SOVEREIGN: An Autonomous Neural System for Incrementally Learning Planned Action Sequences to Navigate Towards a Rewarded Goal

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goaloriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and sizeinvariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.Riverside Reserach Institute; Defense Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225); National Science Foundation (IRI 90-24877, SBE-0345378); Office of Naval Research (N00014-92-J-1309, N00014-91-J-4100, N00014-01-1-0624, N00014-01-1-0624); Pacific Sierra Research (PSR 91-6075-2

The immune system and other cognitive systems

In the following pages we propose a theory on cognitive systems and the common strategies of perception, which are at the basis of their function. We demonstrate that these strategies are easily seen to be in place in known cognitive systems such as vision and language. Furthermore we show that taking these strategies into consideration implies a new outlook on immune function calling for a new appraisal of the immune system as a cognitive system

Towards Contextual Action Recognition and Target Localization with Active Allocation of Attention

Exploratory gaze movements are fundamental for gathering the most relevant information regarding the partner during social interactions. We have designed and implemented a system for dynamic attention allocation which is able to actively control gaze movements during a visual action recognition task. During the observation of a partners reaching movement, the robot is able to contextually estimate the goal position of the partner hand and the location in space of the candidate targets, while moving its gaze around with the purpose of optimizing the gathering of information relevant for the task. Experimental results on a simulated environment show that active gaze control provides a relevant advantage with respect to typical passive observation, both in term of estimation precision and of time required for action recognition. © 2012 Springer-Verlag

Promoting Environments that Measure Outcomes: Partnerships for Change

This paper describes the development of the PrEMO© (Promoting Environments that Measure Outcomes) program. PrEMO© is an innovative model promoting evidence-based practice (EBP) while developing capacity and quality of Level II fieldwork placements. The PrEMO© program is described from initiation to completion, including development of site-specific learning objectives, the twelve week schedule and the role of faculty mentorship. Occupational therapy (OT) students, and university OT program faculty including academic fieldwork coordinators, partner with fieldwork educators at the site to implement EBP using a data-driven decision making (DDDM) process to guide the development of evidence-based practices. PrEMO© appears to be a useful strategy for building Level II fieldwork capacity and enhancing student and fieldwork educators’ knowledge and skills about EBP and outcome measurement in routine OT practice

A tale of two visions: Can a new view of personality help integrate psychology?

Personality psychology studies how psychological systems work together. Consequently, the field can act as a unifying resource for the broader discipline of psychology. Yet personality\u27s current fieldwide organization promotes a fragmented view of the person, seen through such competing theories as the psychodynamic, trait, and humanistic. There exists an alternative--a systems framework for personality--that focuses on 4 topics: identifying personality, personality\u27s parts, its organization, and its development. This new framework and its view of personality are described. The framework is applied to such issues as personality measurement, psychotherapy outcome research, and education. The new framework may better organize the field of personality and help with its mission of addressing how major psychological systems interrelate