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The "myside bias'' in evaluating arguments is an empirically well-confirmed phenomenon that consists of overweighting arguments that endorse one's beliefs or attack alternative beliefs while underweighting arguments that attack one's beliefs or defend alternative beliefs. This paper makes two contributions: First, it proposes a probabilistic model that adequately captures three salient features of myside bias in argument evaluation. Second, it provides a Bayesian justification of this model, thus showing that myside bias has a rational Bayesian explanation under certain conditions.

The "myside bias'' in evaluating arguments is an empirically well-confirmed phenomenon that consists of overweighting arguments that endorse one's beliefs or attack alternative beliefs while underweighting arguments that attack one's beliefs or defend alternative beliefs. This paper makes two contributions: First, it proposes a probabilistic model that adequately captures three salient features of myside bias in argument evaluation. Second, it provides a Bayesian justification of this model, thus showing that myside bias has a rational Bayesian explanation under certain conditions.

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").

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.

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").

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.

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.

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.

Many models in Cognitive Science require data to calibrate parameters. Some modelers calibrate their models’ parameters for each individual in a data set, and others work at the aggregate level. Generally, the accuracy of a model is judged by the degree to which human data are replicated, and the model parameters are interpreted accordingly. It is not too surprising that models that are developed for a particular task and fit to each individual’s data in such a task replicate the human data well. The question is, however, whether those models can make predictions in the absence of human data. In this paper, we present a theory-driven model of a well-known sequential decision task (the Balloon Analog Risk Task, BART) which is able to make predictions in the absence of human data. The cognitive model is grounded on the processes and mechanisms of Instance-Based Learning (IBL) Theory of experiential choice. We demonstrate the simulation predictions from an IBL model and those of a well-known model of the BART, which depends on the fits to human data. We further show that when making predictions without data, the IBL model provides predictions that are both theoretically founded and accurate, while the Two-Parameter model performs much worse than when fit to data. We conclude with a discussion of the benefits of making theory-based predictions in the absence of human data for our community.

Many models in Cognitive Science require data to calibrate parameters. Some modelers calibrate their models’ parameters for each individual in a data set, and others work at the aggregate level. Generally, the accuracy of a model is judged by the degree to which human data are replicated, and the model parameters are interpreted accordingly. It is not too surprising that models that are developed for a particular task and fit to each individual’s data in such a task replicate the human data well. The question is, however, whether those models can make predictions in the absence of human data. In this paper, we present a theory-driven model of a well-known sequential decision task (the Balloon Analog Risk Task, BART) which is able to make predictions in the absence of human data. The cognitive model is grounded on the processes and mechanisms of Instance-Based Learning (IBL) Theory of experiential choice. We demonstrate the simulation predictions from an IBL model and those of a well-known model of the BART, which depends on the fits to human data. We further show that when making predictions without data, the IBL model provides predictions that are both theoretically founded and accurate, while the Two-Parameter model performs much worse than when fit to data. We conclude with a discussion of the benefits of making theory-based predictions in the absence of human data for our community.

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.

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.

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.

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.

Retrieval practice of information through testing has been shown to improve learning. So has studying examples. In this paper, we address inconsistencies in the literature concerning which of these two approaches is best. We test the hypothesis that learning depends on what is being learned; whereas practice emphasizes memorization, studying examples allows for selectivity of encoding, resulting in different information being learned. Accordingly, we predicted that practice will improve learning in situations that emphasize memorization (such as learning facts or simple associations), whereas studying examples will improve learning in situations where there are multiple pieces of information available and selectivity is necessary (such as when learning skills or procedures). We report evidence from a laboratory study using naturalistic materials showing results consistent with these predictions.

Retrieval practice of information through testing has been shown to improve learning. So has studying examples. In this paper, we address inconsistencies in the literature concerning which of these two approaches is best. We test the hypothesis that learning depends on what is being learned; whereas practice emphasizes memorization, studying examples allows for selectivity of encoding, resulting in different information being learned. Accordingly, we predicted that practice will improve learning in situations that emphasize memorization (such as learning facts or simple associations), whereas studying examples will improve learning in situations where there are multiple pieces of information available and selectivity is necessary (such as when learning skills or procedures). We report evidence from a laboratory study using naturalistic materials showing results consistent with these predictions.

