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PubMed2002

Cerebellum activation associated with performance change but not motor learning.

The issue of whether the cerebellum contributes to motor skill learning is controversial, principally because of the difficulty of separating the effects of motor learning from changes in performance. We performed a functional magnetic resonance imaging investigation during an implicit, motor sequence-learning task that was designed to separate these two processes. During the sequence-encoding phase, human participants performed a concurrent distractor task that served to suppress the performance changes associated with learning. Upon removal of the distractor, participants showed evidence of having learned. No cerebellar activation was associated with the learning phase, despite extensive involvement of other cortical and subcortical regions. There was, however, significant cerebellar activation during the expression of learning; thus, the cerebellum does not contribute to learning of the motor skill itself but is engaged primarily in the modification of performance.

Brain↗
PubMed1997

Fragile X, iron, and neurodevelopmental screening in 8 year old children with mild to moderate learning difficulties.

OBJECTIVE: To examine the value of neurodevelopmental examination, fragile X testing, iron studies, and other screening procedures in children with mild to moderate learning difficulties. DESIGN: A cross sectional case-control study. SUBJECTS: A 34% random sample (n = 130) of children with mild to moderate learning difficulties born between 01/07/83 and 30/06/84 and resident in North and West Belfast. Controls were 130 children without learning difficulties matched for age and geographical area. RESULTS: The prevalence of mild to moderate learning difficulties in North and West Belfast was 16%; 115 (89%) of the learning difficulties group and 80 (58%) of the control group consented to participate. Children in the learning difficulties group scored significantly lower in neurodevelopmental testing and were more likely to fail their audiometry assessment than controls. Children in the learning difficulties group were also more likely to be anaemic and had lower serum iron and transferrin saturation than controls. No cases of fragile X were identified. Thyroid function tests and urinary amino acids were all within normal limits There were no significant differences in anthropometry, head circumference, or formal neurological examinations. CONCLUSIONS: Children with learning difficulties are more likely to be neurodevelopmentally immature and iron depleted than controls. Iron depletion should be aggressively sought and treated. The role for routine assessment for fragile X, thyroid function tests, and amino acid chromatography is doubtful.

Amino Acids↗
PubMed2001

The Claude Bernard Distinguished Lecture. In pursuit of meaningful learning.

The Bernard Distinguished Lecturers are individuals who have a history of experience and expertise in teaching that impacts multiple levels of health science education. Dr. Joel Michael more than meets these criteria. Joel earned a BS in biology from CalTech and a PhD in physiology from MIT following which he vigorously pursued his fascination with the mammalian central nervous system under continuous National Institutes of Health funding for a 15-yr period. At the same time, he became increasingly involved in teaching physiology, with the computer being his bridge between laboratory science and classroom teaching. Soon after incorporating computers into his laboratory, he began developing computer-based learning resources for his students. Observing students using these resources to solve problems led to an interest in the learning process itself. This in turn led to a research and development program, funded by the Office of Naval Research (ONR), that applied artificial intelligence to develop smart computer tutors. The impact of problem solving on student learning became the defining theme of National Science Foundation (NSF)-supported research in health science education that gradually moved all of Dr. Michael's academic efforts from neurophysiology to physiology education by the early 1980's. More recently, Joel has been instrumental in developing and maintaining the Physiology Education Research Consortium, a group of physiology teachers from around the nation who collaborate on diverse projects designed to enhance learning of the life sciences. In addition to research in education and learning science, Dr. Michael has devoted much of his time to helping physiology teachers adopt modern approaches to helping students learn. He has organized and presented faculty development workshops at many national and international venues. The topics for these workshops have included computer-based education, active learning, problem-based learning, and the use of general models in teaching physiology.

Computer-Assisted Instruction↗
PubMed2006

Neuronal substrates of motor learning in the velocity storage generated during optokinetic stimulation in the squirrel monkey.

