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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.

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.

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 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.

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.

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.

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.

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.

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.

Having an intention (or not) forms a categorisation system for learning. Correspondingly, incidental learning stands for learning without intention to learn. The current paper examines the influences of incidental and intentional learning. 170 adults participated in three experimental studies. In each experiment, participants examined some animal illustrations and read paragraphs about them. In the first study, animal illustrations were arranged as being common (i.e., low curiosity) or novel to examine the effect of curiosity. Low curiosity level was more effective on incidental learning and vice versa for intentional learning. In the second study, mental time travel manipulation showed focusing on current time increased intentional learning. In the third study, the effect of arousal was examined via the tempo of the music (i.e., faster tempo for high arousal level). Low arousal level increased incidental learning. Ultimately, curiosity, mental time travel, and arousal could be essential in intentional and incidental learning.

The paper explores the impact of social role assignment and the corresponding payoff distribution on cooperation in the Prisoner’s dilemma following the types of relations according to Fiske’s relational models’ theory: communal sharing, authority ranking, equality matching, and market pricing. The corresponding roles and payoff distribution are teammates (each player receives the sum of the payoffs), partners (each player receives half of the sum of the payoffs), boss and subordinate (the boss receives 2/3 and the subordinate 1/3 of the sum of the payoffs), and opponents (each player receives the standard payoff). The results show that in the teammates’ and partners’ conditions cooperation was significantly higher than in the other conditions. Surprisingly, the results for the boss and subordinate condition although sharing a similar payoff distribution rule to the teammates’ and partners’ conditions were more similar to the opponents’ condition with significantly lower cooperation rate.

Knowledge of repeating patterns is foundational for early mathematical thinking. While interventions that rely on direct instruction help children to master specific patterns, they often struggle to transfer this knowledge to new patterns. This paper investigates exploration as a learning strategy for abstract patterning and improving knowledge transfer. In a yoked, between-subjects design, 5- and 6-year-old children (n = 90) were tasked with finding up to three hidden stars in a repeating shape pattern (ABB). Exploration participants (n=45) explored pattern materials themselves, while demonstration participants (n=45) observed the experimenter revealing the contents of each shape in the same order as the exploration counterpart. In a transfer task, children saw the same repeating pattern with different stimuli. We found that 6-year-olds significantly outperformed 5-year-olds in the exploration condition and used more sophisticated patterning strategies. These findings suggest that exploration may support and extend older children’s emerging understanding of underlying repeating pattern units.

When Arabic speakers write in their dialect, they have the choice of using either the standard Arabic script or the non-standard Roman script. Arabizi writing is a new emerging writing system that Arabic speakers use to type their dialects utilizing Roman characters. Although Arabizi is not standardized, people have developed an efficient way to communicate through it. One phenomenon that emerged with this new system is vowel dropping. In this paper, we approach this phenomenon from the perspective of communicative efficiency. We study the informativity of short and long spellings of words and investigate whether the predictability of the word in certain contexts impacts whether the vowel is dropped in that word.

Recent work in judgment and decision making has focused on which actions people consider when solving open-ended problems and found that the actions that come to mind tend to have particular features, such as having a high historical value. Here, we pursue the idea that the process of generating actions for decision-making tasks may actually reflect more general mechanisms for generating kinds of things. We provide evidence that what comes to mind may simply be a reflection of participants sampling from the most relevant part of the representational space they use to encode the type of thing they are generating. In this paper, we (1) introduce an approach for empirically describing a category in terms of the features that people use to represent category members, and for locating category members within that feature space, (2) show that certain locations in a category's feature space predict an item's likelihood of coming to mind, (3) introduce an approach for understanding the relevance of various features to people's representations of category members, and (4) show that features which are most involved in people's representations of category members are also predictors of what comes to mind within a category. We close by proposing that features that are most relevant to our representations of category members and predict coming to mind are those for which it has been historically useful to have information about during past experiences with the category in question.

In this paper we present a novel method for a naive agent to detect novel objects it encounters in an interaction. We train a reinforcement learning policy on a stacking task given a known object type, and then observe the results of the agent attempting to stack various other objects based on the same trained policy. By extracting embedding vectors from a convolutional neural net trained over the results of the aforementioned stacking “play,” we can determine the similarity of a given object to known object types, and determine if the given object is likely dissimilar enough to the known types to be considered a novel class of object. We present the results of this method on two datasets gathered using two different policies and demonstrate what information the agent needs to extract from its environ- ment to make these novelty judgments.

