Evolutionary psychology

    EVOLUTION ADN DESIGN OF INTELLIGENCE     Statistical research on elegant intelligence Flynn effect and other statistical studies IQ data source Intelligence quotients Available statistical IQ data set Wechsler and Stanford Binet scales Individual model of intelligence Social model of intelligence Statistical data of homogenous groups Quantitative approach Nature of intelligence: Method VGI Global model Simulation of the evolution of intelligence Complexity and optimization Esnuka: evolutionary theory game Parametrized Globus model with mate selection Family and identical twin study Bases for the alternative theory of evolution 10 Appendix Methodology of the statistical research Statistical annex   Statistical research on evolution of intelligence 1.a) Research project The purpose of this statistical research is to validate the model about the hereditary nature of relational intelligence, which has been developed to prove the GTCEL (General Theory of Conditional Evolution of Life) through the detection of the existence of the genetic information verification method (GIV) The results of the statistical study have been totally satisfactory; not only does it show the hereditary nature of the scores obtained in the human intelligence quotient measurements (IQ) but also that the genetic information with less intellectual potential is the significant one, as the GTCEL states regarding the concept of conditional intelligence On the other hand, the source data used and the specifications of the model are accurately identified, allowing for the reproduction of the work done and the formal acceptance of the results   Experimental Psychology Psychology statistical survey   It is necessary to be aware that, a priori, there is not a direct link between the GTCEL and the hereditary nature of intelligence like many other inherited characters However, the fact that the GTCEL contributes a logical base for this character to be inherited and that it has been contrasted, must suppose a significant impulse for the acceptance of the new theory or some of its proposals In any case, high correlations of more than 0,8 have been obtained, showing that the genetic component of relational intelligence is much greater than generally accepted until now Considering the difficulties of intelligence measurement and the lack of constant intensity in its manifestation, it is possible to affirm that the genetic component is the only relevant or significant factor The section of related links, includes the four online books of the Global Cognitive Theory: The brain and modern computers Intelligence, intuition and creativity Memory, language and other brain abilities The will, decision making process and artificial intelligence This statistical study is, at the same time, an empirical research about some considerations of the Global Cognitive Theory related with the brain and evolution, in particular the definition of conditional intelligence Another related link is referred to the online book of the Global Theory of the Conditional Evolution of Life The third item of the related links is The EDI Study itself It is a complete statistical survey on the heritability of intelligence performed on the fieldwork database of the Young Adulthood Study, 1939-1967 The results of the statistical survey The EDI Study regarding an elegant intelligence show some important considerations: The hereditary nature of relational intelligence is confirmed The genetic information with less intellectual potential is the significant one, as the GTCEL states regarding the concept of conditional intelligence Likewise, it seems that the main functions of intelligence, or those evolving faster, are fairly concentrated in only one chromosome The most innovative element of this work is undoubtedly the section relating to simulation This section contains the explanation of how the artificial intelligence quotient vectors are generated by using the previsions of the new theory of evolution; they practically behave like the variables that were actually observed, in spite of the intrinsic complexity involved As if that were not enough, with the due caution this subject deserves, the existence of a finalistic or teleological evolution is scientifically proven to agree with that indicated by the General Theory of Conditional Evolution of Life Given that the current results in this book suggest a fairly radical change from the common opinions held by the majority of the scientific community and society, the logical deduction is that more extensive studies, using the same methodology, need to be performed Another result, probably more important than the hereditary nature of relational intelligence and the existence of the GIV method is the validation of the full model of the genetic evolution of intelligence That is to say, according to what the GTCEL discloses, following the basic knowledge of sexual biological reproduction, the increase of the genetic intellectual ability of only a particular ancestor, substantially improves the adjustment of the model in its simulation when using sensitivity analysis This fact would imply the existence of a teleological or finalist evolution and, consequently, that theories of random mutations and natural selection would no longer