"What happens if a bird sings in the forest and no one is there to hear it?
If itâs in the Sierra Nevada, chances are a microphone placed by researchers from Cornell University and the University of Wisconsin will be listening.
A vast network of audio recorders now covers almost 40% of the mountain range in California. They transmit groundbreaking bird-tracking data back to scientists for a practical wildfire study, according to Phys.org.
Hereâs how it works. A joint team from Cornellâs conservation bioacoustics lab and UW partnered with the U.S. Forest Service and several other West Coast schools. It placed enough microphones to cover 6 million acres of forest and gathered over 700,000 hours of tape featuring all kinds of birdsong, Phys.org explained.
In the lab, they ran the recordings through BirdNET, which is powered by artificial intelligence. It can recognize distinct species just from hearing a snippet of sound. The technology was developed by Cornell ornithologists and Chemnitz University of Technology in Germany. With this method, the team analyzed the population levels of spotted owls, woodpeckers, and eight other key birds that reflect the forestâs health, per Phys.org.
Those findings are already pretty useful for birders, conservation efforts, and more research. Plus, getting such a large dataset this way was far more cost-effective than manual observation would have been, researchers said.
Forest Floors Actually Make Different Sounds Depending on Whether Itâs Healthy
But the team did not stop there. It set out to apply its bird presence data to other variables, such as forest density and canopy height. These are the kind of traits that forest managers rely on when strategically removing foliage or running a controlled burn to prevent the spread of wildfires and restore forests.
Yet managers are not always equipped to make those decisions with biodiversity in mind. Now, they can just look at a detailed map informed by hours of birdsong and see where certain species are likely to live, according to Phys.org.
As global temperatures rise, devastating fires are becoming more frequent. They pose intense and long-lasting dangers to both humans and wildlife. With this kind of applicable research, those on the front lines can work to prevent fires and protect habitats at the same time.
âItâs sort of a blueprint for how monitoring birdsongs and calls can inform management,â lead author and Cornell postdoctoral research associate Kristin Brunk said, per Phys.org. âIâm really hoping that weâll hear from other researchers who are trying to do similar things.â
Using the second law of information dynamics and the massâenergyâinformation equivalence principle, we show that gravitational attraction manifests as a requirement to reduce the information entropy of matter objects in space. This is another example of data compression and computational optimization in our universe, which supports the possibility of a simulated or computational universe. Here, we derive Newtonâs gravitational force from information dynamics and show that gravity emerges as an entropic information force governed by the second law of infodynamics. This is fully aligned with Verlindeâs entropic gravity studies published in 2011 but is demonstrated here via a different approach.
Where is my mind? A neurocognitive investigation of mind blanking
Highlights
Ongoing experience comes in shades with varying richness of mental content. Mind blanking (MB) implies that there can be moments that are seemingly devoid of mental content.
MB is gaining attention as a reportable mental state. The multiple definitions it has received point to its current conceptual and methodological ambiguities.
MB may be phenomenologically diverse â different types can bear similarities and differences to âempty mindsâ, such as during meditative practices and sleep (white dreams).
MB reports have distinguishable neurobehavioral profiles, pointing to arousal as a key foundational backbone for MB reportability.
We propose a mechanistic account in which MB is the end-result of physiological, neural, and cognitive changes, which provides insights for future empirical and phenomenological research of MB.
Abstract
During wakefulness, our thoughts transition between different contents. However, there are moments that are seemingly devoid of reportable content, referred to as mind blanking (MB). It remains unclear what these blanks represent, highlighting the definitional and phenomenological ambiguities surrounding MB. We map out MB in terms of its reportable expressions, neurophysiology, and relationship to adjacent phenomenology, including meditative practices and sleep. We propose a mechanistic account linking MB to changes at the physiological, neural, and cognitive levels. We suggest that ongoing experiences are characterized by degrees of richness, and that contentless events represent distinct mental states with their own diversity. We encourage future research to acknowledge MB as a reportable mental category, leading to a comprehensive understanding of ongoing experience.
Study finds liberals show less empathy to political opponents than conservatives do
" In todayâs polarized political landscape, the ability to understand and empathize with those across the aisle has reached concerning lows. New research published in Personality & Social Psychology Bulletin reveals an asymmetry in this empathy deficit: liberals consistently show less empathy toward their conservative peers than vice versa.
President Joe Bidenâs inaugural call to âstand in the other personâs shoesâ highlighted empathy as crucial for healing national divisions. Yet many people find it hard to feel for political opponents. Empathyâdefined as sympathy for and understanding of anotherâs suffering with the aim of reducing itâis widely seen as essential for improving intergroup relations, but tends to diminish when directed toward those outside oneâs political or social group.
