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The frame or relevance problem is a classic problem in cognitive science and philosophy. We attempt to resolve this problem by appealing to predictive processing, a growing theory of cognition. As such, it ought to explain one of the central processes of cognition, that is, how an agent context-sensitively determines relevance. Our solution begins by appealing to Bayesian prior probabilities, which intuitively reflect relevance for a predictive agent. However, prior probabilities are necessary but insufficient for solving the problem with predictive processing. We then turn to the broader predictive processing toolbox, leveraging the concepts of prediction, prediction error, and precision in order to explain relevance. This move reveals that the processes that optimize for prediction error minimization are crucial for realizing relevance. Although, they do not yet solve the entire problem, which also demands an agent select relevant actions, based on considerations about their consequences. By appealing to active inference, decision-making, and planning can be brought to bear on relevance, in addition to perceptual inference. With this final inclusion of action (as inference), we suggest predictive processing has the tools to comprehensively solve the problem of relevance. [ABSTRACT FROM AUTHOR]

Active inference under the Free Energy Principle has been proposed as an across-scales compatible framework for understanding and modelling behaviour and self-maintenance. Crucially, a collective of active inference agents can, if they maintain a group-level Markov blanket, constitute a larger group-level active inference agent with a generative model of its own. This potential for computational scale-free structures speaks to the application of active inference to self-organizing systems across spatiotemporal scales, from cells to human collectives. Due to the difficulty of reconstructing the generative model that explains the behaviour of emergent group-level agents, there has been little research on this kind of multi-scale active inference. Here, we propose a data-driven methodology for characterising the relation between the generative model of a group-level agent and the dynamics of its constituent individual agents. We apply methods from computational cognitive modelling and computational psychiatry, applicable for active inference as well as other types of modelling approaches. Using a simple Multi-Armed Bandit task as an example, we employ the new ActiveInference.jl library for Julia to simulate a collective of agents who are equipped with a Markov blanket. We use sampling-based parameter estimation to make inferences about the generative model of the group-level agent, and we show that there is a non-trivial relationship between the generative models of individual agents and the group-level agent they constitute, even in this simple setting. Finally, we point to a number of ways in which this methodology might be applied to better understand the relations between nested active inference agents across scales. [ABSTRACT FROM AUTHOR]

This article applies the thesis of the extended mind to ambient smart environments. These systems are characterised by an environment, such as a home or classroom, infused with multiple, highly networked streams of smart technology working in the background, learning about the user and operating without an explicit interface or any intentional sensorimotor engagement from the user. We analyse these systems in the context of work on the “classical” extended mind, characterised by conditions such as “trust and glue” and phenomenal transparency, and find that these conditions are ill-suited to describing our engagement with ambient smart environments. We then draw from the active inference framework, a theory of brain function which casts cognition as a process of embodied uncertainty minimisation, to develop a version of the extended mind grounded in a process ontology, where the boundaries of mind are understood to be multiple and always shifting. Given this more fluid account of the extended mind, we argue that ambient smart environments should be thought of as extended allostatic control systems, operating more or less invisibly to support an agent’s biological capacity for minimising uncertainty over multiple, interlocking timescales. Thus, we account for the functionality of ambient smart environments as extended systems, and in so doing, utilise a markedly different version of the classical thesis of extended mind. [ABSTRACT FROM AUTHOR]

Embodied cognition—the idea that mental states and processes should be understood in relation to one's bodily constitution and interactions with the world—remains a controversial topic within cognitive science. Recently, however, increasing interest in predictive processing theories among proponents and critics of embodiment alike has raised hopes of a reconciliation. This article sets out to appraise the unificatory potential of predictive processing, focusing in particular on embodied formulations of active inference. Our analysis suggests that most active-inference accounts invoke weak, potentially trivial conceptions of embodiment; those making stronger claims do so independently of the theoretical commitments of the active-inference framework. We argue that a more compelling version of embodied active inference can be motivated by adopting a diachronic perspective on the way rhythmic physiological activity shapes neural development in utero. According to this visceral afferent training hypothesis, early-emerging physiological processes are essential not only for supporting the biophysical development of neural structures but also for configuring the cognitive architecture those structures entail. Focusing in particular on the cardiovascular system, we propose three candidate mechanisms through which visceral afferent training might operate: (a) activity-dependent neuronal development, (b) periodic signal modeling, and (c) oscillatory network coordination. [ABSTRACT FROM AUTHOR]