The last ten years have seen a steady increase in vignette-based research investigating the folk concept of pain. That research challenges the standard view of pain, according to which pains are unpleasant feelings. However, the results of these studies also suggest that the concept of pain is ambiguous and difficult to pin down. This paper approaches the topic from a new angle, using linguistic tests to decipher what people communicate when making statements such as ‘I have a pain in my arm’. The results suggest that first-person pain reports semantically entail information about both an unpleasant feeling and a disruptive bodily state. This speaks in favor of a pluralist view on the semantic meaning of pain.

Allen Newell laid down highly influential principles for the study of cognition with his Systems Levels framework, but it is less well known that this framework also laid down the foundation for understanding social interaction and culture. Although his book Unified Theories of Cognition was focused on the cognitive level, Newell speculated what implications his theory might have for the study of culture. Although these particular ideas did not receive much attention at the time, this paper argues that Newell’s systems levels provide a valuable insight into the connection between brains and culture.

How do humans generalise to make better decisions? Previous work has investigated this question using reward-guided decision-making tasks with low-dimensional and artificial stimuli. In this paper, we extend this work by presenting participants with a naturalistic decision-making task, in which options were images of real-world objects and the underlying reward function was based on one of their latent dimensions. Even though participants received no explicit instruction about object features, they quickly learned to do the task and generalised to unseen objects. To understand how they accomplished this, we tested a range of computational models and found that human behaviour is overall best explained by a linear model but that participants' strategies changed during the experiment. Lastly, we showed that combining pixel-based representations extracted from convolutional neural networks with the original latent dimensions further improved our models. Taken together, our study offers new insights into human decision making under naturalistic settings.

Measuring the emotion in words is valuable in that it analyzes emotions through language. However, it is difficult to find such measurements in low-resource languages. In this paper, we proposed a method to expand the affective lexicon by utilizing the context of words. The proposed model predicted the Valence and Arousal values of words using their dictionary definitions. In Study 1, we reviewed previous studies about the Korean affective lexicon and integrated data from these studies. The model was trained to minimize the MSE error between the Valence and Arousal values of the words and their predictions. We then checked the distribution of Valence and Arousal values of Korean vocabulary by applying our model to the Korean dictionary. In Study 2, a new affective lexicon was built to empirically validate our model. We found a negatively biased error pattern on model predictions and discussed why it happened.

A key feature of attention is that it moves over time, guided both exogenously by changing external circumstances and endogenously by internal cognitive states. However, the endogenous mechanisms guiding the movement of attention in the absence of external cues remain poorly understood. This paper develops and validates a computational model of how internal attentional states, motivated by the Adaptive Gain Theory of the locus coeruleus-norepinephrine (LC-NE) system, can guide the movement of visual attention over time. By fitting our model to young children's gaze data as they perform a visual object tracking task, we investigate developmental changes in higher-order patterns of attending behavior between 3.5-6 years of age, and we hypothesize about how the LC-NE system might mediate these changes.

People have a remarkable ability to remain steadfast in their beliefs in the face of large-scale disagreement. This has important consequences (e.g., societal polarization), yet its psychological underpinnings are poorly understood. In this paper, we answer foundational questions regarding belief persistence, from its prevalence to variability. Across two Experiments (N = 356, N = 354), we find that participants are aware of societal disagreement about controversial issues, yet overwhelmingly (~85%) do not question their views if asked to reflect on this disagreement. Both studies provide evidence that explanations for persistence vary across domains, with epistemic and meta-epistemic explanations among the most prevalent.

Human communication usually exhibits two fundamental and essential characteristics under environmental pressure, efficiency, i.e., using less communication frequency to achieve comparable performances in cooperation; and robustness, i.e., maintaining a relative performance when communicating in a complicated environment. Since a critical goal of designing artificial agents is making them human-like in many scenarios. How artificial agents could learn a human-like communication mechanisms in terms of efficiency and robustness is a long-existing problem which has not yet been solved. Reinforcement Learning, due to its trail-and-error paradigm, provides a promising framework in solving the above research problem. With reinforcement learning, this paper develops architectures that help agents learn an efficient and robust communication, and carries out extensive experiments which uncover that artificial agents are cognitively capable to learn such human-like communication protocol in various environments (tasks).

