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Recent studies have applied network-based approaches to analyze the organization and retrieval of specific semantic categories, with a focus on the animal category. The current study extended previous studies by using network science tools to quantitatively investigate the structural differences of noun and verb categories of various levels of specificity. Specific (animal and body movement) and general noun and verb networks were constructed from four verbal fluency tasks. Common network measures indicated that the two verb networks were more condensed and less modular than the noun networks, supporting that nouns are more well-organized in the mental lexicon than verbs. Comparing the specific and general networks within each lexical category also corroborated lexical semantic studies that nouns have a more clear hierarchical structure. The results of this paper, along with recent semantic network studies, provide converging evidence for the usefulness of network science in semantic memory research.

Research suggests that people minimize the amount of effort used to generate natural language descriptions of visual scenes. In the case of visual scenes with multiple groups, recent work has found that people tend to generate quantitative descriptions that mention the number and cardinality of groups (e.g., ``two groups of three limes''), but omit the total quantity (e.g., ``six limes''). This finding suggests that people groupitize, that is, they more quickly determine the number of grouped items by rapid enumeration of subgroups, rather than slower item-by-item counting. %This hypothesis predicts that during description, people exert less effort to encode and report only the number and cardinality of groups. A recent proposal predicts that during description, people exert less effort by encoding and reporting only information that is readily available to perception. In previously studied description tasks, people may have omitted the total quantity from their descriptions because of considerations of brevity and informativity. In this paper, we describe a study designed to test how individuals balance effort, brevity, and informativity when evaluating quantified descriptions. The experiment was designed to elicit more fine-grained preferences for descriptions using direct comparisons between two competing descriptive forms. The results suggest that perceptual effort plays a central role in how people describe grouped collections of items.

How do we manage to understand the minds of others and usefully interact with them? In the last decade, the debate on these issues has developed from unitary to pluralist approaches. According to the latter, we make use of multiple socio-cognitive strategies when predicting, interpretating, and reacting to the behavior of others. This means a departure from the view of mindreading as the main strategy underlying social cognition. In this paper, we address the question of the controversial status of mindreading within such a pluralist framework. Contrary to many other accounts, we ascribe mindreading an equal status in a pluralist framework. Mindreading is required for a variety of central situations in life and importantly underlies the way in which we understand other people. Mindreading is also no less reliable than alternative strategies; reliability is not so much a matter of different competing socio-cognitive strategies, but rather of their complementary use.

Language is multimodal: non-linguistic cues, such as prosody, gestures and mouth movements, are always present in face-to-face communication and interact to support processing. In this paper, we ask whether and how multimodal cues affect L2 processing by recording EEG for highly proficient bilinguals when watching naturalistic materials. For each word, we quantified surprisal and the informativeness of prosody, gestures, and mouth movements. We found that each cue modulates the N400: prosodic accentuation, meaningful gestures, and informative mouth movements all reduce N400. Further, effects of meaningful gestures but not mouth informativeness are enhanced by prosodic accentuation, whereas effects of mouth are enhanced by meaningful gestures but reduced by beat gestures. Compared with L1, L2 participants benefit less from cues and their interactions, except for meaningful gestures and mouth movements. Thus, in real-world language comprehension, L2 comprehenders use multimodal cues just as L1 speakers albeit to a lesser extent.

Learnability has been a topic of great interest in phonology. In particular is the question of the relative learnability of process interactions. In both historical and experimental domains, researchers have noted that certain kinds of interactions are harder to learn than others. In both domains, however, the results are seemingly in conflict. One potential source of the conflicting outcome is the types of processes involved. In this paper, we investigate the effect of process types on the learnability of different interaction types, using an ideal minimum-description-length learner (MDL). We find that the model indeed predicts different learnability outcomes for each interaction type; however, the asymmetry is largely independent of the process type. This computational model explains certain elements of both the historical as well as some experimental findings of the relative learnability of linguistics process interactions, while contradicting other behavioral findings.

Categories such as 'animal' or 'furniture' play a pivotal role in processing, organizing, and communicating world knowledge. Many theories and computational models of categorization exist, but evaluation has disproportionately focused on artificially simplified learning problems (e.g., by assuming a given set of relevant features or small data sets); and on English native speakers. This paper presents a large-scale computational study of category and feature learning. We approximate the learning environment with natural language text, and scale previous work in three ways: We (1) model the full complexity of the learning process, acquiring learning categories and structured features jointly; (2) study the generalizability of categorization models to five diverse languages; and (3) learn categorizations comprising hundreds of concepts and thousands of features. Our experiments show that meaningful representations emerge across languages. We further demonstrate a joint model of category and feature acquisition produces more relevant and coherent features than simpler models, suggesting it as an exploratory tool to support cross-cultural categorization studies.

