Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical ; they posit generative models at multiple distinct "levels," whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control (...) comprising levels of generative models. I argue that this view is not entailed by a general Bayesian/predictive processing outlook. Bayesian approaches are compatible with distinct formats of mental representation. Focusing on Bayesian approaches to motor control, I argue that the junctures between different types of mental representation are places where the transitivity of hierarchical prediction may be broken, and I consider the upshot of this conclusion for broader discussions of cognitive architecture. (shrink)

In recent years, the distributed architecture has been widely adopted by security companies with the rapid expansion of their business. A distributed system is comprised of many computing nodes of different components which are connected by high-speed communication networks. With the increasing functionality and complexity of the systems, failures of nodes are inevitable which may result in considerable loss. In order to identify anomalies of the possible failures and enable DevOps engineers to operate in advance, this paper proposes a (...) two-layer prediction architecture based on the monitoring sequences of nodes status. Generally speaking, in the first layer, we make use of EXPoSE anomaly detection technique to derive anomaly scores in constant time which are then used as input data for ensemble learning in the second layer. Experiments are conducted on the data provided by one of the largest security companies, and the results demonstrate the predictability of the proposed approach. (shrink)

Modular approaches to the architecture of the mind claim that some mental mechanisms, such as sensory input processes, operate in special-purpose subsystems that are functionally independent from the rest of the mind. This assumption of modularity seems to be in tension with recent claims that the mind has a predictive architecture. Predictive approaches propose that both sensory processing and higher-level processing are part of the same Bayesian information-processing hierarchy, with no clear boundary between perception and cognition. (...) Furthermore, it is not clear how any part of the predictive architecture could be functionally independent, given that each level of the hierarchy is influenced by the level above. Both the assumption of continuity across the predictive architecture and the seeming non-isolability of parts of the predictive architecture seem to be at odds with the modular approach. I explore and ultimately reject the predictive approach’s apparent commitments to continuity and non-isolation. I argue that predictive architectures can be modular architectures, and that we should in fact expect predictive architectures to exhibit some form of modularity. (shrink)

Predictive processing has recently been advanced as a global cognitive architecture for the brain. I argue that its commitments concerning the nature and format of cognitive representation are inadequate to account for two basic characteristics of conceptual thought: first, its generality—the fact that we can think and flexibly reason about phenomena at any level of spatial and temporal scale and abstraction; second, its rich compositionality—the specific way in which concepts productively combine to yield our thoughts. I consider two (...) strategies for avoiding these objections and I argue that both confront formidable challenges. (shrink)

Predictive processing framework has found wide applications in cognitive science and philosophy. It is an attractive candidate for a unified account of the mind in which perception, action, and cognition fit together in a single model. However, PP cannot claim this role if it fails to accommodate an essential part of cognition—conceptual thought. Recently, Williams argued that PP struggles to address at least two of thought’s core properties—generality and rich compositionality. In this paper, I show that neither necessarily presents (...) a problem for PP. In particular, I argue that because we do not have access to cognitive processes but only to their conscious manifestations, compositionality may be a manifest property of thought, rather than a feature of the thinking process, and result from the interplay of thinking and language. Pace Williams, both of these capacities, constituting parts of a complex and multifarious cognitive system, may be fully based on the architectural principles of PP. Under the assumption that language presents a subsystem separate from conceptual thought, I sketch out one possible way for PP to accommodate both generality and rich compositionality. (shrink)

The global crisis that erupted in 2008 has left unmistakably deep scars in architectural culture. But what is happening now is not solely attributable to what began as a mortgage crisis; many of the causes lie deeper, and go back further than a few years. In a way, the recession has simply accelerated, and exacerbated, various pre-existing trends. Without overstating the case, the West, and above all Europe, is undergoing such major change at the beginning of the twenty-first century that (...) it is no longer logical to expect the future to be simply an extrapolation and continuation of the recent past. And this could well have far-reaching consequences for architecture. Of course, this is not the first time that people have declared that things will never be the same again. And up to now reality has usually turned out to be a good deal better than predicted. But there are reasons why architects in Europe should consider the possibility that this time it really might turn out differently. (shrink)

Task‐relevant actions can facilitate mathematical thinking, even for complex topics, such as mathematical proof. We investigated whether such cognitive benefits also occur for action predictions. The action‐cognition transduction (ACT) model posits a reciprocal relationship between movements and reasoning. Movements—imagined as well as real ones operating on real or imaginary objects—activate feedforward mechanisms for the plausible predicted outcomes of motor system planning, along with feedback from the effect actions have on the world. Thus, ACT posits cognitive influences for making action predictions (...) regardless of whether those actions are performed. Using a two‐by‐two factorial design, we investigated how generating task‐relevant action predictions or performing task‐relevant directed actions influenced undergraduates’ (N = 127) geometry proof performance. As predicted, making action predictions significantly enhanced participants’ proof production. No evidence suggests that combining action predictions and directed actions provided additional benefits, supporting the claim that predicting and performing actions engage overlapping processes, as theorized by ACT. Gestural replays, reenactments of previously performed actions during explanations, were associated with significantly better insight and proof performance for both (actor‐generated) predicted actions and (investigator‐generated) directed actions. Prompting people to predict task‐relevant actions enhances mathematical cognition, possibly through simulated actions of transformations on imagined mathematical objects, as revealed by increased production of speech describing mathematical operations and increased production of gestural replays. We discuss the theoretical implications of these findings regarding the influences of embodied simulation of movements on cognition, and the educational implications of facilitating mathematical reasoning through interventions prompting students to perform and imagine performing task‐relevant body movements. (shrink)

