Search

How do humans adapt when others exploit patterns in their behavior? When can people modify such patterns and when are they simply trapped? The present work explores these questions using the children's game of rock, paper, scissors (RPS). Adult participants played 300 rounds of RPS against one of eight bot opponents. The bots chose a move each round by exploiting unique sequential regularities in participant move choices. In order to avoid losing against their bot opponent, participants needed to recognize the ways in which their own behavior was predictable and disrupt the pattern. We find that for simple biases, participants were able to recognize that they were being exploited and even counter-exploit their opponents. However, for more complex sequential dependencies, participants were unable to change their behavior and lost reliably to the bots. Results provide a quantitative delineation of people's ability to identify and alter patterns in their past decisions.

This is a modern horror story about an innocently misbehavingprojector, and why we beseech everyone to report minimal col-orimetric data about stimulus displays. We present anecdotalexperience of configuring a projector to display video stimuliin a high-tesla MRI room, along with all the gotchas, (broken)technical assumptions, and theoretical rehashings that shouldbe considered by every scientist who uses computers to dis-play visual stimuli. The moral of our story is: (1) check thatyour monitor/projector is actually showing the colors and lu-minances that you think it is, (2) make explicit assumptionsregarding the physical/perceptual space of your stimuli andhow they relate to any model analysis you will perform. Thisis especially important when modeling non-human animals,since most equipment and data formats implicitly assume hu-man perception. We show that innocent changes to displaysettings such as brightness reduction can cause dangerouslyunexpected results. Understanding and reporting colorimetricdata in scientific publications is important for two reasons: (1)reproducibility, and (2) model fidelity.

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.

Deep reinforcement learning (Deep RL) methods can train artificial agents (AAs) to reach or exceed human-level performance. However, in multiagent contexts requiring competitive behavior or where the aim is to use AAs for human training, the qualitative behaviors AAs adopt may be just as important as their performance to ensure representative training. This paper compares human behaviors and performance when competing against either a human expert or an AA opponent trained using Deep RL on a 2-dimensional version of Pong. Results show that participants were not sensitive to the movement differences between the human expert and AA. Further, the participants did not alter their behaviors, except to compensate for differences in the environmental states caused by the opponents. The paper concludes with discussion on the potential impacts of AA training on human behavior with regard to representative design in the areas of skill development and team training.

This paper explores previous arguments that language evolved not from animal communication, as naturally assumed by most scholars, but instead out of animal cognition. It is proposed here that additional support for this argument comes from Cognitive Linguistics, an interdisciplinary branch of linguistics. Cognitive Linguistics mediates communication and cognition but, as with Cognitive Discourse Analysis, studies language use in terms of what it demonstrates about underlying cognitive processes and concepts. The paper demonstrates key examples of animal cognition’s links to language, with Cognitive Linguistics support, as well as the approach’s application to animal cognition in terms of domain-general symbolism, beyond verbal language. However, because communication remains a major function of language, the communicative aspect ought to be maintained in the explanation of language evolution.

It has been shown that in hand-written transcription tasks temporal micro-behavioural chunk signals hold promise as measures of competence in various domains (e.g., Cheng, 2014). But data capture under that an approach requires the use of graphics tablets which are relatively uncommon. In this theoretical paper we propose and explore an alternative method – Competence Assessment by Stimulus Matching (CASM). This new method uses simple mouse-driven interfaces to produce temporal chunk signals as measures of learner’s ability. However, it is not obvious what features of CASM will produce effective competence measures and the design space of CASM tasks is large. Thus, this paper uses GOMS modelling in order to explore the design space to find factors that will maximize the discrimination of chunk measures of competence. The modelling results of this paper show that CASM has potential in using chunk signals to measure competence in the domain of English language.

