SUBMITTED VERSION OF: Commentary on Eva Jablonka and Marion J. Lamb: Evolution in Four Dimensions (2005)
Summarised for Behavioral and Brain Sciences Journal:

Word Counts:
Abstract: 90 words
Main Text: 1353 words
References: 297 words
Total Text: 1740 words

Also available in PDF (revised after the submission was accepted).

Adjust the width of your browser window to make the lines of text the length you prefer.
This web site does not attempt to impose restrictions on line length or font size.

Computational Cognitive Epigenetics

Aaron Sloman
School of Computer Science
University of Birmingham
School of Computer Science, The University of Birmingham
Birmingham, West Midlands, B15 2TT
+44 121 472 8581 (home)

Jackie Chappell
School of Biosciences
University of Birmingham
School of Biosciences, The University of Birmingham
Birmingham, West Midlands, B15 2TT
+44 121 41 43257


The rest of this file is the original submission. The final version (PDF) has been changed in several places, though most of the changes are merely stylistic.


J&L refer only implicitly to aspects of cognitive competence that preceded both evolution of human language and language learning in children. These are important for evolution and development but need to be understood using the 'design-stance', which the book adopts only for molecular and genetic processes, not for behavioural and symbolic processes. Design-based analyses reveal more routes from genome to behaviour than J&L seem to have considered. This both points to gaps in our understanding of evolution and epigenetic processes, and may lead to possible ways of filling the gaps.

J&L's book exposes many tangled connections between genome, behaviour and environment, but skims over gaps in our knowledge about information-processing capabilities underlying observed behaviours -- ignoring important mechanisms with epigenetic features. Much is said about physical and chemical mechanisms involved in development, but behavioural competences are described mostly from 'the outside'. Explaining the internal information processing requires the 'design stance' (Dennett, 1978).

External behaviours of many animals indicate that they have mechanisms concerned with internal symbolic competences required for perceiving or acting in structured situations, including planning, predicting, identifying information gaps to be filled, formulating goals, executing plans, learning generalisations, and creatively combining different competences. We need to explain what these competences are, what mechanisms make them possible, how they develop in individuals, and how they evolved.

Such competences (in humans and other animals) seem to presuppose something like internal symbolic languages with very specific properties.

When the variety of structurally different combinations of situations and goals rules out pre-configured responses, animals need the ability to represent and make inferences about existing and future configurations and changes; e.g., configurations of a partially constructed nest made of interlocking twigs, and the affordances (Gibson 1979) for inserting the next twig. This requires internal formalisms for representing structures and possible processes, and for constructing, comparing and planning, including selecting actions from branching collections of possible future sequences. Later, the animal has to produce the actions under the control of the representation. So action sequences linked to complex internal symbolic structures occurred before external linguistic behaviour evolved.

Animal behaviours demonstrating such competences include tool-related behaviours (Kacelnik et al. 2006), and remarkable symbolic competences of the grey parrot Alex (Pepperberg 2001).

Our epigenetic hypothesis about how information-processing develops under the influence of the environment, avoids two extreme theories: (a) that all animal competences are somehow encoded separately in the genome, possibly in a large collection of innate modules, and (b) that a small collection of general learning mechanisms (e.g. reinforcement learning) is genetically determined, and everything else is a result of applying those general learning processes.

Our 'middle way' also synthesises two apparently opposed views expressed by Karmiloff-Smith (1994): "Decades of developmental research were wasted, in my view, because the focus was entirely on lowering the age at which children could perform a task successfully, without concern for how they processed the information", and Neisser (1976): "we may have been lavishing too much effort on hypothetical models of the mind and not enough on analyzing the environment that the mind has been shaped to meet."

What an individual can learn often changes dramatically during its life, suggesting cascaded development of competences partly under the influence of the environment, including competences to acquire new competences (meta-competences) some of which are themselves the result of interaction of earlier meta-competences with the environment. Chappell and Sloman summarised this in Figure 1, showing multiple routes from the genome to behaviours of various sorts, with competences at different levels of abstraction and different sorts of specificity developed in different ways at different stages. This implies that learning in some parts of the brain is delayed until others have acquired a layer of competences to build on. So if prefrontal lobes are associated with processes further to the right of the diagram, occurring only after many cycles of simpler development, we would expect prefrontal lobes to develop after low level visual and motor control mechanisms. This has recently been reported in human infants, by Gilmore et al. (2007).

Figure 1 (From Chappell & Sloman (to appear)
The environment (including the body and new brain states) can affect all the processes.
There are multiple routes from genome to behaviours, some used only
after others have produced new competences and meta-competences.

J&L discuss the evolution of language and, like many others (e.g. Arbib 2005), focus mainly on external language used for communication. This assumes that first there were simple forms of language (e.g. gestures and sounds), and complex forms evolved later.

In contrast we suggest that language first evolved for 'internal' use. Because some people restrict the label 'language' to symbol systems used for external communication, we use 'g-language' (generalised language) to refer to a wider class including internal languages. A g-language allows rich structural variability of various kinds, and also compositional semantics for dealing with novel configurations of objects or processes.

Most people assume that language started simple and external, then grew more complex, before being internalised, whereas we, like Bridgeman (2005), suggest that complex g-languages evolved in many species and develop in young children, for internal use, providing forms of representation of current and possible future situations and processes that allow wide structural variation in what is represented, with compositional semantics to cope with novelty (Sloman 1979). Humans later started mapping internal structures onto external behaviours for communication. So rich internal g-languages evolved before external human language, and develop earlier in children.

