden box and a single 15 watt incandescent bulb provided illumination inside the test area. Experiments were controlled using a microcomputer and interface (interface and Med- State software, Med Associates Inc. Fairfield, V.T).
Procedure. All mice were deprived of water 24 hours prior to the commencement of a five day pre-training procedure. The first two days consisted of a 15 minute habituation experience to the shaping apparatus. The remaining three days of pre-training involved training the subject to receive and drink water from the nose-poke receptacle. This required the animal to make an overt nose-poke response to obtain water reward.
Following shaping, mice were randomly assigned to the experimental conditions of a perfect or violation sequence (see Appendix) and were individually presented to the octagonal operant chamber, which, while requiring the same nose-poke activity, posed an eight choice procedure with correction for all trials. Mice were exposed to the octagonal chamber to learn a rule governed number sequence in which they were required to nose poke for water reward on a fixed ratio schedule of reinforcement. Throughout the experiment, mice received a single droplet of water reinforcement. In all procedures mice received one such droplet for every correct response and were allowed 0.5 seconds to consume the water for each administration. Each trial began with all eight nose-poke receptacle lights illuminated at the beginning of the trial. The subject was required to make a selection by producing a nose poke response. If a correct choice was made, all lights were turned off and the water droplet was administered. If the response was incorrect, all receptacle lights were turned off except the correct receptacle, which remained illuminated. The mouse was then required to make a nose poke response to the correct receptacle to receive water reward before continuing on the next trial. On each trial the receptacle choice and latency to respond were recorded.
Upon successful completion of all 12 sequences of either the perfect or violation pattern, each mouse was then administered a post-training intraperitoneal injection of either saline or dextrose (30mg/kg with a dose concentration of 10 ml of dextrose per 1kg saline for the dextrose solution, and a 0.9% saline solution) and returned to their home cages. There were four mice for each condition, which were perfect pattern/saline, perfect pattern/dextrose, violation pattern/saline, and violation pattern/dextrose, and each mouse received the injection with in 20 minutes of having completed the sequences. Mice were run on a rolling start / finish for a total of 26 days. Run time was not continuous, however mice were always deprived of water for 24 hours before being tested again.
Upon completion of 26 test days, the dependent variables of Daily Error Rate and Latency were examined. A 2 (glucose, saline) x 2 (perfect, violation pattern) x 26 (days of testing) mixed analysis of variance (ANOVA) was conducted on the Daily Error Rates. The between subject factors were both Drug and Pattern, while the within subject factor was Day. There was a main effect for Day, F (25, 300)= 15.692, p< 0.05, showing that across all groups the Daily Error Rate decreased for the mice (see Figure 1). There was also a main effect for Pattern, F (1,12)= 4.576, p=0.05, indicating that there was a difference in Error Rates between the perfect and violation patterns.
The dependent measure of Daily Error Rates was taken a step further by analyzing the error rate on the individual items responded to on the last test day (see figure 2). A 2 (glucose, saline) x 2 (perfect, violation pattern) x 8 (chunks) x 3 (elements per chunk) mixed ANOVA was run on Individual Item Error Rate. The between group factors were again Pattern and Drug, and the within group factors were Chunk and Element. A main effect was found for Group, F (1,12)=6.703, p=0.02, indicating a difference in Error Rates per individual item in the pattern between the perfect and violation patterns. A Planned Comparison was calculated using the appropriate error term for the Fisher’s LSD procedure. Results showed significance for the first, 11th, 18th, 21st and the 24th and final element. Main effects were also found for the within subject factors of Chunk and Element, indicating that regardless of both Pattern and Drug, all mice made different error responses at different elements of the chunks more so than on other elements.
A second Planned Comparison was conducted to examine the difference in mean error rate at each individual item element. Results showed that for both the perfect and violation conditions, error rates were higher at the first and third elements of the chunk, and least at the second element. An Intrusion Analysis was conducted to examine response types The second dependent measure of Latency was also analyzed as a control to determine that there were no peripheral drug effects. A 2 (glucose, saline) x 2 (perfect, violation pattern) x 8 (chunks) x 3 (elements per chunk) mixed ANOVA was run on Latency to determine differences in response time. Main effects were found for the between subject factors of Chunk, F (7,84)=32.337, p<0.05, Element F (2,24)=12.260, p<0.05, and Chunk X Element, F (14,168)=4.339, p<0.05, once again indicating that regardless of Drug or Pattern, differences in response times to certain parts of the sequence was evidenced in all mice.
