Search

Sentence acceptability judgments are often affected by a pervasive phenomenon called satiation: native speakers give increasingly higher ratings to initially degraded sentences after repeated exposure. Various studies have investigated the satiation effect experimentally, the vast majority of which focused on different types of island-violating sentences in English (sentences with illicit long-distance syntactic movements). However, mixed findings are reported regarding which types of island violations are affected by satiation and which ones are not. This article presents a meta-analysis of past experimental studies on the satiation of island effects in English, with the aim of providing accurate estimates of the rate of satiation for each type of island, testing whether different island effects show different rates of satiation, exploring potential factors that contributed to the heterogeneity in past results, and spotting possible publication bias. The meta-analysis shows that adjunct islands, the Complex NP Constraint (CNPC), subject islands, the that-trace effect, the want-for construction, and whether-islands reliably exhibit satiation, albeit at different rates. No evidence for satiation is found for the Left Branch Condition (LBC). Whether context sentences were presented in the original acceptability judgment experiments predicts the differences in the rates of satiation reported across studies. Potential publication bias is found among studies testing the CNPC and whether-islands. These meta-analytic results can be used to inform debates regarding the nature of island effects and serve as a proof of concept that meta-analysis can be a valuable tool for linguistic research.

Childhood is marked by the rapid accumulation of knowledge and the prolific production of drawings. We conducted a systematic study of how children create and recognize line drawings of visual concepts. We recruited 2-10-year-olds to draw 48 categories via a kiosk at a childrens museum, resulting in >37K drawings. We analyze changes in the category-diagnostic information in these drawings using vision algorithms and annotations of object parts. We find developmental gains in childrens inclusion of category-diagnostic information that are not reducible to variation in visuomotor control or effort. Moreover, even unrecognizable drawings contain information about the animacy and size of the category children tried to draw. Using guessing games at the same kiosk, we find that children improve across childhood at recognizing each others line drawings. This work leverages vision algorithms to characterize developmental changes in childrens drawings and suggests that these changes reflect refinements in childrens internal representations.

The field of psychometrics has undergone substantial evolution over the past several decades, both in terms of advances in methodology and improved software and hardware for deploying new methods. Despite these strides, many of these developments have not been integrated into the broader field of psychology, as highlighted by Embretson (2005) and Borsboom (2006). Understanding and incorporating these psychometric advances is crucial to enable cognitive scientists to address growing concerns about validity and reliability, as well as to develop robust theoretical frameworks for understanding cognition.

Outward differences between cultures are very salient, with Western and East Asian cultures as a prominent comparison pair. A large literature describes cross-cultural variation in cognition, but relatively less research has explored the developmental origins of this variation. This study helps to fill the empirical gap by replicating four prominent findings documenting cross-cultural differences in children's reasoning, visual attention, and social cognition in a cross-sectional sample of 240 3-12-year-olds from the US and China. We observe cross-cultural differences in three of the four tasks and describe the distinct developmental trajectory that each task follows throughout early and middle childhood.

Adults routinely tailor their communication to others' auditory access, such as substituting gestures for speech in noisy environments. Yet, assessing the effectiveness of different communicative acts given others' perceptual access—especially when it differs from one's own—requires mental-state reasoning, which undergoes significant developmental change. Can young children tailor their communication to others' auditory access? In Study 1, parental report (n=98) indicated that most children, by age 4, adjust their communicative behaviors in noisy settings. Study 2 elicited these behaviors experimentally with 4- to 5-year-olds (n=68). Children taught how a novel toy works to a learner who wore headphones playing either loud music or nothing. Children were more likely to use physical demonstrations, and less likely to use verbal explanations, when the learner's auditory access was obstructed. These findings illustrate how mental-state reasoning might support children's ability to communicate successfully across perceptually-compromised contexts and individuals.

What factors contribute to the growth of children's early vocabulary? One method for exploring this question is investigating predictors (e.g., frequency) that differentiate words learnt earlier from those learnt later. A more comprehensive account requires the examination of multiple language families and multiple levels of linguistic representation (e.g., phonological, morphosyntactic, semantic). Here, we studied 10 predictors of word ages of acquisition across 27 languages and dialects. We found that words that were more frequent, concrete, and associated with babies were learnt earlier, whereas words that had greater length in phonemes and mean length of utterance were learnt later. There was no reliable effect of other morphosyntactic predictors, or of phonological neighbourhood. We found evidence of a tradeoff between a language's morphological complexity and the effect of syntactic complexity for predicates, supporting the competition model. Predictor coefficients revealed broad consistency across all languages, along with variability that reflected language family classifications.

Choice deadlines are commonly imposed in decision-making research to incentivize speedy responses and sustained attention to the task settings. However, computational models of choice and response times routinely overlook this deadline, instead simply omitting trials past the deadline from further analysis. This choice is made under the implicit assumption that parameter estimation is not significantly affected by ignoring these omissions. Using new tools from likelihood-free inference, here we elucidate the degree to which omitting omissions, even in seemingly benign settings, can lead researchers astray. We explore the phenomenon using a Sequential Sampling Model (SSM) with collapsing boundaries as a test-bed.

