Difference between revisions of "Evolutionary origin of human traits"

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(Calculations)
(Calculations)
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library(ggplot2)
 
library(ggplot2)
 
summary_data <- opbase.data("Op_en5865",subset="Literary review summary")
 
summary_data <- opbase.data("Op_en5865",subset="Literary review summary")
 +
authors <- opbase.data("Op_en5865",subset="Literary review authors")
 +
 +
authors_summary <- merge(summary_data, authors,by="Article") # Merged tables
 +
 +
authors_short <- authors_summary[,c("Trait","Hypothesis","Result.x","Result.y")] # Irrelevant columns taken out
 +
 +
authors_uniq <- unique(authors_short) # A list of authors only defending of attacking one hypothesis once.
  
 
ggplot(data=summary_data, aes(summary_data$Result)) + geom_histogram()
 
ggplot(data=summary_data, aes(summary_data$Result)) + geom_histogram()
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# Tästä eteenpäin bipedalismi hypoteeseja
 
# Tästä eteenpäin bipedalismi hypoteeseja
  
ggplot(summary_data[summary_data$Hypothesis == "Carrying baby",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Carrying baby") + labs(x="", y="Count") + theme_grey(base_size=24)
+
#ggplot(summary_data[summary_data$Hypothesis == "Carrying baby",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Carrying baby") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 
 +
#ggplot(summary_data[summary_data$Hypothesis == "Carrying food",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Carrying food") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 
 +
#ggplot(summary_data[summary_data$Hypothesis == "Energy efficiency",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Energy efficiency") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 
 +
#ggplot(summary_data[summary_data$Hypothesis == "Thermoregulation-b",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Thermoregulation") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 
 +
#ggplot(summary_data[summary_data$Hypothesis == "Thinner branches",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Thinner branches") + labs(x="", y="Count") + theme_grey(base_size=24)
  
ggplot(summary_data[summary_data$Hypothesis == "Carrying food",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Carrying food") + labs(x="", y="Count") + theme_grey(base_size=24)
+
biped <- ggplot(summary_data[summary_data$Trait == "Bipedalism",], aes(Result)) + geom_histogram(fill="blue") + labs(x="", y="Count") + labs(title="Bipedalism in articles") + theme_grey(base_size=24)
  
ggplot(summary_data[summary_data$Hypothesis == "Energy efficiency",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Energy efficiency") + labs(x="", y="Count") + theme_grey(base_size=24)
+
biped + facet_wrap(~Hypothesis, ncol=3)
  
ggplot(summary_data[summary_data$Hypothesis == "Thermoregulation-b",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Thermoregulation") + labs(x="", y="Count") + theme_grey(base_size=24)
+
biped_authors <- ggplot(authors_uniq[authors_uniq$Trait == "Bipedalism",], aes(Result.x)) + geom_histogram(fill="blue") + labs(x="", y="Count") + labs(title="Bipedalism by author") + theme_grey(base_size=24)
  
ggplot(summary_data[summary_data$Hypothesis == "Thinner branches",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Thinner branches") + labs(x="", y="Count") + theme_grey(base_size=24)
+
biped_authors + facet_wrap(~Hypothesis, ncol=3)
  
 
ggplot(summary_data[summary_data$Trait == "Bipedalism",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Bipedalism") + labs(x="", y="Count") + theme_grey(base_size=24)
 
ggplot(summary_data[summary_data$Trait == "Bipedalism",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Bipedalism") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 +
ggplot(authors_uniq[authors_uniq$Trait == "Bipedalism",], aes(Hypothesis, fill = Result.x)) + geom_histogram() + labs(title="Bipedalism by author") + labs(x="", y="Count") + theme_grey(base_size=24)
  
 
# Tästä eteenpäin aivohypoteeseja
 
# Tästä eteenpäin aivohypoteeseja
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ggplot(summary_data[summary_data$Hypothesis == "Social",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Social brain") + labs(x="", y="Count") + theme_grey(base_size=24)
 
ggplot(summary_data[summary_data$Hypothesis == "Social",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Social brain") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 +
brain <- (summary_data[summary_data$Trait == "Brain",], aes(Result)) + geom_histogram(fill="blue") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 +
brain + facet_wrap(~Hypothesis, ncol=3)
  
 
ggplot(summary_data[summary_data$Trait == "Brain",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Brain") + labs(x="", y="Count") + theme_grey(base_size=24)
 
ggplot(summary_data[summary_data$Trait == "Brain",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Brain") + labs(x="", y="Count") + theme_grey(base_size=24)
  
 
# Tästä eteenpäin rasvahypoteeseja
 
# Tästä eteenpäin rasvahypoteeseja
 +
 +
fat <- (summary_data[summary_data$Trait == "Fat",], aes(Result)) + geom_histogram(fill="blue") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 +
fat + facet_wrap(~Hypothesis, ncol=3)
  
 
ggplot(summary_data[summary_data$Trait == "Fat",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Subcutaneous fat") + labs(x="", y="Count") + theme_grey(base_size=24)
 
ggplot(summary_data[summary_data$Trait == "Fat",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Subcutaneous fat") + labs(x="", y="Count") + theme_grey(base_size=24)
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ggplot(summary_data[summary_data$Hypothesis == "Thermoregulation-h",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Thermoregulation") + labs(x="", y="Count") + theme_grey(base_size=24)
 
ggplot(summary_data[summary_data$Hypothesis == "Thermoregulation-h",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Thermoregulation") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 +
hair <- (summary_data[summary_data$Trait == "Hairlessness",], aes(Result)) + geom_histogram(fill="blue") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 +
hair + facet_wrap(~Hypothesis, ncol=3)
  
 
ggplot(summary_data[summary_data$Trait == "Hairlessness",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Hairlessness") + labs(x="", y="Count") + theme_grey(base_size=24)
 
ggplot(summary_data[summary_data$Trait == "Hairlessness",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Hairlessness") + labs(x="", y="Count") + theme_grey(base_size=24)
  