Learning and memory are complicated processes of the human brain, and it is in our best interest to maximize their capacities. The continuing research into glucose offers us such a means to explore and discover more about the human brain and how it works. With time the mystery of glucose’s effects will be revealed, and along with it more information on what is involved in our learning process. The present study hoped to contribute to this body of knowledge by examining the effects of glucose on a rule- governed serial pattern tracking task in mice. It was hypothesized that glucose would enable the mice to perform faster and with fewer errors than their saline controls. The perfect and violation patterns offered another control of this, measuring any differences between the rule and rote memory techniques being used. It was predicted that the violation pattern would be more difficult to master for the saline control than for the glucose group, since the glucose would enable these mice to more easily memorize the nature and location of the violation. Mice in the Saline / Violation Condition were expected to perform the most poorly, while the Glucose / Perfect Condition was anticipated to perform fastest and with the least errors. This was predicted since the perfect pattern by its very nature is easier to learn than the violation pattern. It can be remembered with a few simple rules, and therefore offers an easier "memory load".
Although the predicted effect of glucose was not observed, the present study supported previous findings that mice are able to abstract and learn serial patterns, evidenced by their improvement from the first to final day of the study. Figure One graphs the mean Daily Error Rates for mice in all four groups. Main effects for Pattern indicated a difference in Error Rate between the perfect and violation pattern. A comparison of means shows that the perfect pattern mice performed with fewer errors than the violation pattern mice (see also Figure One), which was predicted as the perfect pattern offers a completely rule governed sequence as opposed to the more complex violation number sequence.
A closer look at Error Rate is made possible by examining the responses made to each individual item within the pattern sequence. Individual Item Error Rate for the final test day indicated differences among the perfect and violation patterns. A Planned Comparison of these error rates shows that the difference is significant for at least five elements of the sequence, most importantly the first and final/violation elements, which are transition points.
The within subject factors of Chunk and Element were also found to be significant. A Planned comparison of these error responses showed that for both perfect and violation conditions, error responses were highest at the first and third elements of the chunk. Since the chunks in these particular patterns were composed of only three elements, the first and third elements were transition points at which the mouse was required to incorporate knowledge of the lower and higher order rules being used. An Intrusion Analysis was performed to examine the types of errors being made at these boundary trials and at the violation trial .
Depending on the types of errors made at chunk boundaries it may be inferred that the mice are "learning" and incorporating lower and higher-order rules. Over-extension type errors suggest that the mice are sensitive to the structure, yet are learning only the lower-order rule, while "other" type errors may suggest that the mice are aware that one rule is completed, yet are not sure where the next rule beings, or even what it is. Mice in the Glucose / Perfect Condition made 23% over-extension type errors suggesting that they were sensing the distinction between the lower and higher order rules (see Table One). The majority of their mistakes (50%) were "other" type errors suggesting that when the Glucose / Perfect mice made mistakes, they were aware that they had come to the end of a rule, but were not sure of what the next one was or where it began. When the response types of the perfect and violation conditions are compared, it is found that the perfect pattern mice in general made more "other" type responses than the violation pattern mice. This suggests that the violation pattern mice had a more difficult time mastering higher-order rules than the perfect pattern group, perhaps because the violation was too great a distraction from mastering both rules. It is also possible that the higher-order rule was more difficult to master because it was seen less often than the lower-order plus-one rule.
Results for this experiment support hypotheses of previous studies that mice employ rule and rote learning strategies to master serial patterns (Fountain & Annau, 1984, Fountain & Hulse, 1981), however it is unable to support the present hypothesis of glucose as a performance enhancer. There are some potential problems that may account for this. The experiment’s subject pool was limited (N=16), as was run time (26 days). Perhaps with a larger subject group and a longer testing time results would improve. The procedure for this study involved a rolling start / stop with several days off during the testing time. Animals were not run at together for 26 consecutive days. This lack of consistency may have hindered the effects of glucose on the animal’s performance. Another possibility is that the dosage of glucose injected (30mg/kg) to the animals was not great enough to exert any measurable effects. The majority of studies previously run concerning glucose offered a pilot study to determine the appropriate dosing. This was not possible for the present study. Taking these pieces of information into account, it may be wise to consider the present study as a pilot, and run the experiment again with a larger N, controlling for run consistency and length, while also increasing the dose level.
In conclusion, the present study has proved to be a valid supporter for serial pattern learning tasks, illustrating that mice are capable of abstracting rules. It demonstrated that mice have difficulty with pattern violations and that they find lower order rules easier to master than higher order rules. The present study serves also as a reliable pilot study for further experimentation with glucose.
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