How do humans decide what to look at and when to stop looking? The Rational Action, Noisy Choice for Habituation (RANCH) model formulates looking behaviors as a rational information acquisition process. RANCH instantiates a hypothesis about the perceptual encoding process using a neural network-derived embedding space, which allows it to operate on raw images. In this paper, we show that the model not only captures key looking time patterns such as habituation and dishabituation, but also makes fine-grained, out-of-sample predictions about magnitudes of dishabituation to previously unseen stimuli. We validated those predictions experimentally with a self-paced looking time task in adults (N = 468). We also show that model fits are robust across parameters, but that assumptions about the perceptual encoding process, the learning process and the decision process are all critical for predicting human performance.

Human learning is sensitive to rule-like structure and the curriculum of examples used for training. In tasks governed by succinct rules, learning is more robust when related examples are blocked across trials, but in the absence of such rules, interleaving is more effective. To date, no neural model has simultaneously captured these seemingly contradictory effects. Here we show that this same tradeoff spontaneously emerges with “in-context learning” (ICL) both in neural networks trained with metalearning and in large language models (LLMs). ICL is the ability to learn new tasks “in context” — without weight changes — via an inner-loop algorithm implemented in activation dynamics. Experiments with pretrained LLMs and metalearning transformers show that ICL exhibits the blocking advantage demonstrated in humans on a task involving rule-like structure, and conversely, that concurrent in-weight learning reproduces the interleaving advantage observed in humans on tasks lacking such structure.

Models based on the Transformer neural network architecture have seen success on a wide variety of tasks that appear to require complex ``cognitive branching''-- or the ability to maintain pursuit of one goal while accomplishing others. In cognitive neuroscience, success on such tasks is thought to rely on sophisticated frontostriatal mechanisms for selective gating, which enable role-addressable updating-- and later readout-- of information to and from distinct ``addresses'' of memory, in the form of clusters of neurons. However, Transformer models have no such mechanisms intentionally built-in. It is thus an open question how Transformers solve such tasks, and whether the mechanisms that emerge to help them to do so bear any resemblance to the gating mechanisms in the human brain. In this work, we analyze the mechanisms that emerge within a vanilla attention-only Transformer trained on a simple sequence modeling task inspired by a task explicitly designed to study working memory gating in computational cognitive neuroscience. We find that, as a result of training, the self-attention mechanism within the Transformer specializes in a way that mirrors the input and output gating mechanisms which were explicitly incorporated into earlier, more biologically-inspired architectures. These results suggest opportunities for future research on computational similarities between modern AI architectures and models of the human brain.

Humans are efficient social learners who leverage social information to rapidly adapt to new environments, but the computations by which we combine social information with prior knowledge are poorly understood. We study social learning within the context of multi-armed bandits using a novel “asteroid mining” video game where participants learn through active play and passive observation of expert and novice players. We simulate human exploration and social learning using naive versions of Thompson and Upper Confidence Bound (UCB) solvers and hybrid models that use Thompson and UCB solvers for direct learning together with a multi-layer perceptron to estimate what should be learned from other players. Two variants of the hybrid models provide good, parameter-free fits to human performance across a range of learning conditions. Our work shows a route for integrating social learning into reinforcement learning models and suggests that human social learning conforms to the predictions of such models.

Children learn a lot from others, but the effectiveness of their social learning depends on the reliability of others' help. How do children adapt their future learning decisions based on the past reliability of receiving help? In two experiments, 4- to 6-year-olds (N = 60 each) interacted with a researcher who either followed through on promised help (Reliable condition) or failed to do so (Unreliable condition). Experiment 1 was inconclusive. However, with an improved design, Experiment 2 found that children in the Unreliable condition were more likely to forego a harder but more rewarding puzzle as their next task and choose an easier, less rewarding puzzle instead compared to those in the Reliable condition. Such decisions, while seemingly maladaptive at face value, likely reflect an adaptive response to the low likelihood of receiving help. These results extend our understanding of social learning across diverse ecological contexts.

What structures children's early language environment? Large corpora of child-centered naturalistic recordings provide an important window into this question, but most available data centers on young children within the home or in lab contexts interacting primarily with a single caregiver. Here, we characterize children's language experience in a very different kind of environment: the preschool classroom. Children ages 3 – 5 years (N = 26) wore a head-mounted camera in their preschool class, yielding a naturalistic, egocentric view of children's everyday experience across many classroom activity contexts (e.g., sand play, snack time), with >30 hours of video data. Using semi-automatic transcriptions (227,624 words), we find that activity contexts in the preschool classroom vary in both the quality and quantity of the language that children both hear and produce. Together, these findings reinforce prior theories emphasizing the contribution of activity contexts in structuring the variability in children's early learning environments.

The "shape bias" -- the bias to generalize new nouns by their shape rather than other features such as color or texture -- has been argued to facilitate early noun learning for children. However, there is conflicting evidence about the magnitude and nature of this bias, as well as how it changes developmentally and how it varies across cultures. In this paper, we synthesize evidence about the shape bias using meta-analysis and meta-regression. We find strong overall evidence for the shape bias, but the literature is dominated by studies of English-speaking children, making it difficult to assess cross-cultural differences. Large between-study heterogeneity also highlights procedural variation in the literature. Overall, publication bias, heterogeneity, and data sparsity may limit the ability to distinguish theoretical accounts of the shape bias.

