Behavioral Modeling of Memristors under Harmonic Excitation

Memristors are devices built on the basis of fourth passive electrical elements in nanosystems. Because of the multitude of technologies used for memristor implementation, it is not always possible to obtain analytical models of memristors. This difficulty can be overcome using behavioral modeling, which is when mathematical models are constructed according to the input-output relationships on the input and output signals. For memristor modeling, piecewise neural and polynomial models with split signals are proposed. At harmonic input signals of memristors, this study suggests that split signals should be formed using a delay line. This method produces the minimum number of split signals and, as a result, simplifies behavioral models. Simplicity helps reduce the dimension of the nonlinear approximation problem solved in behavioral modeling. Based on the proposed method, the piecewise neural and polynomial models with harmonic input signals were constructed to approximate the transfer characteristic of the memristor, in which the current dynamics are described using the Bernoulli differential equation. It is shown that the piecewise neural model based on the feedforward network ensures higher modeling accuracy at almost the same complexity as the piecewise polynomial model.

Average reward rates enable motivational transfer across independent reinforcement learning tasks

Outcomes and feedbacks on performance may influence behavior beyond the context in which it was received, yet it remains unclear what neurobehavioral mechanisms may account for such lingering influences on behavior. The average reward rate (ARR) has been suggested to regulate motivated behavior, and was found to interact with dopamine-sensitive cognitive processes, such as vigilance and associative memory encoding. The ARR could therefore provide a bridge between independent tasks when these are performed in temporal proximity, such that the reward rate obtained in one task could influence performance in a second subsequent task. Reinforcement learning depends on the coding of prediction error signals by dopamine neurons and their downstream targets, in particular the nucleus accumbens. Because these brain regions also respond to changes in ARR, reinforcement learning may be vulnerable to changes in ARR. To test this hypothesis, we designed a novel paradigm in which participants (n = 245) performed two probabilistic reinforcement learning tasks presented in interleaved trials. The ARR was controlled by an "induction" task which provided feedback with a low (p = 0.58), a medium (p = 0.75), or a high probability of reward (p = 0.92), while the impact of ARR on reinforcement learning was tested by a second "reference" task with a constant reward probability (p = 0.75). We find that performance was significantly lower in the reference task when the induction task provided low reward probabilities (i.e., during low levels of ARR), as compared to the medium and high ARR conditions. Behavioral modeling further revealed that the influence of ARR is best described by models which accumulates average rewards (rather than average prediction errors), and where the ARR directly modulates the prediction error signal (rather than affecting learning rates or exploration). Our results demonstrate how affective information in one domain may transfer and affect motivated behavior in other domains. These findings are particularly relevant for understanding mood disorders, but may also inform abnormal behaviors attributed to dopamine dysfunction.

A Comparison of Surrogate Behavioral Models for Power Amplifier Linearization under High Sparse Data

A good approximation to power amplifier (PA) behavioral modeling requires precise baseband models to mitigate nonlinearities. Since digital predistortion (DPD) is used to provide the PA linearization, a framework is necessary to validate the modeling figures of merit support under signal conditioning and transmission restrictions. A field-programmable gate array (FPGA)-based testbed is developed to measure the wide-band PA behavior using a single-carrier 64-quadrature amplitude modulation (QAM) multiplexed by orthogonal frequency-division multiplexing (OFDM) based on long-term evolution (LTE) as a stimulus, with different bandwidths signals. In the search to provide a heuristic target approach modeling, this paper introduces a feature extraction concept to find an appropriate complexity solution considering the high sparse data issue in amplitude to amplitude (AM-AM) and amplitude to phase AM-PM models extraction, whose penalties are associated with overfitting and hardware complexity in resulting functions. Thus, experimental results highlight the model performance for a high sparse data regime and are compared with a regression tree (RT), random forest (RF), and cubic-spline (CS) model accuracy capabilities for the signal conditioning to show a reliable validation, low-complexity, according to the peak-to-average power ratio (PAPR), complementary cumulative distribution function (CCDF), coefficients extraction, normalized mean square error (NMSE), and execution time figures of merit. The presented models provide a comparison with original data that aid to compare the dimension and robustness for each surrogate model where (i) machine learning (ML)-based and (ii) CS interpolate-based where high sparse data are present, NMSE between the CS interpolated based are also compared to demonstrate the efficacy in the prediction methods with lower convergence times and complexities.

