Semantic memory: A review of methods, models, and current challenges

Stroke Belt birth state and late-life cognition in the Study of Healthy Aging in African Americans (STAR)

PURPOSE

We examined the association of Stroke Belt birth state with late-life cognition in The Study of Healthy Aging in African Americans (STAR).

METHODS

STAR enrolled 764 Black Americans ages 50+ who were long-term Kaiser Permanente Northern California members. Participants completed Multiphasic Health Check-ups (MHC; 1964-1985) where early-life overweight/obesity, hypertension, diabetes, and hyperlipidemia were measured. At STAR (2018), birth state, self-reported early-life socioeconomic status (SES), and executive function, verbal episodic memory, and semantic memory scores were collected. We used linear regression to examine the association between Stroke Belt birth and late-life cognition adjusting for birth year, gender, and parental education. We evaluated early-life SES and cardiovascular risk factors (CVRF) as potential mechanisms.

RESULTS

Twenty-seven percent of participants were born in the Stroke Belt with a mean age of 69 (standard deviation = 9) at STAR. Stroke Belt birth was associated with worse late-life executive function (β [95% confidence interval]: -0.18 [-0.33, -0.02]) and semantic memory (-0.37 [-0.53, -0.21]), but not verbal episodic memory (-0.04 [-0.20, 0.12]). Adjustment for SES and CVRF attenuated associations of Stroke Belt birth with cognition (executive function [-0.05 {-0.25, 0.14}]; semantic memory [-0.28 {-0.49, -0.07}]).

CONCLUSIONS

Black Americans born in the Stroke Belt had worse late-life cognition than those born elsewhere, underscoring the importance of early-life exposures on brain health.

Accounting for item-level variance in recognition memory: Comparing word frequency and contextual diversity

Contextual diversity modifies word frequency by ignoring the repetition of words in context (Adelman, Brown, & Quesada,  2006, Psychological Science, 17(9), 814-823). Semantic diversity modifies contextual diversity by taking into account the uniqueness of the contexts that a word occurs in when calculating lexical strength (Jones, Johns, & Recchia,  2012, Canadian Journal of Experimental Psychology, 66, 115-124). Recent research has demonstrated that measures based on contextual and semantic diversity provide a considerable improvement over word frequency when accounting for lexical organization data (Johns, 2021, Psychological Review, 128, 525-557; Johns, Dye, & Jones, 2020a, Quarterly Journal of Experimental Psychology, 73, 841-855). The article demonstrates that these same findings generalize to word-level episodic recognition rates, using the previously released data of Cortese, Khanna, and Hacker (Cortese et al., 2010, Memory, 18, 595-609) and Cortese, McCarty, and Schock (Cortese et al., 2015, Quarterly Journal of Experimental Psychology, 68, 1489-1501). It was found that including the best fitting contextual diversity model allowed for a very large increase in variance accounted for over previously used variables, such as word frequency, signalling commonality with results from the lexical organization literature. The findings of this article suggest that current trends in the collection of megadata sets of human behavior (e.g., Balota et al., 2007, Behavior Research Methods, 39(3), 445-459) provide a promising avenue to develop new theoretically oriented models of word-level episodic recognition data.

Semantic Memory Search and Retrieval in a Novel Cooperative Word Game: A Comparison of Associative and Distributional Semantic Models

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.

Specifying a relationship between semantic and episodic memory in the computation of a feature-based familiarity signal using MINERVA 2

Approaches to modeling episodic recognition memory often imply a separability from semantic memory insofar as an implicit tabula rasa (i.e., blank slate) assumption is apparent in many simulations. This is evident in the common practice of having new test probes correspond to zero memory traces in the store while old test probes correspond to traces representing instances of items' occurrence on a study list. However, in list-learning studies involving word lists, none of the test items would actually correspond to zero items in the person's memory, as all of the test words are generally known to participants, whether old or new. By focusing on a list-learning recognition phenomenon that likely results from feature-based familiarity detection and necessarily involves a role of preexisting knowledge in its mechanisms-the semantic-feature-based recognition without cued recall phenomenon-we show how incorporating preexisting knowledge into the MINERVA 2 model enables it to simulate previously shown empirical patterns with this phenomenon. The simulation patterns reported here raise new theoretical implications worth further exploration, such as the extent to which the variances change in the signal versus the noise distribution when preexisting knowledge is present versus absent in the simulations.

