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

Advances in semantic dementia: Neuropsychology, pathology & neuroimaging

Semantic dementia is a kind of neurodegenerative disorder, characterized by prominent semantic impairments and anterior temporal lobe atrophy. Since 2010, more studies have devoted to this rare disorder, revealing that it is more complex than we think. Clinical advances include more specific findings of semantic impairments and other higher order cognitive deficits. Neuroimaging techniques can help revealing the different brain networks affected (both structurally and functionally) in this condition. Pathological and genetic studies have also found more complex situations of semantic dementia, which might explain the huge variance existing in semantic dementia. Moreover, the current diagnosis criteria mainly focus on semantic dementia's classical prototype. We further delineated the features of three subtypes of semantic dementia based on atrophy lateralization with three severity stages. In a broader background, as a part of the continuum of neurodegenerative disorders, semantic dementia is commonly compared with other resembling conditions. Therefore, we summarized the differential diagnosis between semantic dementia and them. Finally, we introduced the challenges and achievements of its diagnosis, treatment, care and cross cultural comparison. By providing a comprehensive picture of semantic dementia on different aspects of advances, we hope to deepen the understanding of semantic dementia and promote more inspirations on both clinical and theoretical studies about it.

Using Monte-Carlo simulation to test predictions about the time-course of semantic and lexical access in reading

One of the main theoretical distinctions between reading models is how and when they predict semantic processing occurs. Some models assume semantic activation occurs after word-form is retrieved. Other models assume there is no-word form, and that what people think of as word-form is actually just semantics. These models thus predict semantic effects should occur early in reading. Results showing words with inconsistent spelling-sound correspondences are faster to read aloud if they are imageable/concrete compared to if they are abstract have been used as evidence supporting this prediction, although null-effects have also been reported. To investigate this, I used Monte-Carlo simulation to create a large set of simulated experiments from RTs taken from different databases. The results showed significant main effects of concreteness and spelling-sound consistency, as well as age-of-acquisition, a variable that can potentially confound the results. Alternatively, simulations showing a significant interaction between spelling-sound consistency and concreteness did not occur above chance, even without controlling for age-of-acquisition. These results support models that use lexical form. In addition, they suggest significant interactions from previous experiments may have occurred due to idiosyncratic items affecting the results and random noise causing the occasional statistical error.

Regionally specific cortical lateralization of abstract and concrete verb processing: Magnetic mismatch negativity study

The neural underpinnings of processing concrete and abstract semantics remain poorly understood. Previous fMRI studies have shown that multimodal and amodal neural networks respond differentially to different semantic types; importantly, abstract semantics activates more left-lateralized networks, as opposed to more bilateral activity for concrete words. Due to the lack of temporal resolution, these fMRI results do not allow to easily separate language- and task-specific brain responses and to disentangle early processing stages from later post-comprehension phenomena. To tackle this, we used magnetoencephalography (MEG), a time-resolved neuroimaging technique, in combination with a task-free oddball mismatch negativity (MMN) paradigm, an established approach to tracking early automatic activation of word-specific memory traces in the brain. We recorded the magnetic MMN responses in 30 healthy adults to auditorily presented abstract and concrete action verbs to assess lateralization of word-specific lexico-semantic processing in a set of neocortical areas. We found that MMN responses to these stimuli showed different lateralization patterns of activity in the upper limb motor area (BA4) and parts of Broca's area (BA45/BA47) within ∼100-350 ms after the word disambiguation point. Importantly, the greater leftward response lateralization for abstract semantics was due to the lesser involvement of the right-hemispheric homologues, not increased left-hemispheric activity. These findings suggest differential region-specific involvement of bilateral sensorimotor systems already in the early automatic stages of processing abstract and concrete action semantics.

Unseen but influential associates: Properties of words' associates influence lexical and semantic processing

In many models of lexical and semantic processing, it is assumed that single word processing is a function of the characteristics of the words presented and the distributional properties of the words' networks. Recent research suggests that semantic characteristics of a target word's associates may in fact influence target-word responses in lexical-semantic tasks. The present study extends that previous research to examine whether lexical and semantic properties of target-word associates are recruited during lexical and semantic decision tasks, and whether the type of associate information recruited varies as a function of task and concreteness of the target word. We found that lexical-semantic properties of words' first associates are related to accuracy of responses to words in lexical decision, and that semantic properties of words' first associates are related to both response time and accuracy in semantic decision. Further, these effects differ depending on the target word's concreteness. These findings provide new insight about the way words' associates contribute to semantic representation and processing, even though the associates are not actually presented, moving beyond previous assumptions about lexical-semantic processing of single words.

Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

White matter network underlying semantic processing: evidence from stroke patients

The hub-and-spoke theory of semantic representation fractionates the neural underpinning of semantic knowledge into two essential components: the sensorimotor modality-specific regions and a crucially important semantic hub region. Our previous study in patients with semantic dementia has found that the hub region is located in the left fusiform gyrus. However, because this region is located within the brain damage in patients with semantic dementia, it is not clear whether the semantic deficit is caused by structural damage to the hub region itself or by its disconnection from other brain regions. Stroke patients do not have any damage to the left fusiform gyrus, but exhibit amodal and modality-specific deficits in semantic processing. Therefore, in this study, we validated the semantic hub region from a brain network perspective in 79 stroke patients and explored the white matter connections associated with it. First, we collected data of diffusion-weighted imaging and behavioural performance on general semantic tasks and modality-specific semantic tasks (assessing object knowledge on form, colour, motion, sound, manipulation and function). We then used correlation and regression analyses to examine the association between the nodal degree values of brain regions in the whole-brain structural network and general semantic performance in the stroke patients. The results revealed that the connectivity of the left fusiform gyrus significantly predicted general semantic performance, indicating that this region is the semantic hub. To identify the semantic-relevant connections of the semantic hub, we then correlated the white matter integrity values of each tract connected to the left fusiform gyrus separately with performance on general and modality-specific semantic processing. We found that the hub region accomplished general semantic processing through white matter connections with the left superior temporal pole, middle temporal gyrus, inferior temporal gyrus and hippocampus. The connectivity between the hub region and the left hippocampus, superior temporal pole, middle temporal gyrus, inferior temporal gyrus and parahippocampal gyrus was differentially involved in object form, colour, motion, sound, manipulation and function processing. After statistically removing the effects of potential confounding variables (i.e. whole-brain lesion volume, lesion volume of regions of interest and performance on non-semantic control tasks), the observed effects remained significant. Together, our findings support the role of the left fusiform gyrus as a semantic hub region in stroke patients and reveal its crucial connectivity in the network. This study provides new insights and evidence for the neuroanatomical organization of semantic memory in the human brain.

