Sparse Autoencoders (SAEs) decompose large language model representations into interpretable features, but how these features interact under uncertainty remains poorly understood. We introduce Feature Rivalry -- negatively correlated SAE feature pairs -- and study whether rivalry serves as a mechanistic signature of model uncertainty in Gemma-2-2B using Gemma Scope SAEs. Through a controlled within-domain experiment on PopQA split by response entropy, we find that high-entropy questions produce significantly stronger feature rivalry at layers 0 and 12 relative to low-entropy questions (p=5.3x10^-26 and p=5.8x10^-5 respectively), localizing uncertainty to specific processing stages in the residual stream. We then test whether rivalry is causally upstream of model outputs via activation steering along rivalry axes -- finding that steering along the rivalry direction (vec_A - vec_B) causes more output changes than random directions at low steering multipliers across 15 of 20 rival feature pairs. Finally, a per-prompt rivalry score derived from pairwise cosine similarities of active SAE feature decoder vectors predicts answer correctness (AUROC=0.689), approaching but not matching soft
This study investigates how professional writers' complex relationship with GenAI shapes their work practices and outcomes. Through a cross-sectional survey with writing professionals (n=403) in diverse roles, we show that collaboration and rivalry orientation are associated with differences in work practices and outcomes. Rivalry is primarily associated with relational crafting and skill maintenance. Collaboration is primarily associated with task crafting, productivity, and satisfaction, at the cost of long-term skill deterioration. Combination of the orientations (high rivalry and high collaboration) reconciles these differences, while boosting the association with the outcomes. Our findings argue for a balanced approach where high levels of rivalry and collaboration are essential to shape work practices and generate outcomes aimed at the long-term success of the job. We present key design implications on how to increase friction (rivalry) and reduce over-reliance (collaboration) to achieve a more balanced relationship with GenAI.
Strategic competitions in the real world, from wars to geopolitical rivalries, often involve coalitions competing against rival groups. These contests are not simple interactions between unified entities, but multilayered processes in which coalitions face external competition while dealing with internal conflicts over resources and strategy. Existing game-theoretic models typically treat inter-coalition rivalry and intra-coalition competition separately. This paper introduces the Compound Coalition-Attrition Game (CCAG), a unified framework that integrates a war of attrition between coalitions with a simultaneous war of attrition within each coalition. In this model, the endurance of a coalition in external competition is determined by the strategic choices of its members, who compete internally for shares of the outcome. We prove the nonexistence of pure-strategy equilibria and characterize the unique mixed-strategy Nash equilibrium. The analysis reveals feedback effects: external competition intensifies internal conflict, while internal discord weakens external performance. A case study compares traditional commodity markets, including gold, copper, and silver, with cryptocurren
When conflicting images are presented to either eye, binocular fusion is disrupted. Rather than experiencing a blend of both percepts, often only one eye's image is experienced, whilst the other is suppressed from awareness. Importantly, suppression is transient - the two rival images compete for dominance, with stochastic switches between mutually exclusive percepts occurring every few seconds with law-like regularity. From the perspective of dynamical systems theory, visual rivalry offers an experimentally tractable window into the dynamical mechanisms governing perceptual awareness. In a recently developed visual rivalry paradigm - tracking continuous flash suppression (tCFS) - it was shown that the transition between awareness and suppression is hysteretic, with a higher contrast threshold required for a stimulus to breakthrough suppression into awareness than to be suppressed from awareness. Here, we present an analytically-tractable model of visual rivalry that quantitatively explains the hysteretic transition between periods of awareness and suppression in tCFS. Grounded in the theory of neural dynamics, we derive closed-form expressions for the duration of perceptual domina
Designing experiments that systematically gather data from complex physical systems is central to accelerating scientific discovery. While Bayesian experimental design (BED) provides a principled, information-based framework that integrates experimental planning with probabilistic inference, the selection of utility functions in BED is a long-standing and active topic, where different criteria emphasize different notions of information. Although Kullback--Leibler (KL) divergence has been one of the most common choices, recent studies have proposed Wasserstein distance as an alternative. In this work, we first employ a toy example to illustrate an issue of Wasserstein distance - the value of Wasserstein distance of a fixed-shape posterior depends on the relative position of its main mass within the support and can exhibit false rewards unrelated to information gain, especially with a non-informative prior (e.g., uniform distribution). We then further provide a systematic comparison between these two criteria through a classical source inversion problem in the BED literature, revealing that the KL divergence tends to lead to faster convergence in the absence of model discrepancy, whi