Is the Mind a Network? Maps, Vehicles, and Skyhooks in Cognitive Network Science

Cognitive researchers often carve cognition up into structures and processes. Cognitive processes operate on structures, like vehicles driving over a map. Language alongside semantic and episodic memory are proposed to have structure, as are perceptual systems. Over these structures, processes operate to construct memory and solve problems by retrieving and manipulating information. Network science offers an approach to representing cognitive structures and has made tremendous inroads into understanding the nature of cognitive structure and process. But is the mind a network? If so, what kind? In this article, we briefly review the main metaphors, assumptions, and pitfalls prevalent in cognitive network science (maps and vehicles; one network/process to rule them all), highlight the need for new metaphors that elaborate on the map-and-vehicle framework (wormholes, skyhooks, and generators), and present open questions in studying the mind as a network (the challenge of capturing network change, what should the edges of cognitive networks be made of, and aggregated vs. individual-based networks). One critical lesson of this exercise is that the richness of the mind as network approach makes it a powerful tool in its own right; it has helped to make our assumptions more visible, generating new and fascinating questions, and enriching the prospects for future research. A second lesson is that the mind as a network-though useful-is incomplete. The mind is not a network, but it may contain them.

Beauty and Wellness in the Semantic Memory of the Beholder

Beauty and wellness are terms used often in common parlance, however their meaning and relation to each other is unclear. To probe their meaning, we applied network science methods to estimate and compare the semantic networks associated with beauty and wellness in different age generation cohorts (Generation Z, Millennials, Generation X, and Baby Boomers) and in women and men. These mappings were achieved by estimating group-based semantic networks from free association responses to a list of 47 words, either related to Beauty, Wellness, or Beauty + Wellness. Beauty was consistently related to Elegance, Feminine, Gorgeous, Lovely, Sexy, and Stylish. Wellness was consistently related Aerobics, Fitness, Health, Holistic, Lifestyle, Medical, Nutrition, and Thrive. In addition, older cohorts had semantic networks that were less connected and more segregated from each other. Finally, we found that women compared to men had more segregated and organized concepts of Beauty and Wellness. In contemporary societies that are pre-occupied by the pursuit of beauty and a healthy lifestyle, our findings shed novel light on how people think about beauty and wellness and how they are related across different age generations and by sex.

Semantic Memory and Lexical Availability in Parkinson's Disease: A Statistical Learning Study

Parkinson’s disease (PD) is a neurodegenerative disorder that causes a progressive impairment in motor and cognitive functions. Although semantic fluency deficits have been described in PD, more specific semantic memory (SM) and lexical availability (LA) domains have not been previously addressed. Here, we aimed to characterize the cognitive performance of PD patients in a set of SM and LA measures and determine the smallest set of neuropsychological (lexical, semantic, or executive) variables that most accurately classify groups. Thirty early-stage non-demented PD patients (age 35–75, 10 females) and thirty healthy controls (age 36–76, 12 females) were assessed via general cognitive, SM [three subtests of the CaGi battery including living (i.e., elephant) and non-living things (i.e., fork)], and LA (eliciting words from 10 semantic categories related to everyday life) measures. Results showed that PD patients performed lower than controls in two SM global scores (picture naming and naming in response to an oral description). This impairment was particularly pronounced in the non-living things subscale. Also, the number of words in the LA measure was inferior in PD patients than controls, in both larger and smaller semantic fields, showing a more inadequate recall strategy. Notably, the classification algorithms indicated that the SM task had high classification accuracy. In particular, the denomination of non-living things had a classification accuracy of ∼80%. These results suggest that frontostriatal deterioration in PD leads to search strategy deficits in SF and the potential disruption in semantic categorization. These findings are consistent with the embodied view of cognition.

