Prospective Memory - University of Wisconsin-Platteville

Prospective Memory - University of Wisconsin-Platteville

Chapter 10 Concepts and General Knowledge Semantic Memory Stores Chapter 10 Knowledge Building Blocks of Thought Concepts a mental representation representing a category of objects: stored in long-Term Memory

Category a set or class of objects that belong together (e.g., groupings of similar objects, events, ideas, or people) Concepts provide a kind of mental shorthand, economizing cognitive efforts. Chapter 7 General Knowledge

Classical Approach: Concepts Concepts viewed as definitions with lists of necessary (properties that instances of the concept MUST have) and sufficient (a set of properties that fully define all members of the concept. Chapter 7 General Knowledge Defining the properties

or features on a Concept Formal Concept clear cut defining rules Chapter 7 General Knowledge Real-World or Natural Concepts Fuzzy defining rules Chapter 7 General Knowledge

Classical Approach Assumes: Sharp category boundary (in or out) Equality of members Representation of category is list of necessary and sufficient features If an entity meets the conditions it is a member If an entity is a member we know it meets the conditions

Ludwig Wittgenstein (1953) Used examples such as triangle and square (text example) and friends and games(text example) to demonstrate the amazing ability of humans to conceptualize thoughts that they could not define. Chapter 7 General Knowledge While there are a small set of formals concepts such as scientific categories and geometric

shapes, there are vastly more concepts that humans are able to understand that are realworld. There is no set of necessary and sufficing properties that define them. Instead they are defined by a set of properties that are likely to be true, but not all members of the concept need to have each property, nor is there a set of properties that fully define the concept. Chapter 7 General Knowledge Family Resemblance (Wittgenstein) No one feature

that all have in common. Yet all are in some way similar to the other category members Variation within categories allowed Family Resemblance Assumes:

No strict definition of whats in/out based on individual features Membership based on similarity Some members can be better examples than others Problem: If category membership is based on similarity, the question remains; similar to what? McCloskey & Glucksberg (1978) McCloskey & Glucksberg subjects disagree about whether atypical items belong in a

category: 30/30 apple is a fruit, chicken is not a fruit 16/30 pumpkin is a fruit Subjects change their minds when tested later. Conclusion: Category Boundaries are not always clear-cut. Is X a member of the category Y? Not all members of a category are equally as good members of those categories.

Some members are more typical than others. We are able to more quickly respond to typical than to atypical instances. This is called the typicality effect. Therefore, membership is not equal. Chapter 7 General Knowledge Rosch and Mervis (1975) Participants got category names (bird) and lists of 50 members (robin, canary, ostrich, penguin,

sparrow) Provided rating on how well the item represented the category (typicality ratings) Results: Much agreement on ratings between participants Rosch (1975) Bat Penguin

Owl Poor CATEGORY: BIRDS Telephone Mirror Chin

a Clos et Poor CATEGORY: FURNITURE Sparrow Very Good

Chair, Sofa Very Good Rosch & Mervis (1975) continued New participants list attributes (properties) of each of the instances. Some attributes were listed more frequently than others.

e.g., can fly vs. lives on a farm Results: Typical instances had more frequently listed features than did less typical instances. Chapter 7 General Knowledge Rips, Shoben & Smith, 1973 Verification Task Press a button to answer TRUE or FALSE to the following statements Question

Response A canary is a bird TRUE A ostrich is a bird TRUE fast slow

Typicality effect Other Support Mervis, Catlin & Rosch, (1976) When asked to list instances of a category, typical instances are generally listed first, indicating that they come more quickly to mind. Chapter 7 General Knowledge

Typicality and Language Typical instances are acquired earlier in life than atypical instances. When mentioning two category members together, the more typical member generally is mentioned first. Chapter 7 General Knowledge Semantic Dementia (Fronto-Temporal Degeneration)

A syndrome of progressive deterioration in semantic memory (knowledge of objects, people, concepts and words). can still speak fluently (good grammar and pronunciation) Lose of words for certain items and the knowledge of the meaning of the word. For example, someone may not only forget the word hippopotamus when shown a picture, but also loses all the knowledge they once had about this (e.g. that it is an African animal that lives in rivers). Chapter 7 General Knowledge

may also have difficulty recognizing what things are. Effects all modalities (sound, taste, empathy) Unlike Alzheimer's disease, memory for day-today events may be good. Visuo-spatial skills intact. At later stages, personality is often affected. Deterioration of left anterior temporal lobes. Chapter 7 General Knowledge Animal copies by patient B.F. (shown on

right) demonstrate retention of visuoperceptual ability in SD patients. Naming tests highlight their semantic deficits. When asked to name these animals, B.F. called the cow a dog and the elephant a cow.

