The single-word semantic priming paradigm is a tool for investigating how and when word meaning activation occurs during visual word recognition. The prime task effect refers to the elimination of the typically robust semantic priming effect by a nonsemantic prime task . The purpose of this paper is to provide a tutorial review of the literature examining the prime task effect. Understanding the nature of this effect has implications for delineating how selective attention modulates evidence for semantic activation during word (...) reading. These implications are outlined. Additionally, speculations for how these issues of selective attention relate to awareness are offered. (shrink)

Describing his creative process, David Bowie stated: “I’ll take articles out of newspapers, poems that I’ve written, pieces of other people’s books, and put them all into this little warehouse, thi...

Biases in cognition are ubiquitous. Social psychologists suggested biases and stereotypes serve a multifarious set of cognitive goals, while at the same time stressing their potential harmfulness. Recently, biases and stereotypes became the purview of heated debates in the machine learning community too. Researchers and developers are becoming increasingly aware of the fact that some biases, like gender and race biases, are entrenched in the algorithms some AI applications rely upon. Here, taking into account several existing approaches that address the (...) problem of implicit biases and stereotypes, we propose that a strategy to cope with this phenomenon is to unmask those found in AI systems by understanding their cognitive dimension, rather than simply trying to correct algorithms. To this extent, we present a discussion bridging together findings from cognitive science and insights from machine learning that can be integrated in a state-of-the-art semantic network. Remarkably, this resource can be of assistance to scholars (e.g., cognitive and computer scientists) while at the same time contributing to refine AI regulations affecting social life. We show how only through a thorough understanding of the cognitive processes leading to biases, and through an interdisciplinary effort, we can make the best of AI technology. (shrink)

O'Brien & Opie's (O&O's) theory demands a view of unconscious processing that is incompatible with virtually all current PDP models of neural activity. Relative to the alternatives, the theory is closer to an AI than a parallel distributed processing (PDP) perspective, and its treatment of phenomenology is ad hoc. It raises at least one important question: Could features of network relaxation be the “switch” that turns an unconscious into a conscious network?

An integrated statement is made concerning the semantic status of nodes in a propositional semantic network, claiming that such nodes represent only intensions. Within the network, the only reference to extensionality is via a mechanism to assert that two intensions have the same extension in same world. This framework is employed in three application problems to illustrate the nature of its solutions.The formalism used here utilizes only assertional information and no structural, or definitional, information. This restriction corresponds to many of (...) the psychologically motivated network models. Some of the psychological implications of network processes called node merging and node splitting are discussed. Additionally, it is pointed out that both our networks and the psychologically based networks are prone to memory confusions about knowing unless augmented by domain‐specific inference processes, or by structural information. (shrink)

The Influence of Emotional and Non-emotional Concepts Activation on Information Processing and Unintentional Memorizing The aim of the work is to compare mechanisms of semantic and emotional processing and memory. Targets were primed by category name. The congruency of prime and target was manipulated. The reaction time of lexical decisions and the effects of unintentional memorizing of word targets were measured. Activation of semantic and emotional nodes leads to faster processing of related concepts: congruent targets are processed faster than incongruent. (...) Processing congruent primed emotional concepts depends on their modality: anger and joy words are processed faster than sadness. Thus, congruently primed activating emotional concepts are processed differently from congruently primed deactivating concepts. The effectiveness of unintentional memory of emotional and non-emotional concepts is based on different mechanisms: congruently primed emotional words are better remembered than incongruently primed. The results are discussed in the framework of spreading activation theory and theory of emotional memory. (shrink)

The paper investigates relationship between working memory efficiency, defined as the result of its’ processing & storage capacity and the tendency to create assosiative memory distortions ; yield under the influence of external, suggesting factors. Both issues were examined using extended version of Deese-Roediger-McDermott procedure, modified in order to meet the study demands. Suggestion was contained in an ostentatious feedback information the participants received during the DRM procedure. Working memory was measured by standardized tasks. Study included 3 conditions, differing in (...) the quality of suggestion. Participants were assigned into 3 groups, depending on results they achieved completing the WM tasks. Obtained results alongside the previously set hypothesis, revealed that WM impacts individuals’ tendency to create false memories in DRM and that the individuals showing higher rates in WM tasks are less willing to yield to suggestion compared to those with lesser ones. It also showed that the greater amount to shift, emerges under the negative suggestion condition. Notwithstanding that the interaction effect did not achieve saliency, both analyzed factors are considered as meaningful to explain memory suggestion susceptibility in presented study. Although, obtained results emphasize the crucial role of WM efficiency, that is believed to decide the magnitude of feedback that is influential in every subject. Therefore, issue demands further exploration. (shrink)

