Evidence for preferential attachment: Words that are more well connected in semantic networks are better at acquiring new links in paired-associate learning

Evidence for optimal semantic search throughout adulthood

As people age, they learn and store new knowledge in their semantic memory. Despite learning a tremendous amount of information, people can still recall information relevant to the current situation with ease. To accomplish this, the mind must efficiently organize and search a vast store of information. It also must continue to retrieve information effectively despite changes in cognitive mechanisms due to healthy aging, including a general slowing in information processing and a decline in executive functioning. How effectively does the mind of an individual adjust its search to account for changes due to aging? We tested 746 people ages 25 through 69 on a semantic fluency task (free listing animals) and found that, on average, retrieval follows an optimal path through semantic memory. Participants tended to list a sequence of semantically related animals (e.g., lion, tiger, puma) before switching to a semantically unrelated animal (e.g., whale). We found that the timing of these transitions to semantically unrelated animals was remarkably consistent with an optimal strategy for maximizing the overall rate of retrieval (i.e., the number of animals listed per unit time). Age did not affect an individual's deviation from the optimal strategy given their general performance, suggesting that people adapt and continue to search memory optimally throughout their lives. We argue that this result is more likely due to compensating for a general slowing than a decline in executive functioning.

Age differences in semantic network structure: Acquiring knowledge shapes semantic memory

Computational research suggests that semantic memory, operationalized as semantic memory networks, undergoes age-related changes. Previous work suggests that concepts in older adults' semantic memory networks are more separated, more segregated, and less connected to each other. However, cognitive network research often relies on group averages (e.g., young vs. older adults), and it remains unclear if individual differences influence age-related disparities in language production abilities. Here, we analyze the properties of younger and older participants' individual-based semantic memory networks based on their semantic relatedness judgments. We related individual-based network measures-clustering coefficient (CC; connectivity), global efficiency, and modularity (structure)-to language production (verbal fluency) and vocabulary knowledge. Similar to previous findings, we found significant age effects: CC and global efficiency were lower, and modularity was higher, for older adults. Furthermore, vocabulary knowledge was significantly related to the semantic memory network measures: corresponding with the age effects, CC and global efficiency had a negative relationship, while modularity had a positive relationship with vocabulary knowledge. More generally, vocabulary knowledge significantly increased with age, which may reflect the critical role that the accumulation of knowledge within semantic memory has on its structure. These results highlight the impact of diverse life experiences on older adults' semantic memory and demonstrate the importance of accounting for individual differences in the aging mental lexicon. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

From eye movements to scanpath networks: A method for studying individual differences in expository text reading

Eye movements have been examined as an index of attention and comprehension during reading in the literature for over 30 years. Although eye-movement measurements are acknowledged as reliable indicators of readers' comprehension skill, few studies have analyzed eye-movement patterns using network science. In this study, we offer a new approach to analyze eye-movement data. Specifically, we recorded visual scanpaths when participants were reading expository science text, and used these to construct scanpath networks that reflect readers' processing of the text. Results showed that low ability and high ability readers' scanpath networks exhibited distinctive properties, which are reflected in different network metrics including density, centrality, small-worldness, transitivity, and global efficiency. Such patterns provide a new way to show how skilled readers, as compared with less skilled readers, process information more efficiently. Implications of our analyses are discussed in light of current theories of reading comprehension.

Stochastic Time-Series Analyses Highlight the Day-To-Day Dynamics of Lexical Frequencies

Standard models in quantitative linguistics assume that word usage follows a fixed frequency distribution, often Zipf's law or a close relative. This view, however, does not capture the near daily variations in topics of conversation, nor the short-term dynamics of language change. In order to understand the dynamics of human language use, we present a corpus of daily word frequency variation scraped from online news sources every 20 min for more than 2 years. We construct a simple time-varying model with a latent state, which is observed via word frequency counts. We use Bayesian techniques to infer the parameters of this model for 20,000 words, allowing us to convert complex word-frequency trajectories into low-dimensional parameters in word usage. By analyzing the inferred parameters of this model, we quantify the relative mobility and drift of words on a day-to-day basis, while accounting for sampling error. We quantify this variation and show evidence against "rich-get-richer" models of word use, which have been previously hypothesized to explain statistical patterns in language.

