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A linear threshold model for optimal stopping behavior.

ABSTRACT: In many real-life decisions, options are distributed in space and time, making it necessary to search sequentially through them, often without a chance to return to a rejected option. The optimal strategy in these tasks is to choose the first option that is above a threshold that depends on the current position in the sequence. The implicit decision-making strategies by humans vary but largely diverge from this optimal strategy. The reasons for this divergence remain unknown. We present a model of human stopping decisions in sequential decision-making tasks based on a linear threshold heuristic. The first two studies demonstrate that the linear threshold model accounts better for sequential decision making than existing models. Moreover, we show that the model accurately predicts participants' search behavior in different environments. In the third study, we confirm that the model generalizes to a real-world problem, thus providing an important step toward understanding human sequential decision making.

SUBMITTER: Baumann C

PROVIDER: S-EPMC7293628 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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A linear threshold model for optimal stopping behavior.

Baumann Christiane C Singmann Henrik H Gershman Samuel J SJ von Helversen Bettina B

Proceedings of the National Academy of Sciences of the United States of America 20200527 23

In many real-life decisions, options are distributed in space and time, making it necessary to search sequentially through them, often without a chance to return to a rejected option. The optimal strategy in these tasks is to choose the first option that is above a threshold that depends on the current position in the sequence. The implicit decision-making strategies by humans vary but largely diverge from this optimal strategy. The reasons for this divergence remain unknown. We present a model ...[more]

PMID: 32461363

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Project description:BACKGROUND:The ability to compose biological systems from smaller elements that act independently of the other upon assembly may help make the forward engineering of biological systems practical. Engineering biology in this manner is made difficult by the inherent nonlinear response of organisms to genetic devices. Devices are inevitably coupled to one another in the cell because they share the same transcriptional machinery for expression. Thus, new properties can emerge when devices that had been characterized in isolation are expressed concurrently. We show in this report that, similar to physical systems, the Escherichia coli (E. coli) transcriptional system can exhibit linear behavior under "small" perturbation conditions. This, in turn, allows devices to be treated as independent modules. RESULTS:We developed a framework and model system consisting of three devices to investigate linear system behavior in E. coli. Our framework employed the transfer curve concept to determine the amount of nonlinearity elicited by the E. coli transcriptional system in response to the devices. To this effect, the model system was quantitatively characterized using real-time quantitative PCR to produce device transfer curves (DTCs). Two of the devices encoded the bacterial neomycin phosphotransferase II (nptII) and chloramphenicol acetyl transferase (cat), while the third encoded the jellyfish-originating green fluorescent protein (gfp). The gfp device was the most nonlinear in our system, with nptII and cat devices eliciting linear responses. Superposition experiments verified these findings, with independence among the three devices having been lost when gfp was present at copy numbers above the lowest one used. CONCLUSIONS:We show that linear system behavior is possible in E. coli. Elucidation of the mechanism underlying the nonlinearity observed in gfp may lead to design rules that ensure linear system behavior, enabling the accurate prediction of the quantitative behavior of a system assembled from individually characterized devices. Our work suggests that biological systems follow principles similar to physical ones, and that concepts borrowed from the latter (such as DTCs) may be of use in the characterization and design of biological systems.

Project description:IntroductionEchocardiographic assessment of the left ventricle (LV) begins with the measurement of linear dimensions that approximate its ellipsoid diameter. These linear dimensions have historically been measured at the basal level of the LV, which is not representative of its true diameter. The objective of this study was to determine the optimal level to measure LV cavity dimensions to more accurately estimate its size and mass.MethodsThe derivation study included 75 patients who had a clinically indicated cardiac magnetic resonance (CMR) exam for ischemic heart disease (n = 15), nonischemic cardiomyopathy (n = 25), or normal studies (n = 35). The three-chamber bright blood cine sequence was analyzed using a custom MATLAB program to measure the LV cavity diameter and wall thickness at 15 equidistant levels from base to apex. The linear measurements from each of these levels were compared against the CMR volumetric reference standard. The validation study included 100 patients who had a clinically indicated echocardiogram and CMR within 3 days for ischemic heart disease (n = 20), nonischemic cardiomyopathy (n = 44), and normal or near-normal studies (n = 36). The parasternal long-axis cine sequence was analyzed to measure the LV cavity diameter and wall thickness at the traditional basal level and the midventricular level, which were compared against the CMR volumetric reference standard.ResultsIn both the derivation and validation studies, the midventricular linear dimensions, defined as those located at the true (maximal) diameter of the LV ellipsoid cavity, were found to be more closely correlated with the volumetric reference standard for LV mass, LV end-diastolic size, and LV ejection fraction.ConclusionsMeasurement of linear dimensions at the midventricular level better reflects the ellipsoid geometry of the LV cavity and provides a more accurate estimate of LV mass, size, and systolic function as compared with the traditionally recommended basal level.

Dataset Information

A linear threshold model for optimal stopping behavior.

Publications

A linear threshold model for optimal stopping behavior.

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