scholarly journals Out-of-vocabulary but not meaningless: Evidence for semantic-priming effects in pseudoword processing

2021 ◽  
Author(s):  
Daniele Gatti ◽  
Marco Marelli ◽  
Luca Rinaldi

Non-arbitrary phenomena in language, such as systematic association in the form-meaning interface, have been widely reported in the literature. Exploiting such systematic associations previous studies have demonstrated that pseudowords can be indicative of meaning. However, whether semantic activation from words and pseudowords is supported by the very same processes, activating a common semantic memory system, is currently not known. Here, we take advantage of recent progresses from computational linguistics models allowing to induce meaning representations for out-of-vocabulary strings of letters via domain-general associative-learning mechanisms applied to natural language. We combined these models with data from priming tasks, in which participants are showed two strings of letters presented sequentially one after the other and are then asked to indicate if the latter is a word or a pseudoword. In Experiment 1 we re-analyzed the data of the largest behavioral database on semantic priming, while in Experiment 2 we ran an independent replication on a new language, Italian, controlling for a series of possible confounds. Results were consistent across the two experiments and showed that the prime-word meaning interferes with the semantic pattern elicited by the target pseudoword (i.e., at increasing estimated semantic relatedness between prime word and target pseudoword, participants’ reaction times increased and accuracy decreased). These findings indicate that the same associative mechanisms governing word meaning also subserve the processing of pseudowords, suggesting in turn that human semantic memory can be conceived as a distributional system that builds upon a general-purpose capacity of extracting knowledge from complex statistical patterns.

2009 ◽  
Vol 21 (3-4) ◽  
pp. 137-143 ◽  
Author(s):  
Jacquelyne S. Cios ◽  
Regan F. Miller ◽  
Ashleigh Hillier ◽  
Madalina E. Tivarus ◽  
David Q. Beversdorf

Norepinephrine and dopamine are both believed to affect signal-to-noise in the cerebral cortex. Dopaminergic agents appear to modulate semantic networks during indirect semantic priming, but do not appear to affect problem solving dependent on access to semantic networks. Noradrenergic agents, though, do affect semantic network dependent problem solving. We wished to examine whether noradrenergic agents affect indirect semantic priming. Subjects attended three sessions: one each after propranolol (40 mg) (noradrenergic antagonist), ephedrine (25 mg) (noradrenergic agonist), and placebo. During each session, closely related, distantly related, and unrelated pairs were presented. Reaction times for a lexical decision task on the target words (second word in the pair) were recorded. No decrease in indirect semantic priming occurred with ephedrine. Furthermore, across all three drugs, a main effect of semantic relatedness was found, but no main effect of drug, and no drug/semantic relatedness interaction effect. These findings suggest that noradrenergic agents, with these drugs and at these doses, do not affect indirect semantic priming with the potency of dopaminergic drugs at the doses previously studied. In the context of this previous work, this suggests that more automatic processes such as priming and more controlled searches of the lexical and semantic networks such as problem solving may be mediated, at least in part, by distinct mechanisms with differing effects of pharmacological modulation.


1995 ◽  
Vol 4 (4) ◽  
pp. 148-151
Author(s):  
Amy Hasselkus ◽  
Scott S. Rubin ◽  
Marilyn Newhoff

Studies of both semantic priming and the generation effect (GE) have implicated spreading activation in semantic memory and have provided evidence for a semantic memory access disorder in patients with dementia. Fifteen subjects consisting of young, elderly, and demented patients participated in a semantic priming/GE task to determine whether the act of generating a semantic prime enhanced activation and reduced reaction times to related items. Reaction times were recorded for semantically related and unrelated targets presented after either read or generated word pair cues. From the results it was suggested that generating a prime provided little benefit for young subjects or subjects with dementia; elderly subjects benefited more from generating information than from reading it. Implications for theories of dementia and normal aging are discussed.


2014 ◽  
Vol 49 ◽  
pp. 1-47 ◽  
Author(s):  
E. Bruni ◽  
N. K. Tran ◽  
M. Baroni

Distributional semantic models derive computational representations of word meaning from the patterns of co-occurrence of words in text. Such models have been a success story of computational linguistics, being able to provide reliable estimates of semantic relatedness for the many semantic tasks requiring them. However, distributional models extract meaning information exclusively from text, which is an extremely impoverished basis compared to the rich perceptual sources that ground human semantic knowledge. We address the lack of perceptual grounding of distributional models by exploiting computer vision techniques that automatically identify discrete “visual words” in images, so that the distributional representation of a word can be extended to also encompass its co-occurrence with the visual words of images it is associated with. We propose a flexible architecture to integrate text- and image-based distributional information, and we show in a set of empirical tests that our integrated model is superior to the purely text-based approach, and it provides somewhat complementary semantic information with respect to the latter.


