Psychology 302-50 Chapter 2
Psychology 302-50 Chapter 2 Psych 302-50
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This 6 page Class Notes was uploaded by Alisha orr on Wednesday July 6, 2016. The Class Notes belongs to Psych 302-50 at University of Louisville taught by Lora Haynes in Summer 2016. Since its upload, it has received 27 views. For similar materials see Experimental Psychology in Psychlogy at University of Louisville.
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Date Created: 07/06/16
Chapter 2 Theory – A plausible or scientifically acceptable well sustained explanation of some aspect of the natural world; an organized system of accepted knowledge that applies in a variety of circumstances to explain a specific set of phenomena and predict the characteristics of as yet unobserved phenomena. Only observable, confirmable phenomena fall within the sphere of a scientific theory Theory vs. Hypothesis Hypothesis is a tentative explanation for an observation, phenomenon, or scientific problem that can be tested by further investigation Scientific hypothesis must be posed in a form that allows them to be rejected Hypothesis and theories differ in several ways 1) Unlike theories, hypotheses are not well substantiated. They are actually more like educated guesses and are more limited in scope. 2) Hypothesis are relatively simple; do not involve a complex set of assumptions Theory vs. Law A theory that has been substantially verified is sometimes called a law Laws are usually empirically verified, quantitative relationships between two or more variables. Not normally subject to the disconfirmation that theories are. Theories vs. Model Refers to a specific implementation of a more general theoretical view Can represent an application of a general theory to a specific situation The Rescorla – Wagner model of classical conditioning specifies how the associative strength of a conditional stimulus (CS) is to be calculated following each of a series of trials in which the CS is presented alone or in conjunction with other stimuli. In the case of the Rescorla – Wagner model, the assumptions of the model can be applied to generate predictions for reinforcement of a simple CS, for inhibitory conditioning, for extinction and for discrimination learning. Computer Modeling A computer model is a set of program statements that define the variables to be considered and the ways in which their values will change over the course of time or trials. Developing a computer model offers several advantages: 1) May reveal inconsistencies unspoken assumptions, or other defects 2) Eliminates ambiguity 3) Computer model will show what is to be expected under specific conditions 4) Can be compared with the behavior of real people or animals under actual conditions to determine whether the model behaves realistically 5) Competing theories can be evaluated An interesting example of computer modeling is provided by Josef Nerb and Hans Spada – they were interested in explaining the relationship between cognitive, emotional, and behavior responses to environmental disasters Invented a computer model called intuitive thinking in environmental risk appraisal (or ITERA) Mechanistic Explanations vs. Functional Explanations A mechanistic explanation describes the mechanism ( physical components) and the chain of cause and effect through which conditions act on mechanism to produce its behavior; it describes how something works Functional explanation describes an attribute of something (such as physical attractiveness) in terms of its function; it describes why the attribute or system exist Mechanistic explanations tell you how a system works without telling you why it does what it does; functional explanations refer to the purpose or goal of a given attribute or system without describing how those purposes or goals are achieved. Given the choice between a mechanistic explanation and a functional one, you should prefer the mechanistic one. Classifying Theories Theories can be classified along several dimensions. Three important ones are 1) Quantitative or qualitative 2) Level or description 3) Scope of theory Quantitative Theory – defines the relationships between its variables and constants in a set of mathematical formulas. A good example of a quantitative theory in psychology is information integration theory Qualitative theory – is any theory that is not quantitative. Tend to be stated in verbal rather than mathematical terms A good example of a qualitative theory in psychology is a theory of language acquisition by Noam Chomsky A theory may address itself to the first goal (description), whereas another may address itself to the second (explanation). So some theories are primarily designed to describe a phenomenon whereas others attempt to explain relationships among variables that control a phenomenon. Descriptive theory – a theory the merely describes a relationship An example is Wilhelm Wundts systematic theory of the structure of consciousness. Descriptive theories provide only the weakest form of explanation Analogical theory – explains a relationship through analogy To develop an analogical theory, you equate each variable in a physical system with a variable in the behavioral system to be modeled. You then plug in values for the new variables and apply the rules of the original theory in order to generate predictions. An example of the analogical theory was provided by Konrad Lorenz Fundamental Theories – Do not depend on analogy to provide their basic structure. Instead, they propose a new structure that directly relates the variables and constants of the system. These entities and processes go beyond descriptive theories, which simply describe relationships among observable variables. Fundamental theories are rare in psychology One of the most famous fundamental theories is cognitive dissonance theory prosed by Festinger According to the theory, dissonance is the fundamental process in cognitive dissonance theory. Whenever two (or more) attitudes or behaviors are inconsistent, a negative psychological state called cognitive dissonance is aroused. The arousal of dissonance motivates an individual to reduce dissonance. This can be done by changing behavior or by changing attitudes. The third dimension along which theories differ is domain, or scope. A theory with a wide scope can be applied to a wider range of situations that can a theory with a more limited scope. Gibbon’s Scalar Expectancy Theory is an example of a theory with a relatively limited scope. Theories have several roles to play in science. These roles include providing an understanding of the phenomena for which they account, providing a basis for prediction, and guiding the direction of research Social Impact Theory – proposed by Bibb Latane. Is intended to explain the process of social influence. The amount of influence obtained is influenced obtained is dependent upon the interaction of three factors: 1) The strength of the influence force (S) 2) The immediacy of an influence source (I) 3) The number of influence sources (N) Influence = f (S x I x N) A theory can provide a sound framework for organizing and interpreting research results. Theories are valuable because they often provide ideas for new research. This is known as the heuristic value of a theory. A theory can have a heuristic value even when it is not supported by subsequent empirical research. Even in failing, a theory can have heuristic value Characteristics of a good theory Ability to account for data – To be of value, a theory must account for most of the existing data within its domain. However, a theory that fails to account for wellestablished facts within its domain is in serious trouble. Explanatory Relevance – The explanation for a phenomenon provided by a theory must offer good grounds for believing that the phenomenon would occur under specified conditions. Testability – A theory is testable if it is capable of failing some empirical test. When a theory can provide a seemingly reasonable explanation no matter what the outcome of an observation, you are probably dealing with an untestable theory. Prediction of novel events A good theory should predict new phenomena. Within its domain, a good theory should predict phenomena beyond those for which the theory was originally designed. Parsimony – Okham’s Razor states that a problem should be stated in the simplest of terms and explained with the fewest postulates possible. Today we know this term as the law of parsimony. Strategies for testing theories Confirmational strategy – tested by identifying implications of the theory for a specific situation not yet examined and then setting up the situation and observing whether the predicted effects occur. If the predicted effects are observed, the theory is said to be supported by the results, and your confidence in the theory increases. If the predicted effects do not occur, then the theory is not supported, and your confidence in it weakens. Disconfirmational strategy – You also must determine whether outcomes not expected, according to the theory, do or do not occur. This strategy follows this form: If A is true (the theory is correct), then B will not be true (a certain outcome will not occur); thus, if B is true (the outcome does happen), then A is false (the theory is erroneous). Usually you will pursue a conformational strategy when a theory is fresh and relatively untested. If the theory survives the tests, you will eventually want to pursue a disconfirmational strategy. If unexpected outcomes do occur, it means that the theory is, at best, incomplete. It will have to be developed further so that it can account for the previously unexpected outcome. Strong Inference – can only work if the alternative explanations generate welldefined predictions. The history of science is littered with failed theories: the Ptolemaic system of astronomy, the phlogiston theory of heat, Gall’s phrenology, etc.
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