Chronic motor learning in the vestibuloocular reflex (VOR) results in changes in the gain of this reflex and in other eye movements intimately associated with VOR behavior, e.g., the velocity storage generated by optokinetic stimulation (OKN velocity storage). The aim of the present study was to identify the plastic sites responsible for the change in OKN velocity storage after chronic VOR motor learning. We studied the neuronal responses of vertical eye movement flocculus target neurons (FTNs) during the optokinetic after-nystagmus (OKAN) phase of the optokinetic response (OKR) before and after VOR motor learning. Our findings can be summarized as follows. 1) Chronic VOR motor learning changes the horizontal OKN velocity storage in parallel with changes in VOR gain, whereas the vertical OKN velocity storage is more complex, increasing with VOR gain increases, but not changing following VOR gain decreases. 2) FTNs contain an OKAN signal having opposite directional preferences after chronic high versus low gain learning, suggesting a change in the OKN velocity storage representation of FTNs. 3) Changes in the eye-velocity sensitivity of FTNs during OKAN are correlated with changes in the brain stem head-velocity sensitivity of the same neurons. And 4) these changes in eye-velocity sensitivity of FTNs during OKAN support the new behavior after high gain but not low gain learning. Thus we hypothesize that the changes observed in the OKN velocity storage behavior after chronic learning result from changes in brain stem pathways carrying head velocity and OKN velocity storage information, and that a parallel pathway to vertical FTNs changes its OKN velocity storage representation following low, but not high, gain VOR motor learning.

Adaptation, Physiological↗
PubMed1997

Anatomy of motor learning. I. Frontal cortex and attention to action.

We used positron emission tomography to study new learning and automatic performance in normal volunteers. Subjects learned sequences of eight finger movements by trial and error. In a previous experiment we showed that the prefrontal cortex was activated during new learning but not during during automatic performance. The aim of the present experiment was to see what areas could be reactivated if the subjects performed the prelearned sequence but were required to pay attention to what they were doing. Scans were carried out under four conditions. In the first the subjects performed a prelearned sequence of eight key presses; this sequence was learned before scanning and was practiced until it had become overlearned, so that the subjects were able to perform it automatically. In the second condition the subjects learned a new sequence during scanning. In a third condition the subjects performed the prelearned sequence, but they were required to attend to what they were doing; they were instructed to think about the next movement. The fourth condition was a baseline condition. As in the earlier study, the dorsal prefrontal cortex and anterior cingulate area 32 were activated during new learning, but not during automatic performance. The left dorsal prefrontal cortex and the right anterior cingulate cortex were reactivated when subjects paid attention to the performance of the prelearned sequence compared with automatic performance of the same task. It is suggested that the critical feature was that the subjects were required to attend to the preparation of their responses. However, the dorsal prefrontal cortex and the anterior cingulate cortex were activated more when the subjects learned a new sequence than they were when subjects simply paid attention to a prelearned sequence. New learning differs from the attention condition in that the subjects generated moves, monitored the outcomes, and remembered the responses that had been successful. All these are nonroutine operations to which the subjects must attend. Further analysis is needed to specify which are the nonroutine operations that require the involvement of the dorsal prefrontal and anterior cingulate cortex.

Adult↗
PubMed2003

Isotropic sequence order learning.

In this article, we present an isotropic unsupervised algorithm for temporal sequence learning. No special reward signal is used such that all inputs are completely isotropic. All input signals are bandpass filtered before converging onto a linear output neuron. All synaptic weights change according to the correlation of bandpass-filtered inputs with the derivative of the output. We investigate the algorithm in an open- and a closed-loop condition, the latter being defined by embedding the learning system into a behavioral feedback loop. In the open-loop condition, we find that the linear structure of the algorithm allows analytically calculating the shape of the weight change, which is strictly heterosynaptic and follows the shape of the weight change curves found in spike-time-dependent plasticity. Furthermore, we show that synaptic weights stabilize automatically when no more temporal differences exist between the inputs without additional normalizing measures. In the second part of this study, the algorithm is is placed in an environment that leads to closed sensor-motor loop. To this end, a robot is programmed with a prewired retraction reflex reaction in response to collisions. Through isotropic sequence order (ISO) learning, the robot achieves collision avoidance by learning the correlation between his early range-finder signals and the later occurring collision signal. Synaptic weights stabilize at the end of learning as theoretically predicted. Finally, we discuss the relation of ISO learning with other drive reinforcement models and with the commonly used temporal difference learning algorithm. This study is followed up by a mathematical analysis of the closed-loop situation in the companion article in this issue, "ISO Learning Approximates a Solution to the Inverse-Controller Problem in an Unsupervised Behavioral Paradigm" (pp. 865-884).

Algorithms↗
PubMed2005

Temporal sequence learning, prediction, and control: a review of different models and their relation to biological mechanisms.

