Memory for General Knowledge and Concepts and Categorization

Semantic vs. Episodic Memory Memories for specific events that you yourself were involved in Semantic Memory: General knowledge base Lots of different ways of organization info, each with different predictions Tulving proposed LTM should be divided into two memory types Episodic memory: being able to travel back in time and experience something consciously again, temporally dated (which means that an event happened before, after, or at same time as another event) Semantic: info arranged according to meaning

Memory for General Knowledge Definitions of words Verb tenses Arithmetic facts Historical facts Scientific facts Geography facts All examples of info/knowledge that is general, not specific to yourself Not personal, you don’t remember when you learned it, not specific memories Amnesia example: Gene, damage to frontal/temporal lobes, including left hippocampus cannot remember events from life, but can remember facts (name of high school) Another example: woman w/ damage to front temporal lobe, can remember events but not facts (color of a mouse, where to find soup)

The Semantic Memory Models Hierarchical Semantic Network Model Cognitive economy Breathes air Mammal Live young pointers First models of semantic memory build in 70s when information-processing models prominent, and amount of memory available to computers was limited, so researchers concentrating on how to conserve memory space Think of this and following models as a library, each piece of info is a book, that has a storage place, these places called nodes One idea: reduce redundant information Cognitive economy, you store info for fact at highest level of memory possible Nodes arranged in hierarchies So, prediction: if memory organized like this, it should be faster to verify info closer to activated node than info farther from active node “Bernese mountain dog has an exuberant personality.” “Bernese mountain dog has live young.” Dog Four paws Node Tail Bernese Mountain Dog Black, white, rust fur Exuberant

The Semantic Memory Models Hierarchical Semantic Network Model Problems with model Hierarchy? Animal If memory was arranged in a hierarchy, first sentence should be verified faster than second sentence but that does not happen, second is faster Mammal “A pig is a mammal.” “A pig is an animal.” Pig

The Semantic Memory Models Hierarchical Semantic Network Model Problems with model Typicality effect “A robin is a bird.” Bird Robin Turkey If hierarchy, two sentences should be processed at same time robin is faster “A turkey is a bird.”

The Semantic Memory Models Feature Comparison Model Features Defining Characteristic Memory of a word/concept has a set of ‘features’, a list of features Two types: - must be present - usually present, but not always This is how it works: “a robin is a bird” 1. feature lists activated (for both words, for both types of features) 2. lists scanned 3. decision: overlap? 4. if not conclusive, defining features only compared more closely Explains typicality effect (robin has more “bird” features than turkey Explains fast rejection of a sentence (no overlaps) Explains category size effect (which is, that a word can be subordinate to another word/concept; faster to confirm a sentence that has words from smaller, lower categories than more abstract ones) – ex. a collie is a dog, a collie is an animal (assume lists being compared, smaller category has more to do with dog than with animal) Explains “hedges” (something is sort of but not exactly like another thing, bat, bird) (hedge possible b/c using characteristic features but not defining features) However, arguments that maybe defining features don’t even exist

The Semantic Memory Models Other Network Models Elaboration of network model Main diffs: if one node activated, related nodes activated similar words close together, and have many connections connections between nodes can have diff strengths or weights, activation diminished if words less related no hierarchies, no cognitive economy However, it is difficult to think of data that would falsify model Depiction of spreading activation. Once the node for “bread” is excited, the activation travels to related nodes.

The ACT model (Adaptive Control of Thought) The Semantic Memory Models Other Network Models Does not use semantic/episodic distinction Instead, divides memory into three parts (ignore arrows for moment) working memory (info system currently using) declarative memory (info that can be recalled as words, pictures or symbols, ex. knowledge, facts, info, ideas) procedural memory (info about actions and consequences of actions) (telephone number example, having to visualize make movements) Main diff from other models: there are different kinds of nodes (spatial, abstract, procedural) This model meant to show what happens in general cognition, The ACT model (Adaptive Control of Thought)

The Semantic Memory Models Other Network Models The ACT model production rules: - goal - one or more conditions - one or more actions to take Production rules activated like other nodes, …

The Semantic Memory Models Connectionists Model Very different from other models It proposes that there are no “nodes” Instead there are processing units (neurons), and a fact is encoded as a pattern of activation of these different units Each unit starts out neutral, with neutral connections b/t units, over time/learning, brain learns that when one unit activated, certain other ones should be as well

Schemata Scripts

Implicit vs. Explicit Memory Process Dissociation Framework

Why do we need to know about categories such as “cars,” “people,” “mountains,” and “birds” in order to make sense of our experiences? Do people in different cultures categorize objects in the same way?

Theoretical Descriptions of Concepts Classical View

Theoretical Descriptions of Concepts Prototype View

Theoretical Descriptions of Concepts Exemplar View

Theoretical Descriptions of Concepts Schemata View

Theoretical Descriptions of Concepts Knowledge-Based View