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Semantic memory
- Our org'd knowledge abt the world
- vs. episodic memory (info abt events that happend to us)
- knowledge/info
- doesn't mention how we acquired that info
- broad--includes encyclopedia, lang, & conceptual knowledge
- influences most of our cog activities
- categories & concepts = essential components
- allows you to code objs you encounter
- 4 approaches to semantic memory: feature comparison model, prototype approach, exemplar approach, & network models
- elderly perform quite well on tests of semantic memory
- concept-based knowledge unrelated to specific experiences
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Episodic memory
- contians info abt events that happened to us
- always implies a personal experience
- emphasizes when, where, or how the event happened to you
- vs. semantic memory
- memory of autobiographical events (times, places, emotions, contextual knowledge) that can be explicitly stated
- can be thought of as a "map" that ties together items in semantic memory
- inform semantic knowledge & are relient upon semantic knowledge
- "what," "when," "where"
- memory structure that remembers events that are observed thru experience
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Category
semantic memory
- set of objs that belong together
- ["fruit" rep's category of food items]--your cog system treats these objs as being equivalent
- concept = our mental rep of a category; what defines that category
- prototypes = ex's of a category
- tend to have a structure based on family resemblance--members of a category share attributes w/other members of that category
- no single attribute serves as necessary & sufficient criterion for membership in that category
- ppl rely on representativeness when asked to judge category membership
- (feature comparison model) defining features = something that's absolutely essential for that category [dog has to be mammal]
- (prototype approach) prototype = most typical rep of a category
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Concept
semantic memory
- an essential component of semantic memory
- our mental reps of a category--what defines that category
- [concept of "fruit"--your mental rep of the objs in that category]
- even tho objs arent identical you can combine together a wide variety of similar objs by using a single 1 word concept -> greatly reduces storage space b/c many objs can be stored w/same label
- allow you to make numerous inferences when you encounter new ex's from a category which allow us to go beyond given info greatly expanding our knowledge
- very few concepts we use in everyday life can be captured by a specific list of necessary defining features (prob w/feature comparison model)
- no single attribute is shared by all ex's of a concept
- can be unstable & variable--our notions abt ideal prototype can shift as context changes
- (network model) each concept rep'd as a node
- top-down processing emphasizes these
- stored according to necessary characteristics--what is a mammal?
- (PDP approach) concepts rep'd by pattern of activity thru set of nodes
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Feature comparison model
Semantic memory
- concepts are stored in memory according to a list of necessary features/characteristics; ppl use a decision process to make judgments abt these concepts
- features: either defining or characteristic
- sentence verification technique: 1 method to test
- typicality effect
- major prob: very few of the concepts we use in everyday life can be captured by a specific list of necessary defining features
- another prob: assumption the indiv features are indpendent of 1 another
- doesn't explain how members of categories are related to 1 another
- emphasizes we ignore many details that make each item in a category unique--based on similarity btwn an item & list of features necessary for category membership
- category membership is very clear-cut [all unmarried males must be "bachelors"]
- disadvantage: ppl usu can't create list of necessary & sufficient features for a category
- similar to obj recognition--need certain info to identify a concept/memory
- identification of concepts involves comparison of attributes btwn a stimulus & semantic memory
- evidence: longer reaction times for atypical concepts results from smaller overlap in attributes
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Defining features
Feature comparison model
- attributes necessary to the meaning of the item--something that's absolutely essential for that category [dog has to be mammal]
- [a robin: it's living & has feathers & a red breast; "bachelor": male & unmarried]
- vs. characteristic features
- when ppl encounter an atypical item they need to compare the defining features for the item & its category (requires more time)
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Characteristic features
Feature comparison model
