Dataset Information

Search as a simple take-the-best heuristic.

ABSTRACT: Humans commonly engage in a variety of search behaviours, for example when looking for an object, a partner, information or a solution to a complex problem. The success or failure of a search strategy crucially depends on the structure of the environment and the constraints it imposes on the individuals. Here, we focus on environments in which individuals have to explore the solution space gradually and where their reward is determined by one unique solution they choose to exploit. This type of environment has been relatively overlooked in the past despite being relevant to numerous real-life situations, such as spatial search and various problem-solving tasks. By means of a dedicated experimental design, we show that the search behaviour of experimental participants can be well described by a simple heuristic model. Both in rich and poor solution spaces, a take-the-best procedure that ignores all but one cue at a time is capable of reproducing a diversity of observed behavioural patterns. Our approach, therefore, sheds lights on the possible cognitive mechanisms involved in human search.

SUBMITTER: Yahosseini KS

PROVIDER: S-EPMC6837208 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Search as a simple take-the-best heuristic.

Yahosseini Kyanoush Seyed KS Moussaïd Mehdi M

Royal Society open science 20191030 10

Humans commonly engage in a variety of search behaviours, for example when looking for an object, a partner, information or a solution to a complex problem. The success or failure of a search strategy crucially depends on the structure of the environment and the constraints it imposes on the individuals. Here, we focus on environments in which individuals have to explore the solution space gradually and where their reward is determined by one unique solution they choose to exploit. This type of ...[more]

PMID: 31824690

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Project description:Detection of the relevant brain regions for characterizing the distinction between cognitive conditions is one of the most sought after objectives in neuroimaging research. A popular approach for achieving this goal is the multivariate pattern analysis which is currently conducted through a number of approaches such as the popular searchlight procedure. This is due to several advantages such as being automatic and flexible with regards to size of the search region. However, these approaches suffer from a number of limitations which can lead to misidentification of truly informative regions which in turn results in imprecise information maps. These limitations mainly stem from several factors such as the fact that the information value of the search spheres are assigned to the voxel at the center of them (in case of searchlight), the requirement for manual tuning of parameters such as searchlight radius and shape, and high complexity and low interpretability in commonly used machine learning-based approaches. Other drawbacks include overlooking the structure and interactions within the regions, and the disadvantages of using certain regularization techniques in analysis of datasets with characteristics of common functional magnetic resonance imaging data. In this article, we propose a fully data-driven maximum relevance minimum redundancy search algorithm for detecting precise information value of the clusters within brain regions while alleviating the above-mentioned limitations. Moreover, in order to make the proposed method faster, we propose an efficient algorithmic implementation. We evaluate and compare the proposed algorithm with the searchlight procedure as well as least absolute shrinkage and selection operator regularization-based mapping approach using both real and synthetic datasets. The analysis results of the proposed approach demonstrate higher information detection precision and map specificity compared to the benchmark approaches.

Project description:BackgroundThe combat role of the twenty-first century infantry soldier has changed and accordingly their boots should evolve to meet these new needs and maximize soldier performance.ObjectiveTo evaluate injuries and durability of the hot weather infantry boots (HWIB) in elite infantry training and assess the initial performance of newly designed Israeli infantry boots (NDIB).MethodsIn Phase 1, the durability of the HWIB during elite infantry training was evaluated at weeks 10, 19 and 64 in a cohort of 67 recruits. At each exam recruits removed their boots which were assessed for wear and integrity and photographed. The number of times recruits changed their boots was recorded. In Phase 2, foot injuries were assessed in a cohort of 73 elite infantry recruits wearing HWIB. In Phase 3, 65 infantry recruits were issued the NDIB. Recruits feet were measured for width and shoe size using the Brannock device and then followed for problems associated with their boots. Foot lesions were document by photographs.ResultsPhase 1: The mean longevity of HWIB in training was 5.2 ± 0.2 (SE) months, (95 % CI 4.83-5.61). Phase 2: 38 % of the elite infantry recruits wearing HWIB had at least one complaint and 31 (42 %) were found to have boot related injuries in a total of 56 injured areas. Phase 3: The mean predicted boot size (42.8 ± 1.7) based on Brannock measurements, was less than the size of the NDIB actually worn, 43.1 ± 1.6. Only 34.8 % of the feet were width D (the standard shoe width). At 9 day follow up, 55 of the 65 recruits who wore NDIB reported at least one problem with them (85 %, p < 0.0001, compared to HWIB). By 3 weeks, all but five recruits had returned to wearing the HWIB. Of the recruits wearing NDIB, 47 (72 %) were found to have had at least one boot related injury with a total number of 180 injured foot areas (p = 0.0004, compared to HWIB).ConclusionsThe HWIB was well tolerated by the elite infantry recruits and associated with significantly less foot injuries than the NDIB. The longevity of the HWIB in demanding elite infantry training was five months. Trial registration: NCT02810002 retrospectively registered June 22, 2016.

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Search as a simple take-the-best heuristic.

Publications

Search as a simple take-the-best heuristic.

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