Cross-modal associations and synesthesia: Categorical perception and structure in vowel-color mappings in a large online sample.

We report associations between vowel sounds, graphemes, and colors collected online from over 1,000 Dutch speakers. We also provide open materials, including a Python implementation of the structure measure and code for a single-page web application to run simple cross-modal tasks. We also provide a full dataset of color-vowel associations from 1,164 participants, including over 200 synesthetes identified using consistency measures. Our analysis reveals salient patterns in the cross-modal associations and introduces a novel measure of isomorphism in cross-modal mappings. We found that, while the acoustic features of vowels significantly predict certain mappings (replicating prior work), both vowel phoneme category and grapheme category are even better predictors of color choice. Phoneme category is the best predictor of color choice overall, pointing to the importance of phonological representations in addition to acoustic cues. Generally, high/front vowels are lighter, more green, and more yellow than low/back vowels. Synesthetes respond more strongly on some dimensions, choosing lighter and more yellow colors for high and mid front vowels than do nonsynesthetes. We also present a novel measure of cross-modal mappings adapted from ecology, which uses a simulated distribution of mappings to measure the extent to which participants' actual mappings are structured isomorphically across modalities. Synesthetes have mappings that tend to be more structured than nonsynesthetes', and more consistent color choices across trials correlate with higher structure scores. Nevertheless, the large majority (~ 70%) of participants produce structured mappings, indicating that the capacity to make isomorphically structured mappings across distinct modalities is shared to a large extent, even if the exact nature of the mappings varies across individuals. Overall, this novel structure measure suggests a distribution of structured cross-modal association in the population, with synesthetes at one extreme and participants with unstructured associations at the other.

Phonological and orthographic influences in the bouba-kiki effect.

We examine a high-profile phenomenon known as the bouba-kiki effect, in which non-word names are assigned to abstract shapes in systematic ways (e.g. rounded shapes are preferentially labelled bouba over kiki). In a detailed evaluation of the literature, we show that most accounts of the effect point to predominantly or entirely iconic cross-sensory mappings between acoustic or articulatory properties of sound and shape as the mechanism underlying the effect. However, these accounts have tended to confound the acoustic or articulatory properties of non-words with another fundamental property: their written form. We compare traditional accounts of direct audio or articulatory-visual mapping with an account in which the effect is heavily influenced by matching between the shapes of graphemes and the abstract shape targets. The results of our two studies suggest that the dominant mechanism underlying the effect for literate subjects is matching based on aligning letter curvature and shape roundedness (i.e. non-words with curved letters are matched to round shapes). We show that letter curvature is strong enough to significantly influence word-shape associations even in auditory tasks, where written word forms are never presented to participants. However, we also find an additional phonological influence in that voiced sounds are preferentially linked with rounded shapes, although this arises only in a purely auditory word-shape association task. We conclude that many previous investigations of the bouba-kiki effect may not have given appropriate consideration or weight to the influence of orthography among literate subjects.

The Burden for High-Quality Online Data Collection Lies With Researchers, Not Recruitment Platforms.

A recent article in Perspectives on Psychological Science (Webb & Tangney, 2022) reported a study in which just 2.6% of participants recruited on Amazon's Mechanical Turk (MTurk) were deemed "valid." The authors highlighted some well-established limitations of MTurk, but their central claims-that MTurk is "too good to be true" and that it captured "only 14 human beings . . . [out of] N = 529"-are radically misleading, yet have been repeated widely. This commentary aims to (a) correct the record (i.e., by showing that Webb and Tangney's approach to data collection led to unusually low data quality) and (b) offer a shift in perspective for running high-quality studies online. Negative attitudes toward MTurk sometimes reflect a fundamental misunderstanding of what the platform offers and how it should be used in research. Beyond pointing to research that details strategies for effective design and recruitment on MTurk, we stress that MTurk is not suitable for every study. Effective use requires specific expertise and design considerations. Like all tools used in research-from advanced hardware to specialist software-the tool itself places constraints on what one should use it for. Ultimately, high-quality data is the responsibility of the researcher, not the crowdsourcing platform.