People’s causal judgments exhibit substantial variability, but the processes that lead to this variability are not currently understood. In this paper, we use a repeated-measures design to study the within-participant variability of conditional probability judgments in common-cause networks. We establish that these judgments indeed exhibit substantial within-participant variability. This variability differs by inference type and is related to the extent to which participants commit Markov violations. The consistency and systematicity of this variability suggest that it may be an important source of evidence for the cognitive processes that lead to causal judgments. The systematic study of both within- and between-person variability broadens the scope of behavior that can be studied in causal cognition and promotes the evaluation of formal models of the underlying process. The data and methods provided in this paper provide tools to enable the further study of within-participant variability in causal judgment.

This paper studies social interaction in newborns and of various species, whose behavioral developments attributed to the circular sensory motor Stage 3 of behavior development. The article has associated 16 exciting facts of the social behavior in groups of different species, highlighting three questions: (1) how can newborns successfully classify social phenomena that are abstract or absent from their reality; (2) whether emotional contagion can appear through cues of body language that subjects cannot consciously perceive; (3) how do organisms distinguish identical stimuli by their importance (value) without perceptual driver stimuli. The analysis suggests that organisms can interact at initial stages of development by distinguishing cues of a similar modality by their significance (their value). This ability contributes to the assimilation of knowledge about the initial social phenomena. This paper proposes the model of Coherent intelligence, which is strictly based on experimental evidence in modern literature and laws of physics.

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.

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 simultaneousgaze 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.

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.

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.

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.

When faced with uncertainty, human observers maximize performance by integrating sensory information with learned task-relevant regularities. Does this behavior similarly occur in social settings? In this paper, we explore how reward-seeking behavior in an intergroup context is affected by readily available but task-irrelevant social information (in the form of group membership) when task-relevant reward information can be learned over time. Across two experiments, we show that participants learned and utilized task-relevant regularities to inform their choices. We also show that human observers are not universally biased towards utilizing social information in all settings––participants learned to disregard social information when not relevant to the task at hand. However, learning about the utility of social information (Experiment 2) had a long-term influence on observers’ ability to subsequently learn and utilize available sources of information. Real-world intergroup contexts typically encompass situations and stimuli that have been previously experienced by the observer. Our findings highlight the powerful influence of learning in such contexts.

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.

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.

It is rather uncontroversial that gender should have no influence on treating others as equal epistemic agents. However, is this view reflected in practice? This paper aims to test whether the gender of the testifier and the accused of assault is related to the perception of a testimony’s reliability and the guilt of the potential perpetrator. Two experiments were conducted, in which the subjects (n = 361, 47% females, 53% males) assessed the reliability of the testifier and the guilt of the potential perpetrator in four scenarios of assault accusation, in which the only difference was the gender of the people presented. During the study, we have observed dependencies of gender and ascription of reliability, and, to a lesser extent, of guilt. The results of our research may constitute an argument for the existence of different epistemic status endowed on people depending on their gender and existing gender stereotypes.

Despite the importance of risk attitude on decision making, its role on belief updating has been overlooked. In this paper, we aim to answer this fundamental question. We show, using economic theory, that stronger risk aversion drives more conservative actions, decreases the instrumental value of information relative to the importance of belief-based utility. Thus, with utility for clarity, stronger risk attitude yields to more belief change; whereas with updating cost, stronger risk attitude leads to less belief change. We confirm the result experimentally in two settings where subjects update their belief about their IQ and a randomly drawn number, respectively.

What is the difference between language that describes the world and language that evaluates it? It has been suggested that an essential, distinguishing feature of evaluative language is its potential to guide actions by providing us with reasons to act. Calling an action “cruel” not only evaluates it negatively, its cruelty also provides us with a reason to refrain from it. Descriptive language, in and by itself, is relatively inert in this respect. In this paper, we examine whether this undisputed assumption is empirically adequate. We present three preregistered studies that demonstrate that evaluative language provides reasons for action when an agent contemplates how she should act, and also in conversational contexts. However, we also demonstrate that the speaker can easily deny the intention to provide such reasons to act.

