Letters are crucial features that the visual system attempts to detect during

Letters are crucial features that the visual system

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Letters are crucial features that the visual system attempts to detect during the process of word recognition – became too hard to identify so many letters in just 200msec if the contrast energy was too low o Letters with low contrast energy are weak signals that the visual system will ‘squelch’ Squelching = tendency of the nervous system to inhibit the processing of unclear features Prevents us from just guessing, otherwise we would see many false features Recognition by Components - What are the fundamental elements / size of these features we put together, i.e. for complex 3D objects such as people/cars - Biederman : Recognition by components (RBC) theory = theory that we recognize objects by breaking them down into their fundamental geometric shapes - Geons = the set of 36 basic 3D shapes from which all real-world objects can be constructed o Objects broken into geons, which are then compared with existing geon configurations stored in memory – match input and memory to recognize the object - Tf if objects are parsed into geons, recognition should be a function of the number of geons available to perceive - Experiment – varied number of geons used to depict a given object o More complex objects (penguin) needed more geons than less complex (flashlight) o Objects were flashed for 100msec then covered to interrupt perceptual processing - FINDINGS: o People were good at recognizing objects represented by only 2-3 geons, and recognition improved when more geons were added to add detail o Tf accuracy increased for more detailed penguins than less o Also – more complex objects (penguin) were recognized more efficiently than less complex (flashlight) o ***would have thought more complex require more processing time - Conclusion: complexity and detail lead to more geons and more geons lead to better recognition - Deconstructing objects into geons is a critical component of object recognition
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Context and Knowledge - Context in which features appear is important - Context effects = influence of proximate stimuli and the situation on the perceptual experience of the stimulus o I.e. you see a hand Tf likely on wrist is a bracelet / watch not an elephant - Interesting example of context: Moon illusion = tendency for the moon to appear different in size depending on where it is in the sky Letters in Context - Profound example of the effect of context on letter/word perception: - Jumbled word effect = ability to read words in sentences despite having mixed-up letters in the middle of some of the words. o Expectations regarding what the words will be help you determine what the words actually are - Word superiority effect = its easier to identify a letter if it appears in the word than if it appears alone (e.g. P versus WARP) o Can more efficiently map strings of tentatively identified letters to real words - Context effects can be modelled using connectionist approaches - Parallel distributed processing (PDP) = model of perception according to which different features are processed at the same time by different ‘units’ (simple processing
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