scholarly journals When uncertain, does human self-motion decision-making fully utilize complete information?

2018 ◽  
Vol 119 (4) ◽  
pp. 1485-1496 ◽  
Author(s):  
Torin K. Clark ◽  
Yongwoo Yi ◽  
Raquel C. Galvan-Garza ◽  
María Carolina Bermúdez Rey ◽  
Daniel M. Merfeld
Keyword(s):  
Decision Making ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Complete Information ◽  
Detection Theory ◽  
Forced Choice ◽  
Perceptual Decision Making ◽  
Perceptual Decision ◽  
Available Information ◽  
Self Motion

When forced to choose humans often feel uncertain. Investigations of human perceptual decision-making often employ signal detection theory, which assumes that even when uncertain all available information is fully utilized. However, other studies have suggested or assumed that, when uncertain, human subjects guess totally at random, ignoring available information. When uncertain, do humans simply guess totally at random? Or do humans fully utilize complete information? Or does behavior fall between these two extremes yielding “above chance” performance without fully utilizing complete information? While it is often assumed complete information is fully utilized, even when uncertain, to our knowledge this has never been experimentally confirmed. To answer this question, we combined numerical simulations, theoretical analyses, and human studies performed using a self-motion direction-recognition perceptual decision-making task (did I rotate left or right?). Subjects were instructed to make forced-choice binary (left/right) and trinary (left/right/uncertain) decisions when cued following each stimulus. Our results show that humans 1) do not guess at random when uncertain and 2) make binary and trinary decisions equally well. These findings show that humans fully utilize complete information when uncertain for our perceptual decision-making task. This helps unify signal detection theory and other models of forced-choice decision-making which allow for uncertain responses. NEW & NOTEWORTHY Humans make many perceptual decisions every day. But what if we are uncertain? While many studies assume that humans fully utilize complete information, other studies have suggested and/or assumed that when we're uncertain and forced to decide, information is not fully utilized. While humans tend to perform above chance when uncertain, no earlier study has tested whether available information is fully utilized. Our results show that humans make fully informed decisions even when uncertain.

The Pseudoscientific Structure of “Perceptual Decision-Making” and other “Signal Detection Theory"-based Research (or How Science Works When It Works)

2021 ◽  
Author(s):  
Lydia Maria Maniatis
Keyword(s):  
Decision Making ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Ad Hoc ◽  
Natural World ◽  
Detection Theory ◽  
Perceptual Decision Making ◽  
Research Activity ◽  
Perceptual Decision ◽  
Visual Neuroscience

The assumptions and formulas of “Signal Detection Theory” (SDT) dominate psychophysics and neuroscience, and serve as the basis of visual neuroscience under the rubric of “perceptual decision-making.” Here, I discuss how the overly simple, ad hoc assumptions of SDT served to rationalize the chronic failure of traditional psychophysics to achieve reliable results; how the constraints on outcomes imposed by the traditional methods combined with SDT to artificially immunize core assumptions from empirical challenge; and how consequently, research activity has been reduced to a seemingly uncomplicated - yet still non-replicable - matter of mere measurement and correlation. I contrast the structure of this ever-barren approach to the structure of research that respects reality and expands our knowledge of the natural world.

scholarly journals Quorums enable optimal pooling of independent judgements

2018 ◽  
Author(s):  
James Marshall ◽  
Ralf H.J.M. Kurvers ◽  
Jens Krause ◽  
Max Wolf
Keyword(s):  
Decision Making ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Collective Decision ◽  
Majority Voting ◽  
Single Shot ◽  
Collective Decision Making ◽  
Trade Offs ◽  
Speed Accuracy

Majority-voting and the Condorcet Jury Theorem pervade thinking about collective decision-making. Thus, it is typically assumed that majority-voting is the best possible decision mechanism, and that scenarios exist where individually-weak decision-makers should not pool information. Condorcet and its applications implicitly assume that only one kind of error can be made, yet signal detection theory shows two kinds of errors exist, ‘false positives’ and ‘false negatives’. We apply signal detection theory to collective decision-making to show that majority voting is frequently sub-optimal, and can be optimally replaced by quorum decision-making. While quorums have been proposed to resolve within-group conflicts, or manage speed-accuracy trade-offs, our analysis applies to groups with aligned interests undertaking single-shot decisions. Our results help explain the ubiquity of quorum decision-making in nature, relate the use of sub- and super-majority quorums to decision ecology, and may inform the design of artificial decision-making systems.Impact StatementTheory typically assumes that majority voting is optimal; this is incorrect – majority voting is typically sub-optimal, and should be replaced by sub-majority or super-majority quorum voting. This helps explain the prevalence of quorum-sensing in even the simplest collective systems, such as bacterial communities.

