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PubMed2000

Characteristics of sequential movements during early learning period in monkeys.

We previously demonstrated that the organization of a learned sequential movement, after long-term practice, is based on the entire sequence and that the information pertaining to the sequence is largely specific to the hand used for practice. However, it remained unknown whether these characteristics are present from the beginning of learning. To answer the question, we examined the performance of four monkeys for the same sequential procedure in the early stage of learning. The monkeys' task was to press five consecutive pairs of buttons (which were illuminated), in a correct order for every pair, which they had to find by trial-and-error during a block of trials. We first examined whether the memory of a sequential procedure that was learned once was specific to the hand used for practice. The second time that the monkeys attempted to learn a novel sequence, they were required to use either the same hand they used the first time or the opposite hand. The number of errors decreased to a similar degree in the same-hand condition and in the opposite-hand condition. The performance time decreased in the same-hand condition, but not in the opposite-hand condition. The results suggest that, in the early stage of learning, memory of the correct performance of a sequential procedure is not specific to the hand originally used to perform the sequence (unlike the well-learned stage, where the transfer was incomplete), whereas memory of the fast performance of a sequential procedure is relatively specific to the hand used for practice (like the well-learned stage). We then examined whether memory of a sequential procedure depends on the entire sequence, not individual stimulus sets. For the second learning block, we had the monkey learn the sequence in the same or reversed order. In the reversed order, the order within each set was identical, but the order of sets was reversed. The number of errors decreased in both the same-order and reversed-order conditions to a similar degree for two out of four monkeys; the decrease was larger in the same-order condition for the other two monkeys. For all monkeys, the performance time decreased in the same-order condition, but not in the reversed-order condition. The results suggest that the memory structure for correct performance varies among monkeys in the early stage of learning (unlike the well-learned stage, where the memory of individual sets was consistently absent). On the other hand, memory of the fast performance of a sequential procedure is relatively specific to the learned order used for practice (like the well-learned stage).

Animals↗
PubMed2006

Odour learning and immunity costs in mice.

There is accumulating evidence that learning is metabolically costly. One way in which this may manifest itself is in trade-offs between learning effort and immune function, with learning increasing susceptibility to infection. We tested this idea in the context of odour learning using outbred (BKW) male laboratory mice. Mice were exposed to three experimental treatments in which they were required to learn different numbers of urinary odours. While treatment affected the extent to which mice habituated to test odours during training, differences were not a simple function of the number of odours. The fact that there was also no significant effect of treatment on the degree of preference for novel over familiar odours in subsequent tests suggests mice retained learned odour profiles equally well regardless of the number of odours. That subsequent infection with Babesia microti increased with the number of odours mice had to learn is then consistent with an increased cost to learning effort when more odours were presented. Analysis within treatments, and relationships with the change in corticosterone concentration over the period of the experiment, suggested that it was a failure to learn, rather than maintaining learning performance, in more difficult learning tasks that led to greater infection. As in a previous study of maze learning in the strain, there was no direct relationship between infection and measures of peripheral antibody (total IgG) titre. The results are discussed in relation to studies in other learning contexts and reported relationships between glucocorticoid hormones and learning outcomes.

Animals↗
PubMed2006

Differential hippocampal and prefrontal-striatal contributions to instance-based and rule-based learning.

It is a topic of current interest whether learning in humans relies on the acquisition of abstract rule knowledge (rule-based learning) or whether it depends on superficial item-specific information (instance-based learning). Here, we identified brain regions that mediate either of the two learning mechanisms by combining fMRI with an experimental protocol shown to be able to dissociate both learning mechanisms. Subjects had to learn object-position conjunctions in several trials and blocks. In a learning condition, either objects (Experiment 1) or positions (Experiment 2) were held constant within-blocks. In contrast to a control condition in which object-position conjunctions were trial-unique, a performance increase within and across-blocks was observed in the learning condition of both experiments. We hypothesized that within-block learning mainly relies on instance-based processes, whereas across-block learning might depend on rule-based mechanisms. A within-block parametric fMRI analysis revealed a learning-related increase of lateral prefrontal and striatal activity and a learning-related decrease of hippocampal activity in both experiments. By contrast, across-block learning was associated with an activation modulation in distinct prefrontal-striatal brain regions, but not in the hippocampus. These data indicate that hippocampal and prefrontal-striatal brain regions differentially contribute to instance-based and rule-based learning.

