Elranatamab monotherapy in the real-word setting in relapsed-refractory multiple myeloma: results of the French compassionate use program on behalf of the IFM
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Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics
Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing systems. In this study, we present an approach that integrates an iontronic fluidic memristive (IFM) device with low input impedance and a triboelectric nanogenerator (TENG) based on ferrofluid (FF), which has high input impedance. By incorporating contact separation electromagnetic (EMG) signals with low input impedance into our FF TENG device, we enhance the FF TENG’s performance by increasing energy harvesting, thereby enabling the autonomous powering of IFM devices for self-powered computing. Further, replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic computing. These fluidic devices, composed of soft-matter materials, dynamically adjust their conductance by altering the solution interface. We developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane (PDMS) structures, utilising a fluidic interface of FF and polyacrylic acid partial sodium salt (PAA Na+). The confined ion interactions in this system induce hysteresis in ion transport across various frequencies, resulting in significant ion memory effects. Our IFM successfully replicates diverse electric pulse patterns, making it highly suitable for neuromorphic computing. Furthermore, our system demonstrates synapse-like learning functions, storing and retrieving short-term (STM) and long-term memory (LTM). The fluidic memristor exhibits dynamic synapse-like features, making it a promising candidate for the hardware implementation of neural networks. FF TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials, further enhanced by intricate chemical designs for self-powered electronics.
A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations
This study introduces a unified computational framework connecting acoustic, speech and word-level linguistic structures to study the neural basis of everyday conversations in the human brain. We used electrocorticography to record neural signals across 100 h of speech production and comprehension as participants engaged in open-ended real-life conversations. We extracted low-level acoustic, mid-level speech and contextual word embeddings from a multimodal speech-to-text model (Whisper). We developed encoding models that linearly map these embeddings onto brain activity during speech production and comprehension. Remarkably, this model accurately predicts neural activity at each level of the language processing hierarchy across hours of new conversations not used in training the model. The internal processing hierarchy in the model is aligned with the cortical hierarchy for speech and language processing, where sensory and motor regions better align with the model’s speech embeddings, and higher-level language areas better align with the model’s language embeddings. The Whisper model captures the temporal sequence of language-to-speech encoding before word articulation (speech production) and speech-to-language encoding post articulation (speech comprehension). The embeddings learned by this model outperform symbolic models in capturing neural activity supporting natural speech and language. These findings support a paradigm shift towards unified computational models that capture the entire processing hierarchy for speech comprehension and production in real-world conversations.
Spontaneous thought separates into clusters of negative, positive, and flexible thinking
The nature and frequency of spontaneous thoughts play a critical role in cognitive processes like perception, decision-making, attention, and memory. Deficits in these processes are also greatly associated with the development and maintenance of psychopathology. However, the underlying cognitive dynamics of free and stuck spontaneous thought remain unclear, as these often occur in the absence of measurable behaviors. Here, we analyze free word-association data using attractor-state dynamic modeling, which conceptualizes stuck spontaneous thought as navigating a multidimensional semantic space while in the presence of strong attractor locations. Word-association data was collected from an exploratory sample (N1 = 65), a first replication sample (N2 = 79), and, following pre-registration, a second replication sample (N3 = 222). After the data was embedded into a 3-dimensional semantic space and fit by our dynamic model, unsupervised learning consistently grouped data into four clusters across all independent samples. These clusters were characterized by two distinct patterns of stuck negative thinking, a pattern of protective positive thinking, and a pattern of flexible mind-wandering. Our results support a method for modeling spontaneous thought and isolate distinct sub-types that may not be accessible using retrospective self-report methods. We discuss implications for clinical and cognitive science.
Third-party evaluators perceive AI as more compassionate than expert humans
Empathy connects us but strains under demanding settings. This study explored how third parties evaluated AI-generated empathetic responses versus human responses in terms of compassion, responsiveness, and overall preference across four preregistered experiments. Participants (N = 556) read empathy prompts describing valenced personal experiences and compared the AI responses to select non-expert or expert humans. Results revealed that AI responses were preferred and rated as more compassionate compared to select human responders (Study 1). This pattern of results remained when author identity was made transparent (Study 2), when AI was compared to expert crisis responders (Study 3), and when author identity was disclosed to all participants (Study 4). Third parties perceived AI as being more responsive—conveying understanding, validation, and care—which partially explained AI’s higher compassion ratings in Study 4. These findings suggest that AI has robust utility in contexts requiring empathetic interaction, with the potential to address the increasing need for empathy in supportive communication contexts.
Pathogen stress heightens sensorimotor dimensions in the human collective semantic space
Infectious diseases have been major causes of death throughout human history and are assumed to broadly affect human psychology. However, whether and how conceptual processing, an internal world model central to various cognitive processes, adapts to such salient stress variables remains largely unknown. To address this, we conducted three studies examining the relationship between pathogen severity and semantic space, probed through the main neurocognitive semantic dimensions revealed by large-scale text analyses: one cross-cultural study (across 43 countries) and two historical studies (over the past 100 years). Across all three studies, we observed that increasing pathogen severity was associated with an enhancement of the sensory-motor dimension in the collective semantic space. These patterns remained robust after controlling for the effects of sociocultural variables, including economic wealth and societal norms of tightness. These results highlight the universal dynamic mechanisms of collective semantics, such that pathogen stress potentially drives sensorially oriented semantic processing.
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