Understanding a sequence of images as a visual narrative is challenging because it requires not only the understanding of what is shown at a particular moment but also what has changed, been omitted or is out of frame. The human cognitive system makes inferences about the state of the world based on transitions between sequential frames. In this paper, we present a principled analysis of the stylistic differences between two dominant styles of multi-modal narratives, western comics and manga. These two styles differ in terms of screening, ballooning, layout, language, and reading order. We first provide a systematic account of these differences based on an annotated dataset consisting of both comics and manga. We then annotate these datasets with a new feature set and evaluate the contributions of these features through development of a computational model of multi-modal comprehension. The model evaluation is presented through the cloze test that measures the accuracy of the model in predicting unseen next frames given the prior frames in a sequence. Our results provide initial benchmarks and insight into the fundamental challenges that the multi-modal narrative understanding task presents for computational models both for language and vision.

A large body of work has demonstrated the utility of the Bayesian framework for capturing inference in both specialist and everyday contexts. However, the central tool of the framework, conditionalization via Bayes’ rule, does not apply directly to a common type of learning: the acquisition of conditional information. How should an agent change her beliefs on learning that “If A, then C”? This issue, which is central to both reasoning and argumentation, has recently prompted considerable research interest. In this paper, we critique a prominent proposal and provide a new, alternative, answer.

Verbs such as ‘know’ and ‘think’ help people describe mental states, and reasoners without any training in logic can make epistemic inferences about mental states. For instance, verbs such as ‘know’ are factive, i.e., they describe true propositions, and the statement Ora knows that it’s sunny licenses the inference that it’s sunny. Logicians have accordingly developed epistemic logics capable of characterizing valid and invalid epistemic inferences based on operators that serve as analogs to verbs such as ‘know’ and ‘think’. Recent work suggests that no existing logical system can capture the inferences that naïve individuals tend to make. This paper describes a new theory of epistemic reasoning that operates on the assumption that reasoners represent epistemic relations as spatial models. The theory accords with recent theoretical advances, existing data, as well as two novel experiments that show how reasoners cope with nested epistemic verbs, e.g., Ami knows that Ora thinks it’s sunny.

This paper presents a theoretical framework for modeling human visual attention. The framework's core claim is that three mechanisms drive attention: selection, which picks out an item for further processing; engagement, which tags a selected item as relevant or irrelevant to the current task; and enhancement, which increases sensitivity to task-relevant items and decreases sensitivity to task-irrelevant items. Building on these mechanisms, the framework is able to explain human performance on attentionally demanding tasks like visual search and multiple object tracking, and it supports a broad range of predictions about the interactions between such tasks.

Although Artificial Intelligence (AI) is expected to outperform humans in many domains of decision-making, the process by which AI arrives at its superior decisions is often hidden and too complex for humans to fully grasp. As a result, humans may find it difficult to learn from AI, and accordingly, our knowledge about whether and how humans learn from AI is also limited. In this paper, we aim to expand our understanding by examining human decision-making in the board game Go. Our analysis of 1.3 million move decisions made by professional Go players suggests that people learned to make decisions like AI after they observe reasoning processes of AI, rather than mere actions of AI. Follow-up analyses compared the decision quality of two groups of players: those who had access to AI programs and those who did not. In line with the initial results, decision quality significantly improved for the players with AI access after they gained access to reasoning processes of AI, but not for the players without AI access. Our results demonstrate that humans can learn from AI even in a complex domain where the computation process of AI is also complicated.