One problem language learners face is extracting word meanings from scenes with many possible referents. Despite the ambiguity of individual situations, a large body of empirical work shows that people are able to learn cross-situationally when a word occurs in different situations. Many computational models of cross-situational word learning have been proposed, yet there is little consensus on the main mechanisms supporting learning, in part due to the profusion of disparate studies and models, and lack of systematic model comparisons across a wide range of studies. This study compares the performance of several extant models on a dataset of 44 experimental conditions and a total of 1,696 participants. Using cross-validation, we fit multiple models representing theories of both associative learning and hypothesis-testing theories of word learning, find two best-fitting models, and discuss issues of model and mechanism identifiability. Finally, we test the models' ability to generalize to additional experiments, including develop- mental data.

Attachment is crucial for infants' cognitive development and social relationships. Traditional attachment research has been qualitative, lacking a model to explain how infants' attachment styles develop from experience and how these are influenced by personal traits and environmental factors. We propose such a model, predicting how infants balance interaction with caregivers against exploring their surroundings. Our study is based in a grid-world environment containing an infant and caregiver agent. We vary the infant's temperamental factors (e.g., ability to regulate emotions and preferences for social vs. environmental reward), and caregiver behavior (whether positive or negative interactions are more likely). We find that different equilibria result that qualitatively correspond to different attachment styles. Our findings suggest that the characteristic exploratory behavior of each attachment style in real infants may arise from interactions of infant temperament and caregiver behaviors.

In this paper, we provide guidance for the organization and implementation of EEG studies. This work was inspired by our experience conducting a large-scale, multi-site study, but many elements could be applied to any EEG project. Section 1 focuses on study activities that take place before data collection begins. Topics covered include: establishing and training study teams, considerations for task design and piloting, setting up equipment and software, development of formal protocol documents, and planning communication strategy with all study team members. Section 2 focuses on what to do once data collection has already begun. Topics covered include: (1) how to effectively monitor and maintain EEG data quality, (2) how to ensure consistent implementation of experimental protocols, and (3) how to develop rigorous preprocessing procedures that are feasible for use in a large-scale study. Links to resources are also provided, including sample protocols, sample equipment and software tracking forms, sample code, and tutorial videos (to access resources, please visit: https://osf.io/wdrj3/).

Human learning and decision-making are supported by multiple systems operating in parallel. Recent studies isolating the contributions of reinforcement learning (RL) and working memory (WM) have revealed a trade-off between the two. An interactive WM/RL computational model predicts that although high WM load slows behavioral acquisition, it also induces larger prediction errors in the RL system that enhance robustness and retention of learned behaviors. Here, we tested this account by parametrically manipulating WM load during RL in conjunction with EEG in both male and female participants and administered two surprise memory tests. We further leveraged single-trial decoding of EEG signatures of RL and WM to determine whether their interaction predicted robust retention. Consistent with the model, behavioral learning was slower for associations acquired under higher load but showed parametrically improved future retention. This paradoxical result was mirrored by EEG indices of RL, which were strengthened under higher WM loads and predictive of more robust future behavioral retention of learned stimulus-response contingencies. We further tested whether stress alters the ability to shift between the two systems strategically to maximize immediate learning versus retention of information and found that induced stress had only a limited effect on this trade-off. The present results offer a deeper understanding of the cooperative interaction between WM and RL and show that relying on WM can benefit the rapid acquisition of choice behavior during learning but impairs retention.SIGNIFICANCE STATEMENT Successful learning is achieved by the joint contribution of the dopaminergic RL system and WM. The cooperative WM/RL model was productive in improving our understanding of the interplay between the two systems during learning, demonstrating that reliance on RL computations is modulated by WM load. However, the role of WM/RL systems in the retention of learned stimulus-response associations remained unestablished. Our results show that increased neural signatures of learning, indicative of greater RL computation, under high WM load also predicted better stimulus-response retention. This result supports a trade-off between the two systems, where degraded WM increases RL processing, which improves retention. Notably, we show that this cooperative interplay remains largely unaffected by acute stress.

Infants must balance interacting with a caregiver and navigating the environment, driven by their curiosity and attachment. This decision involves trading off proximity seeking with the caregiver and novelty seeking in the surroundings. Our hypothesis is that different attachment styles may arise as adaptive reward-seeking strategies based on different frequencies and scales of rewards from both caregiver and environment, including negative rewards due to distressing encounters. We implement a computational model using a reinforcement learning framework to model infantsÕ attachment and exploratory behaviors as a bandit problem. We study how infantsÕ exploratory behaviors emerge as they learn from their caregiver and how these learned behaviors transfer to a new caregiver. Our work expands upon existing computational models of attachment and models how social factors can influence curiosity-seeking as infants learn to explore versus exploit.

