AbstractPart of language’s great expressivity comes from its users creating new forms by applying familiar rules to novel items. But linguistic rules aren’t all created equal—some are more readily generalisable than others. In this paper, we focus on how rule generalisation is affected by certain properties of frequency distributions. In an artificial language learning experiment that asks adult learners to generalise using one of two suffixes, we find that they probability-match their input but slightly prefer whichever suffix they encountered with more low-frequency stems. Then with an urn model of learning, we show that previous explanations of generalisation that focus only on a distribution’s type count or its skew fail to capture participants’ behaviour—only the low-frequency preference yields convergent results. We model learners’ behaviour in terms of rational Bayesian inference about how likely a rule is to apply to more word types than somebody has already encountered. Overall, we suggest that linguistic rule generalisation is a self-sustaining process: by creating novel and therefore low-frequency items, rule generalisation produces the very same distributional structure that feeds it.

AbstractA Ganzfeld is a homogeneous visual field, devoid of any focal points. Such a stimulus has been used by researchers to study perceptual phenomena in the absence of changes in sensory structure. Others have used it to study altered states of consciousness (ASCs). Until now, these different facets have been studied separately with little attention for the emotional subjective experience. This study aimed to elucidate the perceptual, phenomenal, and emotional experience of the multifaceted Ganzfeld using a multi-method approach combining behavioral (eye-tracking) and neural (electroencephalography; EEG) measures, with qualitative (interviews) and quantitative (questionnaires) assessments. We show that Ganzfeld spaces induce ASCs and offer immersive, full-body experiences, including bodily effects. Our results pertaining to bodily sensations further prompted us to identify a perceptually grounded cognitive processing type with either an inward-directed or externally-directed focus. We also identified the presence of an abstract cognitive processing type characterized by an introspective focus and meditative experiences. At the behavioral level, decays were characterized by decreased eye movements. The lag in reporting decays and the subjective experience of decays point to the notion of mind blanking. At the neural level, we found increased theta activity preceding decays, further hinting at a potential interrelation between perceptual decays and mind blanking. Finally, decays were characterized by more alpha activity, a pattern often associated with attenuated sensory processing and states of reduced external engagement (Jensen & Mazaheri, 2010), such as relaxation. Our findings contribute to a more in-depth understanding of all the components contributing to the rich Ganzfeld experiences.

AbstractCognitive scientists have discovered normative and heuristic principles that capture human subgoal preferences when partitioning problems into smaller ones. However, it remains unclear where such preferences come from and why they tend to be both effective and efficient. In this work, we study the processes through which these preferences may be implicitly encoded over learning as learners improve towards optimal traversals. We build on the graph-based environments from prior work and use neural networks as model learners to test if learning shortest-path traversal can lead to human-like path decomposition. We find that simple transformer models develop a preference for paths containing nodes that occur frequently on the shortest paths, consistent with human subgoal preferences found in prior work. This preference is observed when models solve shortest path traversals for unseen problems in both known graphs and new graphs, demonstrating that human-like subgoal preferences can arise without requiring explicit preference computation or exhaustively searching over all possible paths. The same preference does not emerge when models learn to perform random or Hamiltonian traversals. Our findings are robust across several transformer variants as well as recurrent neural networks, suggesting they depend more on the data distribution than the network architecture.

AbstractPart of language’s great expressivity comes from its users creating new forms by applying familiar rules to novel items. But linguistic rules aren’t all created equal—some are more readily generalisable than others. In this paper, we focus on how rule generalisation is affected by certain properties of frequency distributions. In an artificial language learning experiment that asks adult learners to generalise using one of two suffixes, we find that they probability-match their input but slightly prefer whichever suffix they encountered with more low-frequency stems. Then with an urn model of learning, we show that previous explanations of generalisation that focus only on a distribution’s type count or its skew fail to capture participants’ behaviour—only the low-frequency preference yields convergent results. We model learners’ behaviour in terms of rational Bayesian inference about how likely a rule is to apply to more word types than somebody has already encountered. Overall, we suggest that linguistic rule generalisation is a self-sustaining process: by creating novel and therefore low-frequency items, rule generalisation produces the very same distributional structure that feeds it.