- attributes that are merely descriptive but not essential/necessar
- [a robin: it flies, perches in trees, isn't domesticated, small; not all dogs & cats have tails]
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Sentence verification technique
Feature comparison model
- ppl see simple sentences & must consult their stored semantic knowledge to determine whether the sentences are true/false
- ["A poodle is a dog"]
- 1 of major tools used to explore feature comparison model
- ppl usu highly accurate on this task so not useful to compare error rates across experimental conditions--measure reaction times instead
- typicality effect: common finding in reearch using this technique
- faster when item is more typical [quicker to answer "is a canary a bird?" vs. "is an ostrich a bird?" b/c share less features of a bird than a canary does]
- problematic when applied to real life
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Typicality effect
Feature comparison model
- ppl reach decisions faster when an item is a typical member of a category (prototype) rather than unusual member (nonprototype)
- faster to make judgments abt something that's more relevant to the category
- common finding in sentence verification technique
- [decide quickly a carrot is a veggie but pause b4 deciding if a rutabaga is]
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Prototype
Prototype approach
- the item that's most typical & representative of a category; more readily available in memory, rehearsed more often
- sometimes the prototype of a category may not even exist
- an abstract, idealized ex
- have a special, privileged status w/in a category & differ from nonprototypical members in several ways
- supplied as ex's of a category--ppl judge some items to be better ex's of a concept than other items
- if someone asks you to name a member of a category, you'll prob name a prototype
- typicality effect: ppl judge prototypes (typical items) faster than nonprototypes
- ppl supply prototypes more often as ex's & make quicker judgments abt category membership when assessing prototypes
- judged more quickly after semantic priming
- share attributes in a family resemblance category
- not the same as a basic-level category--it's the best ex of a category
- our notions abt the ideal prototype can shift as context changes
- experts construct these in a diff fashion--choose diff & more prototypes than novices
- [prototype of a dog would be an idealized rep of a dog, w/avg size for a dog & avg other features--not necessarily like any particular dog you've ever seen (vs. exemplar approach)]
- tends to have most attributes in common w/other items in the category [prototypical dog = golden retreiver]
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Prototype approach
Semantic memory
Emphasizes we ignore many details that make each item in a category unique - Doesn't explain how members of categories are related to 1 another
- Based on similarity btwn an item & idealized obj that reps the category
- You decide whether an item belongs to a category by comparing that item w/a prototype--if the item is similar to the prototype you include it in the category
- [You conclude a robin is a bird b/c it matches your ideal prototype for a bird]
- Members of a category differ in their prototypicality (graded structure)
- Vs. feature comparison model: it'd argue not all members of "bachelor" category are created = & cousin is more prototypical than nephew/priest
- Advantages: it can account for our ability to form concepts for grps that are loosely structured; can be applied to social rel's as well as inanimate objs & nonsocial categories
- Probs: we often store specific info abt indiv ex's of a category--needs to include mechanism for this; doesn't account for experts
- (Exemplar approach) both say we make decisions abt category membership by comparing new item against some stored rep of the category--if similarity is strong enough we conclude this new item belongs to the category
- May be more suitable when considering a category that has numerous members
- Can be abstract--doesn't have to be something you're familiar with/have experienced
- Instead of going down a checklist you compare 1 to the other
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Prototypicality
- The Prototype ApproachDegree to which members of a category are prototypical
- [A robin & sparrow are very prototypical birds but ostriches & penguins are nonprototypes]
- [Tomato = nonprototypical fruit; elevator = nonprototypical vehicle]
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Graded structure
- Prototype approachCategories tend to have this
- Beginning w/most representative/prototypical members & continuing on thru the category's nonprototypical members
- Shows all members of categories are not created equal
- Ppl tend to have an order of how typical things are ["bird": parrot b4 ostrich]
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Semantic priming effect