Compositionality has been argued to a necessary component of interpreting language, yet there appear to be many linguistic phenomena that do not overtly exhibit semantic compositional behavior. One of the challenges involves the phenomena of contextual modulations referred to collectively as semantic coercion or logical metonymy. In this paper, we present a computational model that provides the “compositional flexibility” in the interpretation of a verb with its arguments, for such coercive contexts in English. Specifically, we argue that such constructions typically have surface structural correlates in the form of dense paraphrases, and that these forms can be used to model the masked content in the coerced compositional context. We present preliminary results using a transformer architecture on a masked completion task. Our results show that modeling logical metonymy is a challenging task but can be substantially improved by fine-tuning through dense paraphrasing.

The opportunistic assimilation hypothesis posits that struggling and failing to solve a problem creates failure indexes, or long-term memory traces of the problem, that creates sensitivity to environment hints that trigger insight experiences. Past laboratory research has cast doubt on the usefulness of such hints during incubation breaks, but laboratory work is limited in its ability to recreate the diversity of stimuli in everyday life the opportunistic assimilation hypothesis requires. The current paper evaluates the insight experiences of over 150 participants who solved an insight math puzzle outside the lab for the presence of incidental hints that aided with problem solving. Across two studies, participants reported that chance hints in the wild had helped them to solve the puzzle and triggered insight moments. This suggests that opportunistic assimilation may play a role in everyday insight experiences and should not be discounted in future research.

It is intuitive to believe that humans take considerations of mental effort into account when making decisions. However, it has proved difficult to differentiate theories of mental effort in the absence of direct measurements of this psychological construct. Existing measurements of mental effort using response times and revealed preferences have low reliability. In this paper, we present a new experimental task - selecting between visuomotor lotteries using eye-tracking for sampling lotteries - that enables direct measurement of mental effort. Unlike response time-based measures, effort measurements in this task are not confounded by actual effort allocation. Unlike revealed preference-based measures, effort measurements in this task are acquired on a natural scale unitized by automatic visual selection processes. We also report results from a simple experiment conducted using this task, which reproduce existing findings of costly effort-aversion, and also demonstrate adaptive adjustment of mental effort.

Recent research on thick terms like ‘rude’ and ‘friendly’ has revealed a polarity effect, according to which the evaluative content of positive thick terms like ‘friendly’ and ‘courageous’ can be more easily cancelled than the evaluative content of negative terms like ‘rude’ and ‘selfish’. In this paper, we study the polarity effect in greater detail. We first demonstrate that the polarity effect is insensitive to manipulations of embeddings (Study 1). Second, we show that the effect occurs not only for thick terms but also for thin terms such as ‘good’ or ‘bad’ (Study 2). We conclude that the polarity effect is indicative of a pervasive asymmetry that holds between positive and negative evaluative terms.

Fast and efficient correction of speech errors is essential to effective communication. Yet, despite several accounts of error detection, no computational account exists to explain how humans repair their speech errors. This paper proposes the first such model. We demonstrate that a simple automatic mechanism can form the basis of most repairs. We then demonstrate that augmenting the model with a conflict-based monitoring-control loop allows it to capture more nuanced findings in human speech error repair data.

Children may participate in the design of digital technologies as users, testers, informants, and design partners (Druin, 2002). Yet, more often than not, teachers, researchers, and engineers are design partners, while children remain as mere users and testers (Krumm, Means, & Bienkowski, 2018). This paper explores how children are designers of digital artifacts and learning analytics on an online platform called Knowledge Forum (Scardamalia & Bereiter, 2021). Eight children participated in a group interview to reflect on their online notes and drawings from grade 1 to grade 6, using the analytic tools to mine developmental patterns in their online discourse (e.g., vocabulary growth, diversity and complexity of ideas). Implications of this work are discussed by extending Druin’s framework to consider children’s roles in simultaneously improving usability, augmenting the impact of technology on their learning, and the advancement of learning theories within the context of hybrid learning in a post-pandemic world.

Robots are extraordinary, category-defying entities. Machines that move autonomously, store and communicate information, display emotions, and cultivate social relationships pose a challenge to our most basic assumptions about what kinds of things exists in the world and how we should reason about them. As such, studies of lay people’s beliefs about robots offer new insights into the ordinary functioning of folk ontologies. In this paper, I propose that there are two ontological questions that human reasoners must grapple with in making sense of robots, or any other entity: Which kind of thing is it? and Which causal forces act on it? Each question highlights a distinct way in which robots are extraordinary—albeit, not exceptional—entities for the human cognitive system. A meditation on the dynamic interplay between these two ontological questions provides a new theoretical framework for understanding conceptual change at both the individual and the cultural-historical level.

