Signaling Under Uncertainty: Interpretative Alignment Without a Common Prior

Communication involves a great deal of uncertainty. Prima facie, it is therefore surprising that biological communication systems – from cellular to human – exhibit a high degree of ambiguity and often leave its resolution to contextual cues. This puzzle deepens once we consider that contextual information may diverge between individuals. In the following we lay out a model of ambiguous communication in iterated interactions between subjectively rational agents lacking a common contextual prior. We argue ambiguity’s justification to lie in endowing interlocutors with means to flexibly adapt language use to each other and the context of their interaction to serve their communicative preferences. Linguistic alignment is shown to play an important role in this process; it foments convergence of contextual expectations and thereby leads to agreeing use and interpretation of ambiguous messages. We conclude that ambiguity is ecologically rational when (i) interlocutors’ (beliefs about) contextual expectations are generally in line or (ii) they interact multiple times in an informative context, enabling for the alignment of their expectations. In light of these results meaning multiplicity can be understood as an opportunistic outcome enabled and shaped by linguistic adaptation and contextual information

Signaling Under Uncertainty: Interpretative Alignment Without a Common Prior

Communication involves a great deal of uncertainty. Prima facie, it is therefore surprising that biological communication systems – from cellular to human – exhibit a high degree of ambiguity and often leave its resolution to contextual cues. This puzzle deepens once we consider that contextual information may diverge between individuals. In the following we lay out a model of ambiguous communication in iterated interactions between subjectively rational agents lacking a common contextual prior. We argue ambiguity’s justification to lie in endowing interlocutors with means to flexibly adapt language use to each other and the context of their interaction to serve their communicative preferences. Linguistic alignment is shown to play an important role in this process; it foments convergence of contextual expectations and thereby leads to agreeing use and interpretation of ambiguous messages. We conclude that ambiguity is ecologically rational when (i) interlocutors’ (beliefs about) contextual expectations are generally in line or (ii) they interact multiple times in an informative context, enabling for the alignment of their expectations. In light of these results meaning multiplicity can be understood as an opportunistic outcome enabled and shaped by linguistic adaptation and contextual information

The interaction between cognitive ease and informativeness shapes the lexicons of natural languages

Lexical ambiguity is pervasive in language, and often systematic. Previous work shows that systematic ambiguities involve related meanings. This is attributed to cognitive pressure towards simplicity in language, as it makes lexicons easier to learn and use. The present study examines the interplay between this pressure and competing pressure for languages to support accurate information transfer. We hypothesize that ambiguity is shaped by a balance of the two pressures; and find support for this idea in data from over 1200 languages and 1400 meanings. Our results thus suggest that universal forces shape the lexicons of natural languages

Only, at least, more, and less

Only, at least, more, and les

Diagnosing truth, interactive sincerity, and depictive sincerity

This paper presents two experimental findings pertaining to the semantics and pragmatics of superlative modifiers ("at least", "at most"). First, in a scenario with N objects of a given type, speakers consistently judge it true that there are 'at least N' and 'at most N' objects of that type. This supports the debated position that the ignorance conveyed by superlative modifiers is an implicature, not an entailment, and contrasts with results obtained using an inference-judgment paradigm, suggesting that truth-value judgment tasks are impervious to certain pragmatic infelicities that inference-judgment tasks are sensitive to. The second finding is not predicted by any previous theory: In a scenario with N objects, it is not consistently judged true that there are 'at most N + 1' objects, even though it is consistently judged true that there are 'at least N – 1' objects. To explain this, we propose a novel pragmatic principle requiring that the scenario depicted by a sentence must be considered possible by the speaker (the Maxim of Depictive Sincerity). Put together, the two findings show that truth-value judgment tasks are impervious to some aspects of pragmatics, but not all

Horse or pony? Visual Typicality and Lexical Frequency Affect Variability in Object Naming

Often we can use different names to refer to the same object (e.g., pony vs. horse) and naming choices vary among people. In the present study we explore factors that affect naming variation for visually presented objects. We analyse a large dataset of object naming with realistic images and focus on two factors: (a) the visual typicality of objects and their context for the names used by human annotators and (b) the lexical frequency of these names. We use a novel computational approach to estimate visual typicality by calculating the visual similarity of a given object (or context) and the average visual information of other objects which were given the same name (in an independent dataset). In difference to previous studies, we not only consider the name used by most annotators for a given object (top name) but explore also the role of the second most frequently used name (alternative name). Our results show that naming variation decreases the more typical an object is for its top name and the higher the lexical frequency of this name. For alternative names the opposite is found. Context typicality does not show a general effect in our analysis. Overall our results show that visual and lexical characteristics relating to name candidates beyond the top name are informative for predicting variability in object naming. On a methodological level, our results demonstrate the potential of using large scale datasets with realistic images in conjunction with computational methods to inform models of human object naming

Brief at the risk of being misunderstood: consolidating population- and individual-level tendencies

Communicative pressures can give rise to regular patterns of language use. These patterns, in turn, can come to shape a language’s structure over time. In a recent study, Kanwal et al. (Cognition, 165:45–52, 2017) investigate whether an interaction of such pressures may underlie the cross-linguistic tendency of frequent forms to be shorter. Using a miniature artificial language, they show that speakers follow this tendency if pressured for brevity and accuracy. In this study, we use probabilistic models of varying complexity to shed light on the individual-level factors behind this trend. We find that a hierarchical model that accommodates for subjects’ heterogeneous beliefs about object frequencies best explains the data. At the population level, this model predicts an association of short forms with frequent meanings, in line with past research. At the individual level, however, it reveals a number of patterns that systematically deviate from this trend. On the one hand, these findings support the hypothesis that individual-level pressures may underlie natural languages’ relationship between frequency and brevity. On the other, by characterizing the individual-level dynamics on which this relationship rests, they highlight the importance of consolidating multiple strata of analysis and of understanding where and why they might diverge.This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovarion programme (grant agreement No 715154)

Minimal Requirements for Productive Compositional Signaling

The ability to form complex linguistic units from simpler ones lies at the center of many explanations of the communicative success and robustness of natural language. A closely related ability is that to generalize knowledge about such constructions to novel ones. The present investigation addresses the question what the minimal conditions for the emergence of such productive compositional communication are. Two features are argued to be required for this: relations between elements and classes over their relations. Using signaling games with reinforcement learning we show that a learning bias involving both aspects can lead to the emergence of such generalizable structure

When do languages use the same word for different meanings? The Goldilocks principle in colexification

Lexical ambiguity is pervasive in language, and often systematic. For instance, the Spanish word dedo can refer to a toe or a finger, that is, these two meanings colexify in Spanish; and they do so as well in over one hundred other languages. Previous work shows that related meanings are more likely to colexify. This is attributed to cognitive pressure towards simplicity in language, as it makes lexicons easier to learn and use. The present study examines the interplay between this pressure and the competing pressure for languages to support accurate information transfer. We hypothesize that colexification follows a Goldilocks principle that balances the two pressures: meanings are more likely to attach to the same word when they are related to an optimal degree—neither too much, nor too little. We find support for this principle in data from over 1200 languages and 1400 meanings. Our results thus suggest that universal forces shape the lexicons of natural languages. More broadly, they contribute to the growing body of evidence suggesting that languages evolve to strike a balance between competing functional and cognitive pressures.This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 715154)