constitute the main components of evolution However, the demonstration of a teleological evolution would not imply that all the aspects advocated by the movement of Intelligent Design are correct, bearing in mind their amplitude and heterogeneity It is important to emphasize that the genetic differences due to gender are essential in many areas because of the specialization they imply Doubtlessly, the other ancestor contribution will be carried out by alternative ways, also included in the model An example of further exploration of this study is found in the section that has been added subsequently, which is related to partner choice and intelligence In this section a hypothesis regarding a concrete requisite of the acceptable limit of the difference in intelligence when forming a couple, is confirmed and simultaneously reinforces the model's overall coherence In fact, the requirement refers to the unconscious choice of an unknown intelligence for current psychology The previous additional hypothesis has been validated while observing a substantial increase in the goodness of fit of the intelligence's heritability model under a new relation or condition relative to Intelligent Design; reaching correlations of the 0,97 Finally, I am convinced similar studies on intelligence, with longitudinal data of more extensive population samples, will offer similar results     Flynn effect and other statistical studies   There many arguments that help us to understand the reasons why this subject remains controversial; they are derived from both the intrinsic complexity of intelligence and the different initial premises with which the studies are conducted In any case, the Flynn effect shows and increase in intelligent quotients in different countries The results of the Flynn effect are accepted The problem is with the reasons, causes and interpretation of the facts presented by the Flynn effect Below, the most common views are mentioned 2.a) Lack of a unique definition   This view of the concept of intelligence is somewhat negative 2.b) Francis Galton and regression to the mean   Francis Galton (1822-1911), cousin of Charles Darwin, indicated the necessity of using statistical methods to verify theories; thus, in his important work Natural Inheritance (1889) he introduced the concept of line of regression from a study comparing the statures of parents and children In the descriptive analysis of Galton's data, tall parents were observed to have tall children (but not so tall on average) and that short parents had short children (but not so short on average) This produced what he denominated a regression to the mean Perhaps the phenomena in which the famous regression to the mean takes place can be explained in greater detail with a multifactor analysis approach 2.c) The Bell Curve and correlations below 0,5   Richard J Herrnstein and Charles Murray make many references to studies on human intelligence in their book The Bell Curve with different conclusions about the genetic influence in intelligence, including the famous Flynn effect For the development of their ideas they assume an approximate correlation of 0,5 remaining in between those in favour of genetic influence and those in favor of environmental influence There is no general agreement on the stability of these capacities throughout life, although it seems that it is accepted that the average environmental influence is greater in early ages, followed by a decreasing influence until maturity The latter is contrary to what would be expected 2.d) High correlations in twin studies   In order to try to resolve the controversy on genetic and environmental influences in intelligence, numerous works have been performed, most of which they have been based on the study of identical twins The studies with identical twins have many advantages as they avoid some elements that could cause differences in intelligence Even the Flynn effect is eliminated as the effect would operate in both identical twins Identical twins have a correlation of up to 0.87 as far as intelligence is concerned; in non-twin siblings correlation oscillates around 0.55 This data comprise of a experience of Jensen in 1972, which led to his basic conclusion that 80% of the variance in a population, related to the figures of the intellectual quotient (IQ), can be explained by inherited factors Logically, if this conclusion were correct we would have to assume that intelligence has basically a hereditary nature, although it is not predetermined because there are factors like genetic combination in accordance with the laws of Mendel At this point, it is worthwhile to remember the concept of hereditariness in a strict sense that is established by the relation between the observed and the expected correlations In those cases in which the expected correlation is less than the unit, an upward correction of the observed correlation will be produced for the calculation of the degree of hereditariness 2.e) Flynn effect and complex econometric models   Studies of great statistical complexity have also been made to try to resolve the controversy Two of them drew my interest I believe that one is eminently theoretical and the other practical The article Heritability Estimates Versus Large Environmental Effects: The IQ Paradox Resolved by