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.

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.

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.

Many consider the world to be morally better today than it was in the past and expect moral improvement to continue. How do people explain what drives this change? In this paper, we identify two ways people might think about how moral progress occurs: that it is driven by human action (i.e., if people did not actively work to make the world better, moral progress would not occur) or that it is driven by an unspecified mechanism (i.e., that our world is destined to morally improve, but without specifying a role for human action). In Study 1 (N = 147), we find that those who more strongly believe that the mechanism of moral progress is human action are more likely to believe their own intervention is warranted to correct a moral setback. In Study 2 (N = 145), we find that this translates to intended action: those who more strongly believe moral progress is driven by human action report that they would donate more money to correct a moral setback. In Study 3 (N = 297), participants generate their own explanations for why moral progress occurs. We find that participants' donation intentions are predicted by whether their explanations state that human action drives moral progress. Together, these studies suggest that beliefs about the mechanisms of moral progress have important implications for engaging in social action.

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.

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.

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.

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.

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.

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.

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.

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.

We propose a simple computational model that describes potential mechanisms underlying the organization and development of the lexical-semantic system in 18-month-old infants. We focus on two independent aspects: (i) on potential mechanisms underlying the development of taxonomic and associative priming, and (ii) on potential mechanisms underlying the effect of Inter Stimulus Interval on these priming effects. Our model explains taxonomic priming between words by semantic feature overlap, whereas associative priming between words is explained by Hebbian links between semantic representations derived from co-occurrence relations between words (or their referents). From a developmental perspective, any delay in the emergence of taxonomic priming compared to associative priming during infancy seems paradoxical since feature overlap per se need not be learned. We address this paradox in the model by showing that feature overlap itself is an emergent process. The model successfully replicates infant data related to Inter Stimulus Interval effects in priming experiments and makes testable predictions.

This article evaluates the predictions of an algorithmic-level distributed associative memory model as it introduces, propagates, and resolves ambiguity, and compares it to the predictions of computational-level parallel parsing models in which ambiguous analyses are accounted separately in discrete distributions. By superposing activation patterns that serve as cues to other activation patterns, the model is able to maintain multiple syntactically complex analyses superposed in a finite working memory, propagate this ambiguity through multiple intervening words, then resolve this ambiguity in a way that produces a measurable predictor that is proportional to the log conditional probability of the disambiguating word given its context, marginalizing over all remaining analyses. The results are indeed consistent in cases of complex structural ambiguity with computational-level parallel parsing models producing this same probability as a predictor, which have been shown reliably to predict human reading times.

Filler-gap dependency resolution is often characterized as an active process. We probed the mechanisms that determine where and why comprehenders posit gaps during incremental processing using Norwegian as our test language. First, we investigated why active filler-gap dependency resolution is suspended inside island domains like embedded questions in some languages. Processing-based accounts hold that resource limitations prevent gap-filling in embedded questions across languages, while grammar-based accounts predict that active gap-filling is only blocked in languages where embedded questions are grammatical islands. In a self-paced reading study, we find that Norwegian participants exhibit filled-gap effects inside embedded questions, which are not islands in the language. The findings are consistent with grammar-based, but not processing, accounts. Second, we asked if active filler-gap processing can be understood as a special case of probabilistic ambiguity resolution within an expectation-based framework. To do so, we tested whether word-by-word surprisal values from a neural language model could predict the location and magnitude of filled-gap effects in our behavioral data. We find that surprisal accurately tracks the location of filled-gap effects but severely underestimates their magnitude. This suggests either that mechanisms above and beyond probabilistic ambiguity resolution are required to fully explain active gap-filling behavior or that surprisal values derived from long-short term memory are not good proxies for humans' incremental expectations during filler-gap resolution.

The present study investigated children's understanding that an object's history may increase its significance, an appreciation that underpins the concept of historical authenticity (i.e., the idea that an item's history determines its true identity, beyond its functional or material qualities, leading people to value real items over copies or fakes). We examined the development of historical significance through the lens of parent-child conversations, and children's performance on an authenticity assessment. The final sample was American, 79.2% monoracial White, and mid-high socio-economic status (SES) and included 48 parent-child pairs: 24 with younger children (R = 3.5 to 4.5 years) and 24 with older children (R = 5.5 to 6.5 years). Parent-child pairs discussed three books we created, with three storylines: a museum (culturally authentic) storyline, a clean-up (personally authentic) storyline, and a control storyline. Across measures, conversations suggested that authenticity may begin as a "placeholder concept" that is initially rooted in a broad appreciation for the significance of old objects and only later filled in with specifics. This placeholder initially directs children's learning about authenticity by linking, in an unspecified way, the value and significance of objects to their past. For example, we found that young children appropriately appealed to history (vs. perceptual or functional features of objects) in contexts regarding authentic objects but struggled in determining which objects were more significant on the post-test assessment, suggesting that they attend to object history but are not yet sure how histories matter for making authenticity judgments. We also found some evidence that directing children's attention toward conceptual information related to object history may in turn direct them away from material or perceptual considerations, as seen in trade-offs in parents' and children's conversations. Together, this exploratory report offers many new avenues for work on the development of authenticity concepts in childhood.

What type of conceptual information about an object do we get at a brief glance? In two experiments, we investigated the nature of conceptual tokening-the moment at which conceptual information about an object is accessed. Using a masked picture-word congruency task with dichoptic presentations at "brief" (50-60 ms) and "long" (190-200 ms) durations, participants judged the relation between a picture (e.g., a banana) and a word representing one of four property types about the object: superordinate (fruit), basic level (banana), a high-salient (yellow), or low-salient feature (peel). In Experiment 1, stimuli were presented in black-and-white; in Experiment 2, they were presented in red and blue, with participants wearing red-blue anaglyph glasses. This manipulation allowed for the independent projection of stimuli to the left- and right-hemisphere visual areas, aiming to probe the early effects of these projections in conceptual tokening. Results showed that superordinate and basic-level properties elicited faster and more accurate responses than high- and low-salient features at both presentation times. This advantage persisted even when the objects were divided into categories (e.g., animals, vegetables, vehicles, tools), and when objects contained high-salient visual features. However, contrasts between categories show that animals, fruits, and vegetables tend to be categorized at the superordinate level, while vehicles tend to be categorized at the basic level. Also, for a restricted class of objects, high-salient features representing diagnostic color information (yellow for the picture of a banana) facilitated congruency judgments to the same extent as that of superordinate and basic-level labels. We suggest that early access to object concepts yields superordinate and basic-level information, with features only yielding effects at a later stage of processing, unless they represent diagnostic color information. We discuss these results advancing a unified theory of conceptual representation, integrating key postulates of atomism and feature-based theories.

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.

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.