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This paper explores the use of linguistic strategies, specifically discourse markers like 'but', to express contrasts between expectations and reality when faced with unexpected events. The study concentrates on Denial of Expectation (DofE), themost powerful form of contrast, which arises when the expected value based on background assumptions is not met. The main focus of this paper is to model DofE as a weighted homogeneous relationship between object properties. The aim is to predict DofE for numerical properties in specific contexts. I aim to address a gap in previous models by considering the role of context. This is achieved by analyzing contrastive sentences from German car and motorcycle reviews. The research presents the concept of expectation intervals for scalar properties. These intervals align with expectations and exceeding them triggers a potential contrast. The study incorporates causality, expected behavior, and a shift function in selecting contrastive pairs, transforming the conditions into an algorithm.Keywords: contrast; computational and cognitive modeling; discourse analysis

In this paper, we explore the Principle of Charity. This is an epistemic assumption that people should not judge people to be irrational unless they have an empirically justified account of what they are doing when they violate normative standards. Through two studies, we provide evidence in support of the principle. Study 1 suggests people believe others will arrive at the same conclusions they would themselves given the same information. Study 2 suggests that people assume others may differ in the subjective degrees of belief but that they broadly use the same (Bayesian) updating mechanism when evaluating information about other people. We believe this paper provides the first empirical test of this principle.

Citizens in a democracy must navigate an increasingly dense information landscape. Regulation can aid this navigation by mandating disclosures of the source and nature of political campaign material. In many countries, legislators are increasing transparency requirements for online advertising in particular. The current paper looks at how and if citizens use such disclaimers to infer the intent of political advertisers during the process of a trust evaluation. This paper describes a survey experiment that specifically investigates evaluations of unknown campaigners, theorising such conditions will maximise any effect disclaimers have on trust. Testing both sponsorship and micro-targeting disclaimers, no support is found for the theoretical claim that viewing a disclaimer can increase how trustworthy a political advertiser is perceived to be. There is preliminary support that, for some individuals, viewing a disclaimer increases scepticism.

Recent work using neural networks and crowd-sourced perceptual judgements has shown that human figure drawings contain latent structure that can predict many characteristics of the artist including parent-reported motor function and perceived gender. We extend these approaches to two-dimensional renderings of three-dimensional cubes, assessing whether latent structure in these cube drawings likewise predicts demographic characteristics and motor function measured via a paper-folding task. Drawings produced with marker and paper showed a large predictive relationship with paper-folding (accounting for 59% of the offset variance, 62% of the angle variance, ps < .01). We also observed a complex interaction with gender: better cube-drawings predicted better paper-folding for male-identifying participants, but this relationship was reversed for female-identifying participants, who demonstrated better paper folding abilities overall. The results suggest that cube drawings contain richer structure than previously recognized and can provide a useful nonverbal metric for characterizing aspects of cognitive and motor abilities.

The multiple realizability thesis holds that psychological states may be implemented in a diversity of physical systems. The deep learning revolution seems to be bringing this possibility to life, offering the most plausible examples of man-made realizations of sophisticated cognitive functions to date. This paper explores the implications of deep learning models for the multiple realizability thesis. Among other things, it challenges the widely held view that multiple realizability entails that the study of the mind can and must be pursued independently of the study of its implementation in the brain or in artificial analogues. Although its central contribution is philosophical, the paper has substantial methodological upshots for contemporary cognitive science, suggesting that deep neural networks may play a crucial role in formulating and evaluating hypotheses about cognition, even if they are interpreted as implementation-level models. In the age of deep learning, multiple realizability possesses a renewed significance.

Since the landmark paper of Spivey et al. (2005), there has been an explosion in the use of continuous trajectories for studying decision-making. In these studies, the participant makes their decision by reaching to a target. By extracting trajectory-based measures, it is possible to observe how the decision unfolds over time. However, studies have shown that these trajectories may not continuously track the decision-making process, but rather result from several discrete submovements executed intermittently. Due to this, the conclusions of papers using purely kinematic measures may be affected by these motor control properties. This study presents simulations of trajectories using human motor control models and demonstrates the relationship between non-decision-related parameters and outcome measures typically used. We also comment on the implications of these findings for using kinematic measures in decision-making and make recommendations for how the decision variable can be more appropriately sampled.