Cognitive architectures (i.e., theorized blueprints on the structure of the mind) can be used to make predictions about the effect of multiregion brain activity on the systems level. Recent work has connected one high-level cognitive architecture, known as the “Common Model of Cognition,” to task-based functional MRI data with great success. That approach, however, was limited in that it was intrinsically top-down, and could thus only be compared with alternate architectures that the experimenter could contrive. In this paper, we (...) propose a bottom-up method to infer a cognitive architecture directly from brain imaging data itself, overcoming this limitation. Specifically, Granger causality modeling was applied to the same task-based fMRI data to infer a network of causal connections between brain regions based on their functional connectivity. The resulting network shares many connections with those proposed by the Common Model of Cognition but also suggests important additions likely related to the role of episodic memory. This combined top-down and bottom-up modeling approach can be used to help formalize the computational instantiation of cognitive architectures and further refine a comprehensive theory of cognition. (shrink)

Expanding his collected essays on architectural theory and criticism, Chris Abel pursues his explorations across disciplinary and regional boundaries in search of a deeper understanding of architecture in the evolution of human culture and identity formation. From his earliest writings predicting the computer-based revolution in customised architectural production, through his novel studies on 'tacit knowing' in design or hybridisation in regional and colonial architecture, to his radical theory of the 'extended self', Abel has been a consistently fresh and (...) provocative thinker, contesting both conventions and intellectual fashions. This revised third edition includes a new introduction and six additional chapters by the author covering a broad range of related topics, up to recent concerns with genetic design methods and virtual selves. Together with the former essays, the book presents a unique global perspective on the changing cultural issues and technologies shaping human identities and the built environment in diverse parts of the world, both East and West (from the book cover). (shrink)

Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the “representation wars” of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing’s account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits (...) a resemblance-based representational architecture with organism-relative contents that functions in the service of pragmatic success, not veridical representation. Finally, I argue that internal representation so understood is either impervious to the three anti-representationalist challenges I outline or can actively embrace them. (shrink)

The use of forward models is well established in cognitive and computational neuroscience. We compare and contrast two recent, but interestingly divergent, accounts of the place of forward models in the human cognitive architecture. On the Auxiliary Forward Model account, forward models are special-purpose prediction mechanisms implemented by additional circuitry distinct from core mechanisms of perception and action. On the Integral Forward Model account, forward models lie at the heart of all forms of perception and action. We compare these (...) neighbouring but importantly different visions and consider their implications for the cognitive sciences. We end by asking what kinds of empirical research might offer evidence favouring one or the other of these approaches. (shrink)

Emotion is conceptualised as an emergent, dynamic process based on an individual's subjective appraisal of significant events. It is argued that theoretical models of emotion need to propose an architecture that reflects the essential nature and functions of emotion as a psychobiological and cultural adaptation mechanism. One proposal for such a model and its underlying dynamic architecture, the component process model, is briefly sketched and compared with some of its major competitors. Recent empirical evidence in support of the (...) model is reviewed. Special emphasis is given to the dynamic aspect of emotion processes, in particular the sequence of appraisal checks and the synchronisation of response systems, as well as the capacity of the model to predict individual differences in emotional responding. (shrink)

Cognitive architectures are theories of cognition that try to capture the essential representations and mechanisms that underlie cognition. Research in cognitive architectures has gradually moved from a focus on the functional capabilities of architectures to the ability to model the details of human behavior, and, more recently, brain activity. Although there are many different architectures, they share many identical or similar mechanisms, permitting possible future convergence. In judging the quality of a particular cognitive model, it is pertinent to not just (...) judge its fit to the experimental data but also its simplicity and ability to make predictions. (shrink)

The problem of interdependence is crucial for understanding the climate, with its interactions between land, water and atmosphere, as well as with human activities, past and future. The concept of interdependence expresses two types of relationship, that of causality and that of responsibility. For the problems of climate governance as understood as a statistical average in the Conferences of the parties (COP), causal dependence is impossible to reconstruct precisely, notably because of the complexity of these phenomenons. However, dependence does not (...) only concern the domain of being, falling within the natural sciences and social sciences and human descriptivo-predictive. It also concerns the ought-to-be and therefore the normative sciences (ethics, political thery, law and normative economy). Here interdependence is much more problematic since it is opposed to freedom. The article discusses the various interdependencies and political solutions that are offered to take care of this needs, architectures for discussing climate change politically: systems (N. Luhmann) and deliberation (J. Habermas). He proposes then another solution, that of the moral and political consediration. (shrink)

