Week 5 - Lecture 9 - Categorisation vTB
Page 1: Introduction to Categorisation
Course: PSYC201 - Cognitive Psychology
Instructor: Dr. Tom Beesley
Lecture 5 focuses on categories and their importance in cognitive psychology.
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Page 3: Objectives
Understand categories/concepts and their usefulness.
Appreciate the limitations of classical views on categorisation.
Grasp the two main theories of categorisation:
Exemplar Model
Prototype Model
Page 4: What is a category?
Definition: A category groups instances that share common attributes (e.g., 'bird', 'animal').
Categories allow psychological learning and memory representation of these groupings, referred to as concepts.
Page 5: Functions of Concepts
Classification: Treats diverse entities as equivalent.
Understanding and Prediction: Facilitates anticipation of characteristics and initiates appropriate behaviors for new instances.
Communication: Enhances societal communication through shared knowledge about categories.
Page 6: How do we categorise?
Theories on human cognition's categorization process reflect key inquiries:
Are all category instances stored in memory?
How are instance relationships encoded?
Is category-specific knowledge stored?
Page 7: Classical view (“feature-based” theory)
Proposed by Bruner, Goodnow & Austin (1956).
Mental representations consist of defining features necessary for category membership.
Example: A triangle has three sides and a closed geometric form.
Page 8: Classical view limitations
Members of a category are either included or excluded without gray areas.
All category members are viewed equally, leading to confusion in application (e.g., are all musicians defined equally?).
Page 9: Additional Limitations of the Classical View
All non-members are equally poorly defined.
The process of category evaluation against defining features lacks clarity.
Page 10: Failings of the Classical View
Typicality Effects:
Some instances are more typical than others, demonstrated by comparing members (e.g., differences between a sparrow and a penguin).
Unclear cases:
Ambiguities can arise in classification (e.g., defining 'sport').
Page 11: Continued Failings of the Classical View
Failure to Specify Defining Features:
Simple shapes like triangles are easy, but what defines a bird or musical instrument is complex and hard to pin down.
Page 12: McCloskey & Glucksberg (1978) Study
Participants classified items based on typicality. Repeat tests showed that
Typicality correlates with category assignments.
Greatest changes appeared in instances with intermediate typicality, indicating fuzzy category boundaries.
Page 13: Rosch & Mervis (1975)
Investigated what defines something as typical in a category.
Frequency of occurrence (e.g., sparrows are seen more often than penguins) influences judgement.
Page 14: Further Findings from Rosch & Mervis (1975)
More common attributes among members signify stronger typicality.
Fuzzy categories are further evidence against strict classical definitions of concepts.
Page 15: Typicality and Family Resemblance
Items viewed as prototypical yield higher family resemblance to other category members while showing lower resemblance to other categories (e.g., comparing tomatoes to usual fruit categories).
Page 16: Prototype Theory
Suggests that a summary representation is formed based on experiences with instances.
This representation reflects an ideal or average member of the category, termed the 'Prototype'.
Page 17: Dynamic Nature of Prototype
As more instances are experienced, the prototype evolves reflecting the central tendency or average.
Page 18: Advantages of Prototype Theory
Addresses the difficulties of defining features while allowing for ambiguity in boundary definitions.
Page 19: Evidence for Prototype Abstraction
Posner & Keele (1970):
Used random dot patterns in tasks to assess classifications of prototypes vs. distortions.
Page 20: Outcome of Posner & Keele's Research
Participants performed well with prototypes despite not training on them directly.
Better performance seen with small distortions over larger ones after a week.
Suggests learning involves abstract representation of prototypes during initial exposure.
Page 21: Challenges with Prototypes
Natural categories can present strange prototypes (e.g., defining both flying and non-flying birds).
More likely representation involves ideal features rather than a single item.
Page 22: Exemplar Theory
Proposes storing all exemplars rather than an abstract prototype.
Classifications rely on the similarity of new items to stored instances.
Page 23: Similarity Computation in Exemplar Theory
Classifications function on computed similarities from all stored exemplars, assessing if similarity crosses a threshold for categorization.
Page 24: GCM Model Overview
Nosofsky's Generalised Context Model (1984, 1986) elucidates exemplar theory's mechanisms involving attributes for classification.
Page 25: GCM Decision Making
Decisions are made based on the probability that an exemplar belongs to a category by comparing similarities among group memberships.
Page 26: Prototype Model Relations
Similarity computations compare new instances to the category's prototype rather than all exemplars collectively.
Page 27: Typicality and Classification
Exemplar theory can easily account for variations in typicality and category boundaries, thus explaining much of human cognition in categorisation.
Page 28: Further Research on Prototypes
Evidence supporting prototype theory includes tasks showing a direct correlation between item similarity to prototypical representations.
Page 29: Summary of Findings
The dialogue surrounding category learning emphasizes contrasting exemplary and prototypical theories, with implications for understanding cognitive flexibility and categorization depth.
Page 30: Implications for Learning
The likelihood of individuals grasping strict definitions or sets of features is low, highlighting the propensity towards prototype and exemplar approaches in cognitive processing.
Page 31: Suggested Reading
Murphy, G. (2002). The Big Book of Concepts. Cambridge: MIT Press. Available in the library for deeper understanding.