Search

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.

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.

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.

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.

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.

The lexica of natural languages are ambiguous, but the de- gree of ambiguity is unequal between words. Some words have more meanings than others. However, the exact prop- erties that favor some words over others when acquiring a new meaning are not very well understood. In recent years, several studies suggested that some words gain more meanings than others based on selection for efficient communication, which could explain the correlation between meaning and frequency discovered by Zipf (Piantadosi, Tily, & Gibson, 2012; Gib- son et al., 2019). The object of this study is to assess the role of selection in the meaning-frequency correlation using a neu- tral model that yields a meaning-frequency correlation without selection pressures. We provide a model where words gain additional meanings through reuse. In the neutral model pre- sented in this paper, words are chosen to be reused at random, independently of their frequency, hence there is no selection mechanism favoring efficient communication. Unlike previous attempts to introduce null models of the meaning-frequency correlation (Caplan, Kodner, & Yang, 2020; Trott & Bergen, 2020), it truly does not rely on selection for frequency. We show that statistical regularities related to ambiguity, such as Zipf’s meaning-frequency correlation, can arise in conditions when words are not undergoing any selective pressures. This model has the additional property of matching word frequency distributions of natural languages. It can provide the baseline against which the presence of selection for efficient communi- cation in natural languages can be assessed.

Embodied simulation hypothesis supposes that language processing involves the activation of perceptual-motor systems to recreate the described scene (Bergen, 2012, 2019). The paper investigates whether and how adolescent second language (L2) learners’ online processing of prepositions engages mental simulation. Specifically, the study examines whether any observed mental simulation effect was modulated by prepositions, abstractness of senses, and Stimulus Onset Asynchrony (SOA). 40 Chinese adolescents completed a diagram-picture matching task followed by a semantic priming task in English, where participants saw a diagrammatic prime and made phrasal acceptability judgement. Results showed a compatibility effect of schematic diagrams on adolescent L2 English learners’ accuracy rates (ARs) of processing prepositional phrases (PPs), while response times (RTs) results did not reveal mental simulation effects. The findings suggest that schematic diagrams could serve as effective perceptual cues to prime adolescent L2 learners’ processing of schema-compatible English PPs by improving judgement accuracy but not processing speed.

In this paper, we study human sequential behavior by integrating cognitive, evolutionary, and computational approaches. Our work centers around the emergence of shared vocabularies in the Embodied Communication Game (ECG). Here, participant pairs solve a shared task without access to conventional means of communication, enforcing the emergence of a new communication system. This problem is solved typically by negotiating a shared set of sequential signals that acquire meaning through interactions. Individual differences in Personal Need for Structure (PNS) have been found to influence how this process develops. We trained deep neural networks to mimic the emergence of new communicative systems and used hyperparameter optimization to approximate latent human cognitive variables to explain human behavior. We demonstrate that models based on bidirectional LSTM networks are better at capturing human behavior than unidirectional LSTM networks. This suggests that human sequence processing in the ECG is influenced by expected future states. The approximated variables cannot explain the differences in PNS, but we do provide evidence suggesting that random and uncertainty-directed exploration strategies are combined to develop optimal behavior.

Causal perception is a cornerstone of early cognitive development. A large database of research attests to the fact that infant causal perception emerges between 6 and 10 months of age. However, it remains unknown how infants learn about the causal properties of more realistic categories such as people and objects. For example, how do infants learn that people can cause other people to act and behave at a distance, whereas inanimate objects require contact to move and act? One answer to this question is that this knowledge is present from birth or shortly thereafter and is underpinned by core knowledge systems. An alternative perspective maintains that infants acquire this knowledge via domain-general associative learning. The goal of the present paper is to demonstrate that this alternative perspective—implemented in a connectionist computational model—is sufficient to explain infants’ developing knowledge about people and object causal action.

