Human pattern recognition: Evidence for a switching between strategies in analyzing complex stimuli

In a preceding multidimensional scaling experiment, with size and brightness as parameters, subjects were found to use individually different strategies in processing compound stimuli: Most subjects adhered to either the Euclidean or the City-block metric (Ronacher and Bautz, 1985). In the experiment reported here, participants of the previous study were induced-by a manipulative instruction-to modify their strategy. With 5 out of 10 subjects a switching to another strategy occurred, which manifested itself in a drastic shift of the respective best metric (e.g. from Euclidean to City-block or vice versa). The extent and speed of changes as well as-in some instances-the stability of estimation accuracy show that subjects were not forced by the instruction to develop a new strategy. Results rather suggest that adult subjects have easily available two, or perhaps even more, alternative processing modes, the decision for one of them being a matter of a subject's preferences rather than of individually different abilities.     

Beitrag einzelner parameter zum wahrnehumungsgemäßen unterschied von zusammengesetzten reizen bei der honigbiene

The perceptual difference between stimuli can be regarded as distance within the perceptual space of the bee. The author used this assumption to determine the specific distance function, on the basis of which the differences in the individual perceptual parameters constituted the perceptual difference between the complex stimulus and the reference stimulus. The perceptual differences can be deduced only indirectly from the choice frequency. Consequently, it was necessary to establish a “calibration curve”, to deduce quantitatively the perceptual difference from the choice frequency. The resulting hyperbolic curves for the parameters “brightness” and “size” were almost identical (Fig. 2). The perceptual difference between the complex stimulus and the reference stimulus is greater than one would expect in an Euclidean space. Rather it is the sum of the distances along the perceptual parameters which compose the complex stimulus (Fig. 3). Thus, the bee determines the perceived difference of composite stimuli which affect the perceptual parameters “brightness” and “size” in terms of the city-blockmetric.     

Phylogenetic metrics of community similarity

We derive a new metric of community similarity that takes into account the phylogenetic relatedness among species. This metric, phylogenetic community dissimilarity (PCD), can be partitioned into two components, a nonphylogenetic component that reflects shared species between communities (analogous to S?rensen' s similarity metric) and a phylogenetic component that reflects the evolutionary relationships among nonshared species. Therefore, even if a species is not shared between two communities, it will increase the similarity of the two communities if it is phylogenetically related to species in the other community. We illustrate PCD with data on fish and aquatic macrophyte communities from 59 temperate lakes. Dissimilarity between fish communities associated with environmental differences between lakes often has a phylogenetic component, whereas this is not the case for macrophyte communities. With simulations, we then compare PCD with two other metrics of phylogenetic community similarity, II(ST) and UniFrac. Of the three metrics, PCD was best at identifying environmental drivers of community dissimilarity, showing lower variability and greater statistical power. Thus, PCD is a statistically powerful metric that separates the effects of environmental drivers on compositional versus phylogenetic components of community structure.     

Performance of pairwise shape dissimilarity morphometrics on nonmammalian taxa (Insecta: Neuroptera: Mantispidae)

Morphometric dissimilarity metrics aim to quantify the variation between compared specimens such that inferences about their relatedness and alpha taxonomy can be made. Recently, the technique has developed metrics that purport to quantify shape dissimilarity between specimens—employing the use of least squares regression analysis. These metrics have been well applied by studies in the hominin fossil record with an arguably unsubstantiated backing for the technique. Originally postulated was the log₁₀ se_m metric which subsequently led to the standard error test of the null hypothesis metric. Following this, the standard deviation of logged ratios (S_LR) metric arose as a pairwise dissimilarity metric that constrains the regression to a zero‐intercept, that is, a significant development in the robustness of the technique. This metric was tested on extant primates in order to evaluate its effectiveness alongside the two other metrics. It was shown to be the most reliable for comparisons between specimens of primates, but was unable to discriminate between heterospecific and conspecific comparisons. Arguably, an alternative model organism with which to compare the technique is lacking. This study considers shape dissimilarity metrics with respect to a group of nonmammalian organisms (mantidflies) and tests the metrics against three lines of evidence (morphology, CO1‐DNA, and geographic distribution) that can delimit the species‐level taxonomy for the group. It is shown that the metrics are unable to discriminate between pairwise comparisons of closely related species, resulting in biologically erroneous groupings, and contradicting the groupings derived from morphological, CO1‐DNA, and distributional comparisons. It is thus asserted that the technique is unsuitable for use in alpha taxonomy as an additional line of evidence in mantidflies. It is further supposed that morphometrics in general should be employed with caution in studies of evolutionary history as phylogeny is not the only information contained within morphometric data. J. Morphol. 276:1482–1494, 2015. © 2015 Wiley Periodicals, Inc.     