The N400 component of the event related brain potential is widely used to investigate language and meaning processing. However, despite much research the component’s functional basis remains actively debated. Recent work showed that the update of the predictive representation of sentence meaning (semantic update, or SU) generated by the Sentence Gestalt model (McClelland, St. John, & Taraban, 1989) consistently displayed a similar pattern to the N400 amplitude in a series of conditions known to modulate this event-related potential. These results led (Rabovsky, Hansen, and McClelland,2018) to suggest that the N400 might reflect change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error. However, a limitation of this work is that the model was trained on a small artificial training corpus and thus could not be presented with the same naturalistic stimuli presented in empirical experiments. In the present study, we overcome this limitation and directly model the amplitude of the N400 elicited during naturalistic sentence processing by using as predictor the SU generated by a Sentence Gestalt model trained on a large corpus of texts. The results reported in this paper corroborate the hypothesis that the N400 component reflects the change in a probabilistic representation of meaning after every word presentation. Further analyses demonstrate that the SU of the Sentence Gestalt model and the amplitude of the N400 are influenced similarly by the stochastic and positional properties of the linguistic input.

The present paper reports an experiment testing two views of how reasoners learn and generalize potentially complex causal knowledge. Previous work has focused on reasoners’ ability to learn integration functions that best describe how pre-defined candidate causes combine, potentially interactively, to produce an outcome in a domain. This empirical-function learning view predicts that participants would generalize an acquired integration function based on similarity to stimuli they experienced in the domain. An alternative causal-invariance view recognizes that one’s current representation may not yield invariant/useable causal knowledge –– knowledge that holds true when applied to new circumstances. This view incorporates useable causal knowledge as a goal and deviation from causal invariance as a criterion for knowledge revision. It predicts that participants would re-represent causes such that they do not interact with other causes, even when in participants’ experience all (pre-defined) causes in that domain interact. Our results favor the causal-invariance view.

Our ability to play games like chess and Go relies on both planning several moves ahead and on recognition or gist - intuitively assessing the quality of possible game states without explicit planning. In this paper, we investigate the role of recognition in puzzle solving. We introduce a simple puzzle game to study planning and recognition in a non-adversarial context and a reinforcement learning agent which solves these puzzles relying purely on recognition. The agent relies on a neural network to capture intuitions about which game states are promising. We find that our model effectively predicts the relative difficulty of the puzzles for humans and shows similar qualitative patterns of success and initial moves to humans. Our task and model provide a basis for the study of planning and intuitive notions of fit in puzzle solving that is simple enough for use in developmental studies.

Different patterns of lines can express different emotions, but the reason for the metaphor has not been fully revealed. Some studies speculate that this may be caused by the motion implied by lines. In order to verify this speculation, this paper conducts an experiment on the relationship between the emotional expression of lines and the motion of lines. We created 87 different patterns of lines and visualized the motion implied by the lines as dynamic effects. The subjects chose descriptors from a list of 29 emotion words for samples. The results show that the test samples can well cover the classical two-dimensional emotional space and the emotional expression of lines is obviously related to their implied motion. The implied motion, compared with the static version, tends to shift towards the positive and high arousal in the emotional space. In addition, the speed of the motion affects emotional arousal.

Cognates – words which share form and meaning across two languages – have been extensively studied to understand the bilingual mental lexicon. One consistent finding is that bilingual speakers process cognates faster than non-cognates, an effect known as cognate facilitation. Yet, there is no agreement on the underlying factors driving this effect. In this paper, we use computational modeling to test whether the effect can be explained by the cumulative frequency hypothesis. We train a computational language model on two language pairs (Dutch–English, Norwegian–English) under different conditions of input presentation and test it on sentence stimuli from two existing studies with bilingual speakers of those languages. We find that our model can exhibit a cognate effect, lending support to the cumulative frequency hypothesis. Further analyses reveal that the size of the effect in the model depends on its linguistic accuracy. We interpret our results within the literature on cognate processing.

Current theories of procrastination argue that people put things off into the future with the expectation that they will be better able to do them later. In this paper, we rationalize such expectations within the framework of evidence accumulation models of the choice process. Specifically, we show that it is rational for observers to adopt lower decision thresholds for choices with weak evidence for any alternative, and that observers learning to estimate optimal decision thresholds for tasks that involve decisions will find it reasonable to put the tasks off until the threshold has been sufficiently lowered by time-varying urgency. We designed a computational model and an experiment to differentiate our theory from more general expectancy based temporal motivation accounts. Both simulation and experimental results support our proposal, indicating a large role for choice difficulty in people's self-assessed estimates for how likely they are to procrastinate any given task.

