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.

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.

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.

In this study, we conducted large-scale experiments with novel descriptions of determinism. Our goal was to investigate the effects of desires for punishment and comprehension errors on people's intuitions about free will and moral responsibility in deterministic scenarios. Previous research has acknowledged the influence of these factors, but their total effect has not been revealed. Using a large-scale survey of Japanese participants, we found that the failure to understand causal determination (intrusion) has limited effects relative to other factors and that the conflation of determinism and epiphenomenalism (bypassing) has a significant influence, even when controlling for other variables. This leads to the increased prevalence of incompatibilist responses. Furthermore, our results demonstrated a close association between the attribution of free will/responsibility and retributive desire. While further research is needed to establish the causal relationship between these factors, this association is consistent with Cory Clark and colleagues' study that increased desire contributes to increased compatibilist responses and their claim that a definitive intuition about free will may be elusive.

Moral rules come with exceptions, and moral judgments come with uncertainty. For instance, stealing is wrong and generally punished. Yet, it could be the case that the thief is stealing food for their family. Such information about the thief's context could flip admonishment to praise. To varying degrees, this type of uncertainty regarding the context of another person's behavior is ever-present in moral judgment. Hence, we propose a model of how people evaluate others' behavior: We argue that individuals principally judge the righteousness of another person's behavior by assessing the likelihood that they would act the same way if they were in the person's shoes. That is, if you see another person steal, you will consider the contexts where you too would steal and assess the likelihood that any of these contexts are true, given the available information. This idea can be formalized as a Bayesian model that treats moral judgment as probabilistic reasoning. We tested this model across four studies (N = 601) involving either fictional moral vignettes or economic games. The studies yielded converging evidence showing that the proposed model better predicts moral judgment under uncertainty than traditional theories that emphasize social norms or perceived harm/utility. Overall, the present studies support a new model of moral judgment with the potential to unite research on social judgment, decision-making, and probabilistic reasoning. Beyond this specific model, the present studies also more generally speak to how individuals parse uncertainty by integrating across different possibilities.

Many bioliberals endorse broadly consequentialist frameworks in normative ethics, implying that a progressive stance on matters of bioethical controversy could stem from outcome-based reasoning. This raises an intriguing empirical prediction: encouraging outcome-based reflection could yield a shift toward bioliberal views among nonexperts as well. To evaluate this hypothesis, we identified empirical premises that underlie moral disagreements on seven divisive issues (e.g., vaccines, abortion, or genetically modified organisms). In exploratory and confirmatory experiments, we assessed whether people spontaneously engage in outcome-based reasoning by asking how their moral views change after momentarily reflecting on the underlying empirical questions. Our findings indicate that momentary reflection had no overall treatment effect on the central tendency or the dispersion in moral attitudes when compared to prereflection measures collected 1 week prior. Autoregressive models provided evidence that participants engaged in consequentialist moral reasoning, but this self-guided reflection produced neither moral "progress" (shifts in the distributions' central tendency) nor moral "consensus" (reductions in their dispersion). These results imply that flexibility in people's search for empirical answers may limit the potential for outcome-based reflection to foster moral consensus.

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.