An Instance Theory of Semantic Memory

Jamieson, Randall K.; Avery, Johnathan E.; Johns, Brendan T.; Jones, Michael N.

doi:10.1007/s42113-018-0008-2

Cited by 55 publications

(89 citation statements)

References 69 publications

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“…The finding of word preferences in the UD data is consistent with the perspective that language is a complex adaptive system, a general perspective on language processing and the cultural evolution of language (e.g., Kirby et al, 2007; Christiansen and Chater, 2008; Beckner et al, 2009; Tomasello, 2009; see Johns and Jones, 2015; Jamieson et al, 2018 for computational models of semantics and language processing that embody this perspective). Adaptive theories of language propose that the acquisition and use of language is based in the past interactions that people have had with others in their social environment.…”

Section: Discussionsupporting

confidence: 80%

“…One of the key developments in big data approaches to cognition is the emergence of distributional models of semantics, which learn the meaning of words from statistical patterns contained in very large sources of texts (see Jones et al, 2015 for a review). The original, and best known, model of this class is Latent Semantic Analysis (LSA; Landauer and Dumais, 1997), which spurred the development of many new approaches (e.g., Lund and Burgess, 1996; Griffiths et al, 2007; Jones and Mewhort, 2007; Shaoul and Westbury, 2010; Mikolov et al, 2013; Jamieson et al, 2018). The insight that these models exploit is that lexical semantic behavior seems to be systematically related to the co-occurrence of words within the natural language environment.…”

Section: Introductionmentioning

confidence: 99%

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Mining a Crowdsourced Dictionary to Understand Consistency and Preference in Word Meanings

Johns

2019

Front. Psychol.

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Big data approaches to psychology have become increasing popular ( Jones, 2017 ). Two of the main developments of this line of research is the advent of distributional models of semantics (e.g., Landauer and Dumais, 1997 ), which learn the meaning of words from large text corpora, and the collection of mega datasets of human behavior (e.g., The English lexicon project; Balota et al., 2007 ). The current article combines these two approaches, with the goal being to understand the consistency and preference that people have for word meanings. This was accomplished by mining a large amount of data from an online, crowdsourced dictionary and analyzing this data with a distributional model. Overall, it was found that even for words that are not an active part of the language environment, there is a large amount of consistency in the word meanings that different people have. Additionally, it was demonstrated that users of a language have strong preferences for word meanings, such that definitions to words that do not conform to people’s conceptions are rejected by a community of language users. The results of this article provides insights into the cultural evolution of word meanings, and sheds light on alternative methodologies that can be used to understand lexical behavior.

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Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Mining a Crowdsourced Dictionary to Understand Consistency and Preference in Word Meanings

Johns

2019

Front. Psychol.

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“…The results contained in this article point to the continued power of big-data analyses of behavioral data, and in this particular case, large natural-language corpora. Not only can these corpora be used to develop new models of language processing (e.g., Griffiths, Steyvers, & Tenenbaum, 2007;Jamieson, Avery, Johns, & Jones, 2018;Johns & Jones, 2015;Jones & Mewhort, 2007;Landauer & Dumais, 1997), but can also serve to examine large scale trends in human behavior (e.g., . Language is a central organizer of human cognition (Brysbaert et al, 2018;Johns, Jones, & Mewhort, 2012b;Jones et al, 2017), and a large percentage of our everyday experience consists of linguistic stimuli, with a typical human being reading millions of words per year (Brysbaert, Stevens, Mandera, & Keuleers, 2016).…”

Section: Discussionmentioning

confidence: 99%

Gender bias at scale: Evidence from the usage of personal names

Johns

Dye

2019

Behav Res

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Recent research within the computational social sciences has shown that when computational models of lexical semantics are trained on standard natural-language corpora, they embody many of the implicit biases that are seen in human behavior (Caliskan, Bryson, & Narayanan, 2017). In the present study, we aimed to build on this work and demonstrate that there is a large and systematic bias in the use of personal names in the natural-language environment, such that male names are much more prevalent than female names. This bias holds over an analysis of billions of words of text, subcategorized into different genres within fiction novels, nonfiction books, and subtitles from television and film. Additionally, we showed that this bias holds across time, with more recent work displaying the same patterns as work published tens or hundreds of years previously. Finally, we showed that the main cause of the bias comes from male authors perpetuating the bias toward male names, with female authors showing a much smaller bias. This work demonstrates the potential of big-data analyses to shed light on large-scale trends in human behavior and to elucidate their causes.

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“…However, HDM may be more appropriately understood as a model of semantic memory. A good candidate for an episode memory model is the MINERVA class of memory models (e.g., Hintzman, 1986;Jamieson, Avery, Johns, & Jones, 2018), a vector-based model of human memory that stores one vector for each memory trace (i.e., ACT-R chunk) and has strong commonalities with ACT-R DM (Dimov, 2016).…”

Section: Episodic Memorymentioning

confidence: 99%

Holographic Declarative Memory: Distributional semantics as the architecture of memory

Kelly¹,

Arora²,

West³

et al. 2019

Preprint

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We demonstrate that the key components of cognitive architectures—declarative and procedural memory—and their key capabilities—learning, memory retrieval, judgement, and decision-making—can be implemented as algebraic operations on vectors in a high-dimensional space. High-dimensional vector spaces underlie the success of modern machine learning techniques based on neural networks and deep learning. However, while neural networks have an impressive ability to process data to find patterns, they do not typically model high-level cognition, and it is often unclear how they work. Traditional, symbolic cognitive architectures can capture the complexities of high-level cognition and provide human-readable, explainable models, but have limited ability to detect patterns or learn. Vector-symbolic architectures, where symbols are represented as vectors, bridge the gap between the two approaches. We posit that cognitive architectures, if implemented in a vector-space model, represent a useful, explanatory model of the internal representations of otherwise opaque neural architectures. Our vector-space model accounts for primacy and recency effects in free recall, the fan effect in recognition, probability judgements, and human performance on an iterated decision task. Our model provides a flexible, scalable alternative to symbolic cognitive architectures at a level of description that bridges symbolic, quantum, and neural models of cognition.

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An Instance Theory of Semantic Memory

Cited by 55 publications

References 69 publications

Mining a Crowdsourced Dictionary to Understand Consistency and Preference in Word Meanings

Mining a Crowdsourced Dictionary to Understand Consistency and Preference in Word Meanings

Gender bias at scale: Evidence from the usage of personal names

Holographic Declarative Memory: Distributional semantics as the architecture of memory

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