There is a certain excitement in vision science concerning the idea of applying the tools of bayesian decision theory to explain our perceptual capacities. Bayesian models are thought to be needed to explain how the inverse problem of perception is solved, and to rescue a certain constructivist and Kantian way of understanding the perceptual process. Anticlimactically, I argue both that bayesian outlooks do not constitute good solutions to the inverse problem, and that they are not constructivist (...) in nature. In explaining how visual systems derive a single percept from underdetermined stimulation, orthodox versions of bayesian accounts encounter a problem. The problem shows that such accounts need to be grounded in Natural Scene Statistics (NSS), an approach that takes seriously the Gibsonian insight that studying perception involves studying the statistical regularities of the environment in which we are situated. Additionally, I argue that bayesian frameworks postulate structures that hardly rescue a constructivist way of understanding perception. Except for percepts, the posits of bayesian theory are not representational in nature. bayesian perceptual inferences are not genuine inferences. They are biased processes that operate over nonrepresentational states. (shrink)

This chapter explores a way in which visual processing may involve innate constraints and attempts to show how such processing overcomes one enduring challenge to nativism. In particular, many challenges to nativist theories in other areas of cognitive psychology have focused on the later development of such abilities, and have argued that such development is in conflict with innate origins. Innateness, in these contexts, is seen as antidevelopmental, associated instead with static processes and principles. In contrast, certain perceptual models demonstrate (...) how the very same mental processes can both be innately specified and yet develop richly in response to experience with the environment. This process is entirely unmysterious, as shown in certain formal theories of visual perception, including those that appeal to spontaneous endogenous stimulation and those based on Bayesian inference. (shrink)

In recent years, Bayesian probability theory has emerged not only as a powerful tool for building computational theories of vision, but also as a general paradigm for studying human visual perception. This book provides an introduction to and critical analysis of the Bayesian paradigm. Leading researchers in computer vision and experimental vision science describe general theoretical frameworks for modeling vision, detailed applications to specific problems and implications for experimental studies of human perception. The book provides a (...) dialogue between different perspectives both within chapters, which draw on insights from experimental and computational work, and between chapters, through commentaries written by the contributors on each other's work. Students and researchers in cognitive and visual science will find much to interest them in this thought-provoking collection. (shrink)

We describe a formal framework for analyzing how statistical properties of natural environments and the process of natural selection interact to determine the design of perceptual and cognitive systems. The framework consists of two parts: a Bayesian ideal observer with a utility function appropriate for natural selection, and a Bayesian formulation of Darwin's theory of natural selection. Simulations of Bayesian natural selection were found to yield new insights, for example, into the co‐evolution of camouflage, color vision, and (...) decision criteria. The Bayesian framework captures and generalizes, in a formal way, many of the important ideas of other approaches to perception and cognition. (shrink)

Resource rationality may explain suboptimal patterns of reasoning; but what of “anti-Bayesian” effects where the mind updates in a direction opposite the one it should? We present two phenomena — belief polarization and the size-weight illusion — that are not obviously explained by performance- or resource-based constraints, nor by the authors’ brief discussion of reference repulsion. Can resource rationality accommodate them?

Shepard has argued that a universal law should govern generalization across different domains of perception and cognition, as well as across organisms from different species or even different planets. Starting with some basic assumptions about natural kinds, he derived an exponential decay function as the form of the universal generalization gradient, which accords strikingly well with a wide range of empirical data. However, his original formulation applied only to the ideal case of generalization from a single encountered stimulus to (...) a single novel stimulus, and for stimuli that can be represented as points in a continuous metric psychological space. Here we recast Shepard's theory in a more general Bayesian framework and show how this naturally extends his approach to the more realistic situation of generalizing from multiple consequential stimuli with arbitrary representational structure. Our framework also subsumes a version of Tversky's set-theoretic model of similarity, which is conventionally thought of as the primary alternative to Shepard's continuous metric space model of similarity and generalization. This unification allows us not only to draw deep parallels between the set-theoretic and spatial approaches, but also to significantly advance the explanatory power of set-theoretic models. Key Words: additive clustering; Bayesian inference; categorization; concept learning; contrast model; features; generalization; psychological space; similarity. (shrink)

