Spiking neural networks face hardware limitations as conventional architectures exhibit low array utilization, underperforming GPU-driven artificial neural networks in vision tasks. We present a programmable spiking neurocomputing architecture using CMOS-compatible photonic reconfigurable devices that unify synaptic/neuronal functions in single components. The optical isolation of photonic reconfigurable devices suppresses inter-cell crosstalk while enabling independent neuronal/synaptic programmability without operational redundancy. Programmable spiking neurocomputing architecture maximizes array utilization to enhance computational efficiency for spiking neural networks acceleration. Compared with conventional architectures, our architecture on spiking Visual Geometry Group networks demonstrates 1176× latency reduction and 239× energy savings in static recognition/classification and dynamic detection/tracking tasks, while maintaining equivalent recognition accuracy. This photonic-electronic integration establishes a scalable hardware framework that bridges the performance gap between spiking neural networks and state-of-the-art artificial neural networks, particularly for complex visual information processing applications requiring both efficiency and precision.
This study develops a bioinspired bilayer volatile memristor with a W/SiO₂/Cux(SiO2)100-x/Cu structure. The morphology and crystalline structure of the conductive filaments were directly observed via cross-sectional transmission electron microscopy. Discrete spherical copper-based grains construct conductive filaments with large internal surfaces, some of which are distorted due to stress interactions with the silicon dioxide matrix. According to the results of x-ray photoelectron spectroscopy, the diffusion of copper and redox reactions (involving the valence state transitions of copper to zero valence, positive monovalence, and positive divalence) are the core mechanisms governing the dynamic evolution of conductive filaments. When the stimulus is subsequently removed, the minimization of the thermodynamic surface energy drives the transformation of nonspherical grains into stable spherical grains, leading to conductive filament rupture and spontaneous recovery of the device to the initial state. By regulating the parameters of pulse signals to achieve precise control over conductive filament dynamics, the device successfully reproduces the behavior of nociceptors. A high accuracy of 93.11% for the handwritten digit recognition task in neurocomputing is achieved, showing the multipurpose function of the memristor.
Group membership shapes social behavior, often giving rise to both preferential treatment of ingroups and differential responses to outgroups. However, the neurocomputational mechanisms through which group membership shapes costly punishment remain incompletely understood. Here, we addressed this gap by examining the intergroup bias in both reactive punishment (retaliation against unfairness) and proactive punishment (strategic sanctions against fairness), integrating a second-party punishment paradigm with computational modeling and brain imaging. Participants sanctioned outgroups more than ingroups for both unfair and fair splits, indicating robust intergroup bias across reactive and proactive punishment. Model-based analyses revealed that, in reactive punishment, intergroup bias was associated with attenuated aversion to disadvantageous inequity toward ingroups. This shift in inequity sensitivity was tracked by stronger medial prefrontal cortex responses to disadvantageous offers from ingroups, consistent with processes related to affiliative modulation of valuation. In contrast, in proactive punishment, intergroup bias was associated with increased preference for relative advantage over outgroups. This bias was accompanied by greater engagement of the dorsal anterior cingulate cortex and enhanced coupling with the ventral striatum for fair offers from outgroups, consistent with increased involvement of control-related processes in value-guided decision-making. Together, these findings reveal dissociable computational and neural signatures underlying intergroup bias in reactive and proactive punishment.
We introduce a brain-constrained neurocomputational model designed to simulate higher cognitive functions of the human brain, implemented using NEST, a widely used open-source simulator optimised for high-performance spiking neural network simulations. Previously implemented in the custom-built C-based Felix simulation library, transitioning the model to NEST enhances accessibility, reproducibility, and computational efficiency. At the cellular level, the model comprises spiking excitatory neurons and local inhibitory neurons, whereas at the network level, it replicates the structural and functional organisation of 12 cortical regions spanning frontal, temporal, and occipital cortices, along with their associated inter-area connectivity. Additionally, global inhibition mechanisms and neuronal noise are integrated. Learning in the model follows biologically plausible Hebbian plasticity principles, incorporating both long-term potentiation and long-term depression. To validate the NEST implementation, we replicated previous simulation findings obtained with the Felix-based model. The new implementation successfully reproduced the same topographical distribution of cell assemblies following associative learning of object and action words within action and perception systems, replicating a range of previous neuroimaging results. Although the NEST model produced larger cell assemblies than Felix, the overall topographical patterns remained similar, indicating preservation of fundamental network characteristics. Moreover, the transition to NEST significantly enhanced computational efficiency, reducing simulation runtime nearly sixfold compared to Felix. This improvement in computational speed is crucial for expanding the model to include additional cortical regions, such as extending to the right hemisphere, which necessitates increased computational resources. The online version contains supplementary material available at 10.1007/s11571-026-10415-5.