We introduce a new software package for the Julia programming language, the library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to the growing research community using Julia, we re-implemented the pymdp library for Python. ActiveInference.jl is compatible with cutting-edge Julia libraries designed for cognitive and behavioural modelling, as it is used in computational psychiatry, cognitive science and neuroscience. This means that POMDP active inference models can now be easily fit to empirically observed behaviour using sampling, as well as variational methods. In this article, we show how ActiveInference.jl makes building POMDP active inference models straightforward, and how it enables researchers to use them for simulation, as well as fitting them to data or performing a model comparison. [ABSTRACT FROM AUTHOR]

This paper introduces a theory of mind that positions language as a cognitive tool in its own right for the optimization of biological fitness. I argue that human language reconstruction of reality results from biological memory and adaptation to uncertain environmental conditions for the reaffirmation of the Self-as-symbol. I demonstrate that pretrained language models, such as ChatGPT, lack embodied grounding, which compromises their ability to adequately model the world through language due to the absence of subjecthood and conscious states for event recognition and partition. At a deep level, I challenge the notion that the constitution of a semiotic Self relies on computational reflection, arguing against reducing human representation to data structures and emphasizing the importance of positing accurate models of human representation through language. This underscores the distinction between transformers as posthuman agents and humans as purposeful biological agents, which emphasizes the human capacity for purposeful biological adjustment and optimization. One of the main conclusions of this is that the capacity to integrate information does not amount to phenomenal consciousness as argued by Information Integration Theory. Moreover, while language models exhibit superior computational capacity, they lack the real consciousness providing them with multiscalar experience anchored in the physical world, a characteristic of human cognition. However, the paper anticipates the emergence of new in silico conceptualizers capable of defining themselves as phenomenal agents with symbolic contours and specific goals.

AI Alignment is a term used to summarize the aim of making artificial intelligence (AI) systems behave in line with human intentions and values. There has been little consideration in previous AI Alignment studies of the need for AI Alignment to be adaptive in order to contribute to the survival of human organizations in changing environments. This research gap is addressed here by defining human intentions and values in terms of survival biophysics: entropy, complexity, and adaptive behavior. Furthermore, although technology alignment has been a focus of studies for more than thirty years, there has been little consideration in AI Alignment studies of established resources for aligning technologies. Unlike the current focus of AI Alignment on addressing potential AI risks, technology alignment is generally focused on aligning with opportunities. Established resources include the critical realist philosophy of science, scientific theories, total quality management practices, technology alignment methods, engineering techniques, and technology standards. Here, these established resources are related to the alignment of different types of machine learning with different levels of human organizations. In addition, established resources are related to a well-known hypothetical extreme example of AI Misalignment, and to major constructs in the AI Alignment literature. Overall, it is argued that AI Alignment needs to be adaptive in order for human organizations to be able to survive in changing environments, and that established resources can facilitate Adaptive AI Alignment which addresses risks while focusing on opportunities.

Abstract Being able to robustly interact with and navigate a dynamic environment has been a long-standing challenge in intelligent transportation systems. Autonomous agents can use models that mimic the human brain to learn how to respond to other participants’ actions in the environment and proactively coordinate with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multimodality, and unobservant intents. Active inference may be defined as the Bayesian modeling of a brain with a biologically plausible model of the agent. Its primary idea relies on the free energy principle and the prior preference of the agent. It enables the agent to choose an action that leads to its preferred future observations. An exploring action-oriented model is introduced to address the inference complexity and solve the exploration–exploitation dilemma in unobserved environments. It is conducted by adapting active inference to an imitation learning approach and finding a theoretical connection between them. We present a multimodal self-awareness architecture for autonomous driving systems where the proposed techniques are evaluated on their ability to model proper driving behavior. Experimental results provide the basis for the intelligent driving system to make more human-like decisions and improve agent performance to avoid a collision.