A fundamental understanding of gain provided by motion prediction in wireless ad hoc routing is currently lacking. This paper examines benefits in routing obtainable via prediction. A theoretical best-case non-predictive routing model is quantified in terms of both message overhead and update time for non-predictive routing. This best- case model of existing routing performance is compared with predictive routing. Several specific instances of predictive improvements in routing are examined. The primary contribution of this paper is quantification (...) of predictive gain for wireless ad hoc routing. (shrink)

Sound and listening are intrinsically linked to how we experience and engage with places and communities. This guide invites landscape architects and urban designers to become soundscape architects and offers practical advice on sound and listening applicable to each stage of a design project: from reading the environment to intervening on it. This book foregrounds listening as an affective mediator between subjects and multispecies environments, and a vehicle to think and conceptualise environmental design beyond prevailing visual and human-centred modes. The (...) guide expands landscape architects' and urban designers' tools and skills to assess existing soundscapes, predict how those soundscapes will be altered through their designs, consider sound as a creative and active part of the design process, and envisage how users might perceive and be affected by those soundscapes as they evolve in time. The volume sits in the interface of research and practice, and incorporates theoretical, methodological and creative contributions from acoustic ecology, ecoacoustics, bioacoustics and sound art. Each of the design stages is illustrated through project examples that demonstrate the many advantages of incorporating attentive listening and sound into Landscape Architecture and Urban Design Practice. The book shows how incorporating listening and sounding as part of the design process promotes slow and subtle ways of practice, adds social and ecological value through the reduction of noise pollution and by monitoring the health of habitats, and enables the design of soundscapes that complement the character and design intent of a scheme and elicit joy and wonder. The book will be of interest to landscape architects, together with other design professionals such as urban designers, architects, geographers and engineers, who play a primary role in the composition of the soundscape. (shrink)

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system that relies on generative models to predict the structure of sensory information. Such a view resonates with a body of work in machine learning that has explored the problem-solving capabilities of hierarchically-organized, multi-layer (i.e., deep) neural networks, many of which acquire and deploy generative models of their training data. The present chapter explores the extent to which the ostensible convergence on a common neurocomputational (...) architecture (centred on predictive processing schemes, hierarchical organization, and generative models) might provide inroads into the problem of digital immortality. In contrast to approaches that seek to recapitulate the connectomic microstructure of the human brain, the present chapter advocates an approach that is rooted in the use of machine learning algorithms. The claim is that a future form of deep learning system could be used to acquire generative models of a given individual or (alternatively) the sensory data that is processed by the brain of a given individual during the course of their biological life. The differences between these two forms of digital immortality are explored, as are some of the options for digital resurrection. (shrink)

Model validation in computational cognitive psychology often relies on methods drawn from the testing of theories in experimental physics. However, applications of these methods to computational models in typical cognitive experiments can hide multiple, plausible sources of variation arising from human participants and from stochastic cognitive theories, encouraging a “model fixed, data variable” paradigm that makes it difficult to interpret model predictions and to account for individual differences. This article proposes a likelihood‐based, “data fixed, model variable” paradigm in which models (...) are treated as stochastic processes in experiments with participant‐to‐participant variation that can be applied to a broad range of mechanistic cognitive architectures. This article discusses the implementation and implications of this view in model validation, with a concrete focus on a simple class of ACT‐R models of cognition. This article is not intended as a recipe for broad application of these preliminary, proof‐of‐concept methods, but as a framework for communication between statisticians searching for interesting problems in the cognitive modeling sphere, and cognitive modelers interested in generalizing from deterministic to stochastic model validation, in the face of random variation in human experimental data. (shrink)

The aim of this commentary is to underpin Duffley’s notion of a stable mental content that corresponds to the literal word meaning with a computationally plausible cognitive theory. Our approach is to investigate what these stable contents could be according to the so-called Predictive Processing architecture. We argue that recent advances in cognitive science can make at least two contributions to the debate. First, they can provide some underpinning of Duffley's ideas of a stable linguistic meaning associated with (...) the sign. Second, they provide resources to understand how the semiological principle is compatible with a dynamic and flexible notion of "meaning". (shrink)

An important application of cognitive architectures is to provide human performance models that capture psychological mechanisms in a form that can be “programmed” to predict task performance of human–machine system designs. Although many aspects of human performance have been successfully modeled in this approach, accounting for multitalker speech task performance is a novel problem. This article presents a model for performance in a two-talker task that incorporates concepts from psychoacoustics, in particular, masking effects and stream formation.

