Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “learning”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,117 records · Page 62Linked to original sources
PubMed2006

Effects of sex and insulin/insulin-like growth factor-1 signaling on performance in an associative learning paradigm in Caenorhabditis elegans.

Learning is an adaptive change in behavior in response to environmental stimuli. In mammals, there is a distinct female bias to learn skills that is still unprecedented in other animal taxa. Here we have investigated the biological determinants of performance in an associative learning paradigm in the nematode Caenorhabditis elegans. Using an assay of chemotactic reactions associated with food deprivation, wild-type male worms show inferior learning ability relative to hermaphrodites. Sex-based learning difference is therefore an ancient evolutionary feature appearing even in relatively simple animals. C. elegans mutants with reduced insulin/IGF-1 signaling also exhibit a greatly reduced learning ability in this assay. In addition, hyperactivation of insulin/IGF-1 signaling through loss-of-function mutations in the PTEN phosphatase daf-18, a negative regulator of insulin/IGF-1 signaling, enhances learning ability beyond that of wild type. According to our epistasis analysis, the effect of DAF-2 on learning acts via phosphatidylinositol 3,4,5-trisphosphate (PIP(3)) production, but not the DAF-16 FOXO transcription factor. This implies that the signaling pathway from DAF-2 affecting this learning paradigm branches between PIP(3) production and DAF-16. However, learning capacity of nematodes is lowered by loss-of-function mutations in daf-16, suggesting involvement of noninsulin/IGF-1 signaling-dependent DAF-16 activation in learning. Potentially, sex and insulin/IGF-1 signaling affect performance in this learning assay via effects on the neurobiology of learning.

Animals↗
PubMed2005

Self-directed learning: implications and limitations for undergraduate nursing education.

Self-directed learning (SDL) is an educational concept that has received increasing attention in recent years, particularly in the context of higher education. Whilst the benefits of SDL have been espoused by a number of adult education theorists (Brookfield, S., 1986. Understanding and Facilitating Adult Learning. Jossey-Bass, San Francisco; Houle, C., 1984. Patterns of Learning: New Perspectives on Life-Span Education. Jossey-Bass, San Francisco; Knowles, M., 1998. The Adult Leaner: A Neglected Species, fifth ed., Gulf, Houston; Tough, A., 1979. The Adults Learning Project: A Fresh Approach to Theory and Practice in Adult Learning. Ontario Institute for Studies in Education, Toronto), its introduction into curricula has not always been successful (Nolan, J., Nolan, M., 1997a. Self-directed and student-centred learning in nurse education: 1. British Journal of Nursing 6 (1), 51-55; Nolan, J., Nolan, M., 1997b. Self-directed and student-centred learning in nurse education: 2. British Journal of Nursing 6 (2), 103-107; Slevin, O., Lavery, M., 1991. Self-directed learning and student supervision. Nurse Education Today 11, 368-377). The indiscriminate application of SDL principles and poorly prepared teachers and/or students has at times meant that the introduction of SDL has been resented rather than welcomed (Iwasiw, C., 1987. The role of the teacher in self-directed learning. Nurse Education Today 7, 222-227; Turunen, H., Taskinen, H., Voutilainen, U., Tossavainen, K., Sinkkonen, S., 1997. Nursing and social work students' initial orientation towards their studies. Nurse Education Today 17, 67-71). This paper clarifies and explores these issues by: (a) examining the origins of SDL; (b) discussing the relevance of self-directed learning to Knowles' theory of adult learning and contemporary educational practices such as enquiry based learning and problem based learning; and (c) highlighting the implications and limitations of SDL with regard to adult education in general, and undergraduate nursing education in particular.

Education, Nursing↗
PubMed1995

Improving interface quality: an investigation of human-computer interaction task learning.

