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The received view of computation is methodologically bifurcated: it offers different accounts of computation in the mathematical and physical cases. But little in the way of argument has been given for this approach. This paper rectifies the situation by arguing that the alternative, a unified account, is untenable. Furthermore, once these issues are brought into sharper relief we can see that work remains to be done to illuminate the relationship between physical and mathematical computation.

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.

<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>

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. To appear in: Information and Computation (WoLLIC 2019 Special Issue)

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.

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.

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 .

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.

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.

Abstract Explicit (directly measured) evaluations are widely assumed to be sensitive to logical structure. However, whether implicit (indirectly measured) evaluations are uniquely sensitive to co-occurrence information or can also reflect logical structure has been a matter of theoretical debate. To test these competing ideas, participants (N = 3928) completed a learning phase consisting of a series of two-step trials. In step 1, one or more conditional statements (A → B) containing novel targets co-occurring with valenced adjectives (e.g., “if you see a blue square, Ibbonif is sincere”) were presented. In step 2, a disambiguating stimulus, e.g., blue square (A) or gray blob (¬A) was revealed. Co-occurrence information, disambiguating stimuli, or both were varied between conditions to enable investigating the unique and joint effects of each. Across studies, the combination of conditional statements and disambiguating stimuli licensed different normatively accurate inferences. In Study 1, participants were prompted to use modus ponens (inferring B from A → B and A). In Studies 2–4, the information did not license accurate inferences, but some participants made inferential errors: affirming the consequent (inferring A from A → B and B; Study 2) or denying the antecedent (inferring ¬B from A → B and ¬A; Studies 3A, 3B, and 4). Bayesian modeling using ordinal constraints on condition means yielded consistent evidence for the sensitivity of both explicit (self-report) and implicit (IAT and AMP) evaluations to the (correctly or erroneously) inferred truth value of propositions. Together, these data suggest that implicit evaluations, similar to their explicit counterparts, can reflect logical structure.