Different languages, similar encoding efficiency: Comparable information rates across the human communicative niche

Cognitive control and consciousness in open biological systems

Thermodynamically open biological systems not only sustain a life-supporting mutual relationship with their environment by exchanging matter and energy but also constantly seek information to navigate probabilistic changes in their surroundings. This work argues that cognition and conscious thought should not be viewed in isolation but rather as parts of an integral control of biological systems to identify and act upon meaningful, semantic information to sustain viability. Under this framework, the development of key cognitive control capacities in centralized nervous systems and the resulting behavior are categorized into distinct Markov decision processes: decision-making with partially observable sensory exteroceptive and interoceptive information, learning and memory, and symbolic communication. It is proposed that the state of conscious thought arises from a control mechanism for speech production resembling actuator control in engineered systems. Also known as the phonological loop, this feedback from the motor to the sensory cortex provides a third type of information flowing into the sensory cortex. The continuous, dissipative loop updates the fleeting working memory and provides humans with an advanced layer of control through a sense of self, agency and perception of flow in time. These capacities define distinct degrees of information fitness in the evolution of information-powered organisms.

Exploring the dynamics of Shannon's information and iconicity in language processing and lexeme evolution

This two-part meta-study explores the relationship of Shannon's information and iconicity in American English, with a focus on their implications for cognitive processing and the evolution of lexemes. Part one explores the expression of information in iconic words by calculating phonemic bigram surprisal using a very large corpus of spoken American English and cross referencing it with iconicity ratings. Iconic words-those with a form/meaning resemblance-are known to be processed with a cognitive advantage, so they are included in our tests as a benchmark. Within the framework of the Iconic Treadmill Hypothesis, we posit that as iconic words evolve towards arbitrariness, bigram sequences become more predictable, offsetting some the cognitive costs associated with processing arbitrary words. In part 2, we extend Cognitive Load Theory and the Lossy Context Surprisal Model-both sentence level language processing models-to test our predictions at the bigram level using the results of a battery of existing psycholinguistic experiments. In line with these models that explain the psycholinguistic consequences of hearing improbable words in sentences, our results show that words made up of improbable phonemes are processed with cognitive disadvantage, but that extra processing effort enhances their retention in long term memory. Overall, our findings speak to the cognitive limitations of language processing and how these limitations influence lexeme evolution.

Adversarial Dynamics in Centralized Versus Decentralized Intelligent Systems

Artificial intelligence (AI) is often used to predict human behavior, thus potentially posing limitations to individuals' and collectives' freedom to act. AI's most controversial and contested applications range from targeted advertisements to crime prevention, including the suppression of civil disorder. Scholars and civil society watchdogs are discussing the oppressive dangers of AI being used by centralized institutions, like governments or private corporations. Some suggest that AI gives asymmetrical power to governments, compared to their citizens. On the other hand, civil protests often rely on distributed networks of activists without centralized leadership or planning. Civil protests create an adversarial tension between centralized and decentralized intelligence, opening the question of how distributed human networks can collectively adapt and outperform a hostile centralized AI trying to anticipate and control their activities. This paper leverages multi-agent reinforcement learning to simulate dynamics within a human-machine hybrid society. We ask how decentralized intelligent agents can collectively adapt when competing with a centralized predictive algorithm, wherein prediction involves suppressing coordination. In particular, we investigate an adversarial game between a collective of individual learners and a central predictive algorithm, each trained through deep Q-learning. We compare different predictive architectures and showcase conditions in which the adversarial nature of this dynamic pushes each intelligence to increase its behavioral complexity to outperform its counterpart. We further show that a shared predictive algorithm drives decentralized agents to align their behavior. This work sheds light on the totalitarian danger posed by AI and provides evidence that decentrally organized humans can overcome its risks by developing increasingly complex coordination strategies.

Temporal patterns in the complexity of child-directed song lyrics reflect their functions

Content produced for young audiences is structured to present opportunities for learning and social interactions. This research examines multi-scale temporal changes in predictability in Child-directed songs. We developed a technique based on Kolmogorov complexity to quantify the rate of change of textual information content over time. This method was applied to a corpus of 922 English, Spanish, and French publicly available child and adult-directed texts. Child-directed song lyrics (CDSongs) showed overall lower complexity compared to Adult-directed songs (ADsongs), and lower complexity was associated with a higher number of YouTube views. CDSongs showed a relatively higher information rate at the beginning and end compared to ADSongs. CDSongs and ADSongs showed a non-uniform information rate, but these periodic oscillatory patterns were more predictable in CDSongs compared to ADSongs. These findings suggest that the optimal balance between predictability and expressivity in information content differs between child- and adult-directed content, but also changes over timescales to potentially support multiple children's needs.

Language-like efficiency in whale communication

Vocal communication systems in humans and other animals experience selection for efficiency-optimizing the benefits they convey relative to the costs of producing them. Two hallmarks of efficiency, Menzerath's law and Zipf's law of abbreviation, predict that longer sequences will consist of shorter elements and more frequent elements will be shorter, respectively. Here, we assessed the evidence for both laws in cetaceans by analyzing vocal sequences from 16 baleen and toothed whale species and comparing them to 51 human languages. Eleven whale species exhibit Menzerath's law, sometimes with greater effect sizes than human speech. Two of the five whale species with categorized element types exhibit Zipf's law of abbreviation. On average, whales also tend to shorten elements and intervals toward the end of sequences, although this varies by species. Overall, the results of this study suggest that the vocalizations of many cetacean species have undergone compression for increased efficiency in time.

The unbearable slowness of being: Why do we live at 10 bits/s?

This article is about the neural conundrum behind the slowness of human behavior. The information throughput of a human being is about 10 bits/s. In comparison, our sensory systems gather data at ∼109 bits/s. The stark contrast between these numbers remains unexplained and touches on fundamental aspects of brain function: what neural substrate sets this speed limit on the pace of our existence? Why does the brain need billions of neurons to process 10 bits/s? Why can we only think about one thing at a time? The brain seems to operate in two distinct modes: the "outer" brain handles fast high-dimensional sensory and motor signals, whereas the "inner" brain processes the reduced few bits needed to control behavior. Plausible explanations exist for the large neuron numbers in the outer brain, but not for the inner brain, and we propose new research directions to remedy this.

