Perceptual learning with perceptions
نویسندگان
چکیده
منابع مشابه
Perceptions of perceptual symbols
Various defenses of amodal symbol systems are addressed, including amodal symbols in sensory-motor areas, the causal theory of concepts, supramodal concepts, latent semantic analysis, and abstracted amodal symbols. Various aspects of perceptual symbol systems are clarified and developed, including perception, features, simulators, category structure, frames, analogy, introspection, situated act...
متن کاملLinking perceptual learning with identical stimuli to imagery perceptual learning.
Perceptual learning is usually thought to be exclusively driven by the stimuli presented during training (and the underlying synaptic learning rules). In some way, we are slaves of our visual experiences. However, learning can occur even when no stimuli are presented at all. For example, Gabor contrast detection improves when only a blank screen is presented and observers are asked to imagine G...
متن کاملPerceptual learning with spatial uncertainties
In perceptual learning, stimuli are usually assumed to be presented to a constant retinal location during training. However, due to tremor, drift, and microsaccades of the eyes, the same stimulus covers different retinal positions on sequential trials. Because of these variations the mathematical decision problem changes from linear to non-linear (). This non-linearity implies three predictions...
متن کاملReinforcement Learning with Perceptual Aliasing :
The Perceptual Distinctions Approach Lonnie Chrisman School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 [email protected] Abstract It is known that Perceptual Aliasing may signi cantly diminish the e ectiveness of reinforcement learning algorithms [Whitehead and Ballard, 1991]. Perceptual aliasing occurs when multiple situations that are indistinguishable from immediat...
متن کاملLearning with Recursive Perceptual Representations
Linear Support Vector Machines (SVMs) have become very popular in vision as part of state-of-the-art object recognition and other classification tasks but require high dimensional feature spaces for good performance. Deep learning methods can find more compact representations but current methods employ multilayer perceptrons that require solving a difficult, non-convex optimization problem. We ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Cognitive Neurodynamics
سال: 2010
ISSN: 1871-4080,1871-4099
DOI: 10.1007/s11571-010-9134-9