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Spatial navigation involves the use of various cues. This study examined how cue conflict influences navigation by contrasting landmarks and optic flow. Participants estimated spatial distances under different levels of cue conflict: minimal conflict, large conflict, and large conflict with explicit awareness of landmark instability. Whereas increased cue conflict alone had little behavioral impact, adding explicit awareness reduced reliance on landmarks and impaired the precision of spatial localization based on them. To understand the underlying mechanisms, we tested two cognitive models: a Bayesian causal inference (BCI) model and a non-Bayesian sensory disparity model. The BCI model provided a better fit to the data, revealing two independent mechanisms for reduced landmark reliance: increased sensory noise for unstable landmarks and lower weighting of unstable landmarks when landmarks and optic flow were judged to originate from different causes. Surprisingly, increased cue conflict did not decrease the prior belief in a common cause, even when explicit awareness of landmark instability was imposed. Additionally, cue weighting in the same-cause judgment was determined by bottom-up sensory reliability, while in the different-cause judgment, it correlated with participants’ subjective evaluation of cue quality, suggesting a top-down metacognitive influence. The BCI model further identified key factors contributing to suboptimal cue combination in minimal cue conflicts, including the prior belief in a common cause and prior knowledge of the target location. Together, these findings provide critical insights into how navigators resolve conflicting spatial cues and highlight the utility of the BCI model in dissecting cue interaction mechanisms in navigation. 

Spatial sound reasoning is a fundamental human skill, enabling us to navigate and interpret our surroundings based on sound. In this paper we present BAT, which combines the spatial sound perception ability of a binaural acoustic scene analysis model with the natural language reasoning capabilities of a large language model (LLM) to replicate this innate ability. To address the lack of existing datasets of in-the-wild spatial sounds, we synthesized a binaural audio dataset using AudioSet and SoundSpaces 2.0. Next, we developed SpatialSoundQA, a spatial sound-based question-answering dataset, offering a range of QA tasks that train BAT in various aspects of spatial sound perception and reasoning. The acoustic front end encoder of BAT is a novel spatial audio encoder named Spatial Audio Spectrogram Transformer, or Spatial-AST, which by itself achieves strong performance across sound event detection, spatial localization, and distance estimation. By integrating Spatial-AST with LLaMA-2 7B model, BAT transcends standard Sound Event Localization and Detection (SELD) tasks, enabling the model to reason about the relationships between the sounds in its environment. Our experiments demonstrate BAT's superior performance on both spatial sound perception and reasoning, showcasing the immense potential of LLMs in navigating and interpreting complex spatial audio environments. 

We utilize a combined computational-experimental approach to examine the influence of indium nanoparticle (NP) array distributions on deep-ultraviolet (UV) plasmon resonances. For photon energies < 5.7 eV, analysis of ellipsometric spectra reveals an increase in silicon reflectance induced by indium NP arrays on silicon. For various energies in the range 5.7–7.0 eV, a decrease in reflectance is induced by the NP arrays. Similar trends in reflectance are predicted from finite-difference time-domain (FDTD) simulations using NP size distributions extracted from atomic-force micrographs as input. In addition, in the energy range of 7.4–9.2 eV, the FDTD simulations reveal reflectance minima, characteristic of localized surface plasmon resonances. Electron energy-loss spectroscopy collected from individual indium NPs reveals the presence of LSPR at ≈ 8 eV, further supporting the promise of indium NP arrays on silicon for deep-UV plasmonics. 

Abstract The symbiosis between corals and dinoflagellates of the family Symbiodiniaceae is sensitive to environmental stress. The oxidative bleaching hypothesis posits that extreme temperatures lead to accumulation of photobiont-derived reactive oxygen species ROS, which exacerbates the coral environmental stress response (ESR). To understand how photosymbiosis modulates coral ESRs, these responses must be explored in hosts in and out of symbiosis. We leveraged the facultatively symbiotic coralAstrangia poculata, which offers an opportunity to uncouple the ESR across its two symbiotic phenotypes (brown, white). Colonies of both symbiotic phenotypes were exposed to three temperature treatments for 15 days: (i) control (static 18 °C), (ii) heat challenge (increasing from 18 to 30 °C), and (iii) cold challenge (decreasing from 18 to 4 °C) after which host gene expression was profiled. Cold challenged corals elicited widespread differential expression, however, there were no differences between symbiotic phenotypes. In contrast, brown colonies exhibited greater gene expression plasticity under heat challenge, including enrichment of cell cycle pathways involved in controlling photobiont growth. While this plasticity was greater, the genes driving this plasticity were not associated with an amplified environmental stress response (ESR) and instead showed patterns of a dampened ESR under heat challenge. This provides nuance to the oxidative bleaching hypothesis and suggests that, at least during the early onset of bleaching, photobionts reduce the host’s ESR under elevated temperatures inA. poculata.