People tend to avoid wasting resources, but little is known about when this emerges in development. Though young children are often wasteful with food and other items, previous work suggests that children consider waste in other judgments. Here, we examined if children anticipate that others should minimize waste. In two experiments (total N = 195), children chose which of two foods someone should eat (Experiment 1; 3-7-year-olds) or two papers someone should make a snowflake with (Experiment 2; 5-year-olds). One of the options would result in minimal waste (i.e., a small food/paper) while the other would result in greater waste (i.e., a large food/paper). Children did not anticipate that others would choose smaller foods, however, at around five years they predicted that others would choose smaller paper. These findings contribute to our knowledge of the development of waste aversion and may extend our understanding of waste aversion as a form of efficiency.

During class, taking notes helps the encoding process, especially when students reformulate what is presented. After class, notes can be used for revision. Previous studies, mostly conducted in laboratory contexts, showed that note-taking on paper seems to be more efficient than on computer, probably because students are more likely to reformulate on paper. The originality of this study is to investigate students’ perceptions and strategies depending on their note-taking method during but also after class. Data of 108 students showed that students on paper (30%) declare making more reformulations and less multitasking that students on computer (70%). After class, review sheets are written for a large majority of the students on paper, whatever the method they used during class. These results suggest that even if computer is the most used method during class, paper is still used after class, and with paper are associated better strategies.

How did monolingual bias emerge and why does it persist? What are the epistemologies concerning human language? How do language and languages develop? What do the terms monolingual, bilingual, and multilingual mean? What does “typical” language development entail? What constitutes “atypical” language development? To answer these questions, this paper foregrounds that language is a human cognitive skill for communication, and that Anglo-American and European notions of language diversity, monolingualism, bilingualism, and multilingualism disseminate monolingual bias. Monolingual bias extends to educational and clinical contexts where children from bi/multilingual households and/or communities face inappropriate scrutiny when language practitioners assess and support them based on normative monolingual expectations or standards. Monolingual bias is then linked to conceptual issues on “typical” and “atypical” language development. Ultimately, this paper attempts to explain why Anglo-American and European notions of bi/multilingualism are less relevant for most, if not all, of the world’s multilingual contexts, especially in the Philippines.

The aim of this paper is to import basic concepts from information theory into the epistemological debate about probabilistic measures of coherence. Rather than putting forward and defending a new measure, this paper will sketch an account of `external' coherence, which will be defined as a relation between a target variable of interest, sources containing (more or less) information about the target, and a rule or theoretical hypothesis that postulates the relevant connections between source and target. Relations with and potential insights for standard notions of coherence in formal epistemology are explored. More generaly, the paper explores the potential benefits of applying information theory to the epistemological debate about coherence.

This paper introduces Waymaking, a philosophy that defines cognition by the trajectories of an agent’s movement through its ongoing encounter, whereby those trajectories are themselves dynamic patterns of nested (i.e., whole-body, neural) spatiotemporal movement. Here, an agent’s encounter incudes all realms traditionally named mental, physical, and virtual, distinguishing these by their affordances and sensory landscapes relative to the agent-base. Recent research on the hippocampal formation and entorhinal cortex has opened a way for cognition to be understood as trajectories within nested landscapes: We can now posit knowledge-acquisition, remembering, and spatiotemporal navigation as a common process through statistically diverse clustered regularities. This paper formulates these findings into a general framework that can be used heuristically to provide a practical definition of cognition applicable across disciplines and species, thus alleviating stubborn dichotomies such as those at the heart of the so-called mind-body problem and arguments around potential plant or animal cognition.

English is the dominant language in computer science. In addition to English-based academic papers, English is frequently the only language provided in introduction sections and manuals of command and software libraries, which are essential aspects of computer programming. Hence, English-as-a-second-language (ESL) learners may have difficulty studying computer science because they must learn this field while also learning English. Despite this problem, few studies have assessed the difficulty level of computer science texts for ESL learners.Ideally, the difficulty levels of texts are assessed by having groups of ESL learners read them. However, owing to the excessive time and financial costs involved, such practices can be impractical.Hence, using two highly accurate automatic readability assessors based on natural language processing (NLP) techniques, we assessed the readability of various computer-science-related texts for ESL learners. The first assessor is based on state-of-the-art deep transfer learning, and the second is based on classical machine learning and applied linguistics. For training the assessors, we used a standard corpus employed in NLP, which was annotated by professional English teachers to evaluate the readability of the texts for ESL learners. To conduct the experiments, we built a collection of computer science texts ranging from academic papers to software manuals (READMEs) crawled from a source-code hosting website, namely GitHub. The experimental results showed that intermediate ESL learners were able to read most of the computer science related texts.

