Theory Of Inference Research Articles

The Legitimization Effect of Crowdfunding Success: A Consumer Perspective

Based on legitimacy and consumer inference theory, we examine when, how, and why past crowdfunding success influences the perceptions and behaviors of consumers. Across five studies (four controlled experiments and one field experiment), our findings demonstrate that a young venture’s past crowdfunding success enhances consumers’ perceptions of its cognitive legitimacy. This “legitimization effect of crowdfunding success” leads to positive outcomes with respect to purchase intentions, brand attitudes, and consumers’ willingness to recommend young ventures to others. These effects are robust across different product categories. However, our findings also reveal that these positive effects occur exclusively for young ventures, whereas they disappear or even reverse for established ones.

Inference strength predicts the probability of conditionals better than conditional probability does

According to the philosophical theory of inferentialism and its psychological counterpart, Hypothetical Inferential Theory (HIT), the meaning of an indicative conditional centrally involves the strength of the inferential connection between its antecedent and its consequent. This paper states, for the first time, the implications of HIT for the probabilities of conditionals. We report two experiments comparing these implications with those of the suppositional account of conditionals, according to which the probability of a conditional equals the corresponding conditional probability. A total of 358 participants were presented with everyday conditionals across three different tasks: judging the probability of the conditionals; judging the corresponding conditional probabilities; and judging the strength of the inference from antecedent to consequent. In both experiments, we found inference strength to be a much stronger predictor of the probability of conditionals than conditional probability, thus supporting HIT.

The strange role of brain lesion size in cognitive neuropsychology

The size of brain lesions is a variable that is frequently considered in cognitive neuropsychology. In particular, lesion-deficit inference studies often control for lesion size, and the association of lesion size with post-stroke cognitive deficits and its predictive value are studied. In the present article, the role of lesion size in cognitive deficits and its computational or design-wise consideration is discussed and questioned. First, I argue that the commonly discussed role or effect of lesion size in cognitive deficits eludes us. A generally valid understanding of the causal relation of lesion size, lesion location, and cognitive deficits is unachievable. Second, founded on the theory of causal inference, I argue that lesion size control is no generally appropriate covariate control. Instead, it is identified as a procedure with only situational benefits, which is supported by empirical data. This theoretical background is used to suggest possible research practices in lesion-deficit inference, post-stroke outcome prediction, and behavioural studies. Last, control for lesion size is put into a bigger historical context – it is identified to relate to a long-known association problem in neuropsychology, which was previously discussed from the perspectives of a mislocalisation in lesion-deficit mapping and the symptom complex approach.

Analysis and Research on the Application of Sampling Big Data Statistical Method

Under the current statistical environment and technical conditions, there is a certain lag in the publication of statistical data. This means that there is a time lag in the completion of reports, which may delay the judgment of the current economic situation. Network real-time analysis based on big data analysis has gradually become the main force of data analysis. This paper puts forward the basic idea of understanding the statistical inference problem of non-probabilistic sampling. Sampling methods can consider sample selection based on sample matching, link tracking sampling method, etc., so that the obtained non-probabilistic samples are like probabilistic samples, so the statistical inference theory of probabilistic samples can be adopted. Random sampling technology and non-random sampling technology still have many applicable scenes, which are not only the scenes of traditional sampling survey in the past, but also applied to more modern information scenes with the times.

Accessing Active Inference Theory through Its Implicit and Deliberative Practice in Human Organizations.

Active inference theory (AIT) is a corollary of the free-energy principle, which formalizes cognition of living system’s autopoietic organization. AIT comprises specialist terminology and mathematics used in theoretical neurobiology. Yet, active inference is common practice in human organizations, such as private companies, public institutions, and not-for-profits. Active inference encompasses three interrelated types of actions, which are carried out to minimize uncertainty about how organizations will survive. The three types of action are updating work beliefs, shifting work attention, and/or changing how work is performed. Accordingly, an alternative starting point for grasping active inference, rather than trying to understand AIT specialist terminology and mathematics, is to reflect upon lived experