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CogPsych 9
Chapter 9: Knowledge
| Question | Answer |
|---|---|
| What is conceptual knowledge? | Knowledge that enables us to recognize objects and events and to make inferences about their properties |
| What are concepts? | The mental representation of a class or individual (Smith, 1989), or the meaning of objects, events, and abstract ideas (Kiefer & Pulvermuller, 2012). |
| What is a category? | It includes all possible examples of a particular concept. |
| What is categorization? | The process by which things are placed in categories. |
| What are the benefits of categorization? | 1. Reduces complexity of environment - can focus energy instead on what's special 2. Identification - understand otherwise "strange" behaviours 3. Reduces need for constant learning 4. Allows decision for appropiate actions. |
| What is the definitional approach to categorization? | We decide whether something is a member of a category by determining whether a particular object meets the definition of the category. |
| What is the problem with the definitional approach to categorization? | Not all the members of everyday categories have the same features. |
| What what the family resemblance idea proposed by Wittgenstein (1953)? | Things in a particular category resemble one another in a number of ways, allowing for some variation within a category. |
| What is a prototype, and what is the prototype approach to categorization? | A prototype is a "typical" member of a category. Membership in a category is determined by comparing the object to a prototype that represents the category. |
| How is the prototype defined, according to Rosch (1973)? | It is based on an average of members of a category that are commonly experienced. |
| What is high and low typicality, according to Rosch (1973)? | High typicality means that a category member closely resembles the category prototype, while low typicality means that it does not. |
| How did Rosch and Mervis (1975) show that there was strong relationship between family resemblance and prototypicality? | Good examples of the category "furniture: share many attributes with other members of this category; poor examples do not. |
| Describe Smith et al. ( 1974)'s study using the sentence verification technique. | They used the technique to determine how rapidly peoplecould answer questions about an object's category-- statements; answer yes if true and no if not. Subjects responded faster for objects high in prototypicality. |
| What is the prototypicality effect? | The ability to judge highly prototypical objects more rapidly. |
| What are the effects of prototypicality? | Highly-prototypical items are named first when listing examples of a category, and they are more affected by a priming stimulus than nonprototypical members. |
| Describe Rosch (1975b)'s study that showed that prototypical members primed better. | Subjects heard the prime, a colour. They then saw a pair of colors (good, poor-faded or different) and indicated whether they were the same or not. Found tha priming resulted in faster "same" judgments for the good prototypical colours (610 ms to 780 ms). |
| How does priming affect prototypical colours better according to Rosch (1975)? | When subjects hear the word green, they imagine a prototypical green. The prime facilitates the subject's response to the stimulus if it contains some of the information needed to respond to the stimulus. |
| What is an exemplar, and what is the exemplar approach to categorization? | They are the actual members of a category that a subject has encountered in the past. The standard for the approach involves many such exemplars. |
| How does the exemplar approach explain the typicality effect? | It proposes that objects that are like more of the exemplars are classified faster. |
| What are the advantages of the exemplar approach? | 1. It can deal easier with outlier cases by reclassifying them as exemplars. 2. It can also deal more easily with variable categories like games, by requiring that we only remember some of these varying examples. |
| How may the prototype and exemplar approaches be integrated? | 1. As we initially learn about a category, we may average exemplars into a prototype; the exemplar knowledge would become stronger 2. The exemplar approach works for small categories while the prototype approach works for large categories. |
| What is a hierarchal organization? | Larger, more general categories are divided into smaller, more specific categories creating a number of level of categories. |
| How does Rosch distinguish the levels of categories? | The superordinate/global (furniture), the basic level (table), and the subordinate/specific level (kitchen table). |
| Describe Rosch et al.'s study (1976) on hierarchical organization? | Subjects listed as many common features as they could for objects in a category. Findings; average of 3 common features for global, 9 for basic, and 10.3 for specific. |
| How did Rosch (1976) interpret her study on hierarchical organization? | The basic level is psychologically special because going above it results in a large loss of information and going below it results in little gain of information. |
| What were the findings of Rosch et al's (1976) study on hierarchical organization? | 1. People tended to pick basic level names when asked to name objects 2. Subjects responded faster when a picture of a car was preceded by the word "car" (basic level) than when preced by the word "vehicle" (global level). |
