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  • Open Access
    Authors: 
    Francesca Delogu; Heiner Drenhaus; Matthew W. Crocker;
    Publisher: Springer Science and Business Media LLC

    When reading a text describing an everyday activity, comprehenders build a model of the situation described that includes prior knowledge of the entities, locations, and sequences of actions that typically occur within the event. Previous work has demonstrated that such knowledge guides the processing of incoming information by making event boundaries more or less expected. In the present ERP study, we investigated whether comprehenders’ expectations about event boundaries are influenced by how elaborately common events are described in the context. Participants read short stories in which a common activity (e.g., washing the dishes) was described either in brief or in an elaborate manner. The final sentence contained a target word referring to a more predictable action marking a fine event boundary (e.g., drying) or a less predictable action, marking a coarse event boundary (e.g., jogging). The results revealed a larger N400 effect for coarse event boundaries compared to fine event boundaries, but no interaction with description length. Between 600 and 1000 ms, however, elaborate contexts elicited a larger frontal positivity compared to brief contexts. This effect was largely driven by less predictable targets, marking coarse event boundaries. We interpret the P600 effect as indexing the updating of the situation model at event boundaries, consistent with Event Segmentation Theory (EST). The updating process is more demanding with coarse event boundaries, which presumably require the construction of a new situation model. Electronic supplementary material The online version of this article (10.3758/s13421-017-0766-4) contains supplementary material, which is available to authorized users.

  • Open Access English
    Authors: 
    Harm Brouwer; Matthew W. Crocker; Noortje J. Venhuizen; John Hoeks;
    Publisher: John Wiley and Sons Inc.
    Project: EC | LANPERCEPT (316748)

    Abstract Ten years ago, researchers using event‐related brain potentials (ERPs) to study language comprehension were puzzled by what looked like a Semantic Illusion: Semantically anomalous, but structurally well‐formed sentences did not affect the N400 component—traditionally taken to reflect semantic integration—but instead produced a P600 effect, which is generally linked to syntactic processing. This finding led to a considerable amount of debate, and a number of complex processing models have been proposed as an explanation. What these models have in common is that they postulate two or more separate processing streams, in order to reconcile the Semantic Illusion and other semantically induced P600 effects with the traditional interpretations of the N400 and the P600. Recently, however, these multi‐stream models have been called into question, and a simpler single‐stream model has been proposed. According to this alternative model, the N400 component reflects the retrieval of word meaning from semantic memory, and the P600 component indexes the integration of this meaning into the unfolding utterance interpretation. In the present paper, we provide support for this “Retrieval–Integration (RI)” account by instantiating it as a neurocomputational model. This neurocomputational model is the first to successfully simulate the N400 and P600 amplitude in language comprehension, and simulations with this model provide a proof of concept of the single‐stream RI account of semantically induced patterns of N400 and P600 modulations.

  • Open Access English
    Authors: 
    Adel Al-Janabi; Ehsan Ali Kareem; Radhwan Hussein Abdulzhraa Al Sagheer;
    Publisher: Zenodo

    <span>The work presents new theoretical equipment for the representation of natural languages (NL) in computers. Linguistics: morphology, semantics, and syntax are also presented as components of subtle computer science that form. A structure and an integrated data system. The presented useful theory of language is a new method to learn the language by separating the fields of semantics and syntax.</span>

  • Publication . Article . Preprint . 2022
    Open Access English
    Authors: 
    Noortje J. Venhuizen; Petra Hendriks; Matthew W. Crocker; Harm Brouwer;
    Country: Netherlands