This paper describes the principles of a research programme for cognitive science that exploits recent developments in machine learning (ML) to generate novel hypotheses about the structure of human cognition. Current debate over the interpretability and explainability algorithms usually focuses on the properties of the algorithms themselves in virtue of which they are either interpretable or explainable. However, we argue that there is value in conceptualizing these categories as inherently psychological constructs. Given certain mathematical features of machine learning algorithms – specifically, that many useful ML algorithms are members of Rashomon sets – it is possible to exploit their utility to reason using a principle of parsimony about the inferential structure of certain human cognitive tasks. Algorithms that do something the human mind can do, and are both interpretable and explicable could be, we shall argue, inferential homologues of certain core cognitive processes. We illustrate this proposal with an example drawn from clustering models used in exploratory data analysis, and then conclude with a discussion of some of the philosophical limitations of our proposal.

Humans' capacity for cumulative culture is remarkable: we can build up vast bodies of knowledge over generations. Communication, particularly via language, is a key component of this process. Previous work has described language as enabling posterior passing, where one Bayesian agent transmits a posterior distribution to the next. In practice, we cannot exactly copy our beliefs into the minds of others--we must communicate over the limited channel language provides. In this paper, we analyze cumulative culture as Bayesian reinforcement learning with communication over a rate-limited channel. We implement an agent that solves a crafting task and communicates to the next agent by approximating the optimal rate-distortion trade-off. Our model produces documented effects, such as the benefits of abstraction and selective social learning. It also suggests a new hypothesis: selective social learning can be harmful in tasks where initial exploration is required.

This paper delves into the possible correlations between cognitive impairment due to Alzheimer’s Disease and differences in the ways spatial relations are decoded by groups of individuals with and without AD. We tested participants’ judgments of spatial scenes coded according to intrinsic, relative or undissociated Frames of Reference, ground rotation and use of a locative construction. We found significant differences in the costs of processing spatial information which were considerably higher in elderly and AD groups. On the other hand, judgment ratings reveal that those groups tend to maintain the same Frame of Reference applied by health young individuals to resolve the spatial ambiguity. Furthermore, a general preference for non-relative Frames of Reference was found among participants, all native speakers of Brazilian Portuguese.

A growing body of literature has revealed ordinary causal judgement to be sensitive to normative factors, such that a norm-violating agent is regarded more causal than their non-norm-violating counterpart. In this paper, we explore two competing explanations for this phenomenon: the Responsibility View and the Bias View. The Bias View, but not the Responsibility View, predicts features peripheral to the agent’s responsibility to impact causal attributions. In a series of three preregistered experiments (N = 1162), we present new evidence that the Norm Effect arises from such peripheral features, namely from nonpertinent or entirely silly norm violations. Furthermore, we show that this effect cannot be explained by recourse to the agent’s foreknowledge or desire of the outcome, nor by its foreseeability: the Norm Effect arises even when participants judge the norm-violating agent’s doing as equally foreseeable. This, we argue, provides evidence in favour of the Bias View.

Previous research under the cue-based retrieval framework has assumed that general and discrete retrieval cues, such as [+subject] and [+singular] are employed in selecting a retrieval target during dependency building. However, to explain the effects of semantic compatibility between the target and the head of a dependency, as demonstrated in Cunnings and Sturt (2018), an infinite amount of lexically specific features and retrieval cues are needed. Smith and Vasishth (2020) offered a principled way for feature selection using word embeddings. However, even with a giant corpus, some dependencies are missing. To solve the coverage problem, we leverage pre-trained neural language models (GPT-2) to select features. The metric used in this paper is highly correlated with those in Smith and Vasishth (2020) and can predict the results reported in Cunnings and Sturt (2018). We argue that it the method offers a broader-coverage yet convenient way for feature selection.

Technology to predict responses is a key element in human-to-human messaging that has increasingly been utilized to enable AI-mediated communication. When response suggestions from AI are incorporated into human messaging, it will have an impact on the flow and content of the dialogue. In this paper, we investigated the effect of AI response suggestion sentences used for chat messaging on dialogue flow from two aspects: dialogue act and initiative. Usage rates of response suggestions for different dialogue acts were measured with BLEU scores, and we found that the response suggestions contributed to the establishment of a proper dialogue flow, such as an answer in response to a question. The results of our case study indicated that users who take the initiative in the dialogue tend to utilize response suggestions less frequently. We also found that some written responses were based on the suggested sentence structure but conveyed different messages.