AbstractA Ganzfeld is a homogeneous visual field, devoid of any focal points. Such a stimulus has been used by researchers to study perceptual phenomena in the absence of changes in sensory structure. Others have used it to study altered states of consciousness (ASCs). Until now, these different facets have been studied separately with little attention for the emotional subjective experience. This study aimed to elucidate the perceptual, phenomenal, and emotional experience of the multifaceted Ganzfeld using a multi-method approach combining behavioral (eye-tracking) and neural (electroencephalography; EEG) measures, with qualitative (interviews) and quantitative (questionnaires) assessments. We show that Ganzfeld spaces induce ASCs and offer immersive, full-body experiences, including bodily effects. Our results pertaining to bodily sensations further prompted us to identify a perceptually grounded cognitive processing type with either an inward-directed or externally-directed focus. We also identified the presence of an abstract cognitive processing type characterized by an introspective focus and meditative experiences. At the behavioral level, decays were characterized by decreased eye movements. The lag in reporting decays and the subjective experience of decays point to the notion of mind blanking. At the neural level, we found increased theta activity preceding decays, further hinting at a potential interrelation between perceptual decays and mind blanking. Finally, decays were characterized by more alpha activity, a pattern often associated with attenuated sensory processing and states of reduced external engagement (Jensen & Mazaheri, 2010), such as relaxation. Our findings contribute to a more in-depth understanding of all the components contributing to the rich Ganzfeld experiences.

AbstractCognitive scientists have discovered normative and heuristic principles that capture human subgoal preferences when partitioning problems into smaller ones. However, it remains unclear where such preferences come from and why they tend to be both effective and efficient. In this work, we study the processes through which these preferences may be implicitly encoded over learning as learners improve towards optimal traversals. We build on the graph-based environments from prior work and use neural networks as model learners to test if learning shortest-path traversal can lead to human-like path decomposition. We find that simple transformer models develop a preference for paths containing nodes that occur frequently on the shortest paths, consistent with human subgoal preferences found in prior work. This preference is observed when models solve shortest path traversals for unseen problems in both known graphs and new graphs, demonstrating that human-like subgoal preferences can arise without requiring explicit preference computation or exhaustively searching over all possible paths. The same preference does not emerge when models learn to perform random or Hamiltonian traversals. Our findings are robust across several transformer variants as well as recurrent neural networks, suggesting they depend more on the data distribution than the network architecture.

AbstractThe sperm whale communication system, consisting of groups of clicks called codas, has been primarily analyzed in terms of the number of clicks and their inter-click timing. This paper reports spectral properties in sperm whale vocalizations and demonstrates that spectral properties are highly structured, discretely distributed across codas, and uttered in dialogues, rather than being a physical artefact of whale movement. We report formant structure in whale codas and uncover previously unobserved spectral patterns. We argue that these spectral properties freely combine with the traditionally analyzed properties. We present a visualization technique that allows the description of several previously unobserved patterns. Codas are on many levels analogous to human vowels and diphthongs and can be conceptualized in terms of the source-filter theory: vowel duration and pitch correspond to the number of clicks and their timing (traditional coda types), while spectral properties of clicks correspond to formants in human vowels. We identify two recurrent and discrete coda-level spectral patterns that appear across individual sperm whales and across traditional coda types: the a- and i-coda vowels. We also report that sperm whales have diphthongal patterns on individual codas: with rising, falling, rising-falling and falling-rising formant patterns observed. These uncovered patterns suggest that spectral properties have the potential to add to the communicative complexity of codas independent of the traditionally analyzed properties and add a new dimension to the study of a cetacean communication system.