James P. Casey and colleagues conducted four preregistered studies examining how political ideology shapes intergroup empathy bias and why such differences arise. The researchers recruited 4,737 participants, roughly evenly split between liberals and conservatives, from online platforms including Prolific, CloudResearch, and MTurk.
Study 1 involved 549 U.S. participants, while Study 2 included 958 U.K. participants. Study 3 and Study 4 sampled 1,372 and 1,874 U.S. participants, respectively, with recruitment spanning both conservative and liberal administrations to account for political context.
In each study, participants read a short scenario describing a person undergoing a mild hardship (e.g., a sprained ankle). The person was identified as politically conservative, liberal, or neutral. Participants then rated their emotional responses using several scales: empathic concern (e.g., sympathy), perspective-taking, empathic intentions (e.g., willingness to help), and empathic avoidance.
Mediating variables included perceptions of the targetâs morality, likability, similarity to the self, and, in later studies, the perceived harm caused by the targetâs political group. Ingroup political power was also measured to assess whether the party in power influenced empathic responses.
Across all four studies, participants consistently showed lower empathy for political outgroup members than for ingroup or neutral targets. However, this bias was not symmetrical. Liberals exhibited significantly less empathy for conservatives than conservatives showed for liberals. In Study 1, this asymmetry was driven by liberalsâ stronger negative judgments of conservativesâ morality and likability. Conservativesâ empathic responses remained relatively stable regardless of the targetâs political affiliation.
Study 2 confirmed these findings in the U.K. sample, where British liberals also exhibited stronger empathy bias against conservatives, mediated by perceptions of morality, likability, and similarity. Study 3 demonstrated that even after the shift to a Democratic administration in the U.S., liberals continued to judge conservatives as more harmful and immoral, leading to reduced empathy. Study 4 further validated this pattern with a larger sample, strengthening the evidence for the link between perceived group harm and diminished empathy.
One limitation is that findings rely on hypothetical scenarios rather than real-world interactions, which may limit generalizability of results to real-world political discourse or behavior.
"
" Lucid dreamingâa rare state where the dreamer knows they are dreamingâactivates the brain in ways that are distinct from both normal dreaming and wakefulness. A new study published in The Journal of Neuroscience has mapped the neural activity underlying lucid dreams with unprecedented precision. The researchers found that lucid dreaming produces a unique pattern of brain activity that includes altered communication between brain regions, increased gamma activity, and signatures linked to self-awareness and cognitive control.
Lucid dreaming occurs when a person becomes aware that they are dreaming, sometimes even gaining control over the dreamâs content. Though vivid and immersive, this state is neurologically complex and still poorly understood. Previous studies had proposed possible brain markers of lucid dreaming, but results were often inconsistent. Many used small samples and lacked standardized methods to clean up confounding signals such as those produced by eye movements during rapid eye movement (REM) sleep. The team led by ĂaÄatay Demirel at Radboud University Medical Center sought to overcome these limitations.
âI am a PhD candidate nearing completion, and this project represents the largest chapter of my dissertation,â Demirel explained. âLucid dreaming felt like a strange crack in realityâa moment where you could witness your mind from within and perhaps even take control, even when nothing else felt truly graspable. That paradoxâbeing awake inside a dreamâcaptivated me.â
âOver time, that existential curiosity evolved into a scientific pursuit. Moreover, the inconclusive results regarding electrophysiological correlates in several studies using distinct, small-sample EEG datasets highlighted the need for refinement in the fieldâspecifically, through an EEG mega-analysis by combining various datasets.â
The researchers assembled a large dataset from multiple laboratories in the Netherlands, Germany, Brazil, and the United States, bringing together a final sample of 26 lucid dreamers who contributed a total of 43 usable sleep recordings. These included both low-density and high-density EEG recordings, with up to 128 electrodes monitoring electrical activity across the scalp. Participants were instructed to perform a distinct sequence of eye movements (left-right-left-right) once they realized they were dreaming. This standard signal allowed the researchers to pinpoint the moment lucidity began.
A major innovation in the study was the development of a multi-stage preprocessing pipeline to clean the EEG data. This was essential because both spontaneous and voluntary eye movements during REM sleep can introduce artifacts that mimic brain activity, especially in the gamma frequency band (30â45 Hz). The team implemented techniques to identify and remove these saccadic artifacts using signal processing methods that worked even on low-density EEG setups. This preprocessing ensured that the signals analyzed truly reflected neural activity and not muscle movements or other noise.
The researchers then compared brain activity during lucid REM sleep to that during regular REM sleep and relaxed wakefulness. These comparisons used both broad frequency band analyses and more advanced techniques that measured brain signal complexity and functional connectivity. They found that lucid dreaming had a distinctive neural profile. While some features overlapped with normal REM sleep, such as lower alpha power and higher delta activity compared to waking, other features set lucid dreams apart.
