School of Social Sciences, Humanities, and Arts

What is the connection between the cultural evolution of a language and the rapid processing response to that language in the brains of individual learners? In an iterated communication study that was conducted previously, participants were asked to communicate temporal concepts such as "tomorrow," "day after," "year," and "past" using vertical movements recorded on a touch screen. Over time, participants developed simple artificial 'languages' that used space metaphorically to communicate in nuanced ways about time. Some conventions appeared rapidly and universally (e.g., using larger vertical movements to convey greater temporal durations). Other conventions required extensive social interaction and exhibited idiosyncratic variation (e.g., using vertical location to convey past or future). Here we investigate whether the brain's response during acquisition of such a language reflects the process by which the language's conventions originally evolved. We recorded participants' EEG as they learned one of these artificial space-time languages. Overall, the brain response to this artificial communication system was language-like, with, for instance, violations to the system's conventions eliciting an N400-like component. Over the course of learning, participants' brain responses developed in ways that paralleled the process by which the language had originally evolved, with early neural sensitivity to violations of a rapidly-evolving universal convention, and slowly developing neural sensitivity to an idiosyncratic convention that required slow social negotiation to emerge. This study opens up exciting avenues of future work to disentangle how neural biases influence learning and transmission in the emergence of structure in language.

Opinion dynamics are affected by cognitive biases and noise. While mathematical models have focused extensively on biases, we still know surprisingly little about how noise shapes opinion patterns. Here, we use an agent-based opinion dynamics model to investigate the interplay between confirmation bias-represented as bounded confidence-and different types of noise. After analysing where noise can enter social interaction, we propose a type of noise that has not been discussed so far, ambiguity noise. While previously considered types of noise acted on agents either before, after or independent of social interaction, ambiguity noise acts on communicated messages, assuming that socially transmitted opinions are inherently noisy. We find that noise can induce agreement when confirmation bias is moderate, but different types of noise require quite different conditions for this effect to occur. An application of our model to the climate change debate shows that at just the right mix of confirmation bias and ambiguity noise, opinions tend to converge to high levels of climate change concern. This result is not observed with the other types. Our findings highlight the importance of considering and distinguishing between the various types of noise and the unique role of ambiguity in opinion formation.

Humans regularly solve complex problems in cooperative teams. A wide range of mechanisms have been identified that improve the quality of solutions achieved by those teams on reaching consensus. We argue that many of these mechanisms work via increasing the transient diversity of solutions while the group attempts to reach a consensus. These mechanisms can operate at the level of individual psychology (e.g., behavioral inertia), interpersonal communication (e.g., transmission noise), or group structure (e.g., sparse social networks). Transient diversity can be increased by widening the search space of possible solutions or by slowing the diffusion of information and delaying consensus. All of these mechanisms increase the quality of the solution at the cost of increased time to reach it. We review specific mechanisms that facilitate transient diversity and synthesize evidence from both empirical studies and diverse formal models-including multiarmed bandits, NK landscapes, cumulative-innovation models, and evolutionary-transmission models. Apparent exceptions to this principle occur primarily when problems are sufficiently simple that they can be solved by mere trial and error or when the incentives of team members are insufficiently aligned. This work has implications for our understanding of collective intelligence, problem solving, innovation, and cumulative cultural evolution.

Tinnitus is a prevalent hearing-loss deficit manifested as a phantom (internally generated by the brain) sound that is heard as a high-frequency tone in the majority of afflicted persons. Chronic tinnitus is debilitating, leading to distress, sleep deprivation, anxiety, and even suicidal thoughts. It has been theorized that, in the majority of afflicted persons, tinnitus can be attributed to the loss of high-frequency input from the cochlea to the auditory cortex, known as deafferentation. Deafferentation due to hearing loss develops with aging, which progressively causes tonotopic regions coding for the lost high-frequency coding to synchronize, leading to a phantom high-frequency sound sensation. Approaches to tinnitus remediation that demonstrated promise include inhibitory drugs, the use of tinnitus-specific frequency notching to increase lateral inhibition to the deafferented neurons, and multisensory approaches (auditory-motor and audiovisual) that work by coupling multisensory stimulation to the deafferented neural populations. The goal of this review is to put forward a theoretical framework of a multisensory approach to remedy tinnitus. Our theoretical framework posits that due to visions modulatory (inhibitory, excitatory) influence on the auditory pathway, a prolonged engagement in audiovisual activity, especially during daily discourse, as opposed to auditory-only activity/discourse, can progressively reorganize deafferented neural populations, resulting in the reduced synchrony of the deafferented neurons and a reduction in tinnitus severity over time.

There are two ways, broadly speaking, that one might conceive of meritocratic education. On a standard, ‘narrow’ conception, a meritocratic approach to education is one which distributes certain educational goods and opportunities according to merit. On a second, ‘broader’ conception, however, meritocratic education is an educational system suited to a commitment to meritocracy – where ‘meritocracy’ refers to a particular conception of distributive justice. In this article, I argue that these two conceptions are incompatible with each other, and so the standard ‘narrow’ conception of meritocratic education is, in fact, incompatible with a commitment to meritocracy, at least given the typical way of understanding meritocracy. Of particular importance is that while meritocracy, as a view of distributive justice, requires a commitment to equality of opportunity principles, the narrowly meritocratic conception of education does not. The reason has to do with differences in the underlying justifications of the merit-based principles in each: Meritocracy appeals to moral desert, while the educational desert that is grounded by one’s merits is best thought of as a kind of institutional desert. Thus, I will argue, while meritocracy (and so the broad conception of meritocratic education) is constrained by a fair equality of opportunity requirement, narrowly meritocratic education is not. Recognizing the relationships between meritocracy, meritocratic education, and equality of opportunity, I argue, sheds considerable light on disagreements in the debate over equality of opportunity in education.