AbstractMost deaf and hard-of-hearing children are born to hearing parents, often delaying exposure to their first language. This negatively influences development of not only language, but also many other aspects of cognition, including exact representations of large quantities. The core knowledge view of numeracy predicts that delays in language exposure should not affect nonverbal representations of small quantities (1–3). This study is the first to investigate effects of language modality (spoken vs. signed) and timing of language experience (early, from birth vs. later) on the representation of small quantities of objects. We adapted the “Mr. Elephant” task (Shusterman et al., 2017) and examined whether children (age 3 to 7 years) succeeded on trials involving quantities 2 and 3. A logistic regression found that Timing and Socioeconomic Status significantly predicted Mr. Elephant performance, while Modality and Age did not. Early-exposed children were more likely to succeed on the task than Later-exposed children. For an exploratory follow-up, two measures of language were added into the analysis: Highest Count, which records children’s recitation of the count list, and Give-a-Number (‘Give-N’), which assesses children’s understanding of the cardinal principle (CP). This logistic regression found that Timing and Give-N performance significantly and independently predicted Mr. Elephant performance, but Socioeconomic Status and Highest Count did not. Children who were CP-knowers were more likely to succeed on Mr. Elephant than non-CP-knowers. These results suggest that the representation of small quantity representations is associated with the timing of children’s language exposure and their knowledge of the cardinal principle.

AbstractMetacognition comprises the ability to differentiate the accuracy of predictions about the world. This is often called Type 2 performance (with Type 1 performance being the overall accuracy). Typical measures of metacognition are based on signal detection theory and require the strong assumption of truncated normal noise underlying confidence ratings. To minimize distributional assumptions, measures based on classical information theory have been proposed. We further this approach by providing bounds on its key quantity, the transmitted information. We show that classifiers making predictions with a certain accuracy can transmit information only within a limited range, depending on the underlying noise distribution: The lowest transmitted information indicates the worst Type 2 performance and corresponds to binary noise; the highest transmitted information indicates the best Type 2 performance and corresponds to uniform noise. Because normal noise is only an intermediate case, traditional measures based on this assumption can bias interpretations of Type 2 performance. Based on these bounds, we suggest a new measure: Relative metainformation (RMI). RMI scales from 0 (lower bound) to 1 (upper bound) and therefore advances towards the much-needed decoupling of Type 2 from Type 1 performance measures. To demonstrate the strengths of RMI, we apply it to groups: In a setting where multiple independent group members with fixed accuracies combine their predictions in an optimal way, we show that the group performance depends directly on RMI: Group accuracy is best vs. worst if the group members have highest vs. lowest RMI values. Overall, our theoretical bounds allow to better evaluate measures of Type 2 and group performance.

AbstractMany empirical studies have found a curvilinear (inverted-U) relationship between knowledge and curiosity, such that curiosity is induced when stimuli are neither unknown nor too familiar. While various theoretical accounts have been proposed to explain this phenomenon, no clear link between them have been delineated. In this Perspective, we review seven psychological accounts of the inverted-U relationship between knowledge and curiosity (“the U”) and provide a coherent framework integrating them. According to this framework, the U emerges as a consequence of the imperative to pursue learning progress and thus maximize knowledge. We show that some theories of curiosity address this issue by explicitly stipulating knowledge maximization as the computational objective, and learning-progress maximization as an optimal means of achieving it (i.e., normative theories). Other theories focus on psychological mechanisms or factors that drive curiosity (i.e., process theories). We propose that these process-theoretic mechanisms could also work in a manner that maximizes learning by signaling situations in which some relevant prior knowledge exists, but is incomplete. The implications of this framework for future theoretical work on curiosity and its connections to related phenomena are discussed.

AbstractThe sperm whale communication system, consisting of groups of clicks called codas, has been primarily analyzed in terms of the number of clicks and their inter-click timing. This paper reports spectral properties in sperm whale vocalizations and demonstrates that spectral properties are highly structured, discretely distributed across codas, and uttered in dialogues, rather than being a physical artefact of whale movement. We report formant structure in whale codas and uncover previously unobserved spectral patterns. We argue that these spectral properties freely combine with the traditionally analyzed properties. We present a visualization technique that allows the description of several previously unobserved patterns. Codas are on many levels analogous to human vowels and diphthongs and can be conceptualized in terms of the source-filter theory: vowel duration and pitch correspond to the number of clicks and their timing (traditional coda types), while spectral properties of clicks correspond to formants in human vowels. We identify two recurrent and discrete coda-level spectral patterns that appear across individual sperm whales and across traditional coda types: the a- and i-coda vowels. We also report that sperm whales have diphthongal patterns on individual codas: with rising, falling, rising-falling and falling-rising formant patterns observed. These uncovered patterns suggest that spectral properties have the potential to add to the communicative complexity of codas independent of the traditionally analyzed properties and add a new dimension to the study of a cetacean communication system.

