Knowledge. 1. Semantic (Associative) Memory. 2. Concepts & Categories.

Transcription

1 Knowledge 1. Semantic (Associative) Memory. Measuring Semantic Memory. The Hierarchical Semantic Activation Model. The Spreading Activation Model. Connectionist Models. Hebb's Law & Hebbian Circuits. 2. Concepts & Categories. C&C's defined. Why are C&C's important? Implicit Learning. Four key observations. The process of categorization: Four theories.

2 Semantic vs. Episodic Memory Semantic & Episodic Memory are both forms of declarative knowledge. Episodic - Autobiographic events; includes info about place & time; details; "source". Was "butterfly" on the study list? Semantic - Facts about the world that are not tied to specific events. Aka. General knowledge; Associative memory*. Is a butterfly a bird?

3 Semantic Memory Three major questions define research on semantic memory (knowledge): 1. How is knowledge represented & organized? Representation: Words? Sensations? Actions? Organization: Hierarchical? Associative Strength? 2. What processes are involved in the acquisition and application of knowledge? Activation/Retrieval & Decision Processes. 3. How is knowledge stored in the brain? Areas involved? Local or Distributed? Category vs. Property-based? Embodied?

4 Measuring Semantic Memory Association* Tasks. Free-association (cf. Freud): Party -? Category Association: Fruit -? Word gen. tasks: e.g., Verbal Fluency (FAS) test. Naming & Lexical Decision Tasks (RT). Picture Naming. Attribute Generation. Word Naming: "hint" vs. "pint"; "flag" vs. "fugue". Semantic Priming in LD: e.g., Doctor > Nurse. Sentence-/Category-Verification Tasks (RT). Category: Is a cat a mammal? Property: Does a cat have claws?

5 Sentence Verification Data Collins & Quillian (1969)

6 Hierarchical Semantic Network Model Collins & Quillian (1969) Note how this model is hierarchical. Note how this model exhibits cognitive economy.

7 Problems with a strict hierarchy [ < & > refer to Reaction Time (RT) results] Frequency effects. A dog is an animal < A dog is a mammal. Faster RT despite higher level in hierarchy. Typicality effects. A robin is a bird < A penguin is a bird. Verification faster for more typical exemplars. Negative-judgment effects. A canary is a robin > A canary is a tulip. Verification faster for more distant associations.

8 Spreading Activation Model Collins & Loftus (1975) Node: A representation of a concept; a pattern of neural activity associated with a thing or idea. Connections: Associations between concepts, with distance = strength of association. Spreading Activation: Activity in one node spreads to others, increasing their activation. Assumptions: Associative, not hierarchical; Activation decreases with time & distance.

9 Neural Basis of Associative Memory Hebb's (1949) Law. if Neuron A repeatedly helps fire Neuron B, the connection between them will be strengthened. neurons that fire together wire together Long-Term Potentiation (LTP). Selective strengthening of synapses. Increased excitability of pre & post-synaptic neuron. Growth of new dendritic connections.

10 Hebbian Circuits neurons that fire together wire together The Neural Basis of Associative/Semantic Memory Doggie Woof

11 Spreading Activation Model nodes, connections, distance, spreading activation In addition to semantic priming, this model can nicely explain frequency, typicality, & negative-judgment effects.

12 Connectionist Models (aka. Neural Networks; Parallel Distributed Processing) Units: Neuron-like processing nodes that take on values. Connections: The links between units (cf. axons); connections have (+/-) strngths or "weights" that determine how a unit is affected by activation. Hidden Units: Units that have no connection with the outside world (cf. interneurons). Key features of PDP networks are that knowledge is (a) distributed rather then local; and (b) embedded in the links between nodes, rather the being directly represented. As well, PDP nets are good at (c) completing partial/messy patterns, (d) satisfying multiple constraints, and (d) generalizing to new (untrained) stimuli. Finally, (e) they show "graceful degradation" the ability to still function when damaged (when units are removed or "injured").

13 The Neural Bases of Semantic Memory What does it mean to "know" something? Research on the neural bases of knowledge examines (a) knowledge deficits in brain-damaged patients; & (b) neural activity during knowledge tasks. Much of this research suggests that knowledge is widely distributed across different areas of the brain.

14 The Neural Bases of Semantic Memory Silently naming pictures of tools vs. animals produces very distinct patterns of neural activation (suggesting distinct semantic representations). Similar results (distinctive pattern of activity) have been found with faces (the "FFA"), houses ("PPA"), musical instruments, etc.

15 The Neural Bases of Semantic Memory Amnesic performing semantic task Left prefrontal cortex is often highly active during verbal semantic tasks.

16 Knowledge 1. Semantic (Associative) Memory. Measuring Semantic Memory. The Hierarchical Semantic Activation Model. The Spreading Activation Model. Connectionist Models. Hebb's Law & Hebbian Circuits. 2. Concepts & Categories. C&C's defined. Why are C&C's important? Implicit Learning. Four key observations. The process of categorization: Four theories.