Unveiling the nature of interaction between semantics and phonology in lexical access based on multilayer networks

An essential aspect of human communication is the ability to access and retrieve information from ones’ ‘mental lexicon’. This lexical access activates phonological and semantic components of concepts, yet the question whether and how these two components relate to each other remains widely debated. We harness tools from network science to construct a large-scale linguistic multilayer network comprising of phonological and semantic layers. We find that the links in the two layers are highly similar to each other and that adding information from one layer to the other increases efficiency by decreasing the network overall distances, but specifically affecting shorter distances. Finally, we show how a multilayer architecture demonstrates the highest efficiency, and how this efficiency relates to weak semantic relations between cue words in the network. Thus, investigating the interaction between the layers and the unique benefit of a linguistic multilayer architecture allows us to quantify theoretical cognitive models of lexical access.

Manipulations of List Type in the DRM Paradigm: A Review of How Structural and Conceptual Similarity Affect False Memory

The use of list-learning paradigms to explore false memory has revealed several critical findings about the contributions of similarity and relatedness in memory phenomena more broadly. Characterizing the nature of "similarity and relatedness" can inform researchers about factors contributing to memory distortions and about the underlying associative and semantic networks that support veridical memory. Similarity can be defined in terms of semantic properties (e.g., shared conceptual and taxonomic features), lexical/associative properties (e.g., shared connections in associative networks), or structural properties (e.g., shared orthographic or phonological features). By manipulating the type of list and its relationship to a non-studied critical item, we review the effects of these types of similarity on veridical and false memory. All forms of similarity reviewed here result in reliable error rates and the effects on veridical memory are variable. The results across a variety of paradigms and tests provide partial support for a number of theoretical explanations of false memory phenomena, but none of the theories readily account for all results.

A Critical Review of Network-Based and Distributional Approaches to Semantic Memory Structure and Processes

Some of the earliest work on understanding how concepts are organized in memory used a network-based approach, where words or concepts are represented as nodes, and relationships between words are represented by links between nodes. Over the past two decades, advances in network science and graph theoretical methods have led to the development of computational semantic networks. This review provides a modern perspective on how computational semantic networks have proven to be useful tools to investigate the structure of semantic memory as well as search and retrieval processes within semantic memory, to ultimately model performance in a wide variety of cognitive tasks. Regarding representation, the review focuses on the distinctions and similarities between network-based (based on behavioral norms) approaches and more recent distributional (based on natural language corpora) semantic models, and the potential overlap between the two approaches. Capturing the type of relation between concepts appears to be particularly important in this modeling endeavor. Regarding processes, the review focuses on random walk models and the degree to which retrieval processes demand attention in pursuit of given task goals, which dovetails with the type of relation retrieved during tasks. Ultimately, this review provides a critical assessment of how the network perspective can be reconciled with distributional and machine-learning-based perspectives to meaning representation, and describes how cognitive network science provides a useful conceptual toolkit to probe both the structure and retrieval processes within semantic memory.

Socio-Educational Impact of Augmented Reality (AR) in Sustainable Learning Ecologies: A Semantic Modeling Approach

The current educational processes must be supported by sustainable learning ecologies, where the digitalization of training is enhanced. In this area, augmented reality (AR) plays an important role. It is a technology that for certain educational goals can facilitate the understanding of the course contents and increase the motivation and interest of the student. This research aims to measure the socio-educational impact that AR presents in the teaching processes of university students of social education. These are professionals in training dedicated, precisely, to social and educational actions. In order to reach the pursued objective, an exploratory study of qualitative and descriptive nature was approached from a methodological conception based on the action-research. The study was carried out during three academic courses and consisted of an experience of integration of AR in the classroom in order to determine which applications, and advantages or limitations of a socio-educational nature, were perceived by the participants in that process. The documents generated were analyzed mainly using semantic methods. The main results were that AR is positive overall for its use in learning processes and, specifically in its field, optimal for the development of professional skills within the framework of social education. As to benefits, it highlighted the strengthening of learning dynamism, motivation, and interaction among students; as to limitations, these included the fact that it is not an accessible technology, the need for previous training and that it can reduce sociability. It was also stressed that it can be a useful resource in many areas of social education (childhood and adolescence, gerontology, drug addiction, etc.). In general, therefore, it can be concluded that the use of AR in university training, in this area of knowledge, allows content to be more dynamic and real in a sustainable way, thus achieving a highly transferable and motivating path to develop content and competencies.