Studied by 4 people
deductive reasoning
involves the thinking skills required to evaluate the validity of an argument
(validity referring to a particular quality of this argument such that if the premises are assumed to be true, then the conclusion must also be true)
the form of arguing is the most important, allows us to draw conclusions with certainty
well defined problems
inductive reasoning
involves the thinking skills required to notice patterns and make generalized inferences based on a number of instances or observations
it is judged based on how likely it is to be true
also includes our ability to reason about things that couldve happened but havent
syllogistic reasoning
simple deductive arguments that consist of two premises followed by a conclusion
a valid reasoning is one in which the conclusion follows necessarily from the statements, if they are true then by deductive rules, the conclusion must be true as well
ex; All beagles are dogs (first statement), All dogs are mammals (second premise), therefore, all beagles are mammals(conclusion)
Two-Premise Syllogisms
Quantifier type | Ex with valid conclusion | Ex with Invalid Conclusion |
|---|---|---|
1. Universal Affirmative All A are B | All beagles are dogs All beagles are mammals Therefore, all beagles are mammals | All beagles are dogs All beagles are mammals Therefore, all dogs are mammals |
2. Universal Negative NO A are B | No reptile has fur All snakes are reptiles Therefore, no snake has fur | No cats are dogs Some dogs are not pets Therefore, some pets are not cats |
3. Particular Affirmative SOME A are B | Some foods are veggies All veggies are plants Therefore, some plants are food | Some foods are veggies All veggies are plants Therefore, some plants are not food |
4. Particular Negative SOME A are not B | Some cats are not pets All cats are mammals Therefore, some mammals are not pets | Some professors are not psychologists Some psychologists are musicians Therefore, some musicians are professors |
conditional (propositional) reasoning
has a formal structure that includes the connective words “if” and “then” as part of the premise (plus some other connective words)
most basic form is to assert a second premise consisting of either affirming the antecedent or consequent or denying the antecedent or consequent
ex: if the first premise is “if P then Q” and for the second premise if we affirm the antecedent “P” the valid conclusion for this argument form is “Q”
dual process framework
the idea that cognitive tasks can be performed using two separate and distinct processes
includes type 1 and type 2 processing, 1 is automatic and 2 is controlled processing
when we use inductive reasoning
analogical reasoning, category induction, counterfactual induction, counterfactual thinking, and casual reasoning
the wason 2-4-6 task
lab task that stimulates the discovery of hypotheses to account for a pattern of observations
counterfactual reasoning
our ability to reason about things that couldve happened but havent
take the form of what if and if only sentences
causal reasoning
used to infer cause and effect relationships between events then we can make predictions about or even control our environment
shows evidence of co-occurrence and has the belief that there is mechanism for casual relationship
analogical reasoning
using the structure of one conceptual domain to interpret another domain
Philip Johnson Laird and his colleagues proposed a theory of reasoning that proceeds through 3 stages, the first stage in this theory is model ____?
construction of the premises
conditional reasoning forms
1. modus ponens (valid)
denying the antecedent (invalid)
affirming the consequent (invalid)
modus tollens (valid)
wason selection problems
same logic problem, but more people will get concrete over abstract
mental models
Laird
Stage 1: Model construction
find way to represent statements (ex: all As are Bs, all Bs are Cs)
Stage 2: Conclusion-formulation
combining the two premises and the four possibilities gives us
Stage 3: conclusion-validation
so to evaluate “All As are Cs” we can remove Bs and get both situations that support the conclusion
hypothesis generation vs testing
a process beginning with an educated guess whereas the latter is a process to conclude that the educated guess is true/false or the relationship between the variables is statistically significant or not.
