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Chapter 3

PSYCH 2h03-CHAPTER 3.docx

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Jennifer Ostovich

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CHAPTER 3 PERCEPTIONCHAPTER SUMMARYVisual perception goes beyond the visual input displayed and goes further to interpretation by a series of network detectors influenced by the Gestalt principles context priming despite guidance of features and overall configurationEvidence from neuroscience studies demonstrates that the detection of features is separate from the processes needed to assemble these features into collective complex whole and explains why the detection of features initiates recognitionRepetition priming assisted by tachistoscopic devices reveal correlations between high and low frequencies and their relative recognition threshold These studies have also concluded information about the wordsuperiority effect which refers to the fact that words are more readily perceived by isolated letters In addition wellformed nonwords are more readily perceived than letter strings that do not conform to the rules of normal spellingAnother reliable pattern is that recognition errors when they occur are quite systematic with the input typically perceived as being more regular than it actually is These findings indicate that the recognition is influenced by regularities that exist in our environment Top down influences on recognition help tell us that object recognition is not a selfcontained process but knowledge external to objection recognition is imported into and clearly shapes the processThese findings can be understood in terms of a network of detectors Each detector collects input and fires when the input reaches a threshold level A network of these detectors can accomplish a great deal for example it can interpret ambiguous inputs recover from its own errors and make inferences about barely viewed stimuliThe feature net seems to know the rules of spelling and expects the input to conform to these rules However this knowledge is distributed across the entire network and emerges only through the networks parallel processing This set up leads to enormous efficiency in our commerce with the world because it allows us to recognize patterns and object with relatively little input and under highly diverse circumstances But thee gains come at cost of occasional error This tradeoff may be necessary though if we are to cope with the informational complexity of our world A feature net can be implemented in different ways with or without inhibitory connections for example With some adjustments the net can also recognize threedimensional objects However some stimuli for example faces probably are not recognized through a feature net but instead require different sort of recognition system one that is sensitive to relationships and configurations within the stimulus input
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