Similarity-Based Theories of Object Recognition

These flashcards cover key vocabulary and concepts related to similarity-based theories of object recognition as discussed in the lecture.

Similarity-Based Theories

Theories that suggest category membership is determined by judging how similar an instance is to a relevant concept representation.

Feature Set Theory

Proposes that concepts are represented as sets of semantic features such as defining and characteristic features.

Defining Features

Necessary characteristics that are common to every instance of a concept, which if absent means the instance is not a member of that concept.

Characteristic Features

Typical properties of concepts that are not necessary for membership; instances may or may not exhibit these features.

Stage 1 Processing

Involves a quick, global selection of features for category membership judgments based on similarity.

Stage 2 Processing

A slower, controlled comparison that focuses on defining features to resolve uncertainties in category membership.

Prototype Theory

Suggests concepts are represented as abstract idealized forms or prototypes rather than as specific examples.

Exemplar Theory

States that concepts are represented by all specific examples or instances stored in memory, allowing for categorization based on similarity to stored examples.

TLC Model

Teachable Language Comprehender model, which organizes concepts hierarchically and uses property inheritance to define concept relationships.

Spreading Activation Model

A network model of semantic memory where concept nodes are connected based on semantic relatedness, allowing for individual differences and context influences.

Category Size Effect

The prediction that verification times will be longer for statements about larger categories, as longer search pathways are needed.

Typicality Effect

The phenomenon where more typical category members are verified more quickly than less typical members, even with the same categorization criteria.