COGNITIVE PSYCH FINAL

Heuristic

A "rule of thumb" that provides a best-guess solution to a problem.

Four-card task

A conditional reasoning task developed by Wason that involves four cards. Various versions of this problem have been used to study the mechanisms that determine the outcomes of conditional reasoning tasks. ex. Because finding an odd number on the other side of the E would indicate that the rule is not true, it is necessary to turn over the E to test the rule.

Prototypes

A mental image or best example that incorporates all the features we associate with a category

Parallel distributed processing

A network model of mental operation that proposes that concepts are represented in networks that are modeled after neural networks. This approach to describing the mental representation of concepts is also called the parallel distributed processing (PDP) approach.

input nodes

A node, within a network, that receives at least part of its activation from detectors sensitive to events in the external world. Units in a connectionist network that are activated by stimulation from the environment.

Attribute substitution

A phenomenon observed when individuals must make judgements that are complex but instead substitute a simpler solution or perception.

Pragmatic reasoning vs evolutionary account of conditional reasoning

A pragmatic reasoning schema is a set of rules derived from experience that define the inferences appropriate to a specific situation. The idea that many properties of our minds can be traced to the evolutionary principles of natural selection. According to natural selection, adaptive characteristics—characteristics that help organisms survive to pass their genes to the next generation—will, over time, become basic characteristics of the organism. According to the evolutionary approach, people who can do this will have a better chance of surviving, so "detecting cheating" has become a part of the brain's cognitive makeup. The evolutionary approach proposes that the Wason problem can be understood in terms of cheating. • Why are some versions of the four-card problem difficult and others easy? • Some evolutionary psychologists argue that the difference depends on the fact that one problem is based in the rules of social interaction and the other is not. • According to this proposal, people will reason well when they have to "detect cheaters" who are not following rules of social interaction.

Problem-solving set (situationally produced mental set)

A preconceived notion about how to approach a problem based on a person's experience or what has worked in the past. In these experiments, mental set was created by people's knowledge about the usual uses of objects.

Tower of Hanoi problem

A problem involving moving discs from one set of pegs to another. It has been used to illustrate the process involved in means-end analysis.

Lexical-decision task

A procedure in which a person is asked to decide as quickly as possible whether a particular stimulus is a word or a nonword.

Syllogism

A series of three statements: two premises followed by a conclusion. The conclusion can follow from the premises based on the rules of logic Syllogism 1 - Premise 1: All birds are animals. (All A are B) Premise 2: All animals eat food. (All B are C) Conclusion: Therefore, all birds eat food. (All A are C) Syllogism 2 - All birds are animals. (All A are B) All animals have four legs. (All B are C) All birds have four legs. (All A are C) Validity is about whether the conclusion logically follows from the premises. If it does, and the premises are true, as in Syllogism 1, then the conclusion will be true as well. But if one or both of the premises are not true, the conclusion may not be true, even though the syllogism's reasoning is valid. Returning to Syllogism 2, we see that "All animals have four legs" is not true; that is, it is not consistent with what we know about the world. It is no coincidence, then, that the conclusion, "All birds have four legs," is not true either, even though the syllogism is valid.

Sentence-verification task

A technique in which the participant is asked to indicate whether a particular sentence is true or false. For example, sentences like "An apple is a fruit" have been used in studies on categorization.

Spreading activation

Activity that spreads out along any link in a semantic network that is connected to an activated node. For example, moving through the network from "robin" to "bird" activates the node at "bird" and the link we use to get from robin to bird Thus, activating the canary-to-bird pathway activates additional concepts that are connected to "bird," such as "animal" and other types of birds. The result of this spreading activation is that the additional concepts that receive this activation become "primed" and so can be retrieved more easily from memory. activation levels - summation - threshold -

Heuristics vs algorithms

Algorithms, which are very time consuming, exhaust all possibilities before arriving at a solution. Computers use algorithms. • Heuristics are simple, thinking strategies that allow us to make judgments and solve problems efficiently. Heuristics are less time consuming, but more error- prone than algorithms. - cutting through grocery stores aisles to make shortcuts

Semantic networks

An approach to understanding how concepts are organized in the mind that proposes that concepts are arranged in networks. cognitive economy - A feature of some semantic network models in which properties of a category that are shared by many members of a category are stored at a higher-level node in the network. For example, the property "can fly" would be stored at the node for "bird" rather than at the node for "canary." Inheritance - Exceptions -

Functional fixedness

An effect that occurs when the ideas a person has about an object's function inhibit the person's ability to use the object for a different function. candle problem - A problem, first described by Duncker, in which a person is given a number of objects and is given the task of mounting a candle on a wall so it can burn without dripping wax on the floor. This problem was used to study functional fixedness.