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.

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.

Burns & Krygier (2015) demonstrated that people could exhibit a strong bias towards the smaller first digits, in a way similar to that described by Benford’s law. This paper sought to expand the scope of this phenomenon and to test a possible explanation, the Recognition Hypothesis that a Benford bias is due to life-long environmental exposure to this statistical relationship. Participants completed three numerical tasks: A Generation Task requiring answering trivia questions; a Selection Task requiring selecting between two numerical responses; and an Estimation task requiring estimating the number of jelly beans in a jar. The results found no evidence of any first digit effect in the Recognition Task, some evidence of Benford bias in the Generation Task and strong evidence in the Estimation Task. Future research should focus on alternatives to the Recognition Hypothesis and investigate the parameters of Benford bias in generation tasks

Burns & Krygier (2015) demonstrated that people could exhibit a strong bias towards the smaller first digits, in a way similar to that described by Benford’s law. This paper sought to expand the scope of this phenomenon and to test a possible explanation, the Recognition Hypothesis that a Benford bias is due to life-long environmental exposure to this statistical relationship. Participants completed three numerical tasks: A Generation Task requiring answering trivia questions; a Selection Task requiring selecting between two numerical responses; and an Estimation task requiring estimating the number of jelly beans in a jar. The results found no evidence of any first digit effect in the Recognition Task, some evidence of Benford bias in the Generation Task and strong evidence in the Estimation Task. Future research should focus on alternatives to the Recognition Hypothesis and investigate the parameters of Benford bias in generation tasks

Adaptive scheduling systems aim to estimate the ability of an individual in order to prescribe a personalized training schedule. These adaptive systems are often founded on regularities of human memory such as a learning, forgetting, and the spacing effect. One such model which has been developed to both account for regularities of memory and be used in applied contexts is the Predictive Performance Equation (PPE). One limitation of the PPE is that it is only able to account for and incorporate information about a participant’s accuracy on a task and cannot take into account additional performance measures such as reaction time. To expand the PPE, we propose a simple extension to the model, allowing it to account for both accuracy and reaction time measures. Our paper reports the extension to the PPE as well as a formal model comparison to another model of learning and retention (Pavlik and Anderson, 2005). The results of our model comparison reveal that the extended PPE can both better account and predict an individual’s performance than Pavlik and Anderson (2005) model.

Adaptive scheduling systems aim to estimate the ability of an individual in order to prescribe a personalized training schedule. These adaptive systems are often founded on regularities of human memory such as a learning, forgetting, and the spacing effect. One such model which has been developed to both account for regularities of memory and be used in applied contexts is the Predictive Performance Equation (PPE). One limitation of the PPE is that it is only able to account for and incorporate information about a participant’s accuracy on a task and cannot take into account additional performance measures such as reaction time. To expand the PPE, we propose a simple extension to the model, allowing it to account for both accuracy and reaction time measures. Our paper reports the extension to the PPE as well as a formal model comparison to another model of learning and retention (Pavlik and Anderson, 2005). The results of our model comparison reveal that the extended PPE can both better account and predict an individual’s performance than Pavlik and Anderson (2005) model.

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.

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.

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.

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.

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.

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.

The paper aims to examine participants’ open-text ‘think aloud’ explanations of their reasoning while making a judgement about an ambiguous scenario. It aims to consider this data in light of frameworks such as causal modelling, intuitive theories, coherence and the story model. Consistent with these frameworks, we find that participants bring in a large amount of world knowledge to connect ambiguous evidence to unobserved, inferred variables and, via these, to the target judgement. We attempt to represent these chains of inferences using causal diagrams and find that participants interpretations of the scenario can be lumped into one of two distinct causal models, each presenting an internally coherent ‘image’ of the ambiguous scenario. Furthermore, participants’ judgement predicts which of those two models they adhere to. We discuss the limitations and merits of this methodological approach for investigating these types of frameworks.

