Implementation of Battery Digital Twin: Approach, Functionalities and Benefits

The concept of Digital Twin (DT) is widely explored in literature for different application fields because it promises to reduce design time, enable design and operation optimization, improve after-sales services and reduce overall expenses. While the perceived benefits strongly encourage the use of DT, in the battery industry a consistent implementation approach and quantitative assessment of adapting a battery DT is missing. This paper is a part of an ongoing study that investigates the DT functionalities and quantifies the DT-attributes across the life cycles phases of a battery system. The critical question is whether battery DT is a practical and realistic solution to meeting the growing challenges of the battery industry, such as degradation evaluation, usage optimization, manufacturing inconsistencies or second-life application possibility. Within the scope of this paper, a consistent approach of DT implementation for battery cells is presented, and the main functions of the approach are tested on a Doyle-Fuller-Newman model. In essence, a battery DT can offer improved representation, performance estimation, and behavioral predictions based on real-world data along with the integration of battery life cycle attributes. Hence, this paper identifies the efforts for implementing a battery DT and provides the quantification attribute for future academic or industrial research.

The politics of platform power in surveillance capitalism: A comparative case study of ride-hailing platforms in China and the United States

This article uses a comparative case study of two ride-hailing platforms—DiDi Chuxing in China and Uber in the United States—to explore the comparative politics of platform power in surveillance capitalism. Surveillance capitalism is an emerging economic system that translates human experiences into surveillance assets for behavioral predictions and modifications. Through this comparative study, we demonstrate how DiDi and Uber articulate their operational legitimacy for advancing their corporate interests and visions of datafication in the face of legal uncertainty. Although DiDi and Uber are both “sectoral platforms” in urban mobility with similar visions of datafication and infrastructuralization, we highlight that they deploy different discursive legitimation strategies. Our study shows that Uber adopts a “confrontational” strategy, while DiDi employs a “collaborative” strategy when they need to legitimize their data and business practices to the public and regulatory authorities. This study offers a comparative lens to examine the social and political dynamics of platform firms based in China and the United States and, therefore, contributes to understanding the various aspirational logic of platform thinking in different political contexts.

An association between prediction errors and risk-seeking: Theory and behavioral evidence

Reward prediction errors (RPEs) and risk preferences have two things in common: both can shape decision making behavior, and both are commonly associated with dopamine. RPEs drive value learning and are thought to be represented in the phasic release of striatal dopamine. Risk preferences bias choices towards or away from uncertainty; they can be manipulated with drugs that target the dopaminergic system. Based on the common neural substrate, we hypothesize that RPEs and risk preferences are linked on the level of behavior as well. Here, we develop this hypothesis theoretically and test it empirically. First, we apply a recent theory of learning in the basal ganglia to predict how RPEs influence risk preferences. We find that positive RPEs should cause increased risk-seeking, while negative RPEs should cause risk-aversion. We then test our behavioral predictions using a novel bandit task in which value and risk vary independently across options. Critically, conditions are included where options vary in risk but are matched for value. We find that our prediction was correct: participants become more risk-seeking if choices are preceded by positive RPEs, and more risk-averse if choices are preceded by negative RPEs. These findings cannot be explained by other known effects, such as nonlinear utility curves or dynamic learning rates.

Expectancy-based rhythmic entrainment as continuous Bayesian inference

When presented with complex rhythmic auditory stimuli, humans are able to track underlying temporal structure (e.g., a “beat”), both covertly and with their movements. This capacity goes far beyond that of a simple entrained oscillator, drawing on contextual and enculturated timing expectations and adjusting rapidly to perturbations in event timing, phase, and tempo. Previous modeling work has described how entrainment to rhythms may be shaped by event timing expectations, but sheds little light on any underlying computational principles that could unify the phenomenon of expectation-based entrainment with other brain processes. Inspired by the predictive processing framework, we propose that the problem of rhythm tracking is naturally characterized as a problem of continuously estimating an underlying phase and tempo based on precise event times and their correspondence to timing expectations. We present two inference problems formalizing this insight: PIPPET (Phase Inference from Point Process Event Timing) and PATIPPET (Phase and Tempo Inference). Variational solutions to these inference problems resemble previous “Dynamic Attending” models of perceptual entrainment, but introduce new terms representing the dynamics of uncertainty and the influence of expectations in the absence of sensory events. These terms allow us to model multiple characteristics of covert and motor human rhythm tracking not addressed by other models, including sensitivity of error corrections to inter-event interval and perceived tempo changes induced by event omissions. We show that positing these novel influences in human entrainment yields a range of testable behavioral predictions. Guided by recent neurophysiological observations, we attempt to align the phase inference framework with a specific brain implementation. We also explore the potential of this normative framework to guide the interpretation of experimental data and serve as building blocks for even richer predictive processing and active inference models of timing.