Distributed vector representations are a key bridging point between connectionist and symbolic representations of cognition. It is unclear how uncertainty should be modelled in systems using such representations. One may place vector-valued distributions over vector representations, although that may assign non-zero probabilities to vector symbols that cannot occur. In this paper we discuss how bundles of symbols in Vector Symbolic Architectures (VSAs) can be understood as defining an object that has a relationship to a probability distribution, and how statements in VSAs can be understood as being analogous to probabilistic statements. We sketch novel designs for networks that compute entropy and mutual information. In this paper we restrict ourselves to operators proposed for Holographic Reduced Representations, and representing real-valued data. However, we suggest that the methods presented in this paper should translate to any VSA where the dot product between fractionally bound symbols induces a valid kernel.

Efforts have always been deployed to surpass limitations in human cognitive abilities to enhance aspects such as task accuracy, work effectiveness and error management. Cognitive enhancement is a field aiming at improving human cognition in order to overcome those limitations. It bears important interest from the human factors community given its potential for reducing errors in complex operational environments. Yet, cognitive enhancement strategies are rarely used outside the lab and practical applications are scarce. The current paper presents a brief summary of the literature on human cognitive enhancement and discusses key operational applications of the main methods reported in this field. Using a human factors perspective, the paper also outlines how such techniques could be integrated into decision-support tools to support operators facing cognitive challenges in complex operational domains, including those experiencing functional limitations preventing them to contribute to the workforce.

An important step in children's socio-cognitive development is learning how to engage in coordinated conversations. This requires not only becoming competent speakers but also active listeners. This paper studies children's use of backchannel signaling (e.g., "yeah!" or a head nod) when in the listener's role during conversations with their caregivers via video call. While previous work had found backchannel to be still immature in middle childhood (i.e., 6 to 11 years of age), our use of both more natural/spontaneous conversational settings and more adequate controls allowed us to reveal that school-age children are strikingly close to adult-level mastery in many measures of backchanneling. The broader impact of this paper is to highlight the crucial role of social context in evaluating children's conversational abilities.

This paper presents a connectionist network simulation of Livesey and McLaren’s (2009) results. In that paper they showed that participants with post-discrimination gradients that were initially peak shifted became monotonic as they were exposed to the full range of test stimuli. While the authors suggest that this is the result of rule-based processes ‘taking over’ responding, we show how a connectionist network with an attentional parameter and realistic activation functions for the input can simulate both the peak shifted and monotonic gradients. Although we do not infer that the monotonic gradient obtained in peak shift paradigms is entirely the result of associative, rather than propositional processes, we suggest that perhaps it is a change in the allocation of attention, in conjunction with the underlying representational structures used for the stimuli that facilitates rule induction in this case.

This paper describes an initial cognitive framework that captures the reasoning involved in scientific data analyses, drawing from close collaborations with scientists in different domains over many years. The framework aims to automate data analysis for science. In doing so, existing large repositories of data could be continuously and systematically analyzed by machines, updating findings and potentially making new discoveries as new data becomes available. The framework consists of a cycle with six phases: formulating an investigation, initiating the investigation, getting data, analyzing data, aggregating results, and integrating findings. The paper also describes our implementation of this framework and illustrates it with examples from different science domains.

Cross-domain alignment refers to the task of mapping a concept from one domain to another, for example, ``If a \textit{doctor} were a \textit{color}, what color would it be?''. This seemingly peculiar task was designed to investigate how people represent concrete and abstract concepts through their mappings between categories and their reasoning processes over those mappings. In this paper, we adapt this task from cognitive science to evaluate the conceptualization and reasoning abilities of large language models (LLMs) through a behavioral study. We examine several LLMs by prompting them with a cross-domain mapping task and analyzing their responses at the population level and the individual level. Additionally, we assess the models' ability to reason about their predictions by analyzing and categorizing their explanations for these mappings. The results reveal several similarities between humans' and models' mappings and explanations, suggesting that models represent concepts similarly to humans. This similarity is evident not only at the model representation level but also in their behavior. Furthermore, the models mostly provide valid explanations and deploy reasoning paths that are similar to humans.

To conceptualise biological and mental processes, often a dynamical systems perspective is suggested. In addition to dynamics, the structure of the contextual makeup or world configuration (of an organism or brain) plays a crucial role too, as well as adaptivity of the processes. This paper provides a conceptual perspective where the structure, dynamics, and adaptivity of these processes are distinguished and related to each other via adaptive dynamical systems. Moreover, it is shown how networks can be used to represent this conceptual perspective. Here an adaptive dynamical system of any order of adaptivity can be covered where any level can exert control over the level below. The approach is illustrated by case studies for higher-order adaptive evolutionary processes. One of these case studies shows a fifth-order adaptive dynamical system that models how due to bad environmental influences at a young age, epigenetic effects can lead to a lifelong mental disorder.