In assessing the sense of agency (SoA) in the field, researchers are often confronted with the limitations of conventional methods (e.g., retrospectivity, task-disruption). Generally, facial analysis offers a promising possibility to overcome these obstacles by directly collecting objective data in real-time and without any need for contact. This paper examines the applicability of face tracking for SoA assessment by investigating whether facial expressions can be used to derive information about the extent to which users feel in control in interactions with technical systems. In a laboratory experiment (N = 27) based on the dot control task, we manipulated SoA via task difficulty and explored the effects on facial expression patterns. Three action units in the region of the mouth were observed to be characteristic facial indicators of SoA. These findings support the suitability of facial expressions for SoA evaluation as well as the potential of face tracking for monitoring SoA in various practical fields of application (e.g., automated driving).

In this paper, we simulated true and false recognition in the Deese/Roediger/McDermott (DRM; Deese, 1959; Roediger & McDermott, 1995) paradigm by incorporating word embeddings derived from distributed semantic models (word2vec) into an instance memory model (MINERVA2). Previously, Arndt and Hirshman (1998) demonstrated that MINERVA2 (Hintzman, 1984) could capture multiple classic false recognition findings with randomly generated word representations. However, as randomized representations deviate systematically from semantic representations learned from the natural language environment, there remains uncertainty about whether MINERVA2 can capture the false memory illusion when scaling up to real-life complexity in word representations. To address this uncertainty, we used word2vec embeddings that are derived from large corpora of natural language instead of randomized representations in MINERVA2. Our results showed that MINERVA2 can still capture the standard true and false recognition, and it can also accommodate the true and false recognition effects of various classic manipulations (e.g., associative strength, number of associates, divided attention, retention interval).

Predictability is a prerequisite for effective human control of artificial intelligence (AI). For example, the inability to predict the malfunctioning of AI impedes timely human intervention. In this paper, we employ a computerized navigation task, a lunar lander game, to investigate empirically how AI's predictability compares to humans' predictability. To this end, we ask participants to guess whether the landings of a spaceship in this game performed by AI and humans will succeed. We show that humans are worse at predicting AI performance than at predicting human performance in this environment. Significantly, participants underestimate the differences in the relative predictability of AI and, at times, overestimate their prediction skills. We link the difference in predictability to differences in the approaches, i.e. different landing patterns, employed by AI and humans to succeed in the task. These results highlight important differences in perception of AI and human with implications for human-computer interaction.

Knowledge Graph Reasoning (KGR) is an effective method to solve the incompleteness and sparsity problems of Knowledge Graph (KG), which infers new knowledge based on existing knowledge. Especially, the Graph Convolution Network (GCN)-based approaches can obtain state-of-the-art effectiveness, but there are still some problems such as weak reasoning ability, incomplete local information acquisition, insufficient attention score, and high learning cost, which lead to limited prediction accuracy. This paper proposes a multi-head self-attention mechanism architecture based on cognitive transfer learning, named CogTrans, to make effective improvements in the above problems. Shaped like a cross, CogTrans horizontally includes intuition and reasoning stages, which can achieve a faster convergence rate and obtain prediction results that are more in line with human intuition. Furthermore, CogTrans longitudinally includes source and target domains, and benefit from transfer learning, it can not only obtain the advantages of the horizontal architecture but also can “draw inferences from one instance”, which is more conducive to realizing the human brain-like reasoning effect of the architecture. Extensive experimental results show that our CogTrans architecture can obtain the most advanced accuracy of current GCN-based methods.

Current theory suggests that heterogeneous metapopulation structures can help foster the diffusion of innovations to solve pressing issues including climate change adaptation and promoting public health. In this paper, we develop an agent-based model of the spread of adaptations in simulated populations with minority-majority metapopulation structure, where subpopulations have different preferences for social interactions (i.e., homophily) and, consequently, learn deferentially from their own group. In our simulations, minority-majority-structured populations with moderate degrees of in-group preference better spread and maintained an adaptation compared to populations with more equal-sized groups and weak homophily. Minority groups act as incubators for novel adaptations, while majority groups act as reservoirs for the adaptation once it has spread widely. This suggests that population structure with in-group preference could promote the maintenance of novel adaptations.

