Search for: All records

Abstract BackgroundSince the 1980s, Pacific Black Brant (Branta bernicla nigricans, hereafter brant) have shifted their winter distribution northward from Mexico to Alaska (approximately 4500 km) with changes in climate. Alongside this shift, the primary breeding population of brant has declined. To understand the population-level implications of the changing migration strategy of brant, it is important to connect movement and demographic data. Our objectives were to calculate migratory connectivity, a measure of spatial and temporal overlap during the non-breeding period, for Arctic and subarctic breeding populations of brant, and to determine if variation in migration strategies affected nesting phenology and nest survival. MethodsWe derived a migratory network using light-level geolocator migration tracks from an Arctic site (Colville River Delta) and a subarctic site (Tutakoke River) in Alaska. Using this network, we quantified the migratory connectivity of the two populations during the winter. We also compared nest success rates among brant that used different combinations of winter sites and breeding sites. ResultsThe two breeding populations were well mixed during the winter, as indicated by a migratory connectivity score close to 0 (− 0.06) at the primary wintering sites of Izembek Lagoon, Alaska (n = 11 brant) and Baja California, Mexico (n = 48). However, Arctic birds were more likely to migrate the shorter distance to Izembek (transition probability = 0.24) compared to subarctic birds (transition probability = 0.09). Nest survival for both breeding populations was relatively high (0.88–0.92), and we did not detect an effect of wintering site on nest success the following year. ConclusionsNest survival of brant did not differ among brant that used wintering sites despite a 4500 km difference in migration distances. Our results also suggested that the growing Arctic breeding population is unlikely to compensate for declines in the larger breeding population of brant in the subarctic. However, this study took place in 2011–2014 and wintering at Izembek Lagoon may have greater implications for reproductive success under future climate conditions. 

Human speech recognition transforms a continuous acoustic signal into categorical linguistic units, by aggregating information that is distributed in time. It has been suggested that this kind of information processing may be understood through the computations of a Recurrent Neural Network (RNN) that receives input frame by frame, linearly in time, but builds an incremental representation of this input through a continually evolving internal state. While RNNs can simulate several keybehavioralobservations about human speech and language processing, it is unknown whether RNNs also develop computational dynamics that resemble humanneural speech processing. Here we show that the internal dynamics of long short-term memory (LSTM) RNNs, trained to recognize speech from auditory spectrograms, predict human neural population responses to the same stimuli, beyond predictions from auditory features. Variations in the RNN architecture motivated by cognitive principles further improved this predictive power. Specifically, modifications that allow more human-like phonetic competition also led to more human-like temporal dynamics. Overall, our results suggest that RNNs provide plausible computational models of the cortical processes supporting human speech recognition. 

ABSTRACT The pursuit of sustainable organic synthesis has renewed interest in photochemistry, as sunlight‐driven reactions provide eco‐friendly alternative methods. Although the relationships among structure, properties, and reactivity are well established for ground‐state molecules, the understanding of excited states and reactive intermediates, such as triplet and singlet arylnitrenes, remains limited. Herein, we investigated the properties of triplet and singlet 4‐nitrenopyridine‐1‐pyridine oxide (1N), 3‐nitrenopyridine‐1‐pyridine oxide (2N), and phenylnitrene (PhN) using density functional theory (DFT), complete active space self‐consistent field (CASSCF(10,9)), and complete active space second‐order perturbation theory (CASPT2(10,9)) calculations. Bond length analysis demonstrated that³1Nand¹1N, as well as¹2Nand¹PhN, exhibit significant imine biradical character, whereas the structures of³2Nand³PhNare better described as benzene‐like. Nucleus‐independent chemical shift (NICS(0), NICS(1.7)_ZZ) and anisotropy of induced current density (ACID) calculations were performed to compare the induced magnetic currents in these molecules. These analyses demonstrated that³1Nis weakly aromatic, whereas³2Nand³PhNare best described as having Baird aromaticity. In contrast, singlet nitrenes¹1N,¹2N, and¹PhNare nonaromatic. In addition, irradiation of1in argon matrices verified that³1Nreacts photochemically to form corresponding ketenimine1K. Finally, the absorption difference spectrum of³1Nin a frozen 2‐methyltetrahydrofuran (mTHF) matrix exhibited resolved vibrational structure, suggesting the vibrational coupling to another electronic state. These insights into the structure and aromaticity of heterocyclic nitrenes could provide new avenues for modulating the reactivity of triplet ground state and triplet excited molecules. 