Adult↗
PubMed1999

Use of learning contracts in an office-based primary care clerkship.

OBJECTIVES: This paper describes implementation of the learner-centred learning goal within the primary care clerkship at a Midwestern, United States medical school. DESIGN: The learner-centred learning goal exercise was developed to tailor students' educational activities to their personal level of development and to enhance their commitment to life-long learning in medicine. In the learner-centred learning goal exercise, each student records three specific learning goals early in the primary care clerkship. Students record the methods by which they will pursue and document achievement of each goal. Attainment of the learner-centred learning goal is evaluated based on an oral presentation at the end of the clerkship. We compiled presented learning goals along with the corresponding grade. Students' ratings of the learner-centred learning goal exercise were also compiled. Evaluations and ratings were made on a 1-5 Likert scale, where 1 is the best rating and 5 is worst. SETTING: Department of Medicine, Northwestern University Medical School, Chicago, USA. SUBJECTS: One hundred and seventy-seven third- and fourth-year medical students who presented learner-centred learning goals between 1 July 1995 and 30 June 1996. RESULTS: Students rated pursuing their individual learning goals more worthwhile than most clerkship lectures but less worthwhile than the office experience. Several learning goals were chosen by a disproportionate number of students, potentially indicative of some perceived deficiencies elsewhere in the curriculum. Third-year students ranked the learner-centred learning goal exercise more favourably than fourth-year students (2.14 vs. 2. 51, P = 0.03). CONCLUSIONS: The learner-centred learning goal exercise is a feasible and well-received method within our primary care clerkship. Further study is required to determine whether the exercise promotes independent learning after formal medical school education is completed.

Clinical Clerkship↗
PubMed2000

An internet-based learning portfolio in resident education: the KOALA multicentre programme.

CONTEXT AND OBJECTIVES: To describe the Computerized Obstetrics and Gynecology Automated Learning Anaalysis (KOALAtrade mark), a multicentre, Internet-based learning portfolio and to determine its effects on residents' perception of their self-directed learning abilities. METHODS: The KOALA programme allows residents to record their obstetrical, surgical, ultrasound, and ambulatory patient encounters and to document critical incidents of learning or elements of surprise that arose during these encounters. By prompting the student to reflect on these learning experiences, KOALA encourages residents to articulate questions which can be directly pursued through hypertext links to evidence-based literature. Four Canadian residency training programmes participated in the pilot project, from February to May 1997, using a dynamic relational database with a central server. All participants completed the Self-directed Learning Readiness Scale and a learning habits questionnaire. The impact of the KOALA programme on residents' perception of their self-directed learning abilities was measured by comparing KOALA-naive schools (schools 2, 3, and 4) with school 1 (exposed to the KOALA prototype for 1 year). Ordered variables were compared using the Mann-Whitney U test and continuous variables with the Student t test (statistical significance P < 0. 05). RESULTS: During the study period, 7049 patient and 1460 critical incidents of learning were recorded by 41 residents in the four participating universities. Residents at the exposed school (school 1) had a significantly higher perception of their self-directed learning (P < 0.05) and believed their future learning was less likely to be from continuing medical education (P < 0.028), textbooks (P < 0.04), and didactic lectures (P < 0.011) and would be derived from a learning portfolio with online resources. CONCLUSION: This Internet-based, multi-user, multicentre learning portfolio has a significant effect on residents' perception of their self-directed learning abilities.

Canada↗
PubMed2005

Task-specific disruption of perceptual learning.