- Ppl respond faster to an item if it was preceded by an item w/similar meaning
- [You'd make judgments abt apples more quickly if you'd just seen the word fruit than if you'd seen the word giraffe]
- Helps cog psychologists understand important info abt how we retrieve info from memory
- Priming facilitates responses to prototypes more than to nonprototypes
- Basic-level names are more likely to produce this effect
- If you have a task where you're filling in a word in a sentence ["Joe caught some ____ when he was hunting today"] & showed Bambi b4, the word "deer" is primed to put into that sentence
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Family resemblance
- No single attribute is shared by all ex's of a concept, but each ex has at least 1 attribute in common w/some other ex of the concept
- Most prototypical item usu has largest # attributes in common w/other items in category
- [Hairless dog may not have hair but has other things similar to other dogs even tho that 1 feature is diff]
- Doesn't mean all items w/in a concept have to be defined by 1 universal feature--but certain things will be common throughout the category
- Characteristics will be diff but won't change category membership
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Superordinate-level categories
Prototype approach
Higher-level/more general categories; broad classification - ["Furniture," "animal," "tool"]
- Produced slower than basic-level
- Activates prefrontal cortex (processes lang & associative memory)
- Too general for semantic priming
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Basic-level categories
Prototype approach
Moderately specific--category that's neither too general/specific - ["Chair," "dog," "screwdriver"]
- Not the same as prototype
- Seem to have special status--more useful than superordinate or subordinate levels; used more often
- Used to identify objs--gives enough info w/o being overly detailed
- Ppl produce these names faster than superordinate/subordinate names
- More likely to produce semantic priming effect
- Novices prefer these terms (vs. experts)
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Subordinate-level categories
Prototype approach
Lower-level/more specific categories - ["Desk chair," "collie," "Phillips screwdriver"]
- Produced slower than basic-level--when ppl see these terms they freq remember basic-level version later
- Activates part of parietal region of brain (shift visual attention)
- Experts prefer terms even more specific than these
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Expertise
- Shows consistently exceptional performance on representative tasks in a particular area
- Deliberately challenge themseleves & practics tasks in their specific area
- Must have practiced for at least 10yrs in their area to qualify as an expert
- Construct prototypes in a diff fashion--diff level of categorization (vs. novices)--very specific terms (even more specific than subordinate)
- [Someone really into cars has more prototypes for cars than someone who doesn't know much abt them]
- Concentrate study time on more challenging material
- Have top-down processes that allow them to perform well on many diff components of prob solving in their area of expertise
- Memory skills tend to be very specific [expert chess players--memory for various chess positions]
- More likely to use means-ends heuristic effectively when they encounter a novel prob in their area--divide probs in several subprobs & solve in specified order
- (Analogy approach) more likley to emphasize structural similarity btwn probs not surface (superficial) similarities
- Much faster & solve probs accurately
- More automatic operations & particular stimulus situation quickly triggers a response
- Usu use parallel processing
- Better at monitoring their prob solving
- Domain-specific
- Knowledge base (schemas, etc.) [easier for professor to read a psych textbook than for us--expectations abt material to be read]
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Exemplar approach
Semantic memory
Argues we 1st learn some specific ex's of a concept, then we classify each new stimulus by deciding how closely it resembles those specific ex's - Emphasizes your ex of "dog" would be rep'd by numerous ex's of dogs you've known
- Proposes we don't need any list of featurres b/c all the necessary info is stored in the specific
- exemplars (vs. feature comparison approach)
- We make decisions abt category membership by comparing new item agaisnt stored rep of the category & if similarity's strong enough we conclude new item belongs to category--stored rep is a collection of numerous specific members of the category
- Prob: our semantic memory would quickly become overpop'd w/numerous exemplars for numerous categories--may be more suitable when considering a category that has relatively few members; may be too bulky for some purposes--in many situations it's not effective to use a classification strategy based purely on exemplars