Social influence aligns peoples opinions, but social identities and related in-group biases interfere with this alignment. For instance, the recent rise of young climate activists (e.g. Fridays for Future or Last Generation) has highlighted the importance of generational identities in the climate change debate. It is unclear how social identities affect the emergence of opinion patterns, such as consensus or disagreement, in a society. Here, we present an agent-based model to explore this question. Agents communicate in a network and form opinions through social influence. The agents have fixed social identities which involve homophily in their interaction preferences and in-group bias in their perception of others. We find that the in-group bias has opposing effects depending on the network topology. The bias impedes consensus in highly random networks by promoting the formation of echo chambers within social identity groups. By contrast, the bias facilitates consensus in highly clustered networks by aligning dispersed in-group agents across the network and, thereby, preventing the formation of isolated echo chambers. Our model uncovers the mechanisms underpinning these opposing effects of the in-group bias and highlights the importance of the communication network topology for shaping opinion dynamics.

Theories of innovation often balance contrasting views that either smart people create smart things or smartly constructed institutions create smart things. While population models have shown factors including population size, connectivity and agent behaviour as crucial for innovation, few have taken the individual-central approach seriously by examining the role individuals play within their groups. To explore how network structures influence not only population-level innovation but also performance among individuals, we studied an agent-based model of the Potions Task, a paradigm developed to test how structure affects a groups ability to solve a difficult exploration task. We explore how size, connectivity and rates of information sharing in a network influence innovation and how these have an impact on the emergence of inequality in terms of agent contributions. We find, in line with prior work, that population size has a positive effect on innovation, but also find that large and small populations perform similarly per capita; that many small groups outperform fewer large groups; that random changes to structure have few effects on innovation in the task; and that the highest performing agents tend to occupy more central positions in the network. Moreover, we show that every network factor which improves innovation leads to a proportional increase in inequality of performance in the network, creating genius effects among otherwise dumb agents in both idealized and real-world networks.

[This corrects the article DOI: 10.1098/rsos.160384.][This corrects the article DOI: 10.1098/rsos.160384.].

Prediction is often regarded as an integral aspect of incremental language comprehension, but little is known about the cognitive architectures and mechanisms that support it. We review studies showing that listeners and readers use all manner of contextual information to generate multifaceted predictions about upcoming input. The nature of these predictions may vary between individuals owing to differences in language experience, among other factors. We then turn to unresolved questions which may guide the search for the underlying mechanisms. (i) Is prediction essential to language processing or an optional strategy? (ii) Are predictions generated from within the language system or by domain-general processes? (iii) What is the relationship between prediction and memory? (iv) Does prediction in comprehension require simulation via the production system? We discuss promising directions for making progress in answering these questions and for developing a mechanistic understanding of prediction in language.

Traditionally, pitch variation in a sound stream has been integral to music identity. We attempt to expand music's definition, by demonstrating that the neural code for musicality is independent of pitch encoding. That is, pitchless sound streams can still induce music-like perception and a neurophysiological hierarchy similar to pitched melodies. Previous work reported that neural processing of sounds with no-pitch, fixed-pitch, and irregular-pitch (melodic) patterns, exhibits a right-lateralized hierarchical shift, with pitchless sounds favorably processed in Heschl's gyrus (HG), ascending laterally to nonprimary auditory areas for fixed-pitch and even more laterally for melodic patterns. The objective of this EEG study was to assess whether sound encoding maintains a similar hierarchical profile when musical perception is driven by timbre irregularities in the absence of pitch changes. Individuals listened to repetitions of three musical and three nonmusical sound-streams. The nonmusical streams were comprised of seven 200-ms segments of white, pink, or brown noise, separated by silent gaps. Musical streams were created similarly, but with all three noise types combined in a unique order within each stream to induce timbre variations and music-like perception. Subjects classified the sound streams as musical or nonmusical. Musical processing exhibited right dominant α power enhancement, followed by a lateralized increase in θ phase-locking and spectral power. The θ phase-locking was stronger in musicians than in nonmusicians. The lateralization of activity suggests higher-level auditory processing. Our findings validate the existence of a hierarchical shift, traditionally observed with pitched-melodic perception, underscoring that musicality can be achieved with timbre irregularities alone.NEW & NOTEWORTHY EEG induced by streams of pitchless noise segments varying in timbre were classified as music-like and exhibited a right-lateralized hierarchy in processing similar to pitched melodic processing. This study provides evidence that the neural-code of musicality is independent of pitch encoding. The results have implications for understanding music processing in individuals with degraded pitch perception, such as in cochlear-implant listeners, as well as the role of nonpitched sounds in the induction of music-like perceptual states.