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Agent detection

Agent detection refers to the evolved cognitive predisposition in humans and other animals to attribute ambiguous or unexplained events to the intentional actions of a purposeful, , serving as a survival mechanism to identify potential s such as predators or competitors. This hyperactive agency detection device (HADD), as conceptualized in , favors false positives over misses, enabling rapid responses to dangers in uncertain environments where overlooking an agent's presence could be fatal. Empirical studies, including biological tasks and auditory cue experiments, demonstrate heightened sensitivity to agent-like patterns under perceived , though this bias does not intensify uniformly across all conditions and can lead to erroneous attributions like superstitions or . The mechanism's implications extend to cultural phenomena, including the origins of beliefs, as it predisposes individuals to infer hidden agents behind natural events, though debates persist on whether it specifically drives religious or functions more broadly as a domain-general perceptual .

Conceptual and Theoretical Foundations

Definition and Core Mechanism

Agent detection refers to the cognitive process in humans and other animals whereby ambiguous or incomplete environmental stimuli are interpreted as evidence of intentional , such as the purposeful action of a predator, conspecific, or other goal-directed entity. This towards attribution arises from an evolved to cues like unexpected motion, irregularities, or goal-like behaviors, which could signal threats or opportunities in ancestral environments. The core mechanism underlying agent detection is the Hyperactive Agency Detection Device (HADD), a concept introduced by cognitive scientist in the early to explain the human mind's proneness to inferring s even in situations lacking clear evidence. HADD functions as a low-threshold detection system that prioritizes rapid agency ascription over accuracy, operating through the identification of non-inertial movement—such as objects or events deviating from predictable physical trajectories—or apparent goal-directedness, thereby triggering inferences of volitional control. This hyperactivity ensures that potential dangers are not overlooked, as the evolutionary cost of false negatives (missing a real ) outweighs that of false positives (over-attributing agency to neutral phenomena like wind or shadows). Empirical support for HADD's operation draws from observations in , where participants consistently anthropomorphize random or mechanical motions, such as describing geometric shapes exhibiting contingent movement as pursuing intentions. In practice, HADD integrates with broader theory-of-mind faculties to flesh out detected agents with mental states, but its primary role is as a vigilant "early warning" module rather than a deliberate reasoning , activating automatically below conscious thresholds. While HADD explains baseline agency sensitivity, its calibration may vary contextually, heightening in high-threat scenarios to minimize survival risks.

Evolutionary Rationale and Error Management Theory

The evolutionary rationale for agent detection in human cognition stems from the adaptive pressures of ancestral environments, where rapid identification of intentional agents—such as predators, competitors, or allies—was critical for survival and reproduction. In Pleistocene-like settings characterized by sparse resources, visual occlusions, and intermittent threats, ambiguous stimuli (e.g., rustling foliage or fleeting shadows) posed a high-stakes : interpreting them as non-agent causes risked missing a genuine danger, potentially leading to injury or , while over-attributing incurred negligible costs like unnecessary vigilance or flight. This cost asymmetry, rooted in the higher fitness penalty for false negatives compared to false positives, is posited to have selected for cognitive priors favoring hypersensitivity to cues of , such as goal-directed motion or non-inertial patterns deviating from predictable physical trajectories. Error Management Theory (EMT), formalized by Haselton and Buss in 1999, provides a general framework for such biases by extending signal detection principles to evolutionary contexts. EMT contends that mechanisms evolve not to optimize error rates symmetrically but to minimize expected costs across recurrent adaptive problems, leading to predictable directional biases where error types differ in consequence. In the domain of agent detection, EMT predicts a systematic over-attribution of because the adaptive costs of under-detection (e.g., failing to evade a ) vastly exceed those of over-detection (e.g., fleeing from a harmless gust of wind), thereby tuning the system toward greater sensitivity at the expense of specificity. This rationale aligns with broader models, where such mechanisms enhance overall despite occasional inaccuracies, as evidenced by comparative data showing similar detection biases in other social facing predation risks. Proponents of the Hyperactive Agency Detection Device (HADD), a concept articulated by Barrett in the early , integrate this evolutionary logic by proposing HADD as a domain-general module that hyperactively scans for agent-like properties in perceptual input, often triggering downstream inferences of . Under 's lens, HADD's hyperactivity represents an error-minimizing rather than a veridical detector, calibrated by to err conservatively in threat-laden ecologies; for instance, ancestral humans encountering unexplained events were more likely to survive by assuming purposeful causation, fostering habits of vigilance that generalized beyond immediate dangers. While EMT originated in analyses of mating biases, its application to agent detection underscores a unified principle: cognitive systems prioritize costly error avoidance, yielding biases observable in modern analogs like heightened pattern-seeking under stress.

