Slow Adaptation Research Articles

Enhanced Machine Learning Framework for Autonomous Depression Detection Using Modwave Cepstral Fusion and Stochastic Embedding

Depression is a prevalent mental illness that requires autonomous detection systems due to its complexity. Existing machine learning techniques face challenges such as background noise sensitivity, slow adaptation speed, and imbalanced data. To address these limitations, this study proposes a novel ModWave Cepstral Fusion and Stochastic Embedding Framework for depression prediction. Then, the Gain Modulated Wavelet Technique removes background noise and normalises audio signals. Difficulties with generalisation, which results in a lack of interpretability, hinder extracting relevant characteristics from speech. To address these issues, an Auto Cepstral Fusion extracts relevant features from speech, capturing temporal and spectral characteristics caused by background voice. Feature selection becomes imperative when choosing relevant features for classification. Selecting irrelevant features can result in overfitting, the curse of dimensionality, and less robustness to noise. Hence, the Principal Stochastic Embedding technique handles high-dimensional data, minimising noise influence and dimensionality. Furthermore, the XGBoost classifier differentiates between depressed and non-depressed individuals. As a result, the proposed method uses the DAIC-WOZ dataset from USC for detecting depressions, achieving an accuracy of 97.02%, precision of 97.02%, recall of 97.02%, F1-score of 97.02%, RMSE of 2.00, and MAE of 0.9, making it a promising tool for autonomous depression detection.

Dynamical theory for adaptive systems

Abstract The study of adaptive dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former’s dynamics is crucial for understanding feedback mechanisms underlying evolution and learning. We present a path-integral approach à la Martin–Siggia–Rose-De Dominicis–Janssen to analyse non-equilibrium phase transitions in such dynamical systems. As an illustration, we apply our framework to the adaptation of gene-regulatory networks under a dynamic genotype-phenotype map: phenotypic variations are shaped by the fast stochastic gene-expression dynamics and are coupled to the slowly evolving distribution of genotypes, each encoded by a network structure. We establish that under this map, genotypes corresponding to reciprocal networks of coherent feedback loops are selected within an intermediate range of environmental noise, leading to phenotypic robustness.

Regional tuning of photoreceptor adaptation in the primate retina

Adaptation in cone photoreceptors allows our visual system to effectively operate over an enormous range of light intensities. However, little is known about the properties of cone adaptation in the specialized region of the primate central retina called the fovea, which is densely packed with cones and mediates high-acuity central vision. Here we show that macaque foveal cones exhibit weaker and slower luminance adaptation compared to cones in the peripheral retina. We find that this difference in adaptive properties between foveal and peripheral cones is due to differences in the magnitude of a hyperpolarization-activated current, Ih. This Ih current regulates the strength and time course of luminance adaptation in peripheral cones where it is more prominent than in foveal cones. A weaker and slower adaptation in foveal cones helps maintain a higher sensitivity for a longer duration which may be well-suited for maximizing the collection of high-acuity information at the fovea during gaze fixation between rapid eye movements.

A soft robotic system imitating the multimodal sensory mechanism of human fingers for intelligent grasping and recognition

The sensory function is crucial for achieving precise feedback control and environmental interaction in soft robots. Therefore, the multimodal sensory capabilities that mimic human fingers have always been a research hotspot. This study proposes a design method for a soft robotic gripper that can emulate the multimodal perception mechanism of human fingers and achieve high-precision recognition of grasped objects using machine learning algorithms. In addition, a finger texture structure inspired by human fingerprints is designed using a negative pressure-induced buckling method to enhance the soft gripper's ability to perceive minute surface textures. Inspired by slow adapting (SA) receptors and fast adapting (FA) receptors of human fingers, we have innovatively designed a capacitive curvature sensor (CS) and a triboelectric texture sensor (TS) to detect the size, material, and texture of objects. Furthermore, a non-contact medium identification sensor (MIS), having a sensitivity of more than 1300 times higher compared to the traditional MISs, is fabricated using the principle of electromagnetic resonance. It is shown that by employing a deep learning-based multi-channel feature fusion network, the soft grasping system with multimodal sensing has achieved a recognition accuracy of 98.43 % for different objects. This work provides an innovative approach for the application of soft robots in various fields, such as logistics sorting and food safety.

Comprehensive Assessment of Initial Adaptation of ESBL Positive ST131 Escherichia coli to Carbapenem Exposure.

