From T-patterns to T-strings to T-societies: What makes humans unique?

Over half a century ago, Professor Magnus S Magnusson, now at the Human Behavior Laboratory of the University of Iceland, set out to define what separates humans from all other animal species. By detecting and analysing interaction patterns of behaviour from the nanoscale to human scales, he discovered T-societies in both proteins and humans. This breakthrough led to the realisation that only modern human mass societies are second-order T-societies and delivers a biomathematical definition of human uniqueness.

By the mid-1940s, two world wars in less than 40 years saw many millions killed by what were considered some of the world’s most advanced societies. Researchers wanted to make sense of such a world. Some looked to biology and primatology in their pursuit to better understand human behaviour. In 1967, Desmond Morris published The Naked Ape, detailing his primatological perspective of the social behaviour of humans, and in 1965, Niko Tinbergen’s book Animal Behavior became an influence for many researchers in the field. The 1973 Nobel Prize in Physiology or Medicine was shared by Tinbergen, Konrad Lorenz, and Karl von Frisch for their ethological discoveries regarding social behaviour patterns in animals.

Inspired by this research, Professor Magnus S Magnusson aimed to define what distinguishes humans from all other animal species. During his undergraduate studies and doctoral research from 1972 to 1983 at the University of Copenhagen, for which the university accorded him its Silver Medal in 1979, his research was strongly influenced by the work of B F Skinner, especially on verbal behaviour, N Chomsky’s work on syntactic structure, and their famous debate. Important inspiration came also from S Duncans’s research on human turn-taking and the French ethologist Hubert Montagner’s work on interactions in social insects and children. Magnusson’s attention was drawn to structured mass society and what appeared to be a commonality with external memory – meaning that information is stored in the environment, such as the use of tracks and scent marking in the structured mass society of insects and textual memory aids like notes and books in human society (Magnusson, 1975).

Patterns hidden in human interaction

Magnusson’s research involved defining, detecting, and analysing interaction patterns of behaviour in animals and humans. He developed the mathematical real-time pattern type, T-pattern, which he initially called ‘temporal configuration’ (Magnusson, 1983). T-pattern analysis reveals repeated timed sequences of behavioural events and can be used to examine the temporal behavioural characteristics of species ranging from rodents to humans.

To begin with, Magnusson concentrated on dyadic interactions between children. For instance, his analysis of the interaction of two children playing with a toy for 13.5 minutes revealed a considerable surprise: recurrent patterns that were far more regular and complex than expected (Magnusson, 1982, 1983). He then widened his focus to include adults as well as other species (Magnusson, 1988, 1996, 2000) and, ultimately, interactions in neuronal brain networks and polymer strings (Magnusson, 2016). He found a multitude of T-patterns at different time and spatial scales in all contexts, from proteins and neurons to primates, across many levels of organisation and orders of magnitude that ranged in scale from nano to human.

Computational interaction pattern

While computers made searching for hidden patterns possible, suitable detection algorithms were scarce. Working with computer statistics and SAS specialist Agnar Hoeskuldsson at IBM’s Northern Europe University Computer Center, Magnusson considered using multivariate statistical methods. However, he realised that better pattern models and, with them, suitable detection algorithms and software were required. He thus developed the T-pattern model and implemented the corresponding detection algorithms as his dedicated THEME software package. This has enabled the detection and analysis of T-patterns in many areas of research.

The data, called T-data, is made up of a set of discrete time-stamped event types collected during an observation period, with a series of occurrence times for each event type. T-data can be produced under either experimental or natural conditions and collected in a variety of ways. For example, it can be collected using multi-media coders or chips inserted in brain tissue, coded with a smartwatch app (eg, Theme Watch), or transformed from text or molecular data, including DNA or protein sequences.