17 Concepts and Categories Defined Category - A class or collection of things grouped together on the basis of one or more common properties. objects (animals, dogs, collies, cars, etc.) events (games, parties, arguments, wars, etc.) categories can also be "ad-hoc" (hairy things, fast things, etc.) or "goal-driven" (things to take on a camping trip, things to say to scared child, etc.). Categories can be based on perceptual, biological, or functional properties. Categories as the primary groupings by which we think, communicate, & learn.

18 Concepts and Categories Defined Concept A general idea or understanding about something; the mental representation of a category. concepts as the fundamental units of semantic/ associative memory (i.e., general knowledge). Hebbian circuit for "Dog" Semantic/Associative Network

19 What does it mean to "have" a category? The primary evidence that a person has (understands) a concept is that they can (a) positively classify (include) instances as members of a category; and (b) negatively classify (exclude) instances as not members of a category.

20 Pattern recognition is an act of categorization

21 Types of Categories 1) Nominal categories are artificial (man-made), logical, and well-defined (e.g., letters of the alphabet, geometric shapes, positive integers). 2) Natural categories are those that occur in everyday life. They are often, but not always, "given" by the world and are ill-defined (e.g., dogs, bachelors, games, furniture, running, crying, etc.). 3) Ad-hoc categories are a special case of natural categories and refer to things that satisfy a particular purpose or goal (e.g., things to take on a camping trip, things to save if your house is on fire, things to talk about on a first date, green things).

22 Why Are C&C's Important? 1. Reduce complexity. 50 birds become "a flock"; 50 people, "a crowd". 2. Allow for rapid identification & action. "A shark! Get out of the water". 3. Reduce the need for learning. "I'll bet that shark has sharp teeth!" 4. Provide the foundation for new learning. "although technically a fish, many sharks use a reproductive strategy highly similar to mammals".

23 Implicit Learning The incidental learning of complex information (such as statistical regularities) in the environment. "The process by which knowledge about the rulegoverned complexities of the stimulus environment is acquired independently of conscious attempts to do so" (Reber, 1989, p. 219). Two major paradigms for studying implicit leaning: 1. Serial Pattern Learning. 2. Artificial Grammar Learning.

24 Serial Pattern Learning Subjects respond to a series of lights by pressing matching buttons Unbeknownst to the subjects, there is an underlying pattern to the sequence of lights (and thus to their responses). Example: 3, 1, 4, 1, 3, 2, 3, 1, 4, 1, 3, 2, 2, 4, 3, 4, 1, 3, 2, etc. Implicit serial pattern learning in dyslexic (circles) and control children (squares). As the task progresses, subjects respond faster to the items in the sequence, despite not knowing there was a sequence, or not being able to explicitly reproduce it. Additional evidence for implicit learning is provided by including random sequences* after learning has occurred; evidence for implicit learning is shown by a slow-down on this block of trials.

25 Artificial Grammar Learning Subjects are exposed to a set of letters strings created by a finite state grammar. Examples: TPTS, TTS, VVPS, VXVS, TTXXVS, VXVPXVS, TPTXVPS, etc. After study, subjects are told that the letter strings were based on an underlying set of rules (a "grammar"). A typical finite state grammar. When asked to describe the rules, few can. However Subjects can discriminate between letter strings that do vs. do not correspond to the underlying grammar, even if the specific test strings were never shown in the exposure phase.

26 Knowledge 1. Semantic (Associative) Memory. Measuring Semantic Memory. The Hierarchical Semantic Activation Model. The Spreading Activation Model. Connectionist Models. Hebb's Law & Hebbian Circuits. 2. Concepts & Categories. C&C's defined. Why are C&C's important? Implicit Learning. Four key observations. The process of categorization: Four theories.

27 The Difficulty of Defining Common Things What is a game? What makes hide-and-seek a game? (a) played by children; (b) done for fun; (c) has rules; (d) involves more than one person; (e) is in some ways competitive; (d) done during periods of leisure. As first noted by Wittgenstein, some things are less defined by definition than by family resemblance.

28 The Ease of Defining Uncommon Things Consider the category Things to save from your burning house Which of the following go into that category? As first noted by Basalou, many categories are "ad hoc" defined by goals or temporary needs.

29 Typicality Effects The finding, in numerous paradigms, that more "typical" members of a category hold an advantage over less typical members ,447

30 The Flexibility & Stubbornness of Categories 1. Is it possible to transform a toaster into a coffee maker?? 2. Is it possible to transform a skunk into a raccoon??

31 Four Key Observations about Concepts and Categories 1. Despite categorizing thousands of concepts, people find it difficult to say how they do that, or describe the info that defines a category. 2. People easily & rapidly create new (ad hoc) categories and use them appropriately. 3. People see some members of a category as better or more typical than other members, even when the category is defined "by rule". 4. People believe that (some) categories have an underlying essence (deep structure) that is more important than any perceptual features.

32 Knowledge 1. Semantic (Associative) Memory. Measuring Semantic Memory. The Hierarchical Semantic Activation Model. The Spreading Activation Model. Connectionist Models. Hebb's Law & Hebbian Circuits. 2. Concepts & Categories. C&C's defined. Why are C&C's important? Implicit Learning. Four key observations. The process of categorization: Four theories.