User learning is of critical importance in evaluating interface usability (and in turn interface quality). The focus of this research in on interface learnability, where a stochastic model represents the learning process required for successful completion of human-computer interaction tasks. The parameter used to quantify learning is a learning rate. Of interest here is the validation of learning rate as a measure of interface quality. Learning rate was validated against two traditional measures of interface quality: task completion time, and error frequency. SuperCard, a Macintosh project utility, provided an empirical learning environment in which 32 participants learned 16 fundamental SuperCard tasks. Results of correlation analyses suggested the usefulness of learning rate as an indicator of interface quality. Our learning rate analysis identified four tasks presenting learning difficulties. (Analysis of task completion times identified two of these four tasks, and error frequency analysis identified one). Learning rate data captured all of the information available from the two traditional interface quality measures and identified two tasks disregarded by them. Incorporating learning rates in the interface evaluation process precludes time-intensive video tape analysis typically required by more traditional interface quality measures.

Adult↗
PubMed2006

Parameter learning from stochastic teachers and stochastic compulsive liars.

This paper considers a general learning problem akin to the field of learning automata (LA) in which the learning mechanism attempts to learn from a stochastic teacher or a stochastic compulsive liar. More specifically, unlike the traditional LA model in which LA attempts to learn the optimal action offered by the Environment (also here called the "Oracle"), this paper considers the problem of the learning mechanism (robot, an LA, or in general, an algorithm) attempting to learn a "parameter" within a closed interval. The problem is modeled as follows: The learning mechanism is trying to locate an unknown point on a real interval by interacting with a stochastic Environment through a series of informed guesses. For each guess, the Environment essentially informs the mechanism, possibly erroneously (i.e., with probability p), which way it should move to reach the unknown point. When the probability of a correct response is p > 0.5, the Environment is said to be informative, and thus the case of learning from a stochastic teacher. When this probability p < 0.5, the Environment is deemed deceptive, and is called a stochastic compulsive liar. This paper describes a novel learning strategy by which the unknown parameter can be learned in both environments. These results are the first reported results, which are applicable to the latter scenario. The most significant contribution of this paper is that the proposed scheme is shown to operate equally well, even when the learning mechanism is unaware of whether the Environment ("Oracle") is informative or deceptive. The learning strategy proposed herein, called CPL-AdS, partitions the search interval into d subintervals, evaluates the location of the unknown point with respect to these subintervals using fast-converging E-optimal LRI LA, and prunes the search space in each iteration by eliminating at least one partition. The CPL-AdS algorithm is shown to provably converge to the unknown point with an arbitrary degree of accuracy with probability as close to unity as desired. Comprehensive experimental results confirm the fast and accurate convergence of the search for a wide range of values for the Environment's feedback accuracy parameter p, and thus has numerous potential applications.

Algorithms↗
PubMed2001

Best harmony, unified RPCL and automated model selection for unsupervised and supervised learning on Gaussian mixtures, three-layer nets and ME-RBF-SVM models.

After introducing the fundamentals of BYY system and harmony learning, which has been developed in past several years as a unified statistical framework for parameter learning, regularization and model selection, we systematically discuss this BYY harmony learning on systems with discrete inner-representations. First, we shown that one special case leads to unsupervised learning on Gaussian mixture. We show how harmony learning not only leads us to the EM algorithm for maximum likelihood (ML) learning and the corresponding extended KMEAN algorithms for Mahalanobis clustering with criteria for selecting the number of Gaussians or clusters, but also provides us two new regularization techniques and a unified scheme that includes the previous rival penalized competitive learning (RPCL) as well as its various variants and extensions that performs model selection automatically during parameter learning. Moreover, as a by-product, we also get a new approach for determining a set of 'supporting vectors' for Parzen window density estimation. Second, we shown that other special cases lead to three typical supervised learning models with several new results. On three layer net, we get (i) a new regularized ML learning, (ii) a new criterion for selecting the number of hidden units, and (iii) a family of EM-like algorithms that combines harmony learning with new techniques of regularization. On the original and alternative models of mixture-of-expert (ME) as well as radial basis function (RBF) nets, we get not only a new type of criteria for selecting the number of experts or basis functions but also a new type of the EM-like algorithms that combines regularization techniques and RPCL learning for parameter learning with either least complexity nature on the original ME model or automated model selection on the alternative ME model and RBF nets. Moreover, all the results for the alternative ME model are also applied to other two popular nonparametric statistical approaches, namely kernel regression and supporting vector machine. Particularly, not only we get an easily implemented approach for determining the smoothing parameter in kernel regression, but also we get an alternative approach for deciding the set of supporting vectors in supporting vector machine.