The Semantic Similarity Effect on Short-Term Memory: Null Effects of Affectively Defined Semantic Similarity

Studies on short-term memory have repeatedly demonstrated the beneficial effect of semantic similarity. Although the effect seems robust, the aspects of semantics targeted by these studies (e.g., categorical structure, associative relationship, or dimension of meaning) should be clarified. A recent meta-regression study inspired by Osgood's view, which highlights affective dimensions in semantics, introduced a novel index for quantifying semantic similarity using affective values. Building on the results of the meta-regression of past studies' data with that index, this study predicts that semantic similarity is deleterious to short-term memory if it is manipulated by affective dimensions, after controlling for other confounding factors. This prediction was directly tested. The experimental results of the immediate serial recall task (Study 1) and immediate serial reconstruction of order task (Study 2) indicated null effects of semantic similarity by affective dimensions and thus falsified the prediction. These results suggest that semantic similarity based on affective dimensions is negligible.

Determining the Relativity of Word Meanings Through the Construction of Individualized Models of Semantic Memory

Distributional models of lexical semantics are capable of acquiring sophisticated representations of word meanings. The main theoretical insight provided by these models is that they demonstrate the systematic connection between the knowledge that people acquire and the experience that they have with the natural language environment. However, linguistic experience is inherently variable and differs radically across people due to demographic and cultural variables. Recently, distributional models have been used to examine how word meanings vary across languages and it was found that there is considerable variability in the meanings of words across languages for most semantic categories. The goal of this article is to examine how variable word meanings are across individual language users within a single language. This was accomplished by assembling 500 individual user corpora attained from the online forum Reddit. Each user corpus ranged between 3.8 and 32.3 million words each, and a count-based distributional framework was used to extract word meanings for each user. These representations were then used to estimate the semantic alignment of word meanings across individual language users. It was found that there are significant levels of relativity in word meanings across individuals, and these differences are partially explained by other psycholinguistic factors, such as concreteness, semantic diversity, and social aspects of language usage. These results point to word meanings being fundamentally relative and contextually fluid, with this relativeness being related to the individualized nature of linguistic experience.

Semantic dementia: a complex and culturally influenced presentation

SUMMARY

The variants of frontotemporal dementia (FTD) require careful differentiation from primary psychiatric disorders as the neuropsychiatric manifestations can overshadow the unique cognitive deficits. The language variants of FTD are less readily recognised by trainees despite making up around 43% of cases. This educational article presents an anonymised case of one of the language variants: semantic dementia. The cognitive deficits and neuropsychiatric manifestations (delusions and hyperreligiosity) are explored in terms of aetiology and management. By the end of the article, readers should be able to differentiate FTD from Alzheimer's disease, understand the principles of management and associated risks, and develop a multifaceted approach to hyperreligiosity in dementia.

How word semantics and phonology affect handwriting of Alzheimer's patients: A machine learning based analysis

Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge: non-invasive detection techniques combined with machine learning approaches promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements. In particular, the meaning and phonology of words to copy can compromise writing fluency. In this paper, we investigated how word semantics and phonology affect the handwriting of people affected by Alzheimer's disease. To this aim, we used the data from six handwriting tasks, each requiring copying a word belonging to one of the following categories: regular (have a predictable phoneme-grapheme correspondence, e.g., cat), non-regular (have atypical phoneme-grapheme correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter strings that conform to phoneme-grapheme conversion rules). We analyzed the data using a machine learning approach by implementing four well-known and widely-used classifiers and feature selection. The experimental results showed that the feature selection allowed us to derive a different set of highly distinctive features for each word type. Furthermore, non-regular words needed, on average, more features but achieved excellent classification performance: the best result was obtained on a non-regular, reaching an accuracy close to 90%.

Semantic and episodic processes differently predict false memories in the DRM task

There is a fervent debate about the processes underpinning false memories formation. Seminal theories have suggested that semantic memory would be involved in false memories production, while episodic memory would counter their formation. Yet, direct evidence corroborating such view is still lacking. Here, we tested this possibility by asking participants to perform the Deese-Roediger-McDermott (DRM) task, a typical false memory paradigm, in which they had to study lists of words and subsequently to recognize and distinguish them from new words (i.e., the false memory items). The same participants were also required to perform a semantic task and an episodic-source memory task. Our results showed that a higher number of false memories in the DRM task occurred for those participants with better semantic memory abilities, while a lower number of false memories occurred for participants with better episodic abilities. These findings support a key role of semantic processes in false memory formation and, more generally, help clarify the specific contribution of different memory systems to false recognitions.

Multi-Level Linguistic Alignment in a Dynamic Collaborative Problem-Solving Task

Cocreating meaning in collaboration is challenging. Success is often determined by people's abilities to coordinate their language to converge upon shared mental representations. Here we explore one set of low-level linguistic behaviors, linguistic alignment, that both emerges from, and facilitates, outcomes of high-level convergence. Linguistic alignment captures the ways people reuse, that is, "align to," the lexical, syntactic, and semantic forms of others' utterances. Our focus is on the temporal change of multi-level linguistic alignment, as well as how alignment is related to communicative outcomes within a unique collaborative problem-solving paradigm. The primary task, situated within a virtual educational video game, requires creative thinking between three people where the paths for possible solutions are highly variable. We find that over time interactions are marked by decreasing lexical and syntactic alignment, with a trade-off of increasing semantic alignment. However, greater semantic alignment does not translate into better team performance. Overall, these findings provide greater clarity on the role of linguistic coordination within complex and dynamic collaborative problem-solving tasks.