The human auditory cortex concurrently tracks syllabic and phonemic timescales via acoustic spectral flux

Dynamical theories of speech processing propose that the auditory cortex parses acoustic information in parallel at the syllabic and phonemic timescales. We developed a paradigm to independently manipulate both linguistic timescales, and acquired intracranial recordings from 11 patients who are epileptic listening to French sentences. Our results indicate that (i) syllabic and phonemic timescales are both reflected in the acoustic spectral flux; (ii) during comprehension, the auditory cortex tracks the syllabic timescale in the theta range, while neural activity in the alpha-beta range phase locks to the phonemic timescale; (iii) these neural dynamics occur simultaneously and share a joint spatial location; (iv) the spectral flux embeds two timescales-in the theta and low-beta ranges-across 17 natural languages. These findings help us understand how the human brain extracts acoustic information from the continuous speech signal at multiple timescales simultaneously, a prerequisite for subsequent linguistic processing.

The Effect of Characteristics of Patient Communication on Physician Feedback in Online Health Communities: An Observational Cross-Sectional Study

With the rapid development of e-health and telemedicine, previous studies have explored the relationship between physician-patient communication and patient satisfaction; however, there is a paucity of research on the influence of the characteristics of patient communication on the characteristics of physician feedback. Based on the communication accommodation theory, as well as the computer-mediated communication theory and media richness theory, this study aimed to explore how characteristics of patient communication influence characteristics of physician feedback in online health communities. We employed a crawler software to download the communication data between 1652 physicians and 105,325 patients from the Good Doctor platform, the biggest online health community in China. We built an empirical model using this data and employed a multilevel model to test our hypotheses using Stata and Python software. The results indicate that the amount of patients' rendered information positively influences the physicians' text (α = 0.123, t = 33.147, P < .001) and voice feedback (β = 0.201, t = 40.011, P < .001). Patients' hope for help signals and the provision of their electronic health records weaken the effect of the amount of patients' rendered information on physicians' text feedback (α = -0.040, t = -24.857, P < .001; α = -0.048, t = -15.784, P < .001), whereas, it strengthened the effect of the amount of patients' rendered information on physicians' voice feedback (β = 0.033, t = 14.789, P < .001; β = 0.017, t = 4.208, P < .001). Moreover, the occurrence of high-privacy diseases strengthened the effect of the amount of patients' presented information on physicians' text and voice feedback (α = 0.023, t = 4.870, P < .001; β = 0.028, t = 4.282, P < .001). This research contributes to the development of computer-mediated communication theories and sheds light on service delivery in the online health community.

Age-Related Diversification and Specialization in the Mental Lexicon: Comparing Aggregate and Individual-Level Network Approaches

The mental lexicon changes across the lifespan. Prior work, aggregating data among individuals of similar ages, found that the aging lexicon, represented as a network of free associations, becomes more sparse with age: degree and clustering coefficient decrease and average shortest path length increases. However, because this work is based on aggregated data, it remains to be seen whether or not individuals show a similar pattern of age-related lexical change. Here, we demonstrate how an individual-level approach can be used to reveal differences that vary systematically with age. We also directly compare this approach with an aggregate-level approach, to show how these approaches differ. Our individual-level approach follows the logic of many past approaches by comparing individual data as they are situated within population-level data. To do this, we produce a conglomerate network from population-level data and then identify how data from individuals of different ages are situated within that network. Though we find most qualitative patterns are preserved, individuals produce associates that have a higher clustering coefficient in the conglomerate network as they age. Alongside a reduction in degree, this suggests more specialized but clustered knowledge with age. Older individuals also reveal a pattern of increasing distance among the associates they produce in response to a single cue, indicating a more diverse range of associations. We demonstrate these results for three different languages: English, Spanish, and Dutch, which all show the same qualitative patterns of differences between aggregate and individual network approaches. These results reveal how individual-level approaches can be taken with aggregate data and demonstrate new insights into understanding the aging lexicon.

Dog-human vocal interactions match dogs' sensory-motor tuning

Within species, vocal and auditory systems presumably coevolved to converge on a critical temporal acoustic structure that can be best produced and perceived. While dogs cannot produce articulated sounds, they respond to speech, raising the question as to whether this heterospecific receptive ability could be shaped by exposure to speech or remains bounded by their own sensorimotor capacity. Using acoustic analyses of dog vocalisations, we show that their main production rhythm is slower than the dominant (syllabic) speech rate, and that human-dog-directed speech falls halfway in between. Comparative exploration of neural (electroencephalography) and behavioural responses to speech reveals that comprehension in dogs relies on a slower speech rhythm tracking (delta) than humans' (theta), even though dogs are equally sensitive to speech content and prosody. Thus, the dog audio-motor tuning differs from humans', and we hypothesise that humans may adjust their speech rate to this shared temporal channel as means to improve communication efficacy.

Is the Articulatory Trajectory of Changing Syllables Important for Achieving Higher Syllable Rates Compared to Repeated Syllables?

PURPOSE

The production of speech-like sequences composed of varying syllables has been reported to achieve higher syllable rates than the production of repeated syllables (commonly designed as sequential motion rate [SMR] and alternating motion rate [AMR] tasks, respectively). The faster rate for SMR relative to AMR sequences is explained by different interpretative hypotheses, which remain empirically unexplored. In the present study, we aimed to investigate whether the high syllable rates of SMR sequences are due to the specific sequences used in most studies that involve front-to-back articulatory movements.

METHOD

Syllable rates of SMR sequences composed of different articulatory trajectories (i.e., labial-alveolar-velar [/pateko/], alveolar-velar-labial [/tekopa/], and velar-labial-alveolar [/kopate/]) were compared with those of the AMR sequences /papapa/, /tetete/, and /kokoko/ in 20 participants.

RESULTS

The results show higher syllable rates for each of the three SMR sequences as compared to AMR, suggesting that the trajectory of the articulatory movements in the sequential sequences is not the key to achieving higher syllable rates.

CONCLUSION

The syllable rate advantage for SMR over AMR sequences is not explained by the articulatory trajectories included in the former task, indicating that the front-to-back movements generally included in SMR sequences (i.e., /pataka/) are not decisive in achieving a higher syllable rate.