Sensory errors, mismatches between predicted sensory outcomes of movement and reafferent sensory feedback, drive changes in the feedforward control of future motor behavior that correct for those errors. Across a wide variety of motor behaviors, individuals with cerebellar damage show impairments in these corrections, strongly suggesting a key role of the cerebellum in sensorimotor adaptation. However, the extent to which the cerebellum is involved in controlling vocal pitch is currently unknown. Crucially, vocal pitch differs in several ways from other systems that suggest it relies more on feedback than feedforward control. Adaptation itself also differs in vocal pitch: Rather than the gradual buildup/decay of learning seen in other systems, pitch adaptation and deadaptation are almost immediate. Together, this questions whether adaptation in vocal pitch relies on the same mechanism as other motor domains. Here, we test the hypothesis that the cerebellum underlies sensorimotor adaptation in vocal pitch, testing the domain generality of this neurocomputational process. In both sustained vocalization and a more natural word production task, individuals with cerebellar ataxia fail to adapt to external auditory perturbation of vocal pitch. The lack of adaptation observed, compared to the impaired but present adaptation seen in other systems, suggests that the cerebellum plays an especially critical role in maintaining accurate control of vocal pitch. Conversely, we failed to detect a previously observed increase in online compensation to vocal pitch errors in ataxia, potentially suggesting this may be an idiosyncratic change in control rather than a common trait in this population.
Predicting the success of startup companies, defined as achieving an exit through acquisition or IPO, is a critical problem in entrepreneurship and innovation research. Datasets such as Crunchbase provide both structured information (e.g., funding rounds, industries, and investor networks) and unstructured text (e.g., company descriptions), but effectively leveraging such heterogeneous data for prediction remains challenging. Traditional machine learning approaches often rely only on structured features and achieve moderate accuracy, while large language models (LLMs) offer strong reasoning capabilities but are not readily adapted to domain-specific business data. We present CrunchLLM, a domain-adapted and backbone-agnostic LLM framework for startup success prediction. CrunchLLM integrates structured company attributes with unstructured textual narratives and applies parameter-efficient fine-tuning together with prompt optimization to specialize foundation models for entrepreneurship data. Importantly, our framework introduces a self-verifiable multitask objective, in which the justification loss serves as a training-time constraint on classification, together with a hierarchically ordered input encoding that reduces the tendency of long unstructured company narratives to overshadow structured business attributes. These methodological innovations yield more reliable and feature-grounded predictions than conventional prompt-based LLM adaptation. Our approach achieves 89% accuracy on the Crunchbase startup success prediction task, significantly outperforming traditional classifiers and baseline LLMs. Beyond predictive performance, CrunchLLM generates interpretable reasoning traces that support its predictions, enhancing transparency and trustworthiness for financial and policy decision-makers. Overall, this work demonstrates how domain-aware LLM adaptation and structured-unstructured data fusion can advance predictive modeling of entrepreneurial outcomes, providing both a methodological framework and a practical tool for data-driven decision-making in venture capital and innovation policy.
Large Language Models demonstrate remarkable capabilities but suffer from critical metacognitive deficits, manifesting as overconfidence and hallucination, which severely limit their deployment in high-stakes applications. We introduce Predictive Metacognition, a neurobiologically-inspired framework that integrates principles of predictive processing and anterior cingulate cortex monitoring into transformer architectures. Our approach implements Error-Driven Learning and Dual-Process Monitoring through specialised fine-tuning that trains models to simultaneously generate responses and assess their own performance reliability. We fine-tuned Llama-3-8B-Instruct and Phi-3-Mini-4k-Instruct using LoRA (rank=8, [Formula: see text]) on 4,000 strategically constructed examples spanning varying confidence levels. Comprehensive evaluation against state-of-the-art baselines, including GPT-4o and Claude-3.5-Sonnet, revealed statistically significant improvements in confidence calibration. Our metacognitive models achieved substantial reductions in Brier Score (11.6% and 17.2% respectively) and Expected Calibration Error ([Formula: see text], Cohen's [Formula: see text]). Critically, these improvements generalised robustly to out-of-domain tasks while maintaining competitive task accuracy. This work establishes a computationally tractable implementation of biologically-inspired metacognitive architecture for large language models, offering a principled pathway towards AI systems capable of reliable intrinsic self-monitoring that can more accurately assess their own knowledge boundaries and express appropriate uncertainty.