The use of transcranial Electrical Stimulation in the modulation of cognitive brain functions to improve neuropsychiatric conditions has extensively increased over the decades. tES techniques have also raised new challenges associated with study design, stimulation protocol, functional specificity, and dose-response relationship. In this paper, we addressed challenges through the emerging methodology to investigate the dose-response relationship of High Definition-transcranial Direct Current Stimulation, identifying the role of negative valence in tinnitus perception. In light of the neurofunctional testable framework and tES (...) application, hypotheses were formulated to measure clinical and surrogate endpoints. We posited that conscious pairing adequately pleasant stimuli with tinnitus perception results in correction of the loudness misperception and would be reinforced by concurrent active HD-tDCS on the left Dorsolateral Prefrontal Cortex. The dose-response relationship between HD-tDCS specificity and the loudness perception is also modeled. We conducted a double-blind, randomized crossover pilot study with six recruited tinnitus patients. Accrued data was utilized to design a well-controlled adaptive seamless Bayesian dose-response study. The sample size and optimum interims were anticipated for adaptive decision-making about efficacy, safety, and single session dose parameters. Furthermore, preliminary pilot study results were sufficient to show a significant difference within the longitudinally detected self-report tinnitus loudness between before and under positive emotion induction. This study demonstrated a research methodology used to improve emotion regulation in tinnitus patients. In the projected method, positive emotion induction is essential for promoting functional targeting under HD-tDCS anatomical specificity to indicate the efficacy and facilitate the dose-finding process. The continuous updating of prior knowledge about efficacy and dose during the exploratory stage adapts the anticipated dose-response model. Consequently, the effective dose range to make superiority neuromodulation in correcting loudness misperception of tinnitus will be redefined. Highly effective dose adapts the study to a standard randomized trial and transforms it into the confirmatory stage in which active HD-tDCS protocol is compared with a sham trial. Establishing the HD-tDCS intervention protocols relying on this novel method provides reliable evidence for regulatory agencies to approve or reject the efficacy and safety. Furthermore, this paper supports a technical report for designing multimodality data-driven complementary investigations in emotion regulation, including EEG-driven neuro markers, Stroop-driven attention biases, and neuroimaging-driven brain network dynamics. (shrink)

Viewing the brain as an organ of approximate Bayesian inference can help us understand how it represents the self. We suggest that inferred representations of the self have a normative function: to predict and optimise the likely outcomes of social interactions. Technically, we cast this predict-and-optimise as maximising the chance of favourable outcomes through active inference. Here the utility of outcomes can be conceptualised as prior beliefs about final states. Actions based on interpersonal representations can therefore be understood as (...) minimising surprise – under the prior belief that one will end up in states with high utility. Interpersonal representations thus serve to render interactions more predictable, while the affective valence of interpersonal inference renders self-perception evaluative. Distortions of self-representation contribute to major psychiatric disorders such as depression, personality disorder and paranoia. The approach we review may therefore operationalise the study of interpersonal representations in pathological states. (shrink)

Philosophy and Phenomenological Research, Volume 104, Issue 2, Page 343-363, March 2022.

Moral hypocrisy is typically viewed as an ethical accusation: Someone is applying different moral standards to essentially identical cases, dishonestly claiming that one action is acceptable while otherwise equivalent actions are not. We suggest that in some instances the apparent logical inconsistency stems from different evaluations of a weak argument, rather than dishonesty per se. Extending Corner, Hahn, and Oaksford's (2006) analysis of slippery slope arguments, we develop a Bayesian framework in which accusations of hypocrisy depend on inferences of (...) shared category membership between proposed actions and previous standards, based on prior probabilities that inform the strength of competing hypotheses. Across three experiments, we demonstrate that inferences of hypocrisy increase as perceptions of the likelihood of shared category membership between precedent cases and current cases increase, that these inferences follow established principles of category induction, and that the presence of self-serving motives increases inferences of hypocrisy independent of changes in the actions themselves. Taken together, these results demonstrate that Bayesian analyses of weak arguments may have implications for assessing moral reasoning. (shrink)