It remains unclear how high-risk Escherichia coli lineages, like sequence type (ST) 131, initially adapt to carbapenem exposure in their progression to becoming carbapenem resistant. Carbapenem mutation frequency was measured in multiple subclades of extended-spectrum β-lactamase (ESBL) positive ST131 clinical isolates using a fluctuation assay followed by whole genome sequencing (WGS) characterization. Genomic, transcriptomic, and porin analyses of ST131 C2/ H 30Rx isolate, MB1860, under prolonged, increasing carbapenem exposure was performed using two distinct experimental evolutionary platforms to measure fast vs. slow adaptation. All thirteen ESBL positive ST131 strains selected from a diverse (n=184) ST131 bacteremia cohort had detectable ertapenem (ETP) mutational frequencies with a statistically positive correlation between initial ESBL gene copy number and mutation frequency (r = 0.87, P -value <1e-5). WGS analysis of mutants showed initial response to ETP exposure resulted in significant increases in ESBL gene copy numbers or mutations in outer membrane porin (Omp) encoding genes in the absence of ESBL gene amplification with subclade specific associations. In both experimental evolutionary platforms, MB1860 responded to initial ETP exposure by increasing bla CTX-M-15 copy numbers via modular, insertion sequence 26 (IS 26 ) mediated pseudocompound transposons (PCTns). Transposase activity driven by PCTn upregulation was a conserved expression signal in both experimental evolutionary platforms. Stable mutations in Omp encoding genes were detected only after prolonged increasing carbapenem exposure consistent with clinical observations. ESBL gene amplification is a conserved response to initial carbapenem exposure, especially within the high-risk ST131 C2/ H 30Rx subclade. Targeting such amplification could assist with mitigating carbapenem resistance development.

Perspectives on progression of transboundary disease, one health and ecosystem health management in the Greater Mekong Subregion and beyond

Livestock production in the Greater Mekong Subregion (GMS) reflects the inefficient smallholder ‘keeper’ system that has been slow to adapt to the rapidly expanding demand for animal protein-sourced foods in the region as urban economies have flourished and food preferences altered. The prolonged surge in demand, with only modest increases in local production, has increased the movement of animals and products into and from the GMS, accompanied by surging risks of transboundary animal disease (TAD) incursions, including the one health (OH) threats of zoonoses and antimicrobial resistance. As a consequence, the region has been subjected to epidemics of highly pathogenic avian influenza (HPAI), new strains of foot-and-mouth disease (FMD) virus, with recent incursions of African swine fever (ASF), lumpy skin disease (LSD), porcine reproductive and respiratory syndrome (PRRS) and risks of peste petits ruminants (OPR) occurring in Southeast Asia (SEA) and beyond. These incidents reflect inadequate biosecurity, a sustainability issue that was clearly of relevance in the covid-19 pandemic, reflecting ecosystem health (EH) deficits, including land-use issues and unregulated trading in wildlife-sourced and poorly processed foods through the predominant ‘wet markets’. These challenges are increasingly confounded by slow adaption to the impacts of the climate crisis, including flooding, drought, crop failures and hypothermia episodes. The increase in animal and product movements enhances disease transmission risk, yet coincides with emerging concerns of greenhouse-gas emissions (GHGe) from livestock production, especially from large ruminants, as the world attempts to find pathways in managing the climate crisis. Despite the prolonged collaborative efforts of the SEA China FMD program from 1997 to 2023, a recent review confirmed persistent deficits in biosecurity, vaccine resourcing, disease surveillance, engagement of farmers, and national emergency disease-response capacities. A major project is about to fund major improvements in livestock value chains in Cambodia, including more effective biosecurity, surveillance and emergency disease-response capacities for TADs, antimicrobial resistance (AMR) and zoonoses. Similarly, a private-sector investment in Laos has developed a more climate-resilient livestock-feeding system that decreases GHGe impacts from ruminant production. These developments are likely to extend beyond both countries and be potentially transformational for the livelihoods of many of the poorest citizens in the region.