Exploitation and exploration in text evolution. Quantifying planning and translation flows during writing.

Writing is a complex process at the center of much of modern human activity. Despite appearing to be a linear process, writing conceals many highly non-linear processes. Previous research has focused on three phases of writing: planning, translation and transcription, and revision. While research has shown these are non-linear, they are often treated linearly when measured. Here, we introduce measures to detect and quantify subcycles of planning (exploration) and translation (exploitation) during the writing process. We apply these to a novel dataset that recorded the creation of a text in all its phases, from early attempts to the finishing touches on a final version. This dataset comes from a series of writing workshops in which, through innovative versioning software, we were able to record all the steps in the construction of a text. 61 junior researchers in science wrote a scientific essay intended for a general readership. We recorded each essay as a writing cloud, defined as a complex topological structure capturing the history of the essay itself. Through this unique dataset of writing clouds, we expose a representation of the writing process that quantifies its complexity and the writer's efforts throughout the draft and through time. Interestingly, this representation highlights the phases of "translation flow", where authors improve existing ideas, and exploration, where creative deviations appear as the writer returns to the planning phase. These turning points between translation and exploration become rarer as the writing process progresses and the author approaches the final version. Our results and the new measures introduced have the potential to foster the discussion about the non-linear nature of writing and support the development of tools that can lead to more creative and impactful writing processes.

Noise resistance in communication: Quantifying uniformity and optimality.

Over the past few decades, several lines of research have investigated how information is distributed in language. In particular, research has suggested that speakers distribute the information in linguistic utterances as evenly as possible, in order to make the utterance more robust against noise for the hearer. Several studies have provided evidence for this hypothesis in limited linguistic domains, showing that speakers manipulate acoustic and syntactic features by using redundancy to avoid drastic spikes or troughs in information content. However, this previous work doesn't consider information density across entire utterances, and only rarely has the proposed function of noise resistance been directly explored in detail. Here, we introduce a new descriptive statistic (DORM) that quantifies the uniformity of information across an entire utterance. We present this alongside an algorithm (UIDO) that allows us to quantify the contribution made to an utterance's uniformity by the order of its elements, independent of its level of redundancy. Using a simple simulation with data from an English corpus, we show that utterances whose order results in more uniform distributions of information are, in fact, more robust against noise.

A Social Approach to Rule Dynamics Using an Agent-Based Model.

A well-trod debate at the nexus of cognitive science and linguistics, the so-called past tense debate, has examined how rules and exceptions are individually acquired (McClelland & Patterson, ; Pinker & Ullman, ). However, this debate focuses primarily on individual mechanisms in learning, saying little about how rules and exceptions function from a sociolinguistic perspective. To remedy this, we use agent-based models to examine how rules and exceptions function across populations. We expand on earlier work by considering how repeated interaction and cultural transmission across speakers affects the dynamics of rules and exceptions in language, measuring linguistic outcomes within a social system rather than focusing individual learning outcomes. We consider how population turnover and growth effect linguistic rule dynamics in large and small populations, showing that this method has considerable potential particularly in probing the mechanisms underlying the linguistic niche hypothesis (Lupyan & Dale, ).

The regularity game: Investigating linguistic rule dynamics in a population of interacting agents.

