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- Krieber, Magdalena, et al.
(författare)
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The Relation between Reading Skills and Eye Movement Patterns in Adolescent Readers : Evidence from a Regular Orthography
- 2016
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Over the past decades, the relation between reading skills and eye movement behavior has been well documented in English-speaking cohorts. As English and German differ substantially with regard to orthographic complexity (i.e. grapheme-phoneme correspondence), we aimed to delineate specific characteristics of how reading speed and reading comprehension interact with eye movements in typically developing German-speaking (Austrian) adolescents. Eye movements of 22 participants (14 females; mean age = 13; 6 years; months) were tracked while they were performing three tasks, namely silently reading words, texts, and pseudowords. Their reading skills were determined by means of a standardized German reading speed and reading comprehension assessment (Lesegeschwindigkeits- und -verstandnistest fur Klassen 6-12). We found that (a) reading skills were associated with various eye movement parameters in each of the three reading tasks; (b) better reading skills were associated with an increased efficiency of eye movements, but were primarily linked to spatial reading parameters, such as the number of fixations per word, the total number of saccades and saccadic amplitudes; (c) reading speed was a more reliable predictor for eye movement parameters than reading comprehension; (d) eye movements were highly correlated across reading tasks, which indicates consistent reading performances. Contrary to findings in English-speaking cohorts, the reading skills neither consistently correlated with temporal eye movement parameters nor with the number or percentage of regressions made while performing any of the three reading tasks. These results indicate that, although reading skills are associated with eye movement patterns irrespective of language, the temporal and spatial characteristics of this association may vary with orthographic consistency.
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| 2. |
- Pokorny, Florian B., et al.
(författare)
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Earlier Identification of Children with Autism Spectrum Disorder : An Automatic Vocalisation-based Approach
- 2017
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Ingår i: 18Th Annual Conference Of The International Speech Communication Association (INTERSPEECH 2017), Vols 1-6. - 9781510848764 ; , s. 309-313
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Konferensbidrag (refereegranskat)abstract
- Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually diagnosed in or beyond toddlerhood. ASD is defined by repetitive and restricted behaviours, and deficits in social communication. The early speech-language development of individuals with ASD has been characterised as delayed. However, little is known about ASD-related characteristics of pre-linguistic vocalisations at the feature level. In this study. we examined pre-linguistic vocalisations of 10-month-old individuals later diagnosed with ASD and a matched control group of typically developing individuals (N = 20). We segmented 684 vocalisations from parent-child interaction recordings. All vocalisations were annotated and signal-analytically decomposed. We analysed ASD-related vocalisation specificities on the basis of a standardised set (eGeMAPS) of 88 acoustic features selected for clinical speech analysis applications. 54 features showed evidence for a differentiation between vocalisations of individuals later diagnosed with ASD and controls. In addition, we evaluated the feasibility of automated, vocalisation-based identification of individuals later diagnosed with ASD. We compared linear kernel support vector machines and a 1-layer bidirectional long short-term memory neural network. Both classification approaches achieved an accuracy of 75% for subject-wise identification in a subject-independent 3-fold cross-validation scheme. Our promising results may be an important contribution en-route to facilitate earlier identification of ASD.
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| 3. |
- Krishnan, S., et al.
(författare)
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SWIRL : A SequentialWindowed Inverse Reinforcement Learning Algorithm for Robot Tasks With Delayed Rewards
- 2020
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Ingår i: Springer Proceedings in Advanced Robotics. - Cham : Springer Nature. - 2511-1256. ; 13, s. 672-687
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Tidskriftsartikel (refereegranskat)abstract
- Inverse Reinforcement Learning (IRL) allows a robot to generalize from demonstrations to previously unseen scenarios by learning the demonstrator’s reward function. However, in multi-step tasks, the learned rewards might be delayed and hard to directly optimize. We present Sequential Windowed Inverse Reinforcement Learning (SWIRL), a three-phase algorithm that partitions a complex task into shorter-horizon subtasks based on linear dynamics transitions that occur consistently across demonstrations. SWIRL then learns a sequence of local reward functions that describe the motion between transitions. Once these reward functions are learned, SWIRL applies Q-learning to compute a policy that maximizes the rewards. We compare SWIRL (demonstrations to segments to rewards) with Supervised Policy Learning (SPL - demonstrations to policies) and Maximum Entropy IRL (MaxEnt-IRL demonstrations to rewards) on standard Reinforcement Learning benchmarks: Parallel Parking with noisy dynamics, Two-Link acrobot, and a 2D GridWorld. We find that SWIRL converges to a policy with similar success rates (60%) in 3x fewer time-steps than MaxEnt-IRL, and requires 5x fewer demonstrations than SPL. In physical experiments using the da Vinci surgical robot, we evaluate the extent to which SWIRL generalizes from linear cutting demonstrations to cutting sequences of curved paths.
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