William T Dickens and James R Flynn (Author of the Flynn effect), affirms to have solved the problem by means of the introduction of variables with temporary feedback In my opinion, it is not surprising that, if we are already working with strongly correlated variables and we add a certain feedback, high statistical results can be reached On the other hand, this article tries to explain the observed Flynn effect or gain in IQ throughout different generations, specifically, the 20 point increase that occurred between 1952 and 1982 in some countries The other study, discriminating pre- and postnatal factors, from the Medical School of the University of Pittsburgh, reaches the conclusion that the prenatal maternal environment exerts a powerful influence on intelligence     IQ data set   3.a) Available statistical IQ data set   The current statistical research has been performed with the IQ data set contained in the Young Adulthood Study: 1939-1967 [made accessible in 1979 on electronic files] This IQ data set was collected by Virginia Crandall and made available through an archive at the Henry A Murray Research Center of The Radcliffe Institute for Advanced Study, Harvard University, Cambridge, Massachusetts [Producer and Distributor]    Y OUNG ADULTHOOD STUDY (Statistical IQ data set) Variables Name Mothers M Fathers F Children C1/T1       Reference Period and Statistical data set 186 d12c66 T3 mothers IQ data (otis) 187 d12c70 T3 fathers IQ data (otis) 201 d13cl62T1 Stanford-Binet IQ data, score at ages 3, 6, 10-old/10 C2 217 d14cl62T2 Stanford-Binet IQ data, score at ages 3, 6, 10-old/10 C3 233 d15cl62T3 Stanford-Binet IQ data, score at ages 3, 6, 10-old/10 C4/T4 185 d12c62 T4 IQ data at age 12 C5/WB273 d18c30 T4 Wechsler-Bellevue IQ data, @ 13 yrs, perf C6 318 d20c62 Primary Mental Abilities-ttl (17-18 yrs.) C7 279 d18c54 T4 Wechsler-Bellevue IQ data, recent perf X3     = (C1+C4+C5) / X6   = (C1+C2+C3+ C4+C5+C6) / T1-d     = C1 smoothed tails, 10% of X6   This collection of longitudinal data contains the variables we are interested in: those relative to the intelligence quotients (IQ) of parents and their corresponding children The statistical data reliability is asured After a preliminary analysis of the available statistical IQ data set, one variable for the mothers (M), fathers (F) and children (C4) was used with 70 corresponding values, two more from the children (C1 and C5) with 69 corresponding values, and another set of three variables of the children with less corresponding values (C2, C3, and C6 with values of 58, 42, and 64 respectively) that we will use only to create variable X6, the average of the children's six variables The statistical IQ data set is taken from average class white families, with a mean IQ of 110, slightly above the average For each family, the data source corresponds to the father, the mother, and one child   Intelligence test     Limitations of statistical data set   Sample size of statistical IQ data set This is a limitation that could become very serious, although the sample size is 70 (n=70), when we make the analysis by groups it is reduced to only groups with a sample size of 10 in each one Nevertheless, we the mentioned grouping for values of 2, 3, 4, 5, 6, 7, 8, and 10 In addition, different groupings are created depending on the order the 70 values can be rearranged   Family and identical twin study   Finally, some substantial curiosities of family and IQ identical twin study are mentioned to gain a better understanding about this type of graphs and the Global model and, of course indirectly, the evolution of intelligence's Globus model   Statistical study - Family - Identical twin study   Graphics Subject q071°  q072° Evolution mothers Observations of q081 q082° CI Adjustment evolution Globus Model Identical twins q083° Relation Twins and Twin brothers brothers Clones Replica q053° q084° Clones Replica q056° q085   q086° Order M evolution Mothers & Fathers q087   q088° 0r89° adn Order P adn evolution Sexual selection Super Globus Model Partner ° Internal evolution parameters affect the objective function R and M1F1 order   Similarity of variable C's behaviour in the 0r81 graph can be interpreted in the sense that the coefficients could correspond to identical twins whereas the W would only be a normal sibling since it has been computer generated with the data of the same parents This performance can be found on numerous occasions when the rearrangement criterion is one of the C variables of the children (Standord Binet test, Wechsler intelligence test and others) In the case shown in the 0r82 graph, W is used as the criterion of arrangement, and the behaviour is slightly different; it seems that the four variables correspond to identical twins This is, of course, a particular case Graphs q083° and q084° show how W can resemble one or another C variable of children based on the implied randomness This commentary will be perfectly understood if these images are compared with 0r53 and 0r56 respectively Actually, we know that all C variables correspond to mono-environmental monozygotic twins, whereas W will only be a sibling; for that reason, sometimes they will look alike and others not so much   (Public image domain)   It does not seem hard to imagine some interesting studies on these peculiar