+ Chapter 6 Semantic Memory and Stored Knowledge

+

Chapter 6Semantic Memory and Stored Knowledge

+Semantic vs. Episodic Memory

Declarative memory for general knowledge and facts lacking reference to the episodic context in which it was learned.

Examples: World knowledge Vocabulary Rules, formulae, and

algorithms

“Knowing awareness”

Memory for specific events in context

Comes with a sense of reliving the event Called conscious recollection

or “Mental time-travel”

“Self-knowing”

Semantic Memory Episodic Memory

Subjective Differences

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While all anterograde amnesics have profound deficits in episodic memory, most have only minor (if any) semantic impairments: Spiers, Maguire, and Burgess’s (2001) reviewed 147 cases. Vargha-Khadem’s (1997) patients, Jon and Beth (impaired as

children but developed normal semantic memories).

Patients with retrograde amnesia often have a selective deficit in either episodic or semantic memory Episodic impairment with spared semantic memory:

Tulving’s (2002) patient, KC (intact pre-trauma semantic memory) Semantic impairment with spared pre-trauma episodic memory:

Yasuda, Watanabe, and Ono’s (1997) patient

Are they really distinct?Evidence from Neuropsychological Dissociations

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+Anterograde vs Retrograde Amnesia

Impaired memory for events after the trauma

Impaired memory for events before the trauma

Retrograde

Anterograde


Different brain areas are activated for semantic and episodic memory tasks (Wheeler et al., 1997) During memory encoding:

More left prefrontal cortical activity for episodic tasks than semantic.

During memory retrieval: More right prefrontal cortical activity during episodic memory

retrieval than semantic.

This also suggests that episodic and semantic memory are different types of memory.

Neuroimaging Dissociations

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+Storing Simple Concepts

Studying the effectiveness of different cues can reveal how concepts are stored in semantic memory. Which cues lead to faster retrieval of the word “PINAPPLE”?

Initial letters make for better cues than terminal ones: A FRUIT beginning with “P”? A FRUIT ending with “E”?

Category cues presented before letter cues show faster retrieval: A FRUIT beginning with “P”? A word beginning with “P” that is a FRUIT?

This probably happens because smaller, better-defined categories (e.g. FRUIT) are a more useful starting point than larger, less coherent ones (e.g., words beginning with “P”). This works best with established categories, not for novices (e.g,

Think of a developmental psychologist named “P…”.

Loftus, 1992

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+How is information organized in semantic memory?

The textbook compares two models: Hierarchical organization model. Spreading activation network model.

The spreading activation model better explains some of the phenomena that are problematic for the hierarchical model: Familiarity effect Typicality effect – membership in categories is graded (some items

are better examples of the category than others are). More typical and more familiar items are recalled faster.

+Conceptual Organization

Semantic memory is organized into a series of hierarchical networks. Major concepts are represented

as nodes. Properties/features are

associated with each concept. Cognitive economy rules:

Property information is stored as high up the hierarchy as possible to minimize redundancy.

Hierarchical Network Model (Collins & Quillian, 1969)

Examples:• Nodes: Animal; Bird; Fish; Canary• Features: Has wings; Is dangerous

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Hypothesis: Sentences should take more time to process

as the number of levels between the tested concept and the features increases.

Task: Participants had to quickly verify the veracity

(truth) of statements.

Results: Responses to true statements become

slower as the separation between the subject and property becomes greater.

Conclusion: Unless information is directly linked/stored

with a concept in semantic memory, we infer the answer from properties of higher nodes.

Making more inferences slows verification.

Hierarchical Network Model

Examples:• Fast: “A canary is yellow”• Slow: “A canary can fly”

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http://www.youtube.com/watch?v=kpfsYIN9cZ8


Problems with the hierarchical network model: Hierarchical distance is often confounded with familiarity

Controlling for familiarity greatly reduces the hierarchical distance effect. (e.g., “A canary has skin” is not a familiar sentence.)

Typicality effect: Verification is faster for more representative member categories, independent of hierarchical distance (Rips, Shoben, & Smith, 1973). e.g. “A PENGUIN is a bird” is slower to confirm than “A CANARY

is a bird”. Typical items have more commonalities with other items in their

category than atypical items. Categories are fuzzier than Collins and Quillian believed.

Hierarchical Network Model

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+Categorization of Foods

Is a pumpkin a fruit?

16 said yes, 14 no

8 changed their minds a week later

+Categories are organized by dimensions relevant to item properties.


