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Chapter 3

Chapter 3.docx

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Jonathan Cant

Chapter 3 • Human vision is the dominant sense evident by how much brain area is devoted to vision o If visual info conflicts with information received from other sense, we usually trust our vision most • Form perception: the process through which we manage to see basic shape and size of object • Object recognition: process through which we identify object is o Both are vitally important in perception and applying knowledge to the world o Object recognition is crucial for learning • Gestalt psychologists argue that organization of an image must be contributed by perceiver o Reiterated by Jerome Bruner who referred to it as “beyond the information given” o Ex. Necker cube, vase/figure optical illusion which are reversible figures • Figure/ground organization: determination of what is the figure and what is the ground o When a image has neutral figure/ground organization, the perceiver contributes the information themselves therefore affecting how they view image • Perception guided by principles of proximity and similarity o If within a scene, you see elements that are close to each other or elements that resemble each other, assume these elements are parts of the same object o Tend to assume that contours are smooth, not jagged and avoid interpretations that involve coincidences • Gestalt principles of organization: o Similarity o Proximity o Good continuation: tend to see continuity instead of two separate figures o Closure: biased toward perceiving closer figures rather than incomplete ones o Simplicity: tend to interpret form in the simplest way possible • We appear to interpret input before we start cataloguing input’s basic features o Brain’s parallel processing means that areas that analyze pattern’s basic features do their work at same time as areas analyzing pattern’s large-scale configuration o Perception of features guided by configuration and analysis of configuration is guided by features • Bottom-up influences: influences affecting perception coming from the stimulus itself • Top-down influences: influences that rely on your knowledge • Recognition might begin with identification of visual features of input pattern and with those features catalogued, you could start assembling large units o Proven by the fact that people are slower to searching for target defined as combination of features instead of target with simple features • Damage to parietal cortex can lead to integrative agnosia where people cannot judge how features are bound together to form complex objects i.e. struggled with finding “red and round shape” WORD RECOGNITION Factors Influencing Recognition • Tachistoscope: device designed to present stimuli for precisely controlled amounts of time • Factor influencing recognition is familiarity of stimulus • Another factor is recency of view o If person sees the word and then later, views it again, they will recognize the word more readily the second time o Example of repetition priming The Word-Superiority Effect • Words that are frequently viewed are easier to perceive as are words that have been viewed recently • Word superiority effect: words are easier to perceive than letters Degrees of Well-Formedness • No context effect from string like “HZYE” and therefore would not show word-superiority effect • A word like “FIKE” look like English string and easy to pronounce and therefore dies show a superiority effect o Still do better if the word is actually English • Pronounceable strings are easier to recognize after brief exposure • The more English-like the string, the easier it will be recognize it and greater the context benefit Making Errors • Strong tendency to misread less-common letter sequences as if they were more- common patterns FEATURE NETS AND WORD RECOGNITION The Design of a Feature Net • Idea that there is a network of detectors, organized in layers, with each subsequent layer concerned with more complex, larger-scale objects • “Bottom layer” is for detecting feature  letter detectors bigram detectors  word detectors • Each detector in the network has particular activation level which reflects status of detector at that moment o Strong input will increase activation level by a lot and so will numerous weaker inputs o Activation level will eventually reach detector’s response threshold and then detector will fire • Detectors are likely complex assemblies of neural tissues • If detector is moderately activated at start, only a little input is needed to raise activation level to threshold • Detectors that have fired recently (warm up effect) will have higher activation level as well as detectors that have fired frequently (exercise effect) o Can explain repetition priming: presenting word once will cause relevant detectors to fire, elevating activation levels which means word will be more easily recognized the second time around The Feature Net and Well-Formedness • A layer of the feature net recognizes letter combinations • Bigram detectors: detect familiar letter pairs ex. “HI” “CE” Recovery from Confusion • When visual system
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