Inspired by animal navigation strategies, we introduce a novel computational model to navigate and map a space rooted in biologically inspired principles. Animals exhibit extraordinary navigation prowess, harnessing memory, imagination, and strategic decision-making to traverse complex and aliased environments adeptly. Our model aims to replicate these capabilities by incorporating a dynamically expanding cognitive map over predicted poses within an active inference framework, enhancing our agent's generative model plasticity to novelty and environmental changes. Through structure learning and active inference navigation, our model demonstrates efficient exploration and exploitation, dynamically expanding its model capacity in response to anticipated novel un-visited locations and updating the map given new evidence contradicting previous beliefs. Comparative analyses in mini-grid environments with the clone-structured cognitive graph model (CSCG), which shares similar objectives, highlight our model's ability to rapidly learn environmental structures within a single episode, with minimal navigation overlap. Our model achieves this without prior knowledge of observation and world dimensions, underscoring its robustness and efficacy in navigating intricate environments. [ABSTRACT FROM AUTHOR]

Concept normativity is a prominent subject of inquiry in the philosophical literature on the nature of concepts. Concepts are said to be normative, in that the use of concepts to categorise is associated with an evaluation of the appropriateness of such categorisation measured against some objective external standard. Two broad groups of views have emerged in accounting for the normativity of concepts: a weaker view traces such normativity to the social practice in which the agent using the concept is embedded, while a stronger view traces such normativity to a first-person capacity of reflection. However, both views have drawbacks: the weaker view seems not to do justice to the basic sense of normativity associated with an individual agent using a concept, while the stronger view ties such normativity with the first-person conscious evaluation, which appears to be too strong. Here, we propose a different view of concepts using principles from the Active Inference framework. We reconceive concepts, defining them as Bayesian beliefs—that is, conditional probability distributions—that represent causes and contingencies in the world, their form grounded in the exchange between the agent and its environment. This allows us to present a different view on the source of normativity, with an emphasis on the structure of the agent itself as well as its interaction with the environment. On the Active Inference view, concepts are normative in that they are intrinsically connected to the self-evidencing nature of an agent, whose very structure implies an evaluation of the concepts it employs. [ABSTRACT FROM AUTHOR]

While neurofeedback represents a promising tool for neuroscience and a brain self-regulation approach to psychological rehabilitation, the field faces several problems and challenges. Current research has shown great variability and even failure among human participants in learning to self-regulate target features of brain activity with neurofeedback. A better understanding of cognitive mechanisms, psychological factors and neural substrates underlying self-regulation might help improve neurofeedback's scientific and clinical practices. This article reviews the current understanding of the neural mechanisms of brain self-regulation by drawing on findings from human and animal studies in neurofeedback, brain–computer/machine interfaces and neuroprosthetics. In this article, we look closer at the following topics: cognitive processes and psychophysiological factors affecting self-regulation, theoretical models and neural substrates underlying self-regulation, and finally, we provide an outlook on the outstanding gaps in knowledge and technical challenges. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'. [ABSTRACT FROM AUTHOR]

After more than 30 years since its inception, the utility of brain imaging for understanding and diagnosing mental illnesses is in doubt, receiving well-grounded criticisms from clinical practitioners. Symptom-based correlational approaches have struggled to provide psychiatry with reliable brain-imaging metrics. However, the emergence of computational psychiatry has paved a new path not only for understanding the psychopathology of mental illness but also to provide practical tools for clinical practice in terms of computational metrics, specifically computational phenotypes. However, these phenotypes still lack sufficient test–retest reliability. In this review, we describe recent works revealing that mind and brain-related computational phenotypes show structural (not random) variation over time, longitudinal changes. Furthermore, we show that these findings suggest that understanding the causes of these changes will improve the construct validity of the phenotypes with an ensuing increase in test–retest reliability. We propose that the active inference framework offers a general-purpose approach for causally understanding these longitudinal changes by incorporating brain imaging as observations within partially observable Markov decision processes. [ABSTRACT FROM AUTHOR]

