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Unit 6: Understanding Spoken Words

10 Pages

Course Code
Psychology 2134A/B
Marc Joanisse

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Unit 6Understanding Spoken WordsTheories and Models of Language ProcessingThe Modularity Hypothesis proposes that mental operations are divided into separate modules Each of these is domainspecific this means they process specific types of information only For instance they are specific to language or even a specific aspect of language eg syntax Modularity also proposes Information encapsulation each module only processes data in a fashion that is bottomup or onedirectional It takes inputs and processes them and then passes them along to other modules Information does not flow in the opposite direction ie there is no feedback Finally it proposes Localization of Function each module is isolated to a specific brain region so that the brain can be divided into discrete processing centres each subserving a different functionPredictions of ModularityApplied to language this theory proposes that language is processed 1Using separate mechanisms than other cognitive abilities2In separate brain regions3Bottomup processing onlyBottomup vs Topdown ProcessingThe modularity theory proposes that processing can be divided intoLowlevel processingPerceptionHighlevel processing Memory prior knowledge contextBottomup information consists of perceptual information that is passed to higher levels but not the other way aroundTopdown information consists of perceptual effects are influenced by higherup processesThe ConnectionismPDP approachMore recently a new view of mental processing has emerged called Parallel Distributed Processing This hypothesis holds that our understanding of mental processes should reflect how the brain works The basic unit of processing in the brain is the neuron Information processing happens through connections among neurons So if we want to understand how information is processed we should examine it from the perspective of networks of neuronsIn the PDP framework mental operations are understood using neural network models These consist of simple processing units called nodes that are intended to simulate real neurons They have activation like real neurons and are connected to each other into a network Information passes to other neurons in the network using connections The network learns to process information by changing the strengths of these connectionsReading modelHere is an example of a PDP network that examines how people recognize visual words It was developed by McClellandRumelhart in 1981 Each circle or oval is a separate neuron that encodes a different type of information There are three layers of information visual features bottom individual letters middle and individual words top These are all connected to each other using connections lines Some of these connections are excitatory they increase activation among units lines with arrows others are inhibitory they decrease activation lines with circles For instance the letter A has excitatory connections with the words able trap take and cart Likewise the letter A is connected to trip with an inhibitory connection
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