Relevance of abiotic criteria used in German lake typology for macroinvertebrate fauna

For lake characterisation, top-down typologies are mostly used throughout Europe, including type criteria such as climate, lake area, catchment geology and conductivity. In Germany, a lake typology was applied comprising ecoregion, calcium concentration, Schindler’s ratio, stratification type and residence time. However, the relevance of these criteria for the macroinvertebrate fauna has not been conclusively demonstrated till now. Benthic invertebrate community data and related environmental parameters of pristine or near-pristine lakes in Germany were analysed by multivariate analysis techniques to elucidate which environmental parameters are reflected by invertebrate composition. Moreover, benthic invertebrate data were transformed to metrics expressing ecological attributes and species richness (summarising functional composition, diversity and sensitivity measures). Multivariate statistics were used to test whether information relevant to ordination was lost and whether variation decreases using metrics which combine data with ecological attributes. Analysis of lake-type criteria revealed that ecoregions and prevailing substrates were characterized by different taxonomic compositions of macroinvertebrates. In addition, a relationship was found between community composition and lake size. Creating a novel bottom-up lake typology based on ecoregions, lake size and prevailing substrate gives better separation of distinct macroinvertebrate communities and a higher level of homogeneity within groups compared to top-down typology or single environmental parameters alone, both on species and metrics data. Despite some data variation due to methodological differences (e.g. different sampling and sorting techniques) and interannual and seasonal variation in the data set, NMDS ordination presented well-separated groups of bottom-up lake types. Lake types were more precisely separated by species data than by metric data in both top-down and bottom-up typology. However, as information loss from species lists to calculated metrics is marginal, type-specific benthic invertebrate assemblages are reflected both on the species level and on the metric level. Species and metric data are both suitable for data ordination, while single environmental parameters affecting macroinvertebrate composition can best be obtained using metrics.     

Distance models in ecological network management: A case study of patch connectivity in a grassland network

Landscape connectivity is a key issue of nature conservation and distance parameters are essential for the calculation of patch level metrics. For such calculations the so-called Euclidean and the least cost distance are the most widespread models. In the present work we tested both distance models for landscape connectivity, using connectivity metrics in the case of a grassland mosaic, and the ground beetle Pterostichus melas as a focal species. Our goal was to explore the dissimilarity between the two distance models and the consequent divergence from the calculated values of patch relevance in connectivity. We found that the two distance models calculated the distances similarly, but their estimations were more reliable over short distances (circa 500 m), than long distances (circa 3000 m). The variability in the importance of habitat patches (i.e. patch connectivity indices) was estimated by the difference between the two distance models (Euclidean vs. least cost) according to the patch size. The location of the habitat patches in the matrix seemed to be a more important factor than the habitat size in the estimation of connectivity. The uncertainty of three patch connectivity indices (Integral Index of Connectivity, Probability of Connectance and Flux) became high above a habitat size of 5 ha. Relevance of patches in maintaining connectivity varied even within small ranges depending on the estimator of distance, revealing the careful consideration of these methods in conservation planning.     

Phytoplankton community metrics based on absolute and relative abundance and biomass: implications for multivariate analyses

Phytoplankton and water samples were collected at 12 locations along the temperate lowland Rideau River, Ontario, Canada. The stations were visited twice a month from May to September 1998, 1999, and 2000. Phytoplankton communities were quantified based on cell abundance, entity abundance (colonies, filaments or free-living cells) and biomass (converted from biovolume estimates based on cell shape and biometry), and were expressed as absolute and relative values. The resulting phytoplankton dataset was composed of six different metrics. The general objective was to assess which metric best explained the spatial and temporal variability in the phytoplankton communities of the Rideau River in response to fluctuating environmental variables. Relationships between phytoplankton metrics and water quality variables were assessed using canonical correspondence analyses. The absolute cell abundance metric showed the best relationship with water quality, followed by the cell entity metric. The biomass metric showed the poorest relationship with water quality variables, indicating that accounting for cell size does not provide additional information. The data expressed as absolute values were consistently better predictors of water quality compared to relative values.     

