Moderators of the feature-positive effect in abstract hypothesis-evaluation tasks.

Rusconi, Patrice; Crippa, Franca; Russo, Selena; Cherubini, Paolo

doi:10.1037/a0028173

Cited by 5 publications

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“…There is a large Bayesian reasoning literature on how people evaluate evidence to make judgments (e.g., Beach, 1968;Cherubini, Rusconi, Russo, & Crippa, 2013;Cosmides & Tooby, 1996;Fischhoff & Beyth-Marom, 1983;Gigerenzer & Hoffrage, 1995;Hammerton, 1973;McKenzie, 1994;Rusconi, Crippa, Russo, & Cherubini, 2012;Rusconi, Marelli, Russo, D'Addario, & Cherubini, 2013;Rusconi & McKenzie, in press;Slovic & Lichtenstein, 1971;Villejoubert & Mandel, 2002). The issue of whether people's intuitions about the value of obtained evidence align with theoretically optimal models has been the object of recent studies that noted the theoretical and empirical validity of two norms, namely, measures L and Z (e.g., Crupi, Tentori, & Gonzalez, 2007;Fitelson, 2001Fitelson, , 2006Mastropasqua, Crupi, & Tentori, 2010;Tentori, Crupi, Bonini, & Osherson, 2007).…”

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confidence: 99%

Evidence evaluation: Measure Z corresponds to human utility judgments better than measure L and optimal-experimental-design models.

Rusconi¹,

Marelli²,

D’Addario³

et al. 2014

Journal of Experimental Psychology: Learning, Memory, and Cogni

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Evidence evaluation is a crucial process in many human activities, spanning from medical diagnosis to impression formation. The present experiments investigated which, if any, normative model best conforms to people's intuition about the value of the obtained evidence. Psychologists, epistemologists, and philosophers of science have proposed several models to account for people's intuition about the utility of the obtained evidence with respect either to a focal hypothesis or to a constellation of hypotheses. We pitted against each other the so called optimal-experimental-design models (i.e., Bayesian diagnosticity, log 10 diagnosticity, information gain, Kullback-Leibler distance, probability gain, and impact) and measures L and Z to compare their ability to describe humans' intuition about the value of the obtained evidence. Participants received words-andnumbers scenarios concerning two hypotheses and binary features. They were asked to evaluate the utility of "yes" and "no" answers to questions about some features possessed in different proportions (i.e., the likelihoods) by two types of extraterrestrial creatures (corresponding to two mutually exclusive and exhaustive hypotheses). Participants evaluated either how an answer was helpful or how an answer decreased/increased their beliefs with respect either to a single hypothesis or to both hypotheses. We fitted mixed-effects models and we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) values to compare the competing models of the value of the obtained evidence. Overall, the experiments showed that measure Z was the best-fitting model of participants' judgments of the value of obtained answers. We discussed the implications for the human hypothesis-evaluation process.

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confidence: 99%

Evidence evaluation: Measure Z corresponds to human utility judgments better than measure L and optimal-experimental-design models.

Rusconi¹,

Marelli²,

D’Addario³

et al. 2014

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…Rather, the greater difficulty in belief updating in light of the answers to the asymmetrically disconfirming question reveals people's tendency to overweigh the evidential strength of "yes" versus "no" answers. We found this form of the feature-positive effect (e.g., Cherubini et al, in press;Jenkins & Sainsbury, 1969, 1970Newman et al, 1980;Rusconi, Crippa, et al, 2012; Rusconi et al, in press) in both experiments, as revealed by the significant main effects of answer. This finding is in keeping with previous similar experiments (Slowiaczek et al, 1992, Experiments 1A, 2B, and 2C).…”

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confidence: 57%

“…In this example we consider two exhaustive and mutually exclusive hypotheses, therefore an answer confirms one hypothesis to the same extent as it disconfirms the other (e.g., Nelson, 2005;Nickerson, 1996). Accordingly, in this example, the formula for Impact reduces to the absolute value of the difference between the posterior probability of either hypothesis and .5 (that is, the prior probability of either hypothesis).…”

Section: How To Evaluate the Informativeness Of Answersmentioning

confidence: 99%

“…Whenever the prior probabilities of the hypotheses are equal, the values of utility predicted by Probability Gain are identical to those of another metric, Impact. This measure quantifies the absolute change in beliefs from the prior to the posterior probabilities of the hypotheses (e.g., Nickerson, 1996). In particular, the Impact of a "yes" answer (or, in an equivalent form, the evidential value of the presence of a feature) can be expressed as follows (see e.g.,…”