The question of why human language relies so heavily on vague terms has received a great deal of attention from philosophers, linguists, and more recently cognitive scientists, yet much less is known about their effect on other aspects of language use. In this paper, we propose a model for the interaction between vagueness and implicatures, an important pragmatic phenomenon, incorporating recent work in the RSA framework and insights from the philosophical literature on vagueness. We show that the model offers a good fit of data from earlier studies, and discuss the scope of the model more broadly.

Virtual Reality (VR) technology has been widely used in researching situations that require high ecological validity but are difficult to copy. In this paper, we compare the effect of 360-degree videos in VR head-mounted display and 2D computer screen on evoking four types of emotional states, and innovatively assess the effect of emotion evoking using the Go/No-go attention paradigm. Based on the eye movement data collected from a considerable number of participants (N=48), the result reaches marginally significance in the effect of immersion level of evoking (p=0.075), emotion’s level of arousal (p=0.019), and the interaction between them (p=0.037) under the condition of positive evoking. We find that immersive device can better evoke positive emotions with high arousal, while non-immersive device cannot manipulate the intensity of evoked emotions. Our study empirically demonstrate that high-arousal positive emotion evoking is more effective in VR than on a 2D monitor based on computational affection.

There has been a small but now growing interest in studying decision making in real-world contexts where part of the problem faced by decision makers is to generate candidate options they will actually decide between. While some of this work has employed large decision spaces where options are discrete and valuation is computationally tractable (e.g., chess), very little work has focused on genuinely open-ended decision contexts that more closely mirror mundane real-world decisions. This paper leverages large language models to investigate how people generate options when facing genuinely open-ended problems. Across three experiments, we apply semantic similarity and sentiment analyses to the options that participants sequentially generate for real-world decision problems. We find that the first options generated tend to be sampled from a relatively local region of semantic space and are typically of high value. As additional options are generated, they become increasingly dissimilar and are of lower value. These patterns held both at the level of individual option generation trajectories within a given participant and at the level of individual differences across participants.

A central component of the predictive coding theoretical framework concerns the comparison between predictions and sensory decoding. In the probabilistic setting, this takes the form of assessing the similarity or distance between probability distributions. However, such similarity or distance measures are not associated with explicit probabilistic models, making their assumptions implicit. In this paper, we explore an original variation on probabilistic coherence variables; we define a probabilistic component, that we call a "Bayesian comparator", that mathematically yields a particular similarity measure. A geometrical analogy suggests two variants of this measure. We apply these similarity measures to simulate the comparison of known, predicted patterns to patterns from sensory decoding, first in a simple, illustrative model, and second, in a previous model of visual word recognition. Experimental results suggest that the variant that is scaled by the norms of both predicted and perceived probability distributions yields better robustness and more desirable dynamics.

When people think about the features of common objects, like scissors, they often spontaneously recall a central feature: scissors cut things. They tend not to recall other features of scissors, e.g., that they have handles. The present paper posits a novel explanation for the behavior: the features people recall first and most often reflect semantic generalizations of kinds. A recent taxonomy of such generalizations suggests that people represent privileged links between kinds and their features known as principled connections (Prasada et al., 2013). Several tests diagnose principled connections: for instance, principled connections reflect norms, so one way to diagnose the presence of a principled connection is to test the acceptability of sentences of the form all normal Xs have feature Y, as in all normal cars have four wheels. We tested whether participants accept generalizations about the normality of features produced in a semantic feature production task (Experiments 1 and 2) as well as self-referential generalizations (Experiment 3). The experiments provided participants with generalizations about features listed first and most often as well as features that people list less frequently. They found that people readily accepted generalizations that diagnose the presence of principled connections. The results corroborate the view that principled connections help people recall the features of conceptual categories.

In many real-world settings, it is crucially vital for agents to learn to communicate and cooperate. Different cooperation models have been proposed to represent cooperative relations among agents. However, the intensity of the cooperative relation has not received much attention. In particular, how it varied with spatial-temporal information has not been studied deeply. In this paper, we propose a temporal dynamic weighted graph convolution based multi-agent reinforcement learning framework (TWG-Q). We design a weighted graph convolutional network to capture cooperative information among agents. On top of that, a temporal weight learning mechanism is introduced to characterize intensities of cooperations. We design a novel temporal convolutional network in the temporal dimension to extract effective features for the multi-agent reinforcement learning. Extensive experiments show that our method significantly improves the performance of multi-agent reinforcement learning on the public benchmark of micromanagement tasks in StarCraft II.