AbstractRobots express a great deal of diverse human-like capacities, ranging from communicating in natural languages to displaying emotions to responding to physical touch. Here we examined the role of different kinds of mental capacities on children’s evaluations of, and trust in, robots. We presented 6- to 9-year-olds with identical-looking humanoid robots described as having one (or none) of the following capacities: cognitive-perceptual, social-emotional, or physiological. Across three studies (N = 287), we found that children differentially evaluated (Studies 1A and 1B) and selectively trusted (Study 2) robots with different types of minds. The diverging evaluations (i.e., of benevolence, intelligence, affinity, and epistemic appeal) of robots with different minds emerged between ages 7 and 8 and became stronger with age. Moreover, these differences translated into selective trust choices: children trusted robots with cognitive-perceptual capacities over robots with social-emotional capacities in a factual, but not a social, context, and over robots with bodily capacities across both contexts. Altogether, these findings open avenues for future interdisciplinary research on children’s reasoning about emerging technologies.

AbstractRobots express a great deal of diverse human-like capacities, ranging from communicating in natural languages to displaying emotions to responding to physical touch. Here we examined the role of different kinds of mental capacities on children’s evaluations of, and trust in, robots. We presented 6- to 9-year-olds with identical-looking humanoid robots described as having one (or none) of the following capacities: cognitive-perceptual, social-emotional, or physiological. Across three studies (N = 287), we found that children differentially evaluated (Studies 1A and 1B) and selectively trusted (Study 2) robots with different types of minds. The diverging evaluations (i.e., of benevolence, intelligence, affinity, and epistemic appeal) of robots with different minds emerged between ages 7 and 8 and became stronger with age. Moreover, these differences translated into selective trust choices: children trusted robots with cognitive-perceptual capacities over robots with social-emotional capacities in a factual, but not a social, context, and over robots with bodily capacities across both contexts. Altogether, these findings open avenues for future interdisciplinary research on children’s reasoning about emerging technologies.

AbstractHuman self-beliefs hinge on social feedback, but their formation and revision are not solely based on new information. Biases during learning, such as confirming initial expectations, can lead to inaccurate beliefs. This study uses computational modeling to explore how initial expectations about one’s own and others’ abilities and confidence in these beliefs affect processes of belief formation and belief revision in novel behavioral domains. In the first session, participants formed performance beliefs through trial-by-trial feedback. In the second session, feedback contingencies were reversed to promote a revision of beliefs. Results showed that people form and revise beliefs in a confirmatory manner, with lower initial expectations being linked to more negatively biased belief formation and revision, while growing confidence strengthened these beliefs over time. Once formed, these beliefs proved resistant to change even when faced with contradictory feedback. The findings suggest that newly formed beliefs become entrenched and resistant to new, contradictory information in a short period of time. Understanding how self-beliefs are formed, the role that confidence plays in this process, and why established beliefs are difficult to revise can inform the development of interventions aimed at promoting more adaptive learning in educational, clinical, and social contexts.

AbstractBoth human children and animals seek information following a violation-of-expectation event, but little research suggests the latter do so for the sake of it. In this preregistered experiment, we compared epistemic curiosity—the pursuit of information for its own sake—in kea parrots (Nestor notabilis) and three-year-old human children (Homo sapiens) following a violation-of-expectation event. Subjects were trained to push a tool into an apparatus that produced a reward before the apparatus was surreptitiously made non-functional in following trials. In both functional and non-functional trials, after solving the task, subjects were rewarded and allowed to explore the apparatus for thirty seconds with the opportunity to peek into the side of the apparatus. We found that relatively more kea peeked than children, but the children and not the kea were significantly more likely to peek in the non-functional versus functional trials, particularly when the researcher was absent. While both species showed markers of curiosity in the experiment, we found expectancy-violation-induced epistemic curiosity only in the children and not the kea in this context.

