Semantic maturation during the comprehension-expression gap in late and typical talkers.

This study investigates the influence of semantic maturation on early lexical development by examining the impact of contextual diversity-known to influence semantic development-on word promotion from receptive to productive vocabularies (i.e., comprehension-expression gap). Study 1 compares the vocabularies of 3685 American-English-speaking typical talkers (TTs) and late talkers (LTs; 16-30 months old; 1257 females, 1021 gender unknown; ethnicity unknown; data downloaded in 2018) and finds that LTs, with a longer preverbal phase, produced nouns with lower contextual diversity (R2  = .80), but verbs with higher contextual diversity (R2  = .13). Study 2 compares computational network growth models of semantic maturation and finds that verbs require more semantic maturation than nouns, and TTs produce words that are more semantically mature than LTs.

Neurocognitive free will.

Free will is an apparent paradox because it requires a historical identity to escape its history in a self-guided fashion. Philosophers have itemized design features necessary for this escape, scaling from action to agency and vice versa. These can be organized into a coherent framework that neurocognitive capacities provide and that form a basis for neurocognitive free will. These capacities include (1) adaptive access to unpredictability, (2) tuning of this unpredictability in the service of hierarchical goal structures, (3) goal-directed deliberation via search over internal cognitive representations, and (4) a role for conscious construction of the self in the generation and choice of alternatives. This frames free will as a process of generative self-construction, by which an iterative search process samples from experience in an adaptively exploratory fashion, allowing the agent to explore itself in the construction of alternative futures. This provides an explanation of how effortful conscious control modulates adaptive access to unpredictability and resolves one of free will's key conceptual problems: how randomness is used in the service of the will. The implications provide a contemporary neurocognitive grounding to compatibilist and libertarian positions on free will, and demonstrate how neurocognitive understanding can contribute to this debate by presenting free will as an interaction between our freedom and our will.

The Macroscope: A tool for examining the historical structure of language.

The recent rise in digitized historical text has made it possible to quantitatively study our psychological past. This involves understanding changes in what words meant, how words were used, and how these changes may have responded to changes in the environment, such as in healthcare, wealth disparity, and war. Here we make available a tool, the Macroscope, for studying historical changes in language over the last two centuries. The Macroscope uses over 155 billion words of historical text, which will grow as we include new historical corpora, and derives word properties from frequency-of-usage and co-occurrence patterns over time. Using co-occurrence patterns, the Macroscope can track changes in semantics, allowing researchers to identify semantically stable and unstable words in historical text and providing quantitative information about changes in a word's valence, arousal, and concreteness, as well as information about new properties, such as semantic drift. The Macroscope provides information about both the local and global properties of words, as well as information about how these properties change over time, allowing researchers to visualize and download data in order to make inferences about historical psychology. Although quantitative historical psychology represents a largely new field of study, we see this work as complementing a wealth of other historical investigations, offering new insights and new approaches to understanding existing theory. The Macroscope is available online at http://www.macroscope.tech .

Bad News Has Wings: Dread Risk Mediates Social Amplification in Risk Communication.

Social diffusion of information amplifies risk through processes of birth, death, and distortion of message content. Dread risk-involving uncontrollable, fatal, involuntary, and catastrophic outcomes (e.g., terrorist attacks and nuclear accidents)-may be particularly susceptible to amplification because of the psychological biases inherent in dread risk avoidance. To test this, initially balanced information about high or low dread topics was given to a set of individuals who then communicated this information through diffusion chains, each person passing a message to the next. A subset of these chains were also reexposed to the original information. We measured prior knowledge, perceived risk before and after transmission, and, at each link, number of positive and negative statements. Results showed that the more a message was transmitted the more negative statements it contained. This was highest for the high dread topic. Increased perceived risk and production of negative messages was closely related to the amount of negative information that was received, with domain knowledge mitigating this effect. Reexposure to the initial information was ineffectual in reducing bias, demonstrating the enhanced danger of socially transmitted information.

Humor norms for 4,997 English words.