One key finding was a reduction in theta and beta power in certain brain regions during lucid dreams, particularly in the posterior and right temporoparietal regions of the brain. These areas are involved in attention and self-awareness, suggesting that lucid dreaming might engage neural circuits similar to those used during reflective or metacognitive thinking. At the same time, the researchers observed increased gamma activityâespecially in the 30â36 Hz rangeâaround the moment the lucid dreamer became aware. This activity was most pronounced in the precuneus and prefrontal cortex, areas linked to consciousness and internal monitoring.
Functional connectivity analyses revealed that lucid dreaming was associated with greater long-range communication between brain regions, particularly in the alpha and gamma bands. These patterns involved areas of the brain known to support sensory integration, internal attention, and memoryâfunctions likely involved in recognizing and maintaining lucidity within the dream. Notably, alpha connectivity during lucid dreams formed a network that included the superior temporal and superior frontal gyri, suggesting coordination between auditory, sensory, and executive systems.
Signal complexity analyses also distinguished lucid dreams from other sleep states. Measures like Lempel-Ziv complexity and entropy, which quantify the unpredictability or richness of brain signals, were higher in lucid dreaming than in regular REM sleep. However, these values were still lower than in the waking state. This suggests that lucid dreaming represents an intermediate state of consciousnessâone that is more organized and self-aware than typical dreaming, but still distinct from being awake.
âWe didnât approach with any specific expectations (there is no null hypothesis in this project), as this is an almost entirely exploratory study due to the merging of large datasets from different labs,â Demirel told PsyPost. âHowever, the findings that captivated us most were in the source-level analyses (cortical estimation), which differ from the more traditional sensor-level EEG analyses we also applied. Sensor-level EEG patterns during lucid dreaming regarding power spectral density (PSD) analyses resemble REM sleep in statistical sense. However, source-level findings revealed heightened alpha connectivity during lucid dreaming that lies between REM sleep and wakefulness (there are evidences in the literature regarding alpha connectivity changes related to psychedelics).â
The researchers also studied how brain activity changed in the moments before and after lucid dreamers signaled their awareness with eye movements. In the seconds surrounding this signal, gamma activity spiked, and large-scale increases in connectivity were seen across the cortex. These changes began just before the eye movement signal, suggesting that the brain prepares for lucidity even before the dreamer communicates it. These moments may capture the emergence of self-awareness from a non-conscious dream state.
âThe gamma activation in the precuneus around the onset of lucidity eye signaling was quite a surprising finding,â Demirel said. âConsidering that this activation occurs in comparison to a baseline temporally close to the onset itself, it provides potentially conclusive patterns suggesting that the brain may simulate its own reality, reflecting self-referential awareness and possibly motor awareness. This could be interpreted as a potential sensory awakening into a simulated reality.â
This study sheds light on the neural mechanisms behind lucid dreaming by addressing previous methodological challenges and using both sensor-level and source-level analyses. The findings show that lucid dreaming is not simply a hybrid of dreaming and waking, but a distinct state of consciousness with its own brain dynamics. By tracking both spectral activity and functional connectivity, the researchers provide a more complete picture of how the brain supports self-awareness within a dream.
There are still several open questions. Lucid dreaming remains difficult to induce in experimental settings, which means that researchers often rely on naturally occurring instances. Dream content also varies widely between participants and sessions, making it harder to isolate what aspects of the experience are most responsible for the observed brain patterns. The study also relied on EEG, which has limited spatial resolution and cannot definitively rule out the influence of residual artifactsâespecially in high-frequency bands where eye movements are most disruptive. Future studies using methods like fMRI or intracranial recordings could help resolve these issues.
âThe rarity of very high-density EEG combined with fMRI dataâor the lack of magnetoencephalography (MEG) dataâon lucid dreaming poses a major limitation for performing volumetric estimations of deep brain structures,â Demirel said. âWhile interesting patterns can be captured at the source level, we had to restrict our analyses to cortical estimations. Although we can detect the onset of lucid dreaming through eye signaling via electrooculogram (EOG), the actual experience likely begins earlier, and we still donât know exactly when lucidity truly emerges.â
âThe limitations are shaping the goals. I am working on the development of more in-depth mathematical models to decode EEG patterns and improve sensitivity to phase shifts in non-stationary state decoding. Considering that lucid dreaming is seemingly a transient brain state, methodological reconsideration is essential to distinguish it from stationary signals. This could enable a more precise segmentation of the lucid state. I also view lucid dreaming as a tool for developing methods that could ultimately help redefine the dynamics of sleep and wakefulness, which will also indirectly support research into disorders of consciousness.â
âIâm just happy that the study has finally been published after years of very draining work that, at times, felt like it would never end,â Demirel added. âIâm really excited to finally share these findings with the community.â
Related video: Forest Floors Actually Make Different Sounds Depending on Whether Itâs Healthy (Amaze Lab)