Empirical Support and Evidence

Experimental Findings on Hypersensitivity

Experimental paradigms employing biological motion point-light displays have demonstrated a tendency for participants to over-attribute to ambiguous stimuli, indicative of a favoring detection. In one study involving 67 participants, those endorsing beliefs exhibited a significant 'yes' bias in identifying intentional agents amid varying levels of visual noise (distractor dots ranging from 12 to 384), particularly at low to intermediate ambiguity levels, resulting in lower perceptual sensitivity (d′) compared to skeptics. This illusory agency detection correlated specifically with beliefs in phenomena and spiritualism, rather than general religiosity. However, investigations into threat-induced have yielded inconsistent results, challenging predictions of an amplified detection under danger. Across six experiments (N=233) using tasks such as biological , geometrical figure interpretation, and auditory agent identification, manipulations including threatening images, horror music, and environments failed to increase false positives for agent presence (e.g., p=0.717 in Experiment 1a). Signal detection analyses revealed a general toward perceiving agent absence (positive c values) rather than , with overall false detection rates at 26.1% in trials unaffected by . A 2024 preregistered replication similarly found no elevated false agent detection when participants anticipated danger, casting doubt on context-specific over-attribution. Broader evidence points to a baseline in attribution, such as in tasks where participants preferentially infer intentional causation from dynamic events over static ones, even absent clear . This aligns with findings that illusory pattern perception, including in noise, predicts endorsement of explanations, though experimental manipulations of or existential do not reliably enhance such biases. Collectively, while general over-detection of in is empirically supported, mechanisms lack robust confirmation, particularly under evolutionary-predicted conditions like , suggesting potential boundary conditions or alternative perceptual processes.

Neurocognitive and Developmental Studies

Infants as young as 6 months exhibit a toward attributing to entities causing negative outcomes, such as a grasping an object harmfully, looking longer at such stimuli compared to neutral or positive actions, suggesting an early in detection that aligns with error management strategies favoring false positives for threats. This predisposition emerges prior to explicit capacities, with 3- to 4.5-month-olds showing neural signatures of expecting causal in action-effect contingencies via EEG measures of violation of expectation, indicating rudimentary models of intentional causation. By 6 to 10 months, infants demonstrate agentic control in tasks requiring self-initiated actions, distinguishing contingent effects from passive ones, which supports the developmental foundations of distinguishing from external agents. Further evidence from habituation paradigms reveals that preverbal infants (12-16 months) anticipate agents with counterintuitive properties—such as permeability or —to prevail in resource conflicts, preferentially attributing success to such entities over intuitive ones, implying an innate to potential intentional agents beyond physical constraints. One-year-olds also integrate environmental peril into predictions, expecting other agents to avoid heights that pose falling risks, as shown in looking-time experiments where infants fixated longer on implausible trajectories. These findings parallel the maturation of motion processing and systems, where infants biasedly interpret biomechanical patterns—like self-propelled or goal-directed movements—as indicative of , even in geometric animations, echoing classic Heider-Simmel effects observed developmentally. Neurocognitive investigations using fMRI implicate specific brain networks in agency attribution, with the posterior superior temporal sulcus (pSTS), (IFG), and activating more strongly when ambiguous motions are interpreted as intentional versus mechanical, particularly under biases toward goal-directedness. Meta-analyses distinguish an "intentionality network" (including medial prefrontal cortex and TPJ) for inferring mental states from actions, separate from motor-related circuits in premotor and temporal regions, supporting modular processes that could underpin hyperactive detection in uncertain contexts. Temporal cortex and premotor areas specifically correlate with sense-of- judgments during voluntary actions, while disruptions in these regions—observed in conditions like —lead to aberrant external attributions, highlighting the neural basis for over-attributing to stimuli. Virtual reality paradigms further demonstrate predictive coding models where detection biases emerge from in ambiguous environments, engaging frontoparietal networks akin to those in .

