Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Creativity and the Brain

Neurocognitive approach to higher cognitive functions that bridges the gap between psychological and neural level of description is introduced. Relevant facts about the brain, working memory and representation of symbols in the brain are summarized. Putative brain processes responsible for problem solving, intuition, skill learning and automatization are described. The role of non-dominant brain hemisphere in solving problems requiring insight is conjectured. Two factors seem to be essential for creativity: imagination constrained by experience, and filtering that selects most interesting solutions. Experiments with paired words association are analyzed in details and evidence for stochastic resonance effects is found. Brain activity in the process of invention of novel words is proposed as the simplest way to understand creativity using experimental and computational means. Perspectives on computational models of creativity are discussed

Integrative priming occurs rapidly and uncontrollably during lexical processing

Lexical priming, whereby a prime word facilitates recognition of a related target word (e.g., nurse ? doctor), is typically attributed to association strength, semantic similarity, or compound familiarity. Here, the authors demonstrate a novel type of lexical priming that occurs among unassociated, dissimilar, and unfamiliar concepts (e.g., horse ? doctor). Specifically, integrative priming occurs when a prime word can be easily integrated with a target word to create a unitary representation. Across several manipulations of timing (stimulus onset asynchrony) and list context (relatedness proportion), lexical decisions for the target word were facilitated when it could be integrated with the prime word. Moreover, integrative priming was dissociated from both associative priming and semantic priming but was comparable in terms of both prevalence (across participants) and magnitude (within participants). This observation of integrative priming challenges present models of lexical priming, such as spreading activation, distributed representation, expectancy, episodic retrieval, and compound cue models. The authors suggest that integrative priming may be explained by a role activation model of relational integration

A distributional model of semantic context effects in lexical processinga

One of the most robust findings of experimental psycholinguistics is that the context in which a word is presented influences the effort involved in processing that word. We present a novel model of contextual facilitation based on word co-occurrence prob ability distributions, and empirically validate the model through simulation of three representative types of context manipulation: single word priming, multiple-priming and contextual constraint. In our simulations the effects of semantic context are mod eled using general-purpose techniques and representations from multivariate statistics, augmented with simple assumptions reflecting the inherently incremental nature of speech understanding. The contribution of our study is to show that special-purpose m echanisms are not necessary in order to capture the general pattern of the experimental results, and that a range of semantic context effects can be subsumed under the same principled account.›

Cognitive network science: A review of research on cognition through the lens of network representations, processes, and dynamics

10.1155/2019/2108423Complexity2019210842

Accumulating evidence for myriad alternatives: Modeling the generation of free association

The associative manner by which thoughts follow one another has intrigued scholars for decades. The process by which an association is generated in response to a cue can be explained by classic models of semantic processing through distinct computational mechanisms. Distributed attractor networks implement rich-get-richer dynamics and assume that stronger associations can be reached with fewer steps. Conversely, spreading activation models assume that a cue distributes its activation, in parallel, to all associations at a constant rate. Despite these models’ huge influence, their intractability together with the unconstrained nature of free association have restricted their few previous uses to qualitative predictions. To test these computational mechanisms quantitatively, we conceptualize free association as the product of internal evidence accumulation and generate predictions concerning the speed and strength of people’s associations. To this end, we first develop a novel approach to mapping the personalized space of words from which an individual chooses an association to a given cue. We then use state-of-the-art evidence accumulation models to demonstrate the function of rich-get-richer dynamics on the one hand and of stochasticity in the rate of spreading activation on the other hand, in preventing an exceedingly slow resolution of the competition among myriad potential associations. Furthermore, whereas our results uniformly indicate that stronger associations require less evidence, only in combination with rich-get-richer dynamics does this explain why weak associations are slow yet prevalent. We discuss implications for models of semantic processing and evidence accumulation and offer recommendations for practical applications and individual-differences research. (PsycInfo Database Record (c) 2022 APA, all rights reserved

Computational explorations of semantic cognition

Motivated by the widespread use of distributional models of semantics within the cognitive science community, we follow a computational modelling approach in order to better understand and expand the applicability of such models, as well as to test potential ways in which they can be improved and extended. We review evidence in favour of the assumption that distributional models capture important aspects of semantic cognition. We look at the models’ ability to account for behavioural data and fMRI patterns of brain activity, and investigate the structure of model-based, semantic networks. We test whether introducing affective information, obtained from a neural network model designed to predict emojis from co-occurring text, can improve the performance of linguistic and linguistic-visual models of semantics, in accounting for similarity/relatedness ratings. We find that adding visual and affective representations improves performance, especially for concrete and abstract words, respectively. We describe a processing model based on distributional semantics, in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can account for response time and accuracies in lexical and semantic decision tasks, as well as for concreteness/imageability and similarity/relatedness ratings. We evaluate the differences between concrete and abstract words, in terms of the structure of the semantic networks derived from distributional models of semantics. We examine how the structure is related to a number of factors that have been argued to differ between concrete and abstract words, namely imageability, age of acquisition, hedonic valence, contextual diversity, and semantic diversity. We use distributional models to explore factors that might be responsible for the poor linguistic performance of children suffering from Developmental Language Disorder. Based on the assumption that certain model parameters can be given a psychological interpretation, we start from “healthy” models, and generate “lesioned” models, by manipulating the parameters. This allows us to determine the importance of each factor, and their effects with respect to learning concrete vs abstract words