For more than a century, the process of stabilization has been a central issue in the research of learning and memory. Namely, after a skill or memory is acquired, it must be consolidated before it becomes resistant to disruption by subsequent learning. Although it is clear that there are many cases in which learning can be disrupted, it is unclear when learning something new disrupts what has already been learned. Herein, we provide two answers to this question with the demonstration that perceptual learning of a visual stimulus disrupts or interferes with the consolidation of a previously learned visual stimulus. In this study, we trained subjects on two different hyperacuity tasks and determined whether learning of the second task disrupted that of the first. We first show that disruption of learning occurs between visual stimuli presented at the same orientation in the same retinotopic location but not for the same stimuli presented at retinotopically disparate locations or different orientations at the same location. Second, we show that disruption from stimuli in the same retinotopic location is ameliorated if the subjects wait for 1 h before training on the second task. These results indicate that disruption, at least in visual learning, is specific to features of the tasks and that a temporal delay of 1 h can stabilize visual learning. This research shows that visual learning is susceptible to disruption and elucidates the processes by which the brain can consolidate learning and thus protect what is learned from being overwritten.

Adult↗
PubMed2006

Learning in practice: experiences and perceptions of high-scoring physicians.

PURPOSE: To increase understanding of informal learning in practice (e.g., consulting with colleagues, reading journals) through exploring the experiences and perceptions of physicians perceived to be performing well. Objectives were to find out how physicians learned in practice and maintained their competence, and how they learned about the communication skills domain specifically. METHOD: Of 142 family physicians participating in a formal multisource feedback (360-degree) formative assessment, 25 receiving high scores were invited to participate in interviews conducted in 2003 at Dalhousie University Faculty of Medicine. Twelve responded. Interviews were 1.5 hours each, recorded, transcribed, and analyzed by the research team using accepted qualitative procedures. RESULTS: While formal learning appeared important to most, informal learning, especially through patients and colleagues, appeared to be fundamental. The physicians appeared to learn intentionally from practice and work experiences, and reflection appeared integral to learning and monitoring the impact of learning. Two findings were surprising: participants' conceptions of competence and perceptions that communication skills were innate rather than learned. CONCLUSIONS: These physicians' ways of intentional learning from practice concur with current models of informal learning. However, informal learning is largely unrecognized by formal institutions. Additionally, the physicians did not in general share notions of professional competence held by educators and others in authority. These findings suggest the need to make implicit content and learning processes more explicit. Additional research areas include exploring whether physicians across the range of performance levels demonstrate similar processes of reflective learning.

Adult↗
PubMed2005

Distinguishable brain activation networks for short- and long-term motor skill learning.

The acquisition of a new motor skill is characterized first by a short-term, fast learning stage in which performance improves rapidly, and subsequently by a long-term, slower learning stage in which additional performance gains are incremental. Previous functional imaging studies have suggested that distinct brain networks mediate these two stages of learning, but direct comparisons using the same task have not been performed. Here we used a task in which subjects learn to track a continuous 8-s sequence demanding variable isometric force development between the fingers and thumb of the dominant, right hand. Learning-associated changes in brain activation were characterized using functional MRI (fMRI) during short-term learning of a novel sequence, during short-term learning after prior, brief exposure to the sequence, and over long-term (3 wk) training in the task. Short-term learning was associated with decreases in activity in the dorsolateral prefrontal, anterior cingulate, posterior parietal, primary motor, and cerebellar cortex, and with increased activation in the right cerebellar dentate nucleus, the left putamen, and left thalamus. Prefrontal, parietal, and cerebellar cortical changes were not apparent with short-term learning after prior exposure to the sequence. With long-term learning, increases in activity were found in the left primary somatosensory and motor cortex and in the right putamen. Our observations extend previous work suggesting that distinguishable networks are recruited during the different phases of motor learning. While short-term motor skill learning seems associated primarily with activation in a cortical network specific for the learned movements, long-term learning involves increased activation of a bihemispheric cortical-subcortical network in a pattern suggesting "plastic" development of new representations for both motor output and somatosensory afferent information.

Adult↗
PubMed2006

Bayesian analysis of interleaved learning and response bias in behavioral experiments.