Perceptual rivalry, where conflicting sensory information leads to alternating perceptions crucial for associated cognitive function, has attracted researcher's attention for long. Despite progresses being made, recent studies have revealed limitations and inconsistencies in our understanding across various rivalry contexts. We develop a unified physical framework, where perception undergoes a consecutive phase transition process encompassing different multi-state competitions. We reveal the underlying mechanisms of perceptual rivalry by identifying dominant switching paths among perceptual states and quantifying mean perceptual durations, switching frequencies, and proportions of different perceptions. We uncover the underlying nonequilibrium dynamics and thermodynamics by analyzing average nonequilibrium flux and entropy production rate, while associated time series irreversibility reflects the underlying nonequilibrium mechanism of perceptual rivalry and link thermodynamical results with neuro-electrophysiological experiments. Our framework provides a global and physical understanding of brain perception, which may go beyond cognitive science or psychology but embodies the conne
Brain perceptual rivalry, exemplified by auditory stream segregation of competing tones (A_, B__, ABA_), serves as a core mechanism of brain perception formation. While increasingly recognized as determining by neural connections rather than specific neural groups, the mechanism of brain perception remains uncertain. We demonstrate that auditory stream segregation arises from the topological structure of holistic neural pathways. By constructing a holistic pathway model using existing neurophysiological data, combining nonlinear neural dynamics and nonequilibrium physics, we uncover the biophysical mechanism of perceptual phase transitions from integrated (ABA_) to segregated streams (A_ or B_), as well as the mechanism of temporal dynamics, perceptual switching path, and attention regulation underlying these transitions. Further, we demonstrate how our framework reveals energy consumption of the auditory system and combines it with neuroelectrophysiology. Two psycho-acoustic experiments validate our predictions of perception alternation and attention modulation. Our framework provides a transformative perspective on how brain networks generate complex perceptual experiences, empha
Generative AI (GAI) technologies are disrupting professional writing, challenging traditional practices. Recent studies explore GAI adoption experiences of creative practitioners, but we know little about how these experiences evolve into established practices and how GAI resistance alters these practices. To address this gap, we conducted 25 semi-structured interviews with writing professionals who adopted and/or resisted GAI. Using the theoretical lens of Job Crafting, we identify four strategies professionals employ to reshape their roles. Writing professionals employed GAI resisting strategies to maximize human potential, reinforce professional identity, carve out a professional niche, and preserve credibility within their networks. In contrast, GAI-enabled strategies allowed writers who embraced GAI to enhance desirable workflows, minimize mundane tasks, and engage in new AI-managerial labor. These strategies amplified their collaborations with GAI while reducing their reliance on other people. We conclude by discussing implications of GAI practices on writers' identity and practices as well as crafting theory.
Recent advances in reinforcement learning with social agents have allowed such models to achieve human-level performance on specific interaction tasks. However, most interactive scenarios do not have a version alone as an end goal; instead, the social impact of these agents when interacting with humans is as important and largely unexplored. In this regard, this work proposes a novel reinforcement learning mechanism based on the social impact of rivalry behavior. Our proposed model aggregates objective and social perception mechanisms to derive a rivalry score that is used to modulate the learning of artificial agents. To investigate our proposed model, we design an interactive game scenario, using the Chef's Hat Card Game, and examine how the rivalry modulation changes the agent's playing style, and how this impacts the experience of human players in the game. Our results show that humans can detect specific social characteristics when playing against rival agents when compared to common agents, which directly affects the performance of the human players in subsequent games. We conclude our work by discussing how the different social and objective features that compose the artific
During Stereoscopic 3D (S3D) video transmission, one or both views can be affected by bit errors and packet losses caused by adverse channel conditions, delay or jitter. Typically, the Human Visual System (HVS) is incapable of aligning and fusing stereoscopic content if one view is affected by artefacts caused by compression, transmission and rendering with distorted patterns being perceived as alterations of the original which presents a shimmering effect known as binocular rivalry and is detrimental to a user's Quality of Experience (QoE). This study attempts to quantify the effects of binocular rivalry for stereoscopic videos. Existing approaches, in which one or more frames are lost in one or both views undergo error concealment, are implemented. Then, subjective testing is carried out on the error concealed 3D video sequences. The evaluations provided by these subjects were then combined and analysed using a standard Student t-test thus quantifying the impact of binocular rivalry and allowing the impact to be compared with that of monocular viewing. The main focus is implementing error-resilient video communication, avoiding the detrimental effects of binocular rivalry and imp
Biological and social scientists have long been interested in understanding how to reconcile individual and collective interests in iterated Prisoner's Dilemma. Many effective strategies have been proposed, and they are often categorized into one of two classes, `partners' and `rivals.' More recently, another class, `friendly rivals,' has been identified in longer-memory strategy spaces. Friendly rivals qualify as both partners and rivals: They fully cooperate with themselves, like partners, but never allow their co-players to earn higher payoffs, like rivals. Although they have appealing theoretical properties, it is unclear whether they would emerge in evolving population because most previous works focus on memory-one strategy space, where no friendly rival strategy exists. To investigate this issue, we have conducted large-scale evolutionary simulations in well-mixed and group-structured populations and compared the evolutionary dynamics between memory-one and memory-three strategy spaces. In a well-mixed population, the memory length does not make a major difference, and the key factors are the population size and the benefit of cooperation. Friendly rivals play a minor role b
