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In psychology and cognitive neuroscience, pattern recognition is a cognitive process that matches information from a stimulus with information retrieved from memory. [1]Pattern recognition occurs when information from the environment is received and entered into short-term memory, causing automatic activation of a specific content of long-term memory.
[9] [10] The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems. [11] [12] Optical character recognition is an example of the application of a pattern classifier. The method of signing one's name was captured with stylus and overlay starting in 1990.
In cognitive science, prototype-matching is a theory of pattern recognition that describes the process by which a sensory unit registers a new stimulus and compares it to the prototype, or standard model, of said stimulus. Unlike template matching and featural analysis, an exact match is not expected for prototype-matching, allowing for a more ...
Pattern recognition is a cognitive process that involves retrieving information either from long-term, short-term, or working memory and matching it with information from stimuli. There are three different ways in which this may happen and go wrong, resulting in apophenia.
Used to assess recognition memory based on the pattern of yes-no responses. [21] This is one of the simplest forms of testing for recognition, and is done so by giving a participant an item and having them indicate 'yes' if it is old or 'no' if it is a new item. This method of recognition testing makes the retrieval process easy to record and ...
Attribution (psychology) – Process by which individuals explain causes of behavior and events; Black swan theory – Theory of response to surprise events; Chronostasis – Distortion in the perception of time; Cognitive distortion – Exaggerated or irrational thought pattern; Defence mechanism – Unconscious psychological mechanism
Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information.It describes a number of artificial neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.
An example Bongard problem, the common factor of the left set being convex shapes (the right set are instead all concave). A Bongard problem is a kind of puzzle invented by the Soviet computer scientist Mikhail Moiseevich Bongard (Михаил Моисеевич Бонгард, 1924–1971), probably in the mid-1960s.