Despite its explanatory clout, the theory of evolution has thus far compiled a modest record with respect to predictive power—that other major hallmark of scientific theories. This is considered by many to be an acceptable limitation of a theory that deals with events and processes that are intrinsically random. However, whether this is an inherent restriction or simply the sign of an incomplete theory is an open question. In an attempt to help answer that question, we propose a classification (...) scheme for several types of prediction that might occur with regard to evolutionary systems, then explore the nature of these predictions in a system that simulates the evolution of neural architectures. This provides a platform from which to consider the relevance of such observations for real biological systems and illuminates a variety of key issues pertaining to prediction in those environments. (shrink)

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system that relies on generative models to predict the structure of sensory information. Such a view resonates with a body of work in machine learning that has explored the problem-solving capabilities of hierarchically-organized, multi-layer neural networks, many of which acquire and deploy generative models of their training data. The present chapter explores the extent to which the ostensible convergence on a common neurocomputational architecture (...) might provide inroads into the problem of digital immortality. In contrast to approaches that seek to recapitulate the physical structure of the human brain, the present chapter advocates an approach that is rooted in the use of machine learning algorithms. The claim is that a future form of deep learning system could be used to acquire generative models of a given individual or the sensory data that is processed by the brain of a given individual during the course of their biological life. The differences between these two forms of digital immortality are explored, as are some of the options for digital resurrection. (shrink)

The so-called “dark room problem” makes vivd the challenges that purely predictive models face in accounting for motivation. I argue that the problem is a serious one. Proposals for solving the dark room problem via predictive coding architectures are either empirically inadequate or computationally intractable. The Free Energy principle might avoid the problem, but only at the cost of setting itself up as a highly idealized model, which is then literally false to the world. I draw at least (...) one optimistic conclusion, however. Real-world, real-time systems may embody motivational states in a variety of ways consistent with idealized principles like FEP, including ways that are intuitively embodied and extended. This may allow predictive coding theorists to reconcile their account with embodied principles, even if it ultimately undermines loftier ambitions. (shrink)

Richard Thaler and Cass Sunstein define a nudge as “any aspect of the choice architecture that alters people's behavior in a predictable way without forbidding any options or significantly changing their economic incentives.” Much has been written about the ethics of nudging competent adult patients. Less has been written about the ethics of nudging surrogates’ decision‐making and how the ethical considerations and arguments in that context might differ. Even less has been written about nudging surrogate decision‐making in the context (...) of pediatrics, despite fundamental differences that exist between the pediatric and adult contexts. Yet, as the field of behavioral economics matures and its insights become more established and well‐known, nudges will become more crafted, sophisticated, intentional, and targeted. Thus, the time is now for reflection and ethical analysis regarding the appropriateness of nudges in pediatrics. We argue that there is an even stronger ethical justification for nudging in parental decision‐making than with competent adult patients deciding for themselves. We give three main reasons in support of this: (1) child patients do not have autonomy that can be violated (a concern with some nudges), and nudging need not violate parental decision‐making authority; (2) nudging can help fulfill pediatric clinicians’ obligations to ensure parental decisions are in the child's interests, particularly in contexts where there is high certainty that a recommended intervention is low risk and of high benefit; and (3) nudging can relieve parents’ decisional burden regarding what is best for their child, particularly with decisions that have implications for public health. (shrink)

A typical expository strategy for the predictive processing account begins with perception and then extends to other cognitive domains, such as action or non-human animal cognition. Because this standard, perception-first expository strategy begins at the end of an evolutionary process, it may introduce both diachronic and synchronic distortions into the overall account. As far as the diachronic distortion is concerned, because the perception-first strategy presupposes a highly decoupled cognitive architecture, it invites us to project this architecture onto (...) the coupled cognitive systems of our evolutionary ancestors and other non-human organisms. Turning to the synchronic distortion, as the perception-first strategy is extended to conative states, it invites a cognitivism that seeks to reconstrue motivational states as a species of belief. In this paper, I explore what a predictive processing account would look like if it began at the beginning, with the highly coupled representations carried by our evolutionary ancestors and other non-human organisms. I conclude by exploring the proposal’s implications for a non-cognitivist, predictive processing account of action. (shrink)

Predictive processing as a computational motif of the neocortex needs to be elaborated into theories of higher cognitive functions that include simulating future behavioural outcomes. We contribute to the neuroscientific perspective of predictive processing as a foundation for the proposed representational architectures of the mind.