The paper addresses both issues of first language (L1) attrition and second language (L2) acquisition in the context of two mutually intelligible languages from a psycholinguistic perspective. It summarises results of a study examining how native speakers of Slovak living long-term in Czechia process and produce Slovak and Czech cognates and noncognates. Two experimental sessions consisting of lexical decision task and picture naming task were conducted once with Slovak stimuli and once with Czech stimuli. The results showed that Slovak noncognates were processed and produced more slowly by Slovaks who use their L1 less, and their L2 more. Analogically, this subgroup processed and produced Czech noncognates relatively faster. Thus, the experiments give evidence of (a) L1 attrition (online processing difficulties), and (b) the shift towards L2 patterns depending on the amount of L1 and L2 use even in speakers of two very close and mutually intelligible languages.

Theory of Mind (ToM) or mentalizing is the ability to infer mental states of oneself and other agents. Theory of mind plays a key role in social interactions as it allows one to predict other agents' likely future actions by inferring what they may intend or know. However, there is a wide range of ToM skills of increasing complexity. While most people are generally capable of performing complex ToM reasoning such as recursive belief inference when explicitly prompted, there is much evidence that humans do not always use ToM to their full capabilities. Instead, people often fall back to heuristics and biases, such as an egocentric bias that projects one's beliefs and perspective onto the observed agent. We explore which (internal or external) factors may influence the mentalizing processes that humans employ unsolicitedly, i.e., employ without being primed or explicitly triggered. In this paper we present an online study investigating unbalanced decision problems where one choice is significantly better than the other. Our results demonstrate that participant's are significantly less likely to exhibit an egocentric bias in such situations.

In order to interact with objects in our environment, humans rely on an understanding of the actions that can be performed on them, as well as their properties. When considering concrete motor actions, this knowledge has been called the object affordance. Can this notion be generalized to any type of interaction that one can have with an object? In this paper we introduce a method to represent objects in a space where each dimension corresponds to a broad mode of interaction, based on verb selectional preferences in text corpora. This object embedding makes it possible to predict human judgments of verb applicability to objects better than a variety of alternative approaches. Furthermore, we show that the dimensions in this space can be used to predict categorical and functional dimensions in a state- of-the-art mental representation of objects, derived solely from human judgements of object similarity. These results suggest that interaction knowledge accounts for a large part of mental representations of objects.

Memories for events experienced during infancy and early childhood are rarely recollected later in life—a phenomenon termed infantile and childhood amnesia. The formation and retrieval of such episodic memories relies, in part, on the hippocampus. Characterizing the role of hippocampal development in offsetting infantile and childhood amnesia is key to understanding (i) why infantile and childhood amnesia occur and (ii) how episodic memory capacities develop in early ontogeny. Comparative research is necessary for this enterprise because many paradigms and techniques work better with humans or with non-human animals. The four papers in this symposium gather current work in developmental psychology, developmental cognitive neuroscience, and behavioral neuroscience that characterizes the complex and heterogenous developmental profile of behavioral gains in component processes underlying episodic memory capacity in humans and work on the mammalian hippocampus and how it accompanies development. By leveraging and triangulating multiple levels of analyses, we can gain insights that are unavailable using a siloed approach. This collection of work helps delineate clear future directions for a comparative approach in memory development.

Are mental additions of single-digit numbers solved through direct retrieval from long-term memory or through persistent use of an automatized counting procedure along a mental line? In this paper, we present an experiment based on small additions along an artificial mental line, which tends to show that for very small addends (+2 to +4), counting may still be used at the end of a 3-week training. To investigate this issue, we developed the AutoCoP computational model, which describes how small additions could be solved, based on attention, working memory and experience. The simulations of AutoCoP based on this experiment showed that the effects detected at the behavioral level are reproduced and consistent with the theory, which assumes the use of a counting procedure in experts.

Digitally-enabled means for judgment aggregation have renewed interest in "wisdom of the crowd'' effects and kick-started collective intelligence design as an emerging field in the cognitive and computational sciences. A keenly debated question here is whether social influence helps or hinders collective accuracy on estimation tasks, with recently introduced network theories offering a reconciliation of seemingly contradictory past results. Yet, despite a growing body of literature linking social network structure and the accuracy of collective beliefs, strategies for exploiting network structure to harness crowd wisdom are under-explored. In this paper, we introduce a potential new tool for collective intelligence design informed by such network theories: rewiring algorithms. We provide a proof of concept through agent-based modelling and simulation, showing that rewiring algorithms that dynamically manipulate the structure of communicating social networks can increase the accuracy of collective estimations in the absence of knowledge of the ground truth.