Chapter 7 General Knowledge Drawings by patient G.W. show semantic deficits in spontaneous recall. Distinguishing features are absent: the fish lacks fins, the first bird lacks wings and the elephant lacks a trunk.

Chapter 7 General Knowledge Alternative Approaches to Concepts Prototypes Exemplars World Knowledge Chapter 7 General Knowledge Prototype Approach

a.k.a. Family Resemblance or Probabilistic approach) Eleanor Rosch A Prototype is an abstract, typical representation of the category members. You can think of it as the weighted average of the important features of members of the category. Chapter 7 General Knowledge Prototype Approach

The guy in the center is closest to the prototype (highest prototypically) but he isnt the prototype Prototype isnt here Prototype is abstract Prototype Approach Features: Glasses (yes/no)

Hair (dark/light) Nose (big/small) Ears (big/small) Mustache (yes/no) Prototype 2/3 glasses, 7/9 light hair, 7/9 big nose, 7/9 big ears, 5/9 mustache Center guy has the highest prototypicality Probabilistic (not all or none). Prototypes include characteristic features that are usually

present, not only necessary or sufficient features. Objects that do not share all the characteristics of the prototype are still members of the category but not prototypical ones. Chapter 7 General Knowledge Prototype Theory Rosch (1970s)

suggested that when people categorize items they match them against a "prototype" which contains the most representative . features inside the category. Chapter 7 General Knowledge

Exemplar Approach Medin & Schaffer Exemplars are specific examples Examples of category members are saved in memory (typical as well as atypical) Potential members compared to all exemplars Those with high family resemblance are like more of the exemplars May be more useful for smaller categories (US

presidents, very tall mountains) Novick (2003) Exemplars Change with Experience Extensive media coverage following 9/11 effected the typicality of airplane as a response to the category of vehicle. Airplane was judged to be a more typical vehicle for 1 month following the attacks, relative to a baseline calculated from data collected yearly for 5 years preceding the attacks. By 4.5 months,

however, typicality was back to baseline. Chapter 7 General Knowledge Evidence supporting Exemplars Much of the evidence that supports the prototype model also predicts the exemplar model. Chapter 7 General Knowledge Exemplars or Prototypes

Brooks & Allen (1991) Asked subjects to discriminate between two animals (Diggers and Builders) Two kinds of animals could be distinguished in two different ways (two-out-of-three rule). 3 Relevant Features (leg length, angularity of body, spotted/not spotted). 2 non-relevant features (number of feet and length of neck). Chapter 7 General Knowledge

Half of the participants explicitly told rule, others not told. Classification rule: 2 of (long legs, angular body, spots) => Builder 2 of (short legs, curved body, not spots) => Digger Example of Builder Example of Digger Chapter 7 General Knowledge

Learning Phase: Rule (Prototype) Condition: Participants explicitly told the rule and used it to categorized 8 exemplars. Memory (Exemplar) Condition: Shown 8 exemplars and told which were diggers and which builders without being told the rule. Chapter 7 General Knowledge

Generation of new items for Test phase. New items that varied from the learning phase exemplars were generated by manipulating relevant or non-relevant features. Chapter 7 General Knowledge Good Matches: Changed a feature that did not change the category. In the example below shortening the neck does not change the category.

Original New Chapter 7 General Knowledge Bad Matches: change a relevant feature so that the new item is now a member of the other category. Adding spots Chapter 7 General Knowledge

Results Participants made errors that indicated they were relying on similarity to known exemplars rather than on the rule (prototypes). Even when a rule is known and easy to explicitly state, past examples can override application of the rule! Chapter 7 General Knowledge

A Caveat Evidence shows that we use both prototypes and exemplars depending on context. Natural categories we may have richer prototypes and more complex rules, therefore we rely on prototypes. For Artificial categories we rely on exemplars. Chapter 7 General Knowledge World Knowledge Approach