There is broad consensus that successful repair of severe peripheral nerve injuries requires recreating key structural and cellular features of the natural regenerative process, particularly the action of Bands of Büngner (BoB), longitudinal Schwann cell (SC) structures that guide regenerating axons. Current biomaterial-based strategies have shown limited efficacy, in part because they do not sufficiently reproduce the anisotropic and cellular microenvironment established by BoB, resulting in disorganized axonal growth and reduced regenerative efficiency across long gaps. To address this limitation, a biohybrid scaffold designed to promote Schwann cell self-organization into Büngner-like structures through defined physical cues. Rather than relying solely on biochemical supplementation is developed, this system leverages anisotropic fiber architecture to induce SC alignment and early activation-associated phenotypic modulation. In this study, a self-organizing biohybrid BoB (BBoB) construct formed by Schwann cells within an aligned fiber-based scaffold is presented. It is demonstrated that these engineered structures recapitulate key morphological features of native BoB in vitro and promote enhanced axonal regeneration across a 11 mm sciatic nerve defect in vivo. Together, these findings support the concept that physically programmed Schwann cell organization within biomaterial conduits can enhance peripheral nerve regeneration, using clinically accessible biomaterials and autologous Schwann cells.
Focal transcranial direct current stimulation (tDCS) using center-surround electrode montages enables region-specific cortical targeting, and holds promise for both cognitive neuroscience and clinical interventions. However, systematic examinations of dose-response relationships and their regional differences are lacking, hampering informed selections of suited stimulation parameters. In this preparatory methodological study, we present a modeling-based framework to support harmonized empirical dose-response studies of focal tDCS across different target areas. It covers three steps: Determining the approximate electric field strength that had led to behavioral and physiological effects in related prior tDCS studies. In our case, this led to a field strength of 0.2 V/m on average across magnetic resonance images (MRIs) from 43 participants and eight target areas related to different cognitive and motor functions. Second, optimizing the radii of center-surround montages for each target area to - on average across participants - achieve the intended field strength while maximizing focality. An additional test of cross-sample generalization in an independent sample confirms that the intended target field strength is achieved on average for new participants. Third, the pre-determined montage radii and a method for the individualized positioning of the center-surround electrode montages are provided for prospective planning in empirical dose-response studies. By harmonizing the electric field strength between different target regions at the group level, but preserving inter-individual variability, our framework will enable systematic analyses to relate the field strength to behavioral and neuroimaging outcomes, and to assess differences of these relations across regions. The described computational tools are open-source, allowing other researchers to tailor our framework to their specific research questions; and are currently used in a multi-center study involving approximately 1000 datasets.
The present study designs a new computational approach to study the thermal performance of a shrinking or stretching longitudinal rectangular fin under both convective and radiative conditions. A dimensionless mathematical model is developed to describe the thermal behavior of the fin, including parameters such as Peclet number, convective and radiative coefficients, temperature ratios, and stretching/shrinking parameters. The hybrid neurocomputing method used to solve the nonlinear system of differential equations consists of Cascaded Neural Networks (CNN) along with Genetic Algorithm hybridized with Sequential Quadratic Programming (GA-SQP). CNN modeling offers an approximate solution through a piecewise continuous representation, whereas GA-SQP improves convergence by optimal network weights. A detailed parametric study is done in six scenarios to assess the fin's temperature distribution and tip characteristics. The accuracy and stability of the solver are validated using statistical metrics such as RMSE, MAE, E-VAF, and E-NSE. The outcome shows that the Peclet number increases enhance the fin tip temperature up-to 15% as the temperature ratio increases, the fin tip temperature increases by 7-10%. The increase in the the radiation coefficient reduces the temperature by 8%. Various statistical measures confirm its reliability and effectiveness, making it a strong tool for handling complex ODEs and PDEs. Histogram and boxplot analysis show accuracy ranges up to 10- 9, indicating that almost 90% of total iterations achieved the required accuracy tolerance range. Fitness evaluation attains high rate of convergence shows that CNN-GA-SQP scheme is a fast-converging and accurate computational intelligence approach for solving stiff nonlinear fin heat transfer models.
