Cognitive Science最新文献

The mental lexicon changes across the lifespan. Prior work, aggregating data among individuals of similar ages, found that the aging lexicon, represented as a network of free associations, becomes more sparse with age: degree and clustering coefficient decrease and average shortest path length increases. However, because this work is based on aggregated data, it remains to be seen whether or not individuals show a similar pattern of age-related lexical change. Here, we demonstrate how an individual-level approach can be used to reveal differences that vary systematically with age. We also directly compare this approach with an aggregate-level approach, to show how these approaches differ. Our individual-level approach follows the logic of many past approaches by comparing individual data as they are situated within population-level data. To do this, we produce a conglomerate network from population-level data and then identify how data from individuals of different ages are situated within that network. Though we find most qualitative patterns are preserved, individuals produce associates that have a higher clustering coefficient in the conglomerate network as they age. Alongside a reduction in degree, this suggests more specialized but clustered knowledge with age. Older individuals also reveal a pattern of increasing distance among the associates they produce in response to a single cue, indicating a more diverse range of associations. We demonstrate these results for three different languages: English, Spanish, and Dutch, which all show the same qualitative patterns of differences between aggregate and individual network approaches. These results reveal how individual-level approaches can be taken with aggregate data and demonstrate new insights into understanding the aging lexicon.

Inferring an individual's preferences from their observable behavior is a key step in the development of assistive decision-making technology. Although machine learning models such as neural networks could in principle be deployed toward this inference, a large amount of data is required to train such models. Here, we present an approach in which a cognitive model generates simulated data to augment limited human data. Using these data, we train a neural network to invert the model, making it possible to infer preferences from behavior. We show how this approach can be used to infer the value that people assign to food items from their eye movements when choosing between those items. We demonstrate first that neural networks can infer the latent preferences used by the model to generate simulated fixations, and second that simulated data can be beneficial in pretraining a network for predicting human-reported preferences from real fixations. Compared to inferring preferences from choice alone, this approach confers a slight improvement in predicting preferences and also allows prediction to take place prior to the choice being made. Overall, our results suggest that using a combination of neural networks and model-simulated training data is a promising approach for developing technology that infers human preferences.

Variations in language abilities, use, and production style are ubiquitous within any given population. While research on language evolution has traditionally overlooked the potential importance of such individual differences, these can have an important impact on the trajectory of language evolution and ongoing change. To address this gap, we use a group communication game for studying this mechanism in the lab, in which micro-societies of interacting participants develop and use artificial languages to successfully communicate with each other. Importantly, one participant in the group is assigned a keyboard with a limited inventory of letters (simulating a speech impairment that individuals may encounter in real life), forcing them to communicate differently than the rest. We test how languages evolve in such heterogeneous groups and whether they adapt to accommodate the unique characteristics of individuals with language idiosyncrasies. Our results suggest that language evolves differently in groups where some individuals have distinct language abilities, eliciting more innovative elements at the cost of reduced communicative success and convergence. Furthermore, we observed strong partner-specific accommodation to the minority individual, which carried over to the group level. Importantly, the degree of group-wide adaptation was not uniform and depended on participants’ attachment to established language forms. Our findings provide compelling evidence that individual differences can permeate and accumulate within a linguistic community, ultimately driving changes in languages over time. They also underscore the importance of integrating individual differences into future research on language evolution.

Our self-concept is constantly faced with self-relevant information. Prevailing research suggests that information's valence plays a central role in shaping our self-views. However, the need for stability within the self-concept structure and the inherent alignment of positive feedback with the pre-existing self-views of healthy individuals might mask valence and congruence effects. In this study (N = 30, undergraduates), we orthogonalized feedback valence and self-congruence effects to examine the behavioral and electrophysiological signatures of self-relevant feedback processing and self-concept updating. We found that participants had a preference for integrating self-congruent and dismissing self-incongruent feedback, regardless of its valence. Consistently, electroencephalography results revealed that feedback congruence, but not feedback valence, is rapidly detected during early processing stages. Our findings diverge from the accepted notion that self-concept updating is based on the selective incorporation of positive information. These findings offer novel insights into self-concept dynamics, with implications for the understanding of psychopathological conditions.

There is ample evidence that individual-level cognitive mechanisms active during language learning and use can contribute to the evolution of language. For example, experimental work suggests that learners will reduce case marking in a language where grammatical roles are reliably indicated by fixed word order, a correlation found robustly in the languages of the world. However, such research often assumes homogeneity among language learners and users, or at least does not dig into individual differences in behavior. Yet, it is increasingly clear that language users vary in a large number of ways: in culture, in demographics, and—critically for present purposes—in terms of cognitive diversity. Here, we explore how neurodiversity impacts behavior in an experimental task similar to the one summarized above, and how this behavior interacts with social pressures. We find both similarities and differences between autistic and nonautistic English-speaking individuals, suggesting that neurodiversity can impact language change in the lab. This, in turn, highlights the potential for future research on the role of neurodivergent populations in language evolution more generally.

