Ten years ago, one of us proposed a dynamic hierarchical model of intentions that brought together philosophical work on intentions and empirical work on motor representations and motor control (Pacherie, 2008). The model distinguished among Distal intentions, Proximal intentions, and Motor intentions operating at different levels of action control (hence the name DPM model). This model specified the representational and functional profiles of each type of intention, as well their local and global dynamics, and the ways in which they (...) interact. A core insight of the model was that action control is the result of integrated, coordinated activity across these levels of intention. Since the proposal of the model, empirical and theoretical works in philosophy and cognitive science have emerged that would seem to support and expand on this central insight. In particular, an updated understanding of the nature of sensorimotor processing and motor representations, as well as of how the different levels of intention and control interface and interact, allows for the further specification and precisification of the original DPM model. (shrink)

Law seems to be irremediadibly connected to the experience of coercion and to a structure of hierarchy. This is so because it has traditionally been defined as a set of authoritative prescriptions, usually commands backed by the menace of a sanction, an evil eventually applied through the use of overwhelming violence. Law has also been related to some kind of structure or system which is intrinsically hierarchical, both in the sense of the hierarchy of people whose conduct is addressed (...) by the law and in the sense of the logical interdependence of the legal prescriptions themselves. The British legal philosopher H. L. A. Hart criticizes in his celabrated book The Concept of Law powerfully the command theory and also reshapes the theory of the hierarchical structure of rules. The paper will tries to follow Hart’s attack to the traditional imperativistic view and will then focus on his treatment of international law. The aim is to show on the one side how promising Hart’s approach could be, and on the other side how poor however his conceptualization of international law at the end of the day reveals to be. (shrink)

Law is traditionally related to the practice of command and hierarchy. It seems that a legal rule should immediately establish a relation between a superior and an inferior. This hierarchical and authoritharian view might however be challenged once the phenomenology of the rule is considered from the internal point of view, that is, from the stance of those that can be said to “use” rather than to “suffer” the rules themselves. A practice oriented approach could in this way open (...) up a more liberal, and also somehow less parochial and ideological, road for legal theory. This is – it is argued in the paper – the programme, or better, the promise we can find in Herbert Hart’s main work, The Concept of Law. The article tries to render this promise more transparent while, nonetheless, not eschewing the blind sides of its narrative and argumentative strategy. (shrink)

Law seems to be irremediadibly connected to the experience of coercion and to a structure of hierarchy. This is so because it has traditionally been defined as a set of authoritative prescriptions, usually commands backed by the menace of a sanction, an evil eventually applied through the use of overwhelming violence. Law has also been related to some kind of structure or system which is intrinsically hierarchical, both in the sense of the hierarchy of people whose conduct is addressed (...) by the law and in the sense of the logical interdependence of the legal prescriptions themselves. The British legal philosopher H. L. A. Hart criticizes in his celabrated book The Concept of Law powerfully the command theory and also reshapes the theory of the hierarchical structure of rules. The paper will tries to follow Hart’s attack to the traditional imperativistic view and will then focus on his treatment of international law. The aim is to show on the one side how promising Hart’s approach could be, and on the other side how poor however his conceptualization of international law at the end of the day reveals to be. (shrink)

Hierarchical models are commonly used for modelling uncertainty. They arise whenever there is a `correct' or `ideal' uncertainty model but the modeller is uncertain about what it is. Hierarchical models which involve probability distributions are widely used in Bayesian inference. Alternative models which involve possibility distributions have been proposed by several authors, but these models do not have a clear operational meaning. This paper describes a new hierarchical model which is mathematically equivalent to (...) some of the earlier, possibilistic models and also has a simple behavioural interpretation, in terms of betting rates concerning whether or not a decision maker will agree to buy or sell a risky investment for a specified price. We give a representation theorem which shows that any consistent model of this kind can be interpreted as a model for uncertainty about the behaviour of a Bayesian decision maker. We describe how the model can be used to generate buying and selling prices and to make decisions. (shrink)

