919 research outputs found
Dopamine and the development of executive dysfunction in autism spectrum disorders.
Persons with autism regularly exhibit executive dysfunction (ED), including problems with deliberate goal-directed behavior, planning, and flexible responding in changing environments. Indeed, this array of deficits is sufficiently prominent to have prompted a theory that executive dysfunction is at the heart of these disorders. A more detailed examination of these behaviors reveals, however, that some aspects of executive function remain developmentaly appropriate. In particular, while people with autism often have difficulty with tasks requiring cognitive flexibility, their fundamental cognitive control capabilities, such as those involved in inhibiting an inappropriate but relatively automatic response, show no significant impairment on many tasks. In this article, an existing computational model of the prefrontal cortex and its role in executive control is shown to explain this dichotomous pattern of behavior by positing abnormalities in the dopamine-based modulation of frontal systems in individuals with autism. This model offers excellent qualitative and quantitative fits to performance on standard tests of cognitive control and cognitive flexibility in this clinical population. By simulating the development of the prefrontal cortex, the computational model also offers a potential explanation for an observed lack of executive dysfunction early in life
Learning Representations in Model-Free Hierarchical Reinforcement Learning
Common approaches to Reinforcement Learning (RL) are seriously challenged by
large-scale applications involving huge state spaces and sparse delayed reward
feedback. Hierarchical Reinforcement Learning (HRL) methods attempt to address
this scalability issue by learning action selection policies at multiple levels
of temporal abstraction. Abstraction can be had by identifying a relatively
small set of states that are likely to be useful as subgoals, in concert with
the learning of corresponding skill policies to achieve those subgoals. Many
approaches to subgoal discovery in HRL depend on the analysis of a model of the
environment, but the need to learn such a model introduces its own problems of
scale. Once subgoals are identified, skills may be learned through intrinsic
motivation, introducing an internal reward signal marking subgoal attainment.
In this paper, we present a novel model-free method for subgoal discovery using
incremental unsupervised learning over a small memory of the most recent
experiences (trajectories) of the agent. When combined with an intrinsic
motivation learning mechanism, this method learns both subgoals and skills,
based on experiences in the environment. Thus, we offer an original approach to
HRL that does not require the acquisition of a model of the environment,
suitable for large-scale applications. We demonstrate the efficiency of our
method on two RL problems with sparse delayed feedback: a variant of the rooms
environment and the first screen of the ATARI 2600 Montezuma's Revenge game
A Cognitive Model for Generalization during Sequential Learning
Traditional artificial neural network models of learning suffer from
catastrophic interference. They are commonly trained to perform only
one specific task, and, when trained on a new task, they forget the original
task completely. It has been shown that the foundational neurocomputational principles embodied by the Leabra cognitive modeling framework,
specifically fast lateral inhibition and a local synaptic plasticity model
that incorporates both correlational and error-based components, are sufficient to largely overcome this limitation during the sequential learning
of multiple motor skills. Evidence has also provided that Leabra is able
to generalize the subsequences of motor skills, when doing so is appropriate. In this paper, we provide a detailed analysis of the extent of
generalization possible with Leabra during sequential learning of multiple tasks. For comparison, we measure the generalization exhibited by
the backpropagation of error learning algorithm. Furthermore, we demonstrate the applicability of sequential learning to a pair of movement tasks
using a simulated robotic arm
Dark Energy or Apparent Acceleration Due to a Relativistic Cosmological Model More Complex than FLRW?
We use the Szekeres inhomogeneous relativistic models in order to fit
supernova combined data sets. We show that with a choice of the spatial
curvature function that is guided by current observations, the models fit the
supernova data almost as well as the LCDM model without requiring a dark energy
component. The Szekeres models were originally derived as an exact solution to
Einstein's equations with a general metric that has no symmetries and are
regarded as good candidates to model the true lumpy universe that we observe.
The null geodesics in these models are not radial. The best fit model found is
also consistent with the requirement of spatial flatness at CMB scales. The
first results presented here seem to encourage further investigations of
apparent acceleration using various inhomogeneous models and other constraints
from CMB and large structure need to be explored next.Comment: 6 pages, 1 figure, matches version published in PR
Reliability and Validity of the HD-PRO-TriadTM, a Health-Related Quality of Life Measure Designed to Assess the Symptom Triad of Huntington\u27s Disease.
BACKGROUND: Huntington\u27s disease (HD), is a neurodegenerative disorder that is associated with cognitive, behavioral, and motor impairments that diminish health related quality of life (HRQOL). The HD-PRO-TRIADTM is a quality of life measure that assesses health concerns specific to individuals with HD. Preliminary psychometric characterization was limited to a convenience sample of HD participants who completed measures at home so clinician-ratings were unavailable.
OBJECTIVES: The current study evaluates the reliability and validity of the HD-PRO-TRIADTM in a well-characterized sample of individuals with HD.
METHODS: Four-hundred and eighty-two individuals with HD (n = 192 prodromal, n = 193 early, and n = 97 late) completed the HD-PRO-TRIADTM questionnaire. Clinician-rated assessments from the Unified Huntington Disease Rating Scales, the short Problem Behaviors Assessment, and three generic measures of HRQOL (WHODAS 2.0, RAND-12, and EQ-5D) were also examined.
RESULTS: Internal reliability for all domains and the total HD-PRO-TRIADTM was excellent (all Cronbach\u27s α \u3e0.93). Convergent and discriminant validity were supported by significant associations between the HD-PRO-TRIADTM domains, and other patient reported outcome measures as well as clinician-rated measures. Known groups validity was supported as the HD-PRO-TRIADTM differentiated between stages of the disease. Floor and ceiling effects were generally within acceptable limits. There were small effect sizes for 12-month change over time and moderate effect sizes for 24-month change over time.
CONCLUSIONS: Findings support excellent internal reliability, convergent and discriminant validity, known groups validity, and responsiveness to change over time. The current study supports the clinical efficacy of the HD-PRO-TRIADTM. Future research is needed to assess the test-retest reliability of this measure
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