Elizabeth Torres is a Computational Neuroscientist who has developed
theoretical models of sensory-motor integration and motor control since the late 90's. Her models have been translated to clinical applications across
neurodevelopmental and neurodegenerative disorders, as well as applied to
other neurological disorders. She holds a multi-disciplinary tenure-track Assistant Professorship at Rutgers University. Her appointment is shared among the Psychology, Department, the Computer Science Department and the Rutgers Center for Cognitive Science. She also holds and adjunct
faculty position in Neurology (Movement Disorders) at the Medical School of Indiana University in Indianapolis.
Her lab has created new dynamic behavioral biomarkers of Autism, sub-typed Autism severity and identified autistic female traits to help increase early detection reliability. More recently these findings are being used to implement new concepts for earlier intervention and extended to include neural correlates with novel metrics to objectively track change in the developing child. She has published her work in important peer-reviewed journals including the Journal of Neuroscience, the Journal of
Neurophysiology, and Experimental Brain Research and disseminated her findings in the open access platforms of Biomed Central, Plos One and Frontiers. Her edited research topic in Frontiers in Neuroscience has attracted worldwide interest and gained the attention of Nature Reviews Neuroscience this September 2013.
Elizabeth Torres, PhD
The characterization of autism as a cognitive/social problem has been up to now exclusively based on descriptions of observed behavior. Behavior however is constituted by a continuous flow of movements that are highly variable and have different levels of intent. These levels of intent evolve differently in different contexts, so it is important to promote environments where inter-relations between the child and the environment, including other people spontaneously emerge from the child’s exploration and self-discovery. Under such naturalistic settings the statistics of the patterns of motor output variability from the continuous flow of movements, particularly from those movements that occur largely beneath our awareness, can be objectively quantified at the periphery in non-obtrusive ways.
In the past few years we have developed new methods to individually assess such statistical patterns and track them in real time as individuals interact with their surroundings. We have learned that locked in the minute fluctuations of such patterns are ways to unambiguously detect autism, sub-type its severity in terms of spoken verbal abilities, and steer the autistic individual towards the control of his/her actions at will. This new framework can be paired with DIR-based interventions to quantify spontaneous transitions from random trial and error motions to systematic goal-directed behaviors that the child self-discovers and comes to executively control under prospective planning. Through the improvement of anticipatory sensory-motor control we have been able to positively impact the accuracy and speed of the decisions in non-verbal children with ASD, as well as to evoke intent in their actions. We will discuss our new statistical platform for individualized behavioral analyses and our new conceptual framework to diagnose, track and treat autism spectrum disorders in unprecedented new ways using off-the-shelf wearable sensors to extract the predispositions, preferences and inherent capabilities of the person.
The characterization of autism as a cognitive/social problem has been up to now exclusively based on descriptions of observed behavior. Behavior however is constituted by a continuous flow of movements that are highly variable and have different levels of intent. These levels of intent evolve differently in different contexts, so it is important to promote environments where inter-relations between the child and the environment, including other people spontaneously emerge from the child’s exploration and self-discovery. Under such naturalistic settings the statistics of the patterns of motor output variability from the continuous flow of movements, particularly from those movements that occur largely beneath our awareness, can be objectively quantified at the periphery in non-obtrusive ways.
In the past few years we have developed new methods to individually assess such statistical patterns and track them in real time as individuals interact with their surroundings. We have learned that locked in the minute fluctuations of such patterns are ways to unambiguously detect autism, sub-type its severity in terms of spoken verbal abilities, and steer the autistic individual towards the control of his/her actions at will. This new framework can be paired with DIR-based interventions to quantify spontaneous transitions from random trial and error motions to systematic goal-directed behaviors that the child self-discovers and comes to executively control under prospective planning. Through the improvement of anticipatory sensory-motor control we have been able to positively impact the accuracy and speed of the decisions in non-verbal children with ASD, as well as to evoke intent in their actions. We will discuss our new statistical platform for individualized behavioral analyses and our new conceptual framework to diagnose, track and treat autism spectrum disorders in unprecedented new ways using off-the-shelf wearable sensors to extract the predispositions, preferences and inherent capabilities of the person.
Although all providers in this directory have completed one or more of Profectum’s certificate training courses on the DIR-FCD model, the providers listed in this directory are independent contractors. Providers are not agents nor are they employees and nor are they under the control of Profectum Foundation. Providers are solely responsible for the quality of the services you receive.
Reviews
There are no reviews yet.