General Symbol Machines : The First Stage in the Evolution of Symbolic Communication

Evolutionary Psychology
human-nature.com/ep – 2003. 1: 192-209
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Original Article

General Symbol Machines: The First Stage in the Evolution of
Symbolic Communication

Thomas E. Dickins, School of Psychology, University of East London, United Kingdom. Email:
t.dickins@uel.ac.uk.

Abstract: Humans uniquely form stimulus equivalence (SE) classes of abstract
and unrelated stimuli, i.e. if taught to match A with B and B with C, they will
spontaneously match B with A, and C with B, (the relation of symmetry), and A
with C (transitivity). Other species do not do this. The SE ability is possibly the
consequence of a specific selection event in the Homo lineage. SE is of interest
because it appears to demonstrate a facility that is core to symbolic behavior.
Linguistic symbols, for example, are arbitrarily and symmetrically related to their
referent such that the term banana has no resemblance to bananas but when
processed can be used to discriminate bananas. Equally when bananas are
perceived the term banana is readily produced. This relation is arguably the
defining mark of symbolic representation. In this paper I shall detail the SE
phenomenon and argue that it is evidence for a cognitive device that I term a
General Symbol Machine (GSM). The GSM not only sets the background
condition for subsequent linguistic evolution but also for other symbolic
behaviors such as mathematical reasoning. In so doing the GSM is not
particularly domain-specific. The apparent domain-specificity of, for example,
natural language is a consequence of other computational developments. This
introduces complexity to evolutionary arguments about cognitive architecture.

Keywords: Symbols; stimulus equivalence; learning; modularity; domain-
specific; canalization.

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Introduction

This paper will present a conditional argument about the emergence of
symbolic communication, and as such will constitute a hypothesis about a part of
the evolution of language. Full, natural language is idiosyncratic to humans, for
no other communication system exhibits the quality of recursion (Hauser,

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General Symbol Machines: The First Stage in the Evolution of Symbolic Communication
Chomsky and Fitch, 2002) which is a property of syntax and undoubtedly the
product of evolved cognitive machinery. However, one of the premises of the
conditional argument to be presented is that the recursive property of syntax is
dependent upon having something over which to operate – in this case, symbols.
Symbols have distinctive properties that are not seen in other animal
communication systems, and as a consequence, require an evolutionary
explanation of their own. These properties will be described.
The other premises of the conditional argument are about the kinds of
explanation we should be seeking when theorizing about the evolution of
language. They might be termed epistemic premises or assumptions. As with the
initial assumption that recursion has to operate over something, I am asking the
reader to act as if these assumptions are the case, and instead to focus their critical
effort upon the conclusions I seek to defend.
The first of these epistemic assumptions is a general one about cognitive
science. Cognitive science assumes that there are computational processes
operating within the brain that causally explain input-output relations in
organisms. Much of cognitive science is about delivering functional descriptions
of input-output relations, and trying to hypothesize the kind of algorithms that
might deliver such regularity. What is more, cognitive science aims to reduce
high level functioning to theories that rely only upon dumb, unthinking
mechanisms. This paper is arguing about the characteristics of a dumb,
unthinking mechanism that might underpin symbolic behavior.
The second epistemic assumption is about evolutionary theorizing specifically.
Some contemporary brands of evolutionary psychology have argued from
observations of domain-specific adaptive behavior for domain-specific cognitive
mechanisms that are responsible for that behavior (see Dickins, in press; Samuels,
1998). This is sometimes referred to as a modularity commitment (see below).
One concern with this approach is that although there are sound reasons not to
believe that the brain is a totally domain-general processor it is not clear that
behavioural evidence is sufficient to carve cognition at its joints. It is conceivable
that one cognitive mechanism could instantiate a number of behaviors, and that
one behavior could be the product of a number of mechanisms. The behavioural
data will not always allow you to decide. Another concern is that for every novel
mechanism hypothesized one is effectively hypothesizing a separate selection
‘event’. All too easily, one could have a theory of the evolution of cognition that
relied upon an unlikely number of fortuitous mutations. This paper sides with
Hauser, Chomsky and Fitch (2002) in advocating a long, hard look at comparative
evidence in order to be certain of behavioral discontinuities before advocating a
novel cognition and attendant selection events. This paper maintains that
symbolic behavior is just such a discontinuity, and will speculate about what can
be said with regard to the cognition that enables it [1].
The aspect of language evolution to be discussed, then, is the emergence of
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General Symbol Machines: The First Stage in the Evolution of Symbolic Communication
symbolic communication given the preceding assumptions. Communication will
be defined, in line with the common view from behavioural ecology (see Hauser,
1996), as the transfer of information from an actor to a reactor, such that the
behavior of the reactor is changed. This paper will argue that symbols convey a
different order of information from more common signaling systems, and it is this
that marks the discontinuity with other animal communication systems. The
paper provides a discussion of what symbols are in terms of this difference and it
will outline Deacon’s (1997) hypothesis about the evolutionary transition to
symbols within a communicative context to clarify this point.
Deacon is focused upon in some detail for his work embodies the premises just
discussed. He grounds his hypothesis upon associative learning up until the point
where symbols emerged, and as such Deacon adopts a parsimonious and
comparative approach, albeit an abstract one. It is at the point where symbols
emerge that his hypothesis will then be augmented with a discussion about the
properties of a putative General Symbol Machine (GSM) that allows the
formation of stimulus equivalence classes. This categorical ability is specific to
humans and, it will be claimed, essential for symbolic behavior.