The recognition effects of attribute ambiguity

When examining memory effects of semantic attributes, it is common practice to manipulate normed mean (M) ratings of the attributes (i.e., attribute intensity) in learning materials. Meanwhile, the standard deviations (SDs) of attribute ratings (i.e., attribute ambiguity) are usually treated as indexes of measurement error. However, some recent research found that recall accuracy varied as a function of both the intensity and ambiguity of semantic attributes such as valence, categorization, concreteness, and meaningfulness. These findings challenged the traditional interpretation of attribute rating SDs as noise indexes. In the current study, we examined the recognition effects of ambiguity, intensity, and Ambiguity × Intensity interactions for 21 attributes using mega study data for over 5,000 words. Our results showed that attribute ambiguity had reliable recognition effects beyond those of attribute intensity, and that it sometimes explained more unique variance in recognition than attribute intensity. Thus, we concluded that attribute ambiguity is a distinct psychological dimension of semantic attributes, which is processed separately from attribute intensity during encoding. Two theoretical hypotheses had been proposed for the memory effects of attribute ambiguity. We discuss the implications of our findings for the two theoretical hypotheses about how attribute ambiguity influences episodic memory.

Modeling Brain Representations of Words' Concreteness in Context Using GPT-2 and Human Ratings

The meaning of most words in language depends on their context. Understanding how the human brain extracts contextualized meaning, and identifying where in the brain this takes place, remain important scientific challenges. But technological and computational advances in neuroscience and artificial intelligence now provide unprecedented opportunities to study the human brain in action as language is read and understood. Recent contextualized language models seem to be able to capture homonymic meaning variation ("bat", in a baseball vs. a vampire context), as well as more nuanced differences of meaning-for example, polysemous words such as "book", which can be interpreted in distinct but related senses ("explain a book", information, vs. "open a book", object) whose differences are fine-grained. We study these subtle differences in lexical meaning along the concrete/abstract dimension, as they are triggered by verb-noun semantic composition. We analyze functional magnetic resonance imaging (fMRI) activations elicited by Italian verb phrases containing nouns whose interpretation is affected by the verb to different degrees. By using a contextualized language model and human concreteness ratings, we shed light on where in the brain such fine-grained meaning variation takes place and how it is coded. Our results show that phrase concreteness judgments and the contextualized model can predict BOLD activation associated with semantic composition within the language network. Importantly, representations derived from a complex, nonlinear composition process consistently outperform simpler composition approaches. This is compatible with a holistic view of semantic composition in the brain, where semantic representations are modified by the process of composition itself. When looking at individual brain areas, we find that encoding performance is statistically significant, although with differing patterns of results, suggesting differential involvement, in the posterior superior temporal sulcus, inferior frontal gyrus and anterior temporal lobe, and in motor areas previously associated with processing of concreteness/abstractness.

Local but not global graph theoretic measures of semantic networks generalize across tasks

"Dogs" are connected to "cats" in our minds, and "backyard" to "outdoors." Does the structure of this semantic knowledge differ across people? Network-based approaches are a popular representational scheme for thinking about how relations between different concepts are organized. Recent research uses graph theoretic analyses to examine individual differences in semantic networks for simple concepts and how they relate to other higher-level cognitive processes, such as creativity. However, it remains ambiguous whether individual differences captured via network analyses reflect true differences in measures of the structure of semantic knowledge, or differences in how people strategically approach semantic relatedness tasks. To test this, we examine the reliability of local and global metrics of semantic networks for simple concepts across different semantic relatedness tasks. In four experiments, we find that both weighted and unweighted graph theoretic representations reliably capture individual differences in local measures of semantic networks (e.g., how related pot is to pan versus lion). In contrast, we find that metrics of global structural properties of semantic networks, such as the average clustering coefficient and shortest path length, are less robust across tasks and may not provide reliable individual difference measures of how people represent simple concepts. We discuss the implications of these results and offer recommendations for researchers who seek to apply graph theoretic analyses in the study of individual differences in semantic memory.

The shared and unique neural correlates of personal semantic, general semantic, and episodic memory

One of the most common distinctions in long-term memory is that between semantic (i.e., general world knowledge) and episodic (i.e., recollection of contextually specific events from one's past). However, emerging cognitive neuroscience data suggest a surprisingly large overlap between the neural correlates of semantic and episodic memory. Moreover, personal semantic memories (i.e., knowledge about the self and one's life) have been studied little and do not easily fit into the standard semantic-episodic dichotomy. Here, we used fMRI to record brain activity while 48 participants verified statements concerning general facts, autobiographical facts, repeated events, and unique events. In multivariate analysis, all four types of memory involved activity within a common network bilaterally (e.g., frontal pole, paracingulate gyrus, medial frontal cortex, middle/superior temporal gyrus, precuneus, posterior cingulate, angular gyrus) and some areas of the medial temporal lobe. Yet the four memory types differentially engaged this network, increasing in activity from general to autobiographical facts, from autobiographical facts to repeated events, and from repeated to unique events. Our data are compatible with a component process model, in which declarative memory types rely on different weightings of the same elementary processes, such as perceptual imagery, spatial features, and self-reflection.

Do pictures influence memory and metamemory in Chinese vocabulary learning? Evidence from Russian and Colombian learners

Despite the increasing interest in learning non-alphabetical languages such as Chinese, research about its learning process for alphabet users is scarce. Research conducted on Latin alphabet users on learning languages written in Latin alphabet, or on Chinese language learning in Chinese native speakers, users is undoubtedly useful but it does not inform about the peculiarities of leaning Chinese language by other alphabet users. Additionally, several authors have highlighted the need to inform and extend the current second language acquisition theories on the particular challenges of learning a language that uses another script. In this research we aim to contribute filling this research gap and studied the learning process of Chinese vocabulary by users of scripts different from Chinese. In particular, we examined the role of pictures and translations as learning aids for Chinese language vocabulary learning in participants familiarized with either one or two alphabetical scripts (different from the Chinese logographic script). One hundred thirteen participants studied word-aid pairs in different conditions: Hanzi (Chinese in Chinese characters)-picture; Pinyin (Chinese in Latin alphabet)-picture; Hanzi-translation; Pinyin-translation. Participants evaluated the future recallability of the words and their meanings (i.e., judgements of learning) and completed two recognition tests. Words in Pinyin and words-translation pairs were judged to be easier to remember than Hanzi and word-pictures pairs. Participants remembered the meaning of words written in Hanzi better than in Pinyin, and word-translations pairs better than pictures, but they were more confident about word-picture pairs. These results suggest that pictures boost confidence in learning Chinese, but do not affect performance. These findings suggest that while pictures may boost confidence in learning Chinese, they may not necessarily lead to better performance. Our study provides valuable insights into the interaction of learning aids and writing system in (meta)memory during vocabulary acquisition.