Attempts to recover and reconstruct the origins of mathematics have traditionally focused on identifying evidence of early notational systems of quantification. We aim to show in this paper that archaeological material culture can offer an alternate, more tangible source of information about mathematical knowledge in the deep past, especially when it is paired with ethnographic and cross-cultural data. In addition, when linked to the cognitive science of mathematics, it can support inferences about how humans first began to grasp, learn, and apply mathematical ideas. We focus on fibre technologies and weaving crafts as prime examples of activities that contain and afford mathematical knowledge, in response to Lakoff & Núñez's call to explore the common practices that underlie mathematical ideas and to rethink mathematics as grounded in human experience.

In the domain of Linguistics, the categories of intransitive verbs, namely the unaccusativity, is a long-debated topic. Unaccusativity suggests that intransitive verbs can be divided into unergative and unaccusative verbs, based on their subjects' similarity to the subjects of transitive verbs or the objects of transitive verbs. Previous research has discussed how the meaning of verbs can decide the unaccusativity of intransitive verbs, but the meanings of verbs alone still cannot predict the unaccusativity of intransitive verbs cross-linguistically. Moreover, while the sentential environment can have an impact on the categories of intransitive verbs, previous studies did not investigate how the environment plays a role in the categories. This paper examines this issue from child language acquisition. I select a few sentential environments in the children's corpus of Mandarin and conduct a qualitative analysis that suggests that these sentence environments indeed possess the properties of either category. In a child acquisition experiment, I show that when the category of verbal meanings and sentential environments align, the categorization of verbs is the most obvious and efficient. I introduce the concept of ‘compatibility' to describe this relationship between verb meaning and the sentential environment. These results suggest that speakers can infer the unaccusativity of verbs from a variety of sentence environments in language that may not be directly linked to the concept of unaccusativity, and the concept of ‘compatibility' in language environment is a crucial factor in the categories/categorization of unaccusativity.

Cognitive processes underlie economic relations. In this paper, we develop a conceptual, mathematical, and computational framework for modeling market exchange as a series of dynamically interacting cognitive processes. Specifically, we show how advertisers can build trust and gain confidence in their pricing power to the point that they erode trust and undermine the efficacy of their advertising. Customers conversely orient towards advertisers seeking information or turn away from them as unreliable communicators. These behaviors and the patterns they generate occur inside a state space of unallocated perceived value. They constitute a small subset of the full range of possible strategic and adaptive responses that define cognitive microeconomics.

This paper introduces a novel perspective to significantly mitigate catastrophic forgetting in continuous learning (CL), which emphasizes models' capacity to preserve existing knowledge and assimilate new information. Current replay-based methods treat every task and data sample equally and thus can not fully exploit the potential of the replay buffer.In response, we propose COgnitive REplay (CORE), which draws inspiration from human cognitive review processes. CORE includes two key strategies: Adaptive Quantity Allocation and Quality-Focused Data Selection. The former adaptively modulates the replay buffer allocation for each task based on its forgetting rate, while the latter guarantees the inclusion of representative data that best encapsulates the characteristics of each task within the buffer. Our approach achieves an average accuracy of 37.95\% on split-CIFAR10, surpassing the best baseline method by 6.52\%. Additionally, it significantly enhances the accuracy of the poorest-performing task by 6.30\% compared to the top baseline. Code is available at https://github.com/sterzhang/CORE.

Computational psycholinguistics has traditionally employed a complexity metric called Node Count, which counts the number of syntactic nodes representing syntactic structures and predicts processing costs in human sentence processing. However, Node Count does not dissociate distinct syntactic operations deriving those syntactic structures, so that how much processing cost each syntactic operation induces remains to be investigated. In this paper, we introduce a novel complexity metric dubbed Composition Count, which counts the number of syntactic operations deriving syntactic structures, allowing us to understand the computational system of human sentence processing from the derivational, not representational, perspective. Specifically, employing Combinatory Categorial Grammar (CCG) which is equipped with multiple syntactic operations and thus suitable for the purpose here, we investigate (i) how much distinct syntactic operations of CCG contribute to predicting human reading times, and (ii) whether the same holds across languages. The results demonstrate that distinct syntactic operations of CCG have independent and cross-linguistic contributions to predicting human reading times, while Node Count turns out not to be robust cross-linguistically. In conclusion, these results strongly suggest the importance of Composition Count to dissociate distinct syntactic operations, not whole syntactic representations, and understand the computational system of human sentence processing.