Great storytelling takes us on a journey the way ordinary reality rarely does. But what exactly do we mean by a "journey"? Recently, literary theorist Kukkonen (2014) proposed that storytelling is "probability design": the art of giving an audience pieces of information bit by bit, to craft the *journey of their changing beliefs* about the fictional world. A good "probability design" choreographs a delicate dance of certainty and surprise in the reader's mind as the story unfolds from beginning to end. In this paper, we computationally model this conception of storytelling. Building on the classic Bayesian inverse planning model of human social cognition, we treat storytelling as *inverse* inverse planning: the task of choosing actions to manipulate an inverse planner's inferences, and therefore a human audience's beliefs. First, we use an inverse inverse planner to depict social and physical situations, and present behavioral studies indicating that inverse inverse planning produces more expressive behavior than ordinary "naïve planning." Then, through a series of examples, we demonstrate how inverse inverse planning captures many storytelling elements from first principles: character, narrative arcs, plot twists, irony, flashbacks, and deus ex machina are all naturally encoded in the flexible language of probability design.

We introduce the Cognitive Science Community to the challenges faced in human-in-the-loop (HiTL) decision-making for complex and dynamic industrial chemical processes. A microworld is presented which includes advanced dynamic simulations, an optimal control-based decision-support algorithm and a preliminary human machine interface. Three example applications are discussed, including decision-making in plant startup and shutdown, shared control using human and HiTL supervisory Model Predictive Control (MPC), and a pilot study of operator eye-tracking during a complex startup task. The paper also discusses ongoing and future experimental efforts highlighting the utility of the microworld in understanding human cognition and designing advanced operator training software for various process operation related complex tasks.

Dialogue State Tracking (DST), a key component of task-oriented dialogue systems, tracks user intentions by predicting the values of pre-defined slots in a dialogue. Existing works on DST treat all slots indiscriminately and independently, which ignores the relationships across slots and limits the learning of hard slots (those slots are hard to be predicted correctly), eventually hurting overall performance. In this paper, we propose an iterative learning framework, i.e. iteratively updates the dialogue state with confident slots, to alleviate the aforementioned problem. Specifically, we first employ a scorer to estimate slot confidence. Then, those slots with high confidence are utilized to update the previous state, and the updated state will be fed into the scorer again to recalculate the confidence. In the last iteration, we apply an objective with the confidence penalty to focus on the hard slots. The experiments show that our approach outperforms existing methods on popular datasets.

The Interface Transparency Thesis (ITT) proposes that people tend to use a canonical interpretation of linguistic expressions, even when this interpretation is sub-optimal for the task at hand. The current paper sought to investigate this claim further by adding a time-pressure manipulation to a quantified sentence verification task and analyzing the results through a computational model of decision-making. The results indicate that time pressure -while effectively changing behavioral responses- does not alter cognitive processes associated with quantifier verification, thus supporting the ITT.

The goal of this paper is to analyze the notion of “compositionality” and its use in contemporary cognitive science. We argue that the concept has undergone a series of apparently minor definitional shifts since its initial inception within the field of philosophy of language (as indicated by Janssen, 2012). These changes result in a divergent meaning of the term as it is used in the emergent communication and language evolution communities. Hitherto, this fact has been underappreciated, whereas we believe that it has significant implications for understanding the nature of syntax and the sources of linguistic and conceptual structure. We argue that originally, “compositionality” was understood as pertaining primarily to the process of understanding a compound utterance by a hearer. Other scholars, however, take it to be a prerequisite of the structure of languages. In all contexts, investigating compositionality of natural languages requires making a host of idealizing assumptions. For this reason, we propose to understand compositionality as just one idealized principle influencing the construction of compound expressions in language, necessarily complemented by other principles. This allows for appreciating the structural entanglements permeating natural language and opens new avenues for accounting for them.

