Mapping Husserlian phenomenology onto active inference

Phenomenology is the rigorous descriptive study of conscious experience. Recent attempts to formalize Husserlian phenomenology provide us with a mathematical model of perception as a function of prior knowledge and expectation. In this paper, we re-examine elements of Husserlian phenomenology through the lens of active inference. In doing so, we aim to advance the project of computational phenomenology, as recently outlined by proponents of active inference. We propose that key aspects of Husserl's descriptions of consciousness can be mapped onto aspects of the generative models associated with the active inference approach. We first briefly review active inference. We then discuss Husserl's phenomenology, with a focus on time consciousness. Finally, we present our mapping from Husserlian phenomenology to active inference.Comment: 10 page

Brief Training to Modify the Breadth of Attention Influences the Generalisation of Fear

Background: Generalisation of fear from dangerous to safe stimuli is an important process associated with anxiety disorders. However, factors that contribute towards fear (over)-generalisation remain poorly understood. The present investigation explored how attentional breadth (global/holistic and local/analytic) influences fear generalisation and, whether people trained to attend in a global vs. local manner show more or less generalisation. Methods: Participants (N = 39) were shown stimuli which comprised of large ‘global’ letters and smaller ‘local’ letters (e.g. an F comprised of As) and they either had to identify the global or local letter. Participants were then conditioned to fear a face by pairing it with an aversive scream (75% reinforcement schedule). Perceptually similar, but safe, faces, were then shown. Self-reported fear levels and skin conductance responses were measured. Results: Compared to participants in Global group, participants in Local group demonstrated greater fear for dangerous stimulus (CS +) as well as perceptually similar safe stimuli. Conclusions: Participants trained to attend to stimuli in a local/analytical manner showed higher magnitude of fear acquisition and generalisation than participants trained to attend in a global/holistic way. Breadth of attentional focus can influence overall fear levels and fear generalisation and this can be manipulated via attentional training

Feeling our place in the world: an active inference account of self-esteem

Self-esteem, the evaluation of one’s own worth or value, is a critical aspect of psychological well-being and mental health. In this paper, we propose an active inference account of self-esteem, casting it as a sociometer or an inferential capacity to interpret one’s standing within a social group. This approach allows us to explore the interaction between an individual’s self-perception and the expectations of their social environment.When there is a mismatch between these perceptions and expectations, the individual needs to adjust their actions or update their self-perception to better align with their current experiences. We also consider this hypothesis in relation with recent research on affective inference, suggesting that self-esteem enables the individual to track and respond to this discrepancy through affective states such as anxiety or positive affect. By acting as an inferential sociometer, self-esteem allows individuals to navigate and adapt to their social environment, ultimately impacting their psychological well-being and mental health

Sustainability under Active Inference

In this paper, we explore the known connection among sustainability, resilience, and well-being within the framework of active inference. Initially, we revisit how the notions of well-being and resilience intersect within active inference before defining sustainability. We adopt a holistic concept of sustainability denoting the enduring capacity to meet needs over time without depleting crucial resources. It extends beyond material wealth to encompass community networks, labor, and knowledge. Using the free energy principle, we can emphasize the role of fostering resource renewal, harmonious system–entity exchanges, and practices that encourage self-organization and resilience as pathways to achieving sustainability both as an agent and as a part of a collective. We start by connecting active inference with well-being, building on existing work. We then attempt to link resilience with sustainability, asserting that resilience alone is insufficient for sustainable outcomes. While crucial for absorbing shocks and stresses, resilience must be intrinsically linked with sustainability to ensure that adaptive capacities do not merely perpetuate existing vulnerabilities. Rather, it should facilitate transformative processes that address the root causes of unsustainability. Sustainability, therefore, must manifest across extended timescales and all system strata, from individual components to the broader system, to uphold ecological integrity, economic stability, and social well-being. We explain how sustainability manifests at the level of an agent and then at the level of collectives and systems. To model and quantify the interdependencies between resources and their impact on overall system sustainability, we introduce the application of network theory and dynamical systems theory. We emphasize the optimization of precision or learning rates through the active inference framework, advocating for an approach that fosters the elastic and plastic resilience necessary for long-term sustainability and abundance

Forgetting ourselves in flow: an active inference account of flow states and how we experience ourselves within them

Flow has been described as a state of optimal performance, experienced universally across a broad range of domains: from art to athletics, gaming to writing. However, its phenomenal characteristics can, at first glance, be puzzling. Firstly, individuals in flow supposedly report a loss of self-awareness, even though they perform in a manner which seems to evince their agency and skill. Secondly, flow states are felt to be effortless, despite the prerequisite complexity of the tasks that engender them. In this paper, we unpick these features of flow, as well as others, through the active inference framework, which posits that action and perception are forms of active Bayesian inference directed at sustained self-organisation; i.e., the minimisation of variational free energy. We propose that the phenomenology of flow is rooted in the deployment of high precision weight over (i) the expected sensory consequences of action and (ii) beliefs about how action will sequentially unfold. This computational mechanism thus draws the embodied cognitive system to minimise the ensuing (i.e., expected) free energy through the exploitation of the pragmatic affordances at hand. Furthermore, given the challenging dynamics the flow-inducing situation presents, attention must be wholly focussed on the unfolding task whilst counterfactual planning is restricted, leading to the attested loss of the sense of self-as-object. This involves the inhibition of both the sense of self as a temporally extended object and higher–order, meta-cognitive forms of self-conceptualisation. Nevertheless, we stress that self-awareness is not entirely lost in flow. Rather, it is pre-reflective and bodily. Our approach to bodily-action-centred phenomenology can be applied to similar facets of seemingly agentive experience beyond canonical flow states, providing insights into the mechanisms of so-called selfless experiences, embodied expertise and wellbeing