    Natural language semantics has recently sought to combine the complementary strengths of formal and distributional approaches to meaning. More specifically, proposals have been put forward to augment formal semantic machinery with distributional meaning representations, thereby introducing the notion of semantic similarity into formal semantics, or to define distributional systems that aim to incorporate formal notions such as entailment and compositionality. However, given the fundamentally different 'representational currency' underlying formal and distributional approaches - models of the world versus linguistic co-occurrence - their unification has proven extremely difficult. Here, we define a Distributional Formal Semantics that integrates distributionality into a formal semantic system on the level of formal models. This approach offers probabilistic, distributed meaning representations that are also inherently compositional, and that naturally capture fundamental semantic notions such as quantification and entailment. Furthermore, we show how the probabilistic nature of these representations allows for probabilistic inference, and how the information-theoretic notion of "information" (measured in terms of Entropy and Surprisal) naturally follows from it. Finally, we illustrate how meaning representations can be derived incrementally from linguistic input using a recurrent neural network model, and how the resultant incremental semantic construction procedure intuitively captures key semantic phenomena, including negation, presupposition, and anaphoricity. Comment: To appear in: Information and Computation (WoLLIC 2019 Special Issue)

  • Publication . Preprint . Other literature type . Article . 2022
    Open Access
    Authors: 
    Heyman, Tom; Heyman, Geert;
    Publisher: Center for Open Science

    Recent advances in the field of computational linguistics have led to the development of various prediction-based models of semantics. These models seek to infer word representations from large text collections by predicting target words from neighbouring words (or vice versa). The resulting representations are vectors in a continuous space, collectively called word embeddings. Although psychological plausibility was not a primary concern for the developers of predictive models, it has been the topic of several recent studies in the field of psycholinguistics. That is, word embeddings have been linked to similarity ratings, word associations, semantic priming, word recognition latencies, and so on. Here, we build on this work by investigating category structure. Throughout seven experiments, we sought to predict human typicality judgements from two languages, Dutch and English, using different semantic spaces. More specifically, we extracted a number of predictor variables, and evaluated how well they could capture the typicality gradient of common categories (e.g., birds, fruit, vehicles, etc.). Overall, the performance of predictive models was rather modest and did not compare favourably with that of an older count-based model. These results are somewhat disappointing given the enthusiasm surrounding predictive models. Possible explanations and future directions are discussed.

  • Publication . Other literature type . Article . 2022
    Open Access
    Authors: 
    Adam Michael Bricker;
    Publisher: Open Science Framework

    Despite the ubiquity of knowledge attribution in human social cognition, its associated neural and cognitive mechanisms are poorly documented. A wealth of converging evidence in cognitive neuroscience has identified independent perspective-taking and inhibitory processes for belief attribution, but the extent to which these processes are shared by knowledge attribution isn't presently understood. Here, we present the findings of an EEG study designed to directly address this shortcoming. These findings suggest that belief attribution is not a component process in knowledge attribution, contra a standard attitude taken by philosophers. Instead, observed differences in P3b amplitude indicate that knowledge attribution doesn't recruit the strong self-perspective inhibition characteristic of belief attribution. However, both belief and knowledge attribution were observed to display a late slow wave widely associated with mental state attribution, indicating that knowledge attribution also shares in more general processing of others' mental states. These results provide a new perspective both on how we think about knowledge attribution, as well as Theory of Mind processes generally.

  • Publication . Other literature type . Article . 2022
    Open Access
    Authors: 
    Kurdi, Benedek; Lozano, Shayn; Banaji, Mahzarin;
    Publisher: Springer Science and Business Media LLC

    We introduce the Open Affective Standardized Image Set (OASIS), an open-access online stimulus set containing 900 color images depicting a broad spectrum of themes, including humans, animals, objects, and scenes, along with normative ratings on two affective dimensions-valence (i.e., the degree of positive or negative affective response that the image evokes) and arousal (i.e., the intensity of the affective response that the image evokes). The OASIS images were collected from online sources, and valence and arousal ratings were obtained in an online study (total N = 822). The valence and arousal ratings covered much of the circumplex space and were highly reliable and consistent across gender groups. OASIS has four advantages: (a) the stimulus set contains a large number of images in four categories; (b) the data were collected in 2015, and thus OASIS features more current images and reflects more current ratings of valence and arousal than do existing stimulus sets; (c) the OASIS database affords users the ability to interactively explore images by category and ratings; and, most critically, (d) OASIS allows for free use of the images in online and offline research studies, as they are not subject to the copyright restrictions that apply to the International Affective Picture System. The OASIS images, along with normative valence and arousal ratings, are available for download from www.benedekkurdi.com/#oasis or https://db.tt/yYTZYCga .