Previous computational models of early language acquisition have shown how linguistic structure of speech can be acquired using auditory or audiovisual learning mechanisms. However, real infants have sustained access to both uni- and multimodal sensory experiences. Therefore, it is of interest how the uni- and multimodal learning mechanisms could operate in concert, and how their interplay might affect the acquisition dynamics of different linguistic representations. This paper explores these questions with a computational model capable of simultaneous auditory and audiovisual learning from speech and images. We study how the model’s latent representations reflect phonemic, lexical, and semantic knowledge as a function of language experience. We also test how the findings vary with differential emphasis on the two learning mechanisms. As a result, we find phonemic learning always starting to emerge before lexical learning, followed by semantics. However, there is also notable overlap in their development. The same pattern emerges irrespectively of the emphasis on auditory or audiovisual learning. The result illustrates how the acquisition dynamics of linguistic representations are decoupled from the primary learning objectives (mechanisms) of the learner, and how the emergence of phonemes and words can be facilitated by both auditory and audiovisual learning in a synergetic manner.

Helping is an inherently cooperative behavior, but the cognitive mechanisms underlying this behavior remain relatively underexplored. In this paper, we introduce a novel gamified paradigm for understanding a variety of cognitive behaviors associated with helping. Principals are assigned secret goals in a block-based grid (e.g., move all blue blocks to room C), and helpers can either pass their turn or make a move that could help the principal. We show that principals make useful and pragmatic first moves and helpers accumulate evidence over time before initiating a helpful move. We also introduce a preliminary set of computational models based on recursive pragmatic inference and utility maximization that attempt to account for these behavioral findings.

Converging evidence suggests that natural language meaning systems are efficient by jointly maximizing cognitive simplicity and communicative informativeness. Comparatively less is known about how languages might optimize over time for communicative efficiency. Our goal in this paper is to use minimal dynamic models to give a high-level description of the evolution of efficient meaning systems. To do this, we provide a model of emergent communication combining evolutionary game theory with a recent information theoretic account of efficiency in semantic systems. We perform simulations of adaptive dynamics requiring minimal assumptions about agents’ cognitive resources, and observe that emergent languages converge near the achievable bounds of efficient compression. This unifies existing accounts of communicative efficiency with minimalist accounts of how meaning can emerge ex nihilo.

ChatGPT is a large language model developed by OpenAI as a conversational agent. ChatGPT was trained on data generated by humans and by receiving human feedback. This training process results in a bias toward humans' traits and preferences. In this paper, we stress multiple biases of ChatGPT, and show that its responses demonstrate many human traits. We Begin by showing a very high correlation between the frequency of digits generated by ChatGPT and humans' favorite numbers, with the most frequent digit generated by ChatGPT, matching humans' most favorable number, 7. We continue by showing that ChatGPT's responses in several social experiments are much closer to those of humans' than to those of fully rational agents. Finally, we show that several cognitive biases, known in humans, are also present in ChatGPT's responses.

A positive disjunction (A or B) in natural language typically receives two logical interpretations: inclusive disjunction vs. exclusive disjunction. Within the Gricean paradigm, the inclusive interpretation of disjunction is considered its basic meaning while the exclusive interpretation is caused by Gricean pragmatic reasoning, called exclusivity implicatures. However, pragmatic reasoning is not the only source of exclusivity. In this paper we quantify the role of prior beliefs regarding the compatibility of the two propositions (A, B) in a disjunction (A or B). In two experiments, we tested the impact of prior compatibility on exclusivity implication generation using examples from the literature. Our results show that prior beliefs about compatibility influence the overall exclusivity of a disjunction, but that significant exclusivity remains to be explained by other sources. We conclude that most exclusivity implications are composite implications that feed from minimally two sources: prior beliefs about compatibility and Gricean pragmatic reasoning.

Face stereotypes are prevalent and consequential. In this paper, we investigate an experience sampling account of face stereotyping. In two experiments, we show that, in an experience-sampling-based Trust Game, participants in the role of an investor were much more likely to play the game with trustees with trustworthy faces than those with untrustworthy faces (Experiment 1). Crucially, this endogenous experience sampling bias has amplified the behavioral consequences of the facial trustworthiness stereotype. In contrast, when the information was directly described with no sampling constraint for the participants, the face stereotype had a very limited effect on investment decisions (Experiment 2). We conclude by suggesting that the description-experience gap paves a promising avenue for studying sampling-based accounts in social cognition and behavior.