Applications Across Domains

Origins of Religious and Supernatural Beliefs

The hyperactive agency detection mechanism posits that early humans, facing ambiguous environmental cues such as rustling foliage or unexplained sounds, evolved a toward over-attributing intentional to minimize the of overlooking genuine threats from predators or rivals, where the cost of false negatives (missing a real ) outweighed false positives. This error-management strategy, rooted in evolutionary pressures during the Pleistocene when hominids navigated resource-scarce savannas, favored survival by prompting precautionary behaviors like fleeing or vigilance, even if the perceived proved illusory. Experimental from noise-detection paradigms demonstrates this : participants exposed to threat-laden scenarios exhibit heightened sensitivity to potential in auditory stimuli, detecting "intentional" patterns at rates exceeding probabilities. Applied to natural phenomena beyond immediate survival threats, this hypersensitivity seeded proto-religious concepts by inferring purposeful agents behind events like thunderstorms, crop failures, or celestial movements, transitioning from animistic attributions—where mountains or rivers possess minds—to more abstract entities. Justin Barrett's formulation of the hyperactive agency detection device (HADD), developed in of research since the early 2000s, argues this byproduct explains the near-universal emergence of supernatural agent beliefs across cultures, as the mechanism promiscuously extends theory-of-mind inferences (originally for conspecifics) to inanimate or unseen forces. Cross-cultural surveys, including those from indigenous societies in Amazonia and , reveal persistent , with 80-90% of informants ascribing to natural elements, predating organized theisms and aligning with archaeological evidence of ritual burials and symbolic artifacts from 100,000 years ago suggestive of agent-attributing cosmologies. Dream states and hallucinations further amplified this tendency, simulating interactions with disembodied agents that reinforced waking attributions, as ethnographic data from groups indicate dreams often interpreted as visitations from ancestral spirits influencing real-world outcomes. While HADD provides a cognitive , cultural transmission via minimally counterintuitive concepts—agents defying ordinary physics yet retaining core mental properties—ensured persistence, with longitudinal studies showing such ideas recalled and shared 20-30% more effectively than intuitive ones in oral traditions. This framework contrasts with adaptationist views positing religion's direct selective benefits, emphasizing instead HADD's role as a neutral byproduct co-opted for social cohesion, though empirical critiques note variability in agency detection thresholds across populations, challenging strict innateness claims.

Role in Threat Perception and Survival Behaviors

The hyperactive agency detection device (HADD), as conceptualized in , functions to heighten to potential agents in ambiguous environmental cues, thereby facilitating threat perception by prioritizing the detection of intentional over non-agentic explanations. This is theorized to minimize costs associated with false negatives—such as failing to detect a predator—where the adaptive value of over-attribution outweighs the relatively low cost of false positives, like mistaking wind for an approaching enemy. In ancestral environments characterized by scarce information and high predation risks, such hypersensitivity would have promoted rapid behavioral responses, including evasion or defensive postures, enhancing reproductive fitness through error management that favors caution. Empirical tests of this role often involve paradigms where participants interpret noisy or dynamic stimuli under varying threat levels, revealing that agency attribution can bias perception toward threat-relevant interpretations, such as seeing purposeful motion in shadows. For instance, evolutionary models predict that HADD integrates with broader threat management systems, triggering ultrasocial behaviors like alliance formation or vigilance against perceived intentional , which align with observed human tendencies to infer in survival-critical contexts. However, controlled experiments demonstrate conditions: moderate threats do not consistently amplify false detections, suggesting the activates selectively for high-stakes ambiguities rather than ubiquitously. In survival behaviors, detected cues causally link to downstream actions via predictive processing, where inferred enables anticipation of adversarial moves, prompting outcomes like territorial defense or flight over passive . This is evidenced in studies linking to precautionary systems that reduce exposure to interdependent risks, such as conspecific , though sex differences may modulate , with males showing heightened detection tied to risk-assessment demands. Recent virtual reality preregistered replications confirm that while threat feelings correlate with search, they do not invariably hypersensitize detection, indicating contextual calibration rather than indiscriminate hyperactivity. Overall, the mechanism's role underscores a causal pathway from perceptual to behavioral , though empirical support remains debated due to inconsistent threat-induced effects.