Humor ratings are provided for 4,997 English words collected from 821 participants using an online crowd-sourcing platform. Each participant rated 211 words on a scale from 1 (humorless) to 5 (humorous). To provide for comparisons across norms, words were chosen from a set common to a number of previously collected norms (e.g., arousal, valence, dominance, concreteness, age of acquisition, and reaction time). The complete dataset provides researchers with a list of humor ratings and includes information on gender, age, and educational differences. Results of analyses show that the ratings have reliability on a par with previous ratings and are not well predicted by existing norms.

Correction to "When fairness is not enough: The disproportionate contributions of the poor in a collective action problem" by Malthouse et al. (2023).

Reports an error in "When fairness is not enough: The disproportionate contributions of the poor in a collective action problem" by Eugene Malthouse, Charlie Pilgrim, Daniel Sgroi and Thomas T. Hills (Journal of Experimental Psychology: General, 2023[Nov], Vol 152[11], 3229-3242). The third and final research question in The Collective-Risk Social Dilemma section now appears as follows: 3. If what people perceive as fair is insufficient to solve the problem, under what conditions do groups still manage to succeed? All versions of this article have been corrected. (The following abstract of the original article appeared in record 2023-92402-001.) Many of our most pressing challenges, from combating climate change to dealing with pandemics, are collective action problems: situations in which individual and collective interests conflict with each other. In such situations, people face a dilemma about making individually costly but collectively beneficial contributions to the common good. Understanding which factors influence people's willingness to make these contributions is vital for the design of policies and institutions that support the attainment of collective goals. In this study, we investigate how inequalities, and different causes of inequalities, impact individual-level behavior and group-level outcomes. First, we find that what people judged to be fair was not enough to solve the collective action problem: if they acted according to what they thought was fair, they would collectively fail. Second, the level of wealth (rich vs. poor) altered what was judged to be a fair contribution to the public good more than the cause of wealth (merit vs. luck vs. uncertain). Contributions during the game reflected these fairness judgments, with poorer individuals consistently contributing a higher proportion of their wealth than richer participants, which further increased inequality-particularly in successful groups. Finally, the cause of one's wealth was largely irrelevant, mattering most only when it was uncertain, as opposed to resulting from merit or luck. We discuss implications for policymakers and international climate change negotiations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Confirmation bias emerges from an approximation to Bayesian reasoning.

Confirmation bias is defined as searching for and assimilating information in a way that favours existing beliefs. We show that confirmation bias emerges as a natural consequence of boundedly rational belief updating by presenting the BIASR model (Bayesian updating with an Independence Approximation and Source Reliability). In this model, an individual's beliefs about a hypothesis and the source reliability form a Bayesian network. Upon receiving information, an individual simultaneously updates beliefs about the hypothesis in question and the reliability of the information source. If the individual updates rationally then this introduces numerous dependencies between beliefs, the tracking of which represents an unrealistic demand on memory. We propose that human cognition overcomes this memory limitation by assuming independence between beliefs, evidence for which is provided in prior research. We show how a Bayesian belief updating model incorporating this independence approximation generates many types of confirmation bias, including biased evaluation, biased assimilation, attitude polarisation, belief perseverance and confirmation bias in the selection of sources.

Differences in the semantic structure of the speech experienced by late talkers, late bloomers, and typical talkers.

The present study investigates the relation between language environment and language delay in 63 British-English speaking children (19 typical talkers (TT), 22 late talkers (LT), and 22 late bloomers (LB) aged 13 to 18 months. Families audio recorded daily routines and marked the new words their child produced over a period of 6 months. To investigate how language environments differed between talker types and how environments corresponded with children's developing lexicons, we evaluated contextual diversity-a word property that measures semantic richness-and network properties of language environments in tandem with developing vocabularies. The language environment experienced by the three talker types differed in their structural properties, with LT environments being least contextually diverse and least well-connected in relation to network properties. Notably, LBs' language environments were more like those of TTs. Network properties of language environments also correlate with the rate of vocabulary growth over the study period. By comparing differences between language environments and lexical network development, we also observe results consistent with contributions to lexical development from different learning strategies for expressive vocabularies and different environments for receptive vocabularies. We discuss the potential consequences that structural differences in parental speech might have on language development and the contribution of this work to the debate on quantity versus quality. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Quantifying the retention of emotions across story retellings.