Repeated interaction promotes cooperation among rational individuals under the shadow of future, but it is hard to maintain cooperation when a large number of error-prone individuals are involved. One way to construct a cooperative Nash equilibrium is to find a `friendly-rivalry' strategy, which aims at full cooperation but never allows the co-players to be better off. Recently it has been shown that for the iterated Prisoner's Dilemma in the presence of error, a friendly rival can be designed with the following five rules: Cooperate if everyone did, accept punishment for your own mistake, punish defection, recover cooperation if you find a chance, and defect in all the other circumstances. In this work, we construct such a friendly-rivalry strategy for the iterated $n$-person public-goods game by generalizing those five rules. The resulting strategy makes a decision with referring to the previous $m=2n-1$ rounds. A friendly-rivalry strategy for $n=2$ inherently has evolutionary robustness in the sense that no mutant strategy has higher fixation probability in this population than that of a neutral mutant. Our evolutionary simulation indeed shows excellent performance of the propos
On the basis of the general character and operation of the process of perception, a formalism is sought to mathematically describe the subjective or abstract/mental process of perception. It is shown that the formalism of orthodox quantum theory of measurement, where the observer plays a key role, is a broader mathematical foundation which can be adopted to describe the dynamics of the subjective experience. The mathematical formalism describes the psychophysical dynamics of the subjective or cognitive experience as communicated to us by the subject. Subsequently, the formalism is used to describe simple perception processes and, in particular, to describe the probability distribution of dominance duration obtained from the testimony of subjects experiencing binocular rivalry. Using this theory and parameters based on known values of neuronal oscillation frequencies and firing rates, the calculated probability distribution of dominance duration of rival states in binocular rivalry under various conditions is found to be in good agreement with available experimental data. This theory naturally explains an observed marked increase in dominance duration in binocular rivalry upon perio
When binocular rivalry is induced by opponent motion displays, perceptual reversals are often associated with changed oculomotor behaviour (Frassle et al., 2014; Fujiwara et al., 2017). Specifically, the direction of smooth pursuit phases in optokinetic nystagmus (OKN) typically corresponds to the direction of motion that dominates perceptual appearance at any given time. Here we report an improved analysis that continuously estimates perceived motion in terms of `cumulative smooth pursuit'. In essence, smooth pursuit segments are identified, interpolated where necessary, and joined probabilistically into a continuous record of `cumulative smooth pursuit' (i.e., a probability of eye position disregarding blinks, saccades, signal losses, and artefacts). The analysis is fully automated and robust in healthy, developmental, and patient populations. To validate reliability, we compare volitional reports of perceptual reversals in rivalry displays, and of physical reversals in non-rivalrous control displays. `Cumulative smooth pursuit' detects physical reversals and estimates eye velocity more accurately than existing methods do (Frassle et al., 2014). It also appears to distinguish dom
Supervised Semantic Differential (SSD) is a mixed quantitative-interpretive method that models how text meaning varies with continuous individual-difference variables by estimating a semantic gradient in an embedding space and interpreting its poles through clustering and text retrieval. SSD applies PCA before regression, but currently no systematic method exists for choosing the number of retained components, introducing avoidable researcher degrees of freedom in the analysis pipeline. We propose a PCA sweep procedure that treats dimensionality selection as a joint criterion over representation capacity, gradient interpretability, and stability across nearby values of K. We illustrate the method on a corpus of short posts about artificial intelligence written by Prolific participants who also completed Admiration and Rivalry narcissism scales. The sweep yields a stable, interpretable Admiration-related gradient contrasting optimistic, collaborative framings of AI with distrustful and derisive discourse, while no robust alignment emerges for Rivalry. We also show that a counterfactual using a high-PCA dimension solution heuristic produces diffuse, weakly structured clusters instead
This chapter examines the impact of the geopolitical rivalry between the United States and China on the prospects for inclusive innovation in artificial intelligence (AI) development. We explore three critical aspects of the American and Chinese legal infrastructure that significantly impact AI innovation: data privacy, intellectual property (IP rights), and export restrictions. Through this comparative analysis, we argue that, while China's legal environment may offer certain advantage in terms of access to training data and IP protection, the United States maintains superior resources by enforcing strict export controls on semiconductor chips, AI models, as well as outbound investments in these areas. This nuanced examination helps illuminate how each country's legal framework could influence the ultimate trajectory of AI race and how the technological rivalry has led to exclusionary rulemaking on a global scale.
