Beyond statistical learning? Continuous sequence repetition matters in implicit sequence learning

Typically, statistical learning is investigated by testing the acquisition of specific items or forming general rules. As implicit sequence learning also involves the extraction of regularities from the environment, it can also be considered as an instance of statistical learning. In the present study, a Serial Reaction Time Task was used to test whether the continuous versus interleaved repetition of a sequence affects implicit learning despite the equal exposure to the sequences. The results revealed a sequence learning advantage for the continuous repetition condition compared to the interleaved condition. This suggests that by repetition, additional sequence information was extracted although the exposure to the sequences was identical as in the interleaved condition. The results are discussed in terms of similarities and potential differences between typical statistical learning paradigms and sequence learning.

Statistical Learning, Syllable Processing, and Speech Production in Healthy Hearing and Hearing-Impaired Preschool Children: A Mismatch Negativity Study

OBJECTIVES The objectives of the present study were to investigate temporal/spectral sound-feature processing in preschool children (4 to 7 years old) with peripheral hearing loss compared with age-matched controls. The results verified the presence of statistical learning, which was diminished in children with hearing impairments (HIs), and elucidated possible perceptual mediators of speech production. DESIGN Perception and production of the syllables /ba/, /da/, /ta/, and /na/ were recorded in 13 children with normal hearing and 13 children with HI. Perception was assessed physiologically through event-related potentials (ERPs) recorded by EEG in a multifeature mismatch negativity paradigm and behaviorally through a discrimination task. Temporal and spectral features of the ERPs during speech perception were analyzed, and speech production was quantitatively evaluated using speech motor maximum performance tasks. RESULTS Proximal to stimulus onset, children with HI displayed a difference in map topography, indicating diminished statistical learning. In later ERP components, children with HI exhibited reduced amplitudes in the N2 and early parts of the late disciminative negativity components specifically, which are associated with temporal and spectral control mechanisms. Abnormalities of speech perception were only subtly reflected in speech production, as the lone difference found in speech production studies was a mild delay in regulating speech intensity. CONCLUSIONS In addition to previously reported deficits of sound-feature discriminations, the present study results reflect diminished statistical learning in children with HI, which plays an early and important, but so far neglected, role in phonological processing. Furthermore, the lack of corresponding behavioral abnormalities in speech production implies that impaired perceptual capacities do not necessarily translate into productive deficits.

Implicit task sequence learning

Implicit task sequence learning (TSL) can be considered as an extension of implicit sequence learning which is typically tested with the classical serial reaction time task (SRTT). By design, in the SRTT there is a correlation between the sequence of stimuli to which participants must attend and the sequence of motor movements/key presses with which participants must respond. The TSL paradigm allows to disentangle this correlation and to separately manipulate the presences/absence of a sequence of tasks, a sequence of responses, and even other streams of information such as stimulus locations or stimulus-response mappings. Here I review the state of TSL research which seems to point at the critical role of the presence of correlated streams of information in implicit sequence learning. On a more general level, I propose that beyond correlated streams of information, a simple statistical learning mechanism may also be involved in implicit sequence learning, and that the relative contribution of these two explanations differ according to task requirements. With this differentiation, conflicting results can be integrated into a coherent framework.

Impacts of a word-picture training on reading in youth with mixed intellectual disabilities. A waiting-list control group comparison

Individuals with intellectual disabilities (ID) often struggle with learning how to read. Reading difficulties seem to be the most common secondary condition of ID. Only one in five children with mild or moderate ID achieves even minimal literacy skills. However, literacy education for children and adolescents with ID has been largely overlooked by researchers and educators. While there is little research on reading of children with ID, many training studies have been conducted with other populations with reading difficulties. The most common approach of acquiring literacy skills consists of sophisticated programs that train phonological skills and auditory perception. Only few studies investigated the influence of implicit learning on literacy skills. Implicit learning processes seem to be largely independent of age and IQ. Children are sensitive to the statistics of their learning environment. By frequent word reading they acquire implicit knowledge about the frequency of single letters and letter patterns in written words. Additionally, semantic connections not only improve the word understanding, but also facilitate storage of words in memory. Advances in communication technology have introduced new possibilities for remediating literacy skills. Computers can provide training material in attractive ways, for example through animations and immediate feedback .These opportunities can scaffold and support attention processes central to learning. Thus, the aim of this intervention study was to develop and implement a computer based word-picture training, which is based on statistical and semantic learning, and to examine the training effects on reading, spelling and attention in children and adolescents (9-16 years) diagnosed with mental retardation (general IQ  74). Fifty children participated in four to five weekly training sessions of 15-20 minutes over 4 weeks, and completed assessments of attention, reading, spelling, short-term memory and fluid intelligence before and after training. After a first assessment (T1), the entire sample was divided in a training group (group A) and a waiting control group (group B). After 4 weeks of training with group A, a second assessment (T2) was administered with both training groups. Afterwards, group B was trained for 4 weeks, before a last assessment (T3) was carried out in both groups. Overall, the results showed that the word-picture training led to substantial gains on word decoding and attention for both training groups. These effects were preserved six weeks later (group A). There was also a clear tendency of improvement in spelling after training for both groups, although the effect did not reach significance. These findings highlight the fact that an implicit statistical learning training in a playful way by motivating computer programs can not only promote reading development, but also attention in children with intellectual disabilities.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.

Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model

This paper presents a non-rigid free-from 2D-3D registration approach using statistical deformation model (SDM). In our approach the SDM is first constructed from a set of training data using a non-rigid registration algorithm based on b-spline free-form deformation to encode a priori information about the underlying anatomy. A novel intensity-based non-rigid 2D-3D registration algorithm is then presented to iteratively fit the 3D b-spline-based SDM to the 2D X-ray images of an unseen subject, which requires a computationally expensive inversion of the instantiated deformation in each iteration. In this paper, we propose to solve this challenge with a fast B-spline pseudo-inversion algorithm that is implemented on graphics processing unit (GPU). Experiments conducted on C-arm and X-ray images of cadaveric femurs demonstrate the efficacy of the present approach.