I review several alternative linking hypotheses for relating eye tracking data from the visual world paradigm (VWP) to cognitive theories and models. While some models are able to simulate VWP data surprisingly well (such as the TRACE model), there is still ample ambiguity to resolve in the meaning of fixation proportions over time, despite decades of work with the VWP. I also present a simple fixation model based on probabilistic sampling from an underlying lexical activation that allows simulation of individual trials. Unsurprisingly, a properly-parameterized sampling procedure approximates the underlying activation patterns when sufficient trials are averaged together. However, the utility of simulating trial-level behavior is not in reconstructing central tendencies (which can be derived directly without simulating fixations), but in addressing, for example, individual differences. I also discuss critiques and misunderstandings of linking models to the VWP, and analogies to a simpler paradigm – lexical decision – to illuminate the logic of linking hypotheses in the VWP. 

The adaptive shift that favored stone tool–assisted behavior in hominins began by 3.3 million years ago. However, evidence from early archaeological sites indicates relatively short-distance stone transport dynamics similar to behaviors observed in nonhuman primates. Here we report selective raw material transport over longer distances than expected at least 2.6 million years ago. Hominins at Nyayanga, Kenya, manufactured Oldowan tools primarily from diverse nonlocal stones, pushing back the date for expanded raw material transport by over half a million years. Nonlocal cobbles were transported up to 13 kilometers for on-site reduction, resulting in assemblage patterns inconsistent with accumulations formed by repeated short-distance transport events. These findings demonstrate that early toolmakers moved stones over substantial distances, possibly in anticipation of food processing needs, representing the earliest archaeologically visible signal for the incorporation of lithic technology into landscape-scale foraging repertoires. 

Statistical learning (SL) is typically assumed to be a core mechanism by which organisms learn covarying structures and recurrent patterns in the environment, with the main purpose of facilitating processing of expected events. Within this theoretical framework, the environment is viewed as relatively stable, and SL ‘captures’ the regularities therein through implicit unsupervised learning by mere exposure. Focusing primarily on language— the domain in which SL theory has been most influential—we review evidence that the environment is far from fixed: it is dynamic, in continual flux, and learners are far from passive absorbers of regularities; they interact with their environments, thereby selecting and even altering the patterns they learn from. We therefore argue for an alternative cognitive architecture, where SL serves as a subcomponent of an information foraging (IF) system. IF aims to detect and assimilate novel recurrent patterns in the input that deviate from randomness, for which SL supplies a baseline. The broad implications of this viewpoint and their relevance to recent debates in cognitive neuroscience are discussed. 

Abstract We utilize the cosmological volume simulation FIREbox to investigate how a galaxy’s environment influences its size and dark matter content. Our study focuses on approximately 1200 galaxies (886 central and 332 satellite halos) in the low-mass regime, with stellar masses between 10⁶and 10⁹M_⊙. We analyze the size–mass relation (r₅₀–M_⋆), the inner dark matter mass–stellar mass ( $M_{\mathrm{DM}}^{50}$–M_⋆) relation, and the halo mass–stellar mass (M_halo–M_⋆) relation. At fixed stellar mass, we find that galaxies experiencing stronger tidal influences, indicated by higher Perturbation Indices (PI > 1) are generally larger and have lower halo masses relative to their counterparts with lower Perturbation Indices (PI < 1). Applying a Random Forest regression model, we show that both the environment (PI) and halo mass (M_halo) are significant predictors of a galaxy’s relative size and dark matter content. Notably, becauseM_halois also strongly affected by the environment, our findings indicate that environmental conditions not only influence galactic sizes and relative inner dark matter content directly, but also indirectly, through their impact on halo mass. Our results highlight a critical interplay between environmental factors and halo mass in shaping galaxy properties, affirming the environment as a fundamental driver in galaxy formation and evolution. 

Abstract Estimating correlations among demographic parameters is an important method in population ecology. A recent paper by Deane et al. (Ecology and Evolution13:e9847, 2023) attempted to explore the effects of different priors for covariance matrices on inference when using mark‐recovery data. Unfortunately, Deane et al. (2023) made a mistake when parameterizing some of their models. Rather than exploring the effects of different priors, they examined the effects of the use of incorrect equations on inference. In this manuscript, we clearly describe the mistake in Deane et al. (2023). We then demonstrate the use of an alternative and appropriate method and reach different conclusions regarding the effects of priors on inference. Consistent with other recent literature, informative inverse Wishart priors can lead to flawed inference, while vague priors on covariance matrix components have little impact when sample sizes are adequate.