Associative memory or content-addressable memory is an important component function in computer science and information processing, and at the same time a key concept in cognitive and computational brain science. Many different neural network architectures and learning rules have been proposed to model the brain's associative memory while investigating key component functions like figure-ground segmentation, perceptual reconstruction and rivalry. A less investigated but equally important capability of associative memory is prototype extraction where the training set comprises distorted prototype instances and the task is to recall the correct generating prototype given a new distorted instance. In this paper we benchmark associative memory function of seven different Hebbian learning rules employed in non-modular and modular recurrent networks with winner-take-all dynamics operating on moderately sparse binary patterns. We measure pattern storage and weight information capacity, prototype extraction capabilities, and sensitivity to correlations in data. The original additive Hebb rule comes out with worst capacity, covariance learning proves to be robust but with moderate capacity,
Export control is a policy and legal tool to protect national interests by regulating exports of sensitive goods and technology to foreign nations. It has become central to U.S.-China tech rivalry, especially in AI. Controls cover advanced chips, capital, personnel, and critical minerals for semiconductors. Since October 2022, the U.S. BIS has progressively tightened restrictions on advanced computing components to China. China responded with export curbs on critical minerals and filed a WTO complaint against the U.S. under GATT. This article argues that while export controls are strategic in U.S.-China AI competition, their long-term effectiveness is questionable. They often unintentionally boost China's self-reliance and R&D. Moreover, overly strict or arbitrary controls may violate WTO obligations, complicating dispute resolution and hindering AI progress. The study further examines legal implications of overusing export controls. It advocate for a restrained interpretation of security interests, arguing that commercial or dual-use AI models and semiconductors do not meet the security exception criteria under GATT Article XXI(b).
The rivalry between two football superstars Cristiano Ronaldo and Lionel Messi has always been a subject of extensive discussion. This study aimed to compare the level of consistency between the two players in scoring goals through 6 ways: right-footed kicks, left-footed kicks, penalty kicks, direct free kicks, long-range kicks, and headers. The data analyzed was the duration of time (minutes) each player took to score a goal in every match they played. The data was obtained from a football website called Transfermarkt.com. Competing Failure Modes (CFM) was used to measure the reliability of the two players in scoring goals based on those various ways. The results of CFM exploratory analysis showed that Ronaldo and Messi had the same level of consistency in scoring goals for more than 17 years of their professional football career. Both have been among most talented players in the modern football era with individual and team achievements that are far above other footballers around the world.
The Schelling model is a prototype for agent-based modeling in social systems. We produce a comprehensive analysis of Schelling model rule variants by classifying the space of macroscopic outcomes using phase diagrams. Among 54 rule variants, only 3 phase diagram classes are found, characterized by the number of phase transitions. This classification scheme is found to be robust to the use of sociological and percolation-inspired measures of segregation. The statistical and dynamic drivers of these transitions are elucidated by analyzing the roles of vision, movement criteria, vacancies, the initial state, and rivalry. Schelling's original step function dictating satisfaction is found to be pathological at high thresholds, producing coordination failures as satisfactory sites become increasingly rare. This comprehensive classification gives new insight into the drivers of transitions in the Schelling model and creates a basis for studying more complex Schelling-like models.