communication evolved with sociality

Behavioral technology correlates sociality and communication.  The design of inorganic technologies such as the telephone, email, SMS, and various web-based interfaces for social networking affect both the kinds of social networks among users and characteristics of communication among users.  For example, allowing anonymous commenting typically leads to a higher number of comments but also more rule-violating comments.

Studies of non-human animals show that organic technology also correlates sociality and communication. Among rhesus macaques, females spend much more time grooming other group-mates than do males. Females also more frequently make close-range social vocalizations than do males.  Female bonding characterizes rhesus macaques as a biological species.  That same biology also generates more frequent female social vocalizations.[1]

Across rhesus, pigtail, and stumptail macaques, less frequent and less varied gestural communication are associated with less complex social dynamics. A study of gestural communication in these three species found:

the gestural repertoire of rhesus macaques is generally poor in comparison to that of pigtail macaques, and especially that of stumptail macaques. Rhesus macaques exhibit fewer signals and use some of them with a lower frequency than the other species.[2]

Rhesus macaques’ relatively poor gestural repertoire is associated with a relatively simply social structure:

In a despotic and nepotistic society like that of rhesus macaques there may be little pressure to develop a sophisticated system of affiliative signals and bonding patterns. Maintenance of group structure and coordination of behavior between individuals can be effectively achieved if a few unequivocal indicators of differences in dominance are recognized and if unrelated or distantly-ranked individuals simply avoid each other. In pigtailed macaques, instead, complex dynamics of intergroup cooperation and high levels of social tolerance appear to have led to the evolution of intense affiliative communication and bonding patterns.[3]

The evolutionary forces that created these three distinct species of macaques created both their characteristic social structures and their characteristic patterns of communication.

Comparisons with less detail but covering more species similarly show correlation between increased sociality and better communication capabilities.  In forty-two non-human primate species for which data are available, social group size (the number of animals with which a given animal forms social bonds) correlates with vocal repertoire size (the number of acoustically distinct vocal signals the animal makes).[4]  Assuming equal times for each signal and an equal probability of using each signal, the organism’s signaling bandwidth in bits is proportional to the logarithm of the number of signals it makes.  More generally, biological communication bandwidth increases with an increase in the number of distinct signals that an organism makes. Greater bandwidth indicates better communication capabilities.  In non-human primates, better communication capabilities are thus correlated with larger social group sizes.

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Notes:

[1] Greeno, Nathalie C. and Stuart Semple (2008), “Sex differences in vocal communication among adult rhesus macaques,” Evolution & Human Behavior – 10 November 2008 (10.1016/j.evolhumbehav.2008.09.002).

[2] Maestripieri, Dario (2005), “Gestural communication in three species of macaques (Macaca mulatta, M. nemestrina, M. arctoides),” Gesture 5:1/2, p. 69.

[3] Id. p. 70.

[4] McComb, Karen, and Stuart Semple (2005), “Coevolution of vocal communication and sociality in primates,” Biology Letters, Dec. 22; 1(4): 381-385.  The data for the graph above is from this source and is available in spreadsheet form here. In doing cross-species comparisons, controling for phylogenetic relations is important.  Id. does this, but the graph above does not.  Nonetheless, the picture is similar.

bitter storm engulfs race

A bitter storm overshadowed the most recent running of the legendary Galbi Brothers 800 meter race. Douglas Galbi, who decisively won the contest in 2004 and 2006, entered the race as the clear news-media favorite.  Exploiting the devastating psychological force of his Lanterne Rouge cycling jersey, he started strongly and led by 100 yards going into the second lap.  But Dwight Galbi inexplicably emerged with a huge lead going down the home stretch.  Douglas’s footspeed is unquestionably far superior to Dwight’s, and Douglas courageously and dramatically attempted to close the gap in the last 100 yards.  But Dwight (just barely) managed to outlean him at the line.

However, all fair-minded viewers of the race video will agree that Dwight’s outrageous violation of fundamental racing regulations nullifies his victory.  Across economies around the world today, a lack of respect for rules, regulations, and regulators is causing enormous harm to the common welfare.  All public-spirited persons should insistently declare that Douglas Galbi, morally, virtually, and ideally, if not physically, won the race.

holiday dialogue, c. 2000

I wanna do some joins on a half gigabyte dataset that I’ve set up, but with my 200 MHz Pentium I’d be sitting around all day hearing the disk grind and watching the red light flash. I’m analyzing 26 million first names, the first name of every man, woman, and child in the United Kingdom in 1881.  I’ve worked with some smaller databases; names of 7000 cotton workers in Manchester in 1818 and another 1300 names from 1642 when all adult males were required to indicate whether they supported the King and his religion.  But these databases aren’t big enough to say much other than that John, Richard, and William are common male names, and about 20% of women were named Mary in early 19’th century England.  I need more computing power if I’m going to be able to figure out why people call themselves by certain names.  I’ve seen these monster machines with quad Pentium processors, 500 Mhz each and a gig of RAM.  Man I’d love to get my hands on one of those machines.  It’d standardize 26 million names faster than you can say “join Maggie, Molly and Polly with Margaret, and make Jonathon and Jonny types of Johns.”  Hey, by the way, what’s your name? Do you want to know how many people had your name in Britain in 1881?