04b Goals and Objectives.docx - Module 04b Perception II Goals and Objectives Readings for Unit 04b Lecture Notes Unit 04b Perception II Read

04b Goals and Objectives.docx - Module 04b Perception II...

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Module 04b Perception II Goals and Objectives Readings for Unit 04b: Lecture Notes: Unit 04b Perception II Read phonological, orthographical route At the completion of this unit, you should be able to define and use the following terms: Bigrams : 2 letters of a word; feature detector that helps us recognize the word, if you take them out you cannot get the pronounceability effect Discriminability : (RBC) each geon can be distinguished from all others when viewed from a number of different perspectives Exemplar models Features : the multiple smaller components of a whole thing Frequency Effect : common words are easier to recognize than rarer words Geon : geometric ion, much as any chemical compound is composed of one or more basic chemical elements, a word is composed of letters (geons) Principle of Componential Recovery Pronounceability Effect : letters are easier to recognize when they are presented in a pronounceable non-word, rather than a non- pronounceable non-word. Bigram detectors prove the PE Recognition by Components : every object is composed of one or more geons - To recognize objects, we need to have stored in our long-term memory for each object - a list of the geons comprising that object - information about how those geons connect to one another for that object (how they are arranged in respect to each other) Recency/priming Effect : when we see a word activation level goes up, when we turn the page or shift our gaze to another word, the activation level of the word we just saw does not return to resting level immediately it gradually decreases Resistance to Noise : (RBC) geon can be identified even when partially obscured Structural Description Theories: emphasis on structural relations between the features, RBC is just one of many possible structural description theories Templates : alternative to feature theory; we do not decompose a letter into their individual features but treat them more as whole, we try to match the letters that we are looking at with a whole series of templates in memory Image-based models /View-based models : we learn object descriptions by storing images of actual objects seen in the real-world; this model supports the Tarr & Pinker study that novel objects will take more time to perceive when rotated - these are necessarily viewpoint dependent - they can be incredibly detailed - takes time to recognize
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- images are treated as whole, not broken down into geons and their arrangements View Invariance : (RBC) geon can be identified when viewed from a number of different perspective Word Superiority Effect : a letter is easier to recognize when presented in the context of a word than alone or in the context of a non-word At the completion of this unit, you should be able to answer the following questions: 1. With respect to word recognition, what are the frequency, recency, word superiority, and pronounceability effects? How is each explained within the context of the feature net model?
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  • Spring '14
  • AndrewHendrickson

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