Assumes semantic memory is organized by semantic relatedness/distance Measures of relatedness:

“How related are these two words (e.g. BIRD–CANARY)?”

“What examples of BIRDs can you think of?” The more people that

come up with a particular member (e.g. CANARY), the more related they are

Spreading Activation Model (Collins & Loftus, 1975)

Shorter lines represent greater relatedness

From Collins and Loftus (1975). Copyright © American Psychological Association. Reproduced with permission.

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Whenever a person encounters or thinks of a concept, that node is activated. Activation then spreads to related concepts.

Spreading activation decreases as it gets further away from the original point of activation (i.e. weakly related items receive less spreading activation). Explains the typicality effect Predicts semantic priming:

A semantically-related word facilitates the processing/identification of a target word

e.g. It is faster to say “BUTTER” is a real word if preceded by “BREAD” instead of an unrelated word like “NURSE” (Meyer & Schvaneveldt, 1976).

Spreading Activation Model

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Task: Participants presented with a list of words (e.g. NURSE,

SICK, HOSPITAL, and PATIENT), all of which are related to a missing target word (e.g. DOCTOR).

Participants are then asked to recognize words they saw before, including the missing word.

Prediction: Activation should spread from all the presented words to the

related word (DOCTOR), participants mistakenly recognize having seen the target word before.

Deese–Roediger–McDermott (DRM) Paradigm

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Behavioral Results: Participants tend to mistakenly recognize the missing target

word.

Neuroimaging Results (Schacter et al., 1996) The pattern and intensity of brain activity was the same for

correctly recognized items as for incorrectly identified, related words.

Deese–Roediger–McDermott (DRM) Paradigm

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The spreading activation model is more flexible than the hierarchical network model. Pros of flexibility:

The spreading activation model can account for more empirical findings.

Cons of flexibility: The flexibility also reduces the specificity of the model’s

predictions, making the spreading activation model more difficult to test.

Evaluating the Models

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+Organization of Semantic Memory in the Brain

Semantic memories are represented in the brain as whole objects. Each object/concept has its

own node. e.g. There’s a special neuron

representing your grandmother.

Types of nodes are grouped together (e.g. all living things).

Most evidence suggests that this is not the case.

Different kinds of information about a given object are stored in separate brain regions. e.g. Visual information is

stored in one part of the brain, while the audio linked with that object is stored in another.

This view is becoming increasingly popular.

“Grandmother” Cell Hypothesis Feature-Based Approach

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Neuroimaging Methods: If thinking about different aspects (visual or auditory) of an object

activates the same brain region: It would support the grandmother cells hypothesis

If doing so activates distinct brain regions: It would support the feature-based approach

Neuropsychological Methods: Study individuals with different category-specific deficits and compare

the regions of brain damage. If people with damage to one part of the brain have difficulty

identifying living but not nonliving objects, while other patients with damage elsewhere show the opposite pattern: It would seem to support the feature-based approach

Testing the Approaches

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Recognition Deficit Specifically for:

Brain Region Damaged

Living Objects Anterior, medial, and inferior parts of the temporal lobes

Nonliving Objects Posterior frontal-parietal regions

Neuropsychological Findings (Gainotti, 2000)

Patients tend to have a specific deficit for living objects (80%) rather than for nonliving objects (20% of patients). The nonliving objects were generally more familiar than the living objects.

When familiarity was matched, many patients still showed a living object impairment (Caramazza & Shelton, 1998).

Suggests that concepts are stored in different parts of the brain, supporting the feature-based approach. However, this is NOT necessarily true – there are alternative

explanations.

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Assumptions: Living things are distinguished based on visual/perceptual features (i.e.

what they look like). Nonliving things are distinguished based on functional properties (i.e.

what they’re used for). Based on dictionary entries, there are three times as many visual

descriptors (units) as functional descriptors in the semantic system.

Using a computational model with visual and functional “units”: Damaging the visual units impaired recognition more for living things. Damaging the functional units impaired recognition only for nonliving

things. The theory can potentially explain the neuroscience findings.

Sensory–Functional Theory (Farah & McClelland, 1991)

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Type of Processing Brain Regions with the Strongest ERP Signature at 400 ms:

Visual Fronto-central and anterior–inferior

Functional Occipital, posterior–temporal, and posterior–parietal

Support for the Sensory–Functional Theory

Sitnikova et al. (2006): Sensory–functional theory predicts that:

Viewing living things should produce a visual-processing ERP signature

Viewing nonliving things should produce a feature-processing ERP signature

Results were as predicted.