The child the apple eats: processing of argument structure in Mandarin verb-final sentences

Mandarin Chinese is typologically unusual among the world's languages in having flexible word order despite a near absence of inflectional morphology. These features of Mandarin challenge conventional linguistic notions such as subject and object and the divide between syntax and semantics. In the present study, we tested monolingual processing of argument structure in Mandarin verb-final sentences, where word order alone is not a reliable cue. We collected participants' responses to a forced agent-assignment task while measuring their electroencephalography data to capture real-time processing throughout each sentence. We found that sentence interpretation was not informed by word order in the absence of other cues, and while the coverbs BA and BEI were strong signals for agent selection, comprehension was a result of multiple cues. These results challenge previous reports of a linear ranking of cue strength. Event-related potentials showed that BA and BEI impacted participants' processing even before the verb was read and that role reversal anomalies elicited an N400 effect without a subsequent semantic P600. This study demonstrates that Mandarin sentence comprehension requires online interaction among cues in a language-specific manner, consistent with models that predict crosslinguistic differences in core sentence processing mechanisms.

Speech patterns in responses to questions asked by an intelligent virtual agent can help to distinguish between people with early stage neurodegenerative disorders and healthy controls

Previous research has provided strong evidence that speech patterns can help to distinguish between people with early stage neurodegenerative disorders (ND) and healthy controls. This study examined speech patterns in responses to questions asked by an intelligent virtual agent (IVA): a talking head on a computer which asks pre-recorded questions. The study investigated whether measures of response length, speech rate and pausing in responses to questions asked by an IVA help to distinguish between healthy control participants and people diagnosed with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD). The study also considered whether those measures can further help to distinguish between people with MCI, people with AD, and healthy control participants (HC). There were 38 people with ND (31 people with MCI, 7 people with AD) and 26 HC. All interactions took place in English. People with MCI spoke fewer words compared to HC, and people with AD and people with MCI spoke for less time than HC. People with AD spoke at a slower rate than people with MCI and HC. There were significant differences across all three groups for the proportion of time spent pausing and the average pause duration: silent pauses make up the greatest proportion of responses from people with AD, who also have the longest average silent pause duration, followed by people with MCI then HC. Therefore, the study demonstrates the potential of an IVA as a method for collecting data showing patterns which can help to distinguish between diagnostic groups.

An Intrinsically Explainable Method to Decode P300 Waveforms from EEG Signal Plots Based on Convolutional Neural Networks

This work proposes an intrinsically explainable, straightforward method to decode P300 waveforms from electroencephalography (EEG) signals, overcoming the black box nature of deep learning techniques. The proposed method allows convolutional neural networks to decode information from images, an area where they have achieved astonishing performance. By plotting the EEG signal as an image, it can be both visually interpreted by physicians and technicians and detected by the network, offering a straightforward way of explaining the decision. The identification of this pattern is used to implement a P300-based speller device, which can serve as an alternative communication channel for persons affected by amyotrophic lateral sclerosis (ALS). This method is validated by identifying this signal by performing a brain-computer interface simulation on a public dataset from ALS patients. Letter identification rates from the speller on the dataset show that this method can identify the P300 signature on the set of 8 patients. The proposed approach achieves similar performance to other state-of-the-art proposals while providing clinically relevant explainability (XAI).

Single-Trial Detection and Classification of Event-Related Optical Signals for a Brain-Computer Interface Application

Event-related optical signals (EROS) measure fast modulations in the brain's optical properties related to neuronal activity. EROS offer a high spatial and temporal resolution and can be used for brain-computer interface (BCI) applications. However, the ability to classify single-trial EROS remains unexplored. This study evaluates the performance of neural network methods for single-trial classification of motor response-related EROS. EROS activity was obtained from a high-density recording montage covering the motor cortex during a two-choice reaction time task involving responses with the left or right hand. This study utilized a convolutional neural network (CNN) approach to extract spatiotemporal features from EROS data and perform classification of left and right motor responses. Subject-specific classifiers trained on EROS phase data outperformed those trained on intensity data, reaching an average single-trial classification accuracy of around 63%. Removing low-frequency noise from intensity data is critical for achieving discriminative classification results with this measure. Our results indicate that deep learning with high-spatial-resolution signals, such as EROS, can be successfully applied to single-trial classifications.

On the Mathematical Relationship Between Contextual Probability and N400 Amplitude

Accounts of human language comprehension propose different mathematical relationships between the contextual probability of a word and how difficult it is to process, including linear, logarithmic, and super-logarithmic ones. However, the empirical evidence favoring any of these over the others is mixed, appearing to vary depending on the index of processing difficulty used and the approach taken to calculate contextual probability. To help disentangle these results, we focus on the mathematical relationship between corpus-derived contextual probability and the N400, a neural index of processing difficulty. Specifically, we use 37 contemporary transformer language models to calculate the contextual probability of stimuli from 6 experimental studies of the N400, and test whether N400 amplitude is best predicted by a linear, logarithmic, super-logarithmic, or sub-logarithmic transformation of the probabilities calculated using these language models, as well as combinations of these transformed metrics. We replicate the finding that on some datasets, a combination of linearly and logarithmically-transformed probability can predict N400 amplitude better than either metric alone. In addition, we find that overall, the best single predictor of N400 amplitude is sub-logarithmically-transformed probability, which for almost all language models and datasets explains all the variance in N400 amplitude otherwise explained by the linear and logarithmic transformations. This is a novel finding that is not predicted by any current theoretical accounts, and thus one that we argue is likely to play an important role in increasing our understanding of how the statistical regularities of language impact language comprehension.

Signatures of speech and song: "Universal" links despite cultural diversity

Equitable collaboration between culturally diverse scientists reveals that acoustic fingerprints of human speech and song share parallel relationships across the globe.

Surprisal From Language Models Can Predict ERPs in Processing Predicate-Argument Structures Only if Enriched by an Agent Preference Principle

Language models based on artificial neural networks increasingly capture key aspects of how humans process sentences. Most notably, model-based surprisals predict event-related potentials such as N400 amplitudes during parsing. Assuming that these models represent realistic estimates of human linguistic experience, their success in modeling language processing raises the possibility that the human processing system relies on no other principles than the general architecture of language models and on sufficient linguistic input. Here, we test this hypothesis on N400 effects observed during the processing of verb-final sentences in German, Basque, and Hindi. By stacking Bayesian generalised additive models, we show that, in each language, N400 amplitudes and topographies in the region of the verb are best predicted when model-based surprisals are complemented by an Agent Preference principle that transiently interprets initial role-ambiguous noun phrases as agents, leading to reanalysis when this interpretation fails. Our findings demonstrate the need for this principle independently of usage frequencies and structural differences between languages. The principle has an unequal force, however. Compared to surprisal, its effect is weakest in German, stronger in Hindi, and still stronger in Basque. This gradient is correlated with the extent to which grammars allow unmarked NPs to be patients, a structural feature that boosts reanalysis effects. We conclude that language models gain more neurobiological plausibility by incorporating an Agent Preference. Conversely, theories of human processing profit from incorporating surprisal estimates in addition to principles like the Agent Preference, which arguably have distinct evolutionary roots.

