Why does the word superiority effect occur?

Why does the word superiority effect occur?

The word superiority effect (WSE) refers to the observation that when written stimuli are degraded by noise or brief presentation, letters in words are reported more accurately than single letters and letters embedded in non-words.

What is word recognition model?

To model word recognition and naming, Seidenberg and McClelland (1989) constructed a parallel distributed processing model of the mechanisms by which orthographic representations are mapped to phonological ones. The model provides putative mechanisms for the two major aspects of acquisition of word-recognition skills.

Is word superiority effect top down?

Following the IAM (McClelland and Rumelhart, 1981), most will agree that the word advantage is due to top–down effects on word recognition, that are absent or smaller for single letters.

What was the original motivation for McClelland and Rumelhart’s interactive activation and competition IAC model?

Our initial interest in parallel distributed processing mechanisms grew out of an attempt to capture our ideas about continuous, interactive processes, particularly as they applied to the problems of visual word recognition and reading.

What is the difference between top down and bottom up perceptual processes?

Bottom-up processing begins with the retrieval of sensory information from our external environment to build perceptions based on the current input of sensory information. Top-down processing is the interpretation of incoming information based on prior knowledge, experiences, and expectations.

What is the word recognition experiment?

A new experimental procedure is introduced for studying word recognition. On each trial in the word reading task, subjects are presented a target word, and their response latency, indicating when they have read the word, is recorded.

What is the full form of IAC model in artificial neural networks?

Interactive activation and competition (IAC) networks are artificial neural networks used to model memory and intuitive generalizations. They are made up of nodes or artificial neurons which are arrayed and activated in ways that emulate the behaviors of human memory.