From birth, humans make decisions about what to look at and for how long. A classic framework proposes encoding as a key driver of looking behavior in development - in early stages of encoding, infants and young children prefer to engage with familiar stimuli, while at later stages of encoding they prefer novel stimuli. Though this framework is often invoked when interpreting looking time studies, it is rarely validated empirically. Here, we test these predictions by explicitly manipulating exposure durations within-subjects. While we found robust evidence for habituation and novelty preferences, limiting exposure to visual concepts did not result in familiarity preferences in any age group. Our findings suggest that limited exposure does not generically lead to familiarity preferences, and that interpretations of observed familiarity preferences should be made with care. We argue for the development of formal frameworks which link the learning problem faced by participants to their attentional preferences.

The ability to form novel conventions is a key signature of efficient linguistic communication. Reduction of referring expressions, one measure of convention formation, is found robustly in dyadic repeated reference games when the target images are initially difficult to name. In reference games, participants share the explicit goal of establishing joint reference. However, establishing reference is a key subgoal of many conversations where interlocuters have more complex goals. In the current work, we analyze a dataset where reference was embedded in strategic 3- player negotiation and coordination games. In these more complex games, we found that the patterns of reduction and convergence to conventions still held across two different incentive conditions, with some modest differences between the conditions.

Early abstract reasoning follows qualitatively different developmental trajectories in the US and China (Carstensen et al., 2019), but the causal mechanisms for these differences are unknown. Existing accounts implicate several potential factors that differ between the US and China, including language (Hoyos et al., 2016), executive function (Richland et al., 2010), visual attention (Christie et al., 2020), and social reasoning (Jurkat et al., 2022). While there is extensive work documenting both language and executive function in US and Chinese children, much less is known about the development of cross-cultural differences in visual attention and social reasoning. We document abstract reasoning about relations (Ambiguous cRMTS, Carstensen et al., 2019) alongside the potential moderating factors of visual attention (Free Description; Imada et al., 2013), and social reasoning (Causal Attribution, Seiver et al., 2013; Uniqueness Preference, Kim & Markus, 1999) in a cross-sectional sample of 240 3-12-year-olds, and observe both similarities and differences.

In many cultures, adults use simple, slow, and dynamic speech when talking to infants ("parentese," or infant-directed speech) and make expansive, repetitive movements when demonstrating object properties to infants ("motionese," or infant-directed actions). These modifications enhance infants’ attention to and learning about language and goal-directed actions. Adults’ interactions with infants are also full of emotions—do adults also modify their emotional expressions when interacting with infants? Here we showed parents of infants (aged 7 to 14 months; N = 25) emotion-evoking pictures including colorful bubbles, adorable stuffed animals, yummy snacks, broken toys, dangerous fire, and rotten fruits. We asked parents to describe their feelings about these pictures either to their infant or to an adult partner (i.e., an experimenter). While the parents’ use of emotion words did not differ between conditions, their emotional expressions did: Their infant-directed emotional expressions were more positive when they discussed positive pictures and more negative when they discussed negative pictures compared to their adult-directed emotional expressions. These findings suggest that besides "parentese" and "motionese," there is also a unique form of emotional communication in parent-child interaction—"emotionese."

Despite the unpredictable and ubiquitous nature of noise in the natural acoustic environment, most children still manage to ex- tract the linguistic, cognitive, and social information needed to engage with their world. This is no small feat. We examined what strategies children use to navigate different acoustic environments. One possibility we test is that children can select acoustic contexts that are consistent with particular goals. In Experiment 1, we presented preschool children with a set of auditory stimuli, meant to approximate various acoustic environments, and activity goals to complete within those environments. Children integrated auditory information with goals to select the best environment. To assess the flexibility of children’s decision-making, Experiment 2 built on this framework by replacing familiar activity goals with relatively less familiar ones. In preliminary data, adults and preschoolers reliably evaluated acoustic environments that best matched these less familiar activities, providing evidence for flexible reasoning about goal-consistent environments.

Early language skill is predictive of later life outcomes, and is thus of great interest to developmental psychologists and clinicians. The Communicative Development Inventories (CDIs), parent-reported inventories of early-learned vocabulary items, have proven to be valid and reliable instruments for measuring children's early language skill. CDIs have been painstakingly adapted to dozens of languages, and cross-linguistic comparisons thus far show both consistency and variability in language acquisition trajectories. However, thousands of languages do not yet have CDIs, posing a significant barrier to increasing the diversity of languages that are studied. Here, we propose a method for selecting candidate words to include on new CDIs, leveraging analysis of psychometric properties of translation-equivalent concepts that are frequently included on existing CDIs. Leveraging 26 datasets from existing CDIs, we propose a list of 229 concepts that have low variability in their cross-linguistic learning difficulty. This pool of common concepts---analogous to the "Swadesh" lists used in glottochronology---can be used as a starting point for future CDI adaptations. We test how well the proposed list generalizes to data from 8 additional languages.