AbstractDevelopmental language models (DLMs) aim to replicate the efficiency of child language acquisition but often focus solely on the estimation of exogenous linguistic input. We argue that a child’s linguistic growth is also critically shaped by endogenous processes, including (1) co-opting language in non-linguistic perception and cognition, (2) engaging in private and inner speech, and (3) benefiting from neural replay of linguistic information during sleep. These endogenous processes amplify and refine exogenous linguistic input in ways that current DLMs do not replicate. To align DLMs with child language acquisition, we propose redefining “linguistic exposure“ to encompass both exogenous and endogenous linguistic input. By integrating label feedback, self-generated speech, and sleep-like consolidation, researchers can narrow the gap between artificial and human learning. Collaborations across machine learning, psychology, and linguistics will be essential to ground models in empirical data on child behavior and build DLMs that truly reflect the marvel of language acquisition.

AbstractHumans are remarkably efficient at learning new tasks, in large part by relying on the integration of previously learned knowledge. However, research on task learning typically focuses on the learning of abstract task rules on minimalist stimuli, to study behavior independent of the learning history that humans come equipped with (i.e., semantic knowledge). In contrast, several theories suggest that the use of semantic knowledge and labels may help the learning of new task information. Here, we tested whether providing existing, semantically rich task embeddings and response labels allowed for more robust task rule encoding and less (catastrophic) forgetting and interference. Our results show that providing semantically rich task settings and response labels resulted in less task forgetting (Experiment 1), both when using pictorial symbols or words as labels (Experiment 2), or when contrasted with visually matched shape labels without inherent meaning (Experiment 4). Using a subsequent value-based decision-making task and reinforcement learning modeling (Experiment 3), we demonstrate how the learned embedding of novel stimuli in semantically rich, representations, further allowed for a more efficient, feature-specific processing when learning new task information. Finally, using artificial recurrent neural networks fitted to our participants’ task performance, we found that task separation during learning was more predictive of learning and task performance in the semantically rich conditions. Together, our findings show the benefit of using semantically rich task rules and response labels during novel task learning, thereby offering important insights into why humans excel in continual learning and are less susceptible to catastrophic forgetting compared to most artificial agents.

AbstractDespite cross-linguistic diversity in how kin relations map to terminology, there are constraints on which kin may be categorised together. But what are the constraints on kin term variation, and where do they come from? One proposed constraint is internal co-selection—an evolutionary process where terminological changes in one generation of kin co-occur with parallel changes in other generations. This results in kin categories which are predictable on the basis of other kin categories, a property we call predictive structure. To determine the strength of this constraint, we measured the predictive structure of kinship terminology systems from 731 languages. We found that kinship terminologies exhibit a significant degree of predictive structure, and we argue that its prevalence reflects a cognitive pressure for simplicity imposed during the generalisation of known kin categories to new referent types. We tested this claim using an artificial kin term generalisation task. Our results suggest that people do favour predictive structure when generalising from known kin categories to new referents, but that this preference faces interference from other pressures to distinguish kin by features like gender.

AbstractNavigating the social world is guided by remembering which people know each other. Yet, different factors might influence how social relationships are remembered, where people’s shared attributes could distort a social network’s mnemonic representation. Here, we study whether dyadically shared contexts and personality traits impact how people remember relationships in social networks. Through varying levels of network topological complexity, we find the contexts where people know each other are most memorable and that better contextual retrieval predicts relationship recall. In contrast, shared personality traits affect relationship recall differently depending on social network complexity, where shared negatively valenced traits relate to worse relationship recall in the simple network. Subsequent modeling revealed that as networks become more complex, relationships between more centrally positioned individuals that share negatively valenced traits are better recalled compared to less well-connected individuals. These results suggest contextual memory can serve as a scaffold for remembering relationships in a social network, while affective traits’ impact on social network retrievability depends on emotional valence and the individuals involved. More generally, our findings give insight into how the same social network can be represented differently based on one’s past experience.