Benefiting from trial spacing without the cost of prolonged training: frequency, not duration, of trials with absent stimuli enhances perceived contingency

The statistical relation between two events influences the perception of how one event relates to the presence or absence of another. Interestingly, the simultaneous absence of both events, just like their mutual occurrence, is relevant for describing their contingency. In three experiments, we explored the relevance of coabsent events by varying the duration and frequency of trials without stimuli. We used a rapid trial streaming procedure and found that the perceived association between events is enhanced with increasing frequency of coabsent events, unlike the duration of coabsent events, which had little effect. These findings suggest ways in which the benefits of trial spacing, during which both events are absent, could be obtained without increasing total training time. Centrally, this can be done by frequent repeating of shortened coabsent events, each marked by a trial contextual cue. We discuss four potential accounts of how coabsent experience might be processed contributing to this effect: (a) contingency sensitivity, (b) testing effect, (c) reduced associative interference by the context, and (d) reduced encoding interference. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Designing Ecosystems of Intelligence from First Principles

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants -- what we call ''shared intelligence''. This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world -- also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing -- leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first -- and key -- step towards such an ecology.Comment: 23+18 pages, one figure, one six page appendi

A model of agential learning using active inference

Agential learning refers to the process of forming beliefs regarding one’s degree of control over actions and outcomes in their environment. We first provide an overview and evaluation of associative, statistical, and Bayesian models of agential learning. We then argue that the existing models have limitations in explaining the process of agential learning. Finally, we introduce an active inference account of agential learning, and present results from simulations. We propose that the active inference framework may provide a comprehensive model of agential learning describing three fundamental processes: (i) perception, (ii) learning, and (iii) action

Envisioning a culturally imaginative educational psychology

Culture has mostly been neglected in mainstream educational psychology research. In this paper, we argued for the need to cultivate a cultural imagination and provided seven key recommendations for conducting culturally imaginative research. We explained how these recommendations could prove useful in avoiding the two types of errors that trap cross-cultural researchers. The first type is the cultural attribution error which pertains to attributing any observed difference to culture even if culture is not the relevant factor. The second type is the cultural blind spot error which pertains to the failure to see how culture influences psycho-educational processes and outcomes. We proffered seven recommendations to avoid these twin pitfalls. We reviewed the papers published from 2006 to 2016 in four flagship educational psychology journals including the Journal of Educational Psychology, Contemporary Educational Psychology, Cognition and Instruction, and British Journal of Educational Psychology. Our review focused on how educational psychologists have studied culture over the past decade and how the published studies aligned with our seven recommendations. The content analysis indicated that only a small percentage of the articles dealt with culture, most of the studies drew on Western samples, and that almost all studies relied on an etic approach with very few studies using an emic bottom-up perspective. We ended with a justification for why a culturally imaginative educational psychology is urgently needed in an increasingly diverse world

Sustainability under Active Inference

In this paper, we explore the known connection among sustainability, resilience, and well-being within the framework of active inference. Initially, we revisit how the notions of well-being and resilience intersect within active inference before defining sustainability. We adopt a holistic concept of sustainability denoting the enduring capacity to meet needs over time without depleting crucial resources. It extends beyond material wealth to encompass community networks, labor, and knowledge. Using the free energy principle, we can emphasize the role of fostering resource renewal, harmonious system–entity exchanges, and practices that encourage self-organization and resilience as pathways to achieving sustainability both as an agent and as a part of a collective. We start by connecting active inference with well-being, building on existing work. We then attempt to link resilience with sustainability, asserting that resilience alone is insufficient for sustainable outcomes. While crucial for absorbing shocks and stresses, resilience must be intrinsically linked with sustainability to ensure that adaptive capacities do not merely perpetuate existing vulnerabilities. Rather, it should facilitate transformative processes that address the root causes of unsustainability. Sustainability, therefore, must manifest across extended timescales and all system strata, from individual components to the broader system, to uphold ecological integrity, economic stability, and social well-being. We explain how sustainability manifests at the level of an agent and then at the level of collectives and systems. To model and quantify the interdependencies between resources and their impact on overall system sustainability, we introduce the application of network theory and dynamical systems theory. We emphasize the optimization of precision or learning rates through the active inference framework, advocating for an approach that fosters the elastic and plastic resilience necessary for long-term sustainability and abundance