Story retelling is a fundamental medium for the transmission of information between individuals and among social groups. Besides conveying factual information, stories also contain affective information. Though natural language processing techniques have advanced considerably in recent years, the extent to which machines can be trained to identify and track emotions across retellings is unknown. This study leverages the powerful RoBERTa model, based on a transformer architecture, to derive emotion-rich story embeddings from a unique dataset of 25,728 story retellings. The initial stories were centered around five emotional events (joy, sadness, embarrassment, risk, and disgust-though the stories did not contain these emotion words) and three intensities (high, medium, and low). Our results indicate (1) that RoBERTa can identify emotions in stories it was not trained on, (2) that the five emotions and their intensities are preserved when they are transmitted in the form of retellings, (3) that the emotions in stories are increasingly well-preserved as they experience additional retellings, and (4) that among the five emotions, risk and disgust are least well-preserved, compared with joy, sadness, and embarrassment. This work is a first step toward quantifying situation-driven emotions with machines.

When fairness is not enough: The disproportionate contributions of the poor in a collective action problem.

Many of our most pressing challenges, from combating climate change to dealing with pandemics, are collective action problems: situations in which individual and collective interests conflict with each other. In such situations, people face a dilemma about making individually costly but collectively beneficial contributions to the common good. Understanding which factors influence people's willingness to make these contributions is vital for the design of policies and institutions that support the attainment of collective goals. In this study, we investigate how inequalities, and different causes of inequalities, impact individual-level behavior and group-level outcomes. First, we find that what people judged to be fair was not enough to solve the collective action problem: if they acted according to what they thought was fair, they would collectively fail. Second, the level of wealth (rich vs. poor) altered what was judged to be a fair contribution to the public good more than the cause of wealth (merit vs. luck vs. uncertain). Contributions during the game reflected these fairness judgments, with poorer individuals consistently contributing a higher proportion of their wealth than richer participants, which further increased inequality-particularly in successful groups. Finally, the cause of one's wealth was largely irrelevant, mattering most only when it was uncertain, as opposed to resulting from merit or luck. We discuss implications for policymakers and international climate change negotiations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Structural differences in the semantic networks of younger and older adults.

Cognitive science invokes semantic networks to explain diverse phenomena, from memory retrieval to creativity. Research in these areas often assumes a single underlying semantic network that is shared across individuals. Yet, recent evidence suggests that content, size, and connectivity of semantic networks are experience-dependent, implying sizable individual and age-related differences. Here, we investigate individual and age differences in the semantic networks of younger and older adults by deriving semantic networks from both fluency and similarity rating tasks. Crucially, we use a megastudy approach to obtain thousands of similarity ratings per individual to allow us to capture the characteristics of individual semantic networks. We find that older adults possess lexical networks with smaller average degree and longer path lengths relative to those of younger adults, with older adults showing less interindividual agreement and thus more unique lexical representations relative to younger adults. Furthermore, this approach shows that individual and age differences are not evenly distributed but, rather, are related to weakly connected, peripheral parts of the networks. All in all, these results reveal the interindividual differences in both the content and the structure of semantic networks that may accumulate across the life span as a function of idiosyncratic experiences.

A guide to area-restricted search: a foundational foraging behaviour.