Comparative evaluation of word composition distances for the recognition of SCOP relationships

MOTIVATION: Alignment-free metrics were recently reviewed by the authors, but have not until now been object of a comparative study. This paper compares the classification accuracy of word composition metrics therein reviewed. It also presents a new distance definition between protein sequences, the W-metric, which bridges between alignment metrics, such as scores produced by the Smith-Waterman algorithm, and methods based solely in L-tuple composition, such as Euclidean distance and Information content. RESULTS: The comparative study reported here used the SCOP/ASTRAL protein structure hierarchical database and accessed the discriminant value of alternative sequence dissimilarity measures by calculating areas under the Receiver Operating Characteristic curves. Although alignment methods resulted in very good classification accuracy at family and superfamily levels, alignment-free distances, in particular Standard Euclidean Distance, are as good as alignment algorithms when sequence similarity is smaller, such as for recognition of fold or class relationships. This observation justifies its advantageous use to pre-filter homologous proteins since word statistics techniques are computed much faster than the alignment methods. AVAILABILITY: All MATLAB code used to generate the data is available upon request to the authors. Additional material available at http://bioinformatics.musc.edu/wmetric     

A geometrical model for the perceived line of orientation based on subjective evaluations and human VEP data

A geometric model in proposed based on subjective dissimilarity estimates among differently oriented line segments in the frontal plane and on visual evoked potentials (VEP) recorded in respond to abrupt changes in the orientation of such a line segment. The orientations are represented by points constituting a clised planar curve, with interpoint distances corresponding to interstimulus dissimilarities. The angle of the radius-vertor connecting the center of the configuration with its circumference encodes the orientation of the line stimuli in the visual field, and two Cartesian axes drawn through the center are interpreted as two orientation-opponent channels in the neuronal network processing line orientation. The first channel (the ordinate axis) gives maximal positive and maximal negative responses to, respectively, vertical and horizontal orientations, whereas the second channel (the abscissa axis) gives maximal positive and maximal negative responses to, respectively, 45 and 135 degrees orientations. The VEP analysis shows that the activity of the first channel affects the interpeak amplitudes of both the P1-N2 (P130-N180) and N2-P2 (N180-P230) components, whereas the second channel affects only the late component N180-P230. We propose the hypothesis according to which outputs of the two channels are connected linearly (which is reflected in the city-block metric obtained when distances are computed directly from neuronal activity), but the growth rate of the overall output is inhibited nonlinearly when it reaches large absolute values. This inhibition effectively transforms the "true" city-block metric of the line orientation space into the Euclidean metric one obtains when distances are computed from subjective dissimilarity estimates. VEP amplitudes as intermediate characteristics of visual processing between neuronal activity and subjective estimates represent intermediate metric (between city-block and Euclidean).     

Similarity coefficients for molecular markers in studies of genetic relationships between individuals for haploid, diploid, and polyploid species

Determining true genetic dissimilarity between individuals is an important and decisive point for clustering and analysing diversity within and among populations, because different dissimilarity indices may yield conflicting outcomes. We show that there are no acceptable universal approaches to assessing the dissimilarity between individuals with molecular markers. Different measures are relevant to dominant and codominant DNA markers depending on the ploidy of organisms. The Dice coefficient is the suitable measure for haploids with codominant markers and it can be applied directly to (0,1)-vectors representing banding profiles of individuals. None of the common measures, Dice, Jaccard, simple mismatch coefficient (or the squared Euclidean distance), is appropriate for diploids with codominant markers. By transforming multiallelic banding patterns at each locus into the corresponding homozygous or heterozygous states, a new measure of dissimilarity within locus was developed and expanded to assess dissimilarity between multilocus states of two individuals by averaging across all codominant loci tested. There is no rigorous well-founded solution in the case of dominant markers. The simple mismatch coefficient is the most suitable measure of dissimilarity between banding patterns of closely related haploid forms. For distantly related haploid individuals, the Jaccard dissimilarity is recommended. In general, no suitable method for measuring genetic dissimilarity between diploids with dominant markers can be proposed. Banding patterns of diploids with dominant markers and polyploids with codominant markers represent individuals' phenotypes rather than genotypes. All dissimilarity measures proposed and developed herein are metrics.     

Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity

Introduction

Human body acceleration is often used as an indicator of daily physical activity in epidemiological research. Raw acceleration signals contain three basic components: movement, gravity, and noise. Separation of these becomes increasingly difficult during rotational movements. We aimed to evaluate five different methods (metrics) of processing acceleration signals on their ability to remove the gravitational component of acceleration during standardised mechanical movements and the implications for human daily physical activity assessment.

Methods

An industrial robot rotated accelerometers in the vertical plane. Radius, frequency, and angular range of motion were systematically varied. Three metrics (Euclidian norm minus one [ENMO], Euclidian norm of the high-pass filtered signals [HFEN], and HFEN plus Euclidean norm of low-pass filtered signals minus 1 g [HFEN₊]) were derived for each experimental condition and compared against the reference acceleration (forward kinematics) of the robot arm. We then compared metrics derived from human acceleration signals from the wrist and hip in 97 adults (22–65 yr), and wrist in 63 women (20–35 yr) in whom daily activity-related energy expenditure (PAEE) was available.

Results

In the robot experiment, HFEN₊ had lowest error during (vertical plane) rotations at an oscillating frequency higher than the filter cut-off frequency while for lower frequencies ENMO performed better. In the human experiments, metrics HFEN and ENMO on hip were most discrepant (within- and between-individual explained variance of 0.90 and 0.46, respectively). ENMO, HFEN and HFEN₊ explained 34%, 30% and 36% of the variance in daily PAEE, respectively, compared to 26% for a metric which did not attempt to remove the gravitational component (metric EN).

Conclusion

In conclusion, none of the metrics as evaluated systematically outperformed all other metrics across a wide range of standardised kinematic conditions. However, choice of metric explains different degrees of variance in daily human physical activity.     

Phylogenetic community structure metrics and null models: a review with new methods and software

Competitive exclusion and habitat filtering influence community assembly, but ecologists and evolutionary biologists have not reached consensus on how to quantify patterns that would reveal the action of these processes. Currently, at least 22 α‐diversity and 10 β‐diversity metrics of community phylogenetic structure can be combined with nine null models (eight for β‐diversity metrics), providing 278 potentially distinct approaches to test for phylogenetic clustering and overdispersion. Selecting the appropriate approach for a study is daunting. First, we describe similarities among metrics and null models across variance in phylogeny size and shape, species abundance, and species richness. Second, we develop spatially explicit, individual‐based simulations of neutral, competitive exclusion, or habitat filtering community assembly, and quantify the performance (type I and II error rates) of all 278 metric and null model combinations against each assembly process. Many α‐diversity metrics and null models are at least functionally equivalent, reducing the number of truly unique metrics to 12 and the number of unique metric + null model combinations to 72. An even smaller subset of metric and null model combinations showed robust statistical performance. For α‐diversity metrics, phylogenetic diversity and mean nearest taxon distance were best able to detect habitat filtering, while mean pairwise phylogenetic distance‐based metrics were best able to detect competitive exclusion. Overall, β‐diversity metrics tended to have greater power to detect habitat filtering and competitive exclusion than α‐diversity metrics, but had higher type 1 error in some cases. Across both α‐ and β‐diversity metrics, null model selection affected type I error rates more than metric selection. A null model that maintained species richness, and approximately maintained species occurrence frequency and abundance across sites, exhibited low type I and II error rates. This regional null model simulates neutral dispersal of individuals into local communities by sampling from a regional species pool. We introduce a flexible new R package, metricTester, to facilitate robust analyses of method performance.     

Biometric-genetic analysis of genotype dissimilarity: the model and parameters

An additive-dominant model and parameters for biometric--genetic analysis of pair dissimilarity between genotypes have been developed. The analyzed dissimilarity metrics are presumed to be determined by the quantitative traits of plant lines and F1 hybrids obtained by diallel crossing. Relationships between dissimilarity parameters have been determined, and algorithms for estimating their use, in particular, for detecting the most heterozygous hybrids are suggested.     