- May coexist w/prototype approach--so a concept includes info abt both prototypes & specific exemplars; require same kind of cog process
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Exemplar
Exemplar approach
Each ex of a concept stored in memory - Take up memory
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Netowrk models
Semantic memory
More concerned abt the interconnections among related items (vs. whether an item belongs to a category) - Propose a netlike org of concepts in memory w/many interconnections
- The meaning of a particular concept [apple] depends on concepts to which it's connected
- Includes: Collins & Loftus network model, Anderson's ACT Theories, PDP approach
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Collins & Loftus network model
Network models
Semantic memory is org'd in terms of netlike structures w/numerous interconnections - When we retrieve info activation spreads to related concepts
- Includes nodes & links
- Spreading activation
- Freq used links have greater strenghts -> activation travels faster btwn the nodes (explains typicality effect)
- Useful explanation of semantic priming
- Has been superseded by more complex theories that attempt to explain broader aspects of general knowledge (Anderson's ACT theories & PDP approach)
- Similar to Anderson's ACT theories: links btwn nodes become stronger as they're used more often--practice is vitally important in dvlping more extensive semantic memory
- Interconnections rather than grp membership
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Node
Collins & Loftus network model
Location in the network - Each concept is rep'd as this
- Connected to other nodes by links to form a network
- Activated when the name of a concept is mentioned & spreads from that node to other nodes w/which it's connected
- Links btwn them become stronger as they're used more often
- Also used in PDP approach
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Link
Collins & Loftus network model
Connects a particular node w/another concept node to form a network - Freq used links have greater strengths-> activation travels faster btwn the nodes (typicality effect)
- if many are activated simultaneously each link receives relatively little activation & knowledge will be retrieved slowly
- Also PDP approach: each node has multiple links & activation of these networks is responsible for our cog processes
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Spreading activation
The Collins & Loftus network model
When the name of a concept is mentioned the node rep'ing that concept is activated & the activaiton spreads/expands from that node to other nodes w/which it's connected - Activation requires longer to spread to more remote nodes in the network
- C&Lfocuses on networks for words; vs. ACT-R focuses on larger units of meaning
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ACT-R
Anderson's ACT theories
An acronym for "Automatic Components of Thought-Rational" - Series of network models that attempt to account for all of cognition
- Created to explain all of this textbook's topics
- Declarative knowledge
- Based on larger units of meaning (vs. network model)
- The meaning of a sentence can be rep'd by a propositional network (pattern of interconnected propositions)
- Similar to C&L model: links btwn nodes become stronger as they're used more often--practice is vitally important in dvlping more extensive semantic memory
- Assumes that at any moment as many as 10 noes are rep'd in your working memory
- Activation can spread--but the limited capacity of working memory can restrict spreading
- If many links are activated simultaneously each link receives relatively little action & knowledge will be retreived slowly
- Focused on larger units of meaning (vs. C&L)
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Declarative knowledge
Anderson's ACT theories
Knowledge abt facts & things
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Proposition
Anderson's ACT theories
The smallest unit of knowledge that can be judged either true/false; simplest piece of info you can say yes/no to ["is this a cat?"] - ["Susan gave a cat to Maria"]
- Meaning of a sentence can be rep'd by pattern of interconnected propositions (propositional network)
- Each is rep'd by a node & links rep'd by arrows
- The network rep's the important relations in the propositions but not the exact wording
- Abstract--don't rep a specific set of words
- Each concept in a proposition can be rep'd by its own indiv network--these networks need to be complicated to accurately rep the dozens of associations we have for each item in semantic memory
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Parallel distributed processing (PDP) approach
Network models
- Argues cog processes can be understood in terms of networks that link together neuron-like units
- Cog processes can be rep'd by a model in which activation flows thru networks that link toegether a large # of simple neuron-like units