A scoping review of the relationship between alcohol, memory consolidation and ripple activity: An overview of common methodologies to analyse ripples

Alcohol abuse is not only responsible for 5.3% of the total deaths in the world but also has a substantial impact on neurological and memory disabilities throughout the population. One extensively studied brain area involved in cognitive functions is the hippocampus. Evidence in several rodent models has shown that ethanol produces cognitive impairment in hippocampal-dependent tasks and that the damage is varied according to the stage of development at which the rodent was exposed to ethanol and the dose. To the authors' knowledge, there is a biomarker for cognitive processes in the hippocampus that remains relatively understudied in association with memory impairment by alcohol administration. This biomarker is called sharp wave-ripples (SWRs) which are synchronous neuronal population events that are well known to be involved in memory consolidation. Methodologies for facilitated or automatic identification of ripples and their analysis have been reported for a wider bandwidth than SWRs. This review is focused on communicating the state of the art about the relationship between alcohol, memory consolidation and ripple activity, as well as the use of the common methodologies to identify SWRs automatically.

Misspelled logotypes: the hidden threat to brand identity

Brand names are valuable company assets often accompanied by a unique graphical composition (i.e., as logotypes). Recent research has demonstrated that this uniqueness makes brand names and logotypes susceptible to counterfeiting through misspelling by transposition in tasks that require participants to identify correct spellings. However, our understanding of how brand names are incidentally processed when presented as logotypes is incomplete. To address this gap in knowledge, we conducted a virtual reality experiment to explore the transposed-letter confusability effect on brand name recognition. Participants were immersed in a virtual reality setting and incidentally exposed to logotypes that had correctly spelled brand names or included letter transpositions. Offline analyses revealed that participants were more accurate at recognizing brand names that had been presented with correct spellings than those that had been misspelled. Furthermore, participants exhibited false memories for misspelled logotypes, recalling them as if they had been spelled correctly. Thus, our findings revealed that the incidental processing of misspelled logotypes (e.g., SASMUNG) affects the accuracy of logotype identity recognition, thereby underscoring the challenges faced by individuals when identifying brand names and the elements that make counterfeits so effective.

Lived Experiences of Everyday Memory in Adults with Dyslexia: A Thematic Analysis

Dyslexia-related difficulties with memory are well documented under laboratory conditions and via self-report questionnaires. However, the voice of the individual with dyslexia regarding the lived experience of memory across different memory systems and different daily settings is currently lacking. To address this gap in the literature, semi-structured in-depth interviews were conducted with 12 adult female university students with dyslexia. Questions probed different memory systems and experiences across different settings, with interviewees also being asked about their use of technology to support their memory. Two overarching themes were identified in the subsequent thematic analysis. The theme of fallibility of memory had two sub-themes of (i) a lack of trust and confidence in memory and (ii) factors contributing to memory failure. The second theme, facilitators of memory, also consisted of two sub-themes, relating to (i) a preference for traditional tools to support memory and (ii) the use of digital tools to support memory: benefits and limitations. The current study gives insights into the rich and complex extended and distributed cognitive systems of adults with dyslexia. The implications of the findings for dyslexia theory, support in educational and work settings as well as assistive technology development are considered.

Cortical networks for recognition of speech with simultaneous talkers

The relative contributions of superior temporal vs. inferior frontal and parietal networks to recognition of speech in a background of competing speech remain unclear, although the contributions themselves are well established. Here, we use fMRI with spectrotemporal modulation transfer function (ST-MTF) modeling to examine the speech information represented in temporal vs. frontoparietal networks for two speech recognition tasks with and without a competing talker. Specifically, 31 listeners completed two versions of a three-alternative forced choice competing speech task: "Unison" and "Competing", in which a female (target) and a male (competing) talker uttered identical or different phrases, respectively. Spectrotemporal modulation filtering (i.e., acoustic distortion) was applied to the two-talker mixtures and ST-MTF models were generated to predict brain activation from differences in spectrotemporal-modulation distortion on each trial. Three cortical networks were identified based on differential patterns of ST-MTF predictions and the resultant ST-MTF weights across conditions (Unison, Competing): a bilateral superior temporal (S-T) network, a frontoparietal (F-P) network, and a network distributed across cortical midline regions and the angular gyrus (M-AG). The S-T network and the M-AG network responded primarily to spectrotemporal cues associated with speech intelligibility, regardless of condition, but the S-T network responded to a greater range of temporal modulations suggesting a more acoustically driven response. The F-P network responded to the absence of intelligibility-related cues in both conditions, but also to the absence (presence) of target-talker (competing-talker) vocal pitch in the Competing condition, suggesting a generalized response to signal degradation. Task performance was best predicted by activation in the S-T and F-P networks, but in opposite directions (S-T: more activation = better performance; F-P: vice versa). Moreover, S-T network predictions were entirely ST-MTF mediated while F-P network predictions were ST-MTF mediated only in the Unison condition, suggesting an influence from non-acoustic sources (e.g., informational masking) in the Competing condition. Activation in the M-AG network was weakly positively correlated with performance and this relation was entirely superseded by those in the S-T and F-P networks. Regarding contributions to speech recognition, we conclude: (a) superior temporal regions play a bottom-up, perceptual role that is not qualitatively dependent on the presence of competing speech; (b) frontoparietal regions play a top-down role that is modulated by competing speech and scales with listening effort; and (c) performance ultimately relies on dynamic interactions between these networks, with ancillary contributions from networks not involved in speech processing per se (e.g., the M-AG network).

Contextual dynamics in lexical encoding across the ageing spectrum: A simulation study

The field of psycholinguistics has recently questioned the primacy of word frequency (WF) in influencing word recognition and production, instead focusing on the importance of a word's contextual diversity (CD). WF is operationalised by counting the number of occurrences of a word in a corpus, while a word's CD is a count of the number of contexts that a word occurs in, with repetitions within a context being ignored. Numerous studies have converged on the conclusion that CD is a better predictor of word recognition latency and accuracy than frequency. These findings support a cognitive mechanism based on the principle of likely need over the principle of repetition in lexical organisation. In the current study, we trained the semantic distinctiveness model on communication patterns in social media platforms consisting of over 55-billion-word tokens and examined the ability of theoretically distinct models to explain word recognition latency and accuracy data from over 1 million participants from the Mandera et al. English Crowdsourding Project norms, consisting of approximately 59,000 words across six age bands ranging from ages 10 to 60 years. There was a clear quantitative trend across the age bands, where there is a shift from a social environment-based attention mechanism in the "younger" models, to a clear dominance for a discourse-based attention mechanism as models "aged." This pattern suggests that there is a dynamical interaction between the cognitive mechanisms of lexical organisation and environmental information that emerges across ageing.