2020 ◽  
pp. 1085-1114
Author(s):  
Youngseok Choi ◽  
Jungsuk Oh ◽  
Jinsoo Park

This research proposes a novel method of measuring the dynamics of semantic relatedness. Research on semantic relatedness has a long history in the fields of computational linguistics, psychology, computer science, as well as information systems. Computing semantic relatedness has played a critical role in various situations, such as data integration and keyword recommendation. Many researchers have tried to propose more sophisticated techniques to measure semantic relatedness. However, little research has considered the change of semantic relatedness with the flow of time and occurrence of events. The authors' proposed method is validated by actual corpus data collected from a particular context over a specific period of time. They test the feasibility of our proposed method by constructing semantic networks by using the corpus collected during a different period of time. The experiment results show that our method can detect and manage the changes in semantic relatedness between concepts. Based on the results, the authors discuss the need for a dynamic semantic relatedness paradigm.


2016 ◽  
Vol 27 (2) ◽  
pp. 1-26 ◽  
Author(s):  
Youngseok Choi ◽  
Jungsuk Oh ◽  
Jinsoo Park

This research proposes a novel method of measuring the dynamics of semantic relatedness. Research on semantic relatedness has a long history in the fields of computational linguistics, psychology, computer science, as well as information systems. Computing semantic relatedness has played a critical role in various situations, such as data integration and keyword recommendation. Many researchers have tried to propose more sophisticated techniques to measure semantic relatedness. However, little research has considered the change of semantic relatedness with the flow of time and occurrence of events. The authors' proposed method is validated by actual corpus data collected from a particular context over a specific period of time. They test the feasibility of our proposed method by constructing semantic networks by using the corpus collected during a different period of time. The experiment results show that our method can detect and manage the changes in semantic relatedness between concepts. Based on the results, the authors discuss the need for a dynamic semantic relatedness paradigm.


2015 ◽  
Vol 10 (4) ◽  
pp. 678-686
Author(s):  
Kazuhisa Nagaya ◽  
◽  
Kazuya Nakayachi

When individuals estimate something numerically, their estimation tends to be close to a value perceived beforehand, called an anchor. This tendency is called “the anchoring effect.” We introduce three hypotheses – the numeric priming hypothesis, the semantic priming hypothesis, and the magnitude priming hypothesis – that explain the anchoring effect. We apply them to participants’ estimation of the number of sufferers in order to examine which model explains the anchoring effect best. Experimental results support the numeric priming hypothesis, indicating that the anchoring effect occurs even when no semantic relatedness exists between the number presented as the prime and the successive numerical estimation. Implications for disaster risk communication are discussed based on the results we obtained.


2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Mark Chang ◽  
Monica Chang

One of the main challenges in artificial intelligence or computational linguistics is understanding the meaning of a word or concept. We argue that the connotation of the term “understanding,” or the meaning of the word “meaning,” is merely a word mapping game due to unavoidable circular definitions. These circular definitions arise when an individual defines a concept, the concepts in its definition, and so on, eventually forming a personalized network of concepts, which we call an iWordNet. Such an iWordNet serves as an external representation of an individual’s knowledge and state of mind at the time of the network construction. As a result, “understanding” and knowledge can be regarded as a calculable statistical property of iWordNet topology. We will discuss the construction and analysis of the iWordNet, as well as the proposed “Path of Understanding” in an iWordNet that characterizes an individual’s understanding of a complex concept such as a written passage. In our pilot study of 20 subjects we used a regression model to demonstrate that the topological properties of an individual’s iWordNet are related to his IQ score, a relationship that suggests iWordNets as a potential new methodology to studying cognitive science and artificial intelligence.


2001 ◽  
Vol 4 (2) ◽  
pp. 143-154 ◽  
Author(s):  
Sonja A. Kotz

The current study set out to examine word recognition in early fluent Spanish–English bilinguals using a single word presentation lexical decision task (LDT). Reaction times (RTs) and event-related brain potentials (ERPs) were measured while subjects (16 per language condition) made a lexical decision on words and pseudowords in either Spanish or English. Results show associative priming as measured by RTs, but both associative and categorical priming in the ERPs in both language conditions. The dissociation of RT and ERP effects suggests that the two measures might tap into different underlying processes during semantic priming or reflect different sensitivities towards semantic priming. Furthermore, both RT and ERP measures revealed symmetrical priming in L1 and L2. These data indicate that word recognition in early fluent bilinguals is equivalent for L1 and L2.


Sign in / Sign up

Export Citation Format

Share Document