Robustness of the a b Signal Detection Theory Model of Decision Making

2007 ◽  
Vol 51 (18) ◽  
pp. 1263-1267
Author(s):  
Ernesto A. Bustamante ◽  
Brittany L. Anderson ◽  
Amy R. Thompson ◽  
James P. Bliss ◽  
Mark W. Scerbo
Keyword(s):  
Decision Making ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Classical Model ◽  
Empirical Evaluation ◽  
Detection Theory ◽  
Theory Model ◽  
Extreme Responses

Bustamante, Fallon, and Bliss (2006) showed that the a b Signal Detection Theory (SDT) model was more parsimonious, generalizable, and applicable than the classical SDT model. Additionally, they demonstrated that both models provided statistically equivalent and uncorrelated measures of sensitivity and bias under ideal conditions. The purpose of this research was to show the robustness of the a b model for handling extreme responses. We conducted an empirical evaluation of operators' decision-making and two Monte Carlo simulations. Results from the empirical study showed that the a b model provided equivalent yet independent measures of decision-making accuracy and bias, whereas the classical model failed to provide independent measures in the presence of extreme responses. The Monte Carlo simulations showed a similar trend for the superiority of the a b model. Results from this research provide evidence to support the use of the a b model instead of the classical model.

The Application of Signal Detection Theory to Decision-Making in Forensic Science

2001 ◽  
Vol 46 (2) ◽  
pp. 14962J ◽  
Author(s):  
Victoria L. Phillips ◽  
Michael J. Saks ◽  
Joseph L. Peterson
Keyword(s):  
Decision Making ◽  
Signal Detection ◽  
Forensic Science ◽  
Signal Detection Theory ◽  
Detection Theory

Differential Threshold of Length and Response Criterion for Inspecting Irregular Objects

1997 ◽  
Vol 85 (2) ◽  
pp. 723-735
Author(s):  
Chia-Fen Chi ◽  
Chin-Lung Chen
Keyword(s):  
Decision Making ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Decision Time ◽  
Visual Sensitivity ◽  
Differential Threshold ◽  
Human Beings ◽  
Experimental Conditions ◽  
Defective Items

This research investigated human visual sensitivity and bias in inspecting irregular objects. A preliminary study was conducted using the method of constants to determine the threshold value for judgment of size. A factorial experiment was conducted using payoffs, rate of defective items, and detectability in the signal-detection theory as the factors. In total, eight experimental conditions were tested. 10 college students were recruited as subjects. Each subject was asked to compare 40 teapot shapes to a standard teapot shape under eight experimental conditions. Defective shapes were generated by lengthening the vertical dimension of a standard teapot shape by a factor of 1.01 and 1.04 for ‘low’ and ‘high’ detectability. The decision time and responses of ‘identical’ or ‘different’ were collected under all experimental conditions. Analysis indicates that the decision-making strategy used to inspect this irregular object was very close to maximizing the accuracy of decision-making by considering the rate of defective items. This result is different from most research findings in signal-detection theory in which responses of human beings are similar to degraded Bayes optimizers. The standard deviation of the signal distribution was about 1.30 and 1.41 times that of the noise distributions for ‘low’ and ‘high’ detectability.

scholarly journals Attention and Signal Detection

Information ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 254 ◽  
Author(s):  
Reeves
Keyword(s):  
Visual Field ◽  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Forced Choice ◽  
Focused Attention

In this paper, I first review signal detection theory (SDT) approaches to perception, and then discuss why it is thought that SDT theory implies that increasing attention improves performance. Our experiments have shown, however, that this is not necessarily true. Subjects had either focused attention on two of four possible locations in the visual field, or diffused attention to all four locations. The stimuli (offset letters), locations, conditions, and tasks were all known in advance, responses were forced-choice, subjects were properly instructed and motivated, and instructions were always valid—conditions which should optimize signal detection. Relative to diffusing attention, focusing attention indeed benefitted discrimination of forward from backward pointing Es. However, focusing made it harder to identify a randomly chosen one of 20 letters. That focusing can either aid or disrupt performance, even when cues are valid and conditions are idealized, is surprising, but it can also be explained by SDT, as shown here. These results warn the experimental researcher not to confuse focusing attention with enhancing performance, and warn the modeler not to assume that SDT is unequivocal.

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