An interpretable measure of semantic similarity for predicting eye movements in reading

Predictions about upcoming content play an important role during language comprehension and processing. Semantic similarity as a metric has been used to predict how words are processed in context in language comprehension and processing tasks. This study proposes a novel, dynamic approach for computing contextual semantic similarity, evaluates the extent to which the semantic similarity measures computed using this approach can predict fixation durations in reading tasks recorded in a corpus of eye-tracking data, and compares the performance of these measures to that of semantic similarity measures computed using the cosine and Euclidean methods. Our results reveal that the semantic similarity measures generated by our approach are significantly predictive of fixation durations on reading and outperform those generated by the two existing approaches. The findings of this study contribute to a better understanding of how humans process words in context and make predictions in language comprehension and processing. The effective and interpretable approach to computing contextual semantic similarity proposed in this study can also facilitate further explorations of other experimental data on language comprehension and processing.

Associative thinking at the core of creativity

Creativity has long been thought to involve associative processes in memory: connecting concepts to form ideas, inventions, and artworks. However, associative thinking has been difficult to study due to limitations in modeling memory structure and retrieval processes. Recent advances in computational models of semantic memory allow researchers to examine how people navigate a semantic space of concepts when forming associations, revealing key search strategies associated with creativity. Here, we synthesize cognitive, computational, and neuroscience research on creativity and associative thinking. This Review highlights distinctions between free- and goal-directed association, illustrates the role of associative thinking in the arts, and links associative thinking to brain systems supporting both semantic and episodic memory - offering a new perspective on a longstanding creativity theory.

Lay Definitions of Intelligence, Knowledge, and Memory: Inter- and Independence of Constructs

The present study examined how lay participants define the following concepts used widely in psychology: being intelligent, knowing, and remembering. In the scientific community, knowledge overlaps with the contents of semantic memory, crystallized intelligence reflects the accumulation of knowledge, knowledge and event memory interact, and fluid intelligence and working memory correlate. Naturally, the lay public has implicit theories of these constructs. These theories mainly distinguish between intelligent and unintelligent behaviors and tend to include characteristics outside psychometric studies of intelligence, such as emotional intelligence. Here, we asked lay participants from the online platform Prolific to explain "what does being intelligent mean to you?" as well as "knowing" and "remembering" to understand their degree of alignment with theoretical conceptualizations in the research community. Qualitative coding of participant definitions showed that intelligence and knowledge are closely related, but asymmetrically-when defining what it means to be intelligent, participants reference knowledge, but intelligence is not considered in explaining knowing. Although participants note that intelligence is multi-faceted and related to problem-solving, there is an emphasis (in terms of frequency of mentions) on the crystallized side of intelligence (i.e., knowledge). A deeper understanding of lay participants' mental models of these constructs (i.e., their metacognitions) is essential for bridging gaps between experts and the general public.

Representing melodic relationships using network science

Music is a complex system consisting of many dimensions and hierarchically organized information-the organization of which, to date, we do not fully understand. Network science provides a powerful approach to representing such complex systems, from the social networks of people to modelling the underlying network structures of different cognitive mechanisms. In the present research, we explored whether network science methodology can be extended to model the melodic patterns underlying expert improvised music. Using a large corpus of transcribed improvisations, we constructed a network model in which 5-pitch sequences were linked depending on consecutive occurrences, constituting 116,403 nodes (sequences) and 157,429 edges connecting them. We then investigated whether mathematical graph modelling relates to musical characteristics in real-world listening situations via a behavioral experiment paralleling those used to examine language. We found that as melodic distance within the network increased, participants judged melodic sequences as less related. Moreover, the relationship between distance and reaction time (RT) judgements was quadratic: participants slowed in RT up to distance four, then accelerated; a parallel finding to research in language networks. This study offers insights into the hidden network structure of improvised tonal music and suggests that humans are sensitive to the property of melodic distance in this network. More generally, our work demonstrates the similarity between music and language as complex systems, and how network science methods can be used to quantify different aspects of its complexity.

Overlap in meaning is a stronger predictor of semantic activation in GPT-3 than in humans

Modern large language models generate texts that are virtually indistinguishable from those written by humans and achieve near-human performance in comprehension and reasoning tests. Yet, their complexity makes it difficult to explain and predict their functioning. We examined a state-of-the-art language model (GPT-3) using lexical decision tasks widely used to study the structure of semantic memory in humans. The results of four analyses showed that GPT-3's patterns of semantic activation are broadly similar to those observed in humans, showing significantly higher semantic activation in related (e.g., "lime-lemon") word pairs than in other-related (e.g., "sour-lemon") or unrelated (e.g., "tourist-lemon") word pairs. However, there are also significant differences between GPT-3 and humans. GPT-3's semantic activation is better predicted by similarity in words' meaning (i.e., semantic similarity) rather than their co-occurrence in the language (i.e., associative similarity). This suggests that GPT-3's semantic network is organized around word meaning rather than their co-occurrence in text.

The Role of Semantic Associations as a Metacognitive Cue in Creative Idea Generation

Is my idea creative? This question directs investing in companies and choosing a research agenda. Following previous research, we focus on the originality of ideas and consider their association with self-assessments of idea generators regarding their own originality. We operationalize the originality score as the frequency (%) of each idea within a sample of participants and originality judgment as the self-assessment of this frequency. Initial evidence suggests that originality scores and originality judgments are produced by separate processes. As a result, originality judgments are prone to biases. So far, heuristic cues that lead to such biases are hardly known. We used methods from computational linguistics to examine the semantic distance as a potential heuristic cue underlying originality judgments. We examined the extent to which the semantic distance would contribute additional explanatory value in predicting originality scores and originality judgments, above and beyond cues known from previous research. In Experiment 1, we re-analyzed previous data that compared originality scores and originality judgments after adding the semantic distance of the generated ideas from the stimuli. We found that the semantic distance contributed to the gap between originality scores and originality judgments. In Experiment 2, we manipulated the examples given in task instructions to prime participants with two levels of idea originality and two levels of semantic distance. We replicated Experiment 1 in finding the semantic distance as a biasing factor for originality judgments. In addition, we found differences among the conditions in the extent of the bias. This study highlights the semantic distance as an unacknowledged metacognitive cue and demonstrates its biasing power for originality judgments.