  • Publication . Article . Other literature type . Preprint . 2022
    Open Access

    This is a repository associated with the paper "Positional biases in predictive processing of intonation" and contains materials, data and scripts: Abstract: Real-time speech comprehension is challenging because communicatively relevant information is distributed throughout the entire utterance. In five mouse tracking experiments on German and American English, we probe if listeners, in principle, use non-local, early intonational information to anticipate upcoming referents. Listeners had to select a speaker-intended referent with their mouse guided by intonational cues, allowing them to anticipate their decision by moving their hand toward the referent prior to lexical disambiguation. While German listeners (Exps. 1-3) seemed to ignore early pitch cues, American English listeners (Exps. 4-5) were in principle able to use these early pitch cues to anticipate upcoming referents. However, many listeners showed no indication of doing so. These results suggest that there are important positional asymmetries in the way intonational information is integrated, with early information being paid less attention to than later cues in the utterance.

  • Publication . Article . Other literature type . 2022
    Open Access
    Authors: 
    Nikole D. Patson;
    Publisher: Open Science Framework

    This paper reports the results of two experiments that investigate the nature of plural conceptual representations that are created during sentence comprehension. Previous work has found that comprehenders seem to represent both a singular object and a plural set of objects during the comprehension of plural nouns. The activation of the singular object has been attributed to the pragmatic processing involved in understanding the plural (Patson, Journal of Experimental Psychology: Learning, Memory and Cognition, 42, 1140–1153, 2016a). The goal of the current study was to further investigate this hypothesis. Experiment 1 used a picture-matching paradigm to investigate how comprehenders conceptualize plural nouns quantified with many, which renders the scalar implicature unnecessary. Consistent with the pragmatic processing hypothesis, comprehenders did not activate a singular form when the plural was quantified with many. Experiment 2 was designed to further investigate whether all quantifiers block activation of the singular form. The same picture-matching paradigm was used with numerical quantifiers that specify numbers either within or above the subitization range. When the number was within the subitization range, comprehenders’ conceptual representations contained exactly that number of objects, and importantly did not contain a singular object. When number was above the subitization range, comprehenders’ conceptual representations did not contain an exact number of objects and seemed to activate a singular object. These data are consistent with constraints on how many objects can be represented in visual working memory. Taken together, the results of these two experiments suggest that the plural’s conceptual representation emerged as a result of grammatical processing as well as limits on the visual processing system.

  • Publication . Article . Other literature type . Preprint . 2022
    Open Access
    Authors: 
    Eric Merkley;
    Publisher: Open Science Framework

    Abstract Scholars have maintained that public attitudes often diverge from expert consensus due to ideology-driven motivated reasoning. However, this is not a sufficient explanation for less salient and politically charged questions. More attention needs to be given to anti-intellectualism—the generalized mistrust of intellectuals and experts. Using data from the General Social Survey and a survey of 3,600 Americans on Amazon Mechanical Turk, I provide evidence of a strong association between anti-intellectualism and opposition to scientific positions on climate change, nuclear power, GMOs, and water fluoridation, particularly for respondents with higher levels of political interest. Second, a survey experiment shows that anti-intellectualism moderates the acceptance of expert consensus cues such that respondents with high levels of anti-intellectualism actually increase their opposition to these positions in response. Third, evidence shows anti-intellectualism is connected to populism, a worldview that sees political conflict as primarily between ordinary citizens and a privileged societal elite. Exposure to randomly assigned populist rhetoric, even that which does not pertain to experts directly, primes anti-intellectual predispositions among respondents in the processing of expert consensus cues. These findings suggest that rising anti-elite rhetoric may make anti-intellectual sentiment more salient in information processing.