Our studies of naturally occurring human dialogue have led to the recognition of a class of regularities which characterize impoltant aspects of communication. People appear to interact according to established patterns which span several turns in a dialogue and which recur frequently. These patterns appear to be organized around the goals which the dialogue serves for each participant. Many things which are said later in a dialogue can only be interpreted as pursuit of these goals, established by earlier dialogue.These patterns (...) have been represented b) a set of knowledge structures called Dialogue‐Games, capturing shared, conventional knowledge that people have about communication and how it can be used to achieve goals. A Dialogue‐Game has Parameters, which represent those elements that vary across instances of a particular pattern‐the particular dialogue participants and the content topic. The states of the world which must be in effect for a particular Dialogue‐Game to be employed successfully are represented by Specifications of these Parameters. Finally, the expected sequence of intermediate states that occur during instances of a particular conventional pattern are represented by the Components of the corresponding Dialogue‐Game.Representations for several Dialogue‐Games are presented here, based on our analyses of different kinds of naturally occurring dialogue. A process model is discussed, showing Dialogue‐Game identification, pursuit, and termination as part of the comprehension of dialogue utterances. This Dialogue‐Game model captures some of the important functional aspects of language, especially indirect uses to achieve implicit communication. (shrink)

Preparing words in speech production is normally a fast and accurate process. We generate them two or three per second in fluent conversation; and overtly naming a clear picture of an object can easily be initiated within 600 msec after picture onset. The underlying process, however, is exceedingly complex. The theory reviewed in this target article analyzes this process as staged and feedforward. After a first stage of conceptual preparation, word generation proceeds through lexical selection, morphological and phonological encoding, phonetic (...) encoding, and articulation itself. In addition, the speaker exerts some degree of output control, by monitoring of self-produced internal and overt speech. The core of the theory, ranging from lexical selection to the initiation of phonetic encoding, is captured in a computational model, called WEAVER++. Both the theory and the computational model have been developed in interaction with reaction time experiments, particularly in picture naming or related word production paradigms, with the aim of accounting for the real-time processing in normal word production. A comprehensive review of theory, model, and experiments is presented. The model can handle some of the main observations in the domain of speech errors (the major empirical domain for most other theories of lexical access), and the theory opens new ways of approaching the cerebral organization of speech production by way of high-temporal-resolution imaging. (shrink)

Localist models of spreading activation (SA) and models assuming distributed representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In this study, we implemented SA in an attractor neural network model with distributed representations and created a unified framework for the two approaches. Our models assume a synaptic depression mechanism leading to autonomous transitions between encoded memory patterns (latching dynamics), which account for the major characteristics of automatic semantic priming in (...) humans. Using computer simulations, we demonstrated how findings that challenged attractor‐based networks in the past, such as mediated and asymmetric priming, are a natural consequence of our present model’s dynamics. Puzzling results regarding backward priming were also given a straightforward explanation. In addition, the current model addresses some of the differences between semantic and associative relatedness and explains how these differences interact with stimulus onset asynchrony in priming experiments. (shrink)

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate of semantic (...) transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category-exemplar, mediated and backward prime-target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model. (shrink)

Results from three field network studies show that depending on individuals’ network positions (central or peripheral), experts and novices have varying ethical predispositions (EP). In particular, central experts (vs. peripheral experts) have higher EP, while novices in the same positions (vs. peripheral novices) have lower EP. Results demonstrate individuals’ relational-interdependent self-construal mediates these relationships. Importantly, this research suggests that the interaction between network and individual difference variables uniquely affect individuals’ ethical predisposition. Given the lack of research focus on the impact (...) of structural positions on EP, this article demonstrates the importance of combining network and individual variables to investigate individuals’ EP. (shrink)

Both scientists and children make important structural discoveries, yet their computational underpinnings are not well understood. Structure discovery has previously been formalized as probabilistic inference about the right structural form—where form could be a tree, ring, chain, grid, etc.. Although this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge. Here we (...) introduce a new computational model of how organizing structure can be discovered, utilizing a broad hypothesis space with a preference for sparse connectivity. Given that the inductive bias is more general, the model's initial knowledge shows little qualitative resemblance to some of the discoveries it supports. As a consequence, the model can also learn complex structures for domains that lack intuitive description, as well as predict human property induction judgments without explicit structural forms. By allowing form to emerge from sparsity, our approach clarifies how both the richness and flexibility of human conceptual organization can coexist. (shrink)

Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two‐player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether similarity metrics derived from two large databases of human free association norms, the University of South Florida norms and the Small World of Words norms, and two distributional semantic models based on large language (...) corpora (word2vec and GloVe) predict performance in this game. Participant dyads were presented with 20‐item word boards with word pairs of varying relatedness. The speaker received a word pair from the board (e.g., exam‐algebra) and generated a one‐word semantic clue (e.g., math), which was used by the guesser to identify the word pair on the board across three attempts. Response times to generate the clue, as well as accuracy and latencies for the guessed word pair, were strongly predicted by the cosine similarity between word pairs and clues in random walk‐based associative models, and to a lesser degree by the distributional models, suggesting that conceptual representations activated during free association were better able to capture search and retrieval processes in the game. Further, the speaker adjusted subsequent clues based on the first attempt by the guesser, who in turn benefited from the adjustment in clues, suggesting a cooperative influence in the game that was effectively captured by both associative and distributional models. These results indicate that both associative and distributional models can capture relatively unconstrained search processes in a cooperative game setting, and Connector is particularly suited to examine communication and semantic search processes. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 54-77, January 2022.