Some naturalistic philosophers of mind subscribing to the predictive processing theory of mind have adopted a realist attitude towards the results of Bayesian cognitive science. In this paper, we argue that this realist attitude is unwarranted. The Bayesian research program in cognitive science does not possess special epistemic virtues over alternative approaches for explaining mental phenomena involving uncertainty. In particular, the Bayesian approach is not simpler, more unifying, or more rational than alternatives. It is also contentious that the Bayesian (...) approach is overall better supported by the empirical evidence. So, to develop philosophical theories of mind on the basis of a realist interpretation of results from Bayesian cognitive science is unwarranted. Naturalistic philosophers of mind should instead adopt an anti-realist attitude towards these results and remain agnostic as to whether Bayesian models are true. For continuing on with an exclusive focus and praise of Bayes within debates about the predictive processing theory will impede progress in philosophical understanding of scientific practice in computational cognitive science as well as of the architecture of the mind. (shrink)

There is a trend toward the use of predictive systems in communications networks. At the systems and network management level predictive capabilities are focused on anticipating network faults and performance degradation. Simultaneously, mobile communication networks are being developed with predictive location and tracking mechanisms. The interactions and synergies between these systems present a new set of problems. A new predictive network management framework is developed and examined. The interaction between a predictive mobile network and the (...) proposed network management system is discussed. The Rapidly Deployable Radio Network is used as a specific example to illustrate these interactions. (shrink)

The automatic prediction of court case judgments using Deep Learning and Natural Language Processing is challenged by the variety of norms and regulations, the inherent complexity of the forensic language, and the length of legal judgments. Although state-of-the-art transformer-based architectures and Large Language Models (LLMs) are pre-trained on large-scale datasets, the underlying model reasoning is not transparent to the legal expert. This paper jointly addresses court judgment prediction and explanation by not only predicting the judgment but also providing legal experts (...) with sentence-based explanations. To boost the performance of both tasks we leverage a legal named entity recognition step, which automatically annotates documents with meaningful domain-specific entity tags and masks the corresponding fine-grained descriptions. In such a way, transformer-based architectures and Large Language Models can attend to in-domain entity-related information in the inference process while neglecting irrelevant details. Furthermore, the explainer can boost the relevance of entity-enriched sentences while limiting the diffusion of potentially sensitive information. We also explore the use of in-context learning and lightweight fine-tuning to tailor LLMs to the legal language style and the downstream prediction and explanation tasks. The results obtained on a benchmark dataset from the Indian judicial system show the superior performance of entity-aware approaches to both judgment prediction and explanation. (shrink)

Standard models of lexical production assume that speakers access representations of meaning, grammar, and different aspects of sound in a roughly sequential manner (whether or not they admit cascading or interactivity). In contrast, we review evidence for a parallel activation model in which these representations are accessed in parallel. According to this account, word learning involves the binding of the meaning, grammar, and sound of a word into a single representation. This representation is then activated as a whole during production, (...) and so all linguistic components are available simultaneously. We then note that language comprehension involves extensive use of prediction and argue that comprehenders use production mechanisms to determine (roughly) what they would say next if they were speaking. So far, theories of prediction-by-production have assumed sequential lexical production. We therefore reinterpret such evidence in terms of parallel lexical production. (shrink)

The Proviso Problem is the discrepancy between the predictions of nearly every major theory of semantic presupposition about what is semantically presupposed by conditionals, disjunctions, and conjunctions, versus observations about what speakers of certain sentences are felt to be presupposing. I argue that the Proviso Problem is a more serious problem than has been widely recognized. After briefly describing the problem and two standard responses to it, I give a number of examples which, I argue, show that those responses are (...) inadequate. I conclude by briefly exploring alternate approaches to presupposition that avoid this problem. -/- . (shrink)

O'Brien et al. reported that readers generated elaborative inferences only when a text contained characteristics that made it easy to predict the specific inference that a reader would draw, and virtually eliminated the possibility of the inference being discon-firmed. Garrod et al., however, offered two qualifications to these conclusions. First, the two text characteristics manipulated may have produced different types of elaborative inferencing: a biasing context results in a passive form of elaborative inferencing, involving setting up a context of interpretation, (...) whereas the presence of a demand sentence invites the reader to actively predict a subsequent expression. Secondly, clear evidence for either type of inference will be apparent only with truly anaphoric materials. This work describes how a passive form of elaborative inferencing, reported by Garrod et al, may be implemented in a connectionist manner. We take the connectionist model proposed by Shastri and Ajjanagadde in order to represent a text in the form of a network. Next we analyze and discuss how an attentional focus could operate with the proposed reasoner system in order to deal with inference control and anaphora resolution, during text understanding. Our own suggestion is that the system proposed by Shastri and Ajjanagadde could be extended by incorporating focus in order to apply it to some open problems related to text processing. However, this extension presents implementational problems due to its local character. Distributed models seem to be in a better position to deal with the shift of the attentional states, given that these systems can learn. Particularly, the model proposed by Sun is close to our aims, and so we will discuss its implications in order to design a cognitive architecture for text comprehension. (shrink)

