ࡱ> G NAbjbjَ "4]jjjjjjw&%%%%%%%$-'!)%%"""" %TT%""%%j43B,jj% APPLICATION OF NEURAL NETWORKS IN THE IDENTIFICATION OF TYPES OF PERSONALITY Ksenija Bosnar and Franjo Prot University of Zagreb Konstantin Momirovi and Ankica Hoaek Institute of criminological and sociological research An algorithm for a neural network for cluster analysis with coded name Triatlon was applied in the analysis of 666 male subjects, 18 years old, randomly selected from the population of clinically health and literate population of this age and gender. This sample was described over a set of eleven conative variables selected under an extension of cybernetic model of regulatory functions. The essence of the applied clustering algorithm is a taxonomic neural network based on adaptive multilayer perceptron as a core engine working on the basis of starting classification obtained by a rational method of fuzzy clustering of variables, and then of fuzzy clustering of objects described on fuzzy clusters of variables. An excellent classification of entities is obtained; coefficient of efficacy of neural network attain the value of .994. The identification of types of personality on the basis of all identification structures was very simple due to the very clear pattern of centroid vectors and pattern and structure of discriminant functions. Type 1 (31.68% of examines) was defined by very low control of aggressive impulses, weak control of excitation and high frequency of manifest deviant behavior, so that was clear that subjects that belong to this type are member of classification category of subjects with antisocial personality disorder, that is to the category of psychopaths. Type 2 (32.43% of examines) was defined by very good control of all neural regulatory systems and acceptable level of activity; therefore, it was clear that subjects that belong to this type are conatively sane, and belong to the relatively small segment of population with normal level of conative functions. Type 3 (35.89% of examines) was defined by very low coordination and integration of neural regulatory functions, including low neural control of functions of elementary biological systems, low level of activity but unsatisfactory control of aggression so that belong to the category of neurotics with diffuse neurotic symptoms. KEY WORDS personality / cluster analysis / neural networks 1. INTRODUCTION The problem of existence and identification of types of personality, probably the oldest problem in prescientific and even scientific psychology, remain at yet unsolved due to several theoretical and methodological reasons. The main theoretical argument against existence of psychological types is the fact that almost all psychological characteristics are normally distributed, so that human beings form a normal hyperelipsoid in psychological, and even in whole anthropological space. The main methodological argument against the attempts to discovering psychological types is that taxonomic problems cannot be solved in closed algebraic form, so that the problem is of little true scientific interest. The aim of this paper is to give some arguments against both reasons against the research of taxonomic problems in psychology. 2. METHODS An algorithm for a neural network for cluster analysis with coded name Triatlon was applied in the analysis of 666 male subjects, 18 years old, randomly selected from the population of clinically health and literate population of this age and gender. This sample was described over a set of eleven conative variables selected under an extension (Hoek, 1994) of cybernetic model of regulatory functions (Horga, Ignjatovi, Momirovi and Gredelj, 1982; Momirovi, Horga and Bosnar, 1982). Coded names and basic metric characteristics of instruments applied to assess the personality traits supposed by this model were presented in the table 0. Table 0. Generalizability (psi), Reliability (gamma), Convergence of indicators (alpha) and Homogeneity (hi) of measuring instruments InstrumentTraitpsigammaalphahiEPSExtraversion.803.888.868.650HIPsychosomatic disorders.863.921.963.821ALFAAnxiety.877.933.954.812SIGMAAggressiveness.853.922.784.667DELTADissociation.910.955.890.766ETADisintegration.884.935.925.779DELTA1Psychasthenic