Association (psychology)

Association in psychology and psychoanalysis is the assumption that when learning simple, irreducible elements (usually sensations ) can be linked to each other under certain conditions.

The concept of association serves to explain the phenomenon that two (or more) originally isolated mental content (such as perceptions, feelings, or ideas), also known as association members, enter into such a close connection that calling an Association member pulling the occurrence of one or more other members by association or at least promoted. For example, the sight of a rose and the scent of a rose in memory interconnected, as they usually occur together in learning, while lemon scent perhaps rather the image of a detergent bottle enabled.

After going back to Aristotle, the primary laws of association, the association strength of two stimuli on their spatial and temporal proximity ( contiguity ), equality and opposition hangs during learning.

Thomas Brown (1778-1829) supplemented these laws in the 19th century with its secondary laws of association, after which the bond strength of two stimuli depends on

  • Their respective intensity
  • The frequency of their co-occurrence
  • The time elapsed since the last common occurrence
  • The number of competing with this shortcut links.

The memory performance based on the prevailing opinion on just such chains of association. This results in as a sine qua non of human memory, the ability to associate. This is significant especially in memory and learning research.

Associative learning is the combination of stimuli. Using the example of Pavlov's Dog: A neutral stimulus (eg ringing of a bell ) that is associated normally with a non-specific reaction (possibly turning the head to the sound source ), now triggers a specific ( salivation ), with another stimulus before ( sight or smell of food) linked reaction of (stimulus substitution).

The association learning involves

Association is also called as part of the technical pattern recognition as a property of neural networks.