AI Alignment is a term used to summarize the aim of making artificial intelligence (AI) systems behave in line with human intentions and values. There has been little consideration in previous AI Alignment studies of the need for AI Alignment to be adaptive in order to contribute to the survival of human organizations in changing environments. This research gap is addressed here by defining human intentions and values in terms of survival biophysics: entropy, complexity, and adaptive behavior. Furthermore, although technology alignment has been a focus of studies for more than thirty years, there has been little consideration in AI Alignment studies of established resources for aligning technologies. Unlike the current focus of AI Alignment on addressing potential AI risks, technology alignment is generally focused on aligning with opportunities. Established resources include the critical realist philosophy of science, scientific theories, total quality management practices, technology alignment methods, engineering techniques, and technology standards. Here, these established resources are related to the alignment of different types of machine learning with different levels of human organizations. In addition, established resources are related to a well-known hypothetical extreme example of AI Misalignment, and to major constructs in the AI Alignment literature. Overall, it is argued that AI Alignment needs to be adaptive in order for human organizations to be able to survive in changing environments, and that established resources can facilitate Adaptive AI Alignment which addresses risks while focusing on opportunities. [ABSTRACT FROM AUTHOR]

A central theme of theoretical neurobiology is that most of our cognitive operations require processing of discrete sequences of items. This processing in turn emerges from continuous neuronal dynamics. Notable examples are sequences of words during linguistic communication or sequences of locations during navigation. In this perspective, we address the problem of sequential brain processing from the perspective of active inference, which inherits from a Helmholtzian view of the predictive (Bayesian) brain. Underneath the active inference lies a generative model; namely, a probabilistic description of how (observable) consequences are generated by (unobservable) causes. We show that one can account for many aspects of sequential brain processing by assuming the brain entails a generative model of the sensed world that comprises central pattern generators, narratives, or well-defined sequences. We provide examples in the domains of motor control (e.g., handwriting), perception (e.g., birdsong recognition) through to planning and understanding (e.g., language). The solutions to these problems include the use of sequences of attracting points to direct complex movements—and the move from continuous representations of auditory speech signals to the discrete words that generate those signals. [ABSTRACT FROM AUTHOR]

Social neuroscience has often been criticized for approaching the investigation of the neural processes that enable social interaction and cognition from a passive, detached, third-person perspective, without involving any real-time social interaction. With the emergence of second-person neuroscience, investigators have uncovered the unique complexity of neural-activation patterns in actual, real-time interaction. Social cognition that occurs during social interaction is fundamentally different from that unfolding during social observation. However, it remains unclear how the neural correlates of social interaction are to be interpreted. Here, we leverage the active-inference framework to shed light on the mechanisms at play during social interaction in second-person neuroscience studies. Specifically, we show how counterfactually rich mutual predictions, real-time bodily adaptation, and policy selection explain activation in components of the default mode, salience, and frontoparietal networks of the brain, as well as in the basal ganglia. We further argue that these processes constitute the crucial neural processes that underwrite bona fide social interaction. By placing the experimental approach of second-person neuroscience on the theoretical foundation of the active-inference framework, we inform the field of social neuroscience about the mechanisms of real-life interactions. We thereby contribute to the theoretical foundations of empirical second-person neuroscience. [ABSTRACT FROM AUTHOR]

Asa result of the time lag in transmission, the data obtained by the sensor is delayed and does not reflect the state at the current moment. The effects of input delay are often overlooked in active inference (AIF), which may lead to significant deviations in state estimation and increased prediction errors, particularly when the system is subjected to a sudden external stimulus. In this paper, a theoretical framework of delayed feedback active inference (DAIF) is proposed to enhance the applicability of AIF to real systems. The probability model of DAIF is defined by incorporating a control distribution into that of AIF. The free energy of DAIF is defined as the sum of the quadratic state, sense, and control prediction error. A predicted state derived from previous states is defined and introduced as the expectation of the prior distribution of the real-time state. A proportional-integral (PI)-like control based on the predicted state is taken to be the expectation of DAIF preference control, whose gain coefficient is inversely proportional to the measurement accuracy variance. To adaptively compensate for external disturbances, a second-order inverse variance accuracy replaces the fixed sensory accuracy of preference control. The simulation results of the trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV) show that DAIF performs better than AIF in state estimation and disturbance resistance. [ABSTRACT FROM AUTHOR]

Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors—including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models. [ABSTRACT FROM AUTHOR]