dissociation.884.935.913.752DELTA3Regressive dissociation.764.873.845.591SIGMA1Psychopathic aggressiveness.837.909.847.657SP5Hysteric aggressiveness.869.931.902.709ABERAberrant behavior.837.912.829.574 The essence of the applied clustering algorithm is a taxonomic neural network based on adaptive multilayer perceptron as a core engine working on the basis of starting classification obtained by a rational method of fuzzy clustering of variables, and then of fuzzy clustering of objects described on fuzzy clusters of variables. In an another paper (Momirovi, 2002) is demonstrated that efficacy of Triatlon is, almost always, better of efficacy of K-means algorithm, and in all cases much better then the efficacy of Ward's method of hierarchical clustering for the classification of very different sets of objects described on the quantitative variables from many different fields. 3. RESULTS In Table 1 are presented linear correlation coefficients of applied tests of conative functions. Table 1. Correlations of input variables EPSHIALFASIGMADELTAETADELTA1DELTA3SIGMA1SP5ABEREPS1.000-.095-.105.187-.112-.042-.061.080.114-.004.118HI-.0951.000.715.444.614.712.649.553.451.639.210ALFA-.105.7151.000.451.556.710.695.636.405.659.160SIGMA.187.444.4511.000.537.504.482.529.677.575.514DELTA-.112.614.556.5371.000.749.594.497.523.641.338ETA-.042.712.710.504.7491.000.743.673.506.701.265DELTA1-.061.649.695.482.594.7431.000.689.607.785.322DELTA3.080.553.636.529.497.673.6891.000.596.704.318SIGMA1.114.451.405.677.523.506.607.5961.000.713.617SP5-.004.639.659.575.641.701.785.704.7131.000.426ABER.118.210.160.514.338.265.322.318.617.4261.000 Number of taxons under Plum Brandy criterion was 3, and the same number of taxons was accepted at the end of iterative process. For the classification of variables algorithm needs 6 iterations; for the fuzzy classification of entities 34 iteration, and for final classification of subjects 39 learning attempts. Results obtained through and at the end of iterative process are presented in the following tables. Table 2. Initial input to hidden layer axons  f1f2EPS.913-.480HI-.242.053ALFA-.186-.164SIGMA.342.452DELTA-.221.100ETA-.297-.074DELTA1-.120.187DELTA3.175.008SIGMA1.237.352SP5.010.059ABER.348.411 Table 3. Initial hidden layer to output axons g1g2g3f1.780-.068-.622f2.346-.782.519 Table 4. Initial classification and classification in first iteration g1g2g3g119380g21420916g3151210 Table 5. Number of objects and accordance of initial classifications numberprognosisaccordg1201193.960g2239209.874g3226210.929 Table 6. Final input to hidden layer axons  g1g2EPS-.386.419HI-.235-.440ALFA.449-.257SIGMA1.032.086DELTA-.256-.620ETA.046-.273DELTA1-.119-.289DELTA3.001.038SIGMA1-.310-.160SP5.346.736ABER.602.723 Table 7. Final hidden layer to output axons g1g2g3g1.596-.782.181g2.587.270-.763 Table 8. Centroids of final taxons  PsychopathsnormalneuroticsEPS.304.298-.537HI-.129-.681.730ALFA.029-.826.720SIGMA.676-.941.253DELTA-.043-.733.701ETA-.036-.755.714DELTA1.096-.719.564DELTA3.251-.670.384SIGMA1.511-.671.155SP5.381-.773.362ABER.882-.601-.235 Table 9. Discriminant coefficients  PsychopathsnormalneuroticsEPS.016.415-.389HI-.398.065.293ALFA.117-.420.277SIGMA.665-.784.121DELTA-.516.032.426ETA-.133-.110.217DELTA1-.241.015.199DELTA3.023.009-.029SIGMA1-.278.199.066SP5.638-.072-.499ABER.783-.276-.442 Table 10. Correlations of discriminant functions PsychopathsnormalneuroticsPsychopaths1.000-.451-.512Normal-.4511.000-.536Neurotics-.512-.5361.000 Table 11. Structure of discriminant functions PsychopathsnormalneuroticsEPS.265.262-.503HI-.113-.598.683ALFA.026-.726.674SIGMA.591-.827.237DELTA-.038-.644.656ETA-.031-.664.668DELTA1.084-.632.528DELTA3.219-.589.360SIGMA1.446-.590.145SP5.333-.679.339ABER.770-.529-.220 Table 12. Pattern of discriminant functions PsychopathsnormalneuroticsEPS.174.166-.325HI-.065-.392.439ALFA.031-.480.433SIGMA.414-.561.148DELTA-.013-.425.421ETA-.009-.438.429DELTA1.069-.419.339DELTA3.159-.395.230SIGMA1.312-.401.091SP5.237-.457.215ABER.529-.368-.146 Table 13. Standardized discriminant coefficients  psychopathsnormalneuroticsEPS.014.365-.364HI-.348.057.274ALFA.102-.369.259SIGMA.581-.689.113DELTA-.451.029.399ETA-.116-.097.203DELTA1-.210.013.186DELTA3.020.008-.027SIGMA1-.243.175.062SP5.557-.063-.467ABER.684-.243-.414 