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