Cognitive Science最新文献

Understanding the actions of others is fundamental for human social life. It builds on a grasp of the subjective intentionality behind behavior: one action comprises different things simultaneously (e.g., moving their arm, turning on the light) but which of these constitute intentional actions, in contrast to merely foreseen side-effects (e.g., increasing the electricity bill), depends on the description under which the agent represents the acts. She may be acting intentionally only under the description “turning on the light,” but did not turn on the light in order to increase the electricity bill. In preregistered studies (N = 620), we asked how adults and children engage in such complex subjective action interpretation and evaluation in moral dilemmas. To capture the deep structure of subjects' representations of the intentional structures of actions, we derived “act trees” from their response patterns to questions about the acts. Results suggest that people systematically distinguish between intended main and merely foreseen side-effects in their moral and intentionality judgments, even when main and side-effects were closely related and the latter were harmful. Additional experimental conditions suggest that, when given ambiguous information, the majority of subjects assume that agents act with beneficial main intentions. This “good intention prior” was so strong that participants attributed good intentions even when the harmful action was no longer necessary to resolve the dilemma (Study 2). These methods provide promising new ways to investigate in more subtle and fine-grained ways how reasoners parse, interpret, and evaluate complex actions.

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.

Schuler, W. & Yue, S. (2024), Evaluation of an Algorithmic-Level Left-Corner Parsing Account of Surprisal Effects. Cognitive Science, 48(10), e13500. https://doi.org/10.1111/cogs.13500.

In the above referenced article, author Shisen Yue's name was misspelled as Shizen Yue. This has now been corrected in the original article.

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.