Signal Smoothing and Syntactic Choices: A Critical Reflection on the UID Hypothesis

The Smooth Signal Redundancy Hypothesis explains variations in syllable length as a means to more uniformly distribute information throughout the speech signal. The Uniform Information Density hypothesis seeks to generalize this to choices on all linguistic levels, particularly syntactic choices. While there is some evidence for the Uniform Information Density hypothesis, it faces several challenges, four of which are discussed in this paper. First, it is not clear what exactly counts as uniform. Second, there are syntactic alternations that occur systematically but that can cause notable fluctuations in the information signature. Third, there is an increasing body of negative results. Fourth, there is a lack of large-scale evidence. As to the fourth point, this paper provides a broader array of data-936 sentence pairs for nine syntactic constructions-and analyzes them in a test setup that treats the hypothesis as a classifier. For our data, the Uniform Information Density hypothesis showed little predictive capacity. We explore ways to reconcile our data with theory.

Human languages with greater information density have higher communication speed but lower conversation breadth

Human languages vary widely in how they encode information within circumscribed semantic domains (for example, time, space, colour, human body parts and activities), but little is known about the global structure of semantic information and nothing about its relation to human communication. We first show that across a sample of ~1,000 languages, there is broad variation in how densely languages encode information into words. Second, we show that this language information density is associated with a denser configuration of semantic information. Finally, we trace the relationship between language information density and patterns of communication, showing that informationally denser languages tend towards faster communication but conceptually narrower conversations or expositions within which topics are discussed at greater depth. These results highlight an important source of variation across the human communicative channel, revealing that the structure of language shapes the nature and texture of human engagement, with consequences for human behaviour across levels of society.

Neural tracking of continuous acoustics: properties, speech-specificity and open questions

Human speech is a particularly relevant acoustic stimulus for our species, due to its role of information transmission during communication. Speech is inherently a dynamic signal, and a recent line of research focused on neural activity following the temporal structure of speech. We review findings that characterise neural dynamics in the processing of continuous acoustics and that allow us to compare these dynamics with temporal aspects in human speech. We highlight properties and constraints that both neural and speech dynamics have, suggesting that auditory neural systems are optimised to process human speech. We then discuss the speech-specificity of neural dynamics and their potential mechanistic origins and summarise open questions in the field.

Replication of population-level differences in auditory-motor synchronization ability in a Norwegian-speaking population

The Speech-to-Speech Synchronization test is a powerful tool in assessing individuals' auditory-motor synchronization ability, namely the ability to synchronize one's own utterances to the rhythm of an external speech signal. Recent studies using the test have revealed that participants fall into two distinct groups-high synchronizers and low synchronizers-with significant differences in their neural (structural and functional) underpinnings and outcomes on several behavioral tasks. Therefore, it is critical to assess the universality of the population-level distribution (indicating two groups rather than a normal distribution) across populations of speakers. Here we demonstrate that the previous results replicate with a Norwegian-speaking population, indicating that the test is generalizable beyond previously tested populations of native English- and German-speakers.

Neural speech tracking shifts from the syllabic to the modulation rate of speech as intelligibility decreases

The most prominent acoustic features in speech are intensity modulations, represented by the amplitude envelope of speech. Synchronization of neural activity with these modulations supports speech comprehension. As the acoustic modulation of speech is related to the production of syllables, investigations of neural speech tracking commonly do not distinguish between lower-level acoustic (envelope modulation) and higher-level linguistic (syllable rate) information. Here we manipulated speech intelligibility using noise-vocoded speech and investigated the spectral dynamics of neural speech processing, across two studies at cortical and subcortical levels of the auditory hierarchy, using magnetoencephalography. Overall, cortical regions mostly track the syllable rate, whereas subcortical regions track the acoustic envelope. Furthermore, with less intelligible speech, tracking of the modulation rate becomes more dominant. Our study highlights the importance of distinguishing between envelope modulation and syllable rate and provides novel possibilities to better understand differences between auditory processing and speech/language processing disorders.

Frontal cortex activity during the production of diverse social communication calls in marmoset monkeys

Vocal communication is essential for social behaviors in humans and non-human primates. While the frontal cortex is crucial to human speech production, its role in vocal production in non-human primates has long been questioned. It is unclear whether activities in the frontal cortex represent diverse vocal signals used in non-human primate communication. Here we studied single neuron activities and local field potentials (LFP) in the frontal cortex of male marmoset monkeys while the animal engaged in vocal exchanges with conspecifics in a social environment. We found that both single neuron activities and LFP were modulated by the production of each of the four major call types. Moreover, neural activities showed distinct patterns for different call types and theta-band LFP oscillations showed phase-locking to the phrases of twitter calls, suggesting a neural representation of vocalization features. Our results suggest important functions of the marmoset frontal cortex in supporting the production of diverse vocalizations in communication.

Scale in Language

A central concern of the cognitive science of language since its origins has been the concept of the linguistic system. Recent approaches to the system concept in language point to the exceedingly complex relations that hold between many kinds of interdependent systems, but it can be difficult to know how to proceed when "everything is connected." This paper offers a framework for tackling that challenge by identifying *scale* as a conceptual mooring for the interdisciplinary study of language systems. The paper begins by defining the scale concept-simply, the possibility for a measure to be larger or smaller in different instances of a system, such as a phonemic inventory, a word's frequency value in a corpus, or a speaker population. We review sites of scale difference in and across linguistic subsystems, drawing on findings from linguistic typology, grammatical description, morphosyntactic theory, psycholinguistics, computational corpus work, and social network demography. We consider possible explanations for scaling differences and constraints in language. We then turn to the question of *dependencies between* sites of scale difference in language, reviewing four sample domains of scale dependency: in phonological systems, across levels of grammatical structure (Menzerath's Law), in corpora (Zipf's Law and related issues), and in speaker population size. Finally, we consider the implications of the review, including the utility of a scale framework for generating new questions and inspiring methodological innovations and interdisciplinary collaborations in cognitive-scientific research on language.