Young children acquire a wide range of linguistic and cognitive skills in the first three years of life. Decades of experimental work have established a solid empirical foundation for our understanding of cognitive development. But most experimental studies are limited in statistical power and focus on specific psychological constructs, thus making them unsuitable for describing developmental growth at scale. Here, we turned to meta-analyses of experimental research. We conducted a meta-meta-analysis to consolidate and integrate 23 meta-analyses compiled on MetaLab, a community-augmented meta-analysis platform. We found that most datasets can not meaningfully distinguish different functional forms for developmental change, but in those that could, there is great diversity in the best-fitting functional forms of the age model. We also evaluated the impact of a range of methodological factors. Overall, our work sheds light on the heterogeneous nature of developmental trajectories and the subtle interactions between research methods and experimental outcomes.

Is “cow” more closely related to “grass” or to “chicken”? Speakers of different languages judge similarity in this context differently, but why? One possibility is that cultures covarying with these languages induce differences in conceptualizations of similarity. Specifically, East Asian cultures may promote reasoning about thematic similarity, by which cow and grass are more related, whereasWestern cultures may bias judgments toward taxonomic relations, like cow and chicken. We measure similarity judgments across the US, China, and Vietnam and replicate US-China differences, but do not find that responding in Vietnam patterns with China. Instead, similarity judgments in Vietnam are intermediate between the US and China. We also show that word embedding models (fastText models for each language) are related to judgments within each country, suggesting a possible alternative interpretation of cross cultural differences. Perhaps notions of similarity are similar across contexts, but the statistics of the linguistic environment vary.

BackgroundPsychiatric diagnosis and treatment have historically taken a symptom-based approach, with less attention on identifying underlying symptom-producing mechanisms. Recent efforts have illuminated the extent to which different underlying circuitry can produce phenotypically similar symptomatology (e.g., psychosis in bipolar disorder vs. schizophrenia). Computational modeling makes it possible to identify and mathematically differentiate behaviorally unobservable, specific reinforcement learning differences in patients with schizophrenia versus other disorders, likely owing to a higher reliance on prediction error-driven learning associated with basal ganglia and underreliance on explicit value representations associated with orbitofrontal cortex.MethodsWe used a well-established probabilistic reinforcement learning task to replicate those findings in individuals with schizophrenia both on (n = 120) and off (n = 44) antipsychotic medications and included a patient comparison group of bipolar patients with psychosis (n = 60) and healthy control subjects (n = 72).ResultsUsing accuracy, there was a main effect of group (F3,279 = 7.87, p < .001), such that all patient groups were less accurate than control subjects. Using computationally derived parameters, both medicated and unmediated individuals with schizophrenia, but not patients with bipolar disorder, demonstrated a reduced mixing parameter (F3,295 = 13.91, p < .001), indicating less dependence on learning explicit value representations as well as greater learning decay between training and test (F1,289 = 12.81, p < .001). Unmedicated patients with schizophrenia also showed greater decision noise (F3,295 = 2.67, p = .04).ConclusionsBoth medicated and unmedicated patients showed overreliance on prediction error-driven learning as well as significantly higher noise and value-related memory decay, compared with the healthy control subjects and the patients with bipolar disorder. Additionally, the computational model parameters capturing these processes can significantly improve patient/control classification, potentially providing useful diagnosis insight.

In learning about and building models of their social and physical worlds, children exhibit a wide range of complex, spontaneous, intrinsically motivated, curiosity-driven behaviors. To understand how curiosity-driven behavior in social environments differs in diverse learners, we experimentally measure attention allocation in children on the autism spectrum and their typically-developing peers. We designed an augmented reality smartphone activity where children freely explore novel stimuli. Some agents behave “socially” (i.e., animate), while others have more regular (i.e., periodic, static) or irregular (i.e., random) inanimate behavioral patterns. This project is part of a broader program in which we attempt to model curiosity with artificial intelligence. Our augmented reality systems will lead to large and diverse data acquisition, allowing us to model a comprehensive range of learning behaviors and enable more inclusive, personalized pedagogical and diagnostic tools.

Prior work suggests children understand how speech conveys information and influences others’ minds. Although these studies have focused on communication under ideal conditions, auditory noise plagues the real world, often corrupting the transmission of information. The current study examines how children reason about the impact of auditory noise on communication. Children (N=72, Age:3;0-5;11) watched scenarios where a teacher tells a learner about two toys, but loud auditory noise masks one of the explanations. When asked which toy the learner wants to hear about again, children were more likely to select the noise-masked toy when the learner knew about neither toy (No Knowledge) than when he already knew about the masked toy (Partial Knowledge). However, their preference for the masked toy also increased with age in both conditions. Overall, these results demonstrate children's developing understanding of when and how communication affects listeners' knowledge and information-seeking behaviors.

Children's early language skill has been linked to later educational outcomes, making it important to measure early language accurately. Parent-reported instruments such as the Communicative Development Inventories (CDIs) have been shown to provide reliable and valid measures of children's aggregate early language skill. However, CDIs contain hundreds of vocabulary items, some of which may not be heard (and thus learned) equally often by children of varying backgrounds. This study used a database of American English CDIs to identify words demonstrating strong bias for particular demographic groups of children, on dimensions of sex (male vs. female), race (white vs. non-white), and maternal education (high vs. low). For each dimension, we identified dozens of strongly biased items, and showed that eliminating even some of these items can reduce the magnitude of differences between groups. Additionally, we investigated how well the relative frequency of words spoken to young girls vs. boys predicted sex-based word learning bias, and discuss possible sources of demographic differences in early word learning.