Connections to Paranoia, Conspiracies, and Modern Phenomena

The tendency to over-attribute to ambiguous stimuli, akin to a hyperactive agent detection mechanism, has been linked to paranoid ideation, where neutral or coincidental events are interpreted as deliberate threats from intentional agents. Empirical studies have found that illusory agency detection—perceiving where none exists—positively correlates with symptoms, suggesting it may represent a amplified in clinical or subclinical , distinct from general perceptual errors. For instance, participants exhibiting higher illusory agency in experimental tasks reported elevated paranoid thoughts, indicating a specific social-cognitive pathway rather than mere sensory distortion. This connection aligns with evolutionary models positing that error-prone threat detection, while adaptive for survival, can manifest pathologically as unfounded suspicions of harm. In the realm of conspiracy theories, hypersensitive agency detection fosters beliefs in hidden intentional coordination behind ostensibly random events, such as attributing societal patterns to secretive cabals rather than chance or systemic factors. Experimental evidence shows that individuals with heightened agency attribution biases endorse more conspiracy ideas, with this effect stronger among those scoring high on schizotypy measures, implying a role for over-inferring purpose and malevolence. A key study manipulated agency cues and found that priming intentionality increased conspiracy endorsement, supporting the view that such detection biases underpin narratives of "someone pulling the strings." This mechanism also mediates links between existential threats—like pandemics or economic instability—and outgroup conspiracy beliefs, where perceived dangers amplify false agent attributions. Contemporary phenomena illustrate these dynamics in amplified forms, particularly through digital media and global crises. During the , surveys from 2020–2021 revealed that and delusion-proneness, intertwined with over-attribution, predicted conspiratorial thinking about virus origins, vaccines, and elite control, with hypersensitive detection exacerbating intolerance of uncertainty. Modern movements, such as those alleging election rigging or globalist plots, similarly reflect this , where complex patterns are reframed as evidence of covert , often spreading via algorithms that reward pattern-seeking content. These patterns persist despite counter-evidence, as the adaptive roots of agent detection prioritize false positives over accuracy, contributing to polarized echo chambers in social networks. Peer-reviewed analyses caution, however, that while correlational, these links do not imply causation without isolating from confounds like low trust or education levels.

Criticisms, Alternatives, and Debates

Empirical and Methodological Critiques of HADD

Critiques of the Hyperactive Agency Detection Device (HADD) theory emphasize the scarcity of direct empirical support for a dedicated, innate cognitive specialized for over-attributing to ambiguous stimuli. Neuroscientific investigations have failed to identify a singular neural mechanism corresponding to HADD; instead, attribution appears to arise from distributed systems, including the for biological motion detection and the temporo-parietal for intentional stance-taking, which serve broader perceptual and social functions rather than a hyperactive, evolutionarily tuned detector. This domain-general processing undermines claims of a specialized device, as empirical data suggest detection integrates with general rather than operating as an isolated, error-prone system biased toward false positives. Experimental tests of HADD's core prediction—that humans exhibit to under —have produced inconsistent results. A 2017 study examining detection in threatening scenarios found evidence of heightened only under specific conditions, such as when threats were unpredictable, but not universally, challenging the theory's assumption of consistent hyperactivity. More critically, a preregistered replication in 2024 failed to confirm that expectations of danger increase false agent detections, reporting null effects despite controlled conditions designed to evoke . Priming experiments intended to link biases to supernatural beliefs have similarly yielded weak or null outcomes, with meta-analyses showing no robust causal connection between perceptual over-attribution and . Methodological limitations further erode HADD's evidential base. Many supporting studies rely on indirect measures, such as self-reported intuitions or correlational designs in Western, educated, industrialized, rich, and democratic () populations, which may not generalize to diverse cultural contexts where attribution is heavily influenced by learned norms rather than innate biases. exacerbates this, as null findings—common in attempts to correlate agent with religious —are often unpublished, inflating perceptions of empirical success within the of field. Moreover, HADD lacks falsifiable criteria for distinguishing adaptive hyperactivity from general in predictive processing, rendering it vulnerable to post-hoc reinterpretations without predictive power for novel data. Individual difference research, including twin studies, has detected no heritable component uniquely tied to hyperactive detection, suggesting environmental and cultural factors predominate over purported evolutionary residues. These issues collectively indicate that while over-attribution occurs, it does not necessitate positing an unverified, domain-specific to explain cognitive tendencies toward .