When we do real-world categorization tasks we dont just rely on prototypes or remembered exemplars, we also call on our world knowledge which included richer conceptual representations (uses for, causal and contextual information). Chapter 7 General Knowledge Example: Goal-Derived Categories Goal-derived categories are those that satisfy some goal (e.g., birthday gifts that make the

recipient happy or what you would take with you if your fire alarm went off). Family resemblance scores and prototypes are of little value for these categories since membership is based on goal satisfaction rather than sharing some particular attribute or set of attributes. Chapter 7 General Knowledge The Role of Knowledge in Concept Learning Lin and Murphy (1997)

People learned about objects used in foreign countries. One group learned that the tuk is used for hunting. The hunter holds the handle (3) with one hand behind the hand guard (2) and pulls the end of the rope (4) while slipping the noose (1) around the animals neck. Lin and Murphy (1997)

The other group was told that the tuk was a fertilizing tool. Liquid fertilizer is held in the tank(2), the knob (3) is turned to allow it to come through the outlet pipe (4). The loop is used to hang the tuk up. Chapter 7 General Knowledge After learning a variety of concepts, items were

then presenting missing one or more of the parts in the original object. The missing part really had a big effect on the participants classification of the object as a tuk. Those learning that Part 1 was a noose were far less likely than those learning that Part 1 was a loop hanger to classify the object missing Part 1 as a tuk, presumably because the missing part was more central to its function. Chapter 10 1 Concepts and Knowledge

Tomato: Fruit of Vegetable? Depends on your reason for categorizing! Definitions If you are a gardener, it is a fruit. In the kitchen, it is a vegetable. Chapter 7 General Knowledge

Recently Viewed Presentations

  • WHO/SRNT Treatment Database - treatobacco.net

    WHO/SRNT Treatment Database - treatobacco.net

    It shows that a cigarette is the 'formula one car' of nicotine delivery. In contrast, NRT products provide slower, less variable plasma nicotine concentrations, which means that, while they alleviate nicotine withdrawal symptoms, the addictive potential of these products is...
  • Markov Processes - SMU

    Markov Processes - SMU

    But some people tend to avoid this usage for sake of confusion. Markov Model is also used to refer to all Markov processes that satisfying Markov Property. Hidden Markov Model(HMM) In an Hidden Markov Model(HMM), we don't know the state...
  • Nonholonomic variational systems

    Nonholonomic variational systems

    Nonholonomic structure, reduced equations, constraint calculus. Nonholonomic variational systems, nonholonomic Helmholtz conditions. Examples: ballistic motion, damped planar oscillator, relativistic particle - First, we formulate the problem and present the geometrical background of the theory.
  • 2019 REMSA POLICIES AND PROCEDURE OVERVIEW Overall Policy

    2019 REMSA POLICIES AND PROCEDURE OVERVIEW Overall Policy

    EMS Core Measures will be updated by CA EMSA and REMSA strategies for their implementation discussed with CQILT and other REMSA Advisory Committees through Q1-Q3 2019, once released by CA EMSA. ... - PPV and documentation - Pediatric ETI definition...
  • HR Forum October 2, 2019 UFHR preeminence through

    HR Forum October 2, 2019 UFHR preeminence through

    Jack Causseaux, 294-3558. Diversity & Inclusion Award Contact - Florida Bridgewater-Alford, 846-3903. UFHR . preeminence through people. Training & Organizational Development. Gator Business Administrator Services (GBAS) Nov 20th. 8:30am - 3:30pm.
  • Electronic Student Score Reports (SSRs) for CAASPP

    Electronic Student Score Reports (SSRs) for CAASPP

    Purpose. The purpose of this webcast is to provide the resources needed to review your local educational agency's (LEA's) technological resources to ensure they meet the requirements for the online California Assessment of Student Performance and Progress (CAASPP) tests and...
  • Frequency Distributions - WordPress.com

    Frequency Distributions - WordPress.com

    Frequency Histogram Frequency Polygon Other Information Relative Frequency Histogram Ogive Larson/Farber Ch 2 Frequency Distributions 102 124 108 86 103 82 71 104 112 118 87 95 103 116 85 122 87 100 105 97 107 67 78 125 109...
  • Companding in Fixed Point DSPs - Columbia University

    Companding in Fixed Point DSPs - Columbia University

    Hardware I have succeeded in getting a complete companding system to work on the TI 6713 floating point DSP in real-time. The "analog" parts "run" in floating-point. The digital parts "run" in 8-bit fixed point. I used Simulink's Real Time...