BackgroundLanguages differ greatly both in their syntactic and morphological systems and in the social environments in which they exist. We challenge the view that language grammars are unrelated to social environments in which they are learned and used.Methodology/Principal FindingsWe conducted a statistical analysis of >2,000 languages using a combination of demographic sources and the World Atlas of Language Structures— a database of structural language properties. We found strong relationships between linguistic factors related to morphological complexity, and demographic/socio-historical factors such as the number of language users, geographic spread, and degree of language contact. The analyses suggest that languages spoken by large groups have simpler inflectional morphology than languages spoken by smaller groups as measured on a variety of factors such as case systems and complexity of conjugations. Additionally, languages spoken by large groups are much more likely to use lexical strategies in place of inflectional morphology to encode evidentiality, negation, aspect, and possession. Our findings indicate that just as biological organisms are shaped by ecological niches, language structures appear to adapt to the environment (niche) in which they are being learned and used. As adults learn a language, features that are difficult for them to acquire, are less likely to be passed on to subsequent learners. Languages used for communication in large groups that include adult learners appear to have been subjected to such selection. Conversely, the morphological complexity common to languages used in small groups increases redundancy which may facilitate language learning by infants.Conclusions/SignificanceWe hypothesize that language structures are subjected to different evolutionary pressures in different social environments. Just as biological organisms are shaped by ecological niches, language structures appear to adapt to the environment (niche) in which they are being learned and used. The proposed Linguistic Niche Hypothesis has implications for answering the broad question of why languages differ in the way they do and makes empirical predictions regarding language acquisition capacities of children versus adults.

We investigated the coupling between a speaker's and a listener's eye movements. Some participants talked extemporaneously about a television show whose cast members they were viewing on a screen in front of them. Later, other participants listened to these monologues while viewing the same screen. Eye movements were recorded for all speakers and listeners. According to cross-recurrence analysis, a listener's eye movements most closely matched a speaker's eye movements at a delay of 2 sec. Indeed, the more closely a listener's eye movements were coupled with a speaker's, the better the listener did on a comprehension test. In a second experiment, low-level visual cues were used to manipulate the listeners' eye movements, and these, in turn, influenced their latencies to comprehension questions. Just as eye movements reflect the mental state of an individual, the coupling between a speaker's and a listener's eye movements reflects the success of their communication.

A variety of theoretical frameworks predict the resemblance of behaviors between two people engaged in communication, in the form of coordination, mimicry, or alignment. However, little is known about the time course of the behavior matching, even though there is evidence that dyads synchronize oscillatory motions (e.g., postural sway). This study examined the temporal structure of nonoscillatory actions-language, facial, and gestural behaviors-produced during a route communication task. The focus was the temporal relationship between matching behaviors in the interlocutors (e.g., facial behavior in one interlocutor vs. the same facial behavior in the other interlocutor). Cross-recurrence analysis revealed that within each category tested (language, facial, gestural), interlocutors synchronized matching behaviors, at temporal lags short enough to provide imitation of one interlocutor by the other, from one conversational turn to the next. Both social and cognitive variables predicted the degree of temporal organization. These findings suggest that the temporal structure of matching behaviors provides low-level and low-cost resources for human interaction.

The time course of categorization was investigated in four experiments, which revealed graded competitive effects in a categorization task. Participants clicked one of two categories (e.g., mammal or fish) in response to atypical or typical exemplars (e.g., whale or cat) in the form of words (Experiments 1 and 2) or pictures (Experiments 3 and 4). Streaming x, y coordinates of mouse movement trajectories were recorded. Normalized mean trajectories revealed a graded competitive process: Atypical exemplars produced trajectories with greater curvature toward the competing category than did typical exemplars. The experiments contribute to recent examination of the time course of categorization and carry implications for theories of representation in cognitive science.

Recently, researchers have measured hand movements en route to choices on a screen to understand the dynamics of a broad range of psychological processes. We review this growing body of research and explain how manual action exposes the real-time unfolding of underlying cognitive processing. We describe how simple hand motions may be used to continuously index participants’ tentative commitments to different choice alternatives during the evolution of a behavioral response. As such, hand-tracking can provide unusually high-fidelity, real-time motor traces of the mind. These motor traces cast novel theoretical and empirical light onto a wide range of phenomena and serve as a potential bridge between far-reaching areas of psychological science – from language, to high-level cognition and learning, to social cognitive processes.

Real-time cognition is best described not as a sequence of logical operations performed on discrete symbols but as a continuously changing pattern of neuronal activity. The continuity in these dynamics indicates that, in between describable states of mind, mental activity does not lend itself to the linguistic labels relied on by much of psychology. We discuss eye-tracking and mousetracking evidence for this temporal continuity and provide geometric visualizations of mental activity, depicting it as a continuous trajectory through a state space (a multidimensional space in which locations correspond to mental states). When the state of the system travels toward a frequently visited region of that space, the destination may constitute recognition of a particular word or a particular object; but on the way there, the majority of the mental trajectory is in intermediate regions of that space, revealing graded mixtures of mental states.

This paper describes the package to perform cross-recurrence quantification analysis of two time series of either a categorical or continuous nature. Streams of behavioral information, from eye movements to linguistic elements, unfold over time. When two people interact, such as in conversation, they often adapt to each other, leading these behavioral levels to exhibit recurrent states. In dialog, for example, interlocutors adapt to each other by exchanging interactive cues: smiles, nods, gestures, choice of words, and so on. In order for us to capture closely the goings-on of dynamic interaction, and uncover the extent of coupling between two individuals, we need to quantify how much recurrence is taking place at these levels. Methods available in would allow researchers in cognitive science to pose such questions as how much are two people recurrent at some level of analysis, what is the characteristic lag time for one person to maximally match another, or whether one person is leading another. First, we set the theoretical ground to understand the difference between “correlation” and “co-visitation” when comparing two time series, using an aggregative or cross-recurrence approach. Then, we describe more formally the principles of cross-recurrence, and show with the current package how to carry out analyses applying them. We end the paper by comparing computational efficiency, and results’ consistency, of package, with the benchmark MATLAB toolbox (Marwan, 2013). We show perfect comparability between the two libraries on both levels.

Recent studies of dyadic interaction have examined phenomena of synchronization, entrainment, alignment, and convergence. All these forms of behavioral matching have been hypothesized to play a supportive role in establishing coordination and common ground between interlocutors. In the present study, evidence is found for a new kind of coordination termed complexity matching. Temporal dynamics in conversational speech signals were analyzed through time series of acoustic onset events. Timing in periods of acoustic energy was found to exhibit behavioral matching that reflects complementary timing in turn-taking. In addition, acoustic onset times were found to exhibit power law clustering across a range of timescales, and these power law functions were found to exhibit complexity matching that is distinct from behavioral matching. Complexity matching is discussed in terms of interactive alignment and other theoretical principles that lead to new hypotheses about information exchange in dyadic conversation and interaction in general.