Learning and Causal Reasoning Flashcards
Schemas
- Often called “frames”
- Naive theories or data structures that guide information processing by structuring experiences, regulating behavior, and providing a basis for making inferences
- Schemas are like “default” settings
Scripts
- Data structures that code contextually-based sequences comprising everyday life
- About sequences occurring in order, one thing happens first, then something follows
Schema Function
- Expectations about likely events
- Fill in gaps when listening/reading
- Perception of visual scenes
Hierarchical Semantic Network Model
- Basically linked schemas
- Tested with lexical decision tasks
- “Canary is a bird” = faster than “Canary is an animal”
The Fan Effect
- Activation spreading from an item having many associations will be divided across those many connections
- More connections = each one gets weaker
Encoding Variability
Basic idea: make as many associations as you possibly can with info you need to recall
- Study in lots of locations! If you’re reading this, kudos!
Classical Conditioning
- Ivan Pavlov was studying digestion; dogs salivated whenever assistant came into the room
- Innate reflex responses
- Unconditioned Stimulus = sight or smell of food
- Unconditioned Response = salivation
- Conditioned Stimulus = neutral, temporally associated with US
- Conditioned Response = salivation in response to neutral stimulus
Conditioned Taste Aversion
- Requires only one CS-UCS pairing to be effective
- Gap between CS & UCS can be very long
- Association is selective; odor & taste more often than other stimuli
Operant Conditioning
- Definition: a learning process where the frequency of a behavior is modified by the consequences of the behavior
- Basically, wait until the animal/person does something, then either reward, punish, or withhold reward
Premack Principle
- Definition: high-probability behaviors (frequently performed under free-choice) can be used to reinforce low probability behaviors
- Reinforcers can be responses
- Observe voluntary choices and use preferred behaviors to reinforce
Fundamental Attribution Error
Tendency to make dispositional attributions about others while ignoring the situation.
- It’s just how they ARE.
- We’re less likely to do this to ourselves.
Power-Based Model of Causal Induction
People attribute causation because they can tell a story about the power of the assumed cause to create the event
- Power = the extent to which we know the mechanisms behind causation
Cheng’s Power of Probabilistic Contrast Model
- People’s goal in causal induction is to estimate causal powers from observable covariation
- Using covariation to tell a story