In this review, we compare methods for temporal sequence learning (TSL) across the disciplines machine-control, classical conditioning, neuronal models for TSL as well as spike-timing-dependent plasticity (STDP). This review introduces the most influential models and focuses on two questions: To what degree are reward-based (e.g., TD learning) and correlation-based (Hebbian) learning related? and How do the different models correspond to possibly underlying biological mechanisms of synaptic plasticity? We first compare the different models in an open-loop condition, where behavioral feedback does not alter the learning. Here we observe that reward-based and correlation-based learning are indeed very similar. Machine control is then used to introduce the problem of closed-loop control (e.g., actor-critic architectures). Here the problem of evaluative (rewards) versus nonevaluative (correlations) feedback from the environment will be discussed, showing that both learning approaches are fundamentally different in the closed-loop condition. In trying to answer the second question, we compare neuronal versions of the different learning architectures to the anatomy of the involved brain structures (basal-ganglia, thalamus, and cortex) and the molecular biophysics of glutamatergic and dopaminergic synapses. Finally, we discuss the different algorithms used to model STDP and compare them to reward-based learning rules. Certain similarities are found in spite of the strongly different timescales. Here we focus on the biophysics of the different calcium-release mechanisms known to be involved in STDP.

Forecasting↗
PubMed2006

Modeling sensorimotor learning with linear dynamical systems.

Recent studies have employed simple linear dynamical systems to model trial-by-trial dynamics in various sensorimotor learning tasks. Here we explore the theoretical and practical considerations that arise when employing the general class of linear dynamical systems (LDS) as a model for sensorimotor learning. In this framework, the state of the system is a set of parameters that define the current sensorimotor transformation-the function that maps sensory inputs to motor outputs. The class of LDS models provides a first-order approximation for any Markovian (state-dependent) learning rule that specifies the changes in the sensorimotor transformation that result from sensory feedback on each movement. We show that modeling the trial-by-trial dynamics of learning provides a substantially enhanced picture of the process of adaptation compared to measurements of the steady state of adaptation derived from more traditional blocked-exposure experiments. Specifically, these models can be used to quantify sensory and performance biases, the extent to which learned changes in the sensorimotor transformation decay over time, and the portion of motor variability due to either learning or performance variability. We show that previous attempts to fit such models with linear regression have not generally yielded consistent parameter estimates. Instead, we present an expectation-maximization algorithm for fitting LDS models to experimental data and describe the difficulties inherent in estimating the parameters associated with feedback-driven learning. Finally, we demonstrate the application of these methods in a simple sensorimotor learning experiment: adaptation to shifted visual feedback during reaching.

Algorithms↗
PubMed1998

Online learning from finite training sets and robustness to input bias.

We analyze online gradient descent learning from finite training sets at noninfinitesimal learning rates eta. Exact results are obtained for the time-dependent generalization error of a simple model system: a linear network with a large number of weights N, trained on p = alphaN examples. This allows us to study in detail the effects of finite training set size alpha on, for example, the optimal choice of learning rate eta. We also compare online and offline learning, for respective optimal settings of eta at given final learning time. Online learning turns out to be much more robust to input bias and actually outperforms offline learning when such bias is present; for unbiased inputs, online and offline learning perform almost equally well.

Learning↗
PubMed1996

Hebbian learning of context in recurrent neural networks.

Single electrode recording in the inferotemporal cortex of monkeys during delayed visual memory tasks provide evidence for attractor dynamics in the observed region. The persistent elevated delay activities could be internal representations of features of the learned visual stimuli shown to the monkey during training. When uncorrelated stimuli are presented during training in a fixed sequence, these experiments display significant correlations between the internal representations. Recently a simple model of attractor neural network has reproduced quantitatively the measured correlations. An underlying assumption of the model is that the synaptic matrix formed during the training phase contains in its efficacies information about the contiguity of persistent stimuli in the training sequence. We present here a simple unsupervised learning dynamics that produces such a synaptic matrix if sequences of stimuli are repeatedly presented to the network at fixed order. The resulting matrix is then shown to convert temporal correlations during training into spatial correlations between attractors. The scenario is that, in the presence of selective delay activity, at the presentation of each stimulus, the activity distribution in the neural assembly contain information of both the current stimulus and the previous one (carried by the attractor). Thus the recurrent synaptic matrix can code not only for each of the stimuli presented to the network but also for their context. We combine the idea that for learning to be effective, synaptic modification should be stochastic, with the fact that attractors provide learnable information about two consecutive stimuli. We calculate explicitly the probability distribution of synaptic efficacies as a function of training protocol, that is, the order in which stimuli are presented to the network. We then solve for the dynamics of a network composed of integrate-and-fire excitatory and inhibitory neurons with a matrix of synaptic collaterals resulting from the learning dynamics. The network has a stable spontaneous activity, and stable delay activity develops after a critical learning stage. The availability of a learning dynamics makes possible a number of experimental predictions for the dependence of the delay activity distributions and the correlations between them, on the learning stage and the learning protocol. In particular it makes specific predictions for pair-associates delay experiments.