matters Another substantial curiosity is the different behaviour of W and its variations shown on the images relative to the ancestors M and F as rearrangement criteria       Alternative theory of evolution   Except for error or omission, and with the appropriate caution, the main results of the EDI statistical study as a base of an alternative theory of evolution are presented in the following points: The hereditary nature of relational intelligence is confirmed The historical difficulty to perceive this characteristic of the brain functions is due mainly to the next factors: The multiple functions of the human intellect The lack of a suitable theoretical-philosophical base The fact that not all genetic load is expressed   Cognitive paradigm shift (Imagen de dominio público)   Lack of stability in the manifestation of intellectual power Measurement deviations Shortage of source data Randomness of the Mendelian genetic combination The existence of functional limitations or genetic problems in the expression of the genetic power of the human brain The necessity of a great capacity for statistical calculation and of intuitive understanding of the results of the Globus model In certain cases, different IQ measurements from the same person could have the same deviations as those of monozygotic twins (identical) or those of dizygotics twins (twin brothers), which would be conceptually similar to semimono-environmental siblings as well It looks as if the method of Verification of the Genetic Information (VGI) is operative in the expression of the intelligence power in accordance with alternative theory of evolution GTCEL previsions In regards to the present case, this method shows that the significant genetic information in relation to intelligence will be found in both progenitors The configuration of the concept of intelligence as a basic group of abstract relational abilities that are highly reliable in regards to their efficiency is a direct consequence of the previous The concepts of dominant gene and recessive gene of the Mendel laws are considerably affected by the implications of the existence of this method Approximately 500 million correlation coefficients have been calculated From them, by means of the sensitivity analysis of the parameters implied in the evolution, it seems that male genes are those providing both direct and indirect internal evolution also indicated by the aforementioned alternative theory of evolution GTCEL The percentage of internal evolution of the intelligence potential that optimises the model is 5% for both internal evolutions; representing a total of 10%, again as foreseen by the alternative theory of evolution GTCEL These percentages explain why the IQ test must be normalized each 15 or 20 years The previous points rigorously support the logic of the existence of sexual differentiation and its great advantages; however, we should not forget that they mainly imply differences of a biological nature Another logical point is that the increase generated by men also comes from certain changes due to the improvement of available materials thanks to the amelioration in the quality of males' formation when in the womb On the other hand, it is very possible that women's genes fulfil a backup function to maximize the assurance of the viability of the new being If the previous conclusions were correct, the existence of a non-random and finalist evolution would be established at the same time Therefore, the theory of natural selection would be realigned to a second temporal level as support to the evolutionary processes More extensive studies are strongly recommended, considering the extraordinary obtained results, (r² superior to 0.9) in order to be more accurate in the conclusions regarding qualitative specifications of the model and quantification of the parameters involved These studies could also be referred to other types of conditional intelligence An example of further exploration of this study is found in the section that has been added subsequently, which is related to mate selection and intelligence In this section a hypothesis regarding a concrete requisite of the acceptable limit of the difference in intelligence when forming a couple, is confirmed and simultaneously reinforces the model's overall coherence In fact, the requirement refers to the unconscious selection of an unknown intelligence for current brain psychology The creation of artificial intelligence quotients vectors has been achieved in a computer simulation model of the alternative theory of evolution This can allow the study of the model and its variability by stages; for example, fixing the Mendelian combination, the level of genetic affinity and, finally, the functional limitations or genetic problems, etc It is important to stress that, not only is there a shortage of source data, but also that it can be very expensive to obtain the necessary and appropriate data Up to now, I hope that I have scrupulously respected the scientific method rules From previous conclusions and their philosophical implications, it appears that the current Gods of science, Ra & Dona, straight reminiscences of the Egyptian Goddess Hator and the Mesopotamic God Ale, have not been able to continue hiding the logic or intelligence of the evolution of life, nor to prevent the latter from formally appearing to us, although somehow, still timidly       STATISTICAL ABSTRACT METHODOLOGY OF THE STATISTICAL STUDY   The title of each graph of the of the statistical study indicates the parents variables (R or M & F) to which the correlations are related These correlations are represented by each point of the coloured lines corresponding to each examined C variable (children) Likewise, the variables of unknown order, formed by the different groups of to 10 values from the 70 IQ values of each parent and children variables are placed on the left hand side of the graph The groups of to 10 values located on the right hand side have been previously ordered with the variable mentioned at the bottom of the graph   Statistical data     Each graph condenses more than 5,000 different points of information for the interrelations between: 70 values of each CI variable of the fathers, mothers, and children variables of the CI fathers, mothers, and children variables of individual averages of the previous variables 10 criteria of value arrangement 10 sizes of groupings of individuals 20 values of evolution’s parameters in a sensitivity analysis Countless random variables created in the simulation model The set of graphs collects all of these interrelations, that is, more than 1,000,000 values Note that the average of any two values has its own dynamic and is more or less independent of the other two values As an example of the information’s validity, one could put forth the case of having a historical sample of 70 packets of cigarettes The sample can be considered consisting of 70 elements, or many more if we consider that for each packet the following could be investigated: The number of cigarettes per packet The size of the cigarettes The type of cardboard the packet is made of The color If it has any images If it has any health warnings The type or severity of these warnings Information about the level of nicotine and tar etc Indeed, an almost instantaneous perception of the exactitude of the particular specification is obtained; sixty coefficients of determination (r2) are shown in a way that highlights the global and underlying relations of the involved data In order to facilitate the comparative analysis, a multidimensional correlation index has been defined (hereinafter MCI) to represent the global precision of the adjustments shown in any graph with one number It will be made up of the sum of the determination coefficients of the ten rearranged variables There will be an MCI for each variable and a global MCI for the three variables studied in each graph The maximum G-MCI will be 30, since different variables and ten groups are always used On the right hand side and below the variable, the r² and the GMCI are shown to help understanding the correlations involved The results are surprising, which can be observed both in the graphs of the statistical annex and in the following tables An aspect that will especially allow us to reach some important conclusions is the model sensitivity of the arrangement criterion     DATA SOURCE AND VARIABLES OF THE STATISTICAL STUDY * ** ° *R° M&F T1 T4 * WB T1-d X3 * X6 *W° These variables have been used used to rearranged the groups in certain cases These variables have been used to rearrange the groups in certain cases, but only in the statistical survey to verify the Method of Verification of the Genetic Information (VGI) and the special cases of the progenitors in the Curiosities section These variables, in certain cases, incorporate the effect of the statistical survey evolutionary parameters The objective function R of the statistical study is determined in accordance with the General Theory of Conditional Evolution of Life (GTCEL), Mendelian genetics significance and applying the method VGI to the intelligence quotients of the mothers (M) and fathers (F) Function R is the mathematical expected average of the capacity of the children in agreement with the GTCEL and it will be the sum of the expected averages of each one of the cases weighed by their probabilities according the Mendelian genetics The two IQ vectors are use simultaneously in statistical regression using ordinary least squares with one IQ vector of the children IQ vector of children - Original variable from the Young Adulthood Study - Stanford-Binet intelligence test IQ vector of children - Original variable from the Young Adulthood Study - Stanford-Binet intelligence test applied when children were 12 years old IQ vector of children - Original variable from the Young Adulthood Study - Wechsler Bellevueintelligence test applied when children were 13 years old IQ vector of children - T1 with smoothed tails, 10% of X6 IQ vector of children - Mean of three original variables from the Young Adulthood Study IQ vector of children - Mean of six original variables from the Young Adulthood Study Vectors of artificial intelligence quotients are generated by the computer simulaton of the General Theory of Conditional Evolution of Life They should behave like the observational data source * Vector of IQs are produced by the semi-addition of the intelligence (M+F)/2 quotients of the mother M and the father F * M1F1 ° Vector of IQs obtained with the lowest value of M and F of each family, either the intelligence quotient of the father or the intelligence quotient of the mother ** M IQ vector of the mothers (M) - Original variable from the Young Adulthood Study - OTIS intelligence test ** F IQ vector of the fathers (F) - Original variable from the Young Adulthood Study - OTIS intelligence test ** 2F2M Vector of IQs obtained with the highest value of M and F of each family, either the intelligence quotient of the father or the intelligence quotient of the mother     Appendix: Graphics table   STATISTICAL GRAPHS Social model Data source Social model Centred variables Development Artificial intelligence VGI Method Global model Data source Global model Centred variables Globus model Twins study Sexual selection   Statistical study - Social Model: T1, T4 and WB   Objective function Order R M&F Graphics GMCI r² max Graphics GMCI r² max (M+F)/2 q011 12,48 0,67 q012 13,05 0,80 M1F1 q013 12,17 0,87 q014 13,28 0,87 R q015 12,07 0,74 q016 13,05 0,75 WB q017 13,22 0,92 q018 14,68 0,99     Statistical study - Social Model: T1-d, X3 and X6   Objective function Order R M&F Graphics GMCI r² max Graphics GMCI r² max (M+F)/2 q021 15,71 0,79 q022 16,03 0,80 M1F1 q023 14,98 0,92 q024 16,07 0,92 R q025 15,02 0,89 q026 15,88 0,90 WB q027 15,05 0,91 q028 17,20 0,88         Statistical study Method of Verification Genetics Information (VGI)  Objective function Arrangement Order Graphics R M&F GMCI r² max Graphics GMCI r² max - Original variables T1, T4 and WB M q031 8,48 0,61 q032 9,16 0,69 F q033 9,44 0,59 q034 12,52 0,78 2F2M q035 7,55 0,61 q036 10,25 0,73 - Centred variables T1-d, X3 and X6 M q041 11,79 0,67 q042 12,14 0,71 F q043 12,28 0,69 q044 14,38 0,80 2F2M q045 9,20 0,56 q046 12,39 0,70     Statistical study Development of artificial intelligence quotients   Graphics Subject Observations q050 WMCI Too high q060 WMCI Similar GMCI to   Statistical study - Global Model: T1, T4 and WB   Objective function Order R° M&F Graphics GMCI r² max Graphics GMCI r² max (M+F)/2 q051° 11,73 0,62 q052 13,05 0,80 M1F1° q053° 10,91 0,79 q054° 13,04 0,79 R° q055° 10,83 0,73 q056° 12,63 0,94 WB q057° 12,26 0,89 q058 14,68 0,99 ° Internal evolution parameters affect the objective function R and M1F1 order     Statistical study - Global Model: T1-d, X3 and X6   Objective function Order R° M&F Graphics GMCI r² max Graphics GMCI r² max (M+F)/2 q061° 14,70 0,77 q062 16,03 0,80 M1F1° q063° 15,61 0,89 q064° 17,77 0,89 R° q065° 15,55 0,84 q066° 17,40 0,97 WB q067° 15,05 0,91 q068 17,20 0,88 ° Internal evolution parameters affect the objective function R and M1F1 order   Statistical study - Globus parametrized model   variable X3 q073° variable X6 q076° sexual selection & X6 - q077° Internal evolution adjustment   T1-d, X3 and X6 and order M1F1* Parameters Evo interna° Objective function Direct Indirect R° M&F Mothers Graphics GMCI r² max Graphics GMCI r² max 7   13,45 0,70   13,51 0,70 6   14,11 0,72   14,19 0,72 5 q071° 14,14 0,72 q072° 14,46 0,72 4   13,78 0,74   14,34 0,74 3   14,21 0,82   14,81 0,82 2   13,90 0,87   14,31 0,87 1   13,49 0,80   13,89 0,80             q023 14,98 0,92 q024 16,07 0,92             Null 0 Fathers 1   14,06 0,83   16,10 0,87   14,79 0,87   16,10 0,87 3   15,33 0,84   16,47 0,84 4   15,09 0,84   16,73 0,84 5 q063° 15,61 0,89 0r64° 17,77 0,89 6   14,30 0,95   16,74 0,95 7   13,25 0,83   15,56 0,83 ° Internal evolution parameters affect the objective function R and M1F1 order   Statistical study - Family - Identical twin study   Graphics Subject q071°  q072° Evolution mothers Observations of q081 q082° CI Adjustment evolution Globus Model Identical twins q083° Relation Twins and Twin brothers brothers Clones Replica q053° q084° Clones Replica q056° q085   q086° Order M evolution Mothers & Fathers q087   q088° 0r89° adn Order P adn evolution Sexual selection Super Globus Model Partner ° Internal evolution parameters affect the objective function R and M1F1 order     GRAPHICS - FIGURES INDEX IQ variables from the Young Adulthood Study Correlation of variables, preliminary analysis Normal distribution Genetic combination in the model of intelligence Initial model results Multi dimensional analysis (groups) Table results Social model: T1, T4 and WB (Rm.ori graphics) Table results Social model: T1-d, X3 and X6 (Rm.cen graphics) Table results: GIV Method (Rm.giv graphics) Development of artifical intelligence quotients (Sim.des graphics) Internal evolution sensitivity: Globus model Internal evolution sensitivity:Globus model Table results Global model: T1, T4 and WB (Sim.ori graphics) Table results Global model: T1-d, X3 and X6 (Sim.cen graphics) Internal evolution sensitivity - Sexual selection Table results Real model: Family relation (Sim.cur graphics)     ... the reproduction of the work done and the formal acceptance of the results   Experimental Psychology Psychology statistical survey   It is necessary to be aware that, a priori, there is not a... VGI Global model Simulation of the evolution of intelligence Complexity and optimization Esnuka: evolutionary theory game Parametrized Globus model with mate selection Family and identical twin... In fact, the requirement refers to the unconscious choice of an unknown intelligence for current psychology The previous additional hypothesis has been validated while observing a substantial increase