Advanced search in Research products
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Searching FieldsTerms
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The following results are related to Digital Humanities and Cultural Heritage. Are you interested to view more results? Visit OpenAIRE - Explore.
7,955 Research products, page 1 of 796
  • Open Access
    Authors: 
    Francesca Delogu; Heiner Drenhaus; Matthew W. Crocker;
    Publisher: Springer Science and Business Media LLC

    When reading a text describing an everyday activity, comprehenders build a model of the situation described that includes prior knowledge of the entities, locations, and sequences of actions that typically occur within the event. Previous work has demonstrated that such knowledge guides the processing of incoming information by making event boundaries more or less expected. In the present ERP study, we investigated whether comprehenders’ expectations about event boundaries are influenced by how elaborately common events are described in the context. Participants read short stories in which a common activity (e.g., washing the dishes) was described either in brief or in an elaborate manner. The final sentence contained a target word referring to a more predictable action marking a fine event boundary (e.g., drying) or a less predictable action, marking a coarse event boundary (e.g., jogging). The results revealed a larger N400 effect for coarse event boundaries compared to fine event boundaries, but no interaction with description length. Between 600 and 1000 ms, however, elaborate contexts elicited a larger frontal positivity compared to brief contexts. This effect was largely driven by less predictable targets, marking coarse event boundaries. We interpret the P600 effect as indexing the updating of the situation model at event boundaries, consistent with Event Segmentation Theory (EST). The updating process is more demanding with coarse event boundaries, which presumably require the construction of a new situation model. Electronic supplementary material The online version of this article (10.3758/s13421-017-0766-4) contains supplementary material, which is available to authorized users.

  • Open Access English
    Authors: 
    Harm Brouwer; Matthew W. Crocker; Noortje J. Venhuizen; John Hoeks;
    Publisher: John Wiley and Sons Inc.
    Project: EC | LANPERCEPT (316748)

    Abstract Ten years ago, researchers using event‐related brain potentials (ERPs) to study language comprehension were puzzled by what looked like a Semantic Illusion: Semantically anomalous, but structurally well‐formed sentences did not affect the N400 component—traditionally taken to reflect semantic integration—but instead produced a P600 effect, which is generally linked to syntactic processing. This finding led to a considerable amount of debate, and a number of complex processing models have been proposed as an explanation. What these models have in common is that they postulate two or more separate processing streams, in order to reconcile the Semantic Illusion and other semantically induced P600 effects with the traditional interpretations of the N400 and the P600. Recently, however, these multi‐stream models have been called into question, and a simpler single‐stream model has been proposed. According to this alternative model, the N400 component reflects the retrieval of word meaning from semantic memory, and the P600 component indexes the integration of this meaning into the unfolding utterance interpretation. In the present paper, we provide support for this “Retrieval–Integration (RI)” account by instantiating it as a neurocomputational model. This neurocomputational model is the first to successfully simulate the N400 and P600 amplitude in language comprehension, and simulations with this model provide a proof of concept of the single‐stream RI account of semantically induced patterns of N400 and P600 modulations.

  • Open Access English
    Authors: 
    Adel Al-Janabi; Ehsan Ali Kareem; Radhwan Hussein Abdulzhraa Al Sagheer;
    Publisher: Zenodo

    <span>The work presents new theoretical equipment for the representation of natural languages (NL) in computers. Linguistics: morphology, semantics, and syntax are also presented as components of subtle computer science that form. A structure and an integrated data system. The presented useful theory of language is a new method to learn the language by separating the fields of semantics and syntax.</span>

  • Publication . Article . Preprint . 2022
    Open Access English
    Authors: 
    Noortje J. Venhuizen; Petra Hendriks; Matthew W. Crocker; Harm Brouwer;
    Country: Netherlands