A large quantitative analysis of written language challenges the idea that all languages are equally complex

One of the fundamental questions about human language is whether all languages are equally complex. Here, we approach this question from an information-theoretic perspective. We present a large scale quantitative cross-linguistic analysis of written language by training a language model on more than 6500 different documents as represented in 41 multilingual text collections consisting of ~ 3.5 billion words or ~ 9.0 billion characters and covering 2069 different languages that are spoken as a native language by more than 90% of the world population. We statistically infer the entropy of each language model as an index of what we call average prediction complexity. We compare complexity rankings across corpora and show that a language that tends to be more complex than another language in one corpus also tends to be more complex in another corpus. In addition, we show that speaker population size predicts entropy. We argue that both results constitute evidence against the equi-complexity hypothesis from an information-theoretic perspective.

Planetary Scale Information Transmission in the Biosphere and Technosphere: Limits and Evolution

Information transmission via communication between agents is ubiquitous on Earth, and is a vital facet of living systems. In this paper, we aim to quantify this rate of information transmission associated with Earth's biosphere and technosphere (i.e., a measure of global information flow) by means of a heuristic order-of-magnitude model. By adopting ostensibly conservative values for the salient parameters, we estimate that the global information transmission rate for the biosphere might be ∼1024 bits/s, and that it may perhaps exceed the corresponding rate for the current technosphere by ∼9 orders of magnitude. However, under the equivocal assumption of sustained exponential growth, we find that information transmission in the technosphere can potentially surpass that of the biosphere ∼90 years in the future, reflecting its increasing dominance.

Acoustic correlates of the syllabic rhythm of speech: Modulation spectrum or local features of the temporal envelope

The syllable is a perceptually salient unit in speech. Since both the syllable and its acoustic correlate, i.e., the speech envelope, have a preferred range of rhythmicity between 4 and 8 Hz, it is hypothesized that theta-band neural oscillations play a major role in extracting syllables based on the envelope. A literature survey, however, reveals inconsistent evidence about the relationship between speech envelope and syllables, and the current study revisits this question by analyzing large speech corpora. It is shown that the center frequency of speech envelope, characterized by the modulation spectrum, reliably correlates with the rate of syllables only when the analysis is pooled over minutes of speech recordings. In contrast, in the time domain, a component of the speech envelope is reliably phase-locked to syllable onsets. Based on a speaker-independent model, the timing of syllable onsets explains about 24% variance of the speech envelope. These results indicate that local features in the speech envelope, instead of the modulation spectrum, are a more reliable acoustic correlate of syllables.

Information Theory Opens New Dimensions in Experimental Studies of Animal Behaviour and Communication

Over the last 40–50 years, ethology has become increasingly quantitative and computational. However, when analysing animal behavioural sequences, researchers often need help finding an adequate model to assess certain characteristics of these sequences while using a relatively small number of parameters. In this review, I demonstrate that the information theory approaches based on Shannon entropy and Kolmogorov complexity can furnish effective tools to analyse and compare animal natural behaviours. In addition to a comparative analysis of stereotypic behavioural sequences, information theory can provide ideas for particular experiments on sophisticated animal communications. In particular, it has made it possible to discover the existence of a developed symbolic “language” in leader-scouting ant species based on the ability of these ants to transfer abstract information about remote events.

The channel capacity of multilevel linguistic features constrains speech comprehension

Humans are expert at processing speech but how this feat is accomplished remains a major question in cognitive neuroscience. Capitalizing on the concept of channel capacity, we developed a unified measurement framework to investigate the respective influence of seven acoustic and linguistic features on speech comprehension, encompassing acoustic, sub-lexical, lexical and supra-lexical levels of description. We show that comprehension is independently impacted by all these features, but at varying degrees and with a clear dominance of the syllabic rate. Comparing comprehension of French words and sentences further reveals that when supra-lexical contextual information is present, the impact of all other features is dramatically reduced. Finally, we estimated the channel capacity associated with each linguistic feature and compared them with their generic distribution in natural speech. Our data reveal that while acoustic modulation, syllabic and phonemic rates unfold respectively at 5, 5, and 12 Hz in natural speech, they are associated with independent processing bottlenecks whose channel capacity are of 15, 15 and 35 Hz, respectively, as suggested by neurophysiological theories. They moreover point towards supra-lexical contextual information as the feature limiting the flow of natural speech. Overall, this study reveals how multilevel linguistic features constrain speech comprehension.

The multiple-level framework of developmental dyslexia: the long trace from a neurodevelopmental deficit to an impaired cultural technique

Developmental dyslexia is a neurodevelopmental disorder characterized by an unexpected impairment in literacy acquisition leading to specific poor academic achievement and possible secondary symptoms. The multi-level framework of developmental dyslexia considers five levels of a causal pathway on which a given genotype is expressed and hierarchically transmitted from one level to the next under the increasing influence of individual learning-relevant traits and environmental factors moderated by cultural conditions. These levels are the neurobiological, the information processing and the skill level (prerequisites and acquisition of literacy skills), the academic achievement level and the level of secondary effects. Various risk factors are present at each level within the assumed causal pathway and can increase the likelihood of exhibiting developmental dyslexia. Transition from one level to the next is neither unidirectional nor inevitable. This fact has direct implications for prevention and intervention which can mitigate transitions from one level to the next. In this paper, various evidence-based theories and findings regarding deficits at different levels are placed in the proposed framework. In addition, the moderating effect of cultural impact at and between information processing and skill levels are further elaborated based on a review of findings regarding influences of different writing systems and orthographies. These differences impose culture-specific demands for literacy-specific cognitive procedures, influencing both literacy acquisition and the manifestation of developmental dyslexia.

Value Fulfillment from a Cybernetic Perspective: A New Psychological Theory of Well-Being

Value Fulfillment Theory (VFT) is a philosophical theory of well-being. Cybernetic Big Five Theory (CB5T) is a psychological theory of personality. Both start with a conception of the person as a goal-seeking (or value-pursuing) organism, and both take goals and the psychological integration of goals to be key to well-being. By joining VFT and CB5T, we produce a cybernetic value fulfillment theory in which we argue that well-being is best conceived as the fulfillment of psychologically integrated values. Well-being is the effective pursuit of a set of nonconflicting values that are emotionally, motivationally, and cognitively suitable to the person. The primary difference in our theory from other psychological theories of well-being is that it does not provide a list of intrinsic goods, instead emphasizing that each person may have their own list of intrinsic goods. We discuss the implications of our theory for measuring, researching, and improving well-being.