Animals↗
PubMed2007

The road to chaos by time-asymmetric Hebbian learning in recurrent neural networks.

This letter aims at studying the impact of iterative Hebbian learning algorithms on the recurrent neural network's underlying dynamics. First, an iterative supervised learning algorithm is discussed. An essential improvement of this algorithm consists of indexing the attractor information items by means of external stimuli rather than by using only initial conditions, as Hopfield originally proposed. Modifying the stimuli mainly results in a change of the entire internal dynamics, leading to an enlargement of the set of attractors and potential memory bags. The impact of the learning on the network's dynamics is the following: the more information to be stored as limit cycle attractors of the neural network, the more chaos prevails as the background dynamical regime of the network. In fact, the background chaos spreads widely and adopts a very unstructured shape similar to white noise. Next, we introduce a new form of supervised learning that is more plausible from a biological point of view: the network has to learn to react to an external stimulus by cycling through a sequence that is no longer specified a priori. Based on its spontaneous dynamics, the network decides "on its own" the dynamical patterns to be associated with the stimuli. Compared with classical supervised learning, huge enhancements in storing capacity and computational cost have been observed. Moreover, this new form of supervised learning, by being more "respectful" of the network intrinsic dynamics, maintains much more structure in the obtained chaos. It is still possible to observe the traces of the learned attractors in the chaotic regime. This complex but still very informative regime is referred to as "frustrated chaos."

Algorithms↗
PubMed2006

Two cases requiring external reinforcement in perceptual learning.

The role of external reinforcement is an issue of much debate and uncertainty in perceptual learning research. Although it is commonly acknowledged that external reinforcement, such as performance feedback, can aid in perceptual learning (M. H. Herzog & M. Fahle, 1997), there are many examples in which it is not required (K. Ball & R. Sekuler, 1987; M. Fahle, S. Edelman, & T. Poggio, 1995; A. Karni & D. Sagi, 1991; S. P. McKee & G. Westheimer, 1978; L. P. Shiu & H. Pashler, 1992). Additionally, learning without external reinforcement can occur even for stimuli that are irrelevant to the subject's task (A. R. Seitz & T. Watanabe, 2003). It has been thus hypothesized that internal reinforcement can serve a similar role as external reinforcement in learning (M. H. Herzog & M. Fahle, 1998; A. Seitz & T. Watanabe, 2005). This idea suggests that perceptual learning should occur in the absence of external reinforcement provided that easy exemplars are utilized as a basis for the subject to generate internal reinforcement. Here, we report results from two studies that show that this is not always the case. In the first study, subjects participated in two sessions of a motion direction discrimination task with low-contrast dots moving in directions separated by 90 degrees. In the second study, subjects participated in 12 orientation-discrimination sessions using oriented bars (oriented either 70 degrees or 110 degrees) that were masked by spatial noise. Trials of different signal levels (yielding psychometric functions ranging from chance to ceiling) were randomly interleaved. In both studies, subjects experiencing external reinforcement showed significant learning, whereas subjects receiving no external reinforcement failed to show learning. We conclude that while internal reinforcement is an important learning signal, the presence of easy exemplars is not sufficient to generate reinforcement signals.

Adult↗
PubMedDate not supplied

The effects of extended time on algebra test scores for college students with and without learning disabilities.

The purpose of this study was to assess the effects of extended time on the algebra test performance of community college students with and without learning disabilities. Forty-four students with learning disabilities and 44 students without learning disabilities attending five California community colleges participated in the study. The students each took an algebra test under timed conditions and a comparable test under extended-time conditions. The main results were that the students with learning disabilities scored significantly lower than the students without learning disabilities under timed conditions, the scores of the students with learning disabilities increased significantly with extended time, and the scores of the students with learning disabilities under extended-time conditions did not differ significantly from the timed or extended-time scores of the students without learning disabilities.

Adolescent↗
PubMedDate not supplied

Issues in the definition and diagnosis of learning disabilities: a perspective on Guckenberger v. Boston University.