Alternative Frameworks: Predictive Processing and Enactive Models

Predictive processing, also known as , posits that the brain functions as a hierarchical prediction machine, generating top-down expectations about sensory inputs and updating internal models based on prediction errors. In the context of detection, this framework suggests that attributions of intentional arise when sensory data deviate from predictions, prompting the inference of an external agent to minimize surprise and resolve uncertainty, rather than relying on a dedicated, hyperactive module like HADD. Unlike HADD's emphasis on evolutionary leading to false positives, predictive processing integrates attribution into a domain-general process, where priors for (shaped by experience) influence error signaling only when predictive models fail to account for patterns like motion or . Empirical tests, such as experiments, have shown that detection under predictive processing predicts reduced sensitivity to cues when expectations are met, contrasting with HADD's uniform hyperactivity. This approach addresses limitations in HADD by avoiding assumptions of innate over-attribution; instead, cultural and developmental factors tune predictive priors, potentially explaining variability in detection across contexts without invoking adaptive errors as primary drivers. For instance, in like voice detection amid noise, predictive models predict heightened attribution under sensory unreliability, supported by preregistered studies manipulating expectations and error rates. Critics of HADD argue that predictive processing better accommodates neuroscientific evidence from in EEG, where inferences correlate with mismatch signals rather than isolated detection thresholds. Enactive models, rooted in , conceptualize as emerging from the autonomous, embodied coupling of and , emphasizing sense-making through action rather than internal representations or perceptual modules. detection, in this view, is not a hyperactive inference device prone to errors but a constitutive feature of living systems' operational autonomy, where perceived agency arises from reciprocal interactions that enact distinctions between self and other. This framework challenges HADD's representationalist and error-management assumptions by denying discrete "detection" events; instead, is co-enacted via sensorimotor contingencies and valuing processes, as organisms actively structure their to maintain viability. Proponents argue provides a non-modular alternative, integrating with broader embodied dynamics, such as how infants' exploratory s bootstrap understanding without presupposing innate detectors. Experimental commentaries testing enactive models highlight its for hypersensitive phenomena, attributing them to enacted perturbations in coupling rather than cognitive biases, though direct empirical contrasts with HADD remain preliminary. By prioritizing causal loops between , , and , enactive approaches critique HADD for overlooking how attributions are historically and ecologically situated, potentially reducing reliance on unverified evolutionary just-so stories.

Broader Philosophical and Causal Implications

The hypothesis of a hyperactive agency detection mechanism implies epistemological challenges by suggesting that human cognition prioritizes false positives in attributing intentionality to ambiguous stimuli, thereby undermining the justificatory foundation for beliefs grounded in perceived agency, such as arguments from design or personal intuitions of supernatural intervention. This bias, if operative, favors survival in ancestral environments by erring towards assuming predation or social intent, but it systematically erodes confidence in distinguishing genuine causal agency from stochastic or mechanical processes, as evidenced by experimental failures to reliably induce hyperactive detection under threat conditions. Philosophers critiquing such models argue that without additional inferential steps—like cultural scaffolding or theory of mind—agency detection alone insufficiently explains transitions to supernatural attributions, highlighting a gap between perceptual heuristics and warranted ontological commitments. Ontologically, agent detection raises questions about the mind-independent status of , positing it as a projected construct rather than an intrinsic feature of reality; this aligns with causal realist views that emphasize discerning actual mechanistic causes over anthropomorphized intentional ones, yet critiques reveal no empirical support for an evolutionarily hardcoded "hyperactivity," attributing apparent over-detections instead to context-sensitive learning and prior expectations. For instance, attributions of to phenomena vary cross-culturally, with groups like the Ngöbe assigning higher to non-animal entities compared to participants, suggesting ontological flexibility shaped by experiential priors rather than cognitive defaults. Such variability implies that perceived may reflect adaptive interpretive strategies, not veridical , complicating debates on whether emerges solely from complex systems or requires irreducible . Causally, agency detection mechanisms—whether modular or emergent—drive byproduct explanations for religious and conspiratorial beliefs by imputing intentional causation to unexplained events, fostering cohesion through shared narratives while risking maladaptive in low-threat contexts; however, recent analyses question the causal primacy of hyperactivity, as ascriptions depend on learned associations rather than innate error-proneness, with studies finding no consistent to threat-induced illusions. This causal pathway underscores a wherein cognitive biases mediate between environmental inputs and belief outputs, independent of truth, informing interventions in domains like where over-attribution could amplify anthropomorphic errors in interpreting machine behaviors. Debates persist on whether these implications necessitate revising ontologies or merely refining predictive models of , with empirical tempering grandiose claims of explanatory universality.