The paper aims to examine participants’ open-text ‘think aloud’ explanations of their reasoning while making a judgement about an ambiguous scenario. It aims to consider this data in light of frameworks such as causal modelling, intuitive theories, coherence and the story model. Consistent with these frameworks, we find that participants bring in a large amount of world knowledge to connect ambiguous evidence to unobserved, inferred variables and, via these, to the target judgement. We attempt to represent these chains of inferences using causal diagrams and find that participants interpretations of the scenario can be lumped into one of two distinct causal models, each presenting an internally coherent ‘image’ of the ambiguous scenario. Furthermore, participants’ judgement predicts which of those two models they adhere to. We discuss the limitations and merits of this methodological approach for investigating these types of frameworks.

Cognitive theories for reasoning are about understanding how humans come to conclusions from a set of premises. Starting from hypothetical thoughts, we are interested which are the implications behind basic everyday language and how do we reason with them. A widely studied topic is whether cognitive theories can account for typical reasoning tasks and be confirmed by own empirical experiments. This paper takes a different view and we do not propose a theory, but instead take findings from the literature and show how these, formalized as cognitive principles within a logical framework, can establish a quantitative notion of reasoning, which we call plausibility. For this purpose, we employ techniques from non-monotonic reasoning and computer science, namely, a solving paradigm called answer set programming (ASP). Finally, we can fruitfully use plausibility reasoning in ASP to test the effects of an existing experiment and explain different majority responses.

Cognitive theories for reasoning are about understanding how humans come to conclusions from a set of premises. Starting from hypothetical thoughts, we are interested which are the implications behind basic everyday language and how do we reason with them. A widely studied topic is whether cognitive theories can account for typical reasoning tasks and be confirmed by own empirical experiments. This paper takes a different view and we do not propose a theory, but instead take findings from the literature and show how these, formalized as cognitive principles within a logical framework, can establish a quantitative notion of reasoning, which we call plausibility. For this purpose, we employ techniques from non-monotonic reasoning and computer science, namely, a solving paradigm called answer set programming (ASP). Finally, we can fruitfully use plausibility reasoning in ASP to test the effects of an existing experiment and explain different majority responses.

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.

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.

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.

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.

Speakers' referential expressions often depart from communicative ideals in ways that help illuminate the nature of pragmatic language use. Patterns of overmodification, in which a speaker uses a modifier that is redundant given their communicative goal, have proven especially informative in this regard. It seems likely that these patterns are shaped by the environment a speaker is exposed to in complex ways. Unfortunately, systematically manipulating these factors during human language acquisition is impossible. In this paper, we propose to address this limitation by adopting neural networks (NN) as learning agents. By systematically varying the environments in which these agents are trained, while keeping the NN architecture constant, we show that overmodification is more likely with environmental features that are infrequent or salient. We show that these findings emerge naturally in the context of a probabilistic model of pragmatic communication.

Speakers' referential expressions often depart from communicative ideals in ways that help illuminate the nature of pragmatic language use. Patterns of overmodification, in which a speaker uses a modifier that is redundant given their communicative goal, have proven especially informative in this regard. It seems likely that these patterns are shaped by the environment a speaker is exposed to in complex ways. Unfortunately, systematically manipulating these factors during human language acquisition is impossible. In this paper, we propose to address this limitation by adopting neural networks (NN) as learning agents. By systematically varying the environments in which these agents are trained, while keeping the NN architecture constant, we show that overmodification is more likely with environmental features that are infrequent or salient. We show that these findings emerge naturally in the context of a probabilistic model of pragmatic communication.

Diversity of social identities can improve the performance of groups through varied cognitive and communicative pathways. Recently, research efforts have focused on identifying when we should expect to see these potential benefits in real-world settings. While most research to date has studied this topic at individual and interpersonal levels, in this paper, we develop an agent-based model to explore how various aspects of homophily, the tendency of individuals to associate with similar others, affects performance at a larger scale. Study 1 examines how two types of homophily---identity-driven and opinion-driven---impact collective performance on a sequential decision-making task via modulating network formation and trust relations. Study 2 considers how the presence of identity-based conformity pressure can affect the findings from the first study. Overall, we find that the effect of homophily on performance is complex, depending on the operative dimensions of similarity, mediating pathways, and the specific outcome of interest. Finally, we discuss the implications of our results for policy interventions aiming to improve group performance.