For both adults and children, learning from one's mistakes (error-based learning) has been shown to be advantageous over avoiding errors altogether (errorless learning) in pedagogical settings. However, it remains unclear whether this advantage carries over to nonpedagogical settings in children, who mostly learn language in such settings. Using irregular plurals (e.g., “mice”) as a test case, we conducted a corpus analysis (N = 227) and two preregistered experiments (N = 56, N = 99), to investigate the potency of error-based learning as a mechanism for language acquisition in 3- and 4-year-old children. The results of the corpus analysis showed that incidental feedback after errors, in the form of caregivers’ reformulations of children's errors, was relatively infrequent, had modest informational value, and was rarely used by children to correct their errors immediately. The following two experiments contrasted error-based learning with errorless learning, where the correct utterance was modeled for the child before a potential error was committed. The results showed that error-based learning was not always effective, and when it was, it was certainly not superior to errorless learning. Collectively, these findings question the extension of the benefits of error-based learning from pedagogical to nonpedagogical settings and define constraints under which one mechanism may be more beneficial to learning than the other.

Lexical models diverge on the question of how to represent complex words. Under the morpheme-based approach, each morpheme is treated as a separate unit, while under the word-based approach, morphological structure is derived from complex words. In this paper, we propose a new computational model of morphology that is based on graph theory and is intended to elaborate the word-based network approach. Specifically, we use a key concept of network science, the notion of shortest path, to investigate how complex words are learned, stored, and processed. The notion of shortest path refers to the task of finding the shortest or most optimal path connecting two non-adjacent nodes in a network. Building on this notion, the current study shows (i) that new complex words can be segmented into morphemes through the shortest path analysis; (ii) that attested English words tend to represent the shortest paths in the morphological network; and (iii) that novel (unattested) words receive higher acceptability ratings in experiments when they are formed along established optimal paths. The model's performance is tested in two experiments with human participants as well as against the behavioral data from the English Lexicon Project. We interpret our empirical results from the perspective of a usage-based model of grammar and argue that network science provides a powerful tool for analyzing language structure.

In Semitic languages, the consonantal root is central to morphology, linking form and meaning. While psycholinguistic studies highlight its importance in language processing, the role of meaning in early lexical access and its representation remain unclear. This study investigates when meaning becomes accessible during the processing of Maltese verb forms, using a computational model based on the Discriminative Lexicon framework. Our model effectively comprehends and produces Maltese verbs, while also predicting response times in a masked auditory priming experiment. Results show that meaning is accessible early in lexical access and becomes more prominent after the target word is fully processed. This suggests that semantic information plays a critical role from the initial stages of lexical access, refining our understanding of real-time language comprehension. Our findings contribute to theories of lexical access and offer valuable insights for designing priming studies in psycholinguistics. Additionally, this study demonstrates the potential of computational models in investigating the relationship between form and meaning in language processing.

Naming common odors is a surprisingly difficult task: Odors are frequently misnamed. Little is known about the linguistic properties of odor misnamings. We test whether odor misnamings of old adults carry information about olfactory perception and its connection to lexical-semantic processing. We analyze the olfactory–semantic content of odor source naming failures in a large sample of older adults in Sweden (n = 2479; age 58–100 years). We investigate whether linguistic factors and semantic proximity to the target odor name predict how odors are misnamed, and how these factors relate to overall odor identification performance. We also explore the primary semantic dimensions along which misnamings are distributed. We find that odor misnamings consist of surprisingly many vague and unspecific terms, such as category names (e.g., fruit) or abstract or evaluative terms (e.g., sweet). Odor misnamings are often strongly associated with the correct name, capturing properties such as its category or other abstract features. People are also biased toward misnaming odors with high-frequency terms that are associated with olfaction or gustation. Linguistic properties of odor misnamings and their semantic proximity to the target odor name predict odor identification performance, suggesting that linguistic processing facilitates odor identification. Further, odor misnamings constitute an olfactory–semantic space that is similar to the olfactory vocabulary of English. This space is primarily differentiated along pleasantness, edibility, and concreteness dimensions. Odor naming failures thus contain plenty of information about semantic odor knowledge.

What should judges do when faced with immoral laws? Should they apply them without exception, since “the law is the law?” Or can exceptions be made for grossly immoral laws, such as historically, Nazi law? Surveying laypeople (N = 167) and people with some legal training (N = 141) on these matters, we find a surprisingly strong, monotonic relationship between people's subjective moral evaluation of laws and their judgments that these laws should be applied in concrete cases. This tendency is most pronounced among individuals who endorse natural law (i.e., the legal-philosophical view that immoral laws are not valid laws at all), and is attenuated when disagreement about the moral status of a law is considered reasonable. The relationship is equally strong for laypeople and for those with legal training. We situate our findings within the broader context of morality's influence on legal reasoning that experimental jurisprudence has uncovered in recent years, and consider normative implications.