Reference to events using pronouns like it or demonstratives like that has been difficult to study, because unlike in reference to people or objects, the ground truth of interpretation is harder to establish. In this paper, we introduce a new task to understand the roles of three parameters in event reference resolution: The referential expressions themselves, sentential aspect, and discourse structure. We find that different referring expressions reliably refer to specific parts of a discourse, and confirm previous findings that sentential aspect influences referential accessibility; that the structure of a discourse itself has a major effect on reference resolution, and most notably that, contrary to predictions in the literature, some events that are not on the right-frontier of a discourse are nevertheless available for reference. Our findings contribute to a growing literature on anaphor resolution that finds that the parameter structure that determines reference resolution is multifaceted, but predictable.

Ontology is the basis of knowledge representation, and it is necessary to translate ontologies that are normally expressed in English into other languages in order to achieve exchange across languages. Building a domain-specific translation system is essential due to the extremely focused words used and the inadequacy of contextual information. In this paper, we introduce disentangled representations under cross-lingual agreement to alleviate the aforementioned issues. We introduce semantic and language representations and integrate extra losses to induce disentangled representations that capture different information. To reduce the gap between the ontology label and the hypothesis generated by the translation model, we further integrate adversarial learning. In order to guide the generation of translation candidates, the semantic matching strategy is incorporated into the decoding phase. Experiments on the four English-to-German ontologies of different domains show that the proposed method achieves improvements over the baselines.

When assessing children in laboratory experiments, the measured responses also contain task-irrelevant participant-level variability (“noise”) and measurement noise. Since experimental data are used to make inferences of development of cognitive capabilities with age, it is important to know if reliability of the used measurements depends on child age. Any systematic age-dependent changes in reliability could result in misleading developmental trajectories, as lower reliability will necessarily result in smaller effect sizes. This paper examines age-dependency of task-independent measurement variability in early childhood (3–40 months) by analyzing two large-scale datasets of participant-level experimental responses: the ManyBabies infant-directed speech preference (MB-IDS) dataset and a saccadic reaction time (SRT) dataset collected from rural South Africa. Analysis of participant- and study-level data reveals that MB-IDS shows comparable reliability across the included age range. In contrast, SRTs reflect systematically increasing measurement consistency with increasing age. Potential reasons and implications of this divergence are briefly discussed.

How responsible someone is for an outcome depends on what causal role their actions played, and what those actions reveal about their mental states, such as their intentions. In this paper, we develop a computational account of responsibility attribution that integrates these two cognitive processes: causal attribution and mental state inference. Our model makes use of a shared generative planning algorithm assumed to approximate people's intuitive theory of mind about others' behavior. We test our model on a variety of animated social scenarios in two experiments. Experiment 1 features simple cases of helping and hindering. Experiment 2 features more complex interactions that require recursive reasoning, including cases where one agent affects another by merely signaling their intentions without physically acting on the world. Across both experiments, our model accurately captures participants' counterfactual simulations and intention inferences, and establishes that these two factors together explain responsibility judgments.

Words express various sensory information to various degrees. Norming studies have collected native speakers’ subjective perceptual strength ratings for numerous words in several languages. This paper presents perceptual strength norms for 510 Japanese words, including iconic lexemes called ideophones. The newly collected norms replicated some previous findings, such as visual dominance, olfactory inferiority, and the correlation between overall perceptual strength and iconicity. A systematic comparison between the Japanese and English perceptual strength norms further revealed that Japanese onomatopoeic ideophones tend to be more multisensory than their English equivalents and that Japanese words in general tend to encode more interoceptive information than English words. These findings suggest the usefulness of norming data in typological discussions on lexical semantics.

Working Memory (WM) plays a key role in natural language understanding and generation. To enable a human-like breadth and flexibility of language understanding and generation capabilities, cognitive systems for language-capable robots should feature a human-like WM system in a similarly central role. However, it is still quite unclear how robotic WM should be designed, as a variety of models of human WM have been proposed in cognitive psychology. Moreover, human reliance on WM during language production is sometimes to help the speaker rather than to help hearers. Thus, it is unclear whether different robotic WM systems might harm certain dimensions of interaction for the sake of the robot speaker's ostensible ease of cognitive processing. In this paper we demonstrate how different models of human WM can be implemented into robot cognitive architectures. Our results suggest that these models can be effective in terms of accuracy, perceived naturalness, and perceived human-likeness.