Motivation has been the subject of much research in the sport psychology literature, whereas athlete mental health has received limited attention. Motivational complexities in elite sport are somewhat reflected in the mental health literature, where there is evidence for both protective and risk factors for athletes. Notably, few studies have linked motivation to mental health. Therefore, the key objective of this study was to test four mental health outcomes in the motivational sequence posited by the Hierarchical Model of Intrinsic (...) and Extrinsic Motivation: motivational climate  basic psychological needs  motivation  mental health outcomes. Elite team-sport athletes (140 females, 75 males) completed seven psychometric inventories of motivation-related and mental health variables. Overall, the athletes reported positive motivational patterns, with autonomous motivation and task climate being more prevalent than their less adaptive counterparts. Elevated depressive symptoms and poor sleep quality affected nearly half of the cohort. Structural equation modelling supported pathways between motivational climate, basic needs, motivation, and mood, depressive symptoms, sleep quality, and trait anxiety. Specifically, a task climate was positively associated with the three basic psychological needs, and an ego climate was positively associated with competence. Autonomy and relatedness had positive and negative associations with autonomous and controlled forms of motivation, respectively. Controlled motivation regulations were positively associated with the four mental health outcomes. Integrated regulation had a negative association with anxiety, and intrinsic regulation had a positive association with depressive symptoms. These findings highlight the complexities of and interrelations between motivation and mental health among athletes, and support the importance of considering mental health as an outcome of motivation. (shrink)

The focus of this commentary is what Andy Clark takes to be the most groundbreaking of the philosophical import of the ‘bidirectional hierarchical model of brain functions’, namely, the claim that perceptual representations represent probabilities. This is what makes his account Bayesian and this is a philosophical or theoretical conclusion that neuroscientists and psychologists are also quick and happy to draw. My claim is that nothing in the ‘bidirectional hierarchical models of brain functions’ implies that perceptual representations (...) are probabilistic, or that they represent or ‘encode probability density distributions’. There is a much more parsimonious way of describing the representations in the bidirectional hierarchical model of brain functions: they attribute properties to objects (or to the perceived scene) that are not fully determinate. (shrink)

Verbal learning and memory summaries of older adults have usually been used to describe neuropsychiatric complaints. Bayesian hierarchical models are modern and appropriate approaches for predicting repeated measures data where information exchangeability is considered and a violation of the independence assumption in classical statistics. Such models are complex models for clustered data that account for distributions of hyper-parameters for fixed-term parameters in Bayesian computations. Repeated measures are inherently clustered and typically occur in clinical trials, education, cognitive (...) psychology, and treatment follow-up. The Hopkins Verbal Learning Test is a general verbal knowledge and memory assessment administered repeatedly as part of a neurophysiological experiment to examine an individual’s performance outcomes at different time points. Multiple trial-based scores of verbal learning and memory tests were considered as an outcome measurement. In this article, we attempted to evaluate the predicting effect of individual characteristics in considering within and between-group variations by fitting various Bayesian hierarchical models via the hybrid Hamiltonian Monte Carlo under the Bayesian Regression Models using ‘Stan’ package of R. Comparisons of the fitted models were done using leave-one-out information criteria, Widely applicable information criterion, and K-fold cross-validation methods. The full hierarchical model with varying intercepts and slopes had the best predictive performance for verbal learning tests [from the Advanced Cognitive Training for Independent and Vital Elderly study dataset] using the hybrid Hamiltonian-Markov Chain Monte Carlo approach. (shrink)

In the context of the motor skill literature on observational learning and hierarchical skill structuring, Byrne & Russon's findings call into question their standpoint that great apes imitate the behaviour of role models at the programme level. The authors impose a hierarchical model on their observations without properly considering alternative explanations. One such possibility, which stems from a constraints perspective that they dismiss, is put forward.

Computational models of memory are often expressed as hierarchic sequence models, but the hierarchies in these models are typically fairly shallow, reflecting the tendency for memories of superordinate sequence states to become increasingly conflated. This article describes a broad-coverage probabilistic sentence processing model that uses a variant of a left-corner parsing strategy to flatten sentence processing operations in parsing into a similarly shallow hierarchy of learned sequences. The main result of this article is that a broad-coverage model (...) with constraints on hierarchy depth can process large newspaper corpora with the same accuracy as a state-of-the-art parser not defined in terms of sequential working memory operations. (shrink)