Action is not just behavior. Without beliefs, desires, and intentions as reasons, action cannot be considered action. This is the central idea of the contemporary philosophy of action. But where does the root lie in explaining action by the reasons of belief, desire, and intention? What is the fundamental difference between mental causal states and the brain's neural-dynamic causal states as reasons for action? The literature of philosophy of mind offers various perspectives on these questions. The active inference theory, centered on the free energy principle (FEP), provides new resources for explaining mental causation in animals and humans. The physical explanation of action is limited to efficient causal explanations, while the intentional mental causation emphasized by Davidson and Searle is precisely the internalization of natural teleological causation in animals and humans. This internalization follows the FEP of self-organizing systems; it has evolved through long and complex evolutionary game processes. FEP is not only a telelolgical principle, but also the law of nature that provides the foundation for intentional explanations of action. The active inference that emerges through natural competition and selection follows a second-order natural law, FEP, providing a natural mechanism for resolving mind-body problems such as anomalous monism. [ABSTRACT FROM AUTHOR]

Being able to robustly interact with and navigate a dynamic environment has been a long-standing challenge in intelligent transportation systems. Autonomous agents can use models that mimic the human brain to learn how to respond to other participants' actions in the environment and proactively coordinate with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multimodality, and unobservant intents. Active inference may be defined as the Bayesian modeling of a brain with a biologically plausible model of the agent. Its primary idea relies on the free energy principle and the prior preference of the agent. It enables the agent to choose an action that leads to its preferred future observations. An exploring action-oriented model is introduced to address the inference complexity and solve the exploration–exploitation dilemma in unobserved environments. It is conducted by adapting active inference to an imitation learning approach and finding a theoretical connection between them. We present a multimodal self-awareness architecture for autonomous driving systems where the proposed techniques are evaluated on their ability to model proper driving behavior. Experimental results provide the basis for the intelligent driving system to make more human-like decisions and improve agent performance to avoid a collision. [ABSTRACT FROM AUTHOR]

Cognitive neuroscience and depth psychology confront each other on the models of perception-consciousness-thought. There is a parallelism between the relationship between access/phenomenal consciousness and the repressed/unrepressed unconscious. In order to survive, organisms must minimize the impact of environmental variations on homeostatic parameters, i.e., the surprise given by the deviation of unexpected events from those compatible with life. Friston theorized the principle of free energy, which places an upper limit on surprise, as opposed to the tendency to increase entropy. The brain is a predictive machine that anticipates change and constructs reality by interpreting perceptual data based on unconscious inferences to the best possible explanation based on data in memory and testing predictions on sensory data. Consciousness arises from the detection of homeostatic imbalances and from the adaptive response given by affective feelings. [ABSTRACT FROM AUTHOR]

The free energy principle is a formal theory of adaptive self-organising systems that emerged from statistical thermodynamics, machine learning and theoretical neuroscience and has since been translated into biologically plausible 'process theories' of cognition and behaviour, which fall under the banner of 'active inference'. Despite the promise this theory holds for theorising, research and practical applications in psychology and psychiatry, its impact on these disciplines has only now begun to bear fruit. The aim of this treatment is to consider the extent to which active inference has informed theoretical progress in psychology, before exploring its contributions to our understanding and treatment of psychopathology. Despite facing persistent translational obstacles, progress suggests that active inference has the potential to become a new paradigm that promises to unite psychology's subdisciplines, while readily incorporating the traditionally competing paradigms of evolutionary and developmental psychology. To date, however, progress towards this end has been slow. Meanwhile, the main outstanding question is whether this theory will make a positive difference through applications in clinical psychology, and its sister discipline of psychiatry. [ABSTRACT FROM AUTHOR]

The optimism bias is a cognitive bias where individuals overestimate the likelihood of good outcomes and underestimate the likelihood of bad outcomes. Associated with improved quality of life, optimism bias is considered to be adaptive and is a promising avenue of research for mental health interventions in conditions where individuals lack optimism such as major depressive disorder. Here we lay the groundwork for future research on optimism as an intervention by introducing a domain general formal model of optimism bias, which can be applied in different task settings. Employing the active inference framework, we propose a model of the optimism bias as high precision likelihood biased towards positive outcomes. First, we simulate how optimism may be lost during development by exposure to negative events. We then ground our model in the empirical literature by showing how the developmentally acquired differences in optimism are expressed in a belief updating task typically used to assess optimism bias. Finally, we show how optimism affects action in a modified two-armed bandit task. Our model and the simulations it affords provide a computational basis for understanding how optimism bias may emerge, how it may be expressed in standard tasks used to assess optimism, and how it affects agents’ decision-making and actions; in combination, this provides a basis for future research on optimism as a mental health intervention.