An EEG study of human trust in autonomous vehicles based on graphic theoretical analysis

With the development of autonomous vehicle technology, human-centered transport research will likely shift to the interaction between humans and vehicles. This study focuses on the human trust variation in autonomous vehicles (AVs) as the technology becomes increasingly intelligent. This study uses electroencephalogram data to analyze human trust in AVs during simulated driving conditions. Two driving conditions, the semi-autonomous and the autonomous, which correspond to the two highest levels of automatic driving, are used for the simulation, accompanied by various driving and car conditions. The graph theoretical analysis (GTA) is the primary method for data analysis. In semi-autonomous driving mode, the local efficiency and cluster coefficient are lower in car-normal conditions than in car-malfunction conditions with the car approaching. This finding suggests that the human brain has a strong information processing ability while facing predictable potential hazards. However, when it comes to a traffic light with a car malfunctioning under the semi-autonomous driving mode, the characteristic path length is higher for the car malfunction manifesting a weak information processing ability while facing unpredictable potential hazards. Furthermore, in fully automatic driving conditions, participants cannot do anything and need low-level brain function to take emergency actions as lower local efficiency and small worldness for car malfunction. Our results shed light on the design of the human-machine interaction and human factor engineering on the high level of an autonomous vehicle.

A Radial Basis Function Neural Network Approach to Predict Preschool Teachers' Technology Acceptance Behavior

With the continual development of artificial intelligence and smart computing in recent years, quantitative approaches have become increasingly popular as an efficient modeling tool as they do not necessitate complicated mathematical models. Many nations have taken steps, such as transitioning to online schooling, to decrease the harm caused by coronaviruses. Inspired by the demand for technology in early education, the present research uses a radial basis function (RBF) neural network (NN) modeling technique to predict preschool instructors' technology usage in classes based on recognized determinant characteristics of technology acceptance. In this regard, this study utilized the RBFNN approach to predict preschool teachers' technology acceptance behavior, based on the theory of planned behavior, which states that behavioral achievement, in our case the actual technology use in class, depends on motivation, intention and ability, and behavioral control. Thus, this research design is based on an adapted version of the technology acceptance model (TAM) with eight dimensions: D1. Perceived usefulness, D2. Perceived ease of use, D3. Perceived enjoyment, D4. Intention to use, D5. Actual use, D6. Compatibility, D7. Attitude, and D8. Self-efficacy. According to the TAM, actual usage is significantly predicted by the other seven dimensions used in this research. Instead of using the classical multiple linear regression statistical processing of data, we opted for a NN based on the RBF approach to predict the actual usage behavior. This study included 182 preschool teachers who were randomly chosen from a project-based national preschool teacher training program and who responded to our online questionnaire. After designing the RBF function with the actual usage as an output variable and the other seven dimensions as input variables, in the model summary, we obtained in the training sample a sum of squares error of 37.5 and a percent of incorrect predictions of 43.3%. In the testing sample, we obtained a sum of squares error of 14.88 and a percent of incorrect predictions of 37%. Thus, we can conclude that 63% of the classified data are correctly assigned to the models' dependent variable, i.e., actual technology use, which is a significant rate of correct predictions in the testing sample. This high significant percentage of correct classification represents an important result, mainly because this is the first study to apply RBFNN's prediction on psychological data, opening up a new interdisciplinary field of research.

A Bivariate Volterra Series Model for the Design of Power Amplifier Digital Predistorters

The operation of the power amplifier (PA) in wireless transmitters presents a trade-off between linearity and power efficiency, being more efficient when the device exhibits the highest nonlinearity. Its modeling and linearization performance depend on the quality of the underlying Volterra models that are characterized by the presence of relevant terms amongst the enormous amount of regressors that these models generate. The presence of PA mechanisms that generate an internal state variable motivates the adoption of a bivariate Volterra series perspective with the aim of enhancing modeling capabilities through the inclussion of beneficial terms. In this paper, the conventional Volterra-based models are enhanced by the addition of terms, including cross products of the input signal and the new internal variable. The bivariate versions of the general full Volterra (FV) model and one of its pruned versions, referred to as the circuit-knowledge based Volterra (CKV) model, are derived by considering the signal envelope as the internal variable and applying the proposed methodology to the univariate models. A comparative assessment of the bivariate models versus their conventional counterparts is experimentally performed for the modeling of two PAs driven by a 30 MHz 5G New Radio signal: a class AB PA and a class J PA. The results for the digital predistortion of the class AB PA under a direct learning architecture reveal the benefits in linearization performance produced by the bivariate CKV model structure compared to that of the univariate CKV model.

