final Flashcards
script
knowledge of structure for an event or situation
heuristic
rule of thumb, guideline based on knowledge
schema
mental structure that organizes info
can be used with heuristics to tell us what to expect
Bartletts repeated reproduction technique
first to report that memory retrieval is reconstructive, fragile, reencoded every time its retrieved
we use general knowledge and expectations from experience to organize memories (schemas)
- pts reproduce (verbally or by drawing) something over and over again from memory and it will almost always turn into something more familiar and culturally relevant as it is reencoded
Deese-Roediger-McDermott (DRM) procedure
semantically related lists of words lead pts to falsely remember semantically related words that were NOT on the list
If attention is divided, effect is seen less bc there is less attention available to activate schema
Support for Activation-monitoring theory, a type of source monitoring error in which we use a schema to come to a conclusion and attribute it to a memory
Activation monitoring theory
proposed that we activate a schema and falsely attribute the source to an episodic memory - source monitoring error
Support: DRM procedure
Also, if there is less available attention, there is less source monitoring errors bc less attention available to activate schema
misinformation effect
people tend to recall what they were told about an event rather than their experience of the event - for other people
phases of the misinformation effect
- encoding - show info
- Post event info - either consistent with reality or misinformation (can be subtle or intentional)
- recognition task - which is the correct image?
Implanted memories
false memories regarding yourself
“shopping mall study”
harder to implant but still very effective - easier with photoshop
procedure of shopping mall study
- write down 3 real stories from childhood and add a fake story about being lost in a shopping mall as a child
- send to pt and ask them to add detail repeatedly over several iterations - not prying for more info, just repetition
- When brought in and asked which were fake
Outcome: 25% guessed the wrong story and after 3 iterations, many ppl added inc detail to the fake story
Problems with eye-witness testimony
- Weapon focus and change blindness (attention) - will remember weapon more than person holding it
- Misinformation effect from post event interrogation
- Source monitoring errors - may attribute blame to a bystander they just remember well
- Confirmation bias -confirmation like “everyone said they same story” will increase confidence in memory
knowledge = ___ memory
semantic
category vs concept vs exemplar
category: group of objects that belong together
concept: mental representation of a category
exemplar: an item in the category
classical view of categorization
categories are defined by a list of necessary and sufficient features
necessary vs sufficient
N: must have all
S: no other features required
problems with classical view of categorization
- not all categories have a list of defining features - impossible to agree on a list - maybe we are just bad at coming up with the list but they do exist?
- Graded categories - we rate things on a scale of how “in” they are
- Typicality effects
Typicality effects
- rt faster for typical than atypical
- generate typical more often than atypical
- typical more affected by priming (lexical decision tasks faster for typical pairings - pts decide if something is a word faster if it comes after a semantically paired item)
Prototype theory
Rather than defining features (classical), exemplars have characteristic features (common features that are not required for the category)
We determine category membership by matching item with prototype stored in memory (every member shared at least 1 feature with another member)
Compare stored prototype with exemplars
central tendency for exemplars
categories have a central tendency in which exemplars with the most characteristic features are found
All category members share ____ even if they are atypical members
family resemblance
Levels of categories
superordinate: broad category, e.g. mammal, plant - distinctive but not that informative
basic: moderately specific, e.g. dog, tree - informative and distinctive
Subordinate: specific, e.g. poodle, maple - informative but not distinctive
exemplar theory
“opposite” of prototype
We store exemplars and create a prototype if necessary
Research supports this theory more
Pro: allows access to atypical exemplars thru memory
more characteristics = close to the
prototype
Similarity theories
Exemplar and prototype theory
Problems with similarity theories
- People may give typicality rating bc they are just “playing along”, so we cant use that as reliable evidence
- These theories don’t define which features are important to categorization
- no input of experience
Explanation based theories
categorization based on implicit ideas about categories you learn as you grow up
Based on psychological essentialism based on past experience
Accounts for why some features are more important than others
Psychological Essientialism
The quality of an item - a bird is “birdy”
Essential nature of a cat is a cat but the essential nature of a coffeemaker is to make coffee
Semantic Network Models
Collins and Quillian hierarchical model and Collins and Loftuses Semantic Relatedness model
Collins and Quillians Hierarchical model
nodes contain info and they are connected by ISA and property pathways that are activated via spreading activation
hierarchy based on specificity - superordinate at the top subordinate at the bottom
Important feature: property inheritance
Property inheritance
Important feature of C&Q
As you move down the hierarchy, concepts inherent properties from concepts higher in the hierarchy - efficient
Evidence for C&Q
Property inheritance could be demonstrated thru RT of properties directly related to a word - faster if directly related, slower if related to a related concept (have to mentally travel up the hierarchy to find the feature)
Problem: atypical exemplars are not consistent (chicken=animal is faster than chicken=bird) and so cannot account for typicality effects
Collins and Loftus’ Semantic Relatedness Model
No hierarchy, instead semantic relatedness determines length of connection (and therefore RT)
Nodes still contain info and are connected by ISA or property pathways
Typical exemplars have shorter pathways and you can have multiple nodes for the same thing, therefore accounts for typicality effects
Different ppl can have different pathways which makes the model unfalsifiable and so unfavored
Neural Network Models
Like Artificial neural nets
ANNs
Computing models based on neurons in a brain - connectionist rather than semantic network model
Nodes are like neurons connected by weighted connections (-1-1, inactive, excitatory, inhibitory) making up input, output, and hidden layers
Knowledge is stored in the pattern of activation (distribution of weights across nodes), not individual nodes
Hidden layers
Layers of ANN nodes between input and output, like cognitive processing neurons, we dont know how they work just that they produce the output
Unknown = hidden
Output nodes and input nodes are synonymous to…
motor and sensory neurons
ANNs are consistent with which theory
embodied cognition and memory trace
ANN facilitated development of
AI, self driving cars, cog psych (make models to represent outcomes of a hypothesis and see if ppl respond the same way)
rationalism
priori truths
deduction
priori truths
born knowing
empiricism
posteriori truths
induction
posteriori truths
gain thru observation
deduction
remember and/or apply
prinicple to instance
induction
observe and combine
instance to principle
inferences about what will likely happen - impossible to reach logical conclusion bc we can never be sure of the future
categorical syllogisms
deductive reasoning
draw conclusion from two statements using quanifiers
definite are easier to solve, negation are harder
cant draw logical conclusion = indeterminate
solve using mental models of ALL possibilities
what are mental models limited by
WM, prior knowledge, visual imagery skills
conditional reasoning
deductive
if, then propositions and draw conclusion
propositions made up of…
condition statement is made up of antecedent (if) and consequent (then)
valid reasoning for conditional
affirm the antecedent, therefore affirm the consequent OR
deny the consequent, therefore deny the antecedent
Wason selection task
4 cards, have one thing on front and one on back, how few do you need to turn over to validate the rule?
*remember directional so you must affirm ante or deny consequent (OPPOSITE OF WHAT IS STATED)
wason selection task affected by….
confirmation bias (ppl dont look for information to refute) and harder if abstract
solution to abstract reasoning tasks
pragmatic reasoning schemas - concrete examples to reduce resources required to solve
belief bias effect
relying on knowledge rather than reasoning to come to an incorrect conclusion
inductive reasoning is limited by
WM, prior knowledge, imagery abilities
Expected Utility Theory
assumes ppl make rational decisions based on subjective utility and probability
ppl don’t actually make all decisions perfectly methodically
NORMATIVE
subjective utility and subjective probability
our personal perception of how much utility we will gain from something and how likely it is