Table 14. Neural network and Fisherian classification psychopathsnormalneuroticsPsychopaths20812normal02151Neurotics00239 Table 15. Number of objects and accordance of classifications numprogdiffpsychopaths2112083normal2162151neurotics2392390 As can be seen from the presented tables, an excellent classification of entities is obtained; coefficient of efficacy of neural network attain the value of .994. The identification of types of personality on the basis of all identification structures is very simple due to the very clear pattern of centroid vectors and pattern and structure of discriminant functions. Type 1 (31.68% of examines) is defined by very low control of aggressive impulses, weak control of excitation and high frequency of manifest deviant behavior, so that is clear that subjects that belong to this type are member of classification category of subjects with antisocial personality disorder, that is to the category of psychopaths. Type 2 (32.43% of examines) is defined by very good control of all neural regulatory systems and acceptable level of activity; therefore, it is clear that subjects that belong to this type are conatively sane, and belong to the relatively small segment of population with normal level of conative functions. Type 3 (35.89% of examines) is defined by very low coordination and integration of neural regulatory functions, including low neural control of functions of elementary biological systems, low level of activity but unsatisfactory control of aggression so that belong to the category of neurotics with diffuse neurotic symptoms. Therefore, an acceptable classification of individuals described over a set of normally distributed personality characteristics is possible by essentially very simple neural networks, so that the problem of existence of different personality types must be reconsidered from an other statistical and even substantial point of view. 4. DISCUSSION The main question deserving a serious consideration is the reason of success of classification in spite of the fact that all variables for personality assessment were normally or almost normally distributed. A possible reason is that efficient classification in such a situation is possible if in the distribution of apparently normally distributed variables exists some hidden break points. This assertion is proved in a recent paper (Hoek an Momirovi, 1999) in taxonomic analysis of a set of individuals described over set of ten normally distributed measures of aggressiveness: After transformation of variables in break mirror image form by a procedure based on a simple operationalization of theory of catastrophes, it was possible to detect by Ward's method of hierarchical clustering four very well defined clusters with excellent coefficient of efficacy. Of course, this hypothesis must be examined in the present and other data sets by a series of experimental studies with different attempts to discover specific latent variables underlying every of manifest variables and to discover one or more break points in the distribution of discovered latent variables. Although this can be done also by a suitable constructed neural network, many other data analysis techniques can be applied so that the verification of this hypothesis must be matter of a series of future investigations. REFERENCES Horga, S.; Ignjatovi, I.; Momirovi, K.; Gredelj, M. (1982): Prilog poznavanju strukture konativnih karakteristika. Psihologija, 15, 3: 3-21 i 4: 17-34 Hoek, A. (1994): Predlog postupaka za procenu efikasnosti procesa socijalizacije. ^asopis za klini~ku psihologiju i socijalnu patologiju, 1, 1-2: 229-250. Hoek, A.; Momirovi, K. (1999): Primena transformacija u slike u slomljenom ogledalu u reaavanju taksonomskih problema. Statisti~ka revija, 48, 1-4:60-73. Momirovi, K.; Horga, S.; Bosnar, K. (1982): Prilog formiranju jednog kiberneti~kog modela strukture konativnih faktora. Kineziologija, 14, 5: 83-108. Momirovi, K. (2002): A taxonomic neural network. Technical report, Institute of criminological and sociological research, Belgrade. 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