    Natural language semantics has recently sought to combine the complementary strengths of formal and distributional approaches to meaning. More specifically, proposals have been put forward to augment formal semantic machinery with distributional meaning representations, thereby introducing the notion of semantic similarity into formal semantics, or to define distributional systems that aim to incorporate formal notions such as entailment and compositionality. However, given the fundamentally different 'representational currency' underlying formal and distributional approaches - models of the world versus linguistic co-occurrence - their unification has proven extremely difficult. Here, we define a Distributional Formal Semantics that integrates distributionality into a formal semantic system on the level of formal models. This approach offers probabilistic, distributed meaning representations that are also inherently compositional, and that naturally capture fundamental semantic notions such as quantification and entailment. Furthermore, we show how the probabilistic nature of these representations allows for probabilistic inference, and how the information-theoretic notion of "information" (measured in terms of Entropy and Surprisal) naturally follows from it. Finally, we illustrate how meaning representations can be derived incrementally from linguistic input using a recurrent neural network model, and how the resultant incremental semantic construction procedure intuitively captures key semantic phenomena, including negation, presupposition, and anaphoricity. Comment: To appear in: Information and Computation (WoLLIC 2019 Special Issue)

  • Publication . Preprint . Other literature type . Article . 2022
    Open Access
    Authors: 
    Heyman, Tom; Heyman, Geert;
    Publisher: Center for Open Science

    Recent advances in the field of computational linguistics have led to the development of various prediction-based models of semantics. These models seek to infer word representations from large text collections by predicting target words from neighbouring words (or vice versa). The resulting representations are vectors in a continuous space, collectively called word embeddings. Although psychological plausibility was not a primary concern for the developers of predictive models, it has been the topic of several recent studies in the field of psycholinguistics. That is, word embeddings have been linked to similarity ratings, word associations, semantic priming, word recognition latencies, and so on. Here, we build on this work by investigating category structure. Throughout seven experiments, we sought to predict human typicality judgements from two languages, Dutch and English, using different semantic spaces. More specifically, we extracted a number of predictor variables, and evaluated how well they could capture the typicality gradient of common categories (e.g., birds, fruit, vehicles, etc.). Overall, the performance of predictive models was rather modest and did not compare favourably with that of an older count-based model. These results are somewhat disappointing given the enthusiasm surrounding predictive models. Possible explanations and future directions are discussed.

  • Publication . Other literature type . Article . 2022
    Open Access
    Authors: 
    Adam Michael Bricker;
    Publisher: Open Science Framework

    Despite the ubiquity of knowledge attribution in human social cognition, its associated neural and cognitive mechanisms are poorly documented. A wealth of converging evidence in cognitive neuroscience has identified independent perspective-taking and inhibitory processes for belief attribution, but the extent to which these processes are shared by knowledge attribution isn't presently understood. Here, we present the findings of an EEG study designed to directly address this shortcoming. These findings suggest that belief attribution is not a component process in knowledge attribution, contra a standard attitude taken by philosophers. Instead, observed differences in P3b amplitude indicate that knowledge attribution doesn't recruit the strong self-perspective inhibition characteristic of belief attribution. However, both belief and knowledge attribution were observed to display a late slow wave widely associated with mental state attribution, indicating that knowledge attribution also shares in more general processing of others' mental states. These results provide a new perspective both on how we think about knowledge attribution, as well as Theory of Mind processes generally.

  • Publication . Other literature type . Article . 2022
    Open Access
    Authors: 
    Kurdi, Benedek; Lozano, Shayn; Banaji, Mahzarin;
    Publisher: Springer Science and Business Media LLC

    We introduce the Open Affective Standardized Image Set (OASIS), an open-access online stimulus set containing 900 color images depicting a broad spectrum of themes, including humans, animals, objects, and scenes, along with normative ratings on two affective dimensions-valence (i.e., the degree of positive or negative affective response that the image evokes) and arousal (i.e., the intensity of the affective response that the image evokes). The OASIS images were collected from online sources, and valence and arousal ratings were obtained in an online study (total N = 822). The valence and arousal ratings covered much of the circumplex space and were highly reliable and consistent across gender groups. OASIS has four advantages: (a) the stimulus set contains a large number of images in four categories; (b) the data were collected in 2015, and thus OASIS features more current images and reflects more current ratings of valence and arousal than do existing stimulus sets; (c) the OASIS database affords users the ability to interactively explore images by category and ratings; and, most critically, (d) OASIS allows for free use of the images in online and offline research studies, as they are not subject to the copyright restrictions that apply to the International Affective Picture System. The OASIS images, along with normative valence and arousal ratings, are available for download from www.benedekkurdi.com/#oasis or https://db.tt/yYTZYCga .