To fully understand human memory, it is necessary to understand its lifespan development. However, memory assessments often rely on significantly different methodologies for different age groups, and their results are typically not directly comparable. In this paper, we present a quantitative assessment of memory function spanning an age range of five to 85 years that is based on a model-based memory assessment. This approach yields a uniform metric that is directly interpretable and can be compared across different tasks and materials that are appropriate for different age groups. The results show a robust U-shape function, with long-term memory function at age 5 being comparable to that of cognitively impaired elderly individuals. These results and the method utilized could provide a new foundation for future studies on memory development across life stages.

Philosophers have long been arguing the precise semantics of different deontic terms within normative statements. However, little research has been done on the human reasoning side of understanding such terms. In this paper, we propose a normative scheme with bitstring semantics that is expressive enough to cover the basic normative concepts in most mainstream schemes proposed in deontic logic research. Even though further confirmation is needed, our explorative experiments on human deontic reasoning have shown results that are consistent with our proposed scheme.

In this paper, we explored the attribution of causal responsibility in a causal chain of events, where an agent A instructs an intermediate agent B to execute some harmful action which leads to a bad outcome. In Study 1, participants judged B to be more causally responsible, more blameworthy, and more deserving of punishment than A. In Study 2, we explored the effect of proximity on judgments of the two agents by adding a third, subsequent contributing cause, such that B's action no longer directly caused the final outcome. Participants judged both agents A and B to be less causally responsible and deserving of punishment (but not less blameworthy) when they were less proximal to the outcome, and there were no differences in judgments between the two agents. In Study 3, we varied whether each of the two agents (A and B) intended for the final outcome to occur. We find an interaction between role and intent, where participants only mitigated judgments for A when A did not intend for the outcome to occur – regardless of B's intent. We discuss possible explanations for our findings and its implications for moral and legal decision-making.

Reservoir computing (RC) offers distinct advantages in extracting spatiotemporal information with low training costs by separating recurrent neural networks into a fixed network with recurrent connections. The quality of the fixed network, known as the reservoir, plays a pivotal role in the performance of the RC system. Our work aims to provide a unified synaptic development framework for RC, constructing a more biologically plausible reservoir to model and understand the neural networks development within the human brain. In this paper, we propose an Autonomous Synaptic Development Reservoir Computing model (ASD-RC) based on an adaptive network of phase oscillators. The reservoir autonomously adjusts the distribution of connection weights in response to external stimuli, forming a task-specific structure. Through experiments and theoretical analyses, we demonstrate that ASD-RC can emulate various synaptic development rules of biological neural networks in \textit{vivo}, including the Hebbian rule and STDP. Furthermore, experiments reveal that combining different development rules can enhance performance on prediction tasks compared to using a single development rule, showcasing the emergence and effects of synergistic development that improve information processing capacity.

Computational neuromodeling methods for evaluating representational dynamics involve intricate analysis choices at every stage of the analysis pipeline. Analysis choices for data processing pipelines are generally chosen based upon end to end accuracy metrics and corresponding performance metrics. Psychology research has recently begun to acknowledge the importance of controlling for potential bias introduced by degrees of freedom in data analysis, with specification curve analysis introduced as a principled method for correcting for such biases. In this paper, we conduct a specification curve analysis (SCA) for representational similarity analysis pipelines reported in the literature for fMRI and EEG datasets, respectively. We show that EEG-based RSA analyses are relatively robust to alternative specifications but that fMRI based analyses are not. Using a novel decision-tree analysis to supplement SCA, we present a potentially more robust pipeline for such analyses.

In this paper, we test the effect of manipulating discrimination difficulty on subsequent generalization of learning and in particular, on the peak shift effect. Participants learned a discrimination where one stimulus led to an outcome (S+) and another stimulus led to no outcome (S-). Difficulty was manipulated by varying the degree of similarity between the S+ and S- across groups (easy/medium/hard). In contrast to similar studies in animals, we found that increasing the difficulty of the discrimination resulted in less peak shift. Using a hierarchical mixture model, we characterize the effects of discrimination difficulty on relational- and similarity based responding, and show for the first time, a similar mixture of responding on stimulus identification gradients. We conclude that peak shift on generalization and identification measures can be explained by mixtures of participants responding in different ways.