O'Brien et al. reported that readers generated elaborative inferences only when a text contained characteristics that made it easy to predict the specific inference that a reader would draw, and virtually eliminated the possibility of the inference being discon-firmed. Garrod et al., however, offered two qualifications to these conclusions. First, the two text characteristics manipulated may have produced different types of elaborative inferencing: a biasing context results in a passive form of elaborative inferencing, involving setting up a context of interpretation, (...) whereas the presence of a demand sentence invites the reader to actively predict a subsequent expression. Secondly, clear evidence for either type of inference will be apparent only with truly anaphoric materials. This work describes how a passive form of elaborative inferencing, reported by Garrod et al, may be implemented in a connectionist manner. We take the connectionist model proposed by Shastri and Ajjanagadde in order to represent a text in the form of a network. Next we analyze and discuss how an attentional focus could operate with the proposed reasoner system in order to deal with inference control and anaphora resolution, during text understanding. Our own suggestion is that the system proposed by Shastri and Ajjanagadde could be extended by incorporating focus in order to apply it to some open problems related to text processing. However, this extension presents implementational problems due to its local character. Distributed models seem to be in a better position to deal with the shift of the attentional states, given that these systems can learn. Particularly, the model proposed by Sun is close to our aims, and so we will discuss its implications in order to design a cognitive architecture for text comprehension. (shrink)

How do we ensure that future generally intelligent AI share our values? This is the value-alignment problem. It is a weighty matter. After all, if AI are neutral with respect to our wellbeing, or worse, actively hostile toward us, then they pose an existential threat to humanity. Some philosophers have argued that one important way in which we can mitigate this threat is to develop only AI that shares our values or that has values that ‘align with’ ours. However, there (...) is nothing to guarantee that this policy will be universally implemented—in particular, ‘bad actors’ are likely to flout it. In this paper, I show how the predictive processing model of the mind, currently ascendant in cognitive science, may ameliorate the value-alignment problem. In essence, I argue that there are a plurality of reasons why any future generally intelligent AI will possess a predictive processing cognitive architecture (e.g. because we decide to build them that way; because it is the only possible cognitive architecture that can underpin general intelligence; because it is the easiest way to create AI.). I also argue that if future generally intelligent AI possess a predictive processing cognitive architecture, then they will come to share our pro-moral motivations (of valuing humanity as an end; avoiding maleficent actions; etc.), regardless of their initial motivation set. Consequently, these AI will pose a minimal threat to humanity. In this way then, I conclude, the value-alignment problem is significantly ameliorated under the assumption that future generally intelligent AI will possess a predictive processing cognitive architecture. (shrink)

The energy sector contributes approximately two-thirds of global greenhouse gas emissions. In this context, the sector must adapt to new supply and demand networks for all future energy sources. The ongoing transformation in the European energy field is driven by the ambition of the European Union to reach the climate objectives set for 2030. The main actions are increasing renewable energy production, adapting transition fuels like natural gas to reduce emissions, improving energy efficiency across all economic sectors, prioritizing building, transportation, (...) and industry, developing Carbon Capture and Storage technologies, and ensuring universal access to clean and affordable energy. The significant changes envisaged in the energy sector to increase renewable energy production and consumption require improved integration and more use of predictive tools to support stakeholders' decision-making processes. This article presents a case study to assess the performance of predictive models based on a Feed-Forward Neural Network architecture that employ Root Mean Squared Propagation as their optimization function in terms of choosing the most appropriate activation function for this kind of data input and output. The training and testing phases of the models use data about building energy consumption. The lowest training time and Root Mean Square Error values were similar for Rectified Linear Unit and Tanh activation functions. The model with the Rectified linear Unit activation function performed best. (shrink)

Context: Predicting security and trust vulnerabilities and issues is crucial for IoT interconnected systems and ecosystems, especially when integrating new, third-party or open-source components. Objective: One way to ensure timely predictions is by using a smart monitoring framework to continuously verify functional and non-functional property violations during the executions of the systems and their components. Method: This paper presents a set of guidelines for the Smart Monitoring Framework definition and its application process. Results and Conclusion: The paper provides the reference (...) architecture of the Smart Monitoring Framework and its possible implementation to promptly detect suspicious behavior or property violations. The paper also illustrates how the provided implementation satisfies the defined guidelines by design. (shrink)

We explore the idea that eye-movement strategies in reading are precisely adapted to the joint constraints of task structure, task payoff, and processing architecture. We present a model of saccadic control that separates a parametric control policy space from a parametric machine architecture, the latter based on a small set of assumptions derived from research on eye movements in reading (Engbert, Nuthmann, Richter, & Kliegl, 2005; Reichle, Warren, & McConnell, 2009). The eye-control model is embedded in a decision (...) architecture (a machine and policy space) that is capable of performing a simple linguistic task integrating information across saccades. Model predictions are derived by jointly optimizing the control of eye movements and task decisions under payoffs that quantitatively express different desired speed-accuracy trade-offs. The model yields distinct eye-movement predictions for the same task under different payoffs, including single-fixation durations, frequency effects, accuracy effects, and list position effects, and their modulation by task payoff. The predictions are compared to—and found to accord with—eye-movement data obtained from human participants performing the same task under the same payoffs, but they are found not to accord as well when the assumptions concerning payoff optimization and processing architecture are varied. These results extend work on rational analysis of oculomotor control and adaptation of reading strategy (Bicknell & Levy, ; McConkie, Rayner, & Wilson, 1973; Norris, 2009; Wotschack, 2009) by providing evidence for adaptation at low levels of saccadic control that is shaped by quantitatively varying task demands and the dynamics of processing architecture. (shrink)