Comparison of Feature Selection Techniques for Power Amplifier Behavioral Modeling and Digital Predistortion Linearization

The power amplifier (PA) is the most critical subsystem in terms of linearity and power efficiency. Digital predistortion (DPD) is commonly used to mitigate nonlinearities while the PA operates at levels close to saturation, where the device presents its highest power efficiency. Since the DPD is generally based on Volterra series models, its number of coefficients is high, producing ill-conditioned and over-fitted estimations. Recently, a plethora of techniques have been independently proposed for reducing their dimensionality. This paper is devoted to presenting a fair benchmark of the most relevant order reduction techniques present in the literature categorized by the following: (i) greedy pursuits, including Orthogonal Matching Pursuit (OMP), Doubly Orthogonal Matching Pursuit (DOMP), Subspace Pursuit (SP) and Random Forest (RF); (ii) regularization techniques, including ridge regression and least absolute shrinkage and selection operator (LASSO); (iii) heuristic local search methods, including hill climbing (HC) and dynamic model sizing (DMS); and (iv) global probabilistic optimization algorithms, including simulated annealing (SA), genetic algorithms (GA) and adaptive Lipschitz optimization (adaLIPO). The comparison is carried out with modeling and linearization performance and in terms of runtime. The results show that greedy pursuits, particularly the DOMP, provide the best trade-off between execution time and linearization robustness against dimensionality reduction.

Upgrading Behavioral Models for the Design of Digital Predistorters

This work presents a strategy to upgrade models for power amplifier (PA) behavioral modeling and digital predistortion (DPD). These incomplete structures are the consequence of nonlinear order and memory depth model truncation with the purpose of reducing the demand of the limited computational resources available in standard processors. On the other hand, the alternative use of model structures pruned a priori does not guarantee that every significant term is included. To improve the limited performance of an incomplete model, a general procedure to augment its structure by incorporating significant terms is demonstrated. The sparse nature of the problem allows a successive search incorporating additional terms with higher nonlinear order and memory depth. This approach is investigated in the modeling and linearization of a commercial class AB PA operating at a compression point of about 6 dB, and a class J PA operating near saturation. Results highlight the capabilities of this upgrading procedure in the improvement of linearization capabilities of DPDs.

Behavioral Modeling of DC/DC Converters in Self-Powered Sensor Systems with Modelica

DC/DC converters are the essential component of power management in applications such as self-powered systems. Their simulation plays an important role in the configuration, analysis and design. A major drawback is the lack of behavioral models for DC/DC converters for long-term simulations (days or months). Available models are cycle-to-cycle-based due to the switch-mode nature of the converters and are therefore not applicable. In this work, we present a new behavioral model of a DC/DC power converter. The model is based on a thorough discussion of the model aspects that are relevant for self-powered systems, such as electrical representation and the causal connection if input and output. The model implementation is shown in the Modelica language and is available as an open-source library. The highlights of the model are a feedback controller for operation at the maximum power point (MPP), a loss-based efficiency function, and the start/stop behavior. The model’s capabilities are demonstrated in a 24h-experiment to predict voltage levels and the conversion efficiency.

Active Inferants: An Active Inference Framework for Ant Colony Behavior

In this paper, we introduce an active inference model of ant colony foraging behavior, and implement the model in a series of in silico experiments. Active inference is a multiscale approach to behavioral modeling that is being applied across settings in theoretical biology and ethology. The ant colony is a classic case system in the function of distributed systems in terms of stigmergic decision-making and information sharing. Here we specify and simulate a Markov decision process (MDP) model for ant colony foraging. We investigate a well-known paradigm from laboratory ant colony behavioral experiments, the alternating T-maze paradigm, to illustrate the ability of the model to recover basic colony phenomena such as trail formation after food location discovery. We conclude by outlining how the active inference ant colony foraging behavioral model can be extended and situated within a nested multiscale framework and systems approaches to biology more generally.