Advancements in deep neural networks have led to significant progress in computer vision and natural language processing. These networks, trained on real-world stimuli, develop high-level feature representations of stimuli. It is hypothesized that these representations, stemming from different inputs, should converge into similar conceptual systems, as they reflect various perspectives of the same underlying reality. This paper examines the degree to which different conceptual systems can be aligned in an unsupervised manner, using feature-based representations from deep neural networks. Our investigation centers on the alignment between the image and word representations produced by diverse neural networks, emphasizing those trained via self-supervised learning methods. Subsequently, to probe comparable alignment patterns in human learning, we extend this examination to models trained on developmental headcam data from children. Our findings reveal a more pronounced alignment in models trained through self-supervised learning compared to supervised learning, effectively uncovering higher-level structural connections among categories. However, this alignment was notably absent in models trained with limited developmental headcam data, suggesting more data, more inductive biases, or more supervision are needed to establish alignment from realistic input.

Studies of meaning in human and primate communication face, in principle, similar methodological problems. In both cases, meaning is not observable directly, but must be inferred from more indirect sources, such as directly observable behavior. Recent work in probabilistic cognitive modeling of language use has therefore developed methods of inferring latent se- mantic meaning through the lens of a probabilistic model of language use. In this paper, we explore how to adapt such an approach for insightful investigations of primate communication. Towards this end, we develop a suitable probabilistic model of processes that generate communicative behavior by making use of functionally specified latent meaning representations. As a proof of concept, we apply this model to a rich, annotated data set of orangutan communicative dyadic interaction and conclude that explicit probabilistic modeling can provide additional insights for the study of animal communication pertaining to the context-dependent nature of signals and the gradual evolution of human communication systems.

There has been exciting recent progress in computational modeling of moral cognition. Work in this area tends to describe the cognitive mechanisms of human moral judgment using symbolic models, which are interpretable and written in terms of representations that carry meaning. However, these models fall short of capturing the full human capacity to make moral judgments in that they fail to process natural language inputs but instead rely on formal problem specifications. The inability to interface with natural language also limits the usefulness of symbolic models. Meanwhile, there have been steady advances in conversational AI systems built using large language models (LLMs) that interface with natural language. However, these systems fall short as models of human reasoning, particularly in the domain of morality. In this paper we explore the possibility of building neuro-symbolic models of human moral cognition that use the strengths of LLMs to interface with natural language (specifically, to extract morally relevant features from it) and the strengths of symbolic approaches to reason over representations. Our goal is to construct a model of human moral cognition that interfaces with natural language, predicts human moral judgment with high accuracy, and does so in a way that is transparent and interpretable.

In this paper we investigate whether subject doubling in French is affected by the Uniform Information Density (UID) principle, which states that speakers prefer language encoding that minimizes fluctuations in information density. We show that, other factors being controlled, speakers are more likely to double the NP subject when it has a high surprisal, thus providing further empirical evidence to the UID principle which predicts a surprisal-redundancy trade-off as a property of natural languages. We argue for the importance of employing GPT-2 to investigate complex linguistic phenomena such as subject doubling, as it enables the estimation of subject surprisal by considering a rather large conversational context, a task made possible by powerful language models that incorporate linguistic knowledge through pre-training on extensive datasets.

Helping is a universal human behavior, and is a core aspect of a functioning society. However, the decision to provide help, and what type of help to provide, is a complex cognitive calculation that weights many costs and benefits simultaneously. In this paper, we explore how various costs influence the moment-to-moment decision to help in a simple video game. Participants were paired with another human participant and were asked to make repeated decisions that could benefit either themselves or their partner. Several preregistered manipulations altered the cost each person paid for actions in the environment, the intrinsic resource capacity of individuals to perform the task, the visibility of the other player's score, and the affordances within the environment for helping. The results give novel insight into the cost-benefit analyses that people apply when providing help, and highlight the role of reciprocity in influencing helping decisions.

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.

This paper uses phylogenetic modeling to investigate the evolutionary mechanisms responsible for the maintenance of sound symbolism in the world's languages. Applying our model to sound-meaning correspondences reported in the literature, we find that many previously established associations are weaker than expected when analyzed using our framework. This is possibly because certain sound-meaning associations are artifacts of slow-changing vocabulary items rather than specific preferences for certain sounds in words with certain meanings. For sound-meaning associations for which we find evidence, the maintenance of sound symbolism appears to be due to a tendency to preserve words in certain meanings if certain sounds are present.

In this paper, we report three reading time (RT) experiments (one using self-paced reading and two using A-Maze) that tested the cognitive mechanisms underlying the processing of classifier-noun dependencies in Mandarin Chinese (MC). We leveraged prenominal relative clauses and the contrast between general and specific classifiers in MC, which offered a good testing ground for existing theories of sentence processing. Results from the A-Maze experiments showed both locality and expectation effects. More importantly, we observed an interaction between locality and expectation in the way of Information Locality (Futrell, 2019; Futrell, Gibson, & Levy, 2020): Expectation-driven facilitation was highly constrained by locality effects. To capture the results, we implemented a resource-rational Lossy-Context Surprisal model (Hahn et al., 2022) for MC, which successfully replicated the key patterns in the A-Maze experiments.