Rules are an efficient feature of natural languages which allow speakers to use a finite set of instructions to generate a virtually infinite set of utterances. Yet, for many regular rules, there are irregular exceptions. There has been lively debate in cognitive science about how individual learners acquire rules and exceptions; for example, how they learn the past tense of preach is preached, but for teach it is taught. However, for most population or language-level models of language structure, particularly from the perspective of language evolution, the goal has generally been to examine how languages evolve stable structure, and neglects the fact that in many cases, languages exhibit exceptions to structural rules. We examine the dynamics of regularity and irregularity across a population of interacting agents to investigate how, for example, the irregular teach coexists beside the regular preach in a dynamic language system. Models show that in the absence of individual biases towards either regularity or irregularity, the outcome of a system is determined entirely by the initial condition. On the other hand, in the presence of individual biases, rule systems exhibit frequency dependent patterns in regularity reminiscent of patterns found in natural language. We implement individual biases towards regularity in two ways: through 'child' agents who have a preference to generalise using the regular form, and through a memory constraint wherein an agent can only remember an irregular form for a finite time period. We provide theoretical arguments for the prediction of a critical frequency below which irregularity cannot persist in terms of the duration of the finite time period which constrains agent memory. Further, within our framework we also find stable irregularity, arguably a feature of most natural languages not accounted for in many other cultural models of language structure.

General three-state model with biased population replacement: analytical solution and application to language dynamics.

Empirical evidence shows that the rate of irregular usage of English verbs exhibits discontinuity as a function of their frequency: the most frequent verbs tend to be totally irregular. We aim to qualitatively understand the origin of this feature by studying simple agent-based models of language dynamics, where each agent adopts an inflectional state for a verb and may change it upon interaction with other agents. At the same time, agents are replaced at some rate by new agents adopting the regular form. In models with only two inflectional states (regular and irregular), we observe that either all verbs regularize irrespective of their frequency, or a continuous transition occurs between a low-frequency state, where the lemma becomes fully regular, and a high-frequency one, where both forms coexist. Introducing a third (mixed) state, wherein agents may use either form, we find that a third, qualitatively different behavior may emerge, namely, a discontinuous transition in frequency. We introduce and solve analytically a very general class of three-state models that allows us to fully understand these behaviors in a unified framework. Realistic sets of interaction rules, including the well-known naming game (NG) model, result in a discontinuous transition, in agreement with recent empirical findings. We also point out that the distinction between speaker and hearer in the interaction has no effect on the collective behavior. The results for the general three-state model, although discussed in terms of language dynamics, are widely applicable.

Internal and external dynamics in language: evidence from verb regularity in a historical corpus of English.

Human languages are rule governed, but almost invariably these rules have exceptions in the form of irregularities. Since rules in language are efficient and productive, the persistence of irregularity is an anomaly. How does irregularity linger in the face of internal (endogenous) and external (exogenous) pressures to conform to a rule? Here we address this problem by taking a detailed look at simple past tense verbs in the Corpus of Historical American English. The data show that the language is open, with many new verbs entering. At the same time, existing verbs might tend to regularize or irregularize as a consequence of internal dynamics, but overall, the amount of irregularity sustained by the language stays roughly constant over time. Despite continuous vocabulary growth, and presumably, an attendant increase in expressive power, there is no corresponding growth in irregularity. We analyze the set of irregulars, showing they may adhere to a set of minority rules, allowing for increased stability of irregularity over time. These findings contribute to the debate on how language systems become rule governed, and how and why they sustain exceptions to rules, providing insight into the interplay between the emergence and maintenance of rules and exceptions in language.

What sound does that taste? Cross-modal mappings across gustation and audition.

All people share implicit mappings across the senses, which give us preferences for certain sensory combinations over others (eg light colours are preferentially paired with higher-pitch sounds; Ward et al, 2006 Cortex 42 264-280). Although previous work has tended to focus on the cross-modality of vision with other senses, here we present evidence of systematic cross-modality between taste and sound. We created four sound continua representing to varying extents phonetic qualities of speech (F1, F2, voice discontinuity, and spectral balance). Sixty-five participants selected their preferred sound to accompany each of the basic tastes of sweet, sour, bitter, and salty, at two different concentrations. We found significant shared preferences among our participants to map certain acoustic qualities to certain types of tastes (eg sweet tastes tend to be mapped to a lower spectral balance than sour tastes). We also found a preference for mapping certain sound qualities to different taste concentrations. Together our data provide the first detailed analysis of how phonetic features map systematically to different tastants and concentrations. We examine the roots of these mappings, and discuss how such associations might guide the ways in which human languages are used to name objects with taste.