  • Publication . Article . Other literature type . Preprint . 2022
    Open Access

    This is a repository associated with the paper "Positional biases in predictive processing of intonation" and contains materials, data and scripts: Abstract: Real-time speech comprehension is challenging because communicatively relevant information is distributed throughout the entire utterance. In five mouse tracking experiments on German and American English, we probe if listeners, in principle, use non-local, early intonational information to anticipate upcoming referents. Listeners had to select a speaker-intended referent with their mouse guided by intonational cues, allowing them to anticipate their decision by moving their hand toward the referent prior to lexical disambiguation. While German listeners (Exps. 1-3) seemed to ignore early pitch cues, American English listeners (Exps. 4-5) were in principle able to use these early pitch cues to anticipate upcoming referents. However, many listeners showed no indication of doing so. These results suggest that there are important positional asymmetries in the way intonational information is integrated, with early information being paid less attention to than later cues in the utterance.

  • Publication . Article . Other literature type . 2022
    Open Access
    Authors: 
    Nikole D. Patson;
    Publisher: Open Science Framework

    This paper reports the results of two experiments that investigate the nature of plural conceptual representations that are created during sentence comprehension. Previous work has found that comprehenders seem to represent both a singular object and a plural set of objects during the comprehension of plural nouns. The activation of the singular object has been attributed to the pragmatic processing involved in understanding the plural (Patson, Journal of Experimental Psychology: Learning, Memory and Cognition, 42, 1140–1153, 2016a). The goal of the current study was to further investigate this hypothesis. Experiment 1 used a picture-matching paradigm to investigate how comprehenders conceptualize plural nouns quantified with many, which renders the scalar implicature unnecessary. Consistent with the pragmatic processing hypothesis, comprehenders did not activate a singular form when the plural was quantified with many. Experiment 2 was designed to further investigate whether all quantifiers block activation of the singular form. The same picture-matching paradigm was used with numerical quantifiers that specify numbers either within or above the subitization range. When the number was within the subitization range, comprehenders’ conceptual representations contained exactly that number of objects, and importantly did not contain a singular object. When number was above the subitization range, comprehenders’ conceptual representations did not contain an exact number of objects and seemed to activate a singular object. These data are consistent with constraints on how many objects can be represented in visual working memory. Taken together, the results of these two experiments suggest that the plural’s conceptual representation emerged as a result of grammatical processing as well as limits on the visual processing system.

  • Publication . Article . Other literature type . Preprint . 2022
    Open Access
    Authors: 
    Eric Merkley;
    Publisher: Open Science Framework

    Abstract Scholars have maintained that public attitudes often diverge from expert consensus due to ideology-driven motivated reasoning. However, this is not a sufficient explanation for less salient and politically charged questions. More attention needs to be given to anti-intellectualism—the generalized mistrust of intellectuals and experts. Using data from the General Social Survey and a survey of 3,600 Americans on Amazon Mechanical Turk, I provide evidence of a strong association between anti-intellectualism and opposition to scientific positions on climate change, nuclear power, GMOs, and water fluoridation, particularly for respondents with higher levels of political interest. Second, a survey experiment shows that anti-intellectualism moderates the acceptance of expert consensus cues such that respondents with high levels of anti-intellectualism actually increase their opposition to these positions in response. Third, evidence shows anti-intellectualism is connected to populism, a worldview that sees political conflict as primarily between ordinary citizens and a privileged societal elite. Exposure to randomly assigned populist rhetoric, even that which does not pertain to experts directly, primes anti-intellectual predispositions among respondents in the processing of expert consensus cues. These findings suggest that rising anti-elite rhetoric may make anti-intellectual sentiment more salient in information processing.