Incorporating slow NMDA-type receptors with nonlinear voltage-dependent magnesium block in a next generation neural mass model: derivation and dynamics

We derive a next generation neural mass model of a population of quadratic-integrate-and-fire neurons, with slow adaptation, and conductance-based AMPAR, GABAR and nonlinear NMDAR synapses. We show that the Lorentzian ansatz assumption can be satisfied by introducing a piece-wise polynomial approximation of the nonlinear voltage-dependent magnesium block of NMDAR current. We study the dynamics of the resulting system for two example cases of excitatory cortical neurons and inhibitory striatal neurons. Bifurcation diagrams are presented comparing the different dynamical regimes as compared to the case of linear NMDAR currents, along with sample comparison simulation time series demonstrating different possible oscillatory solutions. The omission of the nonlinearity of NMDAR currents results in a shift in the range (and possible disappearance) of the constant high firing rate regime, along with a modulation in the amplitude and frequency power spectrum of oscillations. Moreover, nonlinear NMDAR action is seen to be state-dependent and can have opposite effects depending on the type of neurons involved and the level of input firing rate received. The presented model can serve as a computationally efficient building block in whole brain network models for investigating the differential modulation of different types of synapses under neuromodulatory influence or receptor specific malfunction.

Supervisi Akademik Kepala Madrasah dalam Peningkatan Kinerja Guru Menghadapi Era Society 5.0

This research aims to identify the patterns of planning, implementation, evaluation, and follow-up in academic supervision by the headmaster to improve teacher performance at MAN 4 East Aceh in facing Society 5.0 era, as well as to explore the constraints faced. The research used a qualitative approach. The results show that academic supervision at MAN 4 East Aceh is carried out systematically through stages of planning, implementation, evaluation, and follow-up. Planning includes developing a supervisory program, assigning tasks to supervisor teams, and creating a supervisory schedule while implementation is done through classroom visits and observations. Evaluation involves assessing learning tools and teaching practices with feedback and continuous coaching as follow-ups. Constraints include scheduling delays; limited human resources; slow technology adaptation; and infrastructure limitations. The practical implications of this study indicate the need for technology training for teachers and investment in infrastructure facilities. Theoretical implications strengthen the importance of comprehensive and adaptive supervision approaches in facing Society 5.0 era. However, this study is limited among others so its results may not be fully generalizable.Recommendations for future research involve expanding studies to other madrasahs use mixed methods for more comprehensive analysis.

Exploring Electrocortical Signatures of Gait Adaptation: Differential Neural Dynamics in Slow and Fast Gait Adapters.

Individuals exhibit significant variability in their ability to adapt locomotor skills, with some adapting quickly and others more slowly. Differences in brain activity likely contribute to this variability, but direct neural evidence is lacking. We investigated individual differences in electrocortical activity that led to faster locomotor adaptation rates. We recorded high-density electroencephalography while young, neurotypical adults adapted their walking on a split-belt treadmill and grouped them based on how quickly they restored their gait symmetry. Results revealed unique spectral signatures within the posterior parietal, bilateral sensorimotor, and right visual cortices that differ between fast and slow adapters. Specifically, fast adapters exhibited lower alpha power in the posterior parietal and right visual cortices during early adaptation, associated with quicker attainment of steady-state step length symmetry. Decreased posterior parietal alpha may reflect enhanced spatial attention, sensory integration, and movement planning to facilitate faster locomotor adaptation. Conversely, slow adapters displayed greater alpha and beta power in the right visual cortex during late adaptation, suggesting potential differences in visuospatial processing. Additionally, fast adapters demonstrated reduced spectral power in the bilateral sensorimotor cortices compared with slow adapters, particularly in the theta band, which may suggest variations in perception of the split-belt perturbation. These findings suggest that alpha and beta oscillations in the posterior parietal and visual cortices and theta oscillations in the sensorimotor cortex are related to the rate of gait adaptation.

Causal evidence for cholinergic stabilization of attractor landscape dynamics

There is substantial evidence that neuromodulatory systems critically influence brain state dynamics; however, most work has been purely descriptive. Here, we quantify, using data combining local inactivation of the basal forebrain with simultaneous measurement of resting-state fMRI activity in the macaque, the causal role of long-range cholinergic input to the stabilization of brain states in the cerebral cortex. Local inactivation of the nucleus basalis of Meynert (nbM) leads to a decrease in the energy barriers required for an fMRI state transition in cortical ongoing activity. Moreover, the inactivation of particular nbM sub-regions predominantly affects information transfer in cortical regions known to receive direct anatomical projections. We demonstrate these results in a simple neurodynamical model of cholinergic impact on neuronal firing rates and slow hyperpolarizing adaptation currents. We conclude that the cholinergic system plays a critical role in stabilizing macroscale brain state dynamics.