Executive functioning moderates the decline of retrieval fluency in time

Prevailing theoretical accounts consider that automatic and controlled processes are uniformly engaged in memory retrieval across performance of the semantic verbal fluency (SVF) task. We tested this proposal against the alternative, namely that a rapid automatic retrieval, exploiting stable associative structure in the early stages of the performance, is followed by a slower, more executively demanding, retrieval in later stages. Eighty-five healthy adults completed low- and high-demand SVF tasks that were assessed for retrieval rate, response typicality, and inter-response similarity across the performance. Additional measures of executive functioning were collected to estimate individual differences in executive control. We found that decrease in fluency in time was associated with lower typicality and weaker semantic similarity of the responses. Critically, the time-dependent retrieval slowing was steeper in individuals with less efficient interference control, particularly in high-demand SVF tasks. Steeper retrieval slowing was also associated with poorer working-memory capacity. Our findings show that the relative contribution of automatic and controlled processes to semantic retrieval changes with associative sparsity over time and across task demands, and provide implications for the use of SVF tasks in clinical assessment.

Different computational relations in language are captured by distinct brain systems

A critical way for humans to acquire information is through language, yet whether and how language experience drives specific neural semantic representations is still poorly understood. We considered statistical properties captured by 3 different computational principles of language (simple co-occurrence, network-(graph)-topological relations, and neural-network-vector-embedding relations) and tested the extent to which they can explain the neural patterns of semantic representations, measured by 2 functional magnetic resonance imaging experiments that shared common semantic processes. Distinct graph-topological word relations, and not simple co-occurrence or neural-network-vector-embedding relations, had unique explanatory power for the neural patterns in the anterior temporal lobe (capturing graph-common-neighbors), inferior frontal gyrus, and posterior middle/inferior temporal gyrus (capturing graph-shortest-path). These results were relatively specific to language: they were not explained by sensory-motor similarities and the same computational relations of visual objects (based on visual image database) showed effects in the visual cortex in the picture naming experiment. That is, different topological properties within language and the same topological computations (common-neighbors) for language and visual inputs are captured by different brain regions. These findings reveal the specific neural semantic representations along graph-topological properties of language, highlighting the information type-specific and statistical property-specific manner of semantic representations in the human brain.

Child-oriented word associations improve models of early word learning

How words are associated within the linguistic environment conveys semantic content; however, different contexts induce different linguistic patterns. For instance, it is well known that adults speak differently to children than to other adults. We present results from a new word association study in which adult participants were instructed to produce either unconstrained or child-oriented responses to each cue, where cues included 672 nouns, verbs, adjectives, and other word forms from the McArthur-Bates Communicative Development Inventory (CDI; Fenson et al., 2006). Child-oriented responses consisted of higher frequency words with fewer letters, earlier ages of acquisition, and higher contextual diversity. Furthermore, the correlations among the responses generated for each pair of cues differed between unconstrained (adult-oriented) and child-oriented responses, suggesting that child-oriented associations imply different semantic structure. A comparison of growth models guided by a semantic network structure revealed that child-oriented associations are more predictive of early lexical growth. Additionally, relative to a growth model based on a corpus of naturalistic child-directed speech, the child-oriented associations explain added unique variance to lexical growth. Thus, these new child-oriented word association norms provide novel insight into the semantic context of young children and early lexical development.

Associative memory of structured knowledge

A long standing challenge in biological and artificial intelligence is to understand how new knowledge can be constructed from known building blocks in a way that is amenable for computation by neuronal circuits. Here we focus on the task of storage and recall of structured knowledge in long-term memory. Specifically, we ask how recurrent neuronal networks can store and retrieve multiple knowledge structures. We model each structure as a set of binary relations between events and attributes (attributes may represent e.g., temporal order, spatial location, role in semantic structure), and map each structure to a distributed neuronal activity pattern using a vector symbolic architecture scheme.We then use associative memory plasticity rules to store the binarized patterns as fixed points in a recurrent network. By a combination of signal-to-noise analysis and numerical simulations, we demonstrate that our model allows for efficient storage of these knowledge structures, such that the memorized structures as well as their individual building blocks (e.g., events and attributes) can be subsequently retrieved from partial retrieving cues. We show that long-term memory of structured knowledge relies on a new principle of computation beyond the memory basins. Finally, we show that our model can be extended to store sequences of memories as single attractors.

Mood effects on semantic processes: Behavioural and electrophysiological evidence

Mood (i.e., our current background affective state) often unobtrusively yet pervasively affects how we think and behave. Typically, theoretical frameworks position it as an embodied source of information (i.e., a biomarker), activating thinking patterns that tune our attention, perception, motivation, and exploration tendencies in a context-dependent manner. Growing behavioural and electrophysiological research has been exploring the mood–language interactions, employing numerous semantics-oriented experimental paradigms (e.g., manipulating semantic associations, congruity, relatedness, etc.) along with mood elicitation techniques (e.g., affectively evocative film clips, music, pictures, etc.). Available behavioural and electrophysiological evidence has suggested that positive and negative moods differently regulate the dynamics of language comprehension, mostly due to the activation of mood-dependent cognitive strategies. Namely, a positive mood has been argued to activate global and heuristics-based processing and a negative mood – local and detail-oriented processing during language comprehension. Future research on mood–language interactions could benefit greatly from (i) a theoretical framework for mood effects on semantic memory, (ii) measuring mood changes multi-dimensionally, (iii) addressing discrepancies in empirical findings, (iv) a replication-oriented approach, and (v) research practices counteracting publication biases.

Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat

Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis - neural word embeddings - can be interpreted as a formal model for cultural learning. Embeddings allow us to empirically model schema learning and activation from natural language data. We illustrate our approach by extracting four lower-order schemas from news articles: the gender, moral, health, and class meanings of body weight. Using these lower-order schemas we quantify how words about body weight "fill in the blanks" about gender, morality, health, and class. Our findings reinforce ongoing concerns that machine-learning models (e.g., of natural language) can encode and reproduce harmful human biases.