A new phenomenon is the spread and acceptance of “fake news” on an individual user level, facilitated by social media such as Twitter. So far, state of the art socio–psychological theories and cognitive models focus on explaining how the accuracy of fake news is judged on average, with little consideration of the individual. This paper takes it to a new level: A breadth of core models are comparatively assessed on their predictive accuracy for the individual decision maker, i.e., how well can models predict an individual’s decision before the decision is made. To conduct this analysis, it requires the raw responses of each individual and the implementation and adaption of theories to predict the individual’s response. We used two previously collected large data sets with a total of 3309 participants and searched for, analyzed and refined existing classical and heuristic modeling approaches. The results suggest that classical reasoning, sentiment analysis models and heuristic approaches can best predict the “Accept” or “Reject” response of a person. A hybrid model that combines those models outperformed the prediction of all individual models pointing to an adaptive tool-box.

While humans routinely encode and retrieve memories in groups, the bulk of our knowledge of human memory comes from paradigms with individuals in isolation. The primary phenomenon of interest within the relatively new field of collaborative memory is collaborative inhibition: the tendency for collaborative groups to underperform in free recall tasks compared to nominal groups of the same size. This effect has been found in a variety of materials and group compositions (Rajaram & Pereira-Pasarin, 2010). However, the majority of research in this field is guided by verbal theories without formal computational models. In this paper we adapt the Search of Associative Memory (SAM; Raaijmakers & Shiffrin, 1981) model to collaborative free recall. We present a framework to scale SAM to collaborative paradigms with multiple SAM models working together. Our simulation results with the collaborative SAM model suggest that retrieval disruption, responsible for the part-set cuing effect in individuals, is also the cause of collaborative inhibition when multiple models are working together. Our work provides an existence proof that SAM can act as a unified theory to explain both individual and collaborative memory effects, and offers a framework for future predictions of scaling to increased group sizes, shared knowledge, and spread of false memories.

In this paper, we test people’s causal judgments when the graphs have inhibitory causal relations. We find evidence that a particularly important class of errors known as Markov vio- lations extend to these settings. These Markov violations are important because they are incompatible with causal graphical models, a theoretical framework that is often used as a com- putational level account of causal cognition. In contrast, the systematic pattern of errors are in line with the predictions of a recently proposed rational process model that models peo- ple as reasoning about concrete cases (Davis & Rehder, 2020). These findings demonstrate that errors in causal reasoning ex- tend across a range of settings, and do so in line with the pre- dictions of a model that describes the process by which causal judgments are drawn.

The role of semantics in inflectional morphology has long been debated (Huang & Pinker, 2010; Pinker & Prince, 1988; Ramscar, 2002) with most of the focus on the English past tense. This paper explores whether an effect of semantics can be found for German noun plural generalisation, a system as yet only poorly understood. German speakers were asked to first freely produce and then rate plural forms of 24 new wug words, presented in a semantically manipulated context. We expected that the German plural class ending in -n should be used more frequently with nouns presented as persons than as objects (Gaeta, 2008). While this hypothesis was not confirmed, the post-hoc discovery of other semantic influences prevents us from completely rejecting the original hypothesis. In light of these results we discuss possible sources of the observed pattern of plural classes and stress the importance of replicating wug studies with novel sets of wug words. We conclude that generalisation of the German plural system cannot easily be explained by phonological nor semantic influences.

Numerous disciplines study the nature of social cognition. Also, in philosophy of mind, there are discourses about socio-cognitive abilities, such as joint action, mindreading, and commitment. However, the so-called standard notions require demanding conditions, which leads to the fact that, for example, abilities of young children and non-human animals cannot be captured by this terminology. By introducing minimal notions, a step has been taken to capture a greater variety of phenomena in the field of social cognition. In this way, current empirical findings can be connected to the theoretical work in philosophy. However, when one characterizes minimal and standard notions by a dichotomous interpretation of a two-system approach, quite a few instances are still falling through the conceptual net. This paper will demonstrate how many instances remain neglected and explore the challenges to develop a disjunctive conceptual schema that can capture the varieties of socio-cognitive abilities.