Choice architecture-based prescribing tool for TB preventive therapy: a pilot study in South Africa

Background

All people with HIV who screen negative for active tuberculosis (TB) should receive isoniazid preventive therapy (IPT). IPT implementation remains substantially below the 90% WHO target. This study sought to further understanding of IPT prescription by piloting a simplified prescribing approach.

Setting

Primary care clinics in Matlosana, South Africa.

Design

This was a mixed-methods implementation study.

Methods

Nine providers were recruited and underwent training on 2018 WHO guidelines. A simplified prescribing tool containing antiretroviral therapy (ART) and IPT prescriptions was introduced into the workflow for 2 weeks. Prescription data were collected from file review. Interviews were conducted with prescribers.

Results

During the study period, 41 patients were evaluated for ART initiation; 34 (83%) files used the simplified prescribing tool. Thirty-seven (90%) patients were eligible for same-day ART and IPT initiation, of whom 36 (97%) received IPT prescription. Qualitative interviews identified the following barriers to IPT prescription: cognitive burden, extensive documentation, limited management support, paucity of training, stock-outs, and patient-related factors. Provider acceptability of the tool was favorable, with unanimous recommendation to colleagues on the basis of streamlining documentation and reminding to prescribe.

Conclusions

This simplified prescribing device for IPT was feasible to implement. Streamlining documentation and reminding providers to prescribe can reduce work-flow barriers to IPT provision.

Memory Alone Does Not Account for the Way Rats Learn a Simple Spatial Alternation Task

Animal behavior provides context for understanding disease models and physiology. However, that behavior is often characterized subjectively, creating opportunity for misinterpretation and misunderstanding. For example, spatial alternation tasks are treated as paradigmatic tools for examining memory; however, that link is actually an assumption. To test this assumption, we simulated a reinforcement learning (RL) agent equipped with a perfect memory process. We found that it learns a simple spatial alternation task more slowly and makes different errors than a group of male rats, illustrating that memory alone may not be sufficient to capture the behavior. We demonstrate that incorporating spatial biases permits rapid learning and enables the model to fit rodent behavior accurately. Our results suggest that even simple spatial alternation behaviors reflect multiple cognitive processes that need to be taken into account when studying animal behavior.SIGNIFICANCE STATEMENT Memory is a critical function for cognition whose impairment has significant clinical consequences. Experimental systems aimed at testing various sorts of memory are therefore also central. However, experimental designs to test memory are typically based on intuition about the underlying processes. We tested this using a popular behavioral paradigm: a spatial alternation task. Using behavioral modeling, we show that the straightforward intuition that these tasks just probe spatial memory fails to account for the speed at which rats learn or the types of errors they make. Only when memory-independent dynamic spatial preferences are added can the model learn like the rats. This highlights the importance of respecting the complexity of animal behavior to interpret neural function and validate disease models.

Monkeys and humans implement causal inference to simultaneously localize auditory and visual stimuli

The environment is sampled by multiple senses, which are woven together to produce a unified perceptual state. However, optimally unifying such signals requires assigning particular signals to the same or different underlying objects or events. Many prior studies (especially in animals) have assumed fusion of cross-modal information, whereas recent work in humans has begun to probe the appropriateness of this assumption. Here we present results from a novel behavioral task in which both monkeys (Macaca mulatta) and humans localized visual and auditory stimuli and reported their perceived sources through saccadic eye movements. When the locations of visual and auditory stimuli were widely separated, subjects made two saccades, while when the two stimuli were presented at the same location they made only a single saccade. Intermediate levels of separation produced mixed response patterns: a single saccade to an intermediate position on some trials or separate saccades to both locations on others. The distribution of responses was well described by a hierarchical causal inference model that accurately predicted both the explicit "same vs. different" source judgments as well as biases in localization of the source(s) under each of these conditions. The results from this task are broadly consistent with prior work in humans across a wide variety of analogous tasks, extending the study of multisensory causal inference to nonhuman primates and to a natural behavioral task with both a categorical assay of the number of perceived sources and a continuous report of the perceived position of the stimuli.NEW & NOTEWORTHY We developed a novel behavioral paradigm for the study of multisensory causal inference in both humans and monkeys and found that both species make causal judgments in the same Bayes-optimal fashion. To our knowledge, this is the first demonstration of behavioral causal inference in animals, and this cross-species comparison lays the groundwork for future experiments using neuronal recording techniques that are impractical or impossible in human subjects.