This article reviews issues related to the definition of learning disabilities in the context of the Guckenberger v. Boston University case. Four major questions are addressed: (a) Who is learning disabled? (b) How should learning disabilities be assessed? (c) Who is qualified to make a decision about whether or not an individual has a learning disability? and (d) What accommodations should be provided by a postsecondary institution and how should they be selected? Although these are complicated and difficult questions, it is possible to develop a simple, reasonable classification system for learning disabilities, and to conduct assessments based on a coherent and relevant set of achievement tests in which individuals who score below a cutoff are considered learning disabled. Scores on IQ tests are irrelevant and not useful and may even be discriminatory. The issues of decision making regarding learning disabilities and appropriate accommodations remain significant dilemmas for the field; resolution of these issues seems virtually impossible without agreement on appropriate procedures for the definition, identification, and assessment of learning disabilities.

Adult↗
PubMed1998

Learning disabilities with and without attention-deficit hyperactivity disorder: parents' and teachers' perspectives.

Our objective was to delineate the educational and behavioral differences between learning disabled children with and without attention-deficit hyperactivity disorder (ADHD). A restrospective (TROHOC) multimeasure comparative design was employed. Parents' and teachers' questionnaires (ANSER system) pertaining to attention-activity, associated behaviors, and scholastic achievements were compared. Parents' questionnaires failed to distinguish between the two groups. Teachers' questionnaires were significantly more sensitive. Significant correlations between educational achievements and attention-activity and associated behaviors scores among children with learning disabilities were evident, no such correlations were found in the group with learning disability with ADHD. The factor analysis identified different educational and behavioral aggregates with language related difficulties and externalizing behaviors more typically aggregated in the learning disabled group with ADHD and recall deficit and internalizing/neurotic behaviors in the group with learning disability only. ADHD appears to be an associated comorbidity and not necessarily a specific learning deficit. However, children with learning disability with ADHD possibly have a different underlying neurocognitive pattern than their peers with learning disabilities only.

Attention Deficit Disorder with Hyperactivity↗
PubMed2006

The basal ganglia in human learning.

For many years, the basal ganglia were described in anatomy courses as strictly motor structures. Certainly, some of the most obvious and debilitating symptoms shown by persons with basal ganglia disorders are problems in motor control. However, the basal ganglia are not limited to motoric aspects of behavior: recent research shows that they are involved in most areas of cognitive and emotional functioning, consistent with their anatomical connections with all areas of the cortex. This review will focus on the roles of the basal ganglia in human learning, particularly sequence learning and category learning. Current areas of research that are discussed include the differing roles of different basal ganglia regions, patterns of interaction between the cortex and basal ganglia, differences in positive and negative association learning, effects of dopaminergic medication on learning, whether basal ganglia-mediated learning is implicit or explicit, and how the basal ganglia learning systems interact with other learning systems, particularly within the medial temporal lobe.

Basal Ganglia↗
PubMed2005

Behavioral and neurophysiological analyses of dynamic learning processes.

In this article, the authors address two topics relevant to the study of the brain basis of associative learning. In Part 1, they compare and contrast the patterns and time course of dynamic learning-related neural activity that have been reported in the medial temporal lobe, premotor cortex, prefrontal cortex, and striatum during various associative learning tasks. In Part 2, they examine the statistical methodologies that have been used to analyze both behavioral learning and learning-related neural activity. They describe a state-space model of behavioral learning that provides accurate estimates of dynamic learning processes and a point-process filter algorithm that tracks the dynamic changes in neural activity on a millisecond time scale. Future challenges for these statistical methodologies and their application to the study of the brain basis of associative learning are discussed.

Association Learning↗
PubMed2006

Computer literacy and attitudes towards e-learning among first year medical students.

BACKGROUND: At the Medical University of Vienna, most information for students is available only online. In 2005, an e-learning project was initiated and there are plans to introduce a learning management system. In this study, we estimate the level of students' computer skills, the number of students having difficulty with e-learning, and the number of students opposed to e-learning. METHODS: The study was conducted in an introductory course on computer-based and web-based training (CBT/WBT). Students were asked to fill out a questionnaire online that covered a wide range of relevant attitudes and experiences. RESULTS: While the great majority of students possess sufficient computer skills and acknowledge the advantages of interactive and multimedia-enhanced learning material, a small percentage lacks basic computer skills and/or is very skeptical about e-learning. There is also a consistently significant albeit weak gender difference in available computer infrastructure and Internet access. As for student attitudes toward e-learning, we found that age, computer use, and previous exposure to computers are more important than gender. A sizable number of students, 12% of the total, make little or no use of existing e-learning offerings. CONCLUSION: Many students would benefit from a basic introduction to computers and to the relevant computer-based resources of the university. Given to the wide range of computer skills among students, a single computer course for all students would not be useful nor would it be accepted. Special measures should be taken to prevent students who lack computer skills from being disadvantaged or from developing computer-hostile attitudes.

Adult↗