Waiting and weighting: Information sampling is a balance between efficiency and error-reduction

Meier, Kimberly; Blair, Mark R.

doi:10.1016/j.cognition.2012.09.014

Cited by 29 publications

(67 citation statements)

References 18 publications

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“…This points to a dynamic relationship between the two movements, with shifting roles and specializations. Although mouse movements, like those in Experiment 10, require less energy than rotating or grasping objects, they require more motor resources than eye movements, and consequently, less shifting should be expected [34]. It is not surprising, then, that we see those kinds of qualitative differences at an aggregate level in the TIPS score between Experiments 9 and 10 (Fig.…”

Section: Discussionmentioning

confidence: 76%

“…Methodological details and technical specifications are available in Meier and Blair [34] who report a different set of findings. Like Experiment 8, this task uses a category structure that was designed to invoke stimulus specific attention (Table 2).…”

Section: Methodsmentioning

confidence: 99%

“…Additional analysis from this experiment, not reported here, are included elsewhere where the methods and materials are also described [34], and the raw data are found through SFU summit, collection 94 entitled “Probability Gain versus Information Gain in Category Learning: Hand Movements”. The critical difference between this experiment and the ones that precede it was the manner in which participants access information.…”

Section: Methodsmentioning

confidence: 99%

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Learning-Induced Changes in Attentional Allocation during Categorization: A Sizable Catalog of Attention Change as Measured by Eye Movements

et al. 2014

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Learning how to allocate attention properly is essential for success at many categorization tasks. Advances in our understanding of learned attention are stymied by a chicken-and-egg problem: there are no theoretical accounts of learned attention that predict patterns of eye movements, making data collection difficult to justify, and there are not enough datasets to support the development of a rich theory of learned attention. The present work addresses this by reporting five measures relating to the overt allocation of attention across 10 category learning experiments: accuracy, probability of fixating irrelevant information, number of fixations to category features, the amount of change in the allocation of attention (using a new measure called Time Proportion Shift - TIPS), and a measure of the relationship between attention change and erroneous responses. Using these measures, the data suggest that eye-movements are not substantially connected to error in most cases and that aggregate trial-by-trial attention change is generally stable across a number of changing task variables. The data presented here provide a target for computational models that aim to account for changes in overt attentional behaviors across learning.

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Section: Discussionmentioning

confidence: 76%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Learning-Induced Changes in Attentional Allocation during Categorization: A Sizable Catalog of Attention Change as Measured by Eye Movements

et al. 2014

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“…If a learner can identify the maximum entropy question among the 100 available queries, he or she will necessarily select the highest information gain question. Tasks that do not fall under the umbrella of Theorem 1 include Meier and Blair's (2013) scenario, one of the scenarios considered by Navarro and Perfors (2011), and the Planet Vuma scenario (Skov & Sherman, 1986). Navarro and Perfors' results also demonstrate that there are cases in which uncertainty sampling maximizes information gain, even if the hypotheses' predictions are not deterministic.…”

Section: The Maximum Entropy Question Heuristic and Information Gainmentioning

confidence: 99%

Towards a theory of heuristic and optimal planning for sequential information search

Nelson

Meder

Jones³

2018

Preprint

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How should tests (or queries, questions, or experiments) be selected? Does it matter if only a single test is allowed, or if a sequential test strategy can be planned in advance? This article contributes two sets of theoretical results bearing on these questions. First, for selecting a single test, several Optimal Experimental Design (OED) ideas have been proposed in statistics and other disciplines. The OED models are mathematically nontrivial. How is it that they often predict human behavior well? One possibility is that simple heuristics can approximate or exactly implement OED models. We prove that heuristics can identify the highest information value queries (as quantified by OED models) in several situations, thus providing a possible algorithmic-level theory of human behavior. Second, we address whether OED models are optimal for sequential search, as is frequently presumed. We consider the Person Game, a 20-questions scenario, as well as a two-category, binary feature scenario, both of which have been widely used in psychological research. In each task, we demonstrate via specific examples and extended computational simulations that neither the OED models nor the heuristics considered in the literature are optimal. Little research addresses human behavior in such situations. We call for experimental research into how people approach the sequential planning of tests, and theoretical research on what sequential planning procedures are most successful, and we offer a number of testable predictions for discriminating among candidate models.

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“…Other measures of the value of obtained evidence have been proposed in the psychological literature (e.g., Crupi, Tentori, & Gonzalez, 2007;Mastropasqua, Crupi, & Tentori, 2010;Nelson, 2005Nelson, , 2008Tentori, Crupi, Bonini, & Osherson, 2007). Among them, Probability Gain is a measure of error reduction that was found to best capture people's intuitions about information acquisition (Nelson, McKenzie, Cottrell, & Sejnowski, 2010; see however Meier & Blair, 2013 for findings showing people's preference for efficiency over Probability Gain). 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.…”

Section: How To Evaluate the Informativeness Of Answersmentioning

confidence: 99%

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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Waiting and weighting: Information sampling is a balance between efficiency and error-reduction

Cited by 29 publications

References 18 publications

Learning-Induced Changes in Attentional Allocation during Categorization: A Sizable Catalog of Attention Change as Measured by Eye Movements

Learning-Induced Changes in Attentional Allocation during Categorization: A Sizable Catalog of Attention Change as Measured by Eye Movements

Towards a theory of heuristic and optimal planning for sequential information search

Insensitivity and Oversensitivity to Answer Diagnosticity in Hypothesis Testing

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