Accurate characterizations of behavior during learning experiments are essential for understanding the neural bases of learning. Whereas learning experiments often give subjects multiple tasks to learn simultaneously, most analyze subject performance separately on each individual task. This analysis strategy ignores the true interleaved presentation order of the tasks and cannot distinguish learning behavior from response preferences that may represent a subject's biases or strategies. We present a Bayesian analysis of a state-space model for characterizing simultaneous learning of multiple tasks and for assessing behavioral biases in learning experiments with interleaved task presentations. Under the Bayesian analysis the posterior probability densities of the model parameters and the learning state are computed using Monte Carlo Markov Chain methods. Measures of learning, including the learning curve, the ideal observer curve, and the learning trial translate directly from our previous likelihood-based state-space model analyses. We compare the Bayesian and current likelihood-based approaches in the analysis of a simulated conditioned T-maze task and of an actual object-place association task. Modeling the interleaved learning feature of the experiments along with the animal's response sequences allows us to disambiguate actual learning from response biases. The implementation of the Bayesian analysis using the WinBUGS software provides an efficient way to test different models without developing a new algorithm for each model. The new state-space model and the Bayesian estimation procedure suggest an improved, computationally efficient approach for accurately characterizing learning in behavioral experiments.

Bayes Theorem↗
PubMed2005

Communication in collaborative discovery learning.

BACKGROUND: Constructivist approaches to learning focus on learning environments in which students have the opportunity to construct knowledge themselves, and negotiate this knowledge with others. Discovery learning and collaborative learning are examples of learning contexts that cater for knowledge construction processes. We introduce a computer-based learning environment in which the two forms of learning are implemented simultaneously. We focus on the interaction between discovery learning and collaborative learning. AIM: We aim to investigate which communicative activities are frequently used in the discovery learning process and which communicative and discovery activities co-occur. SAMPLE: The study involved 21 pairs of 10th-grade students enrolled in pre-university education, ranging from 15 to 17 years of age. METHOD: Participants worked in dyads on separate screens in a shared discovery learning environment. They communicated using a chat box. In order to find a possible relationship between communicative activities and discovery learning processes, correlational analysis and principal component analysis were performed. RESULT: Significant relationships were found between communicative and discovery activities, as well as five factors combining the communicative process and the discovery learning processes. Communicative activities are performed most frequently during the activities in generating hypotheses, experimental design, and conclusion construction. Argumentation occurs less than expected, and is associated with the construction of conclusions, rather than generating hypotheses. CONCLUSION: Communicative activities co-occur with discovery activities most of the time, as we expected. Further research should concentrate on means to augment communicative and discovery activities that are related to positive learning outcomes.

Adolescent↗
PubMed1982

Learning ability of orphan foals, of normal foals and of their mothers.

The maze learning ability of six pony foals that had been weaned at birth was compared to that of six foals reared normally. The foals' learning ability was also compared to their mothers' learning ability at the same task; the correct turn in a single choice point maze. The maze learning test was conducted when the foals were 6 to 8 mo old and after the mothered foals had been weaned. There was no significant difference between the ability of orphaned (weaned at birth) and mothered foals in their ability to learn to turn left (6 +/- .7 and 5.1 +/- .1 trials, respectively) or to learn the reversal, to turn right (6.7 +/- .6 and 6.2 +/- .6 trials, respectively). The orphan foals spent significantly more time in the maze in their first exposure to it than the mothered foals (184 +/- 42 vs 55 +/- 15 s. Mann Whitney U = 7, P less than .05). The mothers of the foals (n = 11) learned to turn left as rapidly as the foals (5.9 +/- .7 trials), but they were slower to learn to turn right (9.8 +/- 1.4 vs 6.4 +/- .4 trials, Mann Whitney U = 33, P less than .05), indicating that the younger horses learned more rapidly. There was no correlation between the trials to criteria of the mare and those of her foal, but there was a significant negative correlation between rank in trials to criteria and age (r = -65, P less than .05) when data from the mare and foal trials were combined. The dominance hierarchy of the mares was determined using a paired feeding test in which two horses competed for one bucket of feed. Although there was no correlation between rank in the hierarchy and maze learning ability, there was a correlation between body weight and rank in the hierarchy (r = .7, P less than .05). This may indicate either that heavier horses are likely to be dominant or that horses high in dominance gain more weight. Maternal deprivation did not appear to seriously retard learning of a simple maze by foals, although the orphans moved more slowly initially. The lack of maternal influence on learning is also reflected in the lack of correlation between the mare's learning ability and that of her foal. Young horses appear to learn more rapidly than older horses.