Vaccines are among the best tools to limit the spread of preventable diseases. yet, recent years have seen a rise in anti-vaccination sentiments for vaccines against COVID-19, the MMR, and more. It is critical to understand the factors that influence whether a person will accept or reject a vaccine for a given disease. This paper tests a Bayesian model to predict attitudes toward vaccines. with five factors (subjective beliefs concerning danger of illness, safety of the vaccine, prevalence of the disease, perceived social norm, and governmental recommendation). To parameterize the model, Study 1 elicits the full conditional probability table while Study 2 tests model predictions by eliciting people’s priors for COVID-19 and the common flu. We find a good fit between predictions and observations, accounting for 53% and 44% of the variance. This suggests the usefulness of a formal model to capture people’s beliefs about vaccination.

Research in child development has shown that embodied experience handling physical objects contributes to many cognitive abilities, including visual learning. One characteristic of such experience is that the learner sees the same object from several different viewpoints. In this paper, we study how learning signals that equate different viewpoints---e.g., assigning similar representations to different views of a single object---can support robust visual learning. We use the Toybox dataset, which contains egocentric videos of humans manipulating different objects, and conduct experiments using a computer vision framework for self-supervised contrastive learning. We find that representations learned by equating different physical viewpoints of an object benefit downstream image classification accuracy. Further experiments show that this performance improvement is robust to variations in the gaps between viewpoints, and that the benefits transfer to several different image classification tasks.

A long tradition of studies in psycholinguistics has examined the formation and generalization of ad hoc conventions in reference games, showing how newly acquired conventions for a given target transfer to new referential contexts. However, another axis of generalization remains understudied: how do conventions formed for one target transfer to completely distinct targets, when specific lexical choices are unlikely to repeat? This paper presents two dyadic studies (N=240) that address this axis of generalization, focusing on the role of nameability --- the a priori likelihood that two individuals will share the same label. We leverage the recently-released KiloGram dataset, a collection of abstract tangram images that is orders of magnitude larger than previously available, exhibiting high diversity of properties like nameability. Our first study asks how nameability shapes convention formation, while the second asks how new conventions generalize to entirely new targets of reference. Our results raise new questions about how ad hoc conventions extend beyond target-specific re-use of specific lexical choices.

Existing studies have suggested that there may be a link between perception and production of speech sounds. In this paper we examine this link using data collected on Singapore English-Mandarin bilingual adults. We used a 2AFC task to measure perception of the Singapore Mandarin [tsʰuə́n]-[ʈʂʰuə́n] alveolar-retroflex contrast, and measured production using centre of gravity (CoG) measurements on speech recordings of alveolar and retroflex words. We found that a perception-production link may exist in our participants, with participants who exhibit less retroflexion in speech showing less sensitivity to retroflexion in ambiguous speech sounds.

Forgiveness plays a significant role in our everyday social life, and, because of that, it has received an increasing amount of attention in academic research. However, philosophers and psychologists are equally worried by the fact that we still lack an empirically adequate characterization of forgiveness. In this paper, we present two preregistered studies in which we explore what ordinary people believe a speaker does when he or she performs the speech act of forgiving by uttering the phrase, “I forgive you”. Study 1 uses a vignette-based stimulus to examine what participants believe to change after the victim granted forgiveness to their wrong-doer. In Study 2, we apply a linguistic test, the cancellability test, to determine whether participants consider forgiving the wrong-doer but still blaming them compatible.

Behavior Trees are widely used in many games, however, they are normally designed in a static way which is hard to deal with dynamic environments. One of the factors that restrict the performance of BTs is the optimization of resources used among subtasks, which has not been paid much attention. In this paper, we concentrate on the problem of dynamic allocation of agents for BTs, i.e., an important resource in games. We design a novel hierarchical learning framework, including the allocation learning module and the action learning module, to find strategies for agent allocation and action selection. Each agent is associated with a network for encoding observations, which is then assigned to a subtask by the allocation learning network. Each subtask uses a network to compute action for the corresponding agent. We design how the losses are computed for the two modules respectively and present the whole training procedure.