This paper advocates for considering disgust as a primary emotional system within Panksepp's Affective Neuroscience framework, which has the potential to improve the efficacy of psychotherapy with obsessive-compulsive disorder, hypochondriasis, and emetophobia. In 2007, Toronchuk and Ellis provided comprehensive evidence that DISGUST system, as they defined it, matched all Panksepp's criteria for a primary emotional system. A debate ensued and was not unambiguously resolved. This paper is an attempt to resume this discussion and supplement it with the data that accumulated since then on DISGUST's relationship with the immune system and the role of DISGUST dysregulation in psychopathology. We hope that renewed research interest in DISGUST has the potential to improve clinical efficacy with hard-to-treat conditions. [ABSTRACT FROM AUTHOR]

Active inference describes (Bayes-optimal) behaviour as being motivated by the minimisation of surprise of one's sensory observations, through the optimisation of a generative model (of the hidden causes of one's sensory data) in the brain. One of active inference's key appeals is its conceptualisation of precision as biasing neuronal communication and, thus, inference within generative models. The importance of precision in perceptual inference is evident—many studies have demonstrated the importance of ensuring precision estimates are correct for normal (healthy) sensation and perception. Here, we highlight the many roles precision plays in action, i.e., the key processes that rely on adequate estimates of precision, from decision making and action planning to the initiation and control of muscle movement itself. Thereby, we focus on the recent development of hierarchical, "mixed" models—generative models spanning multiple levels of discrete and continuous inference. These kinds of models open up new perspectives on the unified description of hierarchical computation, and its implementation, in action. Here, we highlight how these models reflect the many roles of precision in action—from planning to execution—and the associated pathologies if precision estimation goes wrong. We also discuss the potential biological implementation of the associated message passing, focusing on the role of neuromodulatory systems in mediating different kinds of precision. [ABSTRACT FROM AUTHOR]

The representation of intelligence is achieved by patterns of connections among neurons in brains and machines. Brains grow continuously, such that their patterns of connections develop through activity-dependent specification, with the continuing ontogenesis of individual experience. The theory of active inference proposes that the developmental organization of sentient systems reflects general processes of informatic self-evidencing, through the minimization of free energy. We interpret this theory to imply that the mind may be described in information terms that are not dependent on a specific physical substrate. At a certain level of complexity, self-evidencing of living (self-organizing) information systems becomes hierarchical and reentrant, such that effective consciousness emerges as the consequence of a good regulator. We propose that these principles imply that an adequate reconstruction of the computational dynamics of an individual human brain/mind is possible with sufficient neuromorphic computational emulation. [ABSTRACT FROM AUTHOR]

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It is widely believed that play and curiosity are key ingredients as children develop models of the world. There is also an emerging consensus that children are Bayesian learners who combine their structured prior beliefs with estimations of the likelihood of new evidence to infer the most probable model of the world. An influential school of thought within developmental psychology, rational constructivism, combines these two ideas to propose that children learn intuitive theories of how the world works in part by engaging in play activities that allow them to gather new information for testing their theories. There are still, however, at least two pieces missing from rational constructivist theories of development. First, rational constructivism has so far devoted little attention to explaining why children's preferred form of learning, play, feels so fun, enjoyable, and rewarding. Rational constructivism may suggest that children are curious and like to play because reducing uncertainty and learning better theories of the causal workings of the world is enjoyable. What remains unclear, however, is why reducing uncertainty in play is interesting, fun, and joyful, while doing so in other forms of learning can be frustrating or boring. Second, rational constructivism may have overlooked how children, during play, will take control of and manipulate their environment, sometimes in an effort to create ideal niches for surprise‐extraction, sometimes for developing strategies for making the world fit with their predictions. These missing elements from rational constructivism can be provided by understanding the contribution of play to development in terms of predictive processing, an influential framework in cognitive neuroscience that models many of the brain's cognitive functions as processes of model‐based, probabilistic prediction. [ABSTRACT FROM AUTHOR]