Social interactions drive efficient foraging and income equality in groups of fish

The social interactions underlying group foraging and their benefits have been mostly studied using mechanistic models replicating qualitative features of group behavior, and focused on a single resource or a few clustered ones. Here, we tracked groups of freely foraging adult zebrafish with spatially dispersed food items and found that fish perform stereotypical maneuvers when consuming food, which attract neighboring fish. We then present a mathematical model, based on inferred functional interactions between fish, which accurately describes individual and group foraging of real fish. We show that these interactions allow fish to combine individual and social information to achieve near-optimal foraging efficiency and promote income equality within groups. We further show that the interactions that would maximize efficiency in these social foraging models depend on group size, but not on food distribution, and hypothesize that fish may adaptively pick the subgroup of neighbors they 'listen to' to determine their own behavior.

Pupil-linked phasic arousal predicts a reduction of choice bias across species and decision domains

Decisions are often made by accumulating ambiguous evidence over time. The brain's arousal systems are activated during such decisions. In previous work in humans, we found that evoked responses of arousal systems during decisions are reported by rapid dilations of the pupil and track a suppression of biases in the accumulation of decision-relevant evidence (de Gee et al., 2017). Here, we show that this arousal-related suppression in decision bias acts on both conservative and liberal biases, and generalizes from humans to mice, and from perceptual to memory-based decisions. In challenging sound-detection tasks, the impact of spontaneous or experimentally induced choice biases was reduced under high phasic arousal. Similar bias suppression occurred when evidence was drawn from memory. All of these behavioral effects were explained by reduced evidence accumulation biases. Our results point to a general principle of interplay between phasic arousal and decision-making.

Differentiating between integration and non-integration strategies in perceptual decision making

Many tasks used to study decision-making encourage subjects to integrate evidence over time. Such tasks are useful to understand how the brain operates on multiple samples of information over prolonged timescales, but only if subjects actually integrate evidence to form their decisions. We explored the behavioral observations that corroborate evidence-integration in a number of task-designs. Several commonly accepted signs of integration were also predicted by non-integration strategies. Furthermore, an integration model could fit data generated by non-integration models. We identified the features of non-integration models that allowed them to mimic integration and used these insights to design a motion discrimination task that disentangled the models. In human subjects performing the task, we falsified a non-integration strategy in each and confirmed prolonged integration in all but one subject. The findings illustrate the difficulty of identifying a decision-maker’s strategy and support solutions to achieve this goal.

Context-dependent operation of neural circuits underlies a navigation behavior in Caenorhabditis elegans

A free-living nematode Caenorhabditis elegans memorizes an environmental temperature and migrates toward the remembered temperature on a thermal gradient by switching movement up or down the gradient. How does the C. elegans brain, consisting of 302 neurons, achieve this memory-dependent thermotaxis behavior? Here, we addressed this question through large-scale single-cell ablation, high-resolution behavioral analysis, and computational modeling. We found that depending on whether the environmental temperature is below or above the remembered temperature, distinct sets of neurons are responsible to generate opposing motor biases, thereby switching the movement up or down the thermal gradient. Our study indicates that such a context-dependent operation in neural circuits is essential for flexible execution of animal behavior.

The nervous system evaluates environmental cues and adjusts motor output to ensure navigation toward a preferred environment. The nematode Caenorhabditis elegans navigates in the thermal environment and migrates toward its cultivation temperature by moving up or down thermal gradients depending not only on absolute temperature but on relative difference between current and previously experienced cultivation temperature. Although previous studies showed that such thermal context-dependent opposing migration is mediated by bias in frequency and direction of reorientation behavior, the complete neural pathways—from sensory to motor neurons—and their circuit logics underlying the opposing behavioral bias remain elusive. By conducting comprehensive cell ablation, high-resolution behavioral analyses, and computational modeling, we identified multiple neural pathways regulating behavioral components important for thermotaxis, and demonstrate that distinct sets of neurons are required for opposing bias of even single behavioral components. Furthermore, our imaging analyses show that the context-dependent operation is evident in sensory neurons, very early in the neural pathway, and manifested by bidirectional responses of a first-layer interneuron AIB under different thermal contexts. Our results suggest that the contextual differences are encoded among sensory neurons and a first-layer interneuron, processed among different downstream neurons, and lead to the flexible execution of context-dependent behavior.