Information

Information is to be understood in terms of its role in reducing uncertainty.
Through natural selection specific mechanisms will emerge that cause organisms
to react to pertinent input. For example, a frog whose retina is stimulated by a fly
crossing its visual field will produce an appropriate tongue-flick response that will
lead to eating. The way in which the frog’s visual system and tongue-flick system
etc. are constituted renders the visual input information – the frog’s systems can
be in 1+n states and this input determines which of those states they will be in.
The manner in which information is ‘transmitted’ can be organized as follows:
Cues convey information by being permanently on, or constantly present, for
example the yellow and black stripes of a wasp. This is a continuous feature of a
wasp’s abdomen and indicates that the wasp carries a dangerous sting – fatal to
some organisms, a painful irritant to others. This information reduces the
uncertainty about whether or not to approach a wasp.
Cues require no more than perceptual salience and then a learnt association to
be freshly established. The same is true of indices, or indexicals. The difference
between indices and cues is that indices indicate the presence of something by
dint of a causal relationship with that thing, such that smoke is the index of fire,
foaming about the mouth is the index of scurvy.
Signals are unlike cues and indices. A signal gives information about the
changing presence of something and as such can be on or off. Alarm calls are
signals because they are only useful if on in the presence of danger and off in its
absence. There is a sense in which signals are similar to indexical information for
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they are a consequence of the thing they convey information about (Deacon,
1997; see below), but signals such as alarm calls are produced by an organism
with a vested interest in the consequence of that signal being understood and
acted upon. In other words, signals are used communicatively within a social
group and as such are more effortful and have costs and benefits associated with
their production and comprehension. For example, producing an alarm call
makes one a target for the predator who is now aware of one’s location, and
acting on an alarm call opens one to possible deception. On the other hand,
giving an alarm call can save your kin and acting on it can save your skin.
A symbol represents an object, event or state of affairs. Symbols are arbitrarily
related to their referent, meaning that there is no natural relationship between a
symbol and its referent. This arbitrary relationship is established and maintained
through social convention. A symbol is also symmetrically related to its referent,
such that the appropriate symbol can be produced in the presence of the referent
and the appropriate referent can be produced or discriminated in the presence of
the symbol. This key property of symmetry was first noted by Saussure (see
Hurford, 1989, for a discussion).
The word <banana> is a symbol that refers to a certain kind of fruit. There is
nothing in the term <banana> that would indicate its referent naturally; its use is
entirely the consequence of the conventional linguistic history of English speakers
[2]. When a banana is seen the word <banana> can be produced, and when the
word <banana> is uttered the attention of the hearer is drawn to that kind of fruit.
If a token of this kind of fruit is not present then the hearer will have activated an
internal conceptual representation of a [banana], in this way reference can be
displaced temporally and spatially (see Figure 1 overleaf).
The potential informational gains for organisms using symbols are great, for
symbols allow the learning of others within a community to be transmitted and
used by those without the direct experience. Simply by arranging symbols
referents can be alluded to and novel situations involving those referents can be
presented in their absence. In this way the reduction of uncertainty is spread
beyond immediate domains.
Deacon (1997) has collapsed and refined the above taxonomy of information-
bearing entities by proposing three main types – icons, indexicals and symbols –
which owes much to the work of Charles Peirce, as Deacon makes clear. Icons are
the significant addition to the above discussion, achieving informational content
through bearing some similarity, for example, landscape paintings can be
regarded as icons. Deacon’s indexicals are the indices discussed above, however,
he also includes signals within this kind due to their causal relationship with that
which is signaled. His view of symbols is consonant with that already discussed.
Deacon sees the transition from signals (indexicals) to symbols as the first major
transition to language, as breaking the “symbolic threshold”. It is to this account
that we now turn.
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General Symbol Machines: The First Stage in the Evolution of Symbolic Communication
<banana> [banana]
taste
feel
…