Weather variability risks slow climate adaptation: An empirical analysis of forestry

The timing of climate adaptation decisions can have substantial consequences for the assessment of climate damages. Since weather variability can create risks for natural resource management that differ across adaptation choices, such variability has the potential to alter the speed of climate adaptation. This paper estimates the effect of weather variability on the timing of adaptation decisions of forest landowners in the Eastern United States. A discrete-choice econometric model of forest management is estimated and used in a bio-economic simulation that shows how variability in cold temperatures can significantly slow the rate of adapting from cold-tolerant natural hardwood forests to cold-sensitive, but highly valuable pine plantations. The range of weather variability in climate projections and across the landscape generates large differences in adaptation timing. Ignoring projected future decreases in weather variability results in a large downward bias in estimating future paths of climate adaptation. Since pine plantations produce fewer non-market ecosystem services than natural hardwood forests, an important source of future conservation uncertainty is the economic response of private forest landowners to changing weather variability.

Fluctuating Landscapes and Heavy Tails in Animal Behavior

Animal behavior is shaped by a myriad of mechanisms acting on a wide range of scales, which hampers quantitative reasoning and the identification of general principles. Here, we combine data analysis and theory to investigate the relationship between behavioral plasticity and heavy-tailed statistics often observed in animal behavior. Specifically, we first leverage high-resolution recordings of locomotion to show that stochastic transitions among long-lived behaviors exhibit heavy-tailed first-passage-time distributions and correlation functions. Such heavy tails can be explained by slow adaptation of behavior over time. This particular result motivates our second step of introducing a general model where we separate fast dynamics on a quasistationary multiwell potential, from nonergodic, slowly varying modes. We then show that heavy tails generically emerge in such a model, and we provide a theoretical derivation of the resulting functional form, which can become a power law with exponents that depend on the strength of the fluctuations. Finally, we provide direct support for the generality of our findings by testing them in a mutant where adaptation is suppressed and heavy tails thus disappear, and recordings of larval zebrafish swimming behavior where heavy tails are again prevalent. Published by the American Physical Society 2024

Biomimetic dual sensing polymer nanocomposite for biomedical applications.

There is a growing need for sensing materials that can provide multiple sensing capabilities for wearable devices, implantable sensors, and diagnostics tools. As complex human physiology requires materials that can simultaneously detect and respond to slow and fast pressure fluctuations. Mimicking the slow adaptive (SA) and fast adaptive (FA) mechanoreceptors in skin can lead to the development of dual sensing electrospun polymer nanocomposites for biomedical applications. These dual sensing nanocomposites can provide simultaneous sensing of both slow and fast pressure fluctuations, making them ideal for applications such as monitoring vital signs, detecting a wider range of movements and pressures. Here we develop a novel dual sensing PVDF-HFP-based nanocomposite that combines the advantages of capacitive and piezoelectric properties through controling electrospinning environment and processing parameters, polymer solution composition, and addition of nucleating agents such as Carbon Black (CB) to enhance the crystalline development of β-phase, fibre thickness, and morphology. The developed PVDF-HFP/CB nanocomposite presents and response to both slow and fast pressure fluctuations with high capacitance (5.37nF) and output voltage (1.51V) allowing for accurate and reliable measurements.

An All-Protein Multisensory Highly Bionic Skin.

To achieve a highly realistic robot, closely mimicking human skin in terms of materials and functionality is essential. This paper presents an all-protein silk fibroin bionic skin (SFBS) that emulates both fast-adapting (FA) and slow-adapting (SA) receptors. The mechanically different silk film and hydrogel, which exhibited skin-like properties, such as stretchability (>140%), elasticity, low modulus (<10 kPa), biocompatibility, and degradability, were prepared through mesoscopic reconstruction engineering to mimic the epidermis and dermis. Our SFBS, incorporating SA and FA sensors, demonstrated a highly sensitive (1.083 kPa-1) static pressure sensing performance (in vitro and in vivo), showed the ability to sense high-frequency vibrations (50-400 Hz), could discriminate materials and sliding, and could even identify the fine morphological differences between objects. As proof of concept, an SFBS-integrated rehabilitation glove was synthesized, which could help stroke patients regain sensory feedback. In conclusion, this work provides a practical approach for developing skin equivalents, prostheses, and smart robots.