We consider human performance on an optimal stopping problem where people are presented with a list of numbers independently chosen from a uniform distribution. People are told how many numbers are in the list, and how they were chosen. People are then shown the numbers one at a time, and are instructed to choose the maximum, subject to the constraint that they must choose a number at the time it is presented, and any choice below the maximum is incorrect. We (...) present empirical evidence that suggests people use threshold-based models to make decisions, choosing the first currently maximal number that exceeds a fixed threshold for that position in the list. We then develop a hierarchical generative account of this model family, and use Bayesian methods to learn about the parameters of the generative process, making inferences about the threshold decision models people use. We discuss the interesting aspects of human performance on the task, including the lack of learning, and the presence of large individual differences, and consider the possibility of extending the modeling framework to account for individual differences. We also use the modeling results to discuss the merits of hierarchical, generative and Bayesian models of cognitive processes more generally. (shrink)

We present a hierarchical integrated model of self-regulation in which executive function is the cognitive component of the model, together with emotional, behavioral, physiological, and genetic components. These five components in the model are reciprocally and recursively related. The model is supported by empirical evidence, primarily from a single longitudinal study with good measurement at each level of the model. We also find that the model is consistent with current thinking on related topics such as cybernetic theory, the theory (...) of allostasis and allostatic load, and the theory of skill development in harsh and unpredictable environments, referred to as “hidden talents.” Next, we present literature that the integrative processes are susceptible to environmental adversity, poverty-related risk in particular, while positive social interactions with caregivers would promote self-regulatory processes or mitigate the adverse effect of early risk on the processes. A hierarchical integrative model of self-regulation advances our understanding of self-regulatory processes. Future research may consider broader social contexts of the integrative self-regulation system, such as neighborhood/community contexts and structural racism. This can be an integral step to provide children with equitable opportunities to thrive, even among children living in socioeconomically and psychosocially disadvantaged environments. (shrink)

(2013). Hierarchical Bayesian models as formal models of causal reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 36-45. doi: 10.1080/19462166.2012.700321.

In this paper, we consider how certain longstanding philosophical questions about mental representation may be answered on the assumption that cognitive and perceptual systems implement hierarchical generative models, such as those discussed within the prediction error minimization framework. We build on existing treatments of representation via structural resemblance, such as those in Gładziejewski :559–582, 2016) and Gładziejewski and Miłkowski, to argue for a representationalist interpretation of the PEM framework. We further motivate the proposed approach to content by arguing (...) that it is consistent with approaches implicit in theories of unsupervised learning in neural networks. In the course of this discussion, we argue that the structural representation proposal, properly understood, has more in common with functional-role than with causal/informational or teleosemantic theories. In the remainder of the paper, we describe the PEM framework for approximate Bayesian inference in some detail, and discuss how structural representations might arise within the proposed Bayesian hierarchies. After explicating the notion of variational inference, we define a subjectively accessible measure of misrepresentation for hierarchical Bayesian networks by appeal to the Kullbach–Leibler divergence between posterior generative and approximate recognition densities, and discuss a related measure of objective misrepresentation in terms of correspondence with the facts. (shrink)

This article reviews current methods for evaluating models in the cognitive sciences, including theoretically based approaches, such as Bayes factors and minimum description length measures; simulation approaches, including model mimicry evaluations; and practical approaches, such as validation and generalization measures. This article argues that, although often useful in specific settings, most of these approaches are limited in their ability to give a general assessment of models. This article argues that hierarchical methods, generally, and hierarchical Bayesian methods, (...) specifically, can provide a more thorough evaluation of models in the cognitive sciences. This article presents two worked examples of hierarchical Bayesian analyses to demonstrate how the approach addresses key questions of descriptive adequacy, parameter interference, prediction, and generalization in principled and coherent ways. (shrink)

Gut microbiome plays a significant role in defining the health status of subjects, and recent studies highlight the importance of using time series strategies to analyse microbiome dynamics. In this paper, we develop a Bayesian model for microbiota longitudinal data, based on Dirichlet distribution with time-varying parameters, that take into account the compositional paradigm and consider principal balances. The proposed model can be effective for predicting the future dynamics of a microbial community in the short term and for analysing the (...) microbial interactions using the value of the estimated parameters. The usefulness of the proposed model is illustrated with six different datasets, and a comparison study with four alternative models is provided. (shrink)