Spike train metrics

Quantifying similarity and dissimilarity of spike trains is an important requisite for understanding neural codes. Spike metrics constitute a class of approaches to this problem. In contrast to most signal-processing methods, spike metrics operate on time series of all-or-none events, and are, thus, particularly appropriate for extracellularly recorded neural signals. The spike metric approach can be extended to multineuronal recordings, mitigating the 'curse of dimensionality' typically associated with analyses of multivariate data. Spike metrics have been usefully applied to the analysis of neural coding in a variety of systems, including vision, audition, olfaction, taste and electric sense.     

Biometric-genetic analysis of genotype dissimilarity in diallel crosses of spring wheat

A new biometric-genetic model and parameters described in [1] have been used for analysis of pair dissimilarity between spring wheat cultivars and F1 hybrids obtained by diallel crossing. The dissimilarity between the reactions of different genotypes estimated by a growth trait (plant height) served as a metric. The mode of inheritance of dissimilarity has been determined, and the cultivars that are the most dissimilar in allelic composition have been detected.     

Biometric-genetic analysis of genotype dissimilarity: The model and parameters

An additive-dominant model and parameters for biometric-genetic analysis of pair dissimilarity between genotypes have been developed. The analyzed dissimilarity metrics are presumed to be determined by the quantitative traits of plant lines and F₁ hybrids obtained by diallel crossing. Relationships between dissimilarity parameters have been determined, and algorithms for estimating their use, in particular, for detecting the most heterozygous hybrids are suggested.     

Biometric-genetic analysis of genotype dissimilarity in diallel crosses of spring wheat

A new biometric-genetic model and parameters described in [2] have been used for analysis of pair dissimilarity between spring wheat cultivars and F₁ hybrids obtained by diallel crossing. The dissimilarity between shape of the reactions of different genotypes estimated by a growth trait (plant height) served as a metric. The mode of inheritance of dissimilarity has been determined, and the cultivars that are the most dissimilar in allelic composition have been detected.     

Contrasting and comparing sustainable development indicator metrics

Despite the fact that it has been well over a decade since Agenda 21 first called for sustainable development indicators, there is no consensus regarding the best approach to the design and use of SDI models. It is important, therefore, to question the effectiveness of SDIs in an effort to continue advancing sustainability.This paper addresses one aspect of this question by exploring whether our global SDI metrics are sending a clear message to guide us towards sustainable development. Six global SDI metrics are compared by relative ranking in colour coded tabular format and spatially in map format. The combined presentation of results clearly illustrates that the different metrics arrive at varying interpretations about the sustainability of nations. The degree of variability between the metrics is analyzed using correlation analysis. The variability in findings draws attention to the lack of a clear direction at the global level in how best to approach sustainable development. Canada is presented as a case study to highlight and explain the discrepancies between SDI measures.     

Compositional dissimilarity as a robust measure of ecological distance

The robustness of quantitative measures of compositional dissimilarity between sites was evaluated using extensive computer simulations of species' abundance patterns over one and two dimensional configurations of sample sites in ecological space. Robustness was equated with the strength over a range of models, of the linear and monotonic (rank-order) relationship between the compositional dissimilarities and the corresponding Euclidean distances between sites measured in the ecological space. The range of models reflected different assumptions about species' response curve shape, sampling pattern of sites, noise level of the data, species' interactions, trends in total site abundance, and beta diversity of gradients.The Kulczynski, Bray-Curtis and Relativized Manhattan measures were found to have not only a robust monotonic relationship with ecological distance, but also a robust linear (proportional) relationship until ecological distances became large. Less robust measures included Chord distance, Kendall's coefficient, Chisquared distance, Manhattan distance, and Euclidean distance.A new ordination method, hybrid multidimensional scaling (HMDS), is introduced that combines metric and nonmetric criteria, and so takes advantage of the particular properties of robust dissimilarity measures such as the Kulczynski measure.We thank M. P. Austin for encouraging this study, and I. C. Prentice, E. Van der Maarel, and an anonymous reviewer for helpful comments. E. M. Adomeit provided technical assistance.