From classrooms to dinner parties, many of our everyday conversations take place in larger groups where speakers address multiple listeners at once. Such multiparty settings raise a number of challenges for classical theories of communication, which largely focus on dyadic interactions. In this study, we investigated how speakers adapt their referring expressions over time as a function of the feedback they receive from multiple parties. We collected a large corpus of multiparty repeated reference games (98 games, 390 participants, 116K words) where speakers designed referring expressions for groups of 1 to 5 listeners. Larger groups tended to use more words total and to introduce more new words; nonetheless, most groups were able to converge to more efficient conventions regardless of the number of listeners.

From birth, humans constantly make decisions about what to look at and for how long. Yet the mechanism behind such decision-making remains poorly understood. Here we present the rational action, noisy choice for habituation (RANCH) model. RANCH is a rational learning model that takes noisy perceptual samples from stimuli and makes sampling decisions based on Expected Information Gain (EIG). The model captures key patterns of looking time documented in developmental research: habituation and dishabituation. We evaluated the model with adult looking time collected from a paradigm analogous to the infant habituation paradigm. We compared RANCH with baseline models (no learning model, no perceptual noise model) and models with alternative linking hypotheses (Surprisal, KL divergence). We showed that 1) learning and perceptual noise are critical assumptions of the model, and 2) Surprisal and KL are good proxies for EIG under the current learning context.

Children’s early word learning is to a large extent driven by the prevalence of words in their language environment, with words that are spoken more often to children being learned earlier. However, children receive language from a variety of sources, including books, television, and movies meant for children, as well as speech and media that is meant for adults, but over- heard by children. Despite considerable similarity of word frequency distributions from these different input sources, there is also significant and predictable variability between them. For example, function words are far more frequent in books than in everyday speech, while early-learned nouns (e.g., ‘ball’ and ‘mommy’) are more frequent in child-directed speech than in other sources. Children receive a mixture of these different frequency distributions. The goal of this paper is to better understand the shared and unique variance in these input sources – in both English and French – and to evaluate how predictive these distributions are of children’s early word learning.

Previous studies report that children acquire emotion words gradually during ages 3--5 and beyond (e.g., Widen, 2013). Most of this work, however, has used tasks that are demanding for young children (e.g., asking children to produce emotion labels in a free-labeling task), and has asked children to map emotion labels to facial configurations alone. In our study, we tested children's ability to comprehend, rather than produce, emotion words, and used not only facial configurations but also body language. In two pre-registered online experiments, two to four-year-old children (N = 96) were asked to connect emotion words, including happy, sad, angry, and scared, to either facial configurations (Experiment 1) or to combined facial configurations and body postures (Experiment 2). While we found an overall pattern consistent with prior work (i.e., better performance when distractors were from opposite valences than when they were of the same valence), we also found much earlier competence in understanding same-valence emotions. Even 2-year-olds succeeded in differentiating the three negative emotion words and connecting them to facial cues (Experiment 1). Experiment 2 replicated this pattern and further showed that children performed equally well (but not substantially better) given additional body cues. These results suggest that before children can produce emotion words in an adult-like manner, they have some systematic understanding of those words and can map them to emotion cues within valence domains. (Preprint: psyarxiv.com/ka3ed)

Children are navigating a world with massive amounts of auditory input, sometimes relevant while other times purely noise, and must somehow make sense of it all. The early auditory environment is critical for speech perception and recognition, auditory discrimination, and word learning, all of which support language outcomes. What strategies do children use to learn in noisy environments? One potential strategy is environmental selection, which allows children to seek environments that align with particular goals. In the current paper, we examined whether children and adults make decisions about their environments by integrating auditory information and goal-states. While 3- and 4-year olds struggle with discriminating the level of noise in noisy speech streams (and likely do not use this information for environmental selection), 5-year-old children and adults can. Further, we show initial evidence that they can use this information to reason about acoustic environments that are consistent with specific goals.

Is a cow more closely related to grass or to a chicken? Responses vary by culture and age, among other factors. Those from western societies (or independent-leaning regions within interdependent non-western societies) are more likely to endorse the taxonomic match, the chicken, over the thematic match, grass (Chiu, 1972; Talhelm et al., 2014). This preference has been documented -- largely in western cultures -- to increase over development (e.g., Smiley & Brown, 1979). While neither development nor culture occur independently of the other, comparisons across these areas are problematic. We address one potential barrier to comparing cultural and developmental research using this classic paradigm -- stimulus format -- and show that the use of text (versus image) stimuli can bias participants toward taxonomic responding in some contexts. We present stimuli designed for cross-cultural use with children and adults and document country, regional, and demographic variation across the US and Italy.