Performance in an Audiovisual Selective Attention Task Using Speech-Like Stimuli Depends on the Talker Identities, But Not Temporal Coherence

Audiovisual integration of speech can benefit the listener by not only improving comprehension of what a talker is saying but also helping a listener select a particular talker's voice from a mixture of sounds. Binding, an early integration of auditory and visual streams that helps an observer allocate attention to a combined audiovisual object, is likely involved in processing audiovisual speech. Although temporal coherence of stimulus features across sensory modalities has been implicated as an important cue for non-speech stimuli (Maddox et al., 2015), the specific cues that drive binding in speech are not fully understood due to the challenges of studying binding in natural stimuli. Here we used speech-like artificial stimuli that allowed us to isolate three potential contributors to binding: temporal coherence (are the face and the voice changing synchronously?), articulatory correspondence (do visual faces represent the correct phones?), and talker congruence (do the face and voice come from the same person?). In a trio of experiments, we examined the relative contributions of each of these cues. Normal hearing listeners performed a dual task in which they were instructed to respond to events in a target auditory stream while ignoring events in a distractor auditory stream (auditory discrimination) and detecting flashes in a visual stream (visual detection). We found that viewing the face of a talker who matched the attended voice (i.e., talker congruence) offered a performance benefit. We found no effect of temporal coherence on performance in this task, prompting an important recontextualization of previous findings.

Zipfian Distributions in Child-Directed Speech

Across languages, word frequency and rank follow a power law relation, forming a distribution known as the Zipfian distribution. There is growing experimental evidence that this well-studied phenomenon may be beneficial for language learning. However, most investigations of word distributions in natural language have focused on adult-to-adult speech: Zipf's law has not been thoroughly evaluated in child-directed speech (CDS) across languages. If Zipfian distributions facilitate learning, they should also be found in CDS. At the same time, several unique properties of CDS may result in a less skewed distribution. Here, we examine the frequency distribution of words in CDS in three studies. We first show that CDS is Zipfian across 15 languages from seven language families. We then show that CDS is Zipfian from early on (six-months) and across development for five languages with sufficient longitudinal data. Finally, we show that the distribution holds across different parts of speech: Nouns, verbs, adjectives and prepositions follow a Zipfian distribution. Together, the results show that the input children hear is skewed in a particular way from early on, providing necessary (but not sufficient) support for the postulated learning advantage of such skew. They highlight the need to study skewed learning environments experimentally.

Unraveling the functional attributes of the language connectome: crucial subnetworks, flexibility and variability

Language processing is a highly integrative function, intertwining linguistic operations (processing the language code intentionally used for communication) and extra-linguistic processes (e.g., attention monitoring, predictive inference, long-term memory). This synergetic cognitive architecture requires a distributed and specialized neural substrate. Brain systems have mainly been examined at rest. However, task-related functional connectivity provides additional and valuable information about how information is processed when various cognitive states are involved. We gathered thirteen language fMRI tasks in a unique database of one hundred and fifty neurotypical adults (InLang [Interactive networks of Language] database), providing the opportunity to assess language features across a wide range of linguistic processes. Using this database, we applied network theory as a computational tool to model the task-related functional connectome of language (LANG atlas). The organization of this data-driven neurocognitive atlas of language was examined at multiple levels, uncovering its major components (or crucial subnetworks), and its anatomical and functional correlates. In addition, we estimated its reconfiguration as a function of linguistic demand (flexibility) or several factors such as age or gender (variability). We observed that several discrete networks could be specifically shaped to promote key functional features of language: coding-decoding (Net1), control-executive (Net2), abstract-knowledge (Net3), and sensorimotor (Net4) functions. The architecture of these systems and the functional connectivity of the pivotal brain regions varied according to the nature of the linguistic process, gender, or age. By accounting for the multifaceted nature of language and modulating factors, this study can contribute to enriching and refining existing neurocognitive models of language. The LANG atlas can also be considered a reference for comparative or clinical studies involving various patients and conditions.

Corpus-based typology: applications, challenges and some solutions

Over the last few years, the number of corpora that can be used for language comparison has dramatically increased. The corpora are so diverse in their structure, size and annotation style, that a novice might not know where to start. The present paper charts this new and changing territory, providing a few landmarks, warning signs and safe paths. Although no corpus at present can replace the traditional type of typological data based on language description in reference grammars, corpora can help with diverse tasks, being particularly well suited for investigating probabilistic and gradient properties of languages and for discovering and interpreting cross-linguistic generalizations based on processing and communicative mechanisms. At the same time, the use of corpora for typological purposes has not only advantages and opportunities, but also numerous challenges. This paper also contains an empirical case study addressing two pertinent problems: the role of text types in language comparison and the problem of the word as a comparative concept.

Japanese Version of the Mobile App Rating Scale (MARS): Development and Validation

Background

The number of mobile health (mHealth) apps continues to rise each year. Widespread use of the Mobile App Rating Scale (MARS) has allowed objective and multidimensional evaluation of the quality of these apps. However, no Japanese version of MARS has been made available to date.

Objective

The purposes of this study were (1) to develop a Japanese version of MARS and (2) to assess the translated version’s reliability and validity in evaluating mHealth apps.

Methods

To develop the Japanese version of MARS, cross-cultural adaptation was used using a universalist approach. A total of 50 mental health apps were evaluated by 2 independent raters. Internal consistency and interrater reliability were then calculated. Convergent and divergent validity were assessed using multitrait scaling analysis and concurrent validity.

Results

After cross-cultural adaptation, all 23 items from the original MARS were included in the Japanese version. Following translation, back-translation, and review by the author of the original MARS, a Japanese version of MARS was finalized. Internal consistency was acceptable by all subscales of objective and subjective quality (Cronbach α=.78-.89). Interrater reliability was deemed acceptable, with the intraclass correlation coefficient (ICC) ranging from 0.61 to 0.79 for all subscales, except for “functionality,” which had an ICC of 0.40. Convergent/divergent validity and concurrent validity were also considered acceptable. The rate of missing responses was high in several items in the “information” subscale.

Conclusions

A Japanese version of MARS was developed and shown to be reliable and valid to a degree that was comparable to the original MARS. This Japanese version of MARS can be used as a standard to evaluate the quality and credibility of mHealth apps.

