Conceiving “personality”: Psychologist’s challenges and basic fundamentals of the Transdisciplinary Philosophy-of-Science Paradigm for Research on Individuals

Scientists exploring individuals, as such scientists are individuals themselves and thus not independent from their objects of research, encounter profound challenges; in particular, high risks for anthropo-, ethno- and ego-centric biases and various fallacies in reasoning. The Transdisciplinary Philosophy-of-Science Paradigm for Research on Individuals (TPS-Paradigm) aims to tackle these challenges by exploring and making explicit the philosophical presuppositions that are being made and the metatheories and methodologies that are used in the field. This article introduces basic fundamentals of the TPS-Paradigm including the epistemological principle of complementarity and metatheoretical concepts for exploring individuals as living organisms. Centrally, the TPS-Paradigm considers three metatheoretical properties (spatial location in relation to individuals’ bodies, temporal extension, and physicality versus “non-physicality”) that can be conceived in different forms for various kinds of phenomena explored in individuals (morphology, physiology, behaviour, the psyche, semiotic representations, artificially modified outer appearances and contexts). These properties, as they determine the phenomena’s accessibility in everyday life and research, are used to elaborate philosophy-of-science foundations and to derive general methodological implications for the elementary problem of phenomenon-methodology matching and for scientific quantification of the various kinds of phenomena studied. On the basis of these foundations, the article explores the metatheories and methodologies that are used or needed to empirically study each given kind of phenomenon in individuals in general. Building on these general implications, the article derives special implications for exploring individuals’ “personality”, which the TPS-Paradigm conceives of as individual-specificity in all of the various kinds of phenomena studied in individuals

Intelligent methods for solving inverse problems of backscattering spectra with noise : a comparison between neural networks and simulated annealing

This paper investigates two different intelligent techniques - the neural network (NN) method and the simulated annealing (SA) algorithm for solving the inverse problem of Rutherford Backscattering (RBS) with noisy data. The RBS inverse problem is to determine the sample structure information from measured spectra, which can be defined as either a function approximation or a non-linear optimization problem. Early studies emphasized on numerical methods and empirical fitting. In this work, we have applied intelligent techniques and compared their performance and effectiveness for spectral data analysis by solving the inverse problem. Since each RBS spectrum may contain up to 512 data points, principal component analysis is used to make the feature extraction so as to ease the complexity of constructing the network. The innovative aspects of our work include introducing dimensionality reduction and noise modeling. Experiments on RBS spectra from SiGe thin films on a silicon substrate show that the SA is more accurate but the NN is faster, though both methods produce satisfactory results. Both methods are resilient to 10% Poisson noise in the input. These new findings indicate that in RBS data analysis the NN approach should be preferred when fast processing is required; whereas the SA method becomes the first choice should the analysis accuracy be targeted