Perception can’t have disjunctive content. Whereas you can think that a box is blue or red, you can’t see a box as being blue or red. Based on this fact, I develop a new problem for the ambitious predictive processing theory, on which the brain is a machine for minimizing prediction error, which approximately implements Bayesian inference. I describe a simple case of updating a disjunctive belief given perceptual experience of one of the disjuncts, in which Bayesian (...) inference and predictive coding pull in opposite directions, with the former implying that one’s confidence in the belief should increase, and the latter implying that it should decrease. Thus, predictive coding fails to approximately implement Bayesian inference across the interface between belief and perception. (shrink)

The success of the Bayesian approach to perception suggests probabilistic perceptual representations. But if perceptual representation is probabilistic, why doesn't normal conscious perception reflect the full probability distributions that the probabilistic point of view endorses? For example, neurons in MT/V5 that respond to the direction of motion are broadly tuned: a patch of cortex that is tuned to vertical motion also responds to horizontal motion, but when we see vertical motion, foveally, in good conditions, it does not (...) look at all horizontal. This article argues that the best Bayesian approach to this problem does not require probabilistic representation. (shrink)

Rational individuals who perceive information sequentially are confronted to cognitive dissonance and dynamic uncertainty in a way that sets a natural limit to the ex post efficiency of their choices. From the normative perspective which ignores this dynamic uncertainty, their rationality seems limited. Sequential perception is assumed in a model of Bayesian revision of the contingent preference in a repeated choice. This model predicts both the cognitive dissonance phenomenon studied by Festinger and the formation of stable habits. It (...) also shows how an ordinary man can be rational with bounded informational and computational abilities and, for the same reasons, give a disproportionate weight to impressions without any normative value in his own choices and judgments. Sequential perception and cognitive consistency offer a credible alternative to the cognitive bias paradigm for predicting anomalies in choice and game behavior.Des individus logiques percevant l’information de manière séquentielle sont soumis à une dissonance cognitive et une incertitude dynamique qui limitent naturellement l’efficacité ex post de leurs choix. Du point de vue normatif, qui ignore cette incertitude dynamique, leur rationalité apparaît limitée. L’hypothèse de perception séquentielle est introduite dans un modèle de révision bayésienne de la préférence contingente lors d’un choix répété. Ce modèle prédit à la fois le phénomène de dissonance cognitive étudié par Festinger et la formation d’une habitude stable. Il montre aussi comment un homme ordinaire peut être rationnel sans disposer de capacités d’information et de calcul démesurées et, pour les mêmes raisons, subir l’influence disproportionnée d’impressions dénuées de valeur normative dans les choix ou les jugements qu’il porte. La perception séquentielle et la cohérence cognitive offrent une alternative crédible au paradigme des biais cognitifs pour expliquer tous les comportements humains qui réfutent la théorie normative des choix et des jeux. (shrink)

Predictive approaches to the mind claim that perception, cognition, and action can be understood in terms of a single framework: a hierarchy of Bayesian models employing the computational strategy of predictive coding. Proponents of this view disagree, however, over the extent to which perception is direct on the predictive approach. I argue that we can resolve these disagreements by identifying three distinct notions of perceptual directness: psychological, metaphysical, and epistemological. I propose that perception is plausibly construed (...) as psychologically indirect on the predictive approach, in the sense of being constructivist or inferential. It would be wrong to conclude from this, however, that perception is therefore indirect in a metaphysical or epistemological sense on the predictive approach. In the metaphysical case, claims about the inferential properties of constructivist perceptual mechanisms are consistent with both direct and indirect solutions to the metaphysical problem of perception (e.g. naïve realism, representationalism, sense datum theory). In the epistemological case, claims about the inferential properties of constructivist perceptual mechanisms are consistent with both direct and indirect approaches to the justification of perceptual belief. In this paper, I demonstrate how proponents of the predictive approach have conflated these distinct notions of perceptual directness and indirectness, and I propose alternative strategies for developing the philosophical consequences of the approach. (shrink)