Algorithms↗
PubMed2005

Dual-modality impairment of implicit learning of letter-strings versus color-patterns in patients with schizophrenia.

BACKGROUND: Implicit learning was reported to be intact in schizophrenia using artificial grammar learning. However, emerging evidence indicates that artificial grammar learning is not a unitary process. The authors used dual coding stimuli and schizophrenia clinical symptom dimensions to re-evaluate the effect of schizophrenia on various components of artificial grammar learning. METHODS: Letter string and color pattern artificial grammar learning performances were compared between 63 schizophrenic patients and 27 comparison subjects. Four symptom dimensions derived from a Chinese Positive and Negative Symptom Scale ratings were correlated with patients' artificial grammar implicit learning performances along the two stimulus dimensions. Patients' explicit memory performances were assessed by verbal paired associates and visual reproduction subtests of the Wechsler Memory Scales Revised Version to provide a contrast to their implicit memory function. RESULTS: Schizophrenia severely hindered color pattern artificial grammar learning while the disease affected lexical string artificial grammar learning to a lesser degree after correcting the influences from age, education and the performance of explicit memory function of both verbal and visual modalities. Both learning performances correlated significantly with the severity of patients' schizophrenic clinical symptom dimensions that reflect poor abstract thinking, disorganized thinking, and stereotyped thinking. CONCLUSION: The results of this study suggested that schizophrenia affects various mechanisms of artificial grammar learning differently. Implicit learning, knowledge acquisition in the absence of conscious awareness, is not entirely intact in patients with schizophrenia. Schizophrenia affects implicit learning through an impairment of the ability of making abstractions from rules and at least in part decreasing the capacity for perceptual learning.

Journal Article↗
PubMed2002

The learning and application of generic skills by CLSs/MTs who have 'left the field'.

OBJECTIVE: To determine whether generic skills that dinical laboratory scientists (CLSs)/medical technologists (MTs) learned as students and/or practitioners are applied to jobs outside the field of CLS/MT; and to determine if there are any significant differences in learning and/or doing these skills by CLS/MT majors vs. non-CLS/MT majors. DESIGN: An Occupational Change Survey was sent to CLS/MT practitioners who had identified themselves as having left the field (LTF) of CLS/MT. The participants were asked whether or not they were CLS/MT majors as undergraduates, whether they utilized generic baccalaureate level skills in their LTF jobs, and whether or not they learned these skills as CLS/MT students and/or practitioners. The skills were: problem solving, decision making, troubleshooting, analytical reasoning, data correlation, precision studies, quality assessment, teaching, research, communication, technical writing, computer use, utilization review, and supervision. SETTINGS AND PARTICIPANTS: The survey was sent to 105 participants of an ongoing longitudinal study who identified themselves as having LTF. MAIN OUTCOME MEASURES: Responses for doing/utilizing the skills were grouped as 'Yes' if participants indicated they frequently or sometimes used the skills in their LTF jobs, and 'No' if they indicated they rarely or never used the skills in their LTF jobs. Responses for learning the skills were grouped as 'Yes' if participant indicated they learned the skills as CLS/MT students, practitioners or both and 'No' if they indicated they never learned the skills as CLS/MT students, practitioners, or both. Participants indicated whether or not they were CLS/MT majors in college. Chi square analyses were performed to test for any statistical significant (p = 0.05) differences between: doing and learning the skills, doing the skills and being a CLS/MT major, and learning the skills and being a CLS/MT major. RESULTS: The response rate for the survey was 48% (50/103). Chi square analyses could not be performed for doing the skills in the LTF jobs for three variables: problem solving, analytical reasoning, and computer use because all respondents reported that they used these skills. Chi square analyses indicated there were no significant differences between doing and learning the skills in the LTF job for the entire sample group for all remaining skills except supervision. There were no significant differences between doing the skills in the LTF job and being a CLS/MT major. A statistically significant difference in learning the skills was observed between CLS/MT majors and non-CLS/MT majors for the following skills: problem solving, correlating data, precision studies, research, analytical reasoning, and troubleshooting. The 'Yes' answer frequencies for learning the skills was higher for the CLS/ MT majors for all the generic skills except teaching, where they were equal, and utilization studies where they were lower. CONCLUSION: The results indicate that, in general, for this sample group, generic skills learned as CLS/MT students and/or practitioners can be and are applied to a wide variety of LTF jobs. Furthermore, CLS/MT majors learned the generic skills at least as well, if not better, than other baccalaureate level laboratory practitioners who obtained degrees in other areas.