confirmation bias
tendency to focus on info that confirms our hypothesis or existing belief
scientific reasoning
intentional information seeking: asking questions, generating and testing hypotheses, evaluating evidence
three types of theoretical claims
category claim, event claim, causal claim
category claim
asserting that certain entities or phenomena belong to a particular category
event claim
asserting that certain events happen or exist within a specific context or under certain conditions
causal claim
asserting that one variable influences or causes changes in another variable
Ludwig Wittgenstein idea of concept
it may not be possible to identify a list of necessary and sufficient features for many categories, especially real-world categories
family resemblence
points not to a single set of defining features but rather to members of categories connected by overlapping sets of features
concepts are connected by a series of similarities across features, there isnt a set of necessary and sufficient features
typicality effect
a result where more common members of a category show processing advantages
depends on how well the items features match an abstract combination of the most frequent attributes for that category
typical items are more easily judged as members of a category than atypical
prototype approach
views concepts as abstract representations, that summarize the common and distinctive attributes of the members of the category that comprise the concept
basically an average of the important features of its members, with varying levels of importance across these features
better represented as schemata than unstructured lists
more typical members of a category share more features
exemplar approach
proposes that concepts consist of separate representations of examples of the category that we have encountered before
categorization of an object is accomplished by comparing it to all your memories of similar things
most typical items of a concept are those that are similar to many other members of the concept, the more typical the object for a category the more similar it will be to recalled members of that category
major difference between exemplar and prototype approach
comparisons are being made to memories of actual experiences rather than an abstraction of those experiences in the form of an ideal version of a category member
conceptual hierarchies
one way to organize concepts
single objects or events are typically members of many different larger or smaller categories
concepts are structured hierarchically according to the level of detail in the label
stored in memory as networks of relationships
superordinate concepts
categories higher in the figure, higher and broader than those lower
subordinate concepts
categories lower
lower and more detailed than higher up
can inherit the properties of their superordinate categories
basic level categories
one level is typically “privileged” over other levels, meaning the labels at this level are special in how we identify and name objects
share common shapes and movements, they allow for faster categorization or pictures, and they are used more frequently in naming of objects
includes objects that share the greatest number of features with other category members
ex: a parent may refer to picture as a dog rather than an animal or specific breed
cognitive economy
the idea that concept features are stored at the most efficient level of the hierarchy
allows for feature sharing across related concepts and generalization of knowledge to new objects
distance efforts
predicted by cognitive economy, the more “is a” links that need to be traveled through to compare concepts, the longer the verification times should be
feature comparison
alternative to hierarchical network view is that relationships between concepts are determined using reasoning processes rather than being retrieved from a semantic network
ex: compare features of unknown “animal” to features you already know about animals to see if it fits
distributed-only view
theory for how features are brought together for comparison, it suggests that our concepts are directly represented within the connections between these sensorimotor areas of the brain
distributed-plus-hub view
theory for how features are brought together for comparison, it suggests that there are distinct areas of the brain that function to bind these features together, such that there are conceptual representations stored separately from sensory and motor information
associative theory of creativity
creative processing is linked to our semantic memory storage of concepts and knowledge, creating new concepts may involve changing the existing concept knowledge structure one currently has
concept
mental representation used for a variety of cognitive functions, its the smallest unit of knowledge
categorization
process by which things are placed into groups called categories
definitional approach
concepts are represented by defining features
hierarchical semantic network model
concepts are arranged in networks that represent the way these concepts are organized in the mind
Collins and Quillian (1969)
includes nodes (represents a concept in the model) and pathways (by which concepts are linked) and hierarchical structures and cognitive economy and shared inheritance
cognitive economy
principal of networks that properties and facts about a node are stored at the highest level
“is alive” would be stored with the node “animal” rather than each node under “animal”
avoid redundancy
shared inheritance
all features are shared in same categories in hierarchical structure network model
spreading activation
how we retrieve concepts
depending on how we’re asked or think of concept, it is a faster way to retrieve information about a concept
“a canary is a bird” > bird
“a canary is an animal” > bird > animal
category size effect
Collin + Quillian confirm spreading activation & hierarchial model by showing that bigger properties (concepts) take longer to retrieve
“a canary has skin”
semantic priming effect
the finding of faster recognition times to words preceded by related targets
problems with Collins and Quillian’s model
cant explain typicality effect, judging false statements and reverse category size effect
feature comparison model
a concept is represented as a list of defining characteristic features
ex: robin vs bird - each list has defining characteristic and features that they share and overlap
Collins and Loftus model (1975)
modified semantic hierarchical model which was basically a less strict version of the model
however this is unfalsifiable and so not perfect
can explain “judging false statement” idea and typicality effect
stereotype
often negative and could be a cognitively efficient way to interact in social context
connectionist model
how concept is represented in brain
based on connection and weight of connections
Neural networks are simplified models of the brain composed of large numbers of units (the analogs of neurons) together with weights that measure the strength of connections between the units. These weights model the effects of the synapses that link one neuron to another. Experiments on models of this kind have demonstrated an ability to learn such skills as face recognition, reading, and the detection of simple grammatical structure.
