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Stone Age Minds in Modern Medicine: Ancient Footprints Everywhere
Published in Pat Croskerry, Karen S. Cosby, Mark L. Graber, Hardeep Singh, Diagnosis, 2017
Decision making in ancient times, what the evolutionary psychologists refer to as the environment of evolutionary adaptedness (EEA), has been characterized as a form of evolutionary probability gambling. In Unweaving the Rainbow, Richard Dawkins discusses how natural selection has operated on intuitive decision making (System 1) by selecting differentially for false positives (believing something is there when it isn’t) over false negatives (believing something is not there when it is), and describes some human behaviors as evidence of false positive errors: for example, superstitions, phobias, magical thinking, and so on. Like Cartwright, he concludes that “parts of our brains for doing intuitive statistics are still back in the stone age” [7]. Much of survival depended on pattern recognition and pattern matching, and there was probably some value in erring on the side of false positives rather than false negatives. Search satisficing strategies need not be optimal, only sufficient to improve chances of survival.
Evaluation of structural formation of granular materials using anisotropy of magnetic susceptibility
Published in Marine Georesources & Geotechnology, 2023
Xueqian Ni, Yupeng Cao, Feng Zhang, Zhao Zhang
In addition to the intuitive statistics of particle principal directions from image, several studies showed that anisotropy of magnetic susceptibility (AMS) technique had the potential to measure the structural features nondestructively and rapidly both in laboratory test and in situ tests (Uyeda et al. 1963). For the past few decades, the AMS has been widely used in investigation of rocky geology, including rock materials’ paleocurrent or paleowind direction of sedimentary environment (Parés et al. 2007; Ge et al. 2014), tectonic evolution history (Eldredge et al. 1985; Pocoví Juan et al. 2014), and deformation history (Mallik et al. 2009). In addition, investigation with AMS was also extended for loose sediments, not only the intact rock structures. Wassmer (2010) used the AMS to investigate the relationship between the features of tsunami sediments and the wave behaviors. Zhang et al. (2010) analyzed the directions of major and minor AMS axes of loess to evaluate the paleowind direction in northwest China. Bradák-Hayashi (2017) employed the AMS technique to obtain the structures of medium-dense sediments, by which the tsunami wave characterization of 2011 Tohoku-Oki tsunami was analyzed carefully. The aforementioned studies indicated that the AMS technique may also be a possible way to investigate the structure of granular material.
Model of variability estimation: factors influencing human prediction and estimation of variability in continuous information
Published in Theoretical Issues in Ergonomics Science, 2020
Christopher D. Wickens, Benjamin A. Clegg, Jessica K. Witt, C. A. P. Smith, Nathan Herdener, Kimberly S. Spahr
Although not expressly framed within the domain of intuitive statistics, two studies do bear directly on the calibration of variability estimation. Kareev, Arnon, and Horwitz-Zeliger (2002) assessed participants’ judgements of variability of spatial stimuli (e.g. coloured cylinders, coloured matches). Using a variety of methodologies, across five different experiments, they found evidence for underestimation in these spatial analogue quantities, even as they observed that people were sensitive to differences in variability between sets. They propose a model that explains the underestimation effect from an inherent statistical bias in estimating population variance from smaller samples. This is associated with a limited capacity to hold samples in working memory, and they observed that those with smaller working memory capacity showed a larger underestimation bias.