12 Flashcards
Thought
Thought is an extension of perception and memory:
- we form mental representations
- we retrieve representations from semantic
memory
- we mentally manipulate the representations to achieve some purpose
Thinking involves manipulating mental representations (images or words or concepts) for a purpose
Mental Models
Mental models involve a representation that describes, explains, or predicts how things work
- Our model of the synapse
- Our understanding of how cars work
- Atkinson & Shiffrin’s model of memory
Categories
- a class of similar entities that share an essential core or some similarity in properties
- something that exists in the real world
- eg a chair does not fall into the computer category
Concept
a mental representation of a category.
- cat: small, furry, four-legged, eats mice …
- bird: small, feathered, can fly …
Processes of categorisation
Categorisation involves recognising an object as a member of a category
1. comparison with defining features
- characteristics that are essential for membership of the category
2. similarity/dissimilarity to prototypes
- an idealized abstraction across many instances of a
category, an “average” of previous experiences
Levels of categorisation
- Basic
- Subordinate
- Superordinate
Basic categorisation
- most ‘natural’ level, quickest response
- e.g., bird
Subordinate categorisation
- more specific than basic
- e.g., magpie
Superordinate categorisation
- more abstract than basic
- members share few specific features
- e.g., living thing, animal
- level of metaphor
Reasoning
the process of thinking in which conclusions are drawn from given information
- inductive
- deductive
Deductive reasoning
One draws a conclusion that is intended to follow logically from two or more statements (premises)
Specific premises are given, for example:
- The dog always barks when there is someone at
the door, and the dog didn’t bark
Does the premise allow a particular conclusion to be drawn
- So there is no one at the door
requires us to focus on structure (form), not content
Inductive reasoning
A conclusion is made about the probability of some state of affairs, based on the available evidence and past experience
- Brian is a university student.
- Brian studies a lot.
- Therefore all university students study a lot.
Generalising from a specific instance to an entire category
Everyday thinking often involves inductive processes
Deductive V Inductive reasoning
Deductive: general => specific - All dogs bark - Fido is a dog - Therefore Fido barks Inductive: specific => general - Felix is a cat - Felix purrs - Therefore all cats purr
Belief bias
Deductive reasoning gone wrong
Conclusions that concur with real-world knowledge are judged to be valid
- If my finger is cut, then it bleeds.
- My finger is bleeding.
- Therefore, my finger is cut.
Logical structure of premises is overlooked
Relying on real world knowledge is less effortful than formal reasoning
Analogical reasoning
- process by which people understand a novel situation in terms of a familiar one.
- novel and familiar situations must each contain a number of elements that can be mapped onto each other.
- use analogies to categorise novel situations, make inferences, solve problems
Benefits of analogous reasoning
We can use analogous reasoning to make conclusions about things we have not actually seen or experienced first hand • Called Transitive Inference - Tom is happier than Bill - Bill is happier than Mike - Who is happiest?
Problem solving
refers to the process by which we transform one situation into another to meet a goal.
- initial
- goal
- set of operations
Well defined problems - Simon 1973
- have clearly defined initial and goal states
- constraints are clearly specified
- no ambiguity regarding what represents a solution
Problem Clarity
- Well-defined problems are ones where the initial state, goal state, and operators are easily determined (e.g., stats problems).
- Ill-defined problems occur when both the information needed to solve the problem and the criteria for determining when the goal state has been met are vague (e.g., leader tasked with ‘improving morale’).
Algorithms
- Step-by-step procedures that always find a solution, sooner or later
- More likely to exist for well-defined problems
- e.g., rules of algebra
Mental Stimulation
imagining the steps involved in problems solving before undertaking them
Means End Analysis
- involves identifying the principal differences between initial & goal state, then taking actions to reduce those differences
- often involves forming subgoals
- intermediate steps to solution
- dividing problem into subgoals facilitates
solution - Example: Tower of Hanoi
Functional fixedness
A barrier to problem solving
- the tendency to fix on a function for an object and to ignore other possible uses.
Dunckers Candle Problem 1945
3 objects on table
- candle, box of matches, box of thumbtacks
Task
- find a way to attach the candle to the wall in
such a way that the candle burns correctly, using only the objects on the table
Solution
- empty matchbox can be used as a candle
holder
*Involves overcoming functional fixedness
Confirmation bias
A barrier to problems solving
- the tendency to search for confirmation of what we already believe.
Mental set
A barrier to problem solving
- the tendency to keep using the same problem solving techniques that have been successful in the past.
Improving problem solving
- switch to a different (more appropriate) representation
- avoid functional fixedness
- change your mental set
- check for unnecessary constraints
- avoid confirmation bias by looking for disconfirming evidence
Decision making
The process by which an individual weighs the pros and cons of different alternatives in order to make a choice
- Usually weighing the likelihood of things occurring and the value to you individually of those
Heuristics
Cognitive shortcuts (rules of thumb) for selecting amongst alternatives, without carefully considering each one (sometimes irrational)
Decision Making Heuristics
- rules of thumbs or guiding principles used to make decisions
- often yield a correct solution
- can sometimes yield incorrect solutions
- Advantages: simplicity, speed
- Disadvantages: not always correct
Availability Heuristic
- the estimated probability of an event is based on the ease with which relevant instances come to mind
*easier to think of words beginning with a particular letter
than to think of a word with the letter in 3rd position - In general people overestimate the likelihood of
dramatic events which receive much attention in the media and elsewhere (e.g., air crashes, tornadoes, floods, homicide) because instances are more available in memory
Representative Heuristic
- The estimated probability of an event is based on how similar it is to the typical prototype of that event
- Based on a subjective computation of person or event’s similarity to a prototype
- Prototype: most typical instance of a category
- if deviation from p/type small, the odds are good
Bounded Rationality
- People are rational within the bounds imposed by their environment, goals and cognitive resources
- We do not always have complete information
our time is often limited - We make good enough judgments, rather than optimal (Simon, 1956)