Given its simplicity and enormous unifying and explanatory power, the predictive mind approach to mental architecture (predictive processing) is becoming an increasingly attractive way of carrying out theoretical and experimental research in cognitive science. According to this view, the mind is constantly attempting to minimize the discrepancy between its expectations (or sensory predictions) and its actual incoming sensory signals. In the interoceptive inference view of emotion (IIE), the principles of the predictive mind have been extended to (...) account for emotion. IIE holds that, in direct analogy to visual perception, emotions arise from interoceptive predictions of the causes of current interoceptive afferents. In this paper, I argue that this view is problematic, as there are arguably no regularities pertaining to emotion in the physiological inner milieu, from which the relevant interoceptive expectations could be learned. Therefore, it is unlikely that our expectations relative to emotion involve interoceptive expectations in the way required by IIE. The latter view should then be amended. In this respect, I suggest that emotions might arise via external interoceptive active inference: by sampling and modifying the external environment in order to change a valenced feeling. Thus, if the predictive mind approach is on track, emotions are not to be understood in direct analogy to perception (e.g., vision). Rather, I suggest that the view of emotion that emerges from the predictive mind is in line with motivational approaches to emotion. In the suggested view, (almost) just as drives (or ‘homeostatic motivations’), emotions are suited for the active regulation of the inner milieu by sampling the environment in order to finesse our emotion expectations. In this view, emotions are individuated, and differ from drives, in virtue of the distinctive sampling policies (‘actions’) characteristic of the high levels of the predictive hierarchy. (shrink)

This dissertation focuses on generative models in the Predictive Processing framework. It is commonly accepted that generative models are structural representations; i.e. physical particulars representing via structural similarity. Here, I argue this widespread account is wrong: when closely scrutinized, generative models appear to be non-representational control structures realizing an agent’s sensorimotor skills. The dissertation opens (Ch.1) introducing the Predictive Processing account of perception and action, and presenting some of its connectionist implementations, thereby clarifying the role generative models play (...) in Predictive Processing. Subsequently, I introduce the conceptual framework guiding the research (ch.2). I briefly elucidate the metaphysics of representations, emphasizing the specific functional role played by representational vehicles within the systems of which they are part. I close the first half of the dissertation (Ch.3) introducing the claim that generative models are structural representations, and defending it from intuitive but inconclusive objections. I then move to the second half of the dissertation, switching from exposition to criticism. First (Ch.4), I claim that the argument allegedly establishing that generative models are structural representations is flawed beyond repair, for it fails to establish generative models are structurally similar to their targets. I then consider alternative ways to establish that structural similarity, showing they all either fail or violate some other condition individuating structural representations. I further argue (Ch.5) that the claim that generative models are structural representations would not be warranted even if the desired structural similarity were established. For, even if generative models were to satisfy the relevant definition of structural representation, it would still be wrong to consider them as representations. This is because, as currently defined,structural representations fail to play the relevant functional role of representations, and thus cannot be rightfully identified as representations in the first place. This conclusion prompts a direct examination of generative models, to determine their nature (Ch.6). I thus analyze the simplest generative model I know of: a neural network functioning as a robotic “brain” and allowing different robotic creatures to swiftly and intelligently interact with their environments. I clarify how these networks allow the robots to acquire and exert the relevant sensorimotor abilities needed to solve the various cognitive tasks the robots are faced with, and then argue that neither the entire architecture nor any of its parts can possibly qualify as representational vehicles. In this way, the structures implementing generative models are revealed to be non-representational structures that instantiate an agent’s relevant sensorimotor skills. I show that my conclusion generalizes beyond the simple example I considered, arguing that adding computational ingredients to the architecture, or considering altogether different implementations of generative models, will in no way force a revision of my verdict. I further consider and allay a number of theoretical worries that it might generate, and then briefly conclude the dissertation. (shrink)

The issue of the relationship between predictive processing (PP) and the free energy principle (FEP) remains a subject of debate and controversy within the research community. Many researchers have expressed doubts regarding the actual integration of PP with the FEP, questioning whether the FEP can truly contribute significantly to the mechanistic understanding of PP or even undermine such integration altogether. In this paper, I present an alternative perspective. I argue that, from the viewpoint of the constraint-based mechanisms approach, the (...) FEP imposes an important constraint, namely variational free energy, on the mechanistic architecture proposed by PP. According to the constraint-based mechanisms approach, high-level cognitive mechanisms are integral parts of extensive heterarchical networks that govern the physiology and behavior of agents. Consequently, mechanistic explanations of cognitive phenomena should incorporate constraints and flows of free energy as relevant components, given that the implemented constraints operate as long as free energy is available. Within this framework, I contend that the FEP provides a relevant constraint for explaining at least some biological cognitive mechanisms described in terms of Bayesian generative models that minimize prediction errors. (shrink)