Diversity of social identities can improve the performance of groups through varied cognitive and communicative pathways. Recently, research efforts have focused on identifying when we should expect to see these potential benefits in real-world settings. While most research to date has studied this topic at individual and interpersonal levels, in this paper, we develop an agent-based model to explore how various aspects of homophily, the tendency of individuals to associate with similar others, affects performance at a larger scale. Study 1 examines how two types of homophily---identity-driven and opinion-driven---impact collective performance on a sequential decision-making task via modulating network formation and trust relations. Study 2 considers how the presence of identity-based conformity pressure can affect the findings from the first study. Overall, we find that the effect of homophily on performance is complex, depending on the operative dimensions of similarity, mediating pathways, and the specific outcome of interest. Finally, we discuss the implications of our results for policy interventions aiming to improve group performance.

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.

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.

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.

As algorithmic tools increasingly aid experts in making consequential decisions, the need to understand the precise factors that mediate their influence has grown commensurately. In this paper, we present a crowdsourcing vignette study designed to assess the impacts of two plausible factors on AI-informed decision-making. First, we examine homophily---do people defer more to models that tend to agree with them?---by manipulating the agreement during training between participants and the algorithmic tool. Second, we considered incentives---how do people incorporate a (known) cost structure in the hybrid decision-making setting?---by varying rewards associated with true positives vs. true negatives. Surprisingly, we found limited influence of either homophily and no evidence of incentive effects, despite participants performing similarly to previous studies. Higher levels of agreement between the participant and the AI tool yielded more confident predictions, but only when outcome feedback was absent. These results highlight the complexity of characterizing human-algorithm interactions, and suggest that findings from social psychology may require re-examination when humans interact with algorithms.

As algorithmic tools increasingly aid experts in making consequential decisions, the need to understand the precise factors that mediate their influence has grown commensurately. In this paper, we present a crowdsourcing vignette study designed to assess the impacts of two plausible factors on AI-informed decision-making. First, we examine homophily---do people defer more to models that tend to agree with them?---by manipulating the agreement during training between participants and the algorithmic tool. Second, we considered incentives---how do people incorporate a (known) cost structure in the hybrid decision-making setting?---by varying rewards associated with true positives vs. true negatives. Surprisingly, we found limited influence of either homophily and no evidence of incentive effects, despite participants performing similarly to previous studies. Higher levels of agreement between the participant and the AI tool yielded more confident predictions, but only when outcome feedback was absent. These results highlight the complexity of characterizing human-algorithm interactions, and suggest that findings from social psychology may require re-examination when humans interact with algorithms.

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.

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.

This paper describes some epistemic cultural considerations which shape the uses of psychology. I argue the study of mind is bound by the metaphysical background of the given locale and era in which it is practiced. The epistemic setting in which psychology takes place will shape what is worth observing, how it is to be studied, how the data is to be interpreted, and the nature of the ultimate explanatory units. I argue epistemic constraints shape the praxes that arise from structural study of the mind. In order to illustrate this notion of epistemic cultural constraint, I discuss Soviet Psychology and provide a contrast between practical uses of psychoanalysis in India, Egypt, and rural Ghana. In response to these conceptual and practical epistemic limitations, psychology could adapt methods drawn from history and anthropology towards an interdisciplinary psychology.

This paper describes some epistemic cultural considerations which shape the uses of psychology. I argue the study of mind is bound by the metaphysical background of the given locale and era in which it is practiced. The epistemic setting in which psychology takes place will shape what is worth observing, how it is to be studied, how the data is to be interpreted, and the nature of the ultimate explanatory units. I argue epistemic constraints shape the praxes that arise from structural study of the mind. In order to illustrate this notion of epistemic cultural constraint, I discuss Soviet Psychology and provide a contrast between practical uses of psychoanalysis in India, Egypt, and rural Ghana. In response to these conceptual and practical epistemic limitations, psychology could adapt methods drawn from history and anthropology towards an interdisciplinary psychology.