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Other influences include Max Wertheimer's gestalt structure theory and Kant's account of schemas in categorization, as well as studies in experimental psychology on the mental rotation of images. In addition to the dissertation on over by Brugman, Lakoff's use of image schema theory also drew extensively on Talmy and Langacker's theories of ...
The previously mentioned and numerous related studies have led to a relative consensus within cognitive science, psychology, neuroscience, and philosophy on the neural status of mental images. In general, researchers agree that, while there is no homunculus inside the head viewing these mental images, our brains do form and maintain mental ...
Bruno Adolphus Olshausen is an American neuroscientist and professor at the University of California, Berkeley, known for his work on computational neuroscience, vision science, and sparse coding.
In psychology and cognitive science, a schema (pl.: schemata or schemas) describes a pattern of thought or behavior that organizes categories of information and the relationships among them. [ 1 ] [ 2 ] It can also be described as a mental structure of preconceived ideas, a framework representing some aspect of the world, or a system of ...
For example, patient A would be poor at reading printed words while still being normal at understanding spoken words, while the patient B would be normal at understanding written words and be poor at understanding spoken words. Scientists can interpret this information to explain how there is a single cognitive module for word comprehension.
Representationalism (also known as indirect realism) is the view that representations are the main way we access external reality.. The representational theory of mind attempts to explain the nature of ideas, concepts and other mental content in contemporary philosophy of mind, cognitive science and experimental psychology.
In neuroscience, a biological neural network is a physical structure found in brains and complex nervous systems – a population of nerve cells connected by synapses. In machine learning, an artificial neural network is a mathematical model used to approximate nonlinear functions.
The best known example is the Necker cube whose 12 lines can be perceived in one of two different ways in depth. The Necker Cube: The left line drawing can be perceived in one of two distinct depth configurations shown on the right. Without any other cue, the visual system flips back and forth between these two interpretations. [15]