Gaze is cardinal for interaction since it allows us to visually interact with the environment and understand the attention and intent of others. Gaze also plays a critical role in HRI tasks such as object recognition and manipulation, as a robot can use gaze to direct its attention to specific objects or areas of interest in its environment. In this study, we automate gaze estimation for various types of gaze behaviours (such as turn taking, joint attention, gaze following, gaze aversion and mutual attention) in a natural dyadic interaction using videos without wearable cameras or eye trackers that are implementable on a robot. There is no single dataset that covers all of the different gaze and scene combinations that is address in this paper. We propose a model that utilizes the manual annotation of gaze targets in a natural dialogue setting and generate simultaneous gaze prediction for both parties in the video, along with attention heatmaps that provide exclusive information of the target object-of-interest in the scene, by also providing out-of-scene gaze predictions. Our model performs better than the baseline methods that currently exist and the data that was generated is available for different categories of gaze

This paper presents a method for reproducing a simple central pattern generator (CPG) using a modified Echo State Network (ESN). Conventionally, the dynamical reservoir needs to be damped to stabilize and preserve memory. However, we find that a reservoir that develops oscillatory activity without any external excitation can mimic the behaviour of a simple CPG in biological systems. We define the specific neuron ensemble required for generating oscillations in the reservoir and demonstrate how adjustments to the leaking rate, spectral radius, topology, and population size can increase the probability of reproducing these oscillations. The results of the experiments, conducted on the time series simulation tasks, demonstrate that the ESN is able to generate the desired waveform without any input. This approach offers a promising solution for the development of bio-inspired controllers for robotic systems.

Active learning techniques are educational tools that engage students with hands-on and interactive learning experiences. However, despite proven pedagogical advantages, active learning activities can be difficult to implement in traditional university classroom setups. We present a newly developed template for an educational escape room that can be used in a class of any size or discipline, does not require any special equipment, and is supplemented by free and accessible technology. We describe implementation of the escape room template in a plot-driven, dynamic sleep laboratory escape room, using puzzles to reinforce lecture content about circadian rhythms and sleep cycles in a large introductory neuroscience course of 300 students. This proof-of-concept innovative educational escape room framework combines escape room principles, technology, and physical paper elements to provide a new way of engaging students with complex course concepts in an interactive way, connecting best practices in education, applied neuroscience, and computer science.

Contractualist moral theories hold that morality is primarily about acting according to what would be agreed by rational agents. The contractualist tradition has received little attention in empirical work compared to competing normative ethical theories that are primarily concerned with rules (deontology) and consequences (consequentialism). Recent theoretical developments and empirical results suggest that agreement-based considerations and forms of reasoning could play a central role in moral judgment and decision making. However, the computational foundations of moral contractualism have scarcely been explored. In this paper, we present a flexible experimental paradigm to study contractualist moral behavior in an incentivized setting. We then develop a simple computational model of agreement-based moral decision making that captures participants’ behavior in the task. Results suggest that agreement-based cognitive processes are required to explain the nuances of observed behavioral patterns. Contractualism could offer a fruitful framework to understand how humans coordinate their behavior in morally ambiguous contexts.

What determines children's production of syntactic alternations? This study takes up this question with the double PP construction in English as the test case (e.g., write [PP1 on the paper] [PP2 with this new pen]). Leveraging data of spontaneous child-parent interactions, we investigate the roles of dependency length and parent input frequency, with the latter being operationalized as lexical frequency and contextual predictability. We found that when child and parent data was combined, all three factors turned out to have significant predictive power, with dependency length having the most pronounced role. Results from the developmental trajectory of DLM as well as logistic regression analysis suggest that child production of constituent ordering preferences starts becoming more parent-like after the age of 30 months.

The investigation of reasoning involves the study of what is rational as well as the empirical study of human reasoning. We are interested in rational answers to reasoning problems but also in the way human reasoning works. While the first aspect is typically covered by logic or formal epistemology, the second is a matter of empirical psychological research. However, many approaches relate both aspects. This paper discusses these hybrid approaches, their motivation, general criticism that has been raised against them as well as the kind of philosophical commitment behind choosing such kind of framework.

