Lecture 1: Intro to Cognitive Psychology

cognitive psychology

scientific study of the mind & internal processes:

• input

• storage

• transformation

input → ?

• how we take info in

• sensation & perception

storage → ?

• how we keep info

• learning & memory

transformation → ?

• how we use info

• decision making, language, & problem solving

What is the most important factor in successful learning?

what you think about while studying—relates to shallow & deep processing methods

Stephen Chew’s “Beliefs that Make You Stupid”

1. learning is fast

2. knowledge is composed of isolated facts

3. being good at a subject is a matter of inborn talent

4. i’m really good at multi-tasking

learning is fast

dunning & kruger:

• tested college students on various skills (logic, grammar, & humor)

• students in low percentiles estimated they were going to score higher (i.e. 12th percentile → 62nd percentile)

Why is learning not fast?

fluency trap

fluency trap

think you’re fluent/knowledgable about topic/subject because you’re familiar with the material (seen the material & understand it because you are reading over it)

metacognition

awareness & understanding of one’s own thought processes

knowledge is a list of facts

• studying a list → rote memorization

• learning requires understanding relationships

• levels of processing theory

  • craik & tulving

    • method: participants were shown words & asked questions that forced them to use a level of processing

    • result: deep processing → better memory

levels of processing theory

1. structural/shallow - appearance (i.e. spelling)

2. phonemic/deeper - how it sounds like (i.e. what it rhymes wihth

3. semantic/deepest - meaning & relation to other concepts

i’m either a natural at this or i’m not

• ability is malleable ≠ fixed

• dweck’s growth vs. fixed mindset

  • participants: 373 students in 7th grade assigned to a fixed or growth mindset

  • results: both mindsets started off with similar scores → growth mindset achieved higher scores, while fixed mindset achieved the same or lower scores

dweck’s growth vs. fixed mindset

growth mindset:

• brain is a muscle

• intelligence grows & develops

fixed mindset: intelligence is static

i’m good at multi-tasking

• deep processing killer → keeps in shallow processing mode

• heavy time & cognitive cost → requires big shift in attention & less efficient

  • i.e. going on phone during lecture → dedicating less cognitive resources to both activities

• direct violation of how attention works

  • attention bottleneck

  • switch-cost

• cognitive control in media multi-taskers

  • participants: heavy (HMM) & light (LMM) media multi-taskers

    • HMM - jug many streams of info at once

  • method: tested ability to filter out irrelevant information with distractors & no distractors & measured by response time

  • results: HMM were more susceptible to irrelevant information → more you multi-task, the worse it is for you

    • no distractors: HMM & LMM had similar response times

    • distractors: HMM took significantly longer than LMM to filter out junk

attention bottleneck

limited capacity for attentional resources (so much our brain can focus on at one time)

switch-cost

lag & mistakes that occur when multi-tasking

how to study

• retrieval practicing/testing effect

• elaboration

• spacing effect

• interleaving

retrieval practice/testing effect

• prevent forgetting

• examples in class: quizzes, iClicker, closed-book dump

• examples outside of class: close notes & create your own tests & flashcards

elaboration

• asking “why?” & “how does this connect?”

• connecting info to things you already know

• examples in class: contemporary examples, home labs, & textbook

• examples outside of class: relating concepts to personal experiences

the spacing effect

• study in shorter sessions & ≠ one long session

• examples in class: regular at-home quizzes & 4 exams

• examples outside of class: reviewing lectures more often

interleaving

• mixing up learning

• examples in class: revisiting old lecture topics & connecting them to other lecture topics

• examples outside of class: ≠ only revisit lecture of that week but mix it up review other weeks’ content

ebbinghaus forgetting curve

• exponential decay of information in 24 hours

• 70% of content learned in one lecture will be forgotten if it’s ≠ used