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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.

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.

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.

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.

When encountering objects, we readily perceive not only low-level properties (e.g., color and orientation), but also seemingly higher-level ones--including aspects of physics (e.g., mass). Perhaps nowhere is this contrast more salient than in the perception of soft materials such as cloths: the dynamics of these objects (including how their three-dimensional forms vary) are determined by their physical properties such as stiffness, elasticity, and mass. Here we hypothesize that the perception of cloths and their physical properties must involve not only image statistics, but also abstract object representations that incorporate "intuitive physics". We provide behavioral and computational evidence for this hypothesis. We find that humans can visually match the stiffness of cloths with unfamiliar textures from the way they undergo natural transformations (e.g. flapping in the wind) across different scenarios. A computational model that casts cloth perception as mental physics simulation explains important aspects of this behavior. Full paper can be found at https://www.biorxiv.org/content/10.1101/2021.05.12.443806v1.

When encountering objects, we readily perceive not only low-level properties (e.g., color and orientation), but also seemingly higher-level ones--including aspects of physics (e.g., mass). Perhaps nowhere is this contrast more salient than in the perception of soft materials such as cloths: the dynamics of these objects (including how their three-dimensional forms vary) are determined by their physical properties such as stiffness, elasticity, and mass. Here we hypothesize that the perception of cloths and their physical properties must involve not only image statistics, but also abstract object representations that incorporate "intuitive physics". We provide behavioral and computational evidence for this hypothesis. We find that humans can visually match the stiffness of cloths with unfamiliar textures from the way they undergo natural transformations (e.g. flapping in the wind) across different scenarios. A computational model that casts cloth perception as mental physics simulation explains important aspects of this behavior. Full paper can be found at https://www.biorxiv.org/content/10.1101/2021.05.12.443806v1.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

Reasoning about what other people know is an important cognitive ability, known as epistemic reasoning, which has fascinated psychologists, economists, and logicians. In this paper, we propose a computational model of humans’ epistemic reasoning, including higher-order epistemic reasoning—reasoning about what one person knows about another person’s knowledge—that we test in an experiment using a deductive card game called “Aces and Eights”. Our starting point is the model of perfect higher-order epistemic reasoners given by the framework of dynamic epistemic logic. We modify this idealized model with bounds on the level of feasible epistemic reasoning and stochastic update of a player’s space of possibilities in response to new information. These modifications are crucial for explaining the variation in human performance across different participants and different games in the experiment. Our results demonstrate how research on epistemic logic and cognitive models can inform each other.

Reasoning about what other people know is an important cognitive ability, known as epistemic reasoning, which has fascinated psychologists, economists, and logicians. In this paper, we propose a computational model of humans’ epistemic reasoning, including higher-order epistemic reasoning—reasoning about what one person knows about another person’s knowledge—that we test in an experiment using a deductive card game called “Aces and Eights”. Our starting point is the model of perfect higher-order epistemic reasoners given by the framework of dynamic epistemic logic. We modify this idealized model with bounds on the level of feasible epistemic reasoning and stochastic update of a player’s space of possibilities in response to new information. These modifications are crucial for explaining the variation in human performance across different participants and different games in the experiment. Our results demonstrate how research on epistemic logic and cognitive models can inform each other.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Block-building is an early-developing spatial skill in which multiple spatial actions unfold over time. The processes underlying block-building have eluded our understanding, but existing data show that children’s and adults’ construction paths are highly systematic and selective. In this paper, we investigate whether manipulating the stability of block models to be built affects the character of construction paths. We asked participants to build models with either strong or weak support. We measured the step-by-step actions taken and used eye-tracking to assess differences between adults and children in how they collect and use visual information while building. We find that children and adults are highly selective in their construction paths, but models with weak support are more difficult to build, reflected in the specifics of their paths and the eye movements made during building. These results suggest that the stability of a structure drives information-gathering and building strategies in block construction.

Block-building is an early-developing spatial skill in which multiple spatial actions unfold over time. The processes underlying block-building have eluded our understanding, but existing data show that children’s and adults’ construction paths are highly systematic and selective. In this paper, we investigate whether manipulating the stability of block models to be built affects the character of construction paths. We asked participants to build models with either strong or weak support. We measured the step-by-step actions taken and used eye-tracking to assess differences between adults and children in how they collect and use visual information while building. We find that children and adults are highly selective in their construction paths, but models with weak support are more difficult to build, reflected in the specifics of their paths and the eye movements made during building. These results suggest that the stability of a structure drives information-gathering and building strategies in block construction.