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According to the sensory–functional theory, the distinction is between sensory and functional properties, not between two different categories of living vs nonliving objects. Therefore, perceptual information should be processed in the same

brain region, regardless of whether the object is living/nonliving, as should functional information (in a different region). Lee et al. (2002) and Marques et al. (2008) provided

neuroimaging evidence in support of this: Perceptual processing of both living and nonliving things:

Left posterior inferior temporal lobe regions Functional processing of both living and nonliving things:

Middle temporal regions

Support for the Sensory–Functional Theory

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Two properties (sensory and functional) may be too simplistic a distinction to define all categories. Many properties of living things aren’t easily defined as either.

Cree and McRae’s (2003) Multiple Feature Approach: Farah & McClelland argued for a sensory property and a functional

property (what it does or is used for). Cree & McRae subdivided these two categories into parts:

The sensory property is divided into visual, auditory, taste, and tactile sensations

The functional property is divided into: Entity behaviors (what a thing does) Functional information (what humans use it for)

Limitations of the Sensory–Functional Theory

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Cree and McRae (2003) identified seven empirical patterns of category-specific deficits that their approach can explain:

Table from Smith and Kosslyn (2007). Electronically reproduced by permission of Pearson Education, Inc., Upper Saddle River, New Jersey.

Cree and McRae’s (2003) Multiple Feature Approach

Deficit Pattern Shared Properties

Multiple categories consisting of living creatures

Visual motion, visual parts, color

Multiple categories of nonliving things Function, visual parts

Fruits and vegetables Color, function, taste, smell

Fruits and vegetables with living creatures Color

Fruits and vegetables with nonliving things Function

Inanimate foods with living things Function, taste, smell

Musical instruments with living things Sound, color

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The multiple feature approach predicts that each property is stored in a separate region of the brain Evidence in favor of this notion comes from Martin and

Chao’s (2001) neuroimaging study: Category knowledge about color, motion, and shape

activates different brain regions. Generally, these regions map onto the brain areas

responsible for processing that type of visual information.

Any category with a similarly damaged region of the brain will be impaired.

Evidence for the Multiple Feature Approach

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Strengths: Recognizes that most concepts consist of several properties, not

just two. Accounts for the different patterns of deficits in brain-damaged

patients. Consistent with neuroimaging findings suggesting that different

object properties are stored separately.

Still missing: A complete explanation as to how the different properties of a given

object are integrated rapidly and automatically when needed.

Evaluation of the Multiple Feature Approach

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+Learning New Concepts

Learning in one context does not typically transfer (i.e. generalize) to other contexts, unless the two situations closely resemble each other. According to Barnett and Ceci (2002), transfer is less likely if the two

contexts differ in any of six ways: Knowledge domain. Physical context (different environments). Temporal context (different times). Functional context (different purposes). Social context (alone vs. group). Modality (e.g. visual presentation vs. auditory presentation).

Transfer

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+Learning New Concepts

Factors promoting generalization: Slower and deeper learning (Schmidt & Bjork, 1992).

Rapid learning is often superficial and specific to the context. The outline with a different organization promoted more creativity.

Exposing learners to a varied range of examples. However, it is easier/faster to learn a concept when the examples

are all highly consistent. A hybrid approach is best to balance efficiency and transfer:

Initial training should focus on consistent examples; once the concept is learned, additional examples from varied contexts should be presented.

See Nitsch’s CRINCH and MINGE study (waitress & cowhand)

Transfer

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+Crinch ExampleCRINCH – to make someone angry by performing an inappropriate act, originally used by waitresses.

Varied Context Consistent Context

1. When a man does not remove his hat on entering a church.

2. When a spectator at a public event blocks the view of those behind.

3. When someone flicks cigarette ash over a polished table.

4. When diners complain about slow waitress service.

1. When a diner fails to leave a tip.

2. When diners argue about the prices on the menu.

3. When a diner deliberately spills ketchup.

4. When diners complain about slow waitress service.

Hybrid approach – present the consistent examples first, then the varied context examples.

+Schemas

Schemas: Well-integrated chunks of knowledge about the world, events,

people, or actions that strongly influence memory. Schemas serve three purposes:

They allow us to form expectations -- violations of expectations are often memorable and distinctive.

They allow us to draw inferences and fill in gaps when reading or listening to others speak, enhancing understanding.

They allow us to identify indistinct visual images by supplying context.