Career Choice↗
PubMed1996

Coordinate system for learning in the smooth pursuit eye movements of monkeys.

Learning was induced in smooth pursuit eye movements by repeated presentation of targets that moved at one speed for 100 msec and then changed to a second, higher or lower, speed. The learned changes, measured as eye acceleration for the first 100 msec of pursuit, were largest in a "late" interval from 50 to 80 msec after the onset of pursuit and were smaller and less consistent in the earliest 30 msec of pursuit. In each experiment, target motion in one direction consisted of learning trials, whereas target motion in the opposite (control) direction consisted of trials in which targets moved at a constant speed for the entire duration of the trial. Under these conditions, the learning did not generalize to the control direction. For target motion in the learning direction, the changes in pursuit generalized to responses evoked by targets moving at speeds ranging from 15 to 45 degrees/sec as well as to targets of different colors and sizes. Although learning was induced at the initiation of pursuit, it generalized to the response to image motion in the learning direction when it was presented during pursuit in the learning direction. However, learning did not generalize to the response to image motion in the learning direction when it was presented during pursuit in the control direction. The results suggest that the learning does not occur in purely sensory or motor coordinates but in an intermediate reference frame at least partly defined by the direction of eye movement. The selectivity of learning provides new evidence for a previously hypothesized neural "switch" that gates visual information on the basis of movement direction. This selectivity also suggests that the locus of pursuit learning is in pathways related to the operation of the switch.

Animals↗
PubMed2004

Does dietary learning occur outside awareness?

Several forms of dietary learning have been identified in humans. These include flavor-flavor learning, flavor-postingestive learning (including flavor-caffeine learning), and learned satiety. Generally, learning is thought to occur in the absence of contingency (CS-US) or demand awareness. However, a review of the literature suggests that this conclusion may be premature because measures of awareness lack the rigor that is found in studies of other kinds of human learning. If associations do configure outside awareness then this should be regarded as a rare instance of automatic learning. Conversely, if awareness is important, then successful learning may be governed by an individual's beliefs and predilection to attend to stimulus relationships. For researchers of dietary learning this could be critical because it might explain why learning paradigms have a reputation for being unreliable. Since most food preferences are learned, asking questions about awareness can also tell us something fundamental about everyday dietary control.

Association Learning↗
PubMed2006

Neuronal modifications during visuomotor association learning assessed by electric brain tomography.

In everyday life specific situations need specific reactions. Through repetitive practice, such stimulus-response associations can be learned and performed automatically. The aim of the present EEG study was the illustration of learning dependent modifications in neuronal pathways during short-term practice of visuomotor associations. Participants performed a visuomotor association task including four visual stimuli, which should be associated with four keys, learned by trial and error. We assumed that distinct cognitive processes might be dominant during early learning e.g., visual perception and decision making. Advanced learning, however, might be indicated by increased neuronal activation in integration- and memory-related regions. For assessment of learning progress, visual- and movement-related brain potentials were measured and compared between three learning stages (early, intermediate, and late). The results have revealed significant differences between the learning stages during distinct time intervals. Related to visual stimulus presentation, Low Resolution Electromagnetic Brain Tomography (LORETA) revealed strong neuronal activation in a parieto-prefrontal network in time intervals between 100-400 ms post event and during early learning. In relation to the motor response neuronal activation was significantly increased during intermediate compared to early learning. Prior to the motor response (120-360 ms pre event), neuronal activation was detected in the cingulate motor area and the right dorsal premotor cortex. Subsequent to the motor response (68-430 ms post event) there was an increase in neuronal activation in visuomotor- and memory-related areas including parietal cortex, SMA, premotor, dorsolateral prefrontal, and parahippocampal cortex. The present study has shown specific time elements of a visuomotor-memory-related network, which might support learning progress during visuomotor association learning.

