The Dilution Effect and Information Integration in Perceptual Decision Making

Sensory and multisensory reasoning: Is Bayesian updating modality-dependent?

Bayesianism assumes that probabilistic updating does not depend on the sensory modality by which information is processed. In this study, we investigate whether probability judgments based on visual and auditory information conform to this assumption. In a series of five experiments, we found that this is indeed the case when information is acquired through a single modality (i.e., only auditory or only visual) but not necessarily so when it comes from multiple modalities (i.e., audio-visual). In the latter case, judgments prove more accurate when both visual and auditory information individually support (i.e., increase the probability of) the hypothesis they also jointly support (synergy condition) than when either visual or auditory information support one hypothesis that is not the one they jointly support (contrast condition). In the extreme case in which both visual and auditory information individually support an alternative hypothesis to the one they jointly support (i.e., double-contrast condition), participants' accuracy is not only lower than in the synergy condition but near chance. This synergy-contrast effect represents a violation of the assumption that information modality is irrelevant for Bayesian updating and indicates an important limitation of multisensory integration, one which has not been previously documented.

A dilution effect without dilution: When missing evidence, not non-diagnostic evidence, is judged inaccurately

When asked to combine two pieces of evidence, one diagnostic and one non-diagnostic, people show a dilution effect: the addition of non-diagnostic evidence dilutes the overall strength of the evidence. This non-normative effect has been found in a variety of tasks and has been taken as evidence that people inappropriately combine information. In a series of five experiments, we found the dilution effect, but surprisingly it was not due to the inaccurate combination of diagnostic and non-diagnostic information. Because we have objectively correct answers for our task, we could see that participants were relatively accurate in judging diagnostic evidence combined with non-diagnostic evidence, but overestimated the strength of diagnostic evidence alone. This meant that the dilution effect - the gap between diagnostic evidence alone and diagnostic evidence combined with non-diagnostic evidence - was not caused by dilution. We hypothesized that participants were filling in "missing" evidence in a biased fashion when presented with diagnostic evidence alone. This hypothesis best explained the experimental results.

When the Good Looks Bad: An Experimental Exploration of the Repulsion Effect

When people are choosing among different options, context seems to play a vital role. For instance, adding a third option can increase the probability of choosing a similar dominating option. This attraction effect is one of the most widely studied phenomena in decision-making research. Its prevalence, however, has been challenged recently by the tainting hypothesis, according to which the inferior option contaminates the attribute space in which it is located, leading to a repulsion effect. In an attempt to test the tainting hypothesis and explore the conditions under which dominated options make dominating options look bad, we conducted four preregistered perceptual decision-making studies with a total of 301 participants. We identified two factors influencing individuals' behavior: stimulus display and stimulus design. Our results contribute to a growing body of literature showing how presentation format influences behavior in preferential and perceptual decision-making tasks.

Bayesian statistics to test Bayes optimality

We agree with the authors that putting forward specific models and examining their agreement with experimental data are the best approach for understanding the nature of decision making. Although the authors only consider the likelihood function, prior, cost function, and decision rule (LPCD) framework, other choices are available. Bayesian statistics can be used to estimate essential parameters and assess the degree of optimality.