Advances in Psychology Research, Volume 22

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Nova Publishers, 2003 - Psychology - 215 pages
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Advances in Psychology Research presents original research results on the leading edge of psychology. Each chapter has been carefully selected in an attempt to present substantial advances across a broad spectrum. Contents: Preface; Cognitive Psychology: Explicit and Implicit Processes of Metacognition; Behavioural Psychology: A Cross Sectional and Prospective Study of Crying in the First Year of Life; Cognitive Psychology: The Structure and Measurements of Self-Concept for University Students; Behavioural Psychology: Training Behaviours of the Self-employed in Canada: A Decision Tree Analysis; Attenuation of Shock-Elicited Pain by Electrical Prepulses; Social Psychology: Perceptions of Financial Stability in Retirement: Do Americans Really Know What to Expect?; Resilience of Maltreated Children in the Family; The Political Psychology of Interstate Rivalry; Index.
 

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Contents

Explicit and Implicit Processes of Metacognition
3
The Structure and Measurement of SelfConcept for University Students
19
Subjective and Objective Familiarity as Explanations for the Attraction to Average Faces
35
Behavioral Psychology
51
A Cross Sectional and Prospective Study of Crying in the First Year of Life
53
Training Behaviors of the Selfemployed in Canada A Decision Tree Analysis
75
Attenuation of ShockElicited Pain by Electrical Prepulses
97
Personality Moderates Relationship between Rating Context Factors and Rating Behavior
107
Perceptions of Financial Stability in Retirement Do Americans Really Know What to Expect?
123
Resilience of Maltreated Children in the Family
137
The Political Psychology of Interstate Rivalry
155
How to make Friends in Virtual Worlds The Role of Emoticons Motivation Sociability and Skepticism
171
Developmental Psychology
187
General Trends and Individual Differences Perspectives on Normal Speech Development
189
Contents of Earlier Volumes
203
Index
209

Social Psychology
121

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Page 13 - NMa(B) where A and B are two different conditions that lead to the same action a. The measure compares essentially the percentage of positive matches under different conditions A and B (with the Laplace estimator; Lavrac and Dzeroski 1994). If A can improve the percentage to a certain degree over B, then A is considered better than B. In the algorithm, if a rule is better compared with the match-all rule (ie, the rule with the condition that matches all inputs), then the rule is considered successful...
Page 10 - Q(y,a), and y is the new state resulting from action a. Thus, the updating is based on the temporal difference in evaluating the current state and the action chosen. Through successive updates of the Q function, the...
Page 13 - IG(C',C) > 0, where C is the current condition of a matching rule, all refers to no condition at all (with regard to the same action specified by the rule), and C...
Page 9 - A Q- value is an evaluation of the "quality" of an action in a given state: Q(x, a) indicates how desirable action a is in state x (which consists of sensory input).
Page 9 - ... extracts a rule that corresponds to the decision and adds the rule to the rule network. Then, in subsequent interactions with the world, the agent verifies the extracted rule by considering the outcome of applying the rule: if the outcome is not successful, then the rule should be made more specific and exclusive of the current case; if the outcome is successful, the agent may try to generalize the rule to make it more universal.
Page 9 - Q-learning, we chose to use a four-layered network, in which the first three layers form a backpropagation network for computing Q-values and the fourth layer (with only one node) performs stochastic decision making. The output of the third layer (ie, the output layer of the backpropagation network) indicates the Q-value of each action (represented by an individual node), and the node in the fourth layer determines probabilistically the action to be performed based on a Boltzmann distribution (Watkins...
Page 13 - ... for applying the expansion and shrinking operators, on the other hand, is based on the aforementioned statistical test. Expansion amounts to adding an additional value to one input dimension in the condition of a rule, so that the rule will have more opportunities of matching inputs, and shrinking amounts to removing one value from one input dimension in the condition of a rule, so that it will have less opportunities of matching inputs. Here are the detailed descriptions of these operators:...

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