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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.
Psychological statistics is application of formulas, theorems, numbers and laws to psychology. Statistical methods for psychology include development and application statistical theory and methods for modeling psychological data. These methods include psychometrics, factor analysis, experimental designs, and Bayesian statistics. The article ...
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 ...
The AHM, on the other hand, uses statistical pattern recognition where examinees' observed response patterns are compared to response patterns that are consistent with the attribute hierarchy. The purpose of statistical pattern recognition is to identify the attribute combinations that the examinee is likely to possess.
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
Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [ 1 ] [ 2 ] [ 3 ] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data.
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.