Human control of action in routine situations involves a flexible interplay between (a) task-dependent serial ordering constraints; (b) top-down, or intentional, control processes; and (c) bottom-up, or environmentally triggered, affordances. In addition, the interaction between these influences is modulated by learning mechanisms that, over time, appear to reduce the need for top-down control processes while still allowing those processes to intervene at any point if necessary or if desired. We present a model of the acquisition and control of goal-directed action (...) that goes beyond existing models by operationalizing an interface between two putative systems—a routine and a non-routine system—thereby demonstrating how explicitly represented goals can interact with the emergent task representations that develop through learning in the routine system. The gradual emergence of task representations offers an explanation for the transfer of control with experience from the non-routine goal-based system to the routine system. At the same time it allows action selection to be sensitive both to environmental triggers and to biasing from multiple levels within the goal system. (shrink)

Inner speech has been shown to vary in form along several dimensions. Along condensation, condensed inner speech forms have been described, that are supposed to be deprived of acoustic, phonological and even syntactic qualities. Expanded forms, on the other extreme, display articulatory and auditory properties. Along dialogality, inner speech can be monologal, when we engage in internal soliloquy, or dialogal, when we recall past conversations or imagine future dialogues involving our own voice as well as that of others addressing us. (...) Along intentionality, it can be intentional (when we deliberately rehearse material in short-term memory) or it can arise unintentionally (during mind wandering). We introduce the ConDialInt model, a neurocognitive predictive control model of inner speech that accounts for its varieties along these three dimensions. ConDialInt spells out the condensation dimension by including inhibitory control at the conceptualization, formulation or articulatory planning stage. It accounts for dialogality, by assuming internal model adaptations and by speculating on neural processes underlying perspective switching. It explains the differences between intentional and spontaneous varieties in terms of monitoring. We present an fMRI study in which we probed varieties of inner speech along dialogality and intentionality, to examine the validity of the neuroanatomical correlates posited in ConDialInt. Condensation was also informally tackled. Our data support the hypothesis that expanded inner speech recruits speech production processes down to articulatory planning, resulting in a predicted signal, the inner voice, with auditory qualities. Along dialogality, covertly using an avatar’s voice resulted in the activation of right hemisphere homologues of the regions involved in internal own-voice soliloquy and in cerebellar deactivation, consistent with internal model adaptation. Switching from first-person to third-person perspective resulted in activations in precuneus and parietal lobules. Along intentionality, compared with intentional inner speech, mind wandering with inner speech episodes was associated with greater bilateral inferior frontal activation and decreased activation in left temporal regions. This is consistent with the reported subjective evanescence and presumably reflects condensation processes. Our results provide neuroanatomical evidence compatible with predictive control and in favor of the assumptions made in the ConDialInt model. (shrink)

The Model of Hierarchical Complexity is introduced in terms of its main concepts, background, and applications. As a general, quantitative behavioral developmental theory, the Model enables examination of universal patterns of evolution and development. Behavioral tasks are definable and their organization of information in increasingly greater hierarchical, or vertical, complexity is measurable. Fifteen orders of hierarchical complexity account for task performances across domains, ranging from those of machines to creative geniuses. The four most complex orders are demonstrated (...) by postformal stages of thought, which measure beyond formal operations, the highest stage found by Piaget for adults. (shrink)

Critical consideration is given the empirical evidence for psychological models of religious development, its supposed relationship to other domains of psychological development, and especially, moral development. Significant problems with stage conceptions in these models augur a fundamental rethinking of religious development as a construct in developmental psychology. Model of Hierarchical Complexity has demonstrable promise for enabling greater precision in constructs and methods. This may resolve some central problems and advance research in the field.

adjacent phrases, but they typically lack the ability to perform the kind of long-distance reorderings possible with syntax-based systems. In this paper, we present a novel hierarchical phrase reordering model aimed at improving non-local reorderings, which seamlessly integrates with a standard phrase-based system with little loss of computational efficiency. We show that this model can successfully handle the key examples often used to motivate syntax-based systems, such as the rotation of a prepositional phrase around a noun phrase. We contrast (...) our model with reordering models commonly used in phrase-based systems, and show that our approach provides statistically significant BLEU point gains for two language pairs: Chinese-English (+0.53 on MT05 and +0.71 on MT08) and Arabic-English (+0.55 on MT05). (shrink)