A multi-scale investigation of the human communication system's response to visual disruption

In human communication, when the speech is disrupted, the visual channel (e.g. manual gestures) can compensate to ensure successful communication. Whether speech also compensates when the visual channel is disrupted is an open question, and one that significantly bears on the status of the gestural modality. We test whether gesture and speech are dynamically co-adapted to meet communicative needs. To this end, we parametrically reduce visibility during casual conversational interaction and measure the effects on speakers' communicative behaviour using motion tracking and manual annotation for kinematic and acoustic analyses. We found that visual signalling effort was flexibly adapted in response to a decrease in visual quality (especially motion energy, gesture rate, size, velocity and hold-time). Interestingly, speech was also affected: speech intensity increased in response to reduced visual quality (particularly in speech-gesture utterances, but independently of kinematics). Our findings highlight that multi-modal communicative behaviours are flexibly adapted at multiple scales of measurement and question the notion that gesture plays an inferior role to speech.

The science and engineering behind sensitized brain-controlled bionic hands

Advances in our understanding of brain function, along with the development of neural interfaces that allow for the monitoring and activation of neurons, have paved the way for brain-machine interfaces (BMIs), which harness neural signals to reanimate the limbs via electrical activation of the muscles or to control extracorporeal devices, thereby bypassing the muscles and senses altogether. BMIs consist of reading out motor intent from the neuronal responses monitored in motor regions of the brain and executing intended movements with bionic limbs, reanimated limbs, or exoskeletons. BMIs also allow for the restoration of the sense of touch by electrically activating neurons in somatosensory regions of the brain, thereby evoking vivid tactile sensations and conveying feedback about object interactions. In this review, we discuss the neural mechanisms of motor control and somatosensation in able-bodied individuals and describe approaches to use neuronal responses as control signals for movement restoration and to activate residual sensory pathways to restore touch. Although the focus of the review is on intracortical approaches, we also describe alternative signal sources for control and noninvasive strategies for sensory restoration.

An Information Theoretic Approach to Symbolic Learning in Synthetic Languages

An important aspect of using entropy-based models and proposed “synthetic languages”, is the seemingly simple task of knowing how to identify the probabilistic symbols. If the system has discrete features, then this task may be trivial; however, for observed analog behaviors described by continuous values, this raises the question of how we should determine such symbols. This task of symbolization extends the concept of scalar and vector quantization to consider explicit linguistic properties. Unlike previous quantization algorithms where the aim is primarily data compression and fidelity, the goal in this case is to produce a symbolic output sequence which incorporates some linguistic properties and hence is useful in forming language-based models. Hence, in this paper, we present methods for symbolization which take into account such properties in the form of probabilistic constraints. In particular, we propose new symbolization algorithms which constrain the symbols to have a Zipf–Mandelbrot–Li distribution which approximates the behavior of language elements. We introduce a novel constrained EM algorithm which is shown to effectively learn to produce symbols which approximate a Zipfian distribution. We demonstrate the efficacy of the proposed approaches on some examples using real world data in different tasks, including the translation of animal behavior into a possible human language understandable equivalent.

Oral and Laryngeal Diadochokinesis Across the Life Span: A Scoping Review of Methods, Reference Data, and Clinical Applications

PURPOSE

The aim of this study was to conduct a scoping review of research on oral and laryngeal diadochokinesis (DDK) in children and adults, either typically developing/developed or with a clinical diagnosis.

METHOD

Searches were conducted with PubMed/MEDLINE, Google Scholar, CINAHL, and legacy sources in retrieved articles. Search terms included the following: DDK, alternating motion rate, maximum repetition rate, sequential motion rate, and syllable repetition rate.

RESULTS

Three hundred sixty articles were retrieved and included in the review. Data source tables for children and adults list the number and ages of study participants, DDK task, and language(s) spoken. Cross-sectional data for typically developing children and typically developed adults are compiled for the monosyllables /pʌ/, /tʌ/, and /kʌ/; the trisyllable /pʌtʌkʌ/; and laryngeal DDK. In addition, DDK results are summarized for 26 disorders or conditions.

DISCUSSION

A growing number of multidisciplinary reports on DDK affirm its role in clinical practice and research across the world. Atypical DDK is not a well-defined singular entity but rather a label for a collection of disturbances associated with diverse etiologies, including motoric, structural, sensory, and cognitive. The clinical value of DDK can be optimized by consideration of task parameters, analysis method, and population of interest.

The learnability consequences of Zipfian distributions in language

While the languages of the world differ in many respects, they share certain commonalties, which can provide insight on our shared cognition. Here, we explore the learnability consequences of one of the striking commonalities between languages. Across languages, word frequencies follow a Zipfian distribution, showing a power law relation between a word's frequency and its rank. While their source in language has been studied extensively, less work has explored the learnability consequences of such distributions for language learners. We propose that the greater predictability of words in this distribution (relative to less skewed distributions) can facilitate word segmentation, a crucial aspect of early language acquisition. To explore this, we quantify word predictability using unigram entropy, assess it across languages using naturalistic corpora of child-directed speech and then ask whether similar unigram predictability facilitates word segmentation in the lab. We find similar unigram entropy in child-directed speech across 15 languages. We then use an auditory word segmentation task to show that the unigram predictability levels found in natural language are uniquely facilitative for word segmentation for both children and adults. These findings illustrate the facilitative impact of skewed input distributions on learning and raise questions about the possible role of cognitive pressures in the prevalence of Zipfian distributions in language.

Information encoding and transmission profiles of first-language (L1) and second-language (L2) speech

Inspired by information theoretic analyses of L1 speech and language, this study proposes that L1 and L2 speech exhibit distinct information encoding and transmission profiles in the temporal domain. Both the number and average duration of acoustic syllables (i.e., intensity peaks in the temporal envelope) were automatically measured from L1 and L2 recordings of standard texts in English, French, and Spanish. Across languages, L2 acoustic syllables were greater in number (more acoustic syllables/text) and longer in duration (fewer acoustic syllables/second). While substantial syllable reduction (fewer acoustic than orthographic syllables) was evident in both L1 and L2 speech, L2 speech generally exhibited less syllable reduction, resulting in low information density (more syllables with less information/syllable). Low L2 information density compounded low L2 speech rate yielding very low L2 information transmission rate (i.e., less information/second). Overall, this cross-language comparison establishes low information transmission rate as a language-general, distinguishing feature of L2 speech.