Area-restricted search is the capacity to change search effort adaptively in response to resource encounters or expectations, from directional exploration (global, extensive search) to focused exploitation (local, intensive search). This search pattern is used by numerous organisms, from worms and insects to humans, to find various targets, such as food, mates, nests, and other resources. Area-restricted search has been studied for at least 80 years by ecologists, and more recently in the neurological and psychological literature. In general, the conditions promoting this search pattern are: (1) clustered resources; (2) active search (e.g. not a sit-and-wait predator); (3) searcher memory for recent target encounters or expectations; and (4) searcher ignorance about the exact location of targets. Because area-restricted search adapts to resource encounters, the search can be performed at multiple spatial scales. Models and experiments have demonstrated that area-restricted search is superior to alternative search patterns that do not involve a memory of the exact location of the target, such as correlated random walks or Lévy walks/flights. Area-restricted search is triggered by sensory cues whereas concentrated search in the absence of sensory cues is associated with other forms of foraging. Some neural underpinnings of area-restricted search are probably shared across metazoans, suggesting a shared ancestry and a shared solution to a common ecological problem of finding clustered resources. Area-restricted search is also apparent in other domains, such as memory and visual search in humans, which may indicate an exaptation from spatial search to other forms of search. Here, we review these various aspects of area-restricted search, as well as how to identify it, and point to open questions.

Nymph piss and gravy orgies: Local and global contrast effects in relational humor.

How does the relation between two words create humor? In this article, we investigated the effect of global and local contrast on the humor of word pairs. We capitalized on the existence of psycholinguistic lexical norms by examining violations of expectations set up by typical patterns of English usage (global contrast) and within the local context of the words within the word pairs (local contrast). Global contrast was operationalized as lexical-semantic norms for single-words and local contrast was operationalized as the orthographic, phonological, and semantic distance between the two words in the pair. Through crowd-sourced (Study 1) and best-worst (Study 2) ratings of the humor of a large set of word pairs (i.e., compounds), we find evidence of both global and local contrast on compound-word humor. Specifically, we find that humor arises when there is a violation of expectations at the local level, between the individual words that make up the word pair, even after accounting for violations at the global level relative to the entire language. Semantic variables (arousal, dominance, and concreteness) were stronger predictors of word pair humor whereas form-related variables (number of letters, phonemes, and letter frequency) were stronger predictors of single-word humor. Moreover, we also find that semantic dissimilarity increases humor, by defusing the impact of low-valence words-making them seem more amusing-and by enhancing the incongruence of highly imageable pairs of concrete words. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Is the Mind a Network? Maps, Vehicles, and Skyhooks in Cognitive Network Science.

Cognitive researchers often carve cognition up into structures and processes. Cognitive processes operate on structures, like vehicles driving over a map. Language alongside semantic and episodic memory are proposed to have structure, as are perceptual systems. Over these structures, processes operate to construct memory and solve problems by retrieving and manipulating information. Network science offers an approach to representing cognitive structures and has made tremendous inroads into understanding the nature of cognitive structure and process. But is the mind a network? If so, what kind? In this article, we briefly review the main metaphors, assumptions, and pitfalls prevalent in cognitive network science (maps and vehicles; one network/process to rule them all), highlight the need for new metaphors that elaborate on the map-and-vehicle framework (wormholes, skyhooks, and generators), and present open questions in studying the mind as a network (the challenge of capturing network change, what should the edges of cognitive networks be made of, and aggregated vs. individual-based networks). One critical lesson of this exercise is that the richness of the mind as network approach makes it a powerful tool in its own right; it has helped to make our assumptions more visible, generating new and fascinating questions, and enriching the prospects for future research. A second lesson is that the mind as a network-though useful-is incomplete. The mind is not a network, but it may contain them.

Editors' Introduction to Networks of the Mind: How Can Network Science Elucidate Our Understanding of Cognition?

Thinking is complex. Over the years, several types of methods and paradigms have developed across the psychological, cognitive, and neural sciences to study such complexity. A rapidly growing multidisciplinary quantitative field of network science offers quantitative methods to represent complex systems as networks, or graphs, and study the network properties of these systems. While the application of network science to study the brain has greatly advanced our understanding of the brains structure and function, the application of these tools to study cognition has been done to a much lesser account. This topic is a collection of papers that discuss the fruitfulness of applying network science to study cognition across a wide scope of research areas from generalist accounts of memory and encoding, to individual differences, to communities, and finally to cultural and individual change.

Cognitive networks detect structural patterns and emotional complexity in suicide notes.