Direct cellular reprogramming, the conversion of one somatic cell type into another, represents a remarkable advancement in regenerative medicine. Its potential to transform fibrotic tissue into functional parenchyma underscores its therapeutic promise. However, several critical challenges remain unresolved, including limited reprogramming efficiency, the long-term functional stability of converted cells, their integration within pre-existing cellular circuits, and safety concerns related to transgene integration and immunological responses to reprogramming-based viral vectors. Approaches based on the exogenous administration of recombinant proteins and miRNAs have also emerged, though these rely on factors that are naturally prone to exhaustion and degradation, potentially restricting their efficacy. This review is divided into three main sections. The first part addresses direct cellular reprogramming in the context of other cell-based applications, outlining its main applications and current biological limitations. The second part examines how different biomaterials, ranging from hydrogel scaffolds to nanoparticles, can modulate direct cellular reprogramming by providing mechanical and topographical cues and by enabling tighter control over the concentration and spatiotemporal dynamics of reprogramming factors and viral vectors. The third part discusses key findings in biomaterial-assisted reprogramming strategies, highlighting emerging opportunities for clinically translatable approaches. The convergence of regenerative biology and biomaterials science may ultimately generate advanced gel-based and hybrid cellular reprogramming platforms for in vitro testing and, in situ applications, for promoting cell fate stabilization and facilitating the regeneration of damaged tissues and organs.
Background/Objectives: From birth, infants learn how to interact with the world through exploration. It has been proposed that this early learning phase is driven by motor babbling: the spontaneous generation of exploratory movements that are progressively consolidated through associative mechanisms. This process leads to the acquisition of a repertoire of hand movements such as single- or multi-finger flexion, extension, touching, and pushing. Later, in a second phase, some of these movements (e.g., those that happen to enable access to biologically salient stimuli, such as grasping food) are further reinforced and consolidated through rewards obtained from the environment. However, the neural mechanisms underlying these processes remain unclear. Here, we used a fully neuroanatomically and neurophysiologically constrained neural network model to investigate the brain correlates of these processes. Methods: The model consists of six neural maps simulating six human brain areas, including three pre-central (motor-related) and three post-central (sensory-related) regions. Each map is composed of excitatory and inhibitory spiking neurons, with biologically constrained within- and between-area connectivity forming recurrent circuits. Hand action execution and corresponding haptic perception are simulated simply as activity in primary motor and somatosensory model areas, respectively. During an initial "exploratory" phase, the network learned, via Hebbian mechanisms, associations-as emerging distributed cell assembly (CA) circuits-linking "motor" to corresponding "haptic feedback" patterns. As a result of this initial training, the model began to exhibit spontaneous ignitions of these CA circuits, an emergent phenomenon taken to represent internally generated, non-stimulus-driven attempts at hand action exploitation. In a second phase, a global reward signal, simulating dopamine-mediated reward encoding, was applied to only a subset of "successful" actions upon their noise-driven ignition. Results: During the first exploratory phase, the neural architecture autonomously developed "action-perception" circuits corresponding to multiple possible hand actions. During the subsequent exploitation phase, positively reinforced circuits increased in size and, consequently, in frequency of spontaneous ignition, when compared to non-rewarded "actions". Conclusions: These results provide a mechanistic account, at the cortical-circuit level, of the early acquisition of hand actions, of their subsequent consolidation, and of the spontaneous transition of an agent's behavior from exploration to reward-seeking, as typically observed in humans and animals during development.