Humans rely on our social networks to make more accurate inferences about the world. Yet it remains unclear how those inferences are shaped by the medium through which information is exchanged and beliefs are shared. In this paper, we report two experiments where participants (N=645) were asked to make inferences about an unknown probability distribution based on limited private observations. They exchanged messages with neighbors in a small social network and were asked to update their beliefs over repeated rounds. We compared three conditions: a unidirectional message medium, a constrained slider medium, and an interactive chat. All groups were able to converge toward more accurate inferences, but their convergence rates varied across conditions in ways not well-captured by common models. We argue that computational models of collective behavior must move beyond the assumption of direct belief transmission to capture the complexities of sharing information through natural language.

This paper tests the similarity in neural responses across repeated words and morphosyntactic features both within and between two languages. Prior work using priming has revealed robust cross-linguistic lexical effects and effects for shared grammatical form, such as argument structure; these methods have been less successful when applied to morphosyntactic features. Combining machine-learning based neural decoding with EEG data collected from Korean-English bilinguals we, first, replicate prior work showing successful classification of lexical items from EEG signals. We then extend this to demonstrate successful classification of morphosyntactic features of number and tense. Finally, we find that EEG decoding in one language does not successfully generalize to another, even when temporal differences are considered. Taken together, these results point to stable EEG representations for lexical items and morphosyntactic features, but suggest that these representations are different between the two languages investigated here.

Recent years have seen an explosion of theoretical interest, as well as increasingly fraught real-world debate, around issues to do with discourse participation. For example, marginalised groups may find themselves excluded or may exclude themselves from discourse contexts that are hostile. This not only has ethical implications, but likely impacts epistemic outcomes. The nature and scale of such outcomes remain difficult to estimate in practice. In this paper, we use agent-based modelling to explore the implications of a tendency toward `agreeableness' whereby agents might shape their communication so as to reduce direct conflict. Our simulations show that even mild tendencies to avoid disagreement can have significant consequences for information exchange and the resultant beliefs within a population.

Contemporary formal models aim to capture group polarization as the result of deliberation between rational agents. Paradigmatic models do, however, rely on rather limited agents, casting doubt on the conclusion that group polarization can be rationally reconstructed. In this paper, we use a recently developed Bayesian agent-based model of deliberation to investigate this conclusion. This model avoids problems we identify in a group of influential Bayesian polarization models. Our case study shows that a simple mechanism produces realistic patterns of group polarization: limited exchange of evidence across a sparse social network. We reflect on what our results mean for our formal understanding of rational group polarization.

This study investigates cue-based memory retrieval during sentence processing. Cue-based retrieval theories argue that the parser uses lexical and structural information as retrieval cues to retrieve items from memory. Evidence for cue-based memory retrieval comes from research showing that non-target representations matching retrieval cues interfere with target retrieval. However, the degree of susceptibility to this similarity-based interference has been debated, having led to the development of different computational models. This study focuses on two cue-based models and tests their predictions in two experiments. The results suggested similarity-based interference, but its patterns were not fully compatible with these models. To reconcile these findings within the framework of cue-based memory retrieval, this paper presents a model that assigns substantial weight to the structure-based cue and incorporates the notions of initial retrieval and revision. Results from simulations indicate that the model incorporating these assumptions provides a better fit to the observed data.

This paper discusses an experiment conducted with two groups of participants, composed of musicians and non-musicians, in order to investigate the impact that the speed of a sound sequence and the interval size which selected sounds are played on the grouping of sounds into perceptual streams. Significant differences were observed between musicians and non-musicians with respect to the threshold sequence speed at which the sequence was split into two streams. In modern psychoacoustic studies, the qualifying criteria for listeners usually include otologically normal hearing (verified by audiometric test) and age. The differences in the results for the two groups suggest that the musical background of the participating listeners may be a vital factor. The criterion of musical education should be taken into account during experiments so that the results obtained are reliable, uniform and free from interpretive errors.