| How did Tanak & Taylor (1991) show that knowledge affected categorizations? | They asked experts and nonexperts to name pictures of birds. The experts responded by specifying the birds' species but the nonexperts responded by saying only bird. |
| How does expertise affect categorization? | The level that is "special" (focused on) is not the same for everyone. People with more expertise and familiarity with a particular category tend to focus on more specific information that Rosch associated with a specific level. |
| Describe the semantic network approach. | It proposes that concepts are arranged in networks. A network consists of nodes (concepts) that are connected by links (relations between the nodes). In addition, a number of properties are indicated for each concept. |
| What is cognitive economy? | Shared properties are stored just once at a higher-level node. |
| How does the semantic network approach deal with outliers? | Exceptions are made at lower nodes, such as properties. |
| Describe how Collins & Quillian (1969) test their semantic network approach. | They measured the reaction time to a number of different statements. They found that statements that required further travel from "canary" resulted in longer reaction times. |
| What is spreading activation? | Activity that spreads out along any link that is connected to an activated node. Additional concepts that receive this activation become primed and so can be retrieved more easily from memory. |
| What is the lexical decision task (Meyer & Schvaneveldt, 1971)? | Subjects read stimuli, some of which are words and some of which are not words. Their task is to indicate as quickly as possible whether each entry is a word or a nonword. |
| What did Meyer & Schvaneveldt (1971) find in their paired-word variant of the lexical decision task? | They found that reaction time was faster when the two words were associated. Retieving one word from memory triggered a spread of activation to other nearby locations, and more activation would spread to words that were related. |
| What were the criticisms of the Collins and Quillian model of semantic networking? | 1. It couldn't explain the typicality effect 2. People might store specific properties at the node for that concept 3. Rips et al (1973): "a pig is an animal" is verified more quickly than "a pig is a mammal" even though the theory says otherwise. |
| What are the advantages of the connectionist model (McClelland & Rumelhard, 1986)? | 1. It is based on how knowledge is represented in the brain 2. It can explain many findings, including how concepts are learned and how damage to the brain affects people's knowledge about concepts. |
| What is connectionism? | An approach to creating computer models for representing cognitive processes. |
| What are parallel distributed processing models? | They propose that concepts are represented by activity that is distributed across a network. |
| Describe the connectionist network. | Circles represent units (neurons). Lines between circles are connects that transfer information betweenn units (axons). Input units are activated by stimuli from the environments, which then send signals to hidden units, and on to output units. |
| What is a connection weight? | Determines how signals sent from one unit increase/decrease the activity of the next unit. High connection weights result in a strong tendency to excite, lower weights cause less excitation and negative weights decrease excitation/inhibit excitation. |
| What does activation of units in a connectionist network depend on? | 1. The signal that originates in the input units 2. The connection weights throughout the network. |
| What is the basic principle of connectionism? | A stimulus presented to the input units is represented by the pattern of activity that is distributed across the other units. |
| How are units arranged in a connectionist network from left to right? | Concept, Relation & Representation (same level but seperate), Hidden, and Property |
| How is a connectionist network trained? | It involves adjusting the network's connection weights. |
| How is learning achieved in a connectionist network? | Erroneous responses in the property unit (feedback) cause an error signal to be sent back through network through back propagation. Reaching the hidden & representation units-info about weight adjustment so that the correct property units are activated. |
| What is the general learning process in a connectionist network? | Initially weak and undifferentiated activation of property units -> error signals back propagated -> changes in connection weights -> new activation pattern, with more repetitions, approximates closer and closer the correct properties of a concept. |
| What results support the connectionist approach? | 1. The operation of connectionist networks is not totally disrupted by damage - graceful degradation. 2. Connectionist networks can explain generalization of language. |
| How does damage occur in the connectionist network? | Given that information in the network is distributed across many units, disruption of performance occurs only gradually as parts of the network are damaged. |
| How does the connectionist approach explain generalization of learning? | Training a system to recognize the properties of one concept also provides information about other, related concepts. |
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