Under the influence of psychedelics, people often report encountering "entities" who seem to have their own autonomous agency. Depending on the cultural milieu, these entities are reported to take a variety of forms, including spirits, elves, ancestors, or fragments of the self. Encounters with such beings hold a central place in many traditions of psychedelic practice around the world. And yet, mechanistic accounts of these experiences are scarce in the neuroscientific literature. Here, we propose a neurocomputational model to account for experiences of entities, focusing primarily on those occasioned by the serotonergic psychedelic N,N-dimethyltryptamine. Our model builds on earlier theoretical accounts, including the entropic brain model of psychedelics, computational accounts of the felt presence of other minds, and theories of self-other discrimination based on sensory attenuation. We synthesize and expand on these perspectives through an overarching physics-based approach to cognition and brain function-the active inference framework. We propose that the general effects of psychedelics on large-scale neural dynamics may shape the way the brain comes to infer and interpret agentic presences. In particular, the reduction in the predictability of sensory perceptions during the psychedelic state may incline the brain to interpret perceptions, both internal and external, as resulting from non-self-agentic sources. In specifying the neurocomputational mechanisms, our model aims to explain how the brain supports entity encounters while also accounting for the diversity (and similarity) of these experiences across cultural contexts.
The paper presents an All-Digital Aliasing-Free PWM (AF-PWM) transmitter, which combines multiphase band-limited PWM (MP-BLPWM) and accumulated N phase-shift pulse modulation (AN-PSPM), and its FPGA-implementation. As the architecture is based on MP-BLPWM, which generates finite harmonics PWM, this eliminates image and aliasing distortion, and improves spectral performance. However, finite harmonics PWM leads to large amplitude variation, which is converted to two voltage level signals using AN-PSPM, leading toward all-digital implementation. The transmitter's performance is experimentally validated for 5G-NR and LTE signals, both with and without a switched-mode power amplifier (Class-D PA). Measurement results demonstrate that, when used with the PA, the transmitter achieves ACLR values of 37.9 dBc for 5G-NR and 42.2 dBc for LTE signals. Furthermore, the EVM of the proposed transmitter with the Class-D PA is measured at 1.3% for 5G-NR and 0.9% for LTE, highlighting its effectiveness for advanced wireless communication applications.
Visual perception enables goal-directed movement control by mapping sensory input onto motor representations. While neural mechanisms of visuomotor integration have been extensively studied, the temporal dynamics of this process during real and mentally simulated movements remain poorly understood, particularly regarding stimulus-driven versus response-driven motor cortex contributions. We used transcranial magnetic stimulation (TMS) and electroencephalography (EEG) to investigate cortical activity during physical and imagined hand movements in a stimulus-response task. Single-pulse TMS was delivered to the primary motor cortex at 100, 200, and 400 ms following visual stimulus presentation to probe corticospinal excitability. Motor cortex facilitation during early preparation was found to be stimulus-locked rather than motor response-locked, indicating that visual cues drive initial motor cortex activation. Both motor execution (ME) and kinesthetic motor imagery (kMI) showed similar facilitation dynamics during this early stage, supporting functional equivalence during preparatory processing. However, ME and kMI diverged at later response stages: ME showed elevated excitability whereas in kMI it returned to baseline. EEG analyses confirmed this dissociation at later stages. Notably, kMI did not produce significant hemispheric lateralization, whereas ME generated robust lateralized readiness potentials whose duration correlated with pre-response motor excitability and behavioral performance. These findings challenge conventional response-driven conceptualizations of motor imagery and highlight stimulus-driven mechanisms in visuomotor processing during both overt and imagined movements.
The blood oxygen level-dependent (BOLD) signal has been instrumental in characterizing brain activity. While the spatial resolution of fMRI continues to improve, relatively few methods have focused on enhancing and leveraging temporal resolution to investigate the spatiotemporal dynamics of the hemodynamic response. In this study, we applied a reordering method to achieve ultra-high temporal resolution (60 ms) in data acquired during a somatosensory stimulation paradigm. We then used a finite impulse response model (FIR) for each participant (N = 31) to preserve the temporal dynamics in the statistical analysis. At the group level, we employed an ANOVA combined with 4D nonparametric permutation testing to identify significant signal changes in time across the whole brain. Our results characterize the hemodynamic response in terms of both its temporal and spatial patterns and reveal distinct differences in response shapes within the somatosensory system. This method introduces a time-resolved approach to BOLD signal analysis, drawing inspiration from grand-average techniques commonly used in EEG research.
