Search for: All records

Humans count to indefinitely large numbers by recycling words from a finite list, and combining them using rules—for example, combining sixty with unit labels to generate sixty‐one, sixty‐two, and so on. Past experimental research has focused on children learning base‐10 systems, and has reported that this rule learning process is highly protracted. This raises the possibility that rules are slow to emerge because they are not needed in order to represent smaller numbers (e.g., up to 20). Here, we investigated this possibility in adult learners by training them on a series of artificial number “languages” that manipulated the availability of rules, by varying the numerical base in each language. We found (1) that the size of a base—for example, base‐2 versus base‐5—had little effect on learning, (2) that learners struggled to acquire multiplicative rules while they learned additive rules more easily, (3) that memory for number words was greater when they were taught as part of a sequential count list, but (4) that learning numbers as part of a rote list may impair the ability to map them to magnitudes. 

Large Language Models (LLMs) have gained attention in research and industry, aiming to streamline processes and enhance text analysis performance. Thematic Analysis (TA), a prevalent qualitative method for analyzing interview content, often requires at least two human experts to review and analyze data. This study demonstrates the feasibility of LLM-Assisted Thematic Analysis (LATA) using GPT-4 and Gemini. Specifically, we conducted semi-structured interviews with 14 researchers to gather insights on their experiences generating and analyzing Online Social Network (OSN) communications datasets. Following Braun and Clarke's six-phase TA framework with an inductive approach, we initially analyzed our interview transcripts with human experts. Subsequently, we iteratively designed prompts to guide LLMs through a similar process. We compare and discuss the manually analyzed outcomes with responses generated by LLMs and achieve a cosine similarity score up to 0.76, demonstrating a promising prospect for LATA. Additionally, the study delves into researchers' experiences navigating the complexities of collecting and analyzing OSN data, offering recommendations for future research and application designers. 

Abstract Climate change is negatively impacting ecosystems and their contributions to human well‐being, known as ecosystem services. Previous research has mainly focused on the direct effects of climate change on species and ecosystem services, leaving a gap in understanding the indirect impacts resulting from changes in species interactions within complex ecosystems. This knowledge gap is significant because the loss of a species in a food web can lead to additional species losses or “co‐extinctions,” particularly when the species most impacted by climate change are also the species that play critical roles in food web persistence or provide ecosystem services. Here, we present a framework to investigate the relationships among species vulnerability to climate change, their roles within the food web, their contributions to ecosystem services, and the overall persistence of these systems and services in the face of climate‐induced species losses. To do this, we assess the robustness of food webs and their associated ecosystem services to climate‐driven species extinctions in eight empirical rocky intertidal food webs. Across food webs, we find that highly connected species are not the most vulnerable to climate change. However, we find species that directly provide ecosystem services are more vulnerable to climate change and more connected than species that do not directly provide services, which results in ecosystem service provision collapsing before food webs. Overall, we find that food webs are more robust to climate change than the ecosystem services they provide and show that combining species roles in food webs and services with their vulnerability to climate change offer predictions about the impacts of co‐extinctions for future food web and ecosystem service persistence. However, these conclusions are limited by data availability and quality, underscoring the need for more comprehensive data collection on linking species roles in interaction networks and their vulnerabilities to climate change. 

The ability to communicate about exact number is critical to many modern human practices spanning science, industry, and politics. Although some early numeral systems used 1-to-1 correspondence (e.g., ‘IIII' to represent 4), most systems provide compact representations via more arbitrary conventions (e.g., ‘7’ and ‘VII'). When people are unable to rely on conventional numerals, however, what strategies do they initially use to communicate number? Across three experiments, participants used pictures to communicate about visual arrays of objects containing 1–16 items, either by producing freehand drawings or combining sets of visual tokens. We analyzed how the pictures they produced varied as a function of communicative need (Experiment 1), spatial regularities in the arrays (Experiment 2), and visual properties of tokens (Experiment 3). In Experiment 1, we found that participants often expressed number in the form of 1-to-1 representations, but sometimes also exploited the configuration of sets. In Experiment 2, this strategy of using configural cues was exaggerated when sets were especially large, and when the cues were predictably correlated with number. Finally, in Experiment 3, participants readily adopted salient numerical features of objects (e.g., four-leaf clover) and generally combined them in a cumulative-additive manner. Taken together, these findings corroborate historical evidence that humans exploit correlates of number in the external environment – such as shape, configural cues, or 1-to-1 correspondence – as the basis for innovating more abstract number representations. 

The RAFT rate retardation effect is used to classify monomers according to their reactivity. The stability of the radical correlates inversely with the extent of retardation allowing a framework for classifying monomer reactivity to be established. 

Abstract How do children form beliefs about the infinity of space, time, and number? We asked whether children held similar beliefs about infinity across domains, and whether beliefs in infinity for domains like space and time might be scaffolded upon numerical knowledge (e.g., knowledge successors within the count list). To test these questions, 112 U.S. children (aged 4;0–7;11) completed an interview regarding their beliefs about infinite space, time, and number. We also measured their knowledge of counting, and other factors that might impact performance on linguistic assessments of infinity belief (e.g., working memory, ability to respond to hypothetical questions). We found that beliefs about infinity were very high across all three domains, suggesting that infinity beliefs may arise early in development for space, time, and number. Second, we found that—across all three domains—children were more likely to believe that it is always possible to add a unit than to believe that the domain is endless. Finally, we found that understanding the rules underlying counting predicted children’s belief that it is always possible to add 1 to any number, but did not predict any of the other elements of infinity belief.