Communicating one's mindset means transmitting complex relationships between concepts and emotions. Using network science and word co-occurrences, we reconstruct conceptual associations as communicated in 139 genuine suicide notes, i.e., notes left by individuals who took their lives. We find that, despite their negative context, suicide notes are surprisingly positively valenced. Through emotional profiling, their ending statements are found to be markedly more emotional than their main body: The ending sentences in suicide notes elicit deeper fear/sadness but also stronger joy/trust and anticipation than the main body. Furthermore, by using data from the Emotional Recall Task, we model emotional transitions within these notes as co-occurrence networks and compare their structure against emotional recalls from mentally healthy individuals. Supported by psychological literature, we introduce emotional complexity as an affective analog of structural balance theory, measuring how elementary cycles (closed triads) of emotion co-occurrences mix positive, negative and neutral states in narratives and recollections. At the group level, authors of suicide narratives display a higher complexity than healthy individuals, i.e., lower levels of coherently valenced emotional states in triads. An entropy measure identified a similar tendency for suicide notes to shift more frequently between contrasting emotional states. Both the groups of authors of suicide notes and healthy individuals exhibit less complexity than random expectation. Our results demonstrate that suicide notes possess highly structured and contrastive narratives of emotions, more complex than expected by null models and healthy populations.

Bias in Zipf's law estimators.

The prevailing maximum likelihood estimators for inferring power law models from rank-frequency data are biased. The source of this bias is an inappropriate likelihood function. The correct likelihood function is derived and shown to be computationally intractable. A more computationally efficient method of approximate Bayesian computation (ABC) is explored. This method is shown to have less bias for data generated from idealised rank-frequency Zipfian distributions. However, the existing estimators and the ABC estimator described here assume that words are drawn from a simple probability distribution, while language is a much more complex process. We show that this false assumption leads to continued biases when applying any of these methods to natural language to estimate Zipf exponents. We recommend that researchers be aware of the bias when investigating power laws in rank-frequency data.

Identifying Areas of Overlap and Distinction in Early Lexical Profiles of Children with Autism Spectrum Disorder, Late Talkers, and Typical Talkers.

This study compares the lexical composition of 118 children with autism spectrum disorder (ASD) aged 12 to 84 months with 4626 vocabulary-matched typically developing toddlers with and without language delay, aged 8 to 30 months. Children with ASD and late talkers showed a weaker noun bias. Additionally, differences were identified in the proportion of nouns and verbs, and in the semantic categories of animals, toys, household items and vehicles. Most differences appear to reflect the extent of the age differences between the groups. However, children with ASD produced fewer high-social verbs than typical talkers and late talkers, a difference that might be associated with ASD features. In sum, our findings identified areas of overlap and distinction across the developing lexical profiles.

Characterizing the early vocabulary profiles of preverbal and minimally verbal children with autism spectrum disorder.

LAY ABSTRACT: Although preverbal and minimally verbal children with autism spectrum disorder represent a significant portion of the autism spectrum disorder population, we have a limited understanding of and characterization of them. Although it is a given that their lexical profiles contain fewer words, it is important to determine whether (a) the words preverbal and minimally verbal children with autism spectrum disorder produce are similar to the first words typically developing children produce or (b) there are unique features of the limited words that preverbal and minimally verbal children with autism spectrum disorder produce. The current study compared the early word profiles of preverbal and minimally verbal children with autism spectrum disorder to vocabulary-matched typically developing toddlers. Children with autism spectrum disorder produced proportionally more verbs than typically developing toddlers. Also, children with autism spectrum disorder produced proportionally more action and food words, while typically developing toddlers produced proportionally more animal words, animal sounds and sound effects, and people words. Children with autism spectrum disorder also produced "mommy" and "daddy" at lower rates. Our findings identified several areas of overlap in early word learning; however, our findings also point to differences that may be connected to core weaknesses in social communication (i.e. people words). The findings highlight words and categories that could serve as useful targets for communication intervention with preverbal and minimally verbal children with autism spectrum disorder.