ВЕКТОРИ ТРАНСФОРМАЦІЇ УПРАВЛІНСЬКОЇ ПРАКТИКИ ПІДПРИЄМСТВ В УМОВАХ ПЕРЕХОДУ ДО БІОЕКОНОМІЧНОЇ МОДЕЛІ: ВИКЛИКИ ТА ПЕРСПЕКТИВИ

This scientific article is devoted to a comprehensive study of the vectors of transformation in the management practices of enterprises during the transition to a bioeconomic model. The relevance of the topic is driven by the paradigmatic shift in economic systems and the imperative need to ensure sustainable development in the context of global environmental challenges. The aim of the study is to identify and analyze the key vectors of management practice transformation, as well as to uncover emerging challenges and potential opportunities for business structures within the bioeconomic paradigm. The methodological toolkit of the research is based on the integration of systems analysis methods, decomposition, structural-functional analysis, foresight, and scenario planning. The application of STEEP analysis allowed for the identification of key drivers of change, while a combination of the Delphi method and expert panels facilitated the development of a predictive model for the future of management practices. The study conducted a comparative analysis of management practices in traditional and bioeconomic paradigms, revealing fundamental differences in approaches to strategic management, operations management, financial management, marketing, human resource management, and innovation activities. The developed predictive model identifies four potential scenarios for the development of management practices: “Green Revolution”, “Slow Adaptation”, “TechnoOptimism”, and “Regulatory Pressure”. It was established that the synergy between biotechnology and digital technologies is a key factor in shaping new management practices, and adaptability becomes a critical success factor in the bioeconomic context. The emergence of new management models, which consider the complexity of bioeconomic systems and are based on an integrated interdisciplinary knowledge base, is predicted. The practical value of the research lies in forming a theoretical and methodological foundation for developing adaptive strategies for implementing bioeconomic principles into enterprise management practices. The results obtained can be used as a scientific and methodological basis for business process transformation, organizational structure reconfiguration, and the development of new personnel competencies in the context of transitioning to a bioeconomic model. The study emphasizes the need for a proactive approach to the development of management practices, which can provide significant competitive advantages for organizations in the new economic paradigm.

Molecular phylogenetic relationships based on mitochondrial genomes of novel deep-sea corals (Octocorallia: Alcyonacea): Insights into slow evolution and adaptation to extreme deep-sea environments.

A total of 10 specimens of Alcyonacea corals were collected at depths ranging from 905 m to 1 633 m by the manned submersible Shenhai Yongshi during two cruises in the South China Sea (SCS). Based on mitochondrial genomic characteristics, morphological examination, and sclerite scanning electron microscopy, the samples were categorized into four suborders (Calcaxonia, Holaxonia, Scleraxonia, and Stolonifera), and identified as 9 possible new cold-water coral species. Assessments of GC-skew dissimilarity, phylogenetic distance, and average nucleotide identity (ANI) revealed a slow evolutionary rate for the octocoral mitochondrial sequences. The nonsynonymous ( Ka) to synonymous ( Ks) substitution ratio ( Ka/ Ks) suggested that the 14 protein-coding genes (PCGs) were under purifying selection, likely due to specific deep-sea environmental pressures. Correlation analysis of the median Ka/ Ks values of five gene families and environmental factors indicated that the genes encoding cytochrome b (cyt b) and DNA mismatch repair protein ( mutS) may be influenced by environmental factors in the context of deep-sea species formation. This study highlights the slow evolutionary pace and adaptive mechanisms of deep-sea corals.

ПАТОГЕНЕТИЧЕСКАЯ ТЕРАПИЯ СЕРДЕЧНО-СОСУДИСТЫХ НАРУШЕНИЙ У ДЕТЕЙ СТАРШЕГО ВОЗРАСТА И ПОДРОСТКОВ, БОЛЬНЫХ ТУБЕРКУЛЕЗОМ ОРГАНОВ ДЫХАНИЯ С МЛУ/ШЛУ МБТ

We carried out a cohort retrospective-prospective study in order to evaluate the impact of pathogenetic therapy of cardiovascular disorders (CVD) on bedaquiline (Bq) containing TB treatment in older children and adolescents with multi or extensively drug resistant (MDR/XDR) pulmonary TB. The study included 51 patients aged 13–17 years with pulmonary TB and established MDR/pre-XDR/XDR hospitalized in 2014–2023. The prospective study included 35 patients (main group), and the retrospective study included 16 patients (control group). The main group was administered correction of CVD due to QTc interval prolongation without Bq withdrawal; in the control group Bq was withdrawn (according to the current clinical recommendations). We established conduction disturbances in the form of QTc interval prolongation in both main and control groups, 45.7 and 18.7% respectively. In the control group QTc interval prolongation, which required Bq withdrawal, was observed in 3 out of 16 cases. The analysis of proarrhythmogenic factors and comprehensive ECG monitoring in the main group established functional nature of the changes (slow QTc interval adaptation to heart rate changes) in 16 out of 35 cases. Due to pathogenetic therapy (replacement, metabolic therapy, use of β-adrenoblockers) aimed to mitigate the impact of proarrhythmogenic factors we managed to neutralize signs of electrical instability of the heart and continue Bq containing treatment. The comparison of the frequency of Bq withdrawal between the groups associated with correction due to CVD established a significant correlation between the studied risk factor and the outcome (withdrawal of Bq) (χ2 = 3.997; p &lt; 0.05). Thus, our study demonstrated that timely target correction of modifiable risk factors reduced the probability of drug-induced arrhythmia and allowed safe administration of TB drugs with potential cardiotoxic effect.