We investigated the potential and limitations of generative artificial intelligence (AI) in reflecting the authors' cognitive processes through creative expression. The focus is on the AI-generated artwork's ability to understand human intent (alignment) and visually represent emotions based on criteria such as creativity, aesthetic, novelty, amusement, and depth. Results show a preference for images based on the descriptions of the authors' emotions over the main events. We also found that images that overrepresent specific elements or stereotypes negatively impact AI alignment. Our findings suggest that AI could facilitate creativity and the self-expression of emotions. Our research framework with generative AIs can help design AI-based interventions in related fields (e.g., mental health education, therapy, and counseling). The full version of the paper can be found at https://arxiv.org/abs/2304.13324.

This paper presents transdisciplinary research on laryngectomy and a methodological stance to broaden research paradigms for the cognitive sciences. Studying daily experiences of people communicating without biological larynx in an interactive context, we put special emphasis on methodology combining engaged epistemology with ethnographic fieldwork. Our results made evident i) the role of anatomical and physiological adaptations in shaping communication and social relations, ii) the existence of multimodal and context-dependent alternative strategies of conversation, iii) the crucial role of participants’ agency. The dialogue between epistemologically engaged cognitive science and ethnographic fieldwork allowed us to remain open to novel interpretations of the communicative situations and led to unexpected observations. Results of this study point to the importance of integrating qualitative methodologies within research on cognition, and may prove useful for guiding therapeutic interventions and novel technological designs.

Modelling of early language acquisition aims to understand how infants bootstrap their language skills. The modelling encompasses properties of the input data used for training the models, the cognitive hypotheses and their algorithmic implementations being tested, and the evaluation methodologies to compare models to human data. Recent developments have enabled the use of more naturalistic training data for computational models. This also motivates development of more naturalistic tests of model behaviour. A crucial step towards such an aim is to develop representative speech datasets consisting of speech heard by infants in their natural environments. However, a major drawback of such recordings is that they are typically noisy, and it is currently unclear how the sound quality could affect analyses and modelling experiments conducted on such data. In this paper, we explore this aspect for the case of infant-directed speech (IDS) and adult-directed speech (ADS) analysis. First, we manually and automatically annotated audio quality of utterances extracted from two corpora of child-centred long-form recordings (in English and French). We then compared acoustic features of IDS and ADS in an in-lab dataset and across different audio quality subsets of naturalistic data. Finally, we assessed how the audio quality and recording environment may change the conclusions of a modelling analysis using a recent self-supervised learning model. Our results show that the use of modest and high audio quality naturalistic speech data result in largely similar conclusions on IDS and ADS in terms of acoustic analyses and modelling experiments. We also found that an automatic sound quality assessment tool can be used to screen out useful parts of long-form recordings for a closer analysis with comparable results to that of manual quality annotation.

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.

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.

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.

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.

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.

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.

Humans can readily generalize their learning to new visual concepts, and infer their associated meanings. How do people align the different conceptual systems learned from different modalities? In the present paper, we examine emojis— pictographs uniquely situated between visual and linguistic modalities—to explore the role of alignment and multimodality in visual and linguistic semantics. Simulation experiments show that relational structures of emojis captured in visual and linguistic conceptual systems can be aligned, and that the ease of alignment increases as the number of emojis increases. We also found that emojis with subjective impressions of high popularity are easier to align between their visual and linguistic representations. A behavioral experiment was conducted to measure similarity patterns between 48 emojis, and to compare human similarity judgments with three models based on visual, semantic and multimodal-joint representations of emojis. We found that the model trained with multimodal data by aligning visual and semantic spaces best accounts for human judgments.