To date, no account of lie-truth judgement formation has been capable of explaining how core cognitive mechanisms such as memory encoding and retrieval are employed to reach a judgement of either truth or lie. One account, the Adaptive Lie Detector theory (ALIED: Street, Bischof, Vadillo, & Kingstone, 2016) is sufficiently well defined that its assumptions may be implemented in a computational model. In this paper we describe our attempt to ground ALIED in the representations and mechanisms of the ACT-R cognitive architecture and then test the model by comparing it to human data from an experiment conducted by Street et al. (2016). The model provides a close fit to the human data and a plausible mechanistic account of how specific and general information are integrated in the formation of truth-lie judgements.

One hallmark of human language is its combinatoriality---reusing a relatively small inventory of building blocks to create a far larger inventory of increasingly complex structures. In this paper, we explore the idea that combinatoriality in language reflects a human inductive bias toward representational efficiency in symbol systems. We develop a computational framework for discovering structure in a writing system. Built on top of state-of-the-art library learning and program synthesis techniques, our computational framework discovers known linguistic structures in the Chinese writing system and reveals how the system evolves towards simplification under pressures for representational efficiency. We demonstrate how a library learning approach, utilizing learned abstractions and compression, may help reveal the fundamental computational principles that underlie the creation of combinatorial structures in human cognition, and offer broader insights into the evolution of efficient communication systems.

Understanding referring expression generation has long been of interest to psycholinguistics, pragmatics, and sociolinguistics. Experimental data in the former two has shown that referring expression generation is modulated by both pragmatic and cognitive considerations, and the latter suggests that referring expressions have social meaning beyond their literal referential utility. This project integrates these three accounts by extending Burnett (2017)'s socially-enriched implementation of the Rational Speech Act (RSA) framework to account for variation in referring expressions used to denote transgender women in two politically opposed media corpora. Our findings highlight the utility of the RSA framework in explaining socially-modulated variation while also accounting for pragmatic and cognitive considerations. Finally, this paper contributes to growing literatures that address the relationship between (alt-)right ideologies about gender and language by highlighting the use of bioessentialist language such as 'biological male' in the propagation of anti-trans rhetoric in the United States.

Humans learn and generate languages, music, games, and seemingly limitless varieties of other structures across domains. Unlike many AI systems, we often do so from little data. How do we learn such large varieties of richly structured representations so efficiently? One possibility is that people ``learn by programming,'' synthesizing data-generating algorithms to explain what we observe. We examine the nature and origins of this learning mechanism in adults, children, and nonhuman primates (macaque monkeys), using a highly unconstrained sequence prediction task. Although adults and children quickly learn many richly structured sequences, monkeys learn only the simplest sequences (e.g. lines). We test multiple learning models, finding that adults are best explained by a ``Language of Thought''-like program-learning model and monkeys by a simpler extrapolation strategy. Children exhibit varied learning strategies but are best fit in aggregate by an intermediately expressive model. Paper available at https://sites.google.com/view/patternlearning.

Children's acquisition of emotion words is a topic of interest across the domains of emotion research, early language acquisition, and social development. Prior research has shown a slow, gradual process of emotion word acquisition during ages 2–5 years and beyond. However, this research has used tasks that are demanding for young children, such as asking them to label or sort facial expressions. Here, in a preregistered study, we used a child-friendly looking-time paradigm---the "looking-while-listening'' task---to assess children's understanding of four emotion words, "happy,'' "sad,'' "angry,'' and "scared.'' We presented 64 two-year-olds (Mean age = 2.51, range: 2.00-2.97) with facial expressions and measured their preferential looking to the target face upon hearing an emotion word. Both younger and older two-year-olds showed above-chance performance when the target and distractor faces differed in valence (e.g., happy vs. sad). When the target and distractor faces were of the same valence (e.g., angry vs. sad), younger two-year-olds' results did not reach significance, but older two-year-olds' results were significantly above chance. These results suggest that within-valence mappings of emotion words to facial expressions emerges at least during the second half of age two. Full paper here: [https://osf.io/preprints/psyarxiv/nsq5t].

Most current research on commonsense question answering (CQA) has focused on proposing different techniques in natural language processing and text information retrieval. However, for human cognition, retrieving and organizing desired answers from text knowledge related to commonsense questions is far less intuitive and comprehensive than it is when using multi-modal knowledge, such as related images and videos. Motivated by this, we propose a framework for trying the acquisition of diverse modal information, and embedding and integrating it into CQA tasks, further improving the performance and user experience. Specifically, this paper proposes the integration of multi-modal knowledge, including images, image description statements, image scene graphs, and knowledge sub-graphs, into a CQA system. It introduces a parallel embedding technique for this multi-modal knowledge and employs an alignment-interaction-fusion mechanism to facilitate the seamless integration of this multi-modal knowledge. Through extensive experiments, the effectiveness and superiority of our proposed method are demonstrated.