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.

Second Chances in Learning: Does a Resit Prospect Lower Study-Time Investments on a First Test?

Previous studies have shown that the prospect of a resit opportunity lowers hypothetical study-time investments for a first exam, as compared to a single-chance exam (i.e., the resit effect). The present paper describes a first experiment in which we aimed to generalize this effect from hypothetical study-time investments to a learning task allowing for the optimization of actual study-time investments while participants studied pairs of pseudowords for a subsequent multiple-choice test, given either a single chance or two chances to pass. Against our expectations, the results of the experiment showed no resit effect for the amount of actual time participants spent studying the materials in the experimental learning task. To better allow for the optimization of study-time investments, the learning task was adapted for a second experiment to include an indication of passing probability. These results, however, also did not show a resit effect. A third experiment addressed whether it was the investment of actual time that led to this absence of a resit effect with the learning task. The results suggested, however, that it was most likely the lack of a priori deliberation that caused this absence of the effect. Taken together with findings from a fourth questionnaire study showing that students seem to take a resit prospect into account by indicating they would have studied more for an exam if the option to resit would not have been available, these findings lead us to argue that a resit prospect may primarily affect advance study-time allocation decisions.

Barriers to Building More Effective Treatments: Negative Interactions Amongst Smoking Intervention Components

Meaningfully improved mental and behavioral health treatment is an unrealized dream. Across three factorial experiments, inferential tests in prior studies showed a pattern of negative interactions suggesting that better clinical outcomes are obtained when participants receive fewer rather than more intervention components. Further, relatively few significant main effects were found in these experiments. Modeling suggested that negative interactions amongst components may account for these patterns. This paper evaluates factors that may contribute to such declining benefit: increased attentional or effort burden; components that produce their effects via the same capacity limited mechanisms, making their effects subadditive; and a tipping point phenomenon in which those near a hypothesized "tipping point" for change will benefit markedly from weak intervention while those far from the tipping point will benefit little from even strong intervention. New research should explore factors that cause negative interactions amongst components and constrain the development of more effective treatments.

Evidence for preferential attachment: Words that are more well connected in semantic networks are better at acquiring new links in paired-associate learning

Here, we view the mental lexicon as a semantic network where words are connected if they are semantically related. Steyvers and Tenenbaum (Cognitive Science, 29, 41-78, 2005) proposed that the growth of semantic networks follows preferential attachment, the observation that new nodes are more likely to connect to preexisting nodes that are more well connected (i.e., the rich get richer). If this is the case, well-connected known words should be better at acquiring new links than poorly connected words. We tested this prediction in three paired-associate learning (PAL) experiments in which participants memorized arbitrary cue-response word pairs. We manipulated the semantic connectivity of the cue words, indexed by the words' free associative degree centrality. Experiment 1 is a reanalysis of the PAL data from Qiu and Johns (Psychonomic Bulletin & Review, 27, 114-121, 2020), in which young adults remembered 40 cue-response word pairs (e.g., nature-chain) and completed a cued recall task. Experiment 2 is a preregistered replication of Qiu and Johns. Experiment 3 addressed some limitations in Qiu and Johns's design by using pseudowords as the response items (e.g., boot-arruity). The three experiments converged to show that cue words of higher degree centrality facilitated the recall/recognition of the response items, providing support for the notion that better-connected words have a greater ability to acquire new links (i.e., the rich do get richer). Importantly, while degree centrality consistently accounted for significant portions of variance in PAL accuracy, other psycholinguistic variables (e.g., concreteness, contextual diversity) did not, suggesting that degree centrality is a distinct variable that affects the ease of verbal associative learning.