How to Improve Behavioral Parent and Teacher Training for Children with ADHD: Integrating Empirical Research on Learning and Motivation into Treatment

Attention deficit hyperactivity disorder [ADHD] is one of the most common psychiatric disorders of childhood with poor prognosis if not treated effectively. Recommended psychosocial evidence-based treatment for preschool and school-aged children is behavioral parent and teacher training [BPT]. The core elements of BPT are instrumental learning principles, i.e., reinforcement of adaptive and the ignoring or punishment of non-adaptive behaviors together with stimulus control techniques. BPT is moderately effective in reducing oppositional behavior and improving parenting practices; however, it does not reduce blinded ratings of ADHD symptoms. Also after training effects dissipate. This practitioner review proposes steps that can be taken to improve BPT outcomes for ADHD, based on purported causal processes underlying ADHD. The focus is on altered motivational processes (reward and punishment sensitivity), as they closely link to the instrumental processes used in BPT. Following a critical analysis of current behavioral treatments for ADHD, we selectively review motivational reinforcement-based theories of ADHD, including the empirical evidence for the behavioral predictions arising from these theories. This includes consideration of children’s emotional reactions to expected and unexpected outcomes. Next we translate this evidence into potential ADHD-specific adjustments designed to enhance the immediate and long-term effectiveness of BPT programs in addressing the needs of children with ADHD. This includes the use of remediation strategies for proposed deficits in learning not commonly used in BPT programs and cautions regarding the use of punishment. Finally, we address how these recommendations can be effectively transferred to clinical practice.

Optimal policy for attention-modulated decisions explains human fixation behavior

Traditional accumulation-to-bound decision-making models assume that all choice options are processed simultaneously with equal attention. In real life decisions, however, humans tend to alternate their visual fixation between individual items in order to efficiently gather relevant information [46, 23, 21, 12, 15]. These fixations also causally affect one’s choices, biasing them toward the longer-fixated item [38, 2, 25]. We derive a normative decision-making model in which fixating a choice item boosts information about that item. In contrast to previous models [25, 39], we assume that attention enhances the reliability of information rather than its magnitude, consistent with neurophysiological findings [3, 13, 29, 45]. Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation patterns and fixation-related choice biases seen in human decision-makers, and provides a Bayesian computational rationale for the fixation bias. This insight led to additional behavioral predictions that we confirmed in human behavioral data. Finally, we explore the consequences of changing the relative allocation of cognitive resources to the attended versus the unattended item, and show that decision performance is benefited by a more balanced spread of cognitive resources.

Knowledge Attributions and Behavioral Predictions

Recent work has shown that knowledge attributions affect how people think others should behave, more so than belief attributions do. This paper reports two experiments providing evidence that knowledge attributions also affect behavioral predictions more strongly than belief attributions do, and knowledge attributions facilitate faster behavioral predictions than belief attributions do. Thus, knowledge attributions play multiple critical roles in social cognition, guiding judgments about how people should and will behave.

Bayesian estimation on logarithmic scales as an explanation for spatiotemporal interferences with a tendency of deceleration

Spatial and temporal information processing interfere with each other. Kappa effect is a famous spatiotemporal interference, in which the estimated time between two lights increases as an increase of distance between the lights, showing a tendency of deceleration. A classical model attributes the interference to constant speeds and predicts a linear relation, whereas a slowness model attributes the interference to slow speeds and proposes the tendency is the result of the variance of stimuli locations. The present study developed a logarithmic version of the classical model and asserts that the tendency is the result of the Web-Fechner law. These hypotheses were tested in two time discrimination tasks by manipulating the variance of stimuli locations and distance between stimuli. The results demonstrate that estimated time was not modulated by the variance of stimuli locations, and increased as an increase of distance with a tendency of deceleration. The Bayesian model on logarithmic scales made more accurate behavioral predictions than the linear model; the estimated constant speed of the logarithmic Bayesian model was equal to the absolute threshold of speed; the strength of the Kappa effect positively correlated with the variability of time perception. Findings suggest that the interference in the Kappa effect is driven by slow speeds, the strength of the interference is influenced by the variability of time perception, and the tendency of deceleration is the result of the Weber-Fechner law. This Bayesian framework may be useful when applied in the field of time perception and other types of cross-dimensional interferences.

Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention?

Attention is a critical cognitive function, allowing humans to select, enhance, and sustain focus on information of behavioral relevance. Attention contains dissociable neural and psychological components. Nevertheless, some brain networks support multiple attentional functions. Connectome-based Predictive Models (CPM), which associate individual differences in task performance with functional connectivity patterns, provide a compelling example. A sustained attention network model (saCPM) successfully predicted performance for selective attention, inhibitory control, and reading recall tasks. Here we constructed a visual attentional blink (VAB) model (vabCPM), comparing its performance predictions and network edges associated with successful and unsuccessful behavior to the saCPM’s. In the VAB, attention devoted to a target often causes a subsequent item to be missed. Although frequently attributed to attentional limitations, VAB deficits may attenuate when participants are distracted or deploy attention diffusely. Participants (n=73; 24 males) underwent fMRI while performing the VAB task and while resting. Outside the scanner, they completed other cognitive tasks over several days. A vabCPM constructed from these data successfully predicted VAB performance. Strikingly, the network edges that predicted better VAB performance (positive edges) predicted worse selective and sustained attention performance, and vice versa. Predictions from the saCPM mirrored these results, with the network’s negative edges predicting better VAB performance. Furthermore, the vabCPM’s positive edges significantly overlapped with the saCPM’s negative edges, and vice versa. We conclude that these partially overlapping networks each have general attentional functions. They may indicate an individual’s propensity to diffusely deploy attention, predicting better performance for some tasks and worse for others.Significance statementA longstanding question in psychology and neuroscience is whether we have general capacities or domain-specific ones. For such general capacities, what is the common function? Here we addressed these questions using the attentional blink (AB) task and neuroimaging. Individuals searched for two items in a stream of distracting items; the second item was often missed when it closely followed the first. How often the second item was missed varied across individuals, which was reflected in attention networks. Curiously, the networks’ pattern of function that was good for the AB was bad for other tasks, and vice versa. We propose that these networks may represent not a general attentional ability, but rather the tendency to attend in a less focused manner.