It is usually taken as given that consciousness involves superior or more elaborate forms of information processing. Contemporary models equate consciousness with global processing, system complexity, or depth or stability of computation. This is in stark contrast with the powerful philosophical intuition that being conscious is more than just having the ability to compute. I argue that it is also incompatible with current empirical findings. I present a model that is free from the strong assumption that consciousness predicts superior performance. (...) The model is based on Bayesian decision theory, of which signal detection theory is a special case. It reflects the fact that the capacity for perceptual decisions is fundamentally limited by the presence and amount of noise in the system. To optimize performance, one therefore needs to set decision criteria that are based on the behaviour, i.e. the probability distributions, of the internal signals. One important realization is that the knowledge of how our internal signals behave statistically has to be learned over time. Essentially, we are doing statistics on our own brain. This ‘higherorder’ learning, however, may err, and this impairs our ability to set and maintain optimal criteria for perceptual decisions, which I argue is central to perception consciousness. I outline three possibilities of how conscious perception might be affected by failures of ‘higher-order’ representation. These all imply that one can have a dissociation between consciousness and performance. This model readily explains blindsight and hallucinations in formal terms, and is beginning to receive direct empirical support. I end by discussing some philosophical implications of the model. (shrink)

ABSTRACTRational agents have consistent beliefs. Bayesianism is a theory of consistency for partial belief states. Rational agents also respond appropriately to experience. Dogmatism is a theory of how to respond appropriately to experience. Hence, Dogmatism and Bayesianism are theories of two very different aspects of rationality. It's surprising, then, that in recent years it has become common to claim that Dogmatism and Bayesianism are jointly inconsistent: how can two independently consistent theories with distinct subject matter be jointly inconsistent? In this (...) essay I argue that Bayesianism and Dogmatism are inconsistent only with the addition of a specific hypothesis about how the appropriate responses to perceptual experience are to be incorporated into the formal models of the Bayesian. That hypothesis isn't essential either to Bayesianism or to Dogmatism, and so Bayesianism and Dogmatism are jointly consistent. That leaves the matter of how experiences and credences are related, a... (shrink)

Shows how Bayesian thinking should make democratic outcomes so rationally compelling. Bayes's formula provides a mathematical expression for specifying exactly how we ought rationally to update our a priori beliefs in light of subsequent evidence, and the proposal is that voters are modelled in like fashion: votes, let us suppose, constitute ‘reports’ of the voter's experiences and perceptions; further suppose that voters accord ‘evidentiary value’ to the reports they receive from one another through those votes; and further suppose that (...) voters are rational, and that part and parcel of their being rational is being prepared to revise their opinions in light of further evidence. In this process, each of us treats our own experiences and perceptions as one source of evidence, and regards our own report as right; in that sense, we are perfectly sincere when we vote in a particular way, although we also acknowledge that our own experiences and perspectives are particular and peculiar, and hence our own perceptions are themselves inconclusive; because of that, voters striving to behave rationally should sincerely want to adjust their a priori beliefs in the light of all other experiences and perceptions that are reported at an election. Bayesian updating of that sort may well lead people who started out believing one way to end up believing the opposite way, just so long as sufficiently many votes‐cum‐reports point in that different direction; in other words, Bayesian reasoning can, and in politically typical cases ought to, provide people with a compelling reason to accede to the majority verdict. In this way, Bayesianism ‘rationalizes’ majority rule in a pretty strong sense; indeed if anything, it underwrites majoritarianism too strongly. (shrink)

In a Bayesian brain, every perceptual decision will take into account internal priors as well as new incoming evidence. A reality monitoring system—eventually providing the agent with a subjective sense of reality avoids them being confused about whether our experience is perceptual or imagined. Yet not all confusions we experience mean that we wonder whether we may be imagining: some confused experiences feel clearly perceptual but still feel not right. What happens in such confused perceptions, and can the (...) class='Hi'>Bayesian brain explain this kind of confusion? In this paper, we offer a characterisation of perceptual confusion and argue that it requires our subjective sense of reality to be a composite of several subjective markers, including a categorical one that can clearly identify an experience as perceptual and connect us to reality. Our composite account makes new predictions regarding the robustness, the non-linear development and the possible breakdowns of the sense of reality in perception. (shrink)

