System Usability Scale (SUS)

Symbolic cognitive models operate on the principle that cognition is computation involving the manipulation of symbols. These models utilize high-level, explicit representations like propositions, schemas, and rules (e.g., IF-THEN statements) to simulate structured thought processes such as logical reasoning, language use, and problem-solving. They form the basis of classical artificial intelligence, often called 'Good Old-Fashioned AI' (GOFAI).

Connectionist models, also known as parallel distributed processing (PDP) or artificial neural networks, represent cognitive processes as interactions among many simple, interconnected processing units called nodes. Knowledge is not stored in an explicit location but is distributed in the connection weights between these units. Learning occurs by adjusting these weights, often via algorithms like backpropagation, enabling pattern recognition and function approximation.

The Kruithof curve is an empirical graph from psychophysics that outlines a region of illuminance levels and color temperatures considered comfortable or pleasing. It postulates that at low light levels, humans prefer warmer color temperatures (more reddish/yellowish), while at high illuminance levels, cooler color temperatures (more bluish) are preferred. Lighting conditions outside this region may feel unnatural or unpleasant.

A design philosophy and iterative process where the needs, wants, and limitations of end-users are given extensive attention at each stage of the design process. UCD aims to create products that are highly usable and accessible by involving users throughout the design cycle through research, testing, and feedback, rather than relying solely on designer assumptions.

ACT-R (Adaptive Control of Thought—Rational) is a cognitive architecture defining the basic, fixed structures of the human mind. It models cognition as a set of independent modules, such as perceptual, motor, and declarative memory, that interact through a central production system. Information is held in buffers, and production rules fire to manipulate this information, simulating human problem-solving and learning processes.

Bayesian models of cognition frame the mind as a probabilistic inference engine. This approach posits that the brain represents knowledge as probability distributions and updates these beliefs upon receiving new evidence according to Bayes' theorem. It models perception, learning, and reasoning as optimal or near-optimal statistical inference under uncertainty, providing a unifying mathematical framework for many cognitive functions.