Discovery of novel genetic syndromes in Latin America: Opportunities and challenges.

Latin America (LatAm) has a rich and historically significant role in delineating both novel and well-documented genetic disorders. However, the ongoing advancements in the field of human genetics pose challenges to the relatively slow adaption of LatAm in the field. Here, we describe past and present contributions of LatAm to the discovery of novel genetic disorders, often referred as novel gene-disease associations (NGDA). We also describe the current methodologies for discovery of NGDA, taking into account the latest developments in genomics. We provide an overview of opportunities and challenges for NGDA research in LatAm considering the steps currently performed to identify and validate such associations. Given the multiple and diverse needs of populations and countries in LatAm, it is imperative to foster collaborations amongst patients, indigenous people, clinicians and scientists. Such collaborative effort is essential for sustaining and enhancing the LatAm´s contributions to the field of NGDA.

Digitalization of the Legal System: Opportunities and Challenges for Indonesia

This study aims to analyze the impact and challenges of digitalization in the Indonesian legal system, focusing on its implications for legal education, data security, and professional readiness. Adopting a qualitative approach, the research synthesizes discussions on the evolution of legal processes in response to technological advancements. Findings highlight that digitalization offers efficiencies and transparency yet poses significant cybersecurity risks. The study underscores the urgent need for enhanced digital literacy and legal education reform, emphasizing skills in AI and blockchain technology. The implications of this study are pivotal for policymakers and educators as they navigate the balance between embracing digital innovation and addressing the associated risks in the legal domain.&#x0D; Highlights:&#x0D; &#x0D; Digital Technologies' Impact: Digitalization, especially through the E-Court program, significantly enhances the efficiency and transparency of legal processes in Indonesia.&#x0D; Challenges in Implementation: Key obstacles include the digital divide between urban and rural areas, slow adaptation of legal frameworks, and concerns over data privacy and cybersecurity.&#x0D; Need for Strategic Governance: Effective digital transformation in the legal system requires comprehensive strategies, improved cybersecurity, and the adaptation of legal professionals to technological advancements.&#x0D; &#x0D; Keywords: Digitalization, Indonesian Legal System, Big Data, Blockchain, Artificial Intelligence

Disentangling sensory precision and prior expectation of change in autism during tactile discrimination

Predictive coding theories suggest that core symptoms in autism spectrum disorders (ASD) may stem from atypical mechanisms of perceptual inference (i.e., inferring the hidden causes of sensations). Specifically, there would be an imbalance in the precision or weight ascribed to sensory inputs relative to prior expectations. Using three tactile behavioral tasks and computational modeling, we specifically targeted the implicit dynamics of sensory adaptation and perceptual learning in ASD. Participants were neurotypical and autistic adults without intellectual disability. In Experiment I, tactile detection thresholds and adaptation effects were measured to assess sensory precision. Experiments II and III relied on two-alternative forced choice tasks designed to elicit a time-order effect, where prior knowledge biases perceptual decisions. Our results suggest a subtler explanation than a simple imbalance in the prior/sensory weights, having to do with the dynamic nature of perception, that is the adjustment of precision weights to context. Compared to neurotypicals, autistic adults showed no difference in average performance and sensory sensitivity. Both groups managed to implicitly learn and adjust a prior that biased their perception. However, depending on the context, autistic participants showed no, normal or slower adaptation, a phenomenon that computational modeling of trial-to-trial responses helped us to associate with a higher expectation for sameness in ASD, and to dissociate from another observed robust difference in terms of response bias. These results point to atypical perceptual learning rather than altered perceptual inference per se, calling for further empirical and computational studies to refine the current predictive coding theories of ASD.