Figure 1: A symbolic relationship – the symbol <banana> is attached
symmetrically to both the fruit and the concept [banana], which in turn is
associated with a number of banana-related events and experiences such as taste,
touch etc. At whichever point you access this categorical complex you can get to
the other points – for example, on hearing the word <banana> you can accurately
discriminate the fruit from other objects and this will also activate a conceptual
schema.

Deacon’s Symbolic Threshold

Under the definition of symbols adopted by this paper one could argue for a
simple associative model for establishing symbolic reference. Our ancestors
could simply have used novel vocalizations in the presence of certain objects and
given enough stability and exposure an association would be formed between that
vocalization and the object. A name would have been created. Deacon disagrees
with such a pseudo-Skinnerian view, arguing that the correlation between
symbols and their referents is not that frequent or strong in practice and as such, if
symbols were merely associatively linked with their referents there is every
chance that the relationship would quickly extinguish for most symbolic
reference. What Deacon in fact believes is the somewhat counterintuitive claim
that the ‘correspondence between (symbols) and objects is a secondary
relationship, subordinate to a web of associative relationships of a quite different
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sort, which even allows us to reference impossible things’ (1997, p. 70). It is in
order to clarify this claim that Deacon introduces the tripartite taxonomy of icon,
index and symbol.
The discussion of information sources could be used to endorse a passive view
– an organism perceives an index and all of the information necessary to correctly
orient the organism’s behavior is provided by this index. It is just a question of
downloading it. Deacon takes the opposite, behaviorally grounded view.

No particular objects are intrinsically icons, indices, or symbols.
They are interpreted to be so, depending upon what is produced in
response. In simple terms, the differences between iconic,
indexical, and symbolic relationships derive from regarding things
either with respect to their form, their correlations with other
things, or their involvement in systems of conventional
relationships. (Deacon, 1997, p. 71)

