Curiosity-driven learning in adults with and without dyslexia.
Ontology highlight
ABSTRACT:
SUBMITTER: Garvin B
PROVIDER: S-EPMC8600593 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Curiosity-driven learning is foundational to human cognition. By enabling humans to autonomously decide when and what to learn, curiosity has been argued to be crucial for self-organizing temporally extended learning curricula. However, the mechanisms driving people to set intrinsic goals, when they are free to explore multiple learning activities, are still poorly understood. Computational theories propose different heuristics, including competence measures (e.g., percent correct) and learning progress, that could be used as intrinsic utility functions to efficiently organize exploration. Such intrinsic utilities constitute computationally cheap but smart heuristics to prevent people from laboring in vain on unlearnable activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideas by means of a free-choice experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include a learning-progress component provide the best fit to task selection data. These results bridge the research in artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and introduce tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales.
| S-EPMC8514490 | biostudies-literature
Project description:We investigated word learning in university and college students with a diagnosis of dyslexia and in typically-reading controls. Participants read aloud short (4-letter) and longer (7-letter) nonwords as quickly as possible. The nonwords were repeated across 10 blocks, using a different random order in each block. Participants returned 7 days later and repeated the experiment. Accuracy was high in both groups. The dyslexics were substantially slower than the controls at reading the nonwords throughout the experiment. They also showed a larger length effect, indicating less effective decoding skills. Learning was demonstrated by faster reading of the nonwords across repeated presentations and by a reduction in the difference in reading speeds between shorter and longer nonwords. The dyslexics required more presentations of the nonwords before the length effect became non-significant, only showing convergence in reaction times between shorter and longer items in the second testing session where controls achieved convergence part-way through the first session. Participants also completed a psychological test battery assessing reading and spelling, vocabulary, phonological awareness, working memory, nonverbal ability and motor speed. The dyslexics performed at a similar level to the controls on nonverbal ability but significantly less well on all the other measures. Regression analyses found that decoding ability, measured as the speed of reading aloud nonwords when they were presented for the first time, was predicted by a composite of word reading and spelling scores ("literacy"). Word learning was assessed in terms of the improvement in naming speeds over 10 blocks of training. Learning was predicted by vocabulary and working memory scores, but not by literacy, phonological awareness, nonverbal ability or motor speed. The results show that young dyslexic adults have problems both in pronouncing novel words and in learning new written words.
| S-EPMC4018562 | biostudies-literature
Project description:BackgroundDyslexia is a neurological disorder that affects an individual's language processing abilities. Early care and intervention can help dyslexic individuals succeed academically and socially. Recent developments in deep learning (DL) approaches motivate researchers to build dyslexia detection models (DDMs). DL approaches facilitate the integration of multi-modality data. However, there are few multi-modality-based DDMs.MethodsIn this study, the authors built a DL-based DDM using multi-modality data. A squeeze and excitation (SE) integrated MobileNet V3 model, self-attention mechanisms (SA) based EfficientNet B7 model, and early stopping and SA-based Bi-directional long short-term memory (Bi-LSTM) models were developed to extract features from magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG) data. In addition, the authors fine-tuned the LightGBM model using the Hyperband optimization technique to detect dyslexia using the extracted features. Three datasets containing FMRI, MRI, and EEG data were used to evaluate the performance of the proposed DDM.ResultsThe findings supported the significance of the proposed DDM in detecting dyslexia with limited computational resources. The proposed model outperformed the existing DDMs by producing an optimal accuracy of 98.9%, 98.6%, and 98.8% for the FMRI, MRI, and EEG datasets, respectively. Healthcare centers and educational institutions can benefit from the proposed model to identify dyslexia in the initial stages. The interpretability of the proposed model can be improved by integrating vision transformers-based feature extraction.