While the literature has mostly focused on the goal of information transfer, many linguistic phenomena only make sense in the light of further goals pursued by the agent. One such phenomenon is polite language use. In this paper, we propose a new model of polite language production. We suggest that patterns characteristic of polite language, e.g., indirectness, emerge from a tension between two goals: on the one hand, being sufficiently truthful and informative, and on the other hand, being kind to the listener. To capture these pressures, we introduce a novel model of probabilistic language production which combines a strategic choice of content selection with the usual pragmatic choice of content expression. We fit our model to empirical data from a previous experiment using a bespoke Bayesian model. We quantitatively compare our model to a previous model of politeness and discuss some ways in which our account is simpler, more general and better accounts for empirical data and theoretical considerations.

This paper explores the hypothesis of the historical evolution of Semitic morphology from biconsonantal (2C) etymons, to triconsonantal (3C) roots, which make up the majority of words in Biblical Hebrew as well as in other Semitic languages such as modern Hebrew, Arabic, etc. The rules for reducing the 3C roots to their 2C etymons are provided in detail. We use BHSA, a manually annotated corpus of the Hebrew Bible, and Word2Vec, a method for converting words to a vector representing their semantic meaning, to study the hypothesis of evolution from 2C etymons to 3C roots in biblical Hebrew. Namely, we show that words in Hebrew with different roots, that might have originated from the same 2C etymons form a denser cluster than random sets of words of the same size. These differences are statistically significant and strongly support the hypothesis of evolution from 2C etymons to 3C roots.

When choosing how to describe what happened, we have a number of causal verbs at our disposal. In this paper, we develop a model-theoretic formal semantics for nine causal verbs that span the categories of CAUSE, ENABLE, and PREVENT. We use structural causal models (SCMs) to represent participants’ mental construction of a scene when assessing the correctness of causal expressions relative to a presented context. Furthermore, SCMs enable us to model events relating both the physical world as well as agents’ mental states. In experimental evaluations, we find that the proposed semantics exhibits a closer alignment with human evaluations in comparison to prior accounts of the verb families

In this paper we present a new modelling framework for concepts based on quantum theory, and demonstrate how the conceptual representations can be learned from data. Our approach builds upon Gardenfors' classical framework of conceptual spaces, in which cognition is modelled geometrically through the use of convex spaces, which in turn factorise in terms of simpler spaces called domains. We show how concepts from the domains of SHAPE, COLOUR, SIZE and POSITION can be learned from images of simple shapes, where individual images are represented as quantum states and concepts as quantum effects. Concepts are learned by a hybrid classical-quantum network trained to perform concept classification. We also use discarding to produce mixed effects, which can then be used to learn concepts which only apply to a subset of the domains, and show how entanglement (together with discarding) can be used to capture interesting correlations across domains.

From abortion to vaccination, liberals and conservatives may not share the same views, yet they share a tendency to persist in their own beliefs despite knowing that millions of others disagree. In this paper, we investigate whether this shared tendency for persistence is supported by different psychological mechanisms across the two groups. An experiment with a large, nationally-representative sample in the U.S (N = 2,000) focusing on 12 controversies revealed similar rates of persistence across ideologies. However, the drivers of persistence differed across the two groups: Conservatives disproportionately relied on meta-epistemic explanations (i.e., appeals to subjectivity or unknowability) to maintain their controversial scientific views, whereas liberals tended to restrict such explanations to religious and moral controversies. Moreover, reliance on different explanations for persistence predicted differences in affective polarization.

Human language richly invokes our intuitive physical knowledge. We talk about physical objects, scenes, properties, and events; and we can ask questions and answer them with predictions and inferences about physical worlds described entirely in language. How does language construct meanings that connect to our general physical reasoning? In this paper, we propose PiLoT, a computational model that maps language into a probabilistic language of thought—meanings are constructed as probabilistic programs, which provide a formal basis for probabilistic and physical reasoning. Our model uses a large language model (LLM) to map from language to meanings and a probabilistic physics engine to support inferences over scenes described in language. We conduct a linguistic reasoning experiment based on prior psychophysics studies that requires reasoning about physical outcomes based on linguistic descriptions. We show that PiLoT well predicts human judgments across this experiment and outperforms baseline models which use the LLM to directly perform the same task.