Types of problems

Arrangement - (anagram) • Fluency in generating partial solutions • Retrieval of solution • Knowledge of principles to constrain search Inducing structure - (analogy or series) • Series extrapolation • 1 2 8 3 4 6 5 6 ___ • Raven Progressive Matrices • Analogy problems • Merchant is to sell, as customer is to ____ • Miller Analogies Test • GRE • SAT Transformation - Tower of Hanoi

Concepts vs. Categories

CONCEPT - A mental representation of a class or individual. Also, the meaning of objects, events, and abstract ideas. An example of a concept would be the way a person mentally represents "cat" or "house." - set of necessary and sufficient conditions - Classical concepts are arbitrary. Free to vary from person to person and culture to culture. CATEGORY - Groups of objects that belong together because they belong to the same class of objects, such as "houses," "furniture," or "schools." Looked at in this way, concepts provide the rules for creating categories. Thus, the mental representation for "cat" would affect what animals we place in the "cat" category.

Framing effect

Decisions are influenced by how the choices are stated. Tversky and Kahneman concluded that, in general, when a choice is framed in terms of gains (as in the first problem, which is stated in terms of saving lives), people use a risk aversion strategy, and when a choice is framed in terms of losses (as in the second problem, which is stated in terms of losing lives), people use a risk-taking strategy.

Learning algorithms

Error signals - During learning in a connectionist network, the difference between the output signal generated by a particular stimulus and the output that actually represents that stimulus. Back propagation - A process by which learning can occur in a connectionist network, in which an error signal is transmitted backward through the network. This backward-transmitted error signal provides the information needed to adjust the weights in the network to achieve the correct output signal for a stimulus.

Availability heuristic

Events that are more easily remembered are judged to be more probable than events that are less easily remembered What are my "available" memories with respect to the exams I have taken? What do those memories tell me about the success of cramming?

Wittgenstein's problem: defining 'game'

I mean board-games, card-games, ball-games, Olympic games, and so on. For if you look at them you will not see something in common to all, but similarities, relationships, and a whole series of them at that.

The general problem solving procedure

Initial state - In problem solving, the conditions at the beginning of a problem. Goal state - In problem solving, the condition that occurs when a problem has been solved. Operators - In problem solving, permissible moves that can be made toward a problem's solution. Path constraints -

Use of past knowledge (analogy)

Making a comparison in order to show a similarity between two different things. The use of analogies as an aid to solving problems. Typically, a solution to one problem, the source problem, is presented that is analogous to the solution to another problem, the target problem. shooting rays from different directions to kill the tumor at low rates to not kill the person when compared to the fortress solution

Use of mental imagery in problem solving

Mental imagery can also help in problem solving

Normative vs descriptive theories of decision making

Normative predictions: only 30% lawyers, 70% engineers (using the stats) Descriptive outcome: participants guessed entirely based on how closely each description matched their idea of each profession

Category hierarchies and networks

Organization of categories in which larger, more general categories are divided into smaller, more specific categories. These smaller categories can, in turn, be divided into even more specific categories to create a number of levels. Superordinate/global level: furniture Basic level: table Subordinate/specific level: kitchen table

Inductive reasoning (induction)

Reasoning in which a conclusion follows from a consideration of evidence. This conclusion is stated as being probably true rather than definitely true, as can be the case for the conclusions from deductive reasoning. Think about how time-consuming it would be if you had to approach every experience as if you were having it for the first time. Inductive reasoning provides the mechanism for using past experience to guide present behavior. Strength of argument - • Representativeness of observations Number of observations • Quality of observations

Deductive reasoning

Reasoning that involves syllogisms in which a conclusion logically follows from premises. Effects of content

The insight sequence

Representation - how people represent a problem in their mind Solution - how solving a problem involves a reorganization or restructuring of this representation Restructuring - The process of changing a problem's representation. According to the Gestalt psychologists, restructuring is the key mechanism of problem solving.