I present a framework for modeling memory, retrieval, perception, and their interactions. Recent versions of the models were inspired by Bayesian induction: We chose models that make optimal decisions conditioned on a memory/perceptual system with inherently noisy storage and retrieval. The resultant models are, fortunately, largely consistent with my models dating back to the 1960s, and are therefore natural successors. My recent articles have presented simplified models in order to focus on particular applications. This article takes a larger (...) perspective and places the individual models in a more global framework. I will discuss (1) the storage of episodic traces, the accumulation of these into knowledge (e.g., lexical/semantic traces in the case of words), and the changes in knowledge caused by learning; (2) the retrieval of information from episodic memory and from general knowledge; (3) decisions concerning storage, retrieval, and responding. Examples of applications include episodic recognition and cued and free recall, perceptual identification (naming, yes–no and forced‐choice), lexical decision, and long‐term and short‐term priming. (shrink)

This article investigates the predictors of coping willingness among citizens exposed to coastal flooding. We focus especially on how place identity, perceived self-efficacy, anxiety-state and coastal flooding risk perception shape both active and passive coping willingness. Data were obtained from different areas at risk of coastal flooding located in France. The sample is composed of 315 adult participants (mean age = 47; SD = 15). We observe a direct relation between risk perception and active coping willingness. The model (...) did not confirm our expectations that a higher perceived self-efficacy would increase active coping willingness. Concerning passive coping strategies, the model reflects our expectations that a higher anxiety-state increases passive coping willingness, and that place identity acts as a mediator and reinforce the relation between anxiety-state and passive coping willingness. To our knowledge this is an unprecedented model that suggests the importance of anxiety-state and place identity in the study of coastal risks. Moreover, it suggests that place identity is reinforced when the living place is threatened and encourages the willingness to use passive coping strategies. Model fit indices suggest the good fit of our model and Bayesian model comparison reveals a very strong evidence of the better fit of this model compared to its saturated and independent equivalents. (shrink)

Coastal environments are widely recognized as valuable public health resources and therapeutic landscapes. However, limited research has examined how specific coastal interactions that foster close connections with nature influence health outcomes. This study investigates the relationship between the frequency of engaging in high-nature-interaction coastal activities―e.g., beach walking, wildlife spotting, water sports, mountain biking, spending time on the beach, beach sports, watching the sunset, seagoing, and shell collecting― and health outcomes among visitors to the Belgian coast. Using a dataset of 1,939 (...) responses, we analyze how the frequencies of these activities over the past year are associated with self-reported mental and general health. Grounded in the Granular Interaction Thinking Theory (GITT) and employing Bayesian GITT Framework analytics, the study identifies distinct patterns in these associations. While frequent engagement in activities, e.g., beach walking and beach sports, is positively linked to both mental and general health, spending time on the beach and shell collecting are unexpectedly associated with negative health outcomes. Watching the sunset is negatively associated with general health, while mountain biking and water sports show negative associations with mental health. In contrast, wildlife spotting exhibits a positive association with mental well-being. These findings highlight the complex interplay of environmental factors, visitor perceptions, and biophysical-psychological interactions in shaping health outcomes. The study emphasizes the need for targeted coastal management strategies to enhance the therapeutic benefits of blue spaces and calls for further research to elucidate the mechanisms underlying these associations. (shrink)

The expanding integration of artificial intelligence (AI) in various aspects of society makes the infosphere around us increasingly complex. Humanity already faces many obstacles trying to have a better understanding of our own minds, but now we have to continue finding ways to make sense of the minds of AI. The issue of AI’s capability to have independent thinking is of special attention. When dealing with such an unfamiliar concept, people may rely on existing human properties, such as survival desire, (...) to make assessments. Employing information-processing-based Bayesian Mindsponge Framework (BMF) analytics on a dataset of 266 residents in the United States, we found that the more people believe that an AI agent seeks continued functioning, the more they believe in that AI agent’s capability of having a mind of its own. Moreover, we also found that the above association becomes stronger if a person is more familiar with personally interacting with AI. This suggests a directional pattern of value reinforcement in perceptions of AI. As the information processing of AI becomes even more sophisticated in the future, it will be much harder to set clear boundaries about what it means to have an autonomous mind. (shrink)