33 How do we categorize objects and events? 1. Defining Features (rule-based categorization). only works well for well-defined/artificial categories (e.g., triangle; an "ace" in tennis).

34 Categorization The fuzzy nature of categories... Do you know what a "bachelor" is? Bob's an unmarried man, but has been living with the same girl for 20 years. They are happy and Bob has no intention of changing his living situation. Is Bob a bachelor? James is an unmarried man with no girlfriend (or boyfriend). He's also a monk, living in a monastery, and has taken a vow of celibacy. Is James a bachelor? Robert's a married man, but has been separated from his wife for 10 years. He's actively dating, with no intention of ever remarrying. Is Robert a bachelor?

35 Categorization The fuzzy nature of categories... Identify features of the following categories... That would include: But not include: Table: coffee or display table bed, stool, counter Bottle: pill or baby bottle jar, glass, carton Dog: Chihuahua, Greyhound wolf, coyote Furniture: phone, beanbag porch swing, car seat Fruit: Strawberry, Tomato olive, almond, squash

36 How do we categorize objects and events? 1. Defining Features (rule-based categorization). 2. Resemblance (similarity-based categorization). No specific feature(s) that all members have; however, all members share a subset of relevant features, creating a family resemblance. (a) Prototypes - an average of (abstraction across) many different instances (exemplars) of a category. (b) Exemplars - specific instances of a category.

37 Prototype View Concepts are represented as prototypes a summary or average of the features that make up instances of the category (based on family resemblance). Prototypes have "fuzzy boundaries", thus making categorization somewhat ambiguous ("graded membership").

38 Evidence for Prototypes Typicality effects Picture identification (Rosch, Mervis, et al., 1976). Sentence verification (Smith, Rips, & Shoben, 1974). Shown a series of sentences and have to indicate whether they are true or false. Items close to the An apple is a fruit. prototype are more A potato is a fruit. quickly identified A bat is a bird. as members categorizing as A German Shepherd is a dog. judging similarity A fig is a fruit. to prototype. A St. Bernard is a dog.

39 Evidence for Prototypes Typicality effects Picture identification (Rosch, Mervis, et al., 1976). Sentence verification (Smith, Rips, & Shoben, 1974). Exemplar generation (Barsalou, 1983; 1985). Name 5 articles of clothing. Name 5 examples of furniture. Name 5 types of weapons. Items close to the prototype are the earliest and most likely to be mentioned in these production tasks.

40 Evidence for Prototypes Typicality effects Picture identification (Rosch, Mervis, et al., 1976). Sentence verification (Smith, Rips, & Shoben, 1974). Exemplar generation (Barsalou, 1983; 1985). Explicit judgments of cat. membership.

41 Evidence for Prototypes Typicality effects

42 Evidence for Prototypes Typicality effects Picture identification (Rosch, Mervis, et al., 1976). Sentence verification (Smith, Rips, & Shoben, 1974). Exemplar generation (Barsalou, 1983; 1985). Explicit judgments of cat. membership. Induction (extrapolation to novel instances). X

43 Prototypes Name these items.

44 Prototypes How are they organized in semantic memory? Prototypes have a hierarchical structure: (Eleanor Rosch) Superordinate: Fruit Basic: Banana Grape Apple Melon Subordinate: Granny Smith Golden Fuji etc... Basic level categories have special status: (1) best balance of discriminability and info overload; (2) used most often in discourse; (3) develop first in children; (4) come to mind 1 st & fastest; (5) central in memory errors.

45 Exemplar View Concepts are not abstract averages, but are made up of memory for prior encounters with specific objects and events. Emphasis on basic memory mechanisms (e.g., ES, TAP) and the variability with which people process things for specific purposes. Abstract knowledge is computed "on-line",. rather then pre-computed (as with prototypes).

46 Evidence for Exemplars Typicality effects. Availability of info about variability. Availability of info about correlation. Context-sensitivity of categorization. Ad-hoc & goal-driven categories. Effects of specific exemplars on categorization.

47 How do we categorize objects and events? 1. Defining Features (rule-based categorization). only works well for well-defined/artificial categories (e.g., triangle; an "ace" in tennis). 2. Resemblance (similarity-based categorization). Prototypes - an average across many different instances ('exemplars') of a category. Exemplars - specific examples of a category. 3. Explanation (knowledge-based categorization). used to explain the deep structure of concepts or categories, based on essential features and implicit theories about how things are related.

48 Explanation View Concepts as implicit theories, created by "naïve scientists" (us!) trying to understand the world. Concepts and categories as based on a complex web (neural network) of knowledge. For this view, exemplars are to concepts as data are to scientific theories. Knowledge of purpose & history as critical. Psychological essentialism: The (naïve?) assumption that things have an underlying nature that make them what they are.

49 Evidence for Explanation The ease with which we deal with bachelors, drunkenness, etc.. Transformed skunks and toasters. Mutilated lemons. Counterfeits.

50 Categorization All four theories are probably correct... Defining features. Artificial categories; categorization "by rule". Exemplars. Heuristic strategy for novel & distinct things. Prototypes. Heuristic strategy for well-learned things. Explanation. Analytic strategy for complex things/situations.