Theory-of-Mind inference is natural for humans but poses significant computational challenges. The core difficulty can be traced back to the exponential growth of paths to consider in planning given a mental state. In this paper we tackle this problem in a search-and-rescue task implemented in Minecraft. Our goal is to infer differences in knowledge from participants' continuous-time trajectory. By abstracting the spatio-temporal state space and the reward function together, we surface natural decision points, on which we compare the participants' behavior to myopic rational agents of varying knowledgeability. Collectively, the abstraction and rational agent analysis yield successful inference of participants' knowledge states and reveal distinct patterns of their exploratory behavior.

Infant vocabulary development is inevitably dependent on the speech they hear in their environment. This paper reports an investigation of the vocabulary statistics that predict a word’s age of acquisition, focusing on frequency and contextual diversity as derived from child-directed speech data along with associative norms generated by adults. Age of acquisition is operationalised using parental-report of infant word knowledge in a British English-speaking population. The work can be considered an extension of Hills, Maouene, Riordan, and Smith (2010) using a fully British English dataset. We found significant effects of both word frequency and word associations on age of acquisition. Interestingly, the strength of these predictors differed between word classes, with frequency being the strongest predictor for nouns and associations the strongest predictor for function words.

As a step toward computational modeling of cognitive processes and interpretation of creativity, we present an extended theory of computational creative systems that aims to better define the evaluation of creative systems and processes. Previous research has focused largely on the {\em generation} aspect of creative systems. This paper extends this formalism by modeling the {\em judgment} of creativity through computational models of {\em observer} systems. As a concrete illustration of its applicability, we show how this theory can support the interpretation of MacGyver problems---problems defined in the cognitive systems research community as classical planning problems designed to elucidate the cognitive process of creative problem-solving. Finally, we demonstrate how this theory provides an interpretation to previous empirical work in approximation of an aesthetic measure in artistic domains for individual and group preferences over photographs and gestural performances. Our overall contributions are: (a) an initial formal model of creativity that incorporates a generative process and a given observer (b) application of the theory to the interpretation of results from both symbolic (classical planning) and statistical (preference learning) approaches.

In this paper we use pupillometry, a non-invasive, naturalistic method of measuring attention and cognitive load, to measure the effect of stress clash (Chinése shíp) and its metrical repair (Chínese shíp) during auditory sentence processing. We addressed two main research questions. The first question explores whether phonologically-disfavored metrical structures induce processing costs indexed by changes in pupil size. The second investigates whether the application of an optional process of stress retraction called the Rhythm Rule (Liberman & Prince, 1977) ameliorates or compounds any general penalty for stress clash. We find that unrepaired stress clash leads to greater pupil diameter relative to non-clashing sequences, indicating increased attention and cognitive load. We also find that repaired sequences lead to a decrease in overall pupil diameter, indicating facilitation.

This paper examines whether advanced law students are resistant to the blame blocking effect—a tendency to assign higher punishments for failed attempts than failed attempts with independent causal chains leading to intended harm (Cushman, 2008). This effect goes against the criminal law principle that intentionally acting towards committing a crime, and not accidental outcomes, determine liability for attempts. To further investigate whether advanced students of law are judging blame blocking scenarios correctly (in line with their legal expertise), in two experiments, we compared their punishment responses with four populations: beginning law students, advanced philosophy students, advanced natural science students, and laypeople with no academic background. We did not observe the blame blocking effect in either of the four student populations, and it was only partially present in the lay population. We discuss the implications of these findings for research on legal expertise and the blame blocking effect in general.

Understanding how people mentally represent expenditures is crucial to understanding how they manage their money. In this paper, we report three studies that investigate people’s representation of budgeting categories by asking people to categorize common expenditures of money (e.g., rent, dining out, etc.). We then examine the implications of these taxonomic representations of expenditures for how people selectively restrict their uses of money (e.g., when overspending on one item, for which other items would people choose to spend less). We found that there is consensus in people’s representations of expenditures, and that both the category membership and taxonomic distance between items predict how people restrict their spending.

How many rhymes are possible in English? How much alliteration or assonance? This paper explores the space of common phonologically driven poetic devices. I investigate instances of these poetic devices across unique words in the dictionary, assuming perfect matching in relevant corresponding elements of word pairs. Documenting the frequencies of poetic device matches across words provides a baseline for understanding the diversity and use of these forms in the wild. It also allows us to describe the distribution of words across these patterns within each poetic device. We show that certain devices, such as alliteration and stress, support a relatively small number of unique phonological patterns, providing more consolidated and predictable resources than other devices. Forms like masculine and feminine rhyme, assonance, and consonance display relatively more sound patterns respectively, and unique words are distributed across them differently. These results are discussed in terms of their poetic and cognitive implications.