The present paper addresses the study of cross-linguistic phonosemantic correspondences within a deep learning framework. An LSTM-based Recurrent Neural Network is trained to associate the phonetic representation of a word, encoded as a sequence of feature vectors, to its corresponding semantic representation in a multilingual and cross-family vector space. The processing network is then tested, without further training, in a language that does not appear in the training set and belongs to a different language family. The performance of the model is evaluated through a comparison with a monolingual and mono-family upper bound and a randomized baseline. After the assessment of the network's performance, the distribution of phonosemantic properties in the lexicon is inspected in relation to different (psycho)linguistic variables, showing a link between lexical non-arbitrariness and semantic, syntactic, pragmatic, and developmental factors.

The present paper addresses the study of cross-linguistic phonosemantic correspondences within a deep learning framework. An LSTM-based Recurrent Neural Network is trained to associate the phonetic representation of a word, encoded as a sequence of feature vectors, to its corresponding semantic representation in a multilingual and cross-family vector space. The processing network is then tested, without further training, in a language that does not appear in the training set and belongs to a different language family. The performance of the model is evaluated through a comparison with a monolingual and mono-family upper bound and a randomized baseline. After the assessment of the network's performance, the distribution of phonosemantic properties in the lexicon is inspected in relation to different (psycho)linguistic variables, showing a link between lexical non-arbitrariness and semantic, syntactic, pragmatic, and developmental factors.

This paper presents a study of the selection task based on Cognitive Argumentation (CA), a computational framework for dialectic argumentation-based reasoning. CA is built from a theoretical framework of argumentation in AI which is then grounded via cognitive principles from Cognitive Science. The aim is to understand the selection task variations by studying how argumentative reasoning is suitably flexible to uniformly capture the differences among the individuals' selections, the canonical groups and the shifts within the different contexts in which the experiment is carried out. Our approach is assessed with respect to the developed criteria within the meta-analysis of Ragni and Johnson-Laird in 2018.

This paper presents a study of the selection task based on Cognitive Argumentation (CA), a computational framework for dialectic argumentation-based reasoning. CA is built from a theoretical framework of argumentation in AI which is then grounded via cognitive principles from Cognitive Science. The aim is to understand the selection task variations by studying how argumentative reasoning is suitably flexible to uniformly capture the differences among the individuals' selections, the canonical groups and the shifts within the different contexts in which the experiment is carried out. Our approach is assessed with respect to the developed criteria within the meta-analysis of Ragni and Johnson-Laird in 2018.

The block design test (BDT), in which a person has to recreate a visual design using colored blocks, is notable among cognitive assessments because it makes so much of a person's problem-solving strategy ``visible'' through their ongoing manual actions. While, for decades, numerous pockets of research on the BDT have identified certain behavioral variables as being important for certain cognitive or neurological hypotheses, there is no unifying framework for bringing together this spread of variables and hypotheses. In this paper, we identify 25 independent and dependent variables that have been examined as part of published BDT studies across many areas of cognitive science and present a sample of the research on each one. We also suggest variables of interest for future BDT research, especially as made possible with the advent of advanced recording technologies like wearable eye trackers.

The block design test (BDT), in which a person has to recreate a visual design using colored blocks, is notable among cognitive assessments because it makes so much of a person's problem-solving strategy ``visible'' through their ongoing manual actions. While, for decades, numerous pockets of research on the BDT have identified certain behavioral variables as being important for certain cognitive or neurological hypotheses, there is no unifying framework for bringing together this spread of variables and hypotheses. In this paper, we identify 25 independent and dependent variables that have been examined as part of published BDT studies across many areas of cognitive science and present a sample of the research on each one. We also suggest variables of interest for future BDT research, especially as made possible with the advent of advanced recording technologies like wearable eye trackers.

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.

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.

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.

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.

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.

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.

Speakers use different language to communicate with partners in different communities. But how do we learn and represent which conventions to use with which partners? In this paper, we argue that solving this challenging computational problem requires speakers to supplement their lexical representations with knowledge of social group structure. We formalize this idea by extending a recent hierarchical Bayesian model of convention formation with an intermediate layer explicitly representing the latent communities each partner belongs to, and derive predictions about how conventions formed within a group ought to extend to new in-group and out-group members. We then present evidence from two behavioral experiments testing these predictions using a minimal group paradigm. Taken together, our findings provide a first step toward a formal framework for understanding the interplay between language use and social group knowledge.

Speakers use different language to communicate with partners in different communities. But how do we learn and represent which conventions to use with which partners? In this paper, we argue that solving this challenging computational problem requires speakers to supplement their lexical representations with knowledge of social group structure. We formalize this idea by extending a recent hierarchical Bayesian model of convention formation with an intermediate layer explicitly representing the latent communities each partner belongs to, and derive predictions about how conventions formed within a group ought to extend to new in-group and out-group members. We then present evidence from two behavioral experiments testing these predictions using a minimal group paradigm. Taken together, our findings provide a first step toward a formal framework for understanding the interplay between language use and social group knowledge.

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.