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Psychology (9,699)
PSYB57H3 (366)
Dwayne Pare (122)
Chapter 3

PSYB57 – Chapter 3 Notes.docx

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PSYB57 – Chapter 3 Notes  Form perception: the process through which you manage to see what the basic shape and size of an object are  Object recognition: the process through which you identify what the object is  Virtually all knowledge and use of knowledge depend on form perception and object recognition  Early in the 20 century, a group of psychologists called the “Gestalt psychologists” noted that our perception of the visual world is organized in ways that the stimulus input is not o They argued therefore that, the organization must be contributed by the perceiver; this is why, they claimed, the perceptual whole is often different from the sum of its parts o Some years later, Jerome Bruner voiced similar claims and coined the phrase “beyond the information given” to describe some of the ways that our perception of a stimulus differs from (and goes beyond) the stimulus itself  Figure/ground organization: the determination of what is the figure (the depicted object, displayed against a background) and what is the ground o Your perception of this figure, is however not neutral about this point  Your perception contains information – about how the form is arranged in depth, or about which part of the form is figure and which is ground – that is not contained within the stimulus itself; apparently then, this is information contributed by you, the perceiver  A number of observations suggest that our interpretation, our organization of the input, happens before we start cataloguing the input’s basic features, and not after, as a secondary “interpretive” step  At the start, the form seems not to contain the features needed to identify the L, the I, and so on o Once the form is reorganized, though, it does contain these features and the letters are immediately recognized o Apparently, then, the cataloguing of the input’s features depends on a prior step in which the form is organized by the viewer: with one organization, the key features are absent; with another, they’re plainly present  However, the opposite is also the case as well: the features one finds in an input depend on how the figure is interpreted; therefore, it is the interpretation, not the features, that must be first  Solution = brain relies on parallel processing – the brain areas analyzing a pattern’s basic features do their work at the same time as the brain areas analyzing the pattern’s large-scale configuration o These two brain areas constantly interact, so that the perception that is achieved is one that makes sense at both the large-scale and fine- grained levels  The organization determines our most immediate impressions of the stimulus  The perceptual organization also plays a powerful role in deciding whether a form will be recognized as familiar or not  The interpretation achieved by the perceptual system – the organization of figure and ground, and so on – must fit with all the incoming stimulus information  The perceptual apparatus requires a hypothesis that fits with all the data  Second, the perceptual system seems to prefer the simplest explanation of the stimulus  Third, the perceptual system seems to avoid interpretations that involve coincidences  Recognition might begin with the identification of features in the input pattern – features such as vertical lines, curves, or diagonals – with these features appropriately catalogued, you can start assembling the larger units  The features that we use are the ones in our organized perception of the input  We recognize objects by detecting the presence of the relevant features  Advantages of a feature-based system: o Features such as line segments and curves could serve as general- purpose building blocks o Not only would these features serve as the basis for recognizing letters, but they could also serve as the basis for recognizing other, more complex visual patterns, opening the possibility of a single object-recognition system able to deal with a wide variety of targets o Second, focusing on features might allow us to concentrate on what is common to the various As (example), and so might allow us to recognize As despite their apparent diversity o Third, several lines of data indicate that features do have priority in our perception of the world (visual search task: participants have to indicate whether a certain target is or is not present in a display) o Fourth, other results suggest that the detection of features is a separate (and presumably early) step in object recognition, followed by subsequent steps in which the features are assembled into more complex wholes (ex: patients with integrative agnosia – impaired in tasks that require them to judge how particular features are bound together to form complex objects)  Tachistoscope: a device specifically designed to present stimuli for precisely controlled amounts of time o Each stimulus is followed by a post-stimulus mask – often just a random jumble of letters, such as “XJDKEL” o The mask serves to interrupt any continued processing that participants might try to do for the stimulus just presented, and this allows researchers to be certain that a stimulus presented for say 20 ms is visible for exactly 20 ms and no longer o If the stimulus is a word, we can measure familiarity by literally counting how often that word appears in print, and these counts are an excellent predictor of tachistoscopic recognition o Another factor influencing recognition is recency of view; if participants view a word and then, a little later, view it again, they will recognize the word much more readily the second time around o The first exposure primes the participant for the second exposure; more specifically, this is a case of repetition priming  Words themselves are easier to perceive, as compared to isolated letters: word-superiority effect  In study, accuracy rates are higher in the word condition, and so, apparently, recognizing words is easier than recognizing isolated letters; participants are more accurate in identifying letters if those letters appear within a word, as opposed to appearing all by themselves  What matters is whether the string, familiar or not, is well formed according to the rules of the language  If the string is well formed, then it will be easier to recognize and will produce a word-superiority effect  Moreover, a letter string that “follows the rules” closely will produce a stronger word-superiority effect, and will be easier to recognize, than one that only roughly matches the standard patterns  How do we assess “resemblance to English: one way - Pronounceable strings are more easily recognized with tachistoscopic presentations that are unpronounceable strings o Also by statistical terms  There is a strong tendency to misread less-common letter sequences as if they were more-common patterns; irregular patterns are misread as if they were regular patterns  Misspelled words, partial words, or non-words are read in a way that bring them into line with normal spelling  In effect, people perceive the input as being more regular than it actually is, and so these errors are referred to as over-regularization errors – this phenomenon suggests once again that our recognition is guided by some knowledge of spelling patterns  The idea is that there is a network of detectors, organized in layers, with each subsequent layer concerned with more complex, larger-scale objects  The bottom layer is concerned with features, and that is why networks of this sort are often referred to as feature nets  At any point in time, each detector in the network would have a particular activation level, which reflects how activated the detector is at just that moment  The activation level will eventually reach the detector’s response threshold, and at that point the detector will fire – send its signal to the other detectors to which it is connected  If the detector was moderately activated at the start, only a little input is needed to raise the activation level to threshold, and so it will be easy to make this detector fire  If the detector was not at all activated at the start, then a strong input is needed to bring the detector to threshold, and so it will be more difficult to make this detector fire  Detectors that have fired recently will have a higher activation level; detectors that have fired frequently in the past will also have a higher activation level; thus, the activation level is dependent on principles of recency and frequency  Bigram detectors: detectors of letter pairs o These detectors, like all the rest, will be triggered by lower-level detectors, and send their output to higher-level detectors o And just like any other detector, each bigram detector will start out with a certain activation level, influenced by the frequency with which the detector has fired in the past, and also the recency with which it has fired  The detectors for these letter groups (ex: hice) have high activation levels at the start, and so they don’t need much additional input to reach their threshold; as a results, these detector will fire with only weak input  Over-regularization errors come about – in essence, the network is biased, always favouring frequent letter combinations over infrequent ones; this bias facilitates perception if the input is, in fact, a frequent word o But the bias wil
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