Standard semantic information processing models—information in; information processed; information out —lend themselves to standard models of the functioning of the brain in terms, e.g., of threshold-switch neurons connected via classical synapses. That is, in terms of sophisticated descendants of McCulloch and Pitts models. I argue that both the cognition and the brain sides of this framework are incorrect: cognition and thought are not constituted as forms of semantic information processing, and the brain does not function in terms of passive input (...) processing units organized as neural nets. An alternative framework is developed that models cognition and thought not in terms of semantic information processing, and, correspondingly, models brain functional processes also not in terms of semantic information processing. As alternative to such models: I outline a pragmatist oriented, interaction based, model of representation; derive from this model a fundamental framework of constraints on how the brain must function; show that such a framework is in fact found in the brain, and develop the outlines of a broader model of how mental processes can be realized within this alternative framework. Part I of this discussion focuses on some criticisms of standard modeling frameworks for representation and cognition, and outlines an alternative interactivist, pragmatist oriented, model. In part II, the focus is on the fact that the brain does not, in fact, function in accordance with standard passive input processing models—e.g., information processing models. Instead, there are multiple endogenously active processes at multiple spatial and temporal scales across multiple kinds of cells. A micro-functional model that accounts for, and even predicts, these multi-scale phenomena in generating emergent representation and cognition is outlined. That is, I argue that the interactivist model of representation outlined offers constraints on how the brain should function that are in fact empirically found, and, in reverse, that the multifarious details of brain functioning entail the pragmatist representational model—a very strong interrelationship. In the sequel paper, starting with part III, this model is extended to address macro-functioning in the CNS. In part IV, I offer a discussion of an approach to brain functioning that has some similarities with, as well as differences from, the model presented here: sometimes called the predictive brain approach. (shrink)

Cette contribution s’intéresse à la position problématique des arts mécaniques dans les systèmes médiévaux du savoir. Remplaçant la position secondaire assignée aux arts mécaniques du début du Moyen Âge, les solutions proposées par Hugues de Saint-Victor et Gundissalinus eurent une influence forte durant le XIIIe s. Alors que l’intégration des arts mécaniques dans le système de connaissance de Saint-Victor trahit leurs positions encore accessoires vis-à-vis de la considération des arts libéraux, Gundissalinus propose deux principales nouveautés. D’un côté il place les (...) arts mécaniques aux côtés de l’alchimie et des arts de prédiction et de magie. D’un autre côté cependant, utilisant la théorie avancée par Avicenne, il subordonne ces « sciences naturelles » à la philosophie naturelle elle-même, établissant ainsi une architecture du savoir plus vaste et plus organisée hiérarchiquement. Notre contribution examine les implications découlant de ces développements ainsi que leur réception à Paris durant le XIIIe s., soulignant la pertinence des solutions proposées par Gundissalinus dans les discussions qui ont suivi, concernant la structure de la connaissance humaine. (shrink)

The COVID-19 global health emergency has greatly impacted the educational field. Faced with unprecedented stress situations, professors, students, and families have employed various coping and resilience strategies throughout the confinement period. High and persistent stress levels are associated with other pathologies; hence, their detection and prevention are needed. Consequently, this study aimed to design a predictive model of stress in the educational field based on artificial intelligence that included certain sociodemographic variables, coping strategies, and resilience capacity, and to study (...) the relationship between them. The non-probabilistic snowball sampling method was used, involving 337 people from the university education community in south-eastern Spain. The Perceived Stress Scale, Stress Management Questionnaire, and Brief Resilience Scale were administered. The Statistical Package for the Social Sciences was used to design the architecture of artificial neural networks. The results found that stress levels could be predicted by the synaptic weights of coping strategies and timing of the epidemic, with a predictive capacity of over 80% found in the neural network model. Additionally, direct and significant associations were identified between the use of certain coping strategies, stress levels, and resilience. The conclusions of this research are essential for effective stress detection, and therefore, early intervention in the field of educational psychology, by discussing the influence of resilience or lack thereof on the prediction of stress levels. Identifying the variables that maintain a greater predictive power in stress levels is an effective strategy to design more adjusted prevention programs and to anticipate the needs of the community. (shrink)

This paper discusses the concept of climate in relation to architectural space. By elaborating on the notion of atmosphere, that today permeates a wide range of architectural research, I intend to expand its relevance by outlining a relationship between atmosphere and climate analogous to what occurs in meteorological studies. While climate represents a rather stable (if evolving) cycle of recurring conditions, atmospheric events are fleeting and less predictable. Equally, architectural spaces can establish a general climatic scaffolding that increases the possibility (...) of particular atmospheres to unfold, without however evolving into a deterministic cause-effect relationship. By addressing and comparing philosophical notions and architectural questions, I intend to formulate a novel theorisation as a useful tool for both criticism and design. (shrink)