- Aka connectionism/neural networks
- Emphasize the earlier models based on categorization are too restrictive & fail to account for subtlety of our knowledge abt the world
- 3 central characteristics: (1) multiple patterns of activation may occur simultaneously (parallel processing); (2) each node has multiple links & activation of these networks is responsible for our cog processes; (3) concept is rep'd by a pattern of activity distributed throughout a set of nodes
- Extremely complex, sophisticated, & abstract
- Connections btwn these neuron-like units are weighted & the connection weights determine how much activation 1 unit can pass on to another unit
- When a unit reaches a critical level of activation it may affect another unit either by exciting it/inhibiting it
- Every new piece of info you learn will change strength of connections among relevant units by adjusting the connection weights
- Sometimes we have only partial memory for some info rather than complete perfect memory
- Broader than the other semantic memory theories
- Explains overregularization in children: patterns of excitation w/in neural networks
- Filling in gaps in info--when something's unknown/ambiguous--spontaneous generalizations & default assignment
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Serial search
PDP approach
Conducting a complete search of all objs w/the specific characteristic b4 beginning a 2nd search of another characteristic - [Carrot: orange, below-ground, veggie, etc]
- Vs. parallel search
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Parallel search
PDP approach
Consider all attributes simultaneously - VS. serial search
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Spontaneous generalization
PDP approach
Use indiv cases to draw inferences abt general info - Advantage of PDP approach
- Make this when some info is missing in memory
- Accounts for some of the memory errors & distortions
- Can help explain stereotyping--draw conclusion abt a general category ["engineering students"]
- Argues we don't simply retrieve a memory in same fashion we'd retrieve a bk from the lib--we reconstruct a memory & that memory sometimes includes inappropriate info
- Inductive: relies on you drawing generalizations from indiv cases you see; 1 way we eval evidence
- VS. default assignment
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Default assignment
PDP approach
Based on info from other similar ppl/objs - Advantage of PDP approach
- Fill in missing info abt a particular person/obj by making a best guess
- Draw conclusions abt a specific member of a category [a particular enginerring student]
- VS. spontaneous generalization
- Deductive: using your knowledge abt a category to make some assumption abt an indiv item in that category
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Connection weights
PDP approach
Determine how much activation 1 unit can pass on to another unit--since connections btwn the neuron-like units are weighted - If weight is +: during critical level of activation it excites another unit
- If weight is neg: during critical level of activation it inhibits another unit
- Learning a new piece of info changes the strength of connections among relevant units by adjusting these connection weights
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Graceful degradation
PDP approach
Brain's ability to provide partial memory - [Tip-of-the-tongue phenomenon]
- Explains why the brain continues to work somewhat accurately even when an accident, stroke, or dementia has destroyed portions of cortex
- As memory declines, you're able to remember little pieces of it b4 it's completely gone
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Tip-of-the-tongue phenomenon
PDP approach; Metacognition
1 form of graceful degradation - Subjective feeling you have when you're confident you know the target word for which you're searching but you can't recall it
- Metacog: ppl know enough abt their memory for the target word to be able to say "this word is on the tip of my tongue"--likely to identify 1st letter & other attributes of target word w/similar sounding words that resemble it
- Bilinguals experience it more freq than monolinguals (larger vocab -> more opportunity for confusing words)
- Ppl successfully retrieve the word abt 1/2 the time usu w/in 1st 2 min (associated w/strongest feelings)
- These words are likely to be correctly recognized later
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Schema
- Generalized knowledge abt a situation, event, or person
- Influences way we understand a situation/event
- Theories helpful to explain how ppl process complex situations & events--propose ppl encode in their memory "generic" info abt a situation then use this info to understand & remember new ex's of the schema
- Guide your recognition & understanding of new ex's b/c you say to yourself "this is just like what happened when..."