This view leads to the consequence that iconicity is not about brute similarity
between the icon and the referent but is instead about the process ‘based on
recognizing a similarity’ (Deacon, 1997, p. 71). As Deacon says, we can be very
liberal about what features we construe as similar and make an iconic relationship
out of practically anything. One can note similarities between a cheesecake and
the moon, given enough inferential effort, but no one would claim a “natural”
iconicity here. Likewise, a temporal or physical contiguity does not necessarily
instantiate an indexical relationship, and conventional usage does not instantiate a
symbol – it is only when we begin to use them as indexicals or symbols that they
are such. An interpretive “decision” has to be made in each instance.
A consequence of this processing view is that we can begin to see the tripartite
taxonomy as less defined. Icons, indexicals and symbols are not mutually
exclusive categories and the same entity can potentially do the work of all three.
Indeed, Deacon claims that these three classes of information are mutually
interdependent to some extent. For example, we could imagine being in a foreign
land and hearing a particular word used – <arnav> – on a number of occasions.
As symbolic beings we might well realize that this is a symbol simply from the
context in which the utterance is made but we would not have access to the
conventions of what is, in fact, Hebrew linguistic culture and therefore we could
not use the term symbolically. None the less, we might also note that this symbol
is often used in the presence of certain creatures and learn that this is at least a
likely index of the presence of rabbits. This guess will be heavily circumscribed
by various assumptions about the level of categorization appropriate to the term
but none the less, might covary sufficiently to facilitate some useful
understanding. In this example we can refer to the information lost by not being
part of the appropriate symbolic culture – if we spoke Hebrew and English we
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would know that <arnav> meant the same as <rabbit> and not the more generic,
and holophrastic <rabbits are present>, or <long-eared mammals are present> etc.
So, the difference between icon, indexical and symbol is to be understood in
terms of different levels of interpretation and these levels are hierarchically
organized. In the <arnav> example, once symbolic understanding failed the
strategy was to drop down to the next, indexical level and see what information
we could use under the appropriate set of processes. Deacon gives the following
example:

(As) human children become more competent and more
experienced with written words, they gradually replace their iconic
interpretations of these marks as just more writing with indexical
interpretations supported by a recognition of certain regular
correspondences to pictures and spoken sounds, and eventually use
these as support for learning to interpret their symbolic meanings.
(1997p. 74)

Deacon uses this idea as an intuition pump to drive the hypothesis that symbols
are dependent upon indexical reference and indexical reference is dependent upon
iconic reference. Could this hierarchical interdependence be the mark of an
evolutionary transition to symbolic behavior and one based on simple learning
behaviors?
Deacon discusses the different interpretative processes underlying iconic,
indexical and symbolic representation. Iconic representation is the consequence
of recognition, or of regarding the icon as like another thing. Sometimes this
requires absolutely no processing effort at all, and Deacon uses the example of a
bird scanning the bark of a tree to find a moth. The bird moves its head once –
bark – twice – bark – thrice – bark, and so forth. As the moth’s wings are very
similarly patterned to the bark it gets missed. The bird would have to be looking
harder for dissimilarities, rather than maintaining a process of similarity checking,
to get fed – and there are always dissimilarities. To this extent the moth wings are
iconic of the bark.
The obvious line to take when discussing the processes underlying indexical
reference would be to argue for a learning history establishing links between
foaming mouths and scurvy etc. However, as Deacon notes, many things can be
said to have physical or temporal contiguity so there must be something more to
this interpretative process. Deacon claims that it is critically dependent upon
iconic skills, as we would expect given the preceding argument. He uses the
example of smoke indicating fire:

The smell of smoke brings to mind past similar experiences (by
iconically representing them). Each of these experiences comes to
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mind because of their similarities to one another and to the present
event. But what is more, many of these past experiences also share
other similarities. On many of these occasions I also noticed
something burning that was the source of the smoke, and in this
way those experiences were icons of each other. (1997, p. 78)

