ORCID Profile
0000-0003-1283-8313
Current Organisation
University of Granada
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Springer Science and Business Media LLC
Date: 12-11-2010
DOI: 10.3758/S13423-010-0032-2
Abstract: Many theories of contingency learning assume (either explicitly or implicitly) that predicting whether an outcome will occur should be easier than making a causal judgment. Previous research suggests that outcome predictions would depart from normative standards less often than causal judgments, which is consistent with the idea that the latter are based on more numerous and complex processes. However, only indirect evidence exists for this view. The experiment presented here specifically addresses this issue by allowing for a fair comparison of causal judgments and outcome predictions, both collected at the same stage with identical rating scales. Cue density, a parameter known to affect judgments, is manipulated in a contingency learning paradigm. The results show that, if anything, the cue-density bias is stronger in outcome predictions than in causal judgments. These results contradict key assumptions of many influential theories of contingency learning.
Publisher: SAGE Publications
Date: 07-2011
DOI: 10.1080/17470218.2011.552727
Abstract: It is well known that certain variables can bias judgements about the perceived contingency between an action and an outcome, making them depart from the normative predictions. For instance, previous studies have proven that the activity level or probability of responding, P(R), is a crucial variable that can affect these judgements in objectively noncontingent situations. A possible account for the P(R) effect is based on the differential exposure to actual contingencies during the training phase, which is in turn presumably produced by in idual differences in participants' P(R). The current two experiments replicate the P(R) effect in a free-response paradigm, and show that participants' judgements are better predicted by P(R) than by the actual contingency to which they expose themselves. Besides, both experiments converge with previous empirical data, showing a persistent bias that does not vanish as training proceeds. These findings contrast with the preasymptotic and transitory effect predicted by several theoretical models.
Publisher: Elsevier BV
Date: 04-2017
Publisher: Springer Science and Business Media LLC
Date: 26-03-2013
DOI: 10.3758/S13420-013-0108-8
Abstract: Overestimations of null contingencies between a cue, C, and an outcome, O, are widely reported effects that can arise for multiple reasons. For instance, a high probability of the cue, P(C), and a high probability of the outcome, P(O), are conditions that promote such overestimations. In two experiments, participants were asked to judge the contingency between a cue and an outcome. Both P(C) and P(O) were given extreme values (high and low) in a factorial design, while maintaining the contingency between the two events at zero. While we were able to observe main effects of the probability of each event, our experiments showed that the cue- and outcome-density biases interacted such that a high probability of the two stimuli enhanced the overestimation beyond the effects observed when only one of the two events was frequent. This evidence can be used to better understand certain societal issues, such as belief in pseudoscience, that can be the result of overestimations of null contingencies in high-P(C) or high-P(O) situations.
Publisher: Mary Ann Liebert Inc
Date: 04-2007
Abstract: When people try to obtain a desired event and this outcome occurs independently of their behavior, they often think that they are controlling its occurrence. This is known as the illusion of control, and it is the basis for most superstitions and pseudosciences. However, most experiments demonstrating this effect had been conducted many years ago and almost always in the controlled environment of the psychology laboratory and with psychology students as subjects. Here, we explore the generality of this effect and show that it is still today a robust phenomenon that can be observed even in the context of a very simple computer program that users try to control (and believe that they are controlling) over the Internet. Understanding how robust and general this effect is, is a first step towards eradicating irrational and pseudoscientific thinking.
Publisher: Hogrefe Publishing Group
Date: 2016
DOI: 10.1027/1618-3169/A000309
Abstract: Abstract. Decades of research in causal and contingency learning show that people’s estimations of the degree of contingency between two events are easily biased by the relative probabilities of those two events. If two events co-occur frequently, then people tend to overestimate the strength of the contingency between them. Traditionally, these biases have been explained in terms of relatively simple single-process models of learning and reasoning. However, more recently some authors have found that these biases do not appear in all dependent variables and have proposed dual-process models to explain these dissociations between variables. In the present paper we review the evidence for dissociations supporting dual-process models and we point out important shortcomings of this literature. Some dissociations seem to be difficult to replicate or poorly generalizable and others can be attributed to methodological artifacts. Overall, we conclude that support for dual-process models of biased contingency detection is scarce and inconclusive.
Publisher: Informa UK Limited
Date: 06-2010
Publisher: Public Library of Science (PLoS)
Date: 27-09-2012
Publisher: Springer Science and Business Media LLC
Date: 10-2009
DOI: 10.1007/BF03395681
Publisher: Frontiers Media SA
Date: 02-07-2015
Publisher: Springer Science and Business Media LLC
Date: 15-01-2010
DOI: 10.3758/PBR.17.1.117
Publisher: Public Library of Science (PLoS)
Date: 12-09-2017
No related grants have been discovered for Fernando Blanco.