Functional connectivity of brain networks during semantic processing in older adults

The neural systems underlying semantic processing have been characterized with functional neuroimaging in young adults. Whether the integrity of these systems degrade with advanced age remains unresolved. The current study examined functional connectivity during abstract and concrete word processing. Thirty-eight adults, aged 55–91, engaged in semantic association decision tasks during a mixed event-related block functional magnetic resonance imaging (fMRI) paradigm. During the semantic trials, the task required participants to make a judgment as to whether pairs were semantically associated. During the rhyme trials, the task required participants to determine if non-word pairs rhymed. Seeds were placed in putative semantic hubs of the left anterior middle temporal gyrus (aMTG) and the angular gyrus (AG), and also in the left inferior frontal gyrus (IFG), an area considered important for semantic control. Greater connectivity between aMTG, AG, and IFG and multiple cortical areas occurred during semantic processing. Connectivity from the three seeds differed during semantic processing: the left AG and aMTG were strongly connected with frontal, parietal, and occipital areas bilaterally, whereas the IFG was most strongly connected with other frontal cortical areas and the AG in the ipsilateral left hemisphere. Notably, the strength and extent of connectivity differed for abstract and concrete semantic processing; connectivity from the left aMTG and AG to bilateral cortical areas was greater during abstract processing, whereas IFG connectivity with left cortical areas was greater during concrete processing. With advanced age, greater connectivity occurred only between the left AG and supramarginal gyrus during the processing of concrete word-pairs, but not abstract word-pairs. Among older adults, robust functional connectivity of the aMTG, AG, and IFG to widely distributed bilateral cortical areas occurs during abstract and concrete semantic processing in a manner consistent with reports from past studies of young adults. There was not a significant degradation of functional connectivity during semantic processing between the ages of 55 and 85 years. As the study focused on semantic functioning in older adults, a comparison group of young adults was not included, limiting generalizability. Future longitudinal neuroimaging studies that compare functional connectivity of young and older adults under different semantic demands will be valuable.

A test of indirect grounding of abstract concepts using multimodal distributional semantics

How are abstract concepts grounded in perceptual experiences for shaping human conceptual knowledge? Recent studies on abstract concepts emphasizing the role of language have argued that abstract concepts are grounded indirectly in perceptual experiences and language (or words) functions as a bridge between abstract concepts and perceptual experiences. However, this “indirect grounding” view remains largely speculative and has hardly been supported directly by empirical evidence. In this paper, therefore, we test the indirect grounding view by means of multimodal distributional semantics, in which the meaning of a word (i.e., a concept) is represented as the combination of textual and visual vectors. The newly devised multimodal distributional semantic model incorporates the indirect grounding view by computing the visual vector of an abstract word through the visual vectors of concrete words semantically related to that abstract word. An evaluation experiment is conducted in which conceptual representation is predicted from multimodal vectors using a multilayer feed-forward neural network. The analysis of prediction performance demonstrates that the indirect grounding model achieves significantly better performance in predicting human conceptual representation of abstract words than other models that mimic competing views on abstract concepts, especially than the direct grounding model in which the visual vectors of abstract words are computed directly from the images of abstract concepts. This result lends some plausibility to the indirect grounding view as a cognitive mechanism of grounding abstract concepts.

Towards Parallel Selective Attention Using Psychophysiological States as the Basis for Functional Cognition

Attention is a complex cognitive process with innate resource management and information selection capabilities for maintaining a certain level of functional awareness in socio-cognitive service agents. The human-machine society depends on creating illusionary believable behaviors. These behaviors include processing sensory information based on contextual adaptation and focusing on specific aspects. The cognitive processes based on selective attention help the agent to efficiently utilize its computational resources by scheduling its intellectual tasks, which are not limited to decision-making, goal planning, action selection, and execution of actions. This study reports ongoing work on developing a cognitive architectural framework, a Nature-inspired Humanoid Cognitive Computing Platform for Self-aware and Conscious Agents (NiHA). The NiHA comprises cognitive theories, frameworks, and applications within machine consciousness (MC) and artificial general intelligence (AGI). The paper is focused on top-down and bottom-up attention mechanisms for service agents as a step towards machine consciousness. This study evaluates the behavioral impact of psychophysical states on attention. The proposed agent attains almost 90% accuracy in attention generation. In social interaction, contextual-based working is important, and the agent attains 89% accuracy in its attention by adding and checking the effect of psychophysical states on parallel selective attention. The addition of the emotions to attention process produced more contextual-based responses.

Semantic flow and its relation to controlled semantic retrieval deficits in the narrative production of people with aphasia

Aphasia has had a profound influence on our understanding of how language is instantiated within the human brain. Historically, aphasia has yielded an in vivo model for elucidating the effects of impaired lexical-semantic access on language comprehension and production. Aphasiology has focused intensively on single word dissociations. Yet, less is known about the integrity of combinatorial semantic processes required to construct well-formed narratives. Here we addressed the question of how controlled lexical-semantic retrieval deficits (a hallmark of aphasia) might compound over the course of longer narratives. We specifically examined word-by-word flow of taxonomic vs. thematic semantic distance in the storytelling narratives of individuals with chronic post-stroke aphasia (n = 259) relative to age-matched controls (n = 203). We first parsed raw transcribed narratives into content words and computed inter-word semantic distances for every running pair of words in each narrative (N = 232,490 word transitions). The narratives of people with aphasia showed significant reductions in taxonomic and thematic semantic distance relative to controls. Both distance metrics were strongly predictive of offline measures of semantic impairment and aphasia severity. Since individuals with aphasia often exhibit perseverative language output (i.e., repetitions), we performed additional analyses with repetitions excluded. When repetitions were excluded, group differences in semantic distances persisted and thematic distance was still predictive of semantic impairment, although some findings changed. These results demonstrate the cumulative impact of deficits in controlled word retrieval over the course of narrative production. We discuss the nature of semantic flow between words as a novel metric of characterizing discourse and elucidating the nature of lexical-semantic access impairment in aphasia at multiword levels.