Section: How To Evaluate the Informativeness Of Answersmentioning

confidence: 99%

“…In other words, the patient fails to value the evidential strength of the incoming evidence (the appearance of the police car) under hypotheses that are different from the one she is considering. Another way to misweigh evidence is failing to appreciate that the presence of an event might convey a different amount of information than its absence (e.g., Cherubini, Rusconi, Russo, & Crippa, in press; Fischhoff & Beyth-Marom, 1983;Rusconi, Crippa, Russo, & Cherubini, 2012; Rusconi, Marelli, Russo, D'Addario, & Cherubini, in press). For example, an investigator might fail to notice that the absence of a suspect's fingerprints from a crime scene can be more revealing than their presence if the suspect was familiar with the victim.…”

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Insensitivity and Oversensitivity to Answer Diagnosticity in Hypothesis Testing

Rusconi

McKenzie

2013

Quarterly Journal of Experimental Psychology

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Two experiments examined how people perceive the diagnosticity of different answers ("yes" and "no") to the same question. We manipulated whether the "yes" and the "no" answers conveyed the same amount of information or not, as well as the presentation format of the probabilities of the features inquired about. In Experiment 1, participants were presented with only the percentages of occurrence of the features, which most straightforwardly apply to the diagnosticity of "yes" answers. In Experiment 2, participants received in addition the percentages of the absence of features, which serve to assess the diagnosticity of "no" answers. Consistent with previous studies, we found that participants underestimated the difference in the diagnosticity conveyed by different answers to the same question. However, participants' insensitivity was greater when the normative (Bayesian) diagnosticity of the "no" answer was higher than that of the "yes" answer. We also found oversensitivity to answer diagnosticity, whereby participants valued as differentially diagnostic two answers that were normatively equal in terms of their diagnosticity. Presenting to participants the percentages of occurrence of the features inquired about together with their complements increased their sensitivity to the diagnosticity of answers. We discuss the implications of these findings for confirmation bias in hypothesis testing.Keywords: hypothesis testing; answer diagnosticity; insensitivity; oversensitivity; the featurepositive effect. INSENSITIVITY TO ANSWER DIAGNOSTICITY 3 Insensitivity and Oversensitivity to Answer Diagnosticity in Hypothesis TestingAn efficient evaluation of the impact of the information that we receive or we acquire is of critical importance in many everyday life situations as well as in many professional contexts.Consider, for example, the case of a deluded patient who interprets the appearance of a police car in the busy street where she is walking as a cue that the police is chasing her. In this case, the patient is disregarding the probability that the police is chasing another person and not her (Hemsley & Garety, 1986). In other words, the patient fails to value the evidential strength of the incoming evidence (the appearance of the police car) under hypotheses that are different from the one she is considering. Another way to misweigh evidence is failing to appreciate that the presence of an event might convey a different amount of information than its absence (e.g., Cherubini, Rusconi, Russo, & Crippa, in press; Fischhoff & Beyth-Marom, 1983;Rusconi, Crippa, Russo, & Cherubini, 2012; Rusconi, Marelli, Russo, D'Addario, & Cherubini, in press). For example, an investigator might fail to notice that the absence of a suspect's fingerprints from a crime scene can be more revealing than their presence if the suspect was familiar with the victim. Similarly, eyewitness nonidentifications might be at least as informative as identifications (e.g., Clark & Wells, 2008; Wells & Lindsay, 1980). The present artic...

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Individual Differences in the Susceptibility to the Feature-Positive Effect

Rassin

2023

Psychological Test Adaptation and Development

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The feature-positive effect (FPE) is the phenomenon that learning organisms are better at detecting the association between two present stimuli than between the absence of one stimulus and the presence of the other. Although the FPE was first described over 50 years ago, it remains an ill-studied and ill-understood bias. Nonetheless, the FPE can have far-reaching negative consequences in various decision-making contexts. In the present contribution, an attempt was made to document the FPE with a within-subject measurement. Initial psychometric analyses (PCA, Cronbach’s α, test–retest reliability, and concurrent validity correlations) suggest that such intraindividual measurement of the FPE is indeed possible. Consequently, individuals can be expected to differ in their susceptibility to the FPE. This individual difference factor can be measured with the described feature-positive effect test (FPET), which opens new research avenues in which FPE-proneness is taken as a starting point.

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Moderators of the feature-positive effect in abstract hypothesis-evaluation tasks.

Cited by 5 publications

References 46 publications

Evidence evaluation: Measure Z corresponds to human utility judgments better than measure L and optimal-experimental-design models.

Evidence evaluation: Measure Z corresponds to human utility judgments better than measure L and optimal-experimental-design models.

Insensitivity and Oversensitivity to Answer Diagnosticity in Hypothesis Testing

Individual Differences in the Susceptibility to the Feature-Positive Effect

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