AbstractWhen people see an agent perform a task, do they care if the underlying algorithm driving it is ‘intelligent’ or not? More generally, when people intuitively evaluate the performance of others, do they value external performance metrics (intuitive behaviorism) or do they also take into account the underlying algorithm driving the agent’s behavior (intuitive cognitivism)? We propose 3 dimensions for examining this distinction: Action Efficiency, Representation Efficiency, and Generalization. Across 3 tasks (N = 598), we showed people pairs of maze-solving agents, together with the programs driving the agents’ behavior. Participants were asked to pick the ‘better’ of the two programs, based on a single example of the two programs, evaluated on the same maze. Each pair of programs varied along one of our 3 proposed dimensions. Our framework predicts people’s choice of program across the tasks, and the results support the idea that people are intuitive cognitivists.

AbstractJudgment is often described in terms of an intuitive (System 1) versus deliberative (System 2) dichotomy, yet sound deliberation itself can take more than one form. Building on philosophical traditions and distinctions in treatment of sound judgment in economics and law, we propose that lay conceptions revolve around two distinct types of deliberate judgment: rational, emphasizing rule-based and utility-focused reasoning for well-defined problems, and reasonable, prioritizing context-sensitive and socially conscious reasoning for ill-defined problems. Across four studies in English-speaking Western samples (Studies 1–4; N = 2,130) and a Mandarin-speaking Chinese sample (Study 4; N = 697), participants described their notions of “sound” and “good” judgment, evaluated social scenarios, chose between candidates with distinct judgmental profiles, and categorized non-social objects. Results consistently showed that people view both rationality and reasonableness as common forms of deliberate sound judgment, while treating them as distinct. Participants preferred rational deliberation for algorithmic social roles linked to well-defined tasks and reasonable deliberation for interpretive roles linked to ill-defined tasks. Moreover, framing decisions as rational vs. reasonable influenced whether participants relied on rule-based vs. overall-similarity strategies in classification tasks. These findings suggest that lay understanding of sound judgment does not rely on a single standard of judgmental competence. Instead, people recognize that both rationality and reasonableness are critical for competent deliberation on different types of problems in life.

AbstractConspiracy theories have pervaded human thought across time and cultures, often emerging during crises such as the COVID-19 pandemic, where they influenced public behaviors and attitudes, notably in vaccine hesitancy. This research explores the metacognitive foundations of conspiracy beliefs, particularly focusing on how individuals monitor and assess their problem-solving processes. We propose that conspiracy beliefs are linked to high propositional confidence—often unsupported by accurate reasoning. Two studies were conducted to investigate the potential relationship between meta-reasoning inaccuracies (i.e., prospective confidence judgments and commission errors) during problem solving and conspiracy beliefs. Across two studies, we examine metacognitive markers of this overconfidence. Study 1 analyzes archival data from George and Mielicki’s (2023) to investigate how COVID-19 conspiracy beliefs are associated with initial judgments of solvability in solvable and unsolvable Compound Remote Associate (CRA) tasks. Study 2 examines the relationship between commission errors on Rebus puzzles and conspiracy beliefs, while also assessing Socio-Cognitive Polarization (SCP)—a construct encompassing ideological rigidity, intolerance of ambiguity, and xenophobia. Results show that SCP amplified the effects of commission errors on conspiracy beliefs, situating these cognitive patterns within socio-political contexts. These findings offer novel evidence that conspiracy beliefs are not merely a product of what people think, but how they think—underscoring the intertwined roles of flawed meta-reasoning and socio-political attitudes in sustaining conspiratorial worldviews.