| S-EPMC11232624 | biostudies-literature
Project description:Meta-analytic studies suggest that dyslexia is characterized by subtle and spatially distributed variations in brain anatomy, although many variations failed to be significant after corrections of multiple comparisons. To circumvent issues of significance which are characteristic for conventional analysis techniques, and to provide predictive value, we applied a machine learning technique--support vector machine--to differentiate between subjects with and without dyslexia. In a sample of 22 students with dyslexia (20 women) and 27 students without dyslexia (25 women) (18-21 years), a classification performance of 80% (p < 0.001; d-prime = 1.67) was achieved on the basis of differences in gray matter (sensitivity 82%, specificity 78%). The voxels that were most reliable for classification were found in the left occipital fusiform gyrus (LOFG), in the right occipital fusiform gyrus (ROFG), and in the left inferior parietal lobule (LIPL). Additionally, we found that classification certainty (e.g. the percentage of times a subject was correctly classified) correlated with severity of dyslexia (r = 0.47). Furthermore, various significant correlations were found between the three anatomical regions and behavioural measures of spelling, phonology and whole-word-reading. No correlations were found with behavioural measures of short-term memory and visual/attentional confusion. These data indicate that the LOFG, ROFG and the LIPL are neuro-endophenotype and potentially biomarkers for types of dyslexia related to reading, spelling and phonology. In a second and independent sample of 876 young adults of a general population, the trained classifier of the first sample was tested, resulting in a classification performance of 59% (p = 0.07; d-prime = 0.65). This decline in classification performance resulted from a large percentage of false alarms. This study provided support for the use of machine learning in anatomical brain imaging.
| S-EPMC4832088 | biostudies-literature
Project description:In human vision, the brain has to select one view of the world from our two eyes. However, the existence of a clear anatomical asymmetry providing an initial imbalance for normal neural development is still not understood. Using a so-called foveascope, we found that for a cohort of 30 normal adults, the two blue cone-free areas at the centre of the foveas are asymmetrical. The noise-stimulated afterimage dominant eye introduced here corresponds to the circular blue cone-free area, while the non-dominant eye corresponds to the diffuse and irregular elliptical outline. By contrast, we found that this asymmetry is absent or frustrated in a similar cohort of 30 adults with normal ocular status, but with dyslexia, i.e. with visual and phonological deficits. In this case, our results show that the two Maxwell centroid outlines are both circular but lead to an undetermined afterimage dominance with a coexistence of primary and mirror images. The interplay between the lack of asymmetry and the development in the neural maturation of the brain pathways suggests new implications in both fundamental and biomedical sciences.
| S-EPMC5666095 | biostudies-literature
Project description:Multiple cortical (planum temporale, supramarginal gyrus, fusiform gyrus) and subcortical (caudate, putamen, and thalamus) regions have shown different functional lateralization patterns for skilled vs. dyslexic readers. The extent to which skilled and dyslexic adult readers show differential structural lateralization remains to be seen. Method: Participants included 72 adults (N = 41 skilled; N = 31 dyslexic) who underwent a high-resolution MRI brain scan. The grey matter volume of the cortical and subcortical structures was extracted. Results: While there were clear behavioral differences between the groups, there were no differences in any of the isolated structures (i.e., either total size or asymmetry index) and limited evidence for any brain-behavior relationships. We did find a significant cortical-cortical relationship (p = 0.006) and a subcortical-subcortical relationship (p = 0.008), but not cross-over relationships. Overall, this work provides unique information on neural structures as they relate to reading in skilled and dyslexic readers.
| S-EPMC10741947 | biostudies-literature
Project description:Individuals with dyslexia exhibit increased brainstem variability in response to sound. It is unknown as to whether increased variability extends to neocortical regions associated with audition and reading, extends to visual stimuli, and whether increased variability characterizes all children with dyslexia or, instead, a specific subset of children. We evaluated the consistency of stimulus-evoked neural responses in children with (N = 20) or without dyslexia (N = 12) as measured by magnetoencephalography (MEG). Approximately half of the children with dyslexia had significantly higher levels of variability in cortical responses to both auditory and visual stimuli in multiple nodes of the reading network. There was a significant and positive relationship between the number of risk alleles at rs6935076 in the dyslexia-susceptibility gene KIAA0319 and the degree of neural variability in primary auditory cortex across all participants. This gene has been linked with neural variability in rodents and in typical readers. These findings indicate that unstable representations of auditory and visual stimuli in auditory and other reading-related neocortical regions are present in a subset of children with dyslexia and support the link between the gene KIAA0319 and the auditory neural variability across children with or without dyslexia.
