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As previously discussed, identifying individual sensitivity to reward and aversive stimuli is considered crucial in determining the individual optimal training strategy from a positive versus negative reinforcement perspective.
These results are paradoxical from a reinforcement perspective because they violate a basic tenet of animal learning theory that postulates stronger motivational and attentional effects of primary, unconditioned rewards or punishers compared to stimuli conditioned to them.
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From a reinforcement learning perspective, the value of information increases with uncertainty [21].
From a reinforcement learning computational perspective, it has been proposed that contextual uncertainty arbitrates between the use of two dissociable brain control systems.
From the parents' perspective, reinforcement and desensitizing behavior techniques were also ranked as highly acceptable techniques [ 10, 14].
When they do begin to share their perspective, give positive reinforcement.
However, it should also be noted that most tasks can be achieved using combinations of both positive and negative reinforcement, therefore, from a practical perspective, it is perhaps more useful to ascertain individual sensitivity to reward and aversive stimuli as a way of identifying the most effective training strategy.
These perspectives on precision (reinforcement learning rates and Kalman gain) illustrate the formal equivalence between reinforcement learning, predictive coding, and Bayesian filtering, disclosed by the general scheme used here.
This chapter presents a consultant's perspective on galvanized steel reinforcement in concrete.
Baseline cognition was critical to evaluate from a methodological perspective, as Diagnostic Group × Reinforcement interactions would be of considerably less conceptual interest if simply due to ADHD children having more room for improvement [ 9, 16].
The chapter examines the key requirements of materials used from the perspective of the resin, the reinforcement, and ultimately the composite.
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