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Psychological Theories of Intelligence:

• Two-Factor Theory: Intelligence is divided into General Intelligence (g-factor) and Specific Intelligence (s-factor). The g-factor is the sum of s-factors scores.
• Thurstone’s Modified g-factor Intelligence Theory: Instead of viewing intelligence as a single general ability, this theory focused on seven different primary mental abilities.
• Howard Gardener’s Multiple Intelligence Theory: This theory proposed eight different intelligences based on skills and abilities that are valued in different cultures.
• Robert Sternberg’s Triarchic theory of intelligence: Defines intelligence as mental activity directed towards purposive adaptation, selection and shaping of real-world environments relevant to one’s life.

Artificial Intelligence Theories:
• SOFT AI
◦ Minsky’s view on intelligence: Intelligence is the ability to solve hard problems.
◦ Allen Newell’s Demands of Unified theory of Cognition: Allen proposed 13 demands that need to be met for a system to be considered intelligent.
◦ John Anderson’s ACT theory: A common cognitive system for higher level processing supported by the short evolutionary history of human intellectual functions, the plasticity in acquiring the functions and common features among different cognitive processes.
• HARD AI
◦ Dynamic Field Theory: The development of a dynamic system to help us understand the cognitive behaviour of an agent using sensors and actuators.
◦ Body Shaping Cognition: Intelligent agents should exploit their ecological niche and exhibit diverse behavior following certain hard and soft rules.
• HYBRID AI
◦ LIDA: An integrated artificial cognitive system that has has three important phases: an understanding phase, an attention phase and an acting and learning phase.

Synchronization refers to a set of oscillators which have a similar timed response and it’s a phenomenon that can easily be seen in nature.

The Kuramoto Model: A mathematical model used to describe synchronization. More specifically, it is a model for the behavior of a large set of coupled oscillators. This module can be modified to explain the mechanics of neural oscillators in a neural network and thus used to create an artificial network that mimics neural networks found in animals and humans.