Insofar as animals and children can look at different parts of a scene and combine information from most recent saccades with information about parts of the scene that are no longer in view, e.g. when planning what to do, they must use representations of spatial organisation of information as well as temporal organisation. In some ways this requires more complex forms of representation than human spoken languages, combining aspects of verbal language and pictorial languages (analogous to maps, diagrams, drawings). See also Trehub (1991).

G-languages probably evolved for internal information processing and control of behaviour (through the generation of goals, plans or instructions), along with generation of questions to specify missing information, and perhaps to formulate hypotheses, explanations and suppositions. External human language (spoken and gestural) and other symbolically-based aspects of human culture (e.g. music, mathematics etc.) might have built on these pre-existing internal symbolic foundations.

Eventually, instead of a specific g-language, evolution produced competences to acquire a variety of g-languages expressing different kinds of information.

This implies that some non-human animals' behaviour will be directed and shaped by their internal g-languages, which in turn are shaped by the structure of the external environment, directing evolution down particular paths, perhaps causing 'convergent' evolution of closely related cognitive abilities in birds and mammals with overlapping perceptual and manipulative competences.

If abstract and complex g-language constructs have to be learnt at a late stage of development, but are particularly useful to a species, then some of them could become genetically assimilated or accommodated, in which case they will themselves become heritable and can direct development in particular ways. Environmental cues encountered by these animals will be filtered through their cognitive architecture, thus tightening the knots between the genome, behaviour and the environment. Chappell and Sloman (to appear) suggest that this employed a separation between parts of the mechanism producing a general class of behaviours, and parts that provide parameters that select from that class. The generic competence and the particular parameters might undergo separate trajectories in evolution and development.

If J&L's 'assimilate-stretch' principle were extended to cope with evolution and development of internal g-languages and associated mechanisms, this might be a significant, previously unnoticed, factor in evolution of cognition.

Their examples suggest that assimilate-stretch extends behaviour additively. But qualitatively new capabilities might emerge. For example, if a learned capability becomes genetically assimilated or accommodated, it could form a building block for qualitatively diverse competences. Information that some objects can be deformed by manipulation, can be broken into smaller pieces, can be inserted into spaces, and can, if appropriately assembled, produce fairly rigid structures, might form fundamental parts of a very complex collection of learnable competences, including constructing nests, making or using tools or extracting objects from containers.

The ideas in this book may turn out to have far-reaching significance for many disciplines. We have tried to show, briefly, how some of that could impact on studies of cognition, and internal g-languages, with implications for the evolution of language and many forms of learning. As our cited paper suggests, these forms of development may be required also for intelligent robots, learning to cope in a wide variety of environments. These ideas are developed further on the Birmingham CoSy robot project web site:


Arbib, M. A. (2005). From monkey-like action recognition to human language: An evolutionary framework for neurolinguistics. Behavioral and Brain Sciences, 28(2), 105-124. Web site:

Bridgeman, B. (2005). Action planning supplements mirror systems in language evolution. Behavioral and Brain Sciences, 28(2), 129-130.

Chappell, J.M. & Sloman A. (To Appear). Natural and artificial meta-configured altricial information-processing systems. International Journal of Unconventional Computing. Web Site:,

Dennett, D. C. (1978). Brainstorms: Philosophical Essays on Mind and Psychology, Cambridge, MA: MIT Press.

Gibson, J.J. (1979). The Ecological Approach to Visual Perception, Hillsdale, NJ: Lawrence Erlbaum Associates,

Gilmore J.H., Lin, W., Prastawa, M.W, Looney, C.B., Sampath, Y., Vetsa, K., Knickmeyer, R.C., Evans, D.D., Smith, J.K., Hamer, R.M., Lieberman, J.A., and Gerig, G., (2007). Regional Gray Matter Growth, Sexual Dimorphism, and Cerebral Asymmetry in the Neonatal Brain The journal of Neuroscience, 27, 1255-1260. Web site: doi:10.1523/JNEUROSCI.3339-06.2007

Kacelnik, A, Chappell, J, Weir, A A S, & Kenward, B. (2006). Cognitive adaptations for tool-related behaviour in New Caledonian crows, In Wasserman, E A and Zentall, T R, (Eds) Comparative Cognition: Experimental Explorations of Animal Intelligence, (pp. 515-528). Oxford: Oxford University Press Web site:

Karmiloff-Smith, A. (1994). Precis of Beyond modularity: A developmental perspective on cognitive science. Behavioral and Brain Sciences 17 (4): 693-745. Web site:

Neisser, U., (1976) Cognition and Reality San Francisco: W. H. Freeman.

Pepperberg, I. M., (2001). Lessons from Cognitive Ethology: Animal Models for Ethological Computing, In C. Balkenius et al. (Eds), Proceedings of the First International Workshop on Epigenetic Robotics Lund. Web site:

Sloman, A. (1979). The primacy of non-communicative language, In MacCafferty, M. and Gray, K. (Eds) The analysis of Meaning: Informatics 5 Proceedings ASLIB/BCS Conference, Oxford, March 1979, pp. 1--15, Aslib. London. Web site:

Trehub, A. (1991). The Cognitive Brain, Cambridge, MA: MIT Press,


Some of this work arose out of discussions with colleagues in the EU-funded CoSy Robotic project at the University of Birmingham. Chris Miall helped with the diagram. Thanks to Erik Hollnagel for the Neisser quote.