Age Factors↗
PubMed2001

Statistical assessment of the learning curves of health technologies.

OBJECTIVES: (1) To describe systematically studies that directly assessed the learning curve effect of health technologies. (2) Systematically to identify 'novel' statistical techniques applied to learning curve data in other fields, such as psychology and manufacturing. (3) To test these statistical techniques in data sets from studies of varying designs to assess health technologies in which learning curve effects are known to exist. METHODS - STUDY SELECTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): For a study to be included, it had to include a formal analysis of the learning curve of a health technology using a graphical, tabular or statistical technique. METHODS - STUDY SELECTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): For a study to be included, it had to include a formal assessment of a learning curve using a statistical technique that had not been identified in the previous search. METHODS - DATA SOURCES: Six clinical and 16 non-clinical biomedical databases were searched. A limited amount of handsearching and scanning of reference lists was also undertaken. METHODS - DATA EXTRACTION (HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW): A number of study characteristics were abstracted from the papers such as study design, study size, number of operators and the statistical method used. METHODS - DATA EXTRACTION (NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH): The new statistical techniques identified were categorised into four subgroups of increasing complexity: exploratory data analysis; simple series data analysis; complex data structure analysis, generic techniques. METHODS - TESTING OF STATISTICAL METHODS: Some of the statistical methods identified in the systematic searches for single (simple) operator series data and for multiple (complex) operator series data were illustrated and explored using three data sets. The first was a case series of 190 consecutive laparoscopic fundoplication procedures performed by a single surgeon; the second was a case series of consecutive laparoscopic cholecystectomy procedures performed by ten surgeons; the third was randomised trial data derived from the laparoscopic procedure arm of a multicentre trial of groin hernia repair, supplemented by data from non-randomised operations performed during the trial. RESULTS - HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW: Of 4571 abstracts identified, 272 (6%) were later included in the study after review of the full paper. Some 51% of studies assessed a surgical minimal access technique and 95% were case series. The statistical method used most often (60%) was splitting the data into consecutive parts (such as halves or thirds), with only 14% attempting a more formal statistical analysis. The reporting of the studies was poor, with 31% giving no details of data collection methods. RESULTS - NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH: Of 9431 abstracts assessed, 115 (1%) were deemed appropriate for further investigation and, of these, 18 were included in the study. All of the methods for complex data sets were identified in the non-clinical literature. These were discriminant analysis, two-stage estimation of learning rates, generalised estimating equations, multilevel models, latent curve models, time series models and stochastic parameter models. In addition, eight new shapes of learning curves were identified. RESULTS - TESTING OF STATISTICAL METHODS: No one particular shape of learning curve performed significantly better than another. The performance of 'operation time' as a proxy for learning differed between the three procedures. Multilevel modelling using the laparoscopic cholecystectomy data demonstrated and measured surgeon-specific and confounding effects. The inclusion of non-randomised cases, despite the possible limitations of the method, enhanced the interpretation of learning effects. CONCLUSIONS - HEALTH TECHNOLOGY ASSESSMENT LITERATURE REVIEW: The statistical methods used for assessing learning effects in health technology assessment have been crude and the reporting of studies poor. CONCLUSIONS - NON-HEALTH TECHNOLOGY ASSESSMENT LITERATURE SEARCH: A number of statistical methods for assessing learning effects were identified that had not hitherto been used in health technology assessment. There was a hierarchy of methods for the identification and measurement of learning, and the more sophisticated methods for both have had little if any use in health technology assessment. This demonstrated the value of considering fields outside clinical research when addressing methodological issues in health technology assessment. CONCLUSIONS - TESTING OF STATISTICAL METHODS: It has been demonstrated that the portfolio of techniques identified can enhance investigations of learning curve effects. (ABSTRACT TRUNCATED)

Cholecystectomy↗
PubMed2000

The different roles of social learning in vocal communication.