Associative paths (Collins & Quillian, 1969) and searching associative links

Researchers pointed out that this theory couldn't explain the typicality effect, in which reaction times for statements about an object are faster for more typical members of a category than for less typical members (see page 250; Rips et al., 1973). Thus, the statement "A canary is a bird" is verified more quickly than "An ostrich is a bird," but the model predicts equally fast reaction times because "canary" and "ostrich" are both one node away from "bird."

Network account of

State-dependent learning - Context reinstatement - Priming -

Taxonomic structure of categories

Superordinate (global) - furniture Basic object level (important) - chair Subordinate - kitchen you gain more common features when you go down the hierarchy than up we tend to favor the basic level examples Different cultures tend to use the same basic-level categories, at least for living things

Mapping similar problems

Surface structure - what the objects looked like (used by novices to solve problems) Deep structure - the underlying principles involved (used by experts to solve problems)

Belief perseverance

Tendency to think a syllogism is valid if its conclusion is believable or that it is invalid if the conclusion is not believable.

Defining attribute model (definitional approach)

The idea that we can decide whether something is a member of a category by determining whether the object meets the definition of the category. Definitions work well for some things, such as geometric objects. However, for most natural objects (such as birds, trees, and plants) and many human-made objects (like chairs), definitions do not work well at all.

Problem space

The initial state, goal state, and all the possible intermediate states for a particular problem. • Initial state - the knowledge and resources you have at the outset. • Goal state - the state you are working towards. • Operators - available tools or actions. • Path constraints - limits that rule out some operations.

Law of large numbers

The larger the number of individuals that are randomly drawn from a population, the more representative the resulting group will be of the entire population. Conversely, samples of small numbers of individuals will be less representative of the population.

Associations

The phenomenon in learning that states we are better able to remember information if it is paired with something we are familiar with or otherwise stands out.

means-end analysis (heuristic)

The primary goal of means-end analysis is to reduce the difference between the initial and goal states. This is achieved by creating subgoals—approach to problem solving, intermediate states that move the process of solution closer to the goal.

Representativeness heuristic

The probability that an event A comes from class B can be determined by how well A resembles the properties of class B. Our belief that each memory we have is probably representative of all our other memories as well; a single data point is used to represent many data points example: stereotyping

Specificity coding (local representation)

The representation of specific stimuli are signaled by activity in specific neurons

Distributed coding (distributed representation)

The representation of specific stimuli by the pattern of firing of many neurons

Confirmation bias

The tendency to selectively look for information that conforms to our hypothesis and to overlook information that argues against it. ex. mason 2,4,6 sequence and lord and coworkers capital punishment article

Propositions (Anderson's ACT)

Type node - Token node - Time node - Location node - Episodes -

output nodes

Units in a connectionist network that contain the final output of the network.

Well- vs ill-defined problems

Well-defined: correct answer, certain procedures will lead to solution (rare) Ill-defined: path to solution is unclear, no one "correct" answer

Categorization

a cognitive process used to organize information by placing it into larger groupings of information Shared-knowledge - We group objects into categories according to the objects' shared properties. (like different instruments can make music) Object-specific knowledge - like each instrument is different.. a guitar has strings, trumpet can be blown into, drum is slapped

categorical syllogisms

are logical arguments containing two premises and a conclusion If the statements do not follow each other, then it cannot be valid, even if it is true a syllogism can be valid and not true, invalid and true, or valid and true valid, but not true:

Connection weights

determine how strongly signals from one unit increase or decrease activity of next unit

Typicality effects

exemplars that are more average or normal for a given category are likely to be listed first when people are asked to name exemplars of that category, and are more rapidly verified as category members. Fuzzy boundaries - with no clear specification of membership and non- membership Graded membership - the idea that some members (those closer to the prototype) are "better" members of the category than others In sentence-verification task - A sentence like "Robins are birds" can be verified faster than a sentence like "Penguins are birds." In production tasks - If we ask people to name as many birds as they can, they typically start with category members that are closest to the prototype (e.g., robin). • Notice that different people will have different prototypes depending on their experiences. • For instance, consider the prototypical house in the United States compared to Japan. In picture identification task - • response times will be fastest for pictures of category members that are closest to the prototype (e.g., robin). in rating tasks - The more prototypical category members are also "privileged" in rating tasks (the more prototypical you are, the more privileged)