Products made from animal fur and skin have been a major part of human civilization. However, in modern society, the unsustainable consumption of these products – often considered luxury goods – has many negative environmental impacts. This study explores how people’s perceptions of biodiversity affect their attitudes and behaviors toward consumption. To investigate the information process deeper, we add the moderation of beliefs about biodiversity loss. Following the Bayesian Mindsponge Framework (BMF) analytics, we use mindsponge-based reasoning for constructing conceptual (...) models and employ Bayesian analysis aided by Markov Chain Monte Carlo (MCMC) algorithms on a dataset of 535 Vietnamese urban residents. The results show that people’s preference for using products made from animal skin/fur is negatively associated with perceived consequences of biodiversity loss when they believe biodiversity loss is real and a major problem. In contrast, if urban residents believe biodiversity loss is unreal or not a significant issue, the association between perceived consequences of biodiversity loss and personal preference happens in the opposite direction. The same effects of biodiversity loss perception on people’s possession of skin/fur products was not found, indicating a more complex information process on behaviors compared to attitudes. Nevertheless, in the scenario that people believe biodiversity loss is not a significant issue, the higher the perceived consequences of biodiversity loss are, the greater number of animal-based products they likely own. Our results suggest that policymakers should not neglect the factor of personal belief besides knowledge and awareness in environmental campaigns. (shrink)

According to a growing trend in theoretical neuroscience, the human perceptual system is akin to a Bayesian machine. The aim of this article is to clearly articulate the claims that perception can be considered Bayesian inference and that the brain can be considered a Bayesian machine, some of the epistemological challenges to these claims; and some of the implications of these claims. We address two questions: (i) How are Bayesian models used in theoretical neuroscience? (ii) (...) From the use of Bayesian models in theoretical neuroscience, have we learned or can we hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine? From actual practice in theoretical neuroscience, we argue for three claims. First, currently Bayesian models do not provide mechanistic explanations; instead they are useful devices for predicting and systematizing observational statements about people's performances in a variety of perceptual tasks. That is, currently we should have an instrumentalist attitude towards Bayesian models in neuroscience. Second, the inference typically drawn from Bayesian behavioural performance in a variety of perceptual tasks to underlying Bayesian mechanisms should be understood within the three-level framework laid out by David Marr ( [1982] ). Third, we can hope to learn that perception is Bayesian inference or that the brain is a Bayesian machine to the extent that Bayesian models will prove successful in yielding secure and informative predictions of both subjects' perceptual performance and features of the underlying neural mechanisms. (shrink)

Although our eyes receive incomplete and ambiguous information, our perceptual system is usually able to successfully construct a stable representation of the world. In the case of ambiguous figures, however, perception is unstable, spontaneously alternating between equally possible outcomes. The present study compared EEG responses to ambiguous figures and their unambiguous variants. We found that slight figural changes, which turn ambiguous figures into unambiguous ones, lead to a dramatic difference in an ERP (“event-related potential”) component at around 400 ms. (...) This result was obtained across two different categories of figures, namely the geometric Necker cube stimulus and the semantic Old/Young Woman face stimulus. Our results fit well into the Bayesian inference concept, which models the evaluation of a perceptual interpretation’s reliability for subsequent action planning. This process seems to be unconscious and the late.. (shrink)

The only viable formulation of perception, thinking, and action under uncertainty is statistical inference, and the normative way of statistical inference is Bayesian. No wonder, then, that even seemingly non-Bayesian computational frameworks in cognitive science ultimately draw their justification from Bayesian considerations, as enlightened theorists know fully well.