This paper proposes new empirical classifiers for evaluative concepts, including thin concepts like 'good' or 'bad' and thick concepts such as 'honest' or 'disgusting', based on quantitative corpus linguistics. Prior work in experimental philosophy has shown that sentiment analysis can be used to track differences between concept classes. Building on this, Task 1 investigates whether the relationship between sentiment and evaluativeness is parabolic rather than linear. Task 2 extends this question to the differences between evaluative and non-evaluative concept classes. The results of both Tasks show that the linear and the parabolic logistic regression classifiers perform equally well. Interestingly, this study also finds that adjectives attributed to animate entities (e.g. "generous customer") generally have a higher probability to be evaluative concepts than those attributed to inanimate entities (e.g."dry soil").

The present paper reports an experiment (N=254) testing two views of how reasoners learn and generalize potentially complex causal knowledge. Previous work has focused on reasoners’ ability to learn rules describing how pre-defined candidate causes combine, potentially interactively, to produce an outcome in a domain. This empirical-function learning view predicts that reasoners would generalize an acquired combination rule based on similarity to stimuli they experienced in the domain. An alternative causal-invariance view goes beyond empirical learning: it allows for the possibility that one’s current representation may not yield useable (i.e., invariant) causal knowledge –– knowledge that holds true when applied. Accordingly, because useable causal knowledge is the evident aspiration of causal induction, this view posits that deviation from causal invariance is a criterion for knowledge revision. The criterion shapes the empirical functions learned and retained. A discriminating test is whether reasoners would re-represent interacting causes as a whole cause that does not interact with other causes, even when in their relevant experience all (pre-defined) causes in the domain interact. Our results favor the causal-invariance view.

When socially evaluating a speaker, listeners partially rely on context-dependent expectations, giving greater social penalties for using a marked form in a less expected context. The nature of listeners’ expectations can be based on the context in which a form is produced in the current utterance (local use), as well as cumulative information about the context in which a form tends to be produced (typical use). This paper asks which of these kinds of expectations about the English sociolinguistic variable (ING), as in talking vs. talkin, is most relevant to listeners in making social judgments. Results indicate that (ING) words’ typical grammatical functions (as a noun vs. a verb) contributed to social judgments, while the local grammatical use of (ING) words did not, supporting usage-based theories and raising new questions about the cognitive mechanisms that underlie social evaluations based on speech.

The purpose of this paper is to present a novel conceptualisation of an intuitive, primitive form of trust termed proto-trust. This concept is proposed in order to account for the many different senses, types and domains in which trust has traditionally been defined and theorised. A brief review of the literature on affective and intuitive trust is presented, informing the definition and formalisation of proto-trust. Following this, a preliminary empirical investigation of proto-trust is described, where intuitive trust assessments are compared to analytical trust decisions, under various attribution prompts. Results showed effects of attribution prompts on changes to trust assessments from intuitive to deliberative decisions. In addition, qualitative data are presented for the various reasons participants gave for their trust decisions. One of these reasons (emotional reaction) was found to affect the degree of difference between intuitive and deliberative trust assessments.

Dementia is a common and debilitating condition that typically gives rise to increasing language impairment. There is a need to understand the nature of this impairment further so that therapies may be developed, particularly in the case of bilinguals. This paper extends BiLex, an existing computational model of bilingual lexical access, to simulate language decline in dementia. Six lesion types are evaluated for their ability to reproduce the pattern of decline in the semantic variant primary progressive aphasia (svPPA) subtype of dementia. Semantic memory lesions reproduce this pattern of decline best in monolinguals, and further suggest patterns that are likely to be found in longitudinal data from bilingual dementia patients in the future.

This work studies publications in cognitive science and utilizes mathematical techniques to connect the analysis of the papers' content (abstracts) to the context (citation, journals). We apply topic modeling on the abstracts and community detection algorithms on the citation network, and measure content-context discrepancy to find academic communities that study similar topics but do not cite each other or publish in the same venues. These results show a promising, systematic framework to identify opportunities for scientific collaboration in a highly interdisciplinary and diverse field such as cognitive science.