Experts vs. Novices

experts - have much experience with the problem at hand - they think things through - utilize previous knowledge but in an alternative way - make changes as needed One disadvantage is that knowing about the established facts and theories in a field may make experts less open to new ways of looking at problems. novices - try to accomplish the goal as quickly as possible but in a satisfactory manner - just trying to get by

family resemblance

features that appear to be characteristic of category members but may not be possessed by every member For example, chairs and sofas share the characteristics of having legs, having backs, you sit on them, they can have cushions, and so on. When an item's characteristics have a large amount of overlap with the characteristics of many other items in a category, this means that the family resemblance of these items is high.

Working backward

from finish to start (work backwards from airport to home)

Insight vs non-insight problems

insight - Sudden realization of a problem's solution Some point out that people often experience problem solving as an "Aha!" experience—at one point they don't have the answer, and the next minute they have solved the problem—which is one of the characteristics associated with insight problems Janet Metcalfe and David Wiebe (1987) did an experiment designed to distinguish between insight problems and noninsight problems. They hypothesized that there should be a difference in how subjects feel they are progressing toward a solution in insight problems versus noninsight problems. They predicted that subjects working on an insight problem, in which the answer appears suddenly, should not be very good at predicting how near they are to a solution. Subjects working on a noninsight problem, which involves a more methodical process, would be more likely to know when they are getting closer to the solution. For the insight problems (solid line), warmth ratings began at 2 and then didn't change much, until all of a sudden they jumped from 3 to 7 at the end. Thus, 15 seconds before the solution, the median rating was a relatively cold 3 for the insight problems, meaning at this point subjects didn't feel they were close to a solution. In contrast, for the algebra problems (dashed line), the ratings began at 3 and then gradually increased until the problem was solved. Thus, Metcalfe and Wiebe demonstrated that solutions for problems that have been called insight problems do, in fact, occur suddenly, as measured by people's reports of how close they feel they are to a solution.

Hill-climbing strategy

is the heuristic that is the best option that moves you in the direction of the goal (one step in between initial and goal state) A hill-climbing strategy also is not helpful for the "Tower of Hanoi" problem, instead the means end analysis works because we need multiple steps to get to the solution

Conceptual knowledge

knowledge that enables us to recognize objects and events and to make inferences about their properties

Exemplars

the individual instances, or examples, of a concept that are stored in memory from personal experience Thus, if a person has encountered sparrows, robins, and blue jays in the past, each of these would be an exemplar for the category "birds." We can describe this blending of prototypes and exemplars in commonsense terms with the following example: We know generally what cats are (the prototype), but we know our own specific cat the best (an exemplar). An advantage of the exemplar view over prototype theory is that reasoning with more than one exemplar preserves information about variability. • When we first learn about concepts, we may reason based on exemplars, and with more experience, develop prototypes.

Rosch's prototype theory

the prototype is not an actual member of the category but is an "average" representation of the category Of course, not all birds are like robins, blue jays, or sparrows. Owls, buzzards, and penguins are also birds. Rosch describes these variations within categories as representing differences in typicality. • High-prototypicality: category member closely resembles category prototype - "Typical" member - For category "bird" = robin • Low-prototypicality: category member does not closely resemble category prototype - For category "bird" = penguin

hidden units

units in a connectionist network that are located between input units and output units

Conditional syllogisms

• Context is important • Familiarity is not always important Syllogism with two premises and a conclusion, like a categorical syllogism, but whose first premise is an "If ... then" statement.ƒ If I study, I'll get a good grade. I studied. Therefore, I'll get a good grade. • If two premises of a valid syllogism are true, the syllogism's conclusion must be true • Do not confuse "validity" with "truth" • Valid but factually incorrect Valid: If p, then q p therefore, q If p, then q not q therefore, not p Invalid: If p, then q q therefore, p If p, then q not p therefore, not q

Positive vs negative transfer

• Knowing the solution to one problem helped you to solve a second, similarly-structured problem (Positive Transfer) (fortress tumor example of a positive transfer) • Sometimes, though, such a mindset can prevent you from solving a problem (Negative Transfer)