- Emphasize how top-down & bottom-up processing work together
- Allow us to predict what will happen in new situation & predictions usu correct
- Heuristics
- Emphasize active nature of our cog processes--event happens & immediately try to think how it's related to an established schema--if not consistent w/a schema need to reconcile the inconsistency
- Can sometimes lead us astray & make errors (usu rational)
- Used interchangeably w/term script
- Memory selection: sometimes ppl remember material better when consistent w/schema [office schema] & other remember best when it's inconsistent (violates expectations, vivid, surprising)
- Boundary extension: schemas help us fill in missing material during memory tasks--memory stores schema-consistent images rather than partial figures
- Abstraction: remember the gist so our abstracted version is consistent w/original schema; integrate info to construct large schemas (when don't neeed to remember exact words)
- Inferences: use schemas to interpret material & "remember" their own interpretation rather than original message; influence when reading ambiguous material
- Integration: schema-consistent likely when recall is delayed & ppl are performing simultaneous task--material becomes integrated w/existing schemas & recall is altered
- Clearly influence our memory but not completely
- Experts & novices differ substantially--need appropriate schemas to understand a topic properly; knowledge base
- Cognitive maps
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Heuristics
Schema
General rules that are typically accurate - Include schemas
- Cog maps: general prob-solving strategy that usu produces a correct solution; rep relative positions in our mental maps
- It's easier to store a schematic version of an event than a precise version of it that accurately reps all the little details
- Prob-solving: strategy where you ignore some alternatives & explore only those that seem esp. likely to produce a solution; need to weigh benefits of heuristic's speed vs. costs of possibly missing correct solution
- Most widely used heuristics in prob solving: analogy, means-ends heuristic, hill-climbing heuristic
- Decision making: when we need to make difficult decision we use a guideline that's simple & easy to access; usu fail to appreciate their limits so don't always make best decisions
- Short-cut strategy to figuring something out
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Schema therapy
Schemas
Used in clinical psych - Clinician works together w/client in order to create appropriate new schemas that can replace maladaptive schemas dvlpd early in life ["mistrust schema"]
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Script
Schemas
Common kind of schema--subcategory--like many schemas wrapped into 1 - Simple, well-structured sequence of events (in specified order) associated w/highly familiar activity
- Abstraction, prototype of series of events that share underlying similarity
- Often used interchangeably w/schema--but narrower term
- ["Restaurant script"]
- Much of our ed consists of learning scripts we're expected to follow in our culture
- Violation -> can be surprising & unsettling
- When identified early memory recall can be much better [laundry paragraph]
- Operates in cog processing during: selection, boundary extension, abstraction, interpretation, & integration
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Boundary extension
Schemas
- When you store a scene in memory--use schemas/scripts for this
- Tendency to remember having viewed a greater portion of a scene than was actually shown
- We have a schema for a scene [someone's garbage area] & our cog processes fill in the incomplete objs
- Material is visual & our schemas help us fill in missing material during memory tasks
- Activate perceptual schema when comprehending a photo--based on expectations--extend beyond photo as a mental image
- Implication for eye witness testimony
- Our memory stroes more idealized schema-consistent images rather than partial figures
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Abstraction
Schemas
- Memory process that stores the meaning of a message but not the exact words
- We tend to recall the gist/general meaning w/impressive accuracy so our abstracted version of the passage is consistent w/the original schema
- VS. verbatim memory
- Constructive approach vs. pragmatic approach
- Usu we integrate info from indiv sentences so we can construct large schemas--esp when we don't need to remember exact words
- But sometimes we know specific words do matter so pay close attention to precise wording
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Verbatim memory
Schemas
Word-for-word recall - Ppl usu bad w/this, even a few min after passage is presented
- Professional actors need to have a good 1 to remember exact words
- VS. abstraction
- Constructive approach: verbatim impossible
- Pragmatic approach: if ppl realize they need to pay attention to exact wording their verbatim memory can be highly accurate--esp. if insult/criticism
- For most things we don't need this
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False alarm
Schemas
When ppl "remember" an item that wasn't originally presented - During recognition tests ppl usu convinced they'd seen the new items b4
- Particularly likely for complex sentences consistent w/original schema
- Rarely made for sentences that violate meaning of earlier sentences
- Made more for paraphrases of bland sentences than sarcastic ones
