Differential cost, gain, and relative frequency of reward in a sequential choice situation.

Myers, Jerome L.; Reilly, R.E.; Taub, Harvey A.

doi:10.1037/h0047103

Cited by 28 publications

(43 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This provides some explanation of why the earlier studies Myers et al, 1961) On this basis it seems unlikely that the study by Longo et al, involved a classical conditioning experiment. The responses conditioned were not described in detail, but the authors themselves note that "muchof the activity that develops in the appetitive situation may be instrumental in character-a product of adventitious reinforcement."…”

Section: Resultsmentioning

confidence: 88%

Gain, cost, and event probability as determiners of choice behavior

Myers

Suydam

1964

Psychon Sci

Self Cite

View full text Add to dashboard Cite

Differential gain, cost, and event probability were varied in a two-choice situation . Increases in the gain associated with a correct response, increases in the probability of being correct, and decreases in the cost of an incorrect response all result in increases in the probability of that response. Gain had a greater effect than cost, but both variables became less effective as event probability inc reased. Changes in these relationships were noted as a function of trials.' IntroductionThe present study was designed to investigate the effects of differential gains and costs in a two-choice situation. "Differential" means that the gain resulting from a correct de cision was a function of which choice had been made ; similarly, the cost of an incorrect choice depended upon the choice. The resultsofstudiesof·differ-entiaI'gains (Edwards, 1956; and of differential gains and costs suggest that the asymptotic probability of making the response with the highest expected value (EV) is correlated with the difference in expected values (t.EV) associated with the two responses. The acceptance of this conclusion must be qualified by noting that none of these investigations involved more than 150 trials. In a more recent experiment, with more trials and equal event probabilities, Katz (1964) investigated the effects of nine different costgain combinations. He concluded that the t.EV rule does not account for the rank ordering of choice probabilities across conditions. The present study extends Katz's research to two additional levels of event probability, and should therefore test the gene rality of his findings regarding gain-cost combinations. MethodThe probability of the more frequent event (rr),andgain

show abstract

Section: Resultsmentioning

confidence: 88%

Gain, cost, and event probability as determiners of choice behavior

Myers

Suydam

1964

Psychon Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…The evaluation of risks and risk-taking behavior also evidence negativity effects. The potential costs (negative attributes) are weighted more heavily than the potential rewards (positive attributes) of a given choice (Kogan & Wallach, 1967), a result that has been found in studies of subjects ranging from ethical risk-taking (Birnbaum, 1972;Rettig & Pasamanick, 1964;Rettig & Rawson, 1963;Rettig & Sinha, 1966) to gambling (Atthowe, 1960;Katz, 1964;Myers, Reilly, & Taub, 1961;Myers & Suydam, 1964;Myers, Suydam, & Gambino, 1965;Slovic & Lichtenstein, 1968). That negative information is weighted more heav-ily than comparable positive information when one forms an evaluation of social actors, objects, or events is well established.…”

Section: The Negativity Effect Description and Explanationmentioning

confidence: 92%

The Negativity Effect in Interaction It's All in Your Point of View

Kellermann

1989

Human Comm Res

View full text Add to dashboard Cite

The negativity effect is defined as thedisproportionateweightingofnegative information in comparison to equidistantly rialenced positive information in the formation of judgments. The informativeness explanation ofthe negativity effect posits that the evaluative extremity (the distance from a psychological neutral point) and evaluative valence (positivelnegafiael of an event serve as determinants of its informativeness. Traditionally, this informativeness hypothesis explains the Occurrence of a negativity effect by presuming a skcwcd distribution with unequal tails for the frequency of outcomesfor valence; a mirror-image distribution for informativeness as afunction ofmlence; and a negative, linear relationship between typicality and informativeness. These three assumptions were tested directly in this research through thefitting of nonlinear equations. It was determined that (a) a negatively skewed distribution ofoutcomesfor valence does not occur in interaction contexts and that (b) typicality was a determinant ofinformativeness, although its importance as a determinant varies in response to whether one's perspective is that ofan observer or a participant. Thus, reliance on evaluative extremity and evaluative valence as determinants of informativeness was rejected; these variables are only descriptors when the distribution for the frequency of outcomesfor valence is skmed and has uneven tails.onversations contain information and are vehicles for information exchange (Kellermann, 1987). Conversations are C rich in their provisions of both content and relational information, although relatively little of the information available in the conversational stream is remembered by participants. Although the gist of conversations can be recognized at above chance levels

show abstract

“…Because the source of information for learning is the same between the probability learning and the repeated choice tasks, we hypothesize that the same IBL model presented above will predict probability learning accurately. Myers, Reilly, and Taub (1961) examined probabilitylearning behavior in a set of 27 diverse problems (displayed in Table 2). They designed a 3 Â 3 between-subjects experiment where problems varied in the frequency of occurrence of the two lights, the reward for correct predictions, and the cost for incorrect predictions.…”

Section: Probability Learningmentioning

confidence: 99%

“…Participants in Myers et al (1961) were assigned randomly to play one of the 27 problems. They had to predict, in each of 150 trials, which of two lights would turn on.…”

Section: Probability Learningmentioning

confidence: 99%

Instance-based learning: a general model of repeated binary choice

Lejarraga¹

2012

Development and Learning in Organizations: An International Journal

View full text Add to dashboard Cite

A common practice in cognitive modeling is to develop new models specific to each particular task. We question this approach and draw on an existing theory, instance-based learning theory (IBLT), to explain learning behavior in three different choice tasks. The same instance-based learning model generalizes accurately to choices in a repeated binary choice task, in a probability learning task, and in a repeated binary choice task within a changing environment. We assert that, although the three tasks are different, the source of learning is equivalent and therefore, the cognitive process elicited should be captured by one single model. This evidence supports previous findings that instance-based learning is a robust learning process that is triggered in a wide range of tasks from the simple repeated choice tasks to the most dynamic decision making tasks.

show abstract

Differential cost, gain, and relative frequency of reward in a sequential choice situation.

Cited by 28 publications

References 6 publications

Gain, cost, and event probability as determiners of choice behavior

Gain, cost, and event probability as determiners of choice behavior

The Negativity Effect in Interaction It's All in Your Point of View

Instance-based learning: a general model of repeated binary choice

Contact Info

Product

Resources

About