The Experts below are selected from a list of 36807 Experts worldwide ranked by ideXlab platform
Jill C Fodstad - One of the best experts on this subject based on the ideXlab platform.
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the effect of autism spectrum disorders on adaptive independent living skills in adults with severe intellectual disability
Research in Developmental Disabilities, 2009Co-Authors: Johnny L Matson, Timothy Dempsey, Jill C FodstadAbstract:Autism Spectrum Disorders are a class of conditions categorized by communication problems, ritualistic behaviors, and deficits in social behaviors. While evidence supporting a genetic component of Autism Spectrum Disorders (ASDs) is strong, no specific genetic marker has been identified. Thus, professionals have had to utilize Intelligence Tests and measures of adaptive functioning to aid in the diagnosis of individuals with ASD. The present study aimed to isolate specific differences in adaptive functioning in adults with ASD. Two hundred and thirty-four adults with ASD (Autism Spectrum Disorder) or Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS) and intellectual disabilities (IDs) were evaluated with respect to the nature and extent of their independent living skill functioning. The implications of these data for more fully describing and diagnosing autism and PDD-NOS in adults are discussed.
Marek Sergot - One of the best experts on this subject based on the ideXlab platform.
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making sense of sensory input
Artificial Intelligence, 2021Co-Authors: Richard Evans, Jose Hernandezorallo, Johannes Welbl, Pushmeet Kohli, Marek SergotAbstract:Abstract This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws – must be integrated into a coherent whole. On our account, making sense of sensory input is a type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine , that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. In fact, it is able to do all three tasks simultaneously. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction Intelligence Tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction Intelligence Tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve Intelligence Tests, but a general-purpose system that was designed to make sense of any sensory sequence.
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evaluating the apperception engine
arXiv: Artificial Intelligence, 2020Co-Authors: Richard J Evans, Jose Hernandezorallo, Johannes Welbl, Pushmeet Kohli, Marek SergotAbstract:The Apperception Engine is an unsupervised learning system. Given a sequence of sensory inputs, it constructs a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the theory - objects, properties, and laws - must be integrated into a coherent whole. Once a theory has been constructed, it can be applied to predict future sensor readings, retrodict earlier readings, or impute missing readings. In this paper, we evaluate the Apperception Engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction Intelligence Tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The engine performs well in all these domains, significantly outperforming neural net baselines and state of the art inductive logic programming systems. These results are significant because neural nets typically struggle to solve the binding problem (where information from different modalities must somehow be combined together into different aspects of one unified object) and fail to solve occlusion tasks (in which objects are sometimes visible and sometimes obscured from view). We note in particular that in the sequence induction Intelligence Tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve Intelligence Tests, but a general-purpose system that was designed to make sense of any sensory sequence.
Michael G Aman - One of the best experts on this subject based on the ideXlab platform.
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differences in verbal and nonverbal iq test scores in children with autism spectrum disorder
Research in Autism Spectrum Disorders, 2018Co-Authors: Sabrina N Grondhuis, Luc Lecavalier, Eugene L Arnold, Benjamin L Handen, Lawrence Scahill, Christopher J Mcdougle, Michael G AmanAbstract:Abstract Background Intelligence Tests are a fundamental component of diagnostic assessments for children with suspected autism spectrum disorder (ASD). There are many assessments available, but scores across Tests may not be comparable in children with ASD. Method Eighty children (68 boys) age 4–14 years with ASD completed the Stanford-Binet Intelligence Scales, 5th Edition (SB5), a verbal measure of Intelligence, and the Leiter International Performance Scale – Revised (Leiter-R), a nonverbal measure of Intelligence. Results Although discrepancies went in both directions, we found significantly higher mean scores on the Leiter-R than SB5 (9.6 point difference). Children younger than 8 years had more pronounced discrepancies (13.8 points vs. 3.5 points for >8 years), and children with less-developed language skills had greater discrepancies (13.1 points vs. 5.8 points for higher language skills). Conclusion This suggests these IQ Tests are not interchangeable and language demands may produce different results that could impact clinician interpretation. Both clinicians and researchers should be aware of the likely impact of adopting primarily verbal vs. nonverbal Tests when assessing children with ASD, especially those with less language.
H J C Berger - One of the best experts on this subject based on the ideXlab platform.
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flexibility in children with autism spectrum disorders asd inconsistency between neuropsychological Tests and parent based rating scales
Journal of Clinical and Experimental Neuropsychology, 2012Co-Authors: Janpieter Teunisse, R L Roelofs, E W M Verhoeven, Linda Cuppen, H J C BergerAbstract:In this study, we compared neuropsychological Tests and parent-based ratings of flexibility in a sample of children with autism spectrum disorders (ASD). We investigated the discriminant validity of the domain-specific flexibility measures by comparison with the domain general measures, general behavioral problems, general ASD-related traits, and general Intelligence. Tests and parent-based ratings of flexibility were not significantly correlated. Parent-based ratings were strongly related with the three broadband measures, whereas the discriminant validity indices of the neuropsychological Tests were satisfactory. These findings suggest that parent-based ratings do not reflect the specific executive construct of flexibility, but instead reflect a broad spectrum of general child characteristics.
Jose Hernandezorallo - One of the best experts on this subject based on the ideXlab platform.
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making sense of sensory input
Artificial Intelligence, 2021Co-Authors: Richard Evans, Jose Hernandezorallo, Johannes Welbl, Pushmeet Kohli, Marek SergotAbstract:Abstract This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws – must be integrated into a coherent whole. On our account, making sense of sensory input is a type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine , that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. In fact, it is able to do all three tasks simultaneously. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction Intelligence Tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction Intelligence Tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve Intelligence Tests, but a general-purpose system that was designed to make sense of any sensory sequence.
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evaluating the apperception engine
arXiv: Artificial Intelligence, 2020Co-Authors: Richard J Evans, Jose Hernandezorallo, Johannes Welbl, Pushmeet Kohli, Marek SergotAbstract:The Apperception Engine is an unsupervised learning system. Given a sequence of sensory inputs, it constructs a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the theory - objects, properties, and laws - must be integrated into a coherent whole. Once a theory has been constructed, it can be applied to predict future sensor readings, retrodict earlier readings, or impute missing readings. In this paper, we evaluate the Apperception Engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction Intelligence Tests. In each domain, we test our engine's ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The engine performs well in all these domains, significantly outperforming neural net baselines and state of the art inductive logic programming systems. These results are significant because neural nets typically struggle to solve the binding problem (where information from different modalities must somehow be combined together into different aspects of one unified object) and fail to solve occlusion tasks (in which objects are sometimes visible and sometimes obscured from view). We note in particular that in the sequence induction Intelligence Tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve Intelligence Tests, but a general-purpose system that was designed to make sense of any sensory sequence.