Adult↗
PubMed2004

Brain plasticity following psychophysiological treatment in learning disabled/ADHD pre-adolescents.

This research investigated the effects of a psychophysiological treatment methodology on brain plasticity as reflected in event-related brain potential topographic mapping and morphology along with School-marks and Mangina-Test performance in three different groups of pre-adolescents at baseline and 8 months later: (a) Learning Disabled/ADHD pre-adolescents who were treated; (b) Non-treated Learning Disabled/ADHD pre-adolescents; (c) Normal controls. Results indicate that: (1) the Event-Related Brain Potentials topographic mapping was significantly modified in post-treatment condition for the treated Learning/Disabled/ADHD group as opposed to pre-treatment baseline (P < 0.001). This was mainly due to the enhanced pre-frontal and frontal N450 amplitudes along with higher P450 components over posterior regions in post-treatment condition (P < 0.001); (2) for group comparisons at baseline, no significant topographic mapping differences were found between the treated Learning Disabled/ADHD group and the non-treated Learning Disabled/ADHD control group (P > 0.05) and significant differences were present between the treated Leaning Disabled/ADHD and the normal control group (P < 0.001); (3) 8 months later, in post-treatment condition, group comparisons revealed significant topographic mapping differences between the treated Learning Disabled/ADHD group and the non-treated Learning Disabled/ADHD control group (P < 0.001) and none between the treated Learning Disabled/ADHD group and the normal control group (P > 0.05); (4) the topographic mapping of both components was similar at baseline and 8 months later in both control groups (P > 0.05); (5) at baseline, school-marks and Mangina-Test performance of treated Learning Disabled/ADHD were not significantly different than those of the non-treated Learning Disabled/ADHD (P > 0.05) and significantly lower than those of the normal control group (P < 0.001); (6) the treated Learning Disabled/ADHD group in post-treatment condition had significantly higher school-marks and Mangina-Test performance than those of non-treated Learning Disabled/ADHD controls (P < 0.001) and were similar to those of normal controls 8 months later (P > 0.05); (7) school-marks and Mangina-Test performance at baseline for non-treated Learning Disabled/ADHD controls were not modified 8 months later (P > 0.05) and normal controls maintained their high performance within the same time interval (P > 0.05). These findings provide evidence of the impact of the psychophysiological treatment methodology on brain plasticity and regulation as reflected in significantly improved neurophysiology of pre-frontal, frontal and posterior brain regions concomitantly with higher school-marks and neuropsychometric performance in the Mangina-Test.

Attention Deficit Disorder with Hyperactivity↗
PubMed2006

Learning, climate and the evolution of cultural capacity.

Patterns of environmental variation influence the utility, and thus evolution, of different learning strategies. I use stochastic, individual-based evolutionary models to assess the relative advantages of 15 different learning strategies (genetic determination, individual learning, vertical social learning, horizontal/oblique social learning, and contingent combinations of these) when competing in variable environments described by 1/f noise. When environmental variation has little effect on fitness, then genetic determinism persists. When environmental variation is large and equal over all time-scales ("white noise") then individual learning is adaptive. Social learning is advantageous in "red noise" environments when variation over long time-scales is large. Climatic variability increases with time-scale, so that short-lived organisms should be able to rely largely on genetic determination. Thermal climates usually are insufficiently red for social learning to be advantageous for species whose fitness is very determined by temperature. In contrast, population trajectories of many species, especially large mammals and aquatic carnivores, are sufficiently red to promote social learning in their predators. The ocean environment is generally redder than that on land. Thus, while individual learning should be adaptive for many longer-lived organisms, social learning will often be found in those dependent on the populations of other species, especially if they are marine. This provides a potential explanation for the evolution of a prevalence of social learning, and culture, in humans and cetaceans.

Adaptation, Physiological↗
PubMed2006

The mnemonic mechanisms of errorless learning.