Semantic Memory and Creativity: The Costs and Benefits of Semantic Memory Structure in Generating Original Ideas

Despite its theoretical importance, little is known about how semantic memory structure facilitates and constrains creative idea production. We examine whether the semantic richness of a concept has both benefits and costs to creative idea production. Specifically, we tested whether cue set-size-an index of semantic richness reflecting the average number of elements associated with a given concept-impacts the quantity (fluency) and quality (originality) of responses generated during the alternate uses task (AUT). Across four studies, we show that low-association, sparse, AUT cues benefit originality at the cost of fluency compared to high-association, rich, AUT cues. Furthermore, we found an interaction with individual differences in fluid intelligence in the low-association AUT cues, suggesting that constraints of sparse semantic knowledge can be overcome with top-down intervention. The findings indicate that semantic richness differentially impacts the quality and quantity of generated ideas, and that cognitive control processes can facilitate idea production when conceptual knowledge is limited.

Knowledge Representations Derived From Semantic Fluency Data

The semantic fluency task is commonly used as a measure of one’s ability to retrieve semantic concepts. While performance is typically scored by counting the total number of responses, the ordering of responses can be used to estimate how individuals or groups organize semantic concepts within a category. I provide an overview of this methodology, using Alzheimer’s disease as a case study for how the approach can help advance theoretical questions about the nature of semantic representation. However, many open questions surrounding the validity and reliability of this approach remain unresolved.

Simulated visual hallucinations in virtual reality enhance cognitive flexibility

Historically, psychedelic drugs are known to modulate cognitive flexibility, a central aspect of cognition permitting adaptation to changing environmental demands. Despite proof suggesting phenomenological similarities between artificially-induced and actual psychedelic altered perception, experimental evidence is still lacking about whether the former is also able to modulate cognitive flexibility. To address this, we measure participants' cognitive flexibility through behavioral tasks after the exposure to virtual reality panoramic videos and their hallucinatory-like counterparts generated by the DeepDream algorithm. Results show that the estimated semantic network has a flexible structure when preceded by altered videos. Crucially, following the simulated psychedelic exposure, individuals also show an attenuated contribution of the automatic process and chaotic dynamics underlying the decision process. This suggests that simulated altered perceptual phenomenology enhances cognitive flexibility, presumably due to a reorganization in the cognitive dynamics that facilitates the exploration of uncommon decision strategies and inhibits automated choices.

Diagnostic performance and neural basis of the combination of free- and pre-drawn Clock Drawing Test

OBJECTIVES

This study aimed to clarify the diagnostic performance and neural basis of the Clock Drawing Test (CDT) combining free- and pre-drawn methods.

METHODS

This retrospective study included 165 participants (91 with Alzheimer disease [AD], 52 with amnestic mild cognitive impairment [aMCI], and 22 healthy controls [HC]), who were divided into four groups according to their free- and pre-drawn CDT scores: group 1, could do both; group 2, impaired in both; group 3, impaired in pre-drawn CDT; and group 4, impaired in free-drawn CDT. The diagnostic performances of the free-drawn, pre-drawn, and combination methods were compared using receiver operating characteristics analysis; in voxel-based morphometry analysis, the gray matter (GM) volume of groups 2-4 were compared with that of group 1.

RESULTS

The area under the curve of the combination method was greater than that of the free- or pre-drawn method alone when comparing AD with HC or aMCI. Group 2 had a significantly smaller GM volume in the bilateral temporal lobes than group 1. Group 3 had a trend toward smaller GM volumes in the right temporal lobe when a liberal threshold was applied. Group 4 had significantly smaller GM volumes in the left temporal lobe than group 1.

CONCLUSIONS

This study suggests that the combination method may be able to screen for a wider range of brain dysfunction. Combined use of free- and pre-drawn CDT may be useful for screening for AD and its early detection and treatment.

Computational Models of Readers' Apperceptive Mass

Recent progress in machine-learning-based distributed semantic models (DSMs) offers new ways to simulate the apperceptive mass (AM; Kintsch, 1980) of reader groups or individual readers and to predict their performance in reading-related tasks. The AM integrates the mental lexicon with world knowledge, as for example, acquired via reading books. Following pioneering work by Denhière and Lemaire (2004), here, we computed DSMs based on a representative corpus of German children and youth literature (Jacobs et al., 2020) as null models of the part of the AM that represents distributional semantic input, for readers of different reading ages (grades 1–2, 3–4, and 5–6). After a series of DSM quality tests, we evaluated the performance of these models quantitatively in various tasks to simulate the different reader groups' hypothetical semantic and syntactic skills. In a final study, we compared the models' performance with that of human adult and children readers in two rating tasks. Overall, the results show that with increasing reading age performance in practically all tasks becomes better. The approach taken in these studies reveals the limits of DSMs for simulating human AM and their potential for applications in scientific studies of literature, research in education, or developmental science.

DASentimental: Detecting Depression, Anxiety, and Stress in Texts via Emotional Recall, Cognitive Networks, and Machine Learning

Most current affect scales and sentiment analysis on written text focus on quantifying valence/sentiment, the primary dimension of emotion. Distinguishing broader, more complex negative emotions of similar valence is key to evaluating mental health. We propose a semi-supervised machine learning model, DASentimental, to extract depression, anxiety, and stress from written text. We trained DASentimental to identify how N = 200 sequences of recalled emotional words correlate with recallers’ depression, anxiety, and stress from the Depression Anxiety Stress Scale (DASS-21). Using cognitive network science, we modeled every recall list as a bag-of-words (BOW) vector and as a walk over a network representation of semantic memory—in this case, free associations. This weights BOW entries according to their centrality (degree) in semantic memory and informs recalls using semantic network distances, thus embedding recalls in a cognitive representation. This embedding translated into state-of-the-art, cross-validated predictions for depression (R = 0.7), anxiety (R = 0.44), and stress (R = 0.52), equivalent to previous results employing additional human data. Powered by a multilayer perceptron neural network, DASentimental opens the door to probing the semantic organizations of emotional distress. We found that semantic distances between recalls (i.e., walk coverage), was key for estimating depression levels but redundant for anxiety and stress levels. Semantic distances from “fear” boosted anxiety predictions but were redundant when the “sad–happy” dyad was considered. We applied DASentimental to a clinical dataset of 142 suicide notes and found that the predicted depression and anxiety levels (high/low) corresponded to differences in valence and arousal as expected from a circumplex model of affect. We discuss key directions for future research enabled by artificial intelligence detecting stress, anxiety, and depression in texts.

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.