| S-EPMC6969288 | biostudies-literature
Project description:Spiking Neural Networks (SNNs) have shown favorable performance recently. Nonetheless, the time-consuming computation on neuron level and complex optimization limit their real-time application. Curiosity has shown great performance in brain learning, which helps biological brains grasp new knowledge efficiently and actively. Inspired by this leaning mechanism, we propose a curiosity-based SNN (CBSNN) model, which contains four main learning processes. Firstly, the network is trained with biologically plausible plasticity principles to get the novelty estimations of all samples in only one epoch; secondly, the CBSNN begins to repeatedly learn the samples whose novelty estimations exceed the novelty threshold and dynamically update the novelty estimations of samples according to the learning results in five epochs; thirdly, in order to avoid the overfitting of the novel samples and forgetting of the learned samples, CBSNN retrains all samples in one epoch; finally, step two and step three are periodically taken until network convergence. Compared with the state-of-the-art Voltage-driven Plasticity-centric SNN (VPSNN) under standard architecture, our model achieves a higher accuracy of 98.55% with only 54.95% of its computation cost on the MNIST hand-written digit recognition dataset. Similar conclusion can also be found out in other datasets, i.e., Iris, NETtalk, Fashion-MNIST, and CIFAR-10, respectively. More experiments and analysis further prove that such curiosity-based learning theory is helpful in improving the efficiency of SNNs. As far as we know, this is the first practical combination of the curiosity mechanism and SNN, and these improvements will make the realistic application of SNNs possible on more specific tasks within the von Neumann framework.
| S-EPMC7020337 | biostudies-literature
Project description:Reading comprehension is a complex task that depends on multiple cognitive and linguistic processes. According to the updated Simple View of Reading framework, in adults, individual variation in reading comprehension can be largely explained by combined variance in three component abilities: (1) decoding accuracy, (2) fluency, and (3) language comprehension. Here we asked whether the neural correlates of the three components are different in adults with dyslexia as compared to typically-reading adults and whether the relative contribution of these correlates to reading comprehension is similar in the two groups. We employed a novel naturalistic fMRI reading task to identify the neural correlates of individual differences in the three components using whole-brain and literature-driven regions-of-interest approaches. Across all participants, as predicted by the Simple View framework, we found distinct patterns of associations with linguistic and domain-general regions for the three components, and that the left-hemispheric neural correlates of language comprehension in the angular and posterior temporal gyri made the largest contributions to explaining out-of-scanner reading comprehension performance. These patterns differed between the two groups. In typical adult readers, better fluency was associated with greater activation of left occipitotemporal regions, better comprehension with lesser activation in prefrontal and posterior parietal regions, and there were no significant associations with decoding. In adults with dyslexia, better fluency was associated with greater activation of bilateral inferior parietal regions, better comprehension was associated with greater activation in some prefrontal clusters and lower in others, and better decoding skills were associated with lesser activation of bilateral prefrontal and posterior parietal regions. Extending the behavioral findings of skill-level differences in the relative contribution of the three components to reading comprehension, the relative contributions of the neural correlates to reading comprehension differed based on dyslexia status. These findings reveal some of the neural correlates of individual differences in the three components and the underlying mechanisms of reading comprehension deficits in adults with dyslexia.
| S-EPMC7855566 | biostudies-literature
Project description:PurposeThe purpose of this study is to investigate word learning in children with dyslexia to ascertain their strengths and weaknesses during the configuration stage of word learning.MethodChildren with typical development (N = 116) and dyslexia (N = 68) participated in computer-based word learning games that assessed word learning in 4 sets of games that manipulated phonological or visuospatial demands. All children were monolingual English-speaking 2nd graders without oral language impairment. The word learning games measured children's ability to link novel names with novel objects, to make decisions about the accuracy of those names and objects, to recognize the semantic features of the objects, and to produce the names of the novel words. Accuracy data were analyzed using analyses of covariance with nonverbal intelligence scores as a covariate.ResultsWord learning deficits were evident for children with dyslexia across every type of manipulation and on 3 of 5 tasks, but not for every combination of task/manipulation. Deficits were more common when task demands taxed phonology. Visuospatial manipulations led to both disadvantages and advantages for children with dyslexia.ConclusionChildren with dyslexia evidence spoken word learning deficits, but their performance is highly dependent on manipulations and task demand, suggesting a processing trade-off between visuospatial and phonological demands.
| S-EPMC5548075 | biostudies-literature