Recent progress in neuroaesthetics showed that when people appreciate a piece of visual artworks, they can also reconstruct the imaginary stroke sequences from static brushstrokes. This subjective feature is consistent with the general art practice of copying works of art, that is, imitators can interpret the original creative process of practitioners and physically copy their creative tracks. However, this feature has not been measured to characterize artistic styles. This paper attempts to design an experiment to measure this subjective characteristics. 34 pieces of calligraphy and painting works in different styles were chosen as the materials, and 130 testers were invited to draw their imaginary stroke sequences on the chosen pieces. The obtained traces is reinterpreted as AOI(Area of Interests) sequence and modeled as a Markov chain, and its entropy rate with standard error is calculated to measure the randomness. Result show that the measurement can differentiate artistic styles.

Team interaction involves the division of labor and coordination of actions between members to achieve a shared goal. Although the dynamics of interactions that afford effective coordination and performance have been a focus in the cognitive science community, less is known about how to generate these flexible and adaptable coordination patterns. This is important when the goal is to design artificial agents that can augment and enhance team coordination as synthetic teammates. Although previous research has demonstrated the negative impact of model-based agents on the pattern of interactions between members using recurrence quantification methods, more recent work utilizing deep reinforcement learning has demonstrated a promising approach to bootstrap the design of agents to team with humans effectively. This paper explores the impact of artificial agent design on the interaction patterns that are exhibited in human-autonomous agent teams and discusses future directions that can facilitate the design of human-compatible artificial agents.

The current paper brings experimental evidence that Romanian 4-and 5-year-olds are able to understand recursive prepositional modifiers such as "papagalul de lȃngǎ hamsterul de lȃngǎ iepuraş", ‘the parrot next to the hamster next to the bunny’. 23 children engaged in a picture matching task (PMT) where they heard sentences containing either recursive structures or coordinative structures, and they had to choose between a picture corresponding to a recursive interpretation and a picture corresponding to a coordinative interpretation. Interestingly, children provided recursive interpretations to recursive structures to a quite high degree, though their behavior was not fully adult-like. We argue that this can be accounted for through children’s sensitivity to specific recursion cues that are present in Romanian, as well as to the contrast between recursion and coordination, which is activated through the experimental set-up.

Judging if a conclusion follows logically from a given set of premises can depend much more on the believability than on the logical validity of the conclusion. This so-called belief bias effect has been replicated repeatedly for many decades now. An interesting observation is, however, that process models for deductive reasoning and models for the belief bias have not much of an overlap - they have largely been developed independently. Models for the belief bias often just implement first order logic for the reasoning part, thereby neglecting a whole research field. This paper aims to change that by presenting a first attempt at substituting the first order logic components of two models for belief, selective scrutiny and misinterpreted necessity, with two state of the art approaches for modeling human syllogistic reasoning, mReasoner and PHM. In addition, we propose an approach for extending the traditionally dichotomous predictions to numerical rating scales thereby enabling more detailed analysis. Evaluating the models on a dataset published with a recent meta-analysis on the belief bias effect, we demonstrate the general success of the augmented models and discuss the implication of our extensions in terms of the limitations of the current focus of research as well as the potential for future investigation of human reasoning.

Modus tollens is a rule of inference in classical, two-valued logic which allows to derive the negation of the antecedent from a conditional and the negation of its consequent. In this paper, we investigate when humans draw such conclusions and what modulates the application of modus tollens. We consider conditionals which may or may not be obligations and which may or may not have necessary antecedents. We show that humans make significantly more modus tollens inferences in case of obligation conditionals and that the time to make a modus tollens inference is shorter than the time to answer ``nothing follows''. We illustrate how these differences can be modeled within the weak completion semantics.

Many cognitive models assume that stimuli can be represented as points in a latent psychological space. However, it has been difficult to provide these spaces with a geometric structure where the distance between items accurately reflects their subjective dissimilarity. In this paper, we propose a new method to give psychological spaces a geometric structure by equating the amount of change undergone by a stimulus with the arc length of a curve in psychological space. We then assess our method with a categorization experiment where participants classified continuously changing visual stimuli according to their rate of change. Our results indicate that individuals’ judgements are well predicted by arc length, suggesting that it may be a promising geometric constraint for psychological spaces in other contexts.