Exploiting individual differences to bootstrap communication

TL;DR

This work tackles how a scalable communication system can emerge without pre-existing conventions or feedback. It develops a Bayesian online-learning model where agents allocate attention over meanings and learn signals via memory-based, Dirichlet-prior updates, regulated by certainty $C$, alignment $A$, memory decay $\lambda$, and prior strength $\alpha$, enabling emergence across many meanings $M$ and signals $S$. The results reveal three regimes: neutral drift under tight constraints, feedback-driven fixed-point emergence with constrained meaning spaces, and a feedback-free bootstrapping mechanism driven by shared intentionality and population variability that scales to large $M$; a threshold condition $\lambda\alpha < \Gamma$ (with appropriately defined $\Gamma$) delineates when communication arises. These findings imply that language-like coordination can originate from general social-cognitive capacities, with potential implications for AI systems that learn through self-supervised multi-agent interaction.

Abstract

Establishing a communication system is hard because the intended meaning of a signal is unknown to its receiver when first produced, and the signaller also has no idea how that signal will be interpreted. Most theoretical accounts of the emergence of communication systems rely on feedback to reinforce behaviours that have led to successful communication in the past. However, providing such feedback requires already being able to communicate the meaning that was intended or interpreted. Therefore these accounts cannot explain how communication can be bootstrapped from non-communicative behaviours. Here we present a model that shows how a communication system, capable of expressing an unbounded number of meanings, can emerge as a result of individual behavioural differences in a large population without any pre-existing means to determine communicative success. The two key cognitive capabilities responsible for this outcome are behaving predictably in a given situation, and an alignment of psychological states ahead of signal production that derives from shared intentionality. Since both capabilities can exist independently of communication, our results are compatible with theories in which large flexible socially-learned communication systems like language are the product of a general but well-developed capacity for social cognition.

Figures (11)

• Figure 1: Attention of signaller and receiver to specific meanings In each interaction, signaller $i$ and receiver $j$ distribute their attention over a set of meanings (shapes) with varying weights (indicated by size). Variation between interactions at different times for the same agent is quantified by the certainty $0\le C\le1$; variation between signaller and receiver at the same time by the alignment $0\le A\le1$.
• Figure 2: Topic selection and signal production The signaller samples a topic (here, the rabbit) in proportion to the attentional weight (size of the corresponding shape). The lower row of boxes indicates memory of which signal (different coloured circles) was previously interpreted by the signaller as having the corresponding meaning. Only the memories related to the topic are relevant (irrelevant memories are shaded). Memories decay over time, the size of each circle corresponding to the strength of the memory that remains. The signaller samples a signal in proportion to its strength in the memory. Here a blue circle is sampled, which corresponds to the verbalisation 'oyb'.
• Figure 3: Signal interpretation The receiver interprets the signal (here, the vocalisation 'oyb') by focussing on all memories of that signal's production in the past (blue circles). Memories of other signals are irrelevant (hence shaded). These memories are combined with the receiver's attentional weights by scaling their size in proportion to those weights (shown as each box, and its contents, scaled accordingly). The interpreted meaning (here, cat) is sampled according to the resulting combined weight.
• Figure 4: Retention of an interaction in memory After the receiver has drawn an inference (here, cat) given the signal they have encountered (here, the vocalisation 'oyb'), a memory of the interaction is retained. The default is to reinforce the association between the signal and interpreted meaning, operationalised by shrinking the size of memories from earlier interactions, and inserting a unit-sized memory corresponding to the signal (here, a blue circle). When feedback is available, an alternative is to decline to store the memory due to a mismatch between the interpretation and the intended topic. Previous memories shrink, yielding to a reversion to the uniform distribution (right) which occurs because prior knowledge corresponds to circles of fixed equal size (heavy outlines) in this representation.
• Figure 5: Communication systems that emerge under tight meaning constraints. Patch shading indicates the frequency $\phi(s|m)$ with which each signal $s$ is used to convey meaning $m$, averaged across a society of $N=5$ agents. In all cases $\lambda=0.01$, $C=1$, $A=1$ and $S=12$. Meanings increase from $M=14$ (leftmost column) to $M=36$ (rightmost column). Prior strength $\alpha=0.05$ (upper row) or $\alpha=0.01$ (lower row). A single signal dominates for a given meaning when $\phi(s|m)>\frac{1}{2}$. Signals have been ordered by the number of meanings for which they dominate; and meanings ordered so that they are adjacent when the same signal dominates. The horizontal line indicates the boundary between those signals that dominate at least one meaning and those that do not dominate any meaning. The vertical line indicates the boundary between meanings that are dominated by a signal and those that are not. We see that more signals dominate as the prior strength is reduced.