Errorless learning enhances memory relative to errorful, trial-and-error learning, but the extent to which this advantage relies on implicit or explicit memory processes is not clear. Previous attempts to determine the mnemonic mechanisms of errorless learning have relied on contrasts between patient groups or between tasks, but both approaches are problematic. In this study, healthy younger and older adults were engaged in errorless or errorful learning using a process dissociation procedure that provides separate estimates of explicit recollection and implicit familiarity within-subjects and within-task (Hay & Jacoby, 1996). Consistent with much prior research, we found an age-related decrement in recollection, but age-invariance in familiarity. In the young adults, errorless learning reduced the misleading familiarity of prior errors, but this benefit was offset by the non-elaborative nature of the errorless learning condition that also reduced recollection. In the older adults, who are less able to oppose familiarity-based errors because of their lower recollection, errorless learning only reduced the misleading impact of previous errors. Our results support Baddeley and Wilson's (1994) position that the errorless learning effect is mediated by implicit memory processes: individuals with reduced explicit memory benefit from errorless learning because errorless learning bypasses the need to engage explicit error elimination processes. We do not recommend standard errorless learning for individuals with intact explicit memory, such as students trying to learn information in preparation for an examination.

Adolescent↗
PubMedDate not supplied

Isotropic sequence order learning using a novel linear algorithm in a closed loop behavioural system.

In this article, we present an isotropic algorithm for sequence order learning. Its central goal is to learn the causal relation between two (or more) inputs in order to react to the earliest incoming signal after successful learning (like in typical classical conditioning situations). We implement this algorithm in a behaving system (a robot) thereby creating a closed loop situation where the learner's actions influence its own sensor inputs to the end of creating an autonomous agent. Autonomous behaviour implies that learning goals are internally defined within the organism's capabilities. Standard learning models for sequence learning (e.g. temporal difference (TD)-learning) need an externally defined reward. This, however, is in conflict with the requirement of an implicitly defined internal goal in autonomous behaviour. Therefore, in this study we present a system in which the external reward is replaced by a reflex loop. This loop explicitly includes the environment. Every reflex loop has the inherent disadvantage, which is that its re-actions occur each time just after a reflex-eliciting sensor event and thus 'too late'. However, a reflex can serve as the internal reference for sequence order learning, which has the task of eliminating this disadvantage by creating earlier anticipatory actions. In our system learning is achieved by modifying synaptic weights of a linear neuron with a correlation based learning rule which involves the derivative of the neuron's output. All input lines are entirely isotropic. The synaptic weight change curve of this rule is strongly related to the temporal Hebb learning rule, which was found in spike timing experiments. We find that after learning the reflex loop is replaced in functional terms with an earlier anticipatory action (and pathway). In addition, we observed that the synaptic weights stabilise as soon as the reflex remains silent.

Algorithms↗
PubMedDate not supplied

BYY harmony learning, structural RPCL, and topological self-organizing on mixture models.

The Bayesian Ying-Yang (BYY) harmony learning acts as a general statistical learning framework, featured by not only new regularization techniques for parameter learning but also a new mechanism that implements model selection either automatically during parameter learning or via a new class of model selection criteria used after parameter learning. In this paper, further advances on BYY harmony learning by considering modular inner representations are presented in three parts. One consists of results on unsupervisedmixture models, ranging from Gaussian mixture based Mean Square Error (MSE) clustering, elliptic clustering, subspace clustering to NonGaussian mixture based clustering not only with each cluster represented via either Bernoulli-Gaussian mixtures or independent real factor models, but also with independent component analysis implicitly made on each cluster. The second consists of results on supervised mixture-of-experts (ME) models, including Gaussian ME, Radial Basis Function nets, and Kernel regressions. The third consists of two strategies for extending the above structural mixtures into self-organized topological maps. All these advances are introduced with details on three issues, namely, (a) adaptive learning algorithms, especially elliptic, subspace, and structural rival penalized competitive learning algorithms, with model selection made automatically during learning; (b) model selection criteria for being used after parameter learning, and (c) how these learning algorithms and criteria are obtained from typical special cases of BYY harmony learning.

Bayes Theorem↗
PubMed2003

Learning (potential) and social functioning in schizophrenia.