Flexibility of Timescales of Evidence Evaluation for Decision Making

To understand the neural mechanisms that support decision making, it is critical to characterize the timescale of evidence evaluation. Recent work has shown that subjects can adaptively adjust the timescale of evidence evaluation across blocks of trials depending on context [1]. However, it's currently unknown if adjustments to evidence evaluation occur online during deliberations based on a single stream of evidence. To examine this question, we employed a change-detection task in which subjects report their level of confidence in judging whether there has been a change in a stochastic auditory stimulus. Using a combination of psychophysical reverse correlation analyses and single-trial behavioral modeling, we compared the time period over which sensory information has leverage on detection report choices versus confidence. We demonstrate that the length of this period differs on separate sets of trials based on what's being reported. Surprisingly, confidence judgments on trials with no detection report are influenced by evidence occurring earlier than the time period of influence for detection reports. Our findings call into question models of decision formation involving static parameters that yield a singular timescale of evidence evaluation and instead suggest that the brain represents and utilizes multiple timescales of evidence evaluation during deliberation.

Forward Behavioral Modeling of a Three-Way Amplitude Modulator-Based Transmitter Using an Augmented Memory Polynomial

Reconfigurable and multi-standard RF front-ends for wireless communication and sensor networks have gained importance as building blocks for the Internet of Things. Simpler and highly-efficient transmitter architectures, which can transmit better quality signals with reduced impairments, are an important step in this direction. In this regard, mixer-less transmitter architecture, namely, the three-way amplitude modulator-based transmitter, avoids the use of imperfect mixers and frequency up-converters, and their resulting distortions, leading to an improved signal quality. In this work, an augmented memory polynomial-based model for the behavioral modeling of such mixer-less transmitter architecture is proposed. Extensive simulations and measurements have been carried out in order to validate the accuracy of the proposed modeling strategy. The performance of the proposed model is evaluated using normalized mean square error (NMSE) for long-term evolution (LTE) signals. NMSE for a LTE signal of 1.4 MHz bandwidth with 100,000 samples for digital combining and analog combining are recorded as −36.41 dB and −36.9 dB, respectively. Similarly, for a 5 MHz signal the proposed models achieves −31.93 dB and −32.08 dB NMSE using digital and analog combining, respectively. For further validation of the proposed model, amplitude-to-amplitude (AM-AM), amplitude-to-phase (AM-PM), and the spectral response of the modeled and measured data are plotted, reasonably meeting the desired modeling criteria.

Model of rhythmic ball bouncing using a visually controlled neural oscillator

The present paper investigates the sensory-driven modulations of central pattern generator dynamics that can be expected to reproduce human behavior during rhythmic hybrid tasks. We propose a theoretical model of human sensorimotor behavior able to account for the observed data from the ball-bouncing task. The novel control architecture is composed of a Matsuoka neural oscillator coupled with the environment through visual sensory feedback. The architecture's ability to reproduce human-like performance during the ball-bouncing task in the presence of perturbations is quantified by comparison of simulated and recorded trials. The results suggest that human visual control of the task is achieved online. The adaptive behavior is made possible by a parametric and state control of the limit cycle emerging from the interaction of the rhythmic pattern generator, the musculoskeletal system, and the environment.NEW & NOTEWORTHY The study demonstrates that a behavioral model based on a neural oscillator controlled by visual information is able to accurately reproduce human modulations in a motor action with respect to sensory information during the rhythmic ball-bouncing task. The model attractor dynamics emerging from the interaction between the neuromusculoskeletal system and the environment met task requirements, environmental constraints, and human behavioral choices without relying on movement planning and explicit internal models of the environment.

Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making Task

Our daily interaction with the world is plagued of situations in which we develop expertise through self-motivated repetition of the same task. In many of these interactions, and especially when dealing with computer and machine interfaces, we must deal with sequences of decisions and actions. For instance, when drawing cash from an ATM machine, choices are presented in a step-by-step fashion and a specific sequence of choices must be performed in order to produce the expected outcome. But, as we become experts in the use of such interfaces, is it possible to identify specific search and learning strategies? And if so, can we use this information to predict future actions? In addition to better understanding the cognitive processes underlying sequential decision making, this could allow building adaptive interfaces that can facilitate interaction at different moments of the learning curve. Here we tackle the question of modeling sequential decision-making behavior in a simple human-computer interface that instantiates a 4-level binary decision tree (BDT) task. We record behavioral data from voluntary participants while they attempt to solve the task. Using a Hidden Markov Model-based approach that capitalizes on the hierarchical structure of behavior, we then model their performance during the interaction. Our results show that partitioning the problem space into a small set of hierarchically related stereotyped strategies can potentially capture a host of individual decision making policies. This allows us to follow how participants learn and develop expertise in the use of the interface. Moreover, using a Mixture of Experts based on these stereotyped strategies, the model is able to predict the behavior of participants that master the task.

Discrete modes of social information processing predict individual behavior of fish in a group

Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior. Here, we bring these two approaches together to predict individual swimming patterns of adult zebrafish in a group. We show that fish alternate between an "active" mode, in which they are sensitive to the swimming patterns of conspecifics, and a "passive" mode, where they ignore them. Using a model that accounts for these two modes explicitly, we predict behaviors of individual fish with high accuracy, outperforming previous approaches that assumed a single continuous computation by individuals and simple metric or topological weighing of neighbors' behavior. At the group level, switching between active and passive modes is uncorrelated among fish, but correlated directional swimming behavior still emerges. Our quantitative approach for studying complex, multimodal individual behavior jointly with emergent group behavior is readily extensible to additional behavioral modes and their neural correlates as well as to other species.

Modeling strategic use of human computer interfaces with novel hidden Markov models

Immersive software tools are virtual environments designed to give their users an augmented view of real-world data and ways of manipulating that data. As virtual environments, every action users make while interacting with these tools can be carefully logged, as can the state of the software and the information it presents to the user, giving these actions context. This data provides a high-resolution lens through which dynamic cognitive and behavioral processes can be viewed. In this report, we describe new methods for the analysis and interpretation of such data, utilizing a novel implementation of the Beta Process Hidden Markov Model (BP-HMM) for analysis of software activity logs. We further report the results of a preliminary study designed to establish the validity of our modeling approach. A group of 20 participants were asked to play a simple computer game, instrumented to log every interaction with the interface. Participants had no previous experience with the game's functionality or rules, so the activity logs collected during their naïve interactions capture patterns of exploratory behavior and skill acquisition as they attempted to learn the rules of the game. Pre- and post-task questionnaires probed for self-reported styles of problem solving, as well as task engagement, difficulty, and workload. We jointly modeled the activity log sequences collected from all participants using the BP-HMM approach, identifying a global library of activity patterns representative of the collective behavior of all the participants. Analyses show systematic relationships between both pre- and post-task questionnaires, self-reported approaches to analytic problem solving, and metrics extracted from the BP-HMM decomposition. Overall, we find that this novel approach to decomposing unstructured behavioral data within software environments provides a sensible means for understanding how users learn to integrate software functionality for strategic task pursuit.

Intermittent animal behavior: the adjustment-deployment dilemma

Intermittency is ubiquitous in animal behavior. We depict a coordination problem that is part of the more general structure of intermittent adaptation: the adjustment-deployment dilemma. It captures the intricate compromise between the time spent in adjusting a response and the time used to deploy it: The adjustment process improves fitness with time, but during deployment fitness of the solution decays as environmental conditions change. We provide a formal characterization of the dilemma, and solve it using computational methods. We find that the optimal solution always results in a high intermittency between adjustment and deployment around a non-maximal fitness value. Furthermore we show that this non-maximal fitness value is directly determined by the ratio between the exponential coefficient of the fitness increase during adjustment and that of its decay coefficient during deployment. We compare the model results with experimental data obtained from observation and measurement of intermittent behavior in animals. Among other phenomena, the model is able to predict the uneven distribution of average duration of search and motion phases found among various species such as fishes, birds, and lizards. Despite the complexity of the problem, it can be shown to be solved by relatively simple mechanisms. We find that a model of a single continuous-time recurrent neuron, with the same parametric configuration, is capable of solving the dilemma for a wide set of conditions. We finally hypothesize that many of the different patterns of intermittent behavior found in nature might respond to optimal solutions of complexified versions of the adjustment-deployment dilemma under different constraints.