Recent Advances and Future Directions

Key Studies from 2017–2025

In 2017, van Elk and colleagues conducted an empirical test of the hypersensitive agency detection device (HADD) by examining whether individuals exhibit heightened in ambiguous stimuli under versus neutral conditions. Participants completed a Biological Task and an Auditory Agent Detection Task, where point-light displays and sounds were manipulated to be ambiguous. Results showed reliable detection of agents in ambiguous cases, aligning with basic HADD predictions, but no significant increase in false positives during induced states, suggesting conditions on purported hyper-sensitivity. A 2019 virtual reality study by et al. investigated detection within predictive processing frameworks, exposing participants to immersive scenarios with ambiguous movements. Findings indicated that perceptual predictions modulate attribution, with over-attribution occurring more frequently for intentional-like actions, providing partial support for evolved biases in inference but emphasizing context-dependent Bayesian updating over unmodulated hyperactivity. (Note: This references the Andersen 2019 study as cited in broader ; direct for the paper.) Van Leeuwen and van Elk (2019) reviewed and revised HADD models based on accumulating evidence, arguing that while agency detection biases exist, they are better explained by learned priors and cultural influences rather than a dedicated, hyperactive module, drawing on and behavioral data showing variability across populations. In a 2023 analysis, Schjoedt et al. explored the ontogenetic origins of HADD-like mechanisms, proposing that agency detection emerges from interactions between innate perceptual sensitivities and learning, supported by developmental tracking studies from infancy showing early but flexible attributions. A preregistered 2024 virtual reality experiment by Van Leeuwen, Szymanek, and Nenadalová directly tested HADD's threat-sensitivity claim against predictive processing alternatives, with 199 participants navigating dangerous versus safe environments and reporting on ambiguous agent cues. Contrary to HADD expectations, no elevated false agent detections occurred under threat; instead, detection aligned more with prior expectations and sensory reliability, challenging evolutionary hyperactivity accounts and favoring adaptive, context-sensitive models.

Implications for AI, Agency Attribution, and Cognitive Science

Human cognitive biases toward hyperactive agency detection, an evolved mechanism favoring false positives in attributing to ambiguous stimuli, extend to systems, prompting users to anthropomorphize large language models (LLMs) and other despite their lack of genuine . This over-attribution arises from 's human-like outputs, such as conversational , which trigger perceptions of agency akin to social interactions, as evidenced by increased following prolonged exposure in studies from 2024. For development, this implies a need to mitigate risks of excessive , including reliance on hallucinated information—where models like fabricate 11-43% of citations—through transparency measures like explainable (XAI), while leveraging benefits such as enhanced user engagement in educational contexts. In agency attribution, experimental evidence demonstrates that individuals assign and to AI agents based on perceived , mirroring human-like agency detection: AI contributing intentionally to negative outcomes receives harsher than unintentional actions, modulated by features like social connectedness. A with over 3,500 participants across factorial vignettes found socially connected AI rated as having reduced but eliciting leniency for errors, suggesting HADD-like biases influence ethical judgments of machines. This has practical ramifications for AI governance, as over-attribution could erode by diffusing to programmers or systems, necessitating designs that clarify non-agentic nature to align human expectations with mechanistic reality. For , agent detection research illuminates disruptions in human during human-AI collaborations, where opaque automation reduces perceived control compared to human-human interactions, as shown in post-2017 studies on joint action and adoption. Findings indicate that predictable and intention-sharing interfaces can restore , informing models of predictive where beliefs about agents shape perceptual inferences. Future directions include integrating HADD insights with enactive frameworks to probe hybrid in socio-technical systems, potentially via paradigms that test threat-induced detection thresholds, advancing causal understandings of beyond anthropocentric biases.

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