The balance between births and deaths in an age-structured population is strongly influenced by the spatial distribution of sub-populations. Our aim was to describe the demographic process of a fish population in an hierarchical dendritic river network, by taking into account the possible movements of individuals. We tried also to quantify the effect of river network changes (damming or channelling) on the global fish population dynamics. The Salmo trutta life pattern was taken as an example for.We proposed a model (...) which includes the demographic and the migration processes, considering migration fast compared to demography. The population was divided into three age-classes and subdivided into fifteen spatial patches, thus having 45 state variables. Both processes were described by means of constant transfer coefficients, so we were dealing with a linear system of difference equations. The discrete case of the variable aggregation method allowed the study of the system through the dominant elements of a much simpler linear system with only three global variables: the total number of individuals in each age-class. (shrink)

Common methods for analysing response time (RT) tasks, frequently used across different disciplines of psychology, suffer from a number of limitations such as the failure to directly measure the underlying latent processes of interest and the inability to take into account the uncertainty associated with each individual's point estimate of performance. Here, we discuss a Bayesian hierarchical diffusion model and apply it to RT data. This model allows researchers to decompose performance into meaningful psychological processes and to account optimally (...) for individual differences and commonalities, even with relatively sparse data. We highlight the advantages of the Bayesian hierarchical diffusion model decomposition by applying it to performance on Approach–Avoidance Tasks, widely used in the emotion and psychopathology literature. Model fits for two experimental data-sets demonstrate that the model performs well. The Bayesian hierarchical diffusion model overcomes important limitations of current analysis procedures and provides deeper insight in latent psychological processes of interest. (shrink)

In this article, I develop a novel version of the multiverse theory of sets called hierarchical pluralism by introducing the notion of “degrees of intentionality” of theories. The presented view is articulated for the purpose of reconciling epistemological realism and the multiverse theory of sets so as to preserve a considerable amount of epistemic objectivity when working with the multiverse theory. I give some arguments in favor of a hierarchical picture of the multiverse in which theories or (...) class='Hi'>models are thought to be ordered with respect to their plausibility as a manifestation of endorsing the idea that some set theories are more plausible than others. The proposed multiverse account settles the pluralist’s dilemma, the dichotomy that there is a trade-off between the richness of mathematical ontology and the objectivity of mathematical truth. The view also extends and serves as an alternative position to Balaguer’s intention-based Platonism, from which he claims that a certain version of mathematical pluralism follows. (shrink)

The basic Bayesian model of credence states, where each individual’s belief state is represented by a single probability measure, has been criticized as psychologically implausible, unable to represent the intuitive distinction between precise and imprecise probabilities, and normatively unjustifiable due to a need to adopt arbitrary, unmotivated priors. These arguments are often used to motivate a model on which imprecise credal states are represented by sets of probability measures. I connect this debate with recent work in Bayesian cognitive science, where (...) probabilistic models are typically provided with explicit hierarchical structure. Hierarchical Bayesian models are immune to many classic arguments against single-measure models. They represent grades of imprecision in probability assignments automatically, have strong psychological motivation, and can be normatively justified even when certain arbitrary decisions are required. In addition, hierarchical models show much more plausible learning behavior than flat representations in terms of sets of measures, which—on standard assumptions about update—rule out simple cases of learning from a starting point of total ignorance. (shrink)

This chapter explores to what extent some core ideas of predictive processing can be applied to the phenomenology of time consciousness. The focus is on the experienced continuity of consciously perceived, temporally extended phenomena (such as enduring processes and successions of events). The main claim is that the hierarchy of representations posited by hierarchical predictive processing models can contribute to a deepened understanding of the continuity of consciousness. Computationally, such models show that sequences of events can be (...) represented as states of a hierarchy of dynamical systems. Phenomenologically, they suggest a more fine-grained analysis of the perceptual contents of the specious present, in terms of a hierarchy of temporal wholes. Visual perception of static scenes not only contains perceived objects and regions but also spatial gist; similarly, auditory perception of temporal sequences, such as melodies, involves not only perceiving individual notes but also slightly more abstract features (temporal gist), which have longer temporal durations (e.g., emotional character or rhythm). Further investigations into these elusive contents of conscious perception may be facilitated by findings regarding its neural underpinnings. Predictive processing models suggest that sensorimotor areas may influence these contents. (shrink)