An RNA-Based Theory of Natural Universal Computation

Life is confronted with computation problems in a variety of domains including animal behavior, single-cell behavior, and embryonic development. Yet we currently do not know of a naturally existing biological system that is capable of universal computation, i.e., Turing-equivalent in scope. Generic finite-dimensional dynamical systems (which encompass most models of neural networks, intracellular signaling cascades, and gene regulatory networks) fall short of universal computation, but are assumed to be capable of explaining cognition and development. I present a class of models that bridge two concepts from distant fields: combinatory logic (or, equivalently, lambda calculus) and RNA molecular biology. A set of basic RNA editing rules can make it possible to compute any computable function with identical algorithmic complexity to that of Turing machines. The models do not assume extraordinarily complex molecular machinery or any processes that radically differ from what we already know to occur in cells. Distinct independent enzymes can mediate each of the rules and RNA molecules solve the problem of parenthesis matching through their secondary structure. In the most plausible of these models all of the editing rules can be implemented with merely cleavage and ligation operations at fixed positions relative to predefined motifs. This demonstrates that universal computation is well within the reach of molecular biology. It is therefore reasonable to assume that life has evolved - or possibly began with - a universal computer that yet remains to be discovered. The variety of seemingly unrelated computational problems across many scales can potentially be solved using the same RNA-based computation system. Experimental validation of this theory may immensely impact our understanding of memory, cognition, development, disease, evolution, and the early stages of life.

The extended present: an informational context for perception

Several previous authors have proposed a kind of specious or subjective present moment that covers a few seconds of recent information. This article proposes a new hypothesis about the subjective present, renamed the extended present, defined not in terms of time covered but as a thematically connected information structure held in working memory and in transiently accessible form in long-term memory. The three key features of the extended present are that information in it is thematically connected, both internally and to current attended perceptual input, it is organised in a hierarchical structure, and all information in it is marked with temporal information, specifically ordinal and duration information. Temporal boundaries to the information structure are determined by hierarchical structure processing and by limits on processing and storage capacity. Supporting evidence for the importance of hierarchical structure analysis is found in the domains of music perception, speech and language processing, perception and production of goal-directed action, and exact arithmetical calculation. Temporal information marking is also discussed and a possible mechanism for representing ordinal and duration information on the time scale of the extended present is proposed. It is hypothesised that the extended present functions primarily as an informational context for making sense of current perceptual input, and as an enabler for perception and generation of complex structures and operations in language, action, music, exact calculation, and other domains.

The think aloud paradigm reveals differences in the content, dynamics and conceptual scope of resting state thought in trait brooding

Although central to well-being, functional and dysfunctional thoughts arise and unfold over time in ways that remain poorly understood. To shed light on these mechanisms, we adapted a "think aloud" paradigm to quantify the content and dynamics of individuals' thoughts at rest. Across two studies, external raters hand coded the content of each thought and computed dynamic metrics spanning duration, transition probabilities between affective states, and conceptual similarity over time. Study 1 highlighted the paradigm's high ecological validity and revealed a narrowing of conceptual scope following more negative content. Study 2 replicated Study 1's findings and examined individual difference predictors of trait brooding, a maladaptive form of rumination. Across individuals, increased trait brooding was linked to thoughts rated as more negative, past-oriented and self-focused. Longer negative and shorter positive thoughts were also apparent as brooding increased, as well as a tendency to shift away from positive conceptual states, and a stronger narrowing of conceptual scope following negative thoughts. Importantly, content and dynamics explained independent variance, accounting for a third of the variance in brooding. These results uncover a real-time cognitive signature of rumination and highlight the predictive and ecological validity of the think aloud paradigm applied to resting state cognition.

Information-Theoretic Quantification of Dedifferentiation in the Aging of Motor and Executive Functions

A central account of cognitive aging is the dedifferentiation among functions due to reduced processing resources. Previous reports contrasting trends of aging across cognitive domains mostly relied on transformed scores of heterogeneous measures. By quantifying the computational load with information entropy in tasks probing motor and executive functions, this study uncovered interaction among age, task, and load as well as associations among the parametric estimates of these factors at the individual level. Specifically, the linear functions between computational load and performance time differed significantly between motor and executive tasks in the young group but not in the elderly group and showed stronger associations for parameters within and between tasks in the elderly group than in the young group. These findings are in line with the dedifferentiation hypothesis of cognitive aging and provide a more principled approach in contrasting trends of cognitive aging across different domains from the information-theoretic perspective.

Cross-Linguistic Trade-Offs and Causal Relationships Between Cues to Grammatical Subject and Object, and the Problem of Efficiency-Related Explanations

Cross-linguistic studies focus on inverse correlations (trade-offs) between linguistic variables that reflect different cues to linguistic meanings. For example, if a language has no case marking, it is likely to rely on word order as a cue for identification of grammatical roles. Such inverse correlations are interpreted as manifestations of language users' tendency to use language efficiently. The present study argues that this interpretation is problematic. Linguistic variables, such as the presence of case, or flexibility of word order, are aggregate properties, which do not represent the use of linguistic cues in context directly. Still, such variables can be useful for circumscribing the potential role of communicative efficiency in language evolution, if we move from cross-linguistic trade-offs to multivariate causal networks. This idea is illustrated by a case study of linguistic variables related to four types of Subject and Object cues: case marking, rigid word order of Subject and Object, tight semantics and verb-medial order. The variables are obtained from online language corpora in thirty languages, annotated with the Universal Dependencies. The causal model suggests that the relationships between the variables can be explained predominantly by sociolinguistic factors, leaving little space for a potential impact of efficient linguistic behavior.

Interindividual Variation Refuses to Go Away: A Bayesian Computer Model of Language Change in Communicative Networks

Treating the speech communities as homogeneous entities is not an accurate representation of reality, as it misses some of the complexities of linguistic interactions. Inter-individual variation and multiple types of biases are ubiquitous in speech communities, regardless of their size. This variation is often neglected due to the assumption that “majority rules,” and that the emerging language of the community will override any such biases by forcing the individuals to overcome their own biases, or risk having their use of language being treated as “idiosyncratic” or outright “pathological.” In this paper, we use computer simulations of Bayesian linguistic agents embedded in communicative networks to investigate how biased individuals, representing a minority of the population, interact with the unbiased majority, how a shared language emerges, and the dynamics of these biases across time. We tested different network sizes (from very small to very large) and types (random, scale-free, and small-world), along with different strengths and types of bias (modeled through the Bayesian prior distribution of the agents and the mechanism used for generating utterances: either sampling from the posterior distribution [“sampler”] or picking the value with the maximum probability [“MAP”]). The results show that, while the biased agents, even when being in the minority, do adapt their language by going against their a priori preferences, they are far from being swamped by the majority, and instead the emergent shared language of the whole community is influenced by their bias.