BackgroundReduced activation of dopamine D1 receptor signaling may be implicated in reward functioning as a potential driver of negative symptoms in schizophrenia. Phosphodiesterase 10A (PDE10A), an enzyme that is highly expressed in the striatum, modulates both dopamine D2- and D1-dependent signaling.MethodsWe assessed whether augmentation of D1 signaling by the PDE10 inhibitor RG7203 enhances imaging and behavioral markers of reward functions in patients with schizophrenia and negative symptoms. In a 3-period, double-blind, crossover study, we investigated the effects of RG7203 (5 mg and 15 mg doses) and placebo as adjunctive treatment to stable background antipsychotic treatment in patients with chronic schizophrenia with moderate levels of negative symptoms. Effects on reward functioning and reward-based effortful behavior were evaluated using the monetary incentive delay task during functional magnetic resonance imaging and the effort-cost-benefit and working memory reinforcement learning tasks.ResultsPatients (N = 33; 30 male, mean age ± SD 36.6 ± 7.0 years; Positive and Negative Syndrome Scale negative symptom factor score 23.0 ± 3.5 at screening) were assessed at three study centers in the United States; 24 patients completed the study. RG7203 at 5 mg significantly increased reward expectation-related activity in the monetary incentive delay task, but in the context of significantly decreased overall activity across all task conditions.ConclusionsIn contrast to our expectations, RG7203 significantly worsened reward-based effortful behavior and indices of reward learning. The results do not support the utility of RG7203 as adjunctive treatment for negative symptoms in patients with schizophrenia.

From the earliest months of life, infants prefer listening to and learn better from infant-directed speech (IDS) than adult-directed speech (ADS). Yet, IDS differs within communities, across languages, and across cultures, both in form and in prevalence. This large-scale, multi-site study used the diversity of bilingual infant experiences to explore the impact of different types of linguistic experience on infants' IDS preference. As part of the multi-lab ManyBabies 1 project, we compared lab-matched samples of 333 bilingual and 385 monolingual infants' preference for North-American English IDS (cf. ManyBabies Consortium, 2020: ManyBabies 1), tested in 17 labs in 7 countries. Those infants were tested in two age groups: 6-9 months (the younger sample) and 12-15 months (the older sample). We found that bilingual and monolingual infants both preferred IDS to ADS, and did not differ in terms of the overall magnitude of this preference. However, amongst bilingual infants who were acquiring North-American English (NAE) as a native language, greater exposure to NAE was associated with a stronger IDS preference, extending the previous finding from ManyBabies 1 that monolinguals learning NAE as a native language showed a stronger preference than infants unexposed to NAE. Together, our findings indicate that IDS preference likely makes a similar contribution to monolingual and bilingual development, and that infants are exquisitely sensitive to the nature and frequency of different types of language input in their early environments.

Is “cow” more closely related to “grass” or “chicken”? Speakers of different languages judge similarity in this context differently, but why? One possibility is that cultures co-varying with these languages induce variation in conceptualizations of similarity. Specifically, East Asian cultures may promote reasoning about thematic similarity, by which cow and grass are more related whereas Western cultures may bias similarity judgements toward taxonomic relations, like cow-chicken. This difference in notions of similarity is the consensus interpretation for cross-cultural variation in this paradigm. We consider, and provide evidence for, an alternative possibility, by which notions of similarity are equivalent across contexts, but the statistics of the environment vary. On this account, similarity judgements are guided by co-occurrence in experience, and observing or hearing about cows and grass or cows and chickens more often could induce preferences for the relevant grouping, and account for apparent differences in notions of similarity across contexts.

Human communication involves far more than words; speakers’ utterances are often accompanied by various kinds of emotional expressions. How do listeners represent and integrate these distinct sources of information to make communicative inferences? We first show that people, as listeners, integrate both verbal and emotional information when inferring true states of the world and others' communicative goals, and then present computational models that formalize these inferences by considering different ways in which these signals might be generated. Results suggest that while listeners understand that utterances and emotional expressions are generated by a balance of speakers’ informational and social goals, they additionally consider the possibility that emotional expressions are noncommunicative signals that directly reflect the speaker’s internal states. These results are consistent with the predictions of a probabilistic model that integrates goal inferences with linguistic and emotional signals, moving us towards a more complete formal theory of human communicative reasoning.

Relational reasoning is an important component of abstract thought that emerges early in development but shows substantial variation across contexts, with children in the US and China following distinct developmental trajectories in one paradigm between 18m and 4y. To understand the mechanisms through which variation in the learning environment influences the development of relational reasoning, we examine early relational reasoning in Punjabi speakers in India, who share some cultural and linguistic elements of their experience with children in both the US and China. In a causal relational match-to-sample task, we find that 3-year-olds in India exhibit performance that is intermediate to their high-performing peers in China and the relatively poor performance observed in the US at this age. These results suggest complexity and variability in the development of relational reasoning and lay a foundation for future research designed to tease apart the factors associated with early diversity in relational reasoning.

One problem language learners face is extracting word meanings from scenes with many possible referents. Despite the ambiguity of individual situations, a large body of empirical work shows that people are able to learn cross-situationally when a word occurs in different situations. Many computational models of cross-situational word learning have been proposed, yet there is little consensus on the main mechanisms supporting learning, in part due to the profusion of disparate studies and models, and lack of systematic model comparisons across a wide range of studies. This study compares the performance of several extant models on a dataset of 44 experimental conditions and a total of 1,696 participants. Using cross-validation, we fit multiple models representing theories of bothassociative learning and hypothesis-testing theories of word learning, find two best-fitting models, and discuss issues of model and mechanism identifiability. Finally, we test the models' ability to generalize to additional experiments, including developmental data.

