Chapter 8 - The Organization of Knowledge in the Mind Flashcards
Concept
The fundamental unit of symbolic knowledge (knowledge of correspondence between symbols and their meaning).
Category
A hierarchy of concepts placed together since they share common features, or are similar to a certain prototype; a category is a concept with members (for example, the concept bird has the members hawk, blue jay etc).
Natural categories
Groupings that occur naturally, like birds or trees.
Artifact categories
Groupings that are designed or invented by humans to serve a particular purpose.
Ad hoc categories
Categories where the content varies, depending on the context.
Basic level
A level in the hierarchy of a categiry that is preferred, that is neither the most specific or the most abstract. The level with the most distinctive features that set it off from other concepts at the same level.
Feature-based view of concepts
Concepts have a few defining features that they need to have in order to fit into the concept.
Prototype theory
Grouping things together by their similarity to an averaged model of the category.
Prototype
An abstract average of all the objects in the category we previously have encountered. Objects that are prototypical have a high family resemblance.
Characteristic features
Features that describe the prototype, and are commonly but not always present.
Classical concepts
Categories that can be readily defined through defining features; tend to be inventions that experts have devised for labelling a class that has associated defining features.
Fuzzy concepts
Concepts that can not be easily defined through defining features; tend to evolve naturally.
Exemplar theory
Instead of using a single abstract prototype for categorizing a concept, we use multiple, specific exemplars. Categories are set up by creating a rule and then by storing examples as exemplars, and objects are compared to them to decide if they belong in the category.
Exemplars
Typical representatives of a category, not necessarily averaged over all objects, can be of different subtypes of objects within a category.
The varying abstraction model (VAM)
Prototypes and exemplars are just two extremes on a continuum of abstraction - we often use a number of intermediate representations. A theory of categorization can combine defining and characteristic features in a synthesis. Each category can have both a core and a prototype.
Core
The defining features something must have to be considered an example of a category.
Theory-based view of meaning
People understand and categorize concepts in terms of implicit theories, or general ideas they have regarding those concepts. People can distinguish between essential and incidental features of concepts because they have complex mental representations of these concepts.
Essentialism
Certain categories have an underlying reality that cannot be observed directly.
Semantic-network models
Knowledge is represented in the form of concepts that are connected in a web-like form.
Collins and Quillian’s network model
Knowledge is represented in a hierarchical semantic network. The elements in the network (nodes) are typically concepts, and the connections between nodes are called relationships. This allows for a high degree of cognitive economy due to inheritance.
Inheritance
Lower level items in a semantic network inherit the properties of higher level items.
Schemas
Mental frameworks for organizing knowledge. Schemas are more task-oriented than semantic networks. How elaborate a schema is varies with individuals, their profession and experience with the concept. Schemas can contain many subschemas, and encompass typical, general facts that vary slightly from one instance to another. They also vary in their degree of abstraction.
Schemas can also include information about relationships among concepts, attributed within concepts, attributes in related concepts, causal (if-then) relationships etc.
Script
A schema that contains information about the particular order in which things occur; less flexible than schemas in general. Scripts include default values for the actors, props, setting, and expected sequence of events. Scripts seem to guide what people recall and recognize, and ultimately, what they know. We also have scripts for typical life stories (marriage, college, having children etc). The frontal and parietal lobes seem to be involved in the generation of scripts.
Production
The generation and output of a procedure.
Production system
Encompasses the entire set of rules (productions) for executing the task or using the skill.
Semantic priming
Primed by a meaningful context/meaningful information.
Repetition priming
Primed by repeated exposure to information.
Anderson’s adaptive control of thought (ACT) model
Procedural knowledge is represented in the form of production systems, and declarative knowledge is represented in propositional (propositions = the smallest unit of knowledge that can be judged to be either true or false) networks.
The ACT-R model
Integrating a network representation for declarative knowledge and a production system representation for procedural knowledge. In the ACT-R, networks include images of objects and corresponding spatial configurations and relationships, as well as temporal information involving sequencing of events. According to ACT-R, nodes can be either active or inactive at a given time, and nodes are activated by external (sensations) or internal (thoughts, memories) stimuli. Declarative knowledge may be learned through the strengthening of connections resulting from frequent use. Representation of procedural knowledge occurs in three stages: cognitive, associative, and autonomous.
Proceduralization
The transformation of slow, explicit information about procedures into speedy, implicit implementations of procedures.
Production tuning
We learn to generalize existing rules and apply them to new conditions, and we learn to discriminate new criteria for meeting the new condition.
Parallel distributed processing models/connectionist models
We handle very large numbers of cognitive operations at once through a network distributed across incalculable numbers of locations in the brain. In the PDP model, the network is made up of neuron-like units, and do not represent concepts or categories. The pattern of connections represents the knowledge, not the specific units. Differing cognitive processes are handled by differing patterns of activation, rather than as a result of a different set of instructions from a CPU.
Modular mind
The mind is divided into discrete modules working independently, each handling only one kind of input - an extremely domain specific view.