While vocal learning has been studied extensively in birds and mammals, little effort has been made to define what exactly constitutes vocal learning and to classify the forms that it may take. We present such a theoretical framework for the study of social learning in vocal communication. We define different forms of social learning that affect communication and discuss the required methodology to show each one. We distinguish between contextual and production learning in animal communication. Contextual learning affects the behavioural context or serial position of a signal. It can affect both usage and comprehension. Production learning refers to instances where the signals themselves are modified in form as a result of experience with those of other individuals. Vocal learning is defined as production learning in the vocal domain. It can affect one or more of three systems: the respiratory, phonatory and filter systems. Each involves a different level of control over the sound production apparatus. We hypothesize that contextual learning and respiratory production learning preceded the evolution of phonatory and filter production learning. Each form of learning potentially increases the complexity of a communication system. We also found that unexpected genetic or environmental factors can have considerable effects on vocal behaviour in birds and mammals and are often more likely to cause changes or differences in vocalizations than investigators may assume. Finally, we discuss how production learning is used in innovation and invention, and present important future research questions. Copyright 2000 The Association for the Study of Animal Behaviour.

Journal Article↗
PubMedDate not supplied

Active learning with support vector machine applied to gene expression data for cancer classification.

There is growing interest in the application of machine learning techniques in bioinformatics. The supervised machine learning approach has been widely applied to bioinformatics and gained a lot of success in this research area. With this learning approach researchers first develop a large training set, which is a time-consuming and costly process. Moreover, the proportion of the positive examples and negative examples in the training set may not represent the real-world data distribution, which causes concept drift. Active learning avoids these problems. Unlike most conventional learning methods where the training set used to derive the model remains static, the classifier can actively choose the training data and the size of training set increases. We introduced an algorithm for performing active learning with support vector machine and applied the algorithm to gene expression profiles of colon cancer, lung cancer, and prostate cancer samples. We compared the classification performance of active learning with that of passive learning. The results showed that employing the active learning method can achieve high accuracy and significantly reduce the need for labeled training instances. For lung cancer classification, to achieve 96% of the total positives, only 31 labeled examples were needed in active learning whereas in passive learning 174 labeled examples were required. That meant over 82% reduction was realized by active learning. In active learning the areas under the receiver operating characteristic (ROC) curves were over 0.81, while in passive learning the areas under the ROC curves were below 0.50.

Artificial Intelligence↗
PubMed2001

An Investigation of Undergraduate Athletic Training Students' Learning Styles and Program Admission Success.

OBJECTIVE: The phrase learning style refers to the method one uses to obtain and use information to learn. Personal learning styles can be assessed by specifically designed inventories. We conducted this study to determine if undergraduate athletic training students possess a dominant learning style, according to the Kolb Learning Style Inventory IIA (KLSI IIA), the newest version of the Kolb Learning Style Inventory (KLSI), and whether this style is related to education program admission success. DESIGN AND SETTING: A 1 x 4 factorial design was used. The independent variable was learning style type with 4 levels (converger, diverger, assimilator, or accommodator). The dependent variable was successful versus unsuccessful admission into selected programs. SUBJECTS: Forty undergraduate students (21 men, 19 women) from 3 institutions (mean +/- SD age, 20.7 +/- 1.7 years; mean +/- SD grade point average, 3.26 +/- 0.43) participated in this study. No subjects had previously taken the KLSI IIA, and none had a diagnosed learning disability. MEASUREMENTS: The KLSI IIA was administered to the participants at their respective institutions. We used 2 separate chi(2) analyses to determine if the observed distribution of learning styles differed from the expected distribution. Additionally, a Mann-Whitney U test was performed to determine if the learning style distributions of those subjects who were successfully admitted to the selected programs differed from those who were not. RESULTS: No significant differences existed between the observed distribution and the expected distribution for those admitted and those not admitted (chi2(3) = 3.8, P =.28; and chi2(3) = 3.1, P =.4, respectively). Also, no significant differences existed between the learning style distributions of the groups when compared with each other (Mann-Whitney U = 158, P =.5). CONCLUSIONS: Learning styles can be easily identified through the use of the KLSI IIA. We found no dominant learning style among undergraduate athletic training students and no particular learning style led to program admission.

