scholarly journals The Redundancy Effect on Human Predictive Learning: Evidence against a Propositional Interpretation

2020 ◽  
Vol 12 (3) ◽  
pp. 105-115
Author(s):  
Jorge A. Pinto ◽  
Daniel E. Núñez
Keyword(s):  
Common Error ◽  
Redundancy Effect ◽  
Predictive Learning ◽  
Discrimination Procedure

The redundancy effect is the finding of greater learning when an X stimulus is trained in an A+ AX+ blocking procedure, than when a Y stimulus is trained in a BY+ CY- discrimination procedure. These findings are new and theoretically challenging for all conditioning theories that calculate learning based on a common error. For this reason, we alternatively examined the possibility that the phenomenon is the result of a propositional reasoning. In an experiment, we replicated the basic effect and we found out that the addition of instructions on the occurrence of the consequences at a submaximal level does not have a significant impact on the redundancy effect. These findings are discussed with regard to a propositional and associative approach based on the assumption that the experimental stimuli share a common feature.

scholarly journals A test for a difference in the associability of blocked and uninformative cues in human predictive learning

2018 ◽  
Vol 72 (2) ◽  
pp. 222-237 ◽  
Author(s):  
Metin Uengoer ◽  
Dominic M Dwyer ◽  
Stephan Koenig ◽  
John M Pearce
Keyword(s):  
Error Term ◽  
Simple Discrimination ◽  
Common Error ◽  
Predictive Learning ◽  
Stage 1

In human predictive learning, blocking, A+ AB+, and a simple discrimination, UX+ VX–, result in a stronger response to the blocked, B, than the uninformative cue, X (where letters represent cues and + and – represent different outcomes). To assess whether these different treatments result in more attention being paid to blocked than uninformative cues, Stage 1 in each of three experiments generated two blocked cues, B and E, and two uninformative cues, X and Y. In Stage 2, participants received two simple discriminations: either BX+ EX– and BY+ EY–, or BX+ BY– and EX+ EY–. If more attention is paid to blocked than uninformative cues, then the first pair of discriminations will be solved more readily than the second pair. In contrast to this prediction, both discriminations were acquired at the same rate. These results are explained by the theory of Mackintosh, by virtue of the assumption that learning is governed by an individual rather than a common error term.

The fate of redundant cues in human predictive learning: The outcome ratio effect

2019 ◽  
Vol 72 (8) ◽  
pp. 1945-1960
Author(s):  
Metin Uengoer ◽  
Harald Lachnit ◽  
John M Pearce
Keyword(s):  
Allergic Reaction ◽  
Associative Strength ◽  
Error Term ◽  
Common Error ◽  
Ratio Effect ◽  
Hypothetical Patient ◽  
Predictive Learning

In four experiments, participants were shown a sequence of pairs of pictures of food and asked to predict whether each pair signalled an allergic reaction in a hypothetical patient. The pairs of pictures were used to present two simple discriminations that differed in their outcome ratio. A rich discrimination, 3AX+ BX−, involved three trials in which the compound of two foods, AX, was followed by a reaction, for every trial in which the compound BX was not followed by the outcome. A lean discrimination, CY+ 3DY− was based on the opposite outcome ratio. Upon the completion of this training, participants were asked to rate how likely an individual food would be followed by the allergic reaction. In each experiment, the rating for X was stronger than for Y. This outcome ratio effect poses a challenge for theories of learning that assume changes in associative strength are governed by a common error term, based on the significance of all the cues present on a trial. Instead, the results are consistent with the assumption that changes in associative strength are governed by an individual error term, based on the significance of a single cue.

Combining Values

2002 ◽  
Vol 7 (2) ◽  
pp. 1-4, 12 ◽  
Author(s):  
Christopher R. Brigham
Keyword(s):  
Lower Extremity ◽  
Upper Extremity ◽  
Evaluation Method ◽  
Proximal Phalanx ◽  
Common Error ◽  
The Third ◽  
Whole Person ◽  
Impairment Ratings ◽  
Upper Extremity Impairment ◽  
The Individual

Abstract To account for the effects of multiple impairments, evaluating physicians must provide a summary value that combines multiple impairments so the whole person impairment is equal to or less than the sum of all the individual impairment values. A common error is to add values that should be combined and typically results in an inflated rating. The Combined Values Chart in the AMA Guides to the Evaluation of Permanent Impairment, Fifth Edition, includes instructions that guide physicians about combining impairment ratings. For example, impairment values within a region generally are combined and converted to a whole person permanent impairment before combination with the results from other regions (exceptions include certain impairments of the spine and extremities). When they combine three or more values, physicians should select and combine the two lowest values; this value is combined with the third value to yield the total value. Upper extremity impairment ratings are combined based on the principle that a second and each succeeding impairment applies not to the whole unit (eg, whole finger) but only to the part that remains (eg, proximal phalanx). Physicians who combine lower extremity impairments usually use only one evaluation method, but, if more than one method is used, the physician should use the Combined Values Chart.

The Effect of Outcome Probability on Generalization in Predictive Learning

2019 ◽  
Vol 66 (1) ◽  
pp. 23-39 ◽  
Author(s):  
Hadar Ram ◽  
Dieter Struyf ◽  
Bram Vervliet ◽  
Gal Menahem ◽  
Nira Liberman
Keyword(s):  
High Probability ◽  
Lower Probability ◽  
Perceptual Similarity ◽  
Learning Paradigm ◽  
Predictive Learning ◽  
Lightning Bolt ◽  
Outcome Probability

Abstract. People apply what they learn from experience not only to the experienced stimuli, but also to novel stimuli. But what determines how widely people generalize what they have learned? Using a predictive learning paradigm, we examined the hypothesis that a low (vs. high) probability of an outcome following a predicting stimulus would widen generalization. In three experiments, participants learned which stimulus predicted an outcome (S+) and which stimulus did not (S−) and then indicated how much they expected the outcome after each of eight novel stimuli ranging in perceptual similarity to S+ and S−. The stimuli were rings of different sizes and the outcome was a picture of a lightning bolt. As hypothesized, a lower probability of the outcome widened generalization. That is, novel stimuli that were similar to S+ (but not to S−) produced expectations for the outcome that were as high as those associated with S+.

Widening the Diagnostic Focus: A Case for Including Human Strengths and Environmental Resources

2009 ◽  
pp. 70-88 ◽  
Author(s):  
Beatrice A. Wright ◽  
Shane J. Lopez
Keyword(s):  
Positive Psychology ◽  
Professional Practice ◽  
Cognitive Complexity ◽  
Professional Psychology ◽  
Environment Interaction ◽  
Personal Integrity ◽  
Common Error ◽  
Psychology Today ◽  
Whole Person ◽  
And Behavior

In positive psychology, we must challenge a common error of professional psychology, today: making diagnostic, treatment, and policy decisions primarily on deficiencies of the person instead of giving serious consideration to “deficits” and “strengths” of both person and environment. This mission may seem disheartening in that it requires greater rather than less cognitive complexity. Yet this multifaceted focus is crucial if two system concepts—whole person and behavior as a function of person in interaction with environment—are to betaken seriously (Lewin, 1935 ). Practice and research that fall short of attending to this person-environment interaction does a disservice to remedial possibilities and personal integrity. We have divided this chapter into two parts. In the first part, we present enlightening concepts together with supporting research. In the second part, we apply the insights gained to professional practice and research and make specific recommendations regarding each of the issues raised.

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