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Constructive model of memory
Schemas & Memory abstraction
Ppl integrate info from indiv sentences in order to construct larger ideas - Later they beleive they've already seen the complex sentences b/c they've combined the various facts in memory
- Once sentences are fused in memory we
- can't untangle them into their original components & recall them verbatim
- Emphasizes active nature of our cog processes: sentences don't passively enter memory where each is stored separately--we combine them into a coherent story fitting related pieces together
- Can lead us astray if applied inappropriately [when testing verbatim memory]
- Listen to strories -> recognition test = combos
- Integrate info from indiv sentences to form broader ideas
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Pragmatic view of memory
Schemas & Memory Abstraction
Proposes that ppl pay attention to aspect of a message that's most relevant to current goals - Ppl know they usu need to recall gist quite accurately but can ignore specific sentences
- But if they realize they do need to pay attention to exact wording their verbatim memory can be highly accurate--esp if criticism/insult
- Perhaps we're esp sensitive abt emotionally threatening material so we make effort to recall exact words
- Sarcastic vs. neutral condition
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Inferences
Schemas; Reading
- Allow us to go beyond given info, greatly expanding our knowledge
- During interpretation phase
- Recall can contain these
- Logical interpretations & conclusions that weren't part of original stimulus material
- Usu ppl use own schemas to interpret material & "remember" their own interpretation rather than original message
- A person's unique interests & personal background often shape contents of memory
- Schemas influence them when we're reading ambiguous material--our cog processes actively work to make sense of it so our top-down shapes memory for complex material
- Background knowledge can mislead ppl causing them to "remember" inferences that weren't actually stated
- Gender stereotypes: make inferences abt indiv's personal characteristics based on gender
- Persuasion: ads, "remembering" inferences never actually stated
- Reading: draw on our world knowledge to activate info that's not explicitly stated in a written passage--draw reasonable conclusion
- Constructionist view: readers draw inferences abt causes of events & rel's btwn events
- Readers create these to integrate discourse & construct a well-org'd story
- We're sometimes just as likely to remember our inferences as the statements actually said--our inferences blend w/text forming cohesive story
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Gender stereotypes
Schemas/Inferences
Widely shared sets of beliefs abt the characteristics of females & males - Even when 1 is partially accurate it can't be applied to every indiv of the gender
- When ppl know someone's gender they often make inferences abt the indiv's personal characteristics [men more competitive than women]
- During recognition tests most will think they originally saw a sentence consistent w/widely held gender stereotype--make stereotype-consistent inference
- Implicit memory task: assess ppl's gender stereotypes w/o asking directly (ERP, IAT)
- Have the power to influence ppl's self images & sense of academic competence
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Explicit memory task
- Directly instructs participants to remember info previously learned (recognize/recall)
- Ppl may guess what researchers are measuring for -> designed implicit memory tasks
- Gender stereotypes: better recognition for schema-consistent info [women were gossiping at water cooler vs. talking abt sports]
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Implicit memory task
- Designed to prevent participants guessing motivations (explicit memory task)
- Ppl perform a cog task that doesn't directly ask for recall/recognition
- Goal: assess ppl's gender stereotypes w/o asking them directly
- Supposed to discourage ppl from providing socially desirable answers
- Assess ppl's semantic memory--their general knowledge abt gender in a culture & tendency to make gender-consistent inferences
- 2 kinds: ERP & IAT
- Elderly: requires participants to perform perceptual/cog task; past experience w/the material facilitates performance on task (read familiar sentence faster)--good at this
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Event-related potential (ERP) technique
Implicit memory task
Records tiny fluctuations in brain's electrical activity in response to a stimulus - Quickly change in response to a surprising word (stereotype-inconsistent info)
- Assess gender stereotypes using this neuroscience technique
- Very rapid measure--immediately see change when reading inconsistent info [women talking abt sports]
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Implicit Association Test (IAT)
Implicit memory task
Used to assess implicit gender stereotypes - Based on principle that ppl can mentally pair related words together much more easily than they can pair unrelated words
- Ppl w/strong gender stereotypes would think math & males fit in same category while arts & females in another -> fast response
- Inconsistent pairings: difficulty associating math terms w/women & arts w/men -> responses much slower
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