Journal Article↗
PubMedDate not supplied

Behavioral methods for measuring effects of drugs on learning and memory in animals.

This review describes methods for measuring effects of drugs on learning and memory in animals, proceeding from relatively simple nonassociative learning (habituation) to classical and instrumental conditioning, and concluding with complex measures for measuring learning and memory repeatedly in the individual animal. Procedures for separating drug effects specific to learning and memory from non-specific effects on activity, motivation, sensory and motor capacity, etc., were emphasized. For each method, selected experimental examples were presented which described the action of drugs on learning and memory, elucidated the behavioral processes involved in the drug effects, or illustrated methodological points. The various procedures used to measure drug effects on learning and memory in animals have yielded a bewildering array of often-contradictory results. Quantitative differences in effectiveness of drugs in the different procedures are common. Drugs (for example, the nootropics) that alter learning or memory in a few procedures may be totally without activity in many others. How are these discrepancies to be interpreted? The apparent inconsistencies in the data can, for the most part, be understood in terms of the nature of learning and memory. "Learning" and "memory" are hypothetical processes presumed to underlie enduring changes in behavior resulting from the organism's interaction with environmental stimuli. Given such a broad definition, the prevalence of inconsistencies in the data is hardly surprising. It is unlikely that the same mechanisms should underlie all of the wide variety of behavioral changes included under the rubrics "learning" or "memory." (For a contrary view, based on consistencies among results obtained in the diverse procedures, see Zornetzer). How, then, should drug effects on learning and memory be identified or measured? The first step, of course, is to rule out those drug effects that do not conform to the definition of learning or memory. This review has described strategies and procedures by which this can be accomplished. However, even when this is done there is no single procedure that can detect drug effects on learning and memory in general, nor, in view of the heterogeneous behaviors involved, is it likely that such a universal procedure will ever be found. Thus, a multi-faceted strategy will be required. Some of the simpler procedures described in this review may be adequate for the initial identification of interesting effects.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗
PubMed1992

Effects of lesion of the inferior olivary complex by 3-acetylpyridine on learning and memory in the rat.

DA/HAN-strained male rats (pigmented rats) were submitted to two experimental tasks consisting of spatial learning (water-escape) and a passive avoidance conditioning. Both these tasks were performed by different animals. In order to destroy the inferior olivary complex, the animals were injected with 3-acetylpyridine either 9 days prior to the initial learning session or 24 h after completion of the learning task. They were retested (retrieval test) 10 days after the initial learning was achieved. Learning and retention were compared to those noted in control rats. Administration of 3-acetylpyridine before the initial learning did not prevent the spatial learning but the scores were greatly altered and the number of trials needed to reach the fixed learning criterion was much greater than in controls. However, 10 days later the animals had memorized their initial experience. Injection of 3-acetylpyridine after the initial learning session impaired memory: the animals had completely forgotten their initial learning. It can therefore be concluded that lesion of the afferent climbing fibres to the cerebellar cortex alters learning and retention of a spatial task. Such a lesion does not interfere with learning and retention of a passive avoidance conditioning, since in this condition the experimental animals injected with 3-acetylpyridine either before or after the initial learning behave similarly to controls. The effects of the inferior olivary complex lesion are obviously different according to the task to be learnt, suggesting that these two tasks do not require the integrity of the same nervous structures.

Animals↗
PubMed2003

The multifaceted nature of unsupervised category learning.

A substantial portion of category-learning research has focused on one learning mode--namely, classification learning (a supervised learning mode). Subsequently, theories of category learning have focused on how the abstract structure of categories (i.e., the co-occurrence patterns of feature values) affects acquisition. Recent work in supervised learning has shown that a learner's interactions with the stimulus set also plays an important role in acquisition. The present study extends this work to unsupervised learning situations involving simple one-dimensional stimuli. The results suggest that categorization performance is a function of both learning mode (i.e., study conditions) and learning problem (i.e., category structure). Unsupervised learning, like supervised learning, appears to be multifaceted, with different learning modes best paired with certain learning problems.

Adult↗