Recent work in cognitive and computational neuroscience depicts the brain as in some sense implementing probabilistic inference. This suggests a puzzle. If the processing that enables perceptual experience involves representing or approximating probability distributions, why does experience itself appear univocal and determinate, apparently bearing no traces of those probabilistic roots? In this paper, I canvass a range of responses, including the denial of univocality and determinacy itself. I argue that there is reason to think that it is our conception of (...) perception itself that is flawed. Once we see perception aright, as the slave of action, the puzzlement recedes. Perceptual determinacy reflects only the mundane fact that we are embodied, active, agents who must constantly engage the world they perceptually encounter. (shrink)

The chapter deals with misconceptions in perception theory that are based on the idea of slicing the nature of perception along the joints of physics and on corresponding ill-conceived ʹpurposesʹ and ʹgoalsʹ of the perceptual system. It argues that the conceptual structure underlying the percept cannot be inferentially attained from the sensory input. The output of the perceptual system, namely meaningful categories, is evidently vastly underdetermined by the sensory input, namely physico-geometric energy patterns. Thus, the core task of (...) perception theory is to understand the internal conceptual structure with which our perceptual system is endowed. The conceptual structure underlying the semantic distinctions that characterise the output of the perceptual system can, by conceptual necessity, only be expressed by a logical language that is strictly more powerful than the logical language by which sensory notions can be expressed. Consequently, the internal structure underlying perceptual meaning – including core notions such as ‘Gestalt’ or ‘perceptual object’ – cannot be derived, by whatever kind of general inductive machinery, from the sensory input, or, more generally, from experience. (shrink)

The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision-making, and motor control. This article provides an up-to-date introduction to the two most influential theories within this framework: predictive coding and active inference. The first half of the paper (Sections 2–5) reviews the evolution of predictive coding, from early ideas about efficient coding in the visual (...) system to a more general model encompassing perception, cognition, and motor control. The theory is characterized in terms of the claims it makes at Marr's computational, algorithmic, and implementation levels of description, and the conceptual and mathematical connections between predictive coding, Bayesian inference, and variational free energy (a quantity jointly evaluating model accuracy and complexity) are explored. The second half of the paper (Sections 6–8) turns to recent theories of active inference. Like predictive coding, active inference models assume that perceptual and learning processes minimize variational free energy as a means of approximating Bayesian inference in a biologically plausible manner. However, these models focus primarily on planning and decision-making processes that predictive coding models were not developed to address. Under active inference, an agent evaluates potential plans (action sequences) based on their expected free energy (a quantity that combines anticipated reward and information gain). The agent is assumed to represent the world as a partially observable Markov decision process with discrete time and discrete states. Current research applications of active inference models are described, including a range of simulation work, as well as studies fitting models to empirical data. The paper concludes by considering future research directions that will be important for further development of both models. (shrink)

Marine and coastal ecosystems are crucial in maintaining human livelihood, facilitating social development, and reducing climate change impacts. Studies have examined how the benefit perception of aquatic ecosystems, knowledge, and emotion about climate change affect peoples’ support for marine protection. However, their interaction effects remain understudied. The current study explores the intricate interaction effect of the benefit perception of aquatic ecosystems, knowledge, and worry about climate change on marine protection support. Bayesian Mindsponge Framework (BMF) analytics was employed (...) on a dataset of 709 stakeholders from 42 countries generated by MaCoBioS—a research project funded by the European Commission Horizon 2020. The statistical analysis shows that the impacts of benefit perception of ocean ecosystems, knowledge, and worry about climate change on marine protection support vary due to their interactions. Specifically, when stakeholders perceive ocean ecosystems to have little utility in mitigating climate change, greater climate change knowledge and concern are associated with a higher level of marine protection support. Nevertheless, in the scenarios where stakeholders perceive the benefits of ocean ecosystems, the effect of climate change knowledge becomes conditional on the worry level. If stakeholders are concerned about climate change, those with a greater level of climate change knowledge will associate with a higher level of marine protection support. Otherwise, greater climate change knowledge will result in lower support. These findings highlight emotion’s importance in directing climate change knowledge’s effect on marine protection support. Linking people’s “objects of care” to the consequences of climate change can help improve climate change communication effectiveness. (shrink)