Schemas in semantic memory include: Scripts -- knowledge about events and consequences of events. Frames -- knowledge structures referring to some aspect of the

world containing fixed structural information and slots for variable information.

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+A Typical Restaurant Script

73% of respondents reported these common events when going to a restaurant: Sit down Look at menu Order Eat Pay bill Leave

48% also included: Enter restaurant Give reservation name Order drinks Discuss menu Talk Eat appetizer Order dessert Eat dessert Leave a tip

Bower, Black, and Turner (1979)

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+Schemas and Visual Scenes

The probability of identifying an object is facilitated when it is in an expected context and inhibited when the context is inappropriate.

Contextual match condition: Kitchen–Bread

No context condition: No context–Mailbox

Contextual mismatch condition: Kitchen–Mailbox

Palmer (1975)

Easier

Harder

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+Schemas

There are costs associated with the use of schemas: Bartlett (1932) presented folk tales, such as The war of the ghosts

to students and then asked them to recall the story. Students tended to recall stories that were:

Shorter More coherent More closely associated with the students’ own perspectives

Especially when their expectations were incompatible with the story.

Bartlett concluded that intrusions of schematic knowledge caused systematic errors in recall.

Errors and Distortions

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+Tests of Bartlett’s Theory

Hypothesis: Schema-driven errors are more likely at longer retention intervals

because schematic information is more durable than rote recall.

Task and Results: Presented all participants with the same story about a dictator,

whose name was either: Gerald Martin (an unknown) or Adolf Hitler (someone with associated semantic knowledge).

Asked participants whether they remembered reading a statement that the dictator “hated Jews,” which did not appear in the story at two delays: Short (5 minutes): No difference between groups. Long (1 week): Participants who read about Hitler were more

likely to incorrectly agree that they had read the statement, having been influenced by schematic knowledge about the real Hitler.

Sulin and Dooling (1974)

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+Schemas in Everyday Life

Studied schema-driven memory errors in incidental memory, by walking participants into a naturalistic setting (an office) In this office were:

Schema-consistent objects (e.g. a desk, calendar, and eraser) Schema-inconsistent objects (e.g. a skull, a toy top)

Missing from this office were some schema-consistent objects (e.g. books)

Participants were then surprised with a test asking them to: First, recall all the objects they could remember Second, recognize items actually in the office from those that were

not

Brewer and Treyens (1981)

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+Brewer & Treyens (1981)

Books and filing cabinets were recalled but were not present in the room.


Results: Participants recalled more schema-consistent than schema-

inconsistent items. True both for items that were present and items that weren’t.

Objects that weren’t present in the room but were recognized with high confidence were uniformly schema-consistent.

Participants recognized more objects than they recalled. Recalled items were most likely to be objects very consistent with

the schema (e.g., typewriter).

Conclusions: Schemas lead to errors in memory. Schemas are often used as a retrieval mechanism to facilitate recall.

Brewer and Treyens (1981)

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People tend to remember information consistent with their own views better than they remember inconsistent information. For people with limited knowledge of a controversial subject:

It is easier to add schema-consistent information to the existing knowledge base.

This is because they don’t have a schema for information contrary to their view.

For people with a more complete understanding of both sides of an issue: It is just as easy to remember information from both sides. This is because they have schematic support for both sides.

Consistency Bias (Wiley, 2005)

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+Schema Theories Evaluated

Successes: Schema theories explain why many memories become distorted. They are strongly supported by evidence showing we possess schemas.

Limitations: The theories tend to be underspecified, as are the schemas. Generally can’t predict when we invoke schemas to draw inferences.

Only participants with high reading skills drew predictive inferences rapidly and automatically (Husband threw vase – infer “break”).

Memory representations are more complex than simple schema theories predict (e.g., fast food restaurants differ from sit-down restaurants).

They predict that we should make more mistakes than we actually do.

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+Disorders of Concept- and Schema-Based Memory

Deficit: Examples:

Semantic Dementia:Difficulty identifying and describing abstract concepts for word meanings.

Patients EP and KE• Find it difficult to identify/describe the meaning of common objects.

• But still able to use the objects normally.

Goal-directed use of schemas and scripts.

Problems ordering/assembling actions within a script, despite being able to name appropriate actions.Associated with damage to frontal lobe regions.

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+Disorders of Schema-based Memory

Semantic and sequencing errors made by patients with semantic dementia, temporo-frontal patients, and normal controls. Data from Cosentino et al. (2006).

Documents

+ Chapter 6 Semantic Memory and Stored Knowledge