imagery
mental recreations of sensory information from the outside world and can be visual, auditory, olfactory, or tactile
also is important in cognitive/bx tasks such as problem solving, navigation, sports performance and mind wandering
can be helpful in defining a problem, determining the resources available, and possible constraints as well as exploring possible strategies and solutions for the prolem
the two primary ideas about how images are stored and manipulated
one idea is that mental images are represented spatially, in the same way that objects or scenes are perceived when looking
the other idea is that mental images are represented propositionally
spatial representation
the idea that visual information is represented in analog form in the mind
the description of images as spatial proposed by Kosslyn illustrates representational cognition
ex: a video showing twelve blackbirds flying and landing on top of a large tree
mental scanning
when participants are given a task in which they have to access different locations of a scene or object represented in their minds, they would show longer response times in a task that involves “moving” longer distance across that scene or object
relates to spatial coding
tactic knowledge
implicit assumption of participants will have when asked them to scan images. they will act in the way they assume researcher wants
demand characteristics
want to behave in the ways participants think the experimenter wants
propositional representation
the idea that visual information is represented non spatially in the mind
knowing the purpose of each of the parts allows you to interpret the sentence and understand the ideas presented in it
it is consistent with ideas about the way language is represented in the mind
suggested that accessing a spatial mental image doesnt necessarily mean that this is the mode in which our mind is representing the image
very difficult to test
ex: “twelve blackbirds flew through the cloudless sky and landed at the top of a large oak tree”
concreteness effect
people remember more concrete items on a list than abstract words
relates to dual coding
picture superiority effect
people remember pictures better (and more accurately) than labels
dual-coding theory
pictures produce automatic encoding in two modalities whereas words only produce one modality
words produce only a verbal code when studied but pictures produce the image (analog/depoctive) code and verbal code, both of these codes are automatically encoded into memory when they are studied
this results in 2 separate and distinct cues that can be accessed at retrieval and provides a better opportunity for one to retrieve a studied picture compared with a studied word that can be retrieved by the verbal code
bizarreness/distinctiveness effect
info that evokes unusual or distinct image is better remembered than typical image
method of loci
the more bizarre the images created when using the technique, the better they are remembered
peg-word mnemonic
specific words that rhyme with numbers are used as place holders in an ordered list, (one-bun, two-shoe etc)
they are then associated with items you wish to remember in order
prospective cognition
our ability to make predictions about how things will occur in the future
imagery allows knowledge to be generated about specific events, which then allows for predictions to be made about those events aka, allows for various solutions to problems from the knowledge gained in the mental stimulation problem
rule-based strategies
used to solve problems, involves using a propositional representation of how to solve the problem
scenographic imagery
what one would see walking through the environment
abstract imagery
a map-like image overview of the environment
wayfinding
used by 2 different working map representations, scenographic and abstract
the effectiveness of each imagery may depend of the complexity of the environment, the means of following instructions, individual differences in sense of direction or other factors
types of nonvisual imagery
kinesthetic imagery, internal imagery (muscular imagining) and motor imagery
motor imagery
a mental rep of motor movements
benefits in sports performances
may also be related to social skills and interactions
ex: imagining yourself jumping over a small fence
mental rotation
shepard and metzler (1971), cooper and shepard (1973)
imagination inflation effect
imagining events can increase the confidence that the events actually happened
the different aspects of visual imagery
seeing an embedded property of a shape
visualizing with high resolution
mentally amalgamating separate shaped to form a whole
mentally rotating a pattern in an image
Perky (1910)
studied how images can be affected by sensory input
findings: sometimes ppl cant tell difference between actual sensory image and mental image
IDEAL framework
Identify the problem
Define and mentally represent the problem
Explore possible solution strategies
Act on the solution
Look back and learn
represents one way to describe the typical sequence of cognitive processes involved in solving problems
Identifying a problem
problem solving describes a problem as a situation in which there is a difference between a current or initial state and a desired goal state
it is also the process of developing a solution designed to move from the initial state to the goal state in the context of potential constraints (limits within which you must work, including big picture issues like rules or laws that shouldnt be broken)
problem identification
first step in the problem solving process
includes defining characteristics of a problem: the initial state, a goal state, constraints, and a sequence of cognitive operations
they are goal-directed and require metacognition to monitor the process
cognitive operations
retrieving, identifying, organizing and elaborating mental representations
well-defined problem
one where the goal and constraints are known and, by correctly applying specific procedures, the correct solution can be fond
ex: sudoko, algebra problems, logic truth tables, geometry proofs
ill-defined problems
includes multiple possible ways to move from the initial state to the goal state and may lack a clearly defined goal state, and may have too many constraints or not enough
basically they are challenging because they can be difficult to mentally represent and generate solutions for
ex: arranging your day to fit everything in
defining a problem
the process of determining which features of a problem solving situation are relevant and which are irrelevant
exploring solutions
includes domain-general strategies and domain-specific solutions (ex: mnemonic for PEMDAS, please excuse my dear aunt sally)
problem space
a mental representation of the initial and goal states, possible subgoals, constraints, and operators (the actions that can be performed to change a state)
ex: think of searching through your apartment for your keys
algorithm
strategy guaranteed to solve a problem
typically used for well defined problems
ex: relating to searching through the “problem space” i.e your apartment, “searching every square inch” is this term
heuristic
problem-solving strategy that doesn’t guarantee a correct solution, but it works well enough most the time, aka "rule of thumb”
this search only considers part of the space
typically used in ill-defined problems, for solutions that are deemed “good enough” (aka satisfying)
trial and error strategy
aka generate test strategy
a strategy that involves generating possible solutions, trying those solutions, trying those solutions, and then repeating the process
works well when there is few possible solutions to consider or when there is only one correct goal state
think-aloud protocol
verbalizing what one is thinking about while performing a task
means-ends strategy
guides the search through the problem space by repeatedly comparing the current state of the problem to the goal state, identifying the differences and developing subgoals
hill-climbing strategy
involves selecting the operator the results in a change most similar to the goal state
aka most direct route to a goal
heuristic
working-backward strategy
involves searching through the problem space backward, starting from the goal state
factors that hinder effective problem solving
incorrect problem definition, mental set, functional fixedness, failing to notice analogous solutions
functional fixedness
focusing on how things are usually used while ignoring other potential uses
analogical transfer
occurs when we notice that it is possible to use the same solution for two different problems with the same underlying structure
isomorphic problems
where the underlying features are the same,
Gick and Holyoak (1980)
insight
the “aha” phenomenon
solvers struggle to find a solution and have often stopped consciously thinking about the problem when the correct solution suddenly emerges into consciousness
Note 
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