Cognitive dysfunction in schizophrenia has well-known functional consequences. The ability to learn (learning potential) may be an important mediator. This study examines the relationship between learning and functional status in schizophrenia patients before and after participation in a rehabilitation program. We reasoned that learning is a broad construct, encompassing controlled, effortful as well as automatic (learning by doing) mechanisms, called explicit and implicit learning, respectively. Both types of learning ability are important in daily life. The study included 44 medicated schizophrenia patients and 79 healthy controls. We included measures of implicit and explicit learning as well as measures of the cognitive domains for which significant relationships with functional outcome have been established: immediate and secondary verbal memory, card sorting and vigilance. Learning potential and the patient's 'learner status' were also assessed. The results show that learning, as assessed by measures of explicit and implicit learning and learning potential, was not associated with social functioning or rehabilitation outcome. The highest correlations between cognitive functioning and social functioning were found for more or less 'static' performance measures when they were assessed for a second time with or without instructions on how to do the test. Optimized cognitive performance (i.e. performance after instruction or training) seems to be a better predictor of complex domains of functioning than naive or everyday performance.

Adult↗
PubMed2005

[Learning curve--calculation and value in laparoscopic surgery].

The learning curve shows the progress in mastering a new method. It is completed when the monitored parameters reach a steady state and when the final results can be compared with literature. The earlier used analysis of the performance-improvement with its "on the spots" appraisals at certain time-intervals is replaced by a continuous assessment. The multimode learning curve is particularly useful for it, because not only one parameter (f.e. operation-time), but also several important factors can be put together into one single graphic. For the operation-time, the Moving Average Method is useful. For incidents, which may happen or not like a conversion from laparoscopy to laparotomy as well as complications, the Cusum-method is of practical use. The learning curves of the technique of laparoscopic cholecystectomy, colo-rectal surgery, fundoplicatio and hernia surgery have been completed. Also, the learning curve of the industry is well advanced. Reliable data for the learning curves of individual surgeons for certain operations cannot be given, as, only now, young doctors are being trained on a large scale in laparoscopic technique as used to be the case in the open abdominal surgery. This will influence greatly the learning curves and will shorten the time till their completion. Different bias concerning the individual surgeons and their clinics prohibit the production of comparable curves. Several factors like the patient respectively his abdomen are complicating all this. That's why the learning curves cannot be used as benchmarks to compare different surgeons or clinics, as long as no valid scoring system concerning the complexity of a surgical intervention exists. Learning curves which become quality curves after reaching a steady state, can be used for the individual monitoring of a surgeon's performance and serve as a quality measurement of a clinic. The learning curves of the laparoscopic cholecystectomy, fundoplicatio, colo-rectal surgery and hernia surgery are discussed in particular The mandatory number of operations needed to learn a new method cannot yet be established today, even if all the existing data are consulted. Therefore, the learning curve is a useful instrument to monitor the individual progress and the results of a clinic in the meaning of an individual quality-management. After completion of the learning curve, a quality curve using the same parameters will be given, which shows the deviations of its own standard.

Cholecystectomy, Laparoscopic↗
PubMed1997

Task difficulty and the specificity of perceptual learning.

Practising simple visual tasks leads to a dramatic improvement in performing them. This learning is specific to the stimuli used for training. We show here that the degree of specificity depends on the difficulty of the training conditions. We find that the pattern of specificities maps onto the pattern of receptive field selectivities along the visual pathway. With easy conditions, learning generalizes across orientation and retinal position, matching the spatial generalization of higher visual areas. As task difficulty increases, learning becomes more specific with respect to both orientation and position, matching the fine spatial retinotopy exhibited by lower areas. Consequently, we enjoy the benefits of learning generalization when possible, and of fine grain but specific training when necessary. The dynamics of learning show a corresponding feature. Improvement begins with easy cases (when the subject is allowed long processing times) and only subsequently proceeds to harder cases. This learning cascade implies that easy conditions guide the learning of hard ones. Taken together, the specificity and dynamics suggest that learning proceeds as a countercurrent along the cortical hierarchy. Improvement begins at higher generalizing levels, which, in turn, direct harder-condition learning to the subdomain of their lower-level inputs. As predicted by this reverse hierarchy model, learning can be effective using only difficult trials, but on condition that learning onset has previously been enabled. A single prolonged presentation suffices to initiate learning. We call this single-encounter enabling effect 'eureka'.

Humans↗