Early language learning relies on statistical regularities that exist across timescales in infants’ lives. Two types of these statistical regularities are the routine activities that make up their day, such as mealtime and play, and the real-time repeated behaviors that make up the moment-by-moment dynamics of those routines. These two types of regularities are different in nature and are embedded at two different temporal scales, which led to divergent research in the literature – those who collect long-form recordings and observations of at-home behavior and those who use eye trackers and micro-level analyses to quantify real-time behavior in laboratories. The goal of present paper is to jointly examine and connect the statistical regularities at these two timescales. Towards this goal, we brought wearable eye trackers to English- and Spanish-speaking families’ homes to record parent and toddler visual attention during daily routines. We transcribed parent speech during object play and mealtime and coded toddler visual attention during naming moments. We found that parents and toddlers jointly interacted with the unique vocabularies of the two activities. Although naming and attention were more coordinated during object play, mealtime still afforded opportunities for high-quality naming moments. Our results lay the building blocks for connecting these two lines of research and demonstrate the feasibility of at-home data collection with eye trackers.

When deciding whether and how to punish, people consider not only the potential direct consequences, but also, how their choice will affect observers’ judgements about the values and motives underlying the choice. We formalize the decision to punish as a rational communicative social action (RCSA). The model generates rational decisions to punish, incorporating anticipated observers’ judgements obtained from a recursive model of inference using an intuitive theory of mind. Using this model, we synthesize patterns of human punishment from recently published papers. RCSA thus offers a formal model of the cognitive process that humans use to balance preferences for how they are perceived, with other goals for punishing.

Causal learning is shaped by people’s prior beliefs, including their expectations. In this paper, we specifically examine expectations of determinism: do they vary with perceptual features of physical causal events, and how do they influence subsequent causal learning from data? We show that perceptual features lead adults to different expectations of determinism for different causes of launching (Exps. 1A & 1B). Those expectations lead to significant differences in responses to causal “failures”; that is, we show a difference in violation-of-expectation effect after a failed launch (Exp. 2). Actual data can reduce or eliminate the impact of these expectations, but they do not override the effect of perceptual features (Exp. 3). Overall, spatiotemporal contiguity cues and expectation of determinism have similar effects on causal learning outcomes, but neither is fully reducible to the other.

Elucidating the mechanisms that underlie clustering and switching behavior is essential to understanding semantic memory search and retrieval. Hills, Jones, and Todd (2012) proposed a model of semantic foraging based on the observation that statistical signatures in memory search resemble optimal foraging in animal behavior. However, the original model was postdictive in explaining when a switch would occur, as opposed to predictive, and was agnostic as to the cues used by humans to make a decision to switch from local to global information. In this paper, we proposed a switching mechanism, \textit{Semantic Scent}, as a predictive model underlying such behavior. Semantic Scent extends optimal foraging theory, reproducing the same switch behavior observed animal foraging behavior in memory search. We evaluated Semantic Scent against competing models including \textit{Random Walk} and \textit{Fixed Count} to determine its effectiveness in classifying switches made in fluency tasks. A quantitative model comparison between the switch models demonstrated Semantic Scent's superior performance in fitting human data. These results provide further evidence of the importance of optimal foraging theory to semantic memory search.

The Shapley value is one of the most important normative division schemes in cooperative game theory, satisfying basic axioms. However, some allocation according to the Shapley value may seem unfair to humans. In this paper, we develop an automatic method that generates intuitive explanations for a Shapley-based payoff allocation, which utilizes the basic axioms. Given any coalitional game, our method decomposes it to sub-games, for which it is easy to generate verbal explanations, and shows that the given game is composed of the sub-games. Since the payoff allocation for each sub-game is perceived as fair, the Shapley-based payoff allocation for the given game should seem fair as well. We run an experiment with 630 human participants and show that when applying our method, humans perceive the Shapley-based payoff allocation as more fair than the Shapley-based payoff allocation without any explanation or with explanations generated by other methods.

Given considerable advancements in automated driving systems, the day when autonomous vehicles will be regularly present in our everyday life is impending. It is, therefore, very significant to put emphasis on the effect that giving up autonomy might have on an individual. We take into consideration an experimental data set regarding participants' reported discomfort levels to tackle the following questions: How can we represent a discomfort measurement in a meaningful way? Using this representation, can future discomfort reactions be predicted? We identify key features, identify baseline models, and develop a new approach based on the k-nearest neighbor model to considerably improve the prediction of individual user's discomfort measurements. A discussion of limits and potentials concludes the paper.