From T-Pattern to T-string to T-Society

A T-pattern is a repeated structure produced by a series of simultaneous or sequential events that occur in the same order and with similar time intervals more frequently than one would expect if each event type occurred independently and randomly over time. If we picture the T-data running horizontally with one row for the occurrences of each event type, then a T-pattern connects some (critically related) points of different rows, level-by-level, until it outlines the occurrences of each complete pattern (Magnusson, 1996, 2000, 2004, 2020, 2023, 2024).

T-patterns are binary trees where each node has a maximum of two child nodes at all levels with the same statistical relation between them, called a critical interval. They thus have a self-similar fractal-like structure. While in mathematics, self-similarity in fractal structures is infinite, in nature, biomathematical self-similarity holds across a range of scales. T-pattern detection and analysis (TPA) using the THEME software (PatternVision) has uncovered self-similarity between interaction patterns in animals and between neurons in their brains.

T-strings are physical strings, such as text, proteins, and DNA, that contain T-patterns. The T-strings in DNA, proteins, and texts thus all display similarities in their patterning over different scales and across varying levels of biological organisation.

Magnusson recognised a similarity between protein and literate human mass societies. They are both based on what he calls Giant Extra-Individual Purely Informational T-strings (GEIPIT) that establish the characteristics of the various types of individuals. Just as the genome is the complete set of DNA in a protein society, a textome is the complete set of text in a literate human society. Genomes and textomes are both GEIPITs, made up of complete sets of T-strings.

T-pattern analyses of text and DNA revealed a distinct self-similarity between the modern human literate mass societies and the protein societies in their body cells. Both have GEIPITs defining the possible behaviours of types of individuals. Magnusson calls these societies ‘T-societies’. They don’t occur in other animals or illiterate humans, only in most literate modern human mass societies and protein societies.

Human uniqueness

In a major evolutionary transition, humans are the only species to have developed the same type of social organisation that occurs in every biological cell. Text offers a precise, unlimited external T-string memory. The growth of knowledge and better access to it, particularly in the twentieth century, has enabled the upsurge in the many kinds of specialisations of individuals supported by external memory T-strings, and likewise, the rapid growth of knowledge in areas such as science, law, and technology.

Writing, invented around 3500 BC, made human T-societies possible. The introduction of texts and progressively more effective ways to copy and distribute them in print and more recently electronically marked a departure from other primates and the start of human advancement towards T-societies. The arrival of mass education together with the standardised curricula used by school have been instrumental in the development of the numerous different brain patterns, and with them the behavioural capabilities of the many specialised humans. These unique modern human mass societies are a first for the extracellular world.

Humans are made up of cells that in themselves are protein T-societies. This means that human T-societies are in fact T-societies of T-societies. Magnusson calls this second-order T-societies. This T-society self-similarity does not happen in proteins and is unique to humans.

A biomathematical definition of uniqueness

Over half a century ago, Magnusson set out to find something that clearly separates modern humans from all other animal species. Along the way, he discovered T-patterns and T-strings. These enabled his discovery of T-societies in both proteins and humans. This breakthrough led to the realisation that only modern human mass societies are second-order T-societies and that this unified view of culture and biology provides a biomathematical definition of the unique modern human mass-social organisation.

Magnusson explains that most people seem to agree that the invention of writing was essential for the development of human possibilities. However, proteins in their mass societies are specialised in numerous ways, each defined by DNA (T-string) segments external to the protein, called a gene. Similarly, in modern human mass societies the numerous specialisations of individuals are each defined by text segments called a curriculum. T-string segments external to the individuals determine their specialisation, a defining aspect of T-societies. This is common and exclusive to protein and human mass societies and happened very recently in humans, since around 1800 AD with the advent of mass-education (Magnusson, 2022, 2023, 2024).

Following the invention of text that provides unlimited external memory and a multitude of specialities and specialists, only humans have become aware of the Universe, from the elemental components of matter and the microscopic constituents of our bodies to phenomena of an astronomical scale, such as galaxy clusters. At last, it appears that the human species has earned its name: homo sapiens (wise humans).