People perceive, think, and act in a multitude of different ways across cultures, and there is an extensive history of research documenting these differences. At the heart of much of this work is a contrast between Western and East Asian cultures that has inspired important efforts to document human psychology in populations outside of the WEIRD (Western, Educated, Industrial, Rich, Democratic) demographic, which is much overrepresented within psychological research (Henrich, Heine, & Norenzayan, 2010). Recent recommendations for measuring cultural distance (Muthukrishna et al., 2020) profile the US and China as focal points for cultural comparisons, but define cultural distance using explicit self-report measures. Here, we evaluate cross-cultural differences between the US and China using implicit and experimental measures. We attempt to reproduce and test extensions of prior work demonstrating cross-cultural differences in reasoning, vision, and social cognition with convenience snowball samples of university students. Few of these differences appeared in our sample.

Syntactic priming may be a key mechanism underlying children’s learning of novel words. Havron et al. (2019) exposed French-speaking children (ages 3 to 4) to a speaker biased by the use of either familiar verbs or nouns presented in the same syntactic context. This influenced participants’ interpretations of ambiguous novel words presented in the same syntactic frame. In Experiment 1, we successfully replicated Havron et al. with 77 French-speaking adults, using a web-based eye-tracking paradigm. Experiment 2 adapted this paradigm to English: Repeated exposure to a syntactic structure induced 102 English-speaking adults to update their expectations about the meanings of novel words. Our results indicate participants adapted to the specific linguistic structure used, not just the speaker’s tendency to mention actions or objects. These findings support the role of rapid adaptation during word learning. Experiments in progress investigate whether the English paradigm yields successful learning in 3- to 5-year-old children.

Curiosity is a fundamental driver of human behavior, and yet because of its open-ended nature and the wide variety of behaviors it inspires in different contexts, it is remarkably difficult to study in a laboratory context. A promising approach to developing and testing theories of curiosity is to instantiate them in artificial agents that are able to act and explore in a simulated environment, and then compare the behavior of these agents to humans exploring the same stimuli. Here we propose a new experimental paradigm for examining children's -- and AI agents' -- curiosity about objects' physical interactions. We let them choose which object to drop another object onto in order to create the most interesting effect. We compared adults' (N=155) and children's choices (N=66; 3-7 year-olds) and found that both children and adults show a strong preference for choosing target objects that could potentially contain the dropped object. Adults alone also make choices consistent with achieving support relations. We contextualize our results using heuristic computational models based on 3D physical simulations of the same scenarios judged by participants.

Curiosity drives much of human behavior, but its open-ended nature makes it hard to study in the laboratory. Moreover, computational theories of curiosity -- models of how intrinsic motivation promotes complex behaviors -- have been challenging to test because of technical limits. To circumvent this problem, we develop a new way to assess intrinsic motivation for building: we assume people build what they find interesting, so we asked them to rate the "interestingness" of visual stimuli -- in this case, simple block towers.Adults gave a range of ratings to towers built by children, with taller towers rated higher.To probe interestingness further, we developed controlled tower stimuli in a simulated 3D environment. While tower height predicted much of the variation in ratings, people also favored more precarious towers, as inferred from geometric features and simulated dynamics. These ratings and features therefore give a clear target for computational accounts of curiosity to explain.

What do infants and young children tend to see in their everyday lives? Relatively little work has examined the categories and objects that tend to be in the infant view during everyday experience, despite the fact that this knowledge is central to theories of category learning. Here, we analyzed the prevalence of the categories (e.g., people, animals, food) in the infant view in a longitudinal dataset of egocentric infant visual experience. Overall, we found a surprising amount of consistency in the broad characteristics of children's visual environment across individuals and across developmental time, in contrast to prior work examining the changing nature of the social signals in the infant view. In addition, we analyzed the distribution and identity of the categories that children tended touch and interact with in this dataset, generalizing previous findings that these objects tended to be distributed in a Zipfian manner. Taken together, these findings take a first step towards characterizing infants' changing visual environment, and call for future work to examine the generalizability of these results and to link them to learning outcomes.

The ability to rapidly recognize words and link them to referents in context is central to children's early language development. This ability, often called word recognition in the developmental literature, is typically studied in the looking-while-listening paradigm, which measures infants' fixation on a target object (vs. a distractor) after hearing a target label. We present a large-scale, open database of infant and toddler eye-tracking data from looking-while-listening tasks. The goal of this effort is to address theoretical and methodological challenges in measuring vocabulary development. We present two analyses of the current database (N=1,320): (1) capturing age-related changes in infants' word recognition while generalizing across item-level variability and (2) assessing how a central methodological decision -- selecting the time window of analysis -- impacts the reliability of measurement. Future efforts will expand the scope of the current database to advance our understanding of participant-level and item-level variation in children's vocabulary development.

Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect (p