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Paired association learning can be defined as a system of learning in which items (such as words, letters, numbers, symbols etc.) are matched so that presentation of one member of the pair will cue the recall of the other member. [3] It is this learning which constitutes the basics in a paired-associate task. These tasks can be divided into the ...
For example, if subjects are asked to memorize word pairs (e.g., donkey-tree and dog-tree), interference will occur when two pairs share a common associate (in this example, tree). A study using paired-associate tasks by Wickens, Born, and Allen (1963) [15] showed that if target material and interfering material decrease in similarity, a ...
An example of a grammatical string produced using this grammar is ZGGF. An example of an ungrammatical string is ZGFG. Artificial grammar learning (AGL) is a task designed to test the process of implicit learning, which is the unconscious acquisition of knowledge and the use of this knowledge without consciously activating it. [26]
For example in a lexical decision task a participant observes a string of characters and must respond whether the string is a "word" or "non-word". Another example is the random dot kinetogram task, in which a participant must decide whether a group of moving dots are predominately moving "left" or "right".
In most cases, a single input parameter or an interaction between two parameters is what causes a program's bugs. [2] Bugs involving interactions between three or more parameters are both progressively less common [3] and also progressively more expensive to find, such testing has as its limit the testing of all possible inputs. [4]
In this design any difference in preference between subjects would have to be based on whether the letter occurred in their name. For example, for the fictitious pair Irma Maes and Jef Jacobs the first stimulus was A and U: the last letter in Irma's first name and a letter not in her name. Both subjects had to circle the letter they preferred.
Approaches for integration are diverse. [10] Henry Kautz's taxonomy of neuro-symbolic architectures [11] follows, along with some examples: . Symbolic Neural symbolic is the current approach of many neural models in natural language processing, where words or subword tokens are the ultimate input and output of large language models.
Allan Paivio's dual-coding theory is a basis of picture superiority effect. Paivio claims that pictures have advantages over words with regards to coding and retrieval of stored memory because pictures are coded more easily and can be retrieved from symbolic mode, while the dual coding process using words is more difficult for both coding and retrieval.
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