So the extra process that is placed on top of iconic processing is that of noting
repeated correlations, in this case between smoke and fire. The transition across
the symbolic threshold is the next stage and this transition is, in Deacon’s view,
the establishment of relationships between indexicals, in a similar fashion to that
in which indexicals are constituted by relationships between icons. In this way,
symbols are not merely associatively linked to their referent. However, symbols
do retain their indexical properties as a consequence of the inter-relationships
between symbols as used in linguistic practice. Deacon exhorts us to think ‘of the
way a dictionary or thesaurus works. They each map one word onto other words.
If this shared meaning breaks down between users … the reference will also fail’
(1997, p. 82). The intensionality of a linguistic symbol or word is established and
maintained by the word-word relations, whilst the indexical element or word-use
provides the extension – word-word relations ‘allow words to be about indexical
relationships’ (Deacon, 1997, p. 83). Indeed, contextual information provided by
words often supports our comprehension of new terms.
How could this symbolic system establish itself in an ancestral population?
Deacon’s claim is that what establishes a symbol-symbol relation is a form of
insight learning. He supports the notion by discussing child development and lays
claim to bursts of learning within the language domain that are indicative of
ongoing insights. It is at this point in Deacon’s theory that there is a gap to be
filled.

Inferential Effort

The standard view of language acquisition in modern human infants is that
much of it is governed by innate mechanisms. For syntactic elements of language
these mechanisms are highly structured modular devices that effectively impose a
set of principles on a child’s learning of their native tongue. For word learning –
i.e. basic symbol acquisition – there is less evidence of a specific device, and
instead much discussion about canalizing learning with a number of innate
constraints, such as a whole-object bias, sensitivity to ostensive cues, novel
objects and novel speech sounds (Bloom and Markson, 1998). These constraints
triangulate the referent to which a given word is related such that an infant hears
the novel sound being uttered by an adult, looks to ascertain the direction of
attention (primarily from gaze direction), fixes the new object and assumes the
word refers to that whole object.
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It would appear that word learning does rely upon associative learning, but this
learning is heavily directed in order to deliver specific associations. Deacon
would argue that initially words are acquired as indices and only later do they
gain intensional properties once symbol-symbol relations are established. What is
surprising is the speed with which children acquire words and the lack of explicit
associative training that they receive, specifically negative training. To some
extent the canalization argument will account for this effect, for it reduces the
number of possible associations that can be made to “sound x” goes with “whole
object y”, but under normal associative learning paradigms one might expect a
few trials to be undertaken before such a novel link is made (see below).
It would appear reasonable to look at the kinds of associative learning that
might be operating under the canalizing constraints. Some discussion of the
nature of the learning might actually enable us to say something more about the
inferential effort required to interpret something as a symbol.
Using a straightforward matching-to-sample (MTS) paradigm with abstract
stimuli Sidman (1971, 1986, and 1994) was able to demonstrate a number of
emergent relational properties in human participants. A simple MTS procedure
consists of a training phase and then a test phase. In the training phase
participants are taught, through feedback, to pair abstract and unrelated stimuli
according to an undisclosed pattern. The experimenter might have three sets (A,
B and C) each of three stimuli (1, 2, and 3; see Figure 2a) and would train A1-B1
(which means that in the presence of sample stimulus A1 the comparison stimulus
B1 should be selected from B1, B2, and B3), and A2-B2 and A3-B3; and then B1-
C1, B2-C2, and B3-C3.

Figure 2a: Nine abstract stimuli for use in the formation of three three-member
stimulus equivalence classes. The classes to be formed are A1-B1-C1, A2-B2-
C2, and A3-B3-C3. In this example characters from the Klingon alphabet have
been used. None of the characters have a natural relationship within their
categories.

A B
C





1



2

3


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Figure 2b: A test of A1-A1 identity (with outlined correct response)


Sample






Comparisons



Figure 2c: A test of B1-A1 symmetry (with outlined correct response)


Sample






Comparisons



Figure 2d: A test of A1-C1 transitivity (with outlined correct response)


Sample





Comparisons




Figure 2e: A test of full C1-A1 equivalence (with outlined correct response)


Sample






Comparisons



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Document Outline

• Evolutionary Psychology
  • Information
  • ??
  • Inferential Effort
  • Evolved mechanisms and a General Symbol Machine
  • Conclusion
  • Notes
  • References

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