Human pain perception is not solely driven by sensory input but is dynamically modulated by what we expect to feel and how confident we are in those expectations. Yet, the temporal mechanisms through which evolving expectations shape pain remain poorly understood. Here, we combined a probabilistic cueing paradigm with computational modeling and EEG to dissociate two core components of expectation: strength (a recency-weighted estimate of predicted pain) and precision (the inverse variability of recent predictions). Trial-wise strength estimates closely tracked subjective expectations and outperformed static cue labels, validating the model's psychological relevance. Expectation strength and precision exerted dissociable effects on pain processing: strength enhanced, whereas precision suppressed, pain-evoked responses. Critically, anticipatory α-band activity mediated these effects via distinct topographical patterns-expectation strength reduced fronto-central α power (reflecting heightened vigilance), while precision increased contralateral sensorimotor α-synchronization (supporting sensory gating). Source-level mediation analyses identified a right-lateralized dorsolateral prefrontal-sensorimotor cortices (DLPFC-SM1) integrating both components, with strength-specific engagement of the medial prefrontal cortex (mPFC). These effects were supported by Bayesian inference and pooled mega-analyses, underscoring their robustness. Together, these findings highlight cortical α-oscillations as dual-control mechanisms for predictive integration, with DLPFC-SM1 as a shared expectation hub and mPFC as a strength-specific node. By moving beyond static cue-based models, this framework captures the adaptive dynamics of expectation and provides a neurocomputational foundation for targeted interventions in chronic pain.
The neuroscience of planning has long been analogized to search algorithms in artificial intelligence (AI), which simulate future actions to guide immediate choices. We argue that advances in both neuroscience and AI suggest that planning is better understood to encompass a broader class of computations where mental simulation supports learning, often well before a decision is needed. We review three neurocomputational mechanisms that illustrate this shift. First, hippocampal replay resembles search but also often occurs prospectively or offline, likely training downstream circuits rather than directly guiding choice. Second, temporally abstract representations, such as grid cells, can enable planning without iterative search. Third, metalearning may shape how prefrontal dynamics implement task-specific planning strategies, echoing how AI systems learn to adapt across contexts. This view recasts the brain's planning machinery as a family of learning processes that leverage simulations to build representations and strategies, with forward search as one special case.
Among brain areas, axonal projections carry channels of information that can be mixed to varying degrees. Here, we assess the rules for the network consisting of the primary visual cortex and higher visual areas (V1-HVA) in mice. We use large field-of-view two-photon calcium imaging to measure correlated variability (i.e. noise correlations, NCs) among thousands of neurons, forming over a million unique pairs, distributed across multiple cortical areas simultaneously. The amplitude of NCs is proportional to functional connectivity in the network, and we find that they are robust, reproducible statistical measures and are remarkably similar across stimuli, thus providing effective constraints to network models. We used these NCs to measure the statistics of functional connectivity among tuning classes of neurons in V1 and HVAs. Using a data-driven clustering approach, we identify approximately 60 distinct tuning classes found in V1 and HVAs. We find that NCs are higher between neurons from the same tuning class, both within and across cortical areas. Thus, in the V1-HVA network, mixing of channels is avoided. Instead, distinct channels of visual information are broadcast within and across cortical areas, at both the micron and millimeter length scales. This principle for the functional organization and correlation structure at the individual neuron level across multiple cortical areas can inform and constrain computational theories of neocortical networks.
Reinforcement learning with reversal is a paradigm frequently employed in neuropsychology to assess the adaptive capacity and flexibility in individuals in non-stationary environments. Each single behavior, however, depends on the superimposition of multiple factors, which are still not completely understood. We optimize a neurocomputational model of the Basal Ganglia to simulate the behavior of 18 patients with Parkinson's disease (both ON and OFF medication) and 14 control subjects during a two-choice probabilistic reversal learning task (with 80-20% reward probabilities). The individual behavior (in terms of the cumulative number of correct responses during a 40-trials direct phase and a 40-trial reversal phase) is reproduced by fitting a few model parameters for each individual, representing the tonic dopamine level, Hebbian learning, and the exploratory attitude (noise level). Results show that very different responses can be explained quite well, ascribing them to the varying combination of the aforementioned individual factors. The tonic dopamine level is significantly different for patients in ON and OFF medication, while the other parameters were not statistically different. A regression analysis reveals that the value of the Hebbian learning rate is correlated with the subject's sensitivity to punishments. In contrast, the noise standard deviation is correlated with exploration, i.e., the tendency to modify the choice even after a reward. The results provide a mechanistic explanation of the various factors that affect adaptation and flexibility in reinforcement learning, representing a first step toward characterizing and understanding the diverse behaviors on an individual basis.