Even though potential impacts of political and legal environments of business on ethical behavior of firms (EBOF) have been conceptually recognized, not much evidence (i.e., empirical work) has been produced to clarify their role. In this paper, using Bayesian causal maps (BCMs) methodology, relationships between legal and political environments of business and EBOF are investigated. The unique design of our study allows us to analyze these relationships based on the stages of development in 92 countries around the world. The (...) EBOF models structured through BCMs are used to explain how EBOF in a given country group are shaped by how managers perceive political, legislative, and protective environments of business in these countries. The results suggest that irregular payments and bribes are the most influential factors affecting managers’ perceptions of business ethics in relatively more advanced economies, whereas intellectual property protection is the most influential factor affecting managers’ perceptions of business ethics in less-advanced economies. The results also suggest that regardless of where the business is conducted in the world, judicial independence is the driving force behind managers’ perceptions of business ethics. In addition, the results of this study provide further support for scholars who argue that business ethics is likely to vary among countries based on their socio-economic factors. In addition to its managerial implications, the study provides directions for policy makers to improve the ethical conduct of businesses in their respective countries. (shrink)

In this paper, I use the predictive processing theory of perception to tackle the question of how perceptual states can be rationally involved in cognition by justifying other mental states. I put forward two claims regarding the epistemological implications of PP. First, perceptual states can confer justification on other mental states because the perceptual states are themselves rationally acquired. Second, despite being inferentially justified rather than epistemically basic, perceptual states can still be epistemically responsive to the mind-independent world. My (...) main goal is to elucidate the epistemology of perception already implicit in PP. But I also hope to show how it is possible to peacefully combine central tenets of foundationalist and coherentist accounts of the rational powers of perception while avoiding the well-recognized pitfalls of either. (shrink)

Perception is not an independent, in‐the‐moment event. Instead, perceiving involves integrating prior expectations with current observations. How does this ability develop from infancy through adulthood? We examined how prior visual experience shapes visual perception in infants, children, and adults. Using an identical task across age groups, we exposed participants to pairs of colorful stimuli and implicitly measured their ability to discriminate relative saturation levels. Results showed that adult participants were biased by previously experienced exemplars, and exhibited weakened in‐the‐moment (...) discrimination between different levels of saturation. In contrast, infants and children showed less influence of memory in their perception, and they actually outperformed adults in discriminating between current levels of saturation. Our findings suggest that as humans develop, their perception relies more on prior experience and less on current observation. (shrink)

In a seminal 1977 article, Rumelhart argued that perception required the simultaneous use of multiple sources of information, allowing perceivers to optimally interpret sensory information at many levels of representation in real time as information arrives. Building on Rumelhart's arguments, we present the Interactive Activation hypothesis—the idea that the mechanism used in perception and comprehension to achieve these feats exploits an interactive activation process implemented through the bidirectional propagation of activation among simple processing units. We then examine the (...) interactive activation model of letter and word perception and the TRACE model of speech perception, as early attempts to explore this hypothesis, and review the experimental evidence relevant to their assumptions and predictions. We consider how well these models address the computational challenge posed by the problem of perception, and we consider how consistent they are with evidence from behavioral experiments. We examine empirical and theoretical controversies surrounding the idea of interactive processing, including a controversy that swirls around the relationship between interactive computation and optimal Bayesian inference. Some of the implementation details of early versions of interactive activation models caused deviation from optimality and from aspects of human performance data. More recent versions of these models, however, overcome these deficiencies. Among these is a model called the multinomial interactive activation model, which explicitly links interactive activation and Bayesian computations. We also review evidence from neurophysiological and neuroimaging studies supporting the view that interactive processing is a characteristic of the perceptual processing machinery in the brain. In sum, we argue that a computational analysis, as well as behavioral and neuroscience evidence, all support the Interactive Activation hypothesis. The evidence suggests that contemporary versions of models based on the idea of interactive activation continue to provide a basis for efforts to achieve a fuller understanding of the process of perception. (shrink)