AbstractUsing our hands to move a stick along a path differs in systematic ways from using our hands to communicate about moving the stick. Kinematic signatures (e.g., enlarged moving trajectories) have been found to mark a movement as communicative, relative to its non-communicative counterpart. But communicative movements are frequently embedded within an expressive system and might differ as a function of that system. For example, deaf signers move their hands when they communicate with sign language, which is a linguistic system. Hearing speakers also move their hands—they gesture along with speech—but those gestures do not form a linguistic system unto themselves. Do the communicative movements signers and speakers use to describe the same event differ as a function of the expressive systems within which they are embedded? Because some signs are highly iconic, researchers often assume that movements in these signs have the same properties as speakers’ gestures. To test this assumption, we compared spontaneous hand gestures produced by hearing speakers when they talk (co-speech gesture) to productive iconic hand signs produced by deaf signers when the signs superficially resemble co-speech gestures (classifier signs). We used motion tracking and kinematic analyses to disentangle the spatial and temporal kinematic patterns of communicative movements in 33 English-speakers and 10 American Sign Language (ASL) signers, using each group’s non-communicative movements as a control. Participants copied a movement on an object performed by a model (non-communicative movement) and then described what they did with the object (communicative movement). We found no differences between groups in how non-communicative movements related to communicative movements for spatial kinematics. However, for temporal kinematics, speakers’ co-speech movements were less rhythmic and jerkier than their non-communicative movements, but signers’ communicative movements were more rhythmic and smoother than their non-communicative movements. We thus found differences in the temporal aspects of co-speech gestures vs. classifier signs, leading to 3 conclusions: (i) Communicative movements do not always have the same kinematic signatures but depend on the expressive system within which they are embedded. (ii) Since signers’ and speakers’ communicative movements have different kinematic features, even highly iconic signed movements cannot be considered entirely gestural. (iii) We need fine-grained techniques to measure communicative movements, particularly when trying to identify the gestural aspects of sign. Communicative movements, even when superficially similar, differ as a function of the system they are part of.

AbstractMany advances in NLP, starting with distributional semantics, are founded on the insight that the semantics of words can be revealed by their co-occurrences (the frequency for two words to be present in the same sentence). And the most sophisticated modern machines are built to reproduce the statistics of even larger sets of words. Yet, these systems do not provide a transparent description of these statistics. We reveal a second-order Zipf’s law: the frequencies of co-occurrences follow an inverse power law in all 17 languages we tested. To assess this, we use the most conservative methods to detect a power law (and even develop its extension in the natural case of a finite vocabulary, which reveals new insights). We show that this second-order Zipf’s law is not a consequence of random processes, it is not a consequence of the usual Zipf’s law on word frequencies (it is less robust in a shuffled corpus), and it is not a consequence of syntactic constraints (it is less robust in a shuffled corpus that respects syntactic categories). The second-order Zipf’s law may therefore be more foundational than Zipf’s original law, and able to provide (transparent) information about semantic aspects of language.

AbstractTo maintain and develop close relationships, people need to accurately represent the minds of their social partners. Although studies have characterized many aspects of children’s intuitive theory of the mind and children’s intuitive theory of relationships, it is largely unknown whether and how children think about mental state reasoning within relationships. In three experiments, we asked whether children think accurate mental state reasoning is a cue to social closeness. In Experiment 1 (n = 145), we found that 5- to 9-year-old children, but not 4-year-old children, inferred that characters who engage in affective touch (making physical contact, as though nuzzling, while moving together in synchrony) are socially closer and know about each other’s goals and desires. In Experiment 2 (n = 137), we found that 6- to 9-year-old children, but not younger children, inferred that characters who are correct about each other’s minds are socially close. Children did not think that being correct about external states of the world was evidence that a character was close to another. In Experiment 3 (n = 79), we conceptually replicated the main findings from Experiments 1 and 2, and we found that 6- to 9-year-old children did not form the same inferences concerning knowledge about observable features of individuals (e.g., an individual’s outfit); children’s inferences were specific to unobservable mental content. Thus, by 6 years of age, children integrate their intuitive theories of the mind and relationships to make sense of whether and how people are connected to each other, as well as the strength and nature of those connections.