 
# Tästä eteenpäin kurkunpäähypoteeseja
 
# Tästä eteenpäin kurkunpäähypoteeseja
 +
 +
larynx <- (summary_data[summary_data$Trait == "Larynx",], aes(Result)) + geom_histogram(fill="blue") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 +
larynx + facet_wrap(~Hypothesis, ncol=3)
  
 
ggplot(summary_data[summary_data$Trait == "Larynx",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Larynx") + labs(x="", y="Count") + theme_grey(base_size=24)
 
ggplot(summary_data[summary_data$Trait == "Larynx",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Larynx") + labs(x="", y="Count") + theme_grey(base_size=24)
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#Tästä eteenpäin puhehypoteeseja
 
#Tästä eteenpäin puhehypoteeseja
  
ggplot(summary_data[summary_data$Hypothesis == "Sociality",], aes(Result)) + geom_histogram(fill="blue") + labs(title="Sociality") + labs(x="", y="Count") + theme_grey(base_size=24)
+
speech <- (summary_data[summary_data$Trait == "Speech",], aes(Result)) + geom_histogram(fill="blue") + labs(x="", y="Count") + theme_grey(base_size=24)
 +
 
 +
speech + facet_wrap(~Hypothesis, ncol=3)
  
 
ggplot(summary_data[summary_data$Trait == "Speech",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Speech") + labs(x="", y="Count") + theme_grey(base_size=24)
 
ggplot(summary_data[summary_data$Trait == "Speech",], aes(Hypothesis, fill = Result)) + geom_histogram() + labs(title="Speech") + labs(x="", y="Count") + theme_grey(base_size=24)
 
</rcode>
 
 
 
'''A code for drawing histograms from the literary review data showing the number of authors who have (co)authored articles supporting and arguing against different hypotheses.
 
 
<rcode graphics="1">
 
library(OpasnetUtils)
 
library(ggplot2)
 
summary <- opbase.data("Op_en5865",subset="Literary review summary")
 
authors <- opbase.data("Op_en5865",subset="Literary review authors")
 
 
authors_summary <- merge(summary, authors,by="Article") # Merged tables
 
 
authors_short <- authors_summary[,c("Trait","Hypothesis","Result.x","Result.y")] # Irrelevant columns taken out
 
 
authors_uniq <- unique(authors_short) # A list of authors only defending of attacking one hypothesis once.
 
 
oprint(authors_uniq)
 
 
ggplot(authors_uniq[authors_uniq$Hypothesis == "Thinner branches",], aes(Result.x)) + geom_histogram(fill="blue") + labs(title="Thinner branches by authors") + labs(x="", y="Count") + theme_grey(base_size=24)
 
 
ggplot(authors_uniq[authors_uniq$Trait == "Bipedalism",], aes(Hypothesis, fill = Result.x)) + geom_histogram() + labs(title="Bipedalism by author") + labs(x="", y="Count") + theme_grey(base_size=24)
 
  
 
</rcode>
 
</rcode>

Revision as of 07:17, 7 August 2016



Question

Why and how did humans become so different from other apes?

Answer

Rationale

There are conflicting hypotheses to explain why the traits that so clearly distinguish humans from other primates originally evolved. One idea is that the ancestors of humans came to live in a different kind of environment than the ancestors of chimpanzees and gorillas, and hence experienced different selection pressures and obtained a suite of unique traits as adaptations to the new environment. What that new environment was and which selection pressures were most important has been debated, however, and a number of hypotheses based on ideas other than environmental adaptation have also been proposed. To date, general discussion on the topic seems mostly to have focused on finding merit or flaws in one hypothesis at a time. The purpose of this page is to provide a structured forum for the general evaluation and comparison of the different hypotheses on human origins.

The answerers' opinions should be used as continuous [0,1] variables in such a way that each answer is transformed into its quantile of all answers to that question. Then neural networks, Bayesian belief network analyzers (such as B-source), or other statistical tools can be applied.

For single answer analyses, multinomial regression models should be applied.

Calculations

+ Show code

  • Data version 1: raw data in Excel from Webropol: free text columns removed [1]
  • Data version 2: cleaned with this code [2].

number of X column (1-25):

number of fill column (empty = all):

Encryption key:

+ Show code


A code for drawing histograms from the literary review data showing the number of articles supporting and arguing against different hypotheses.

+ Show code

Literary review summary

Hidden below you can find a summary of the articles found in a literary review on the evolution of human traits done in the summer 2016 to find out, which hypothesis on which human traits are currently discussed in scientific articles, and which according to them seem the most likely ways the human traits have evolved. F, A and N in the last column stand for For, Against and Neutral, according to which point of view is argued in the article about the hypothesis in question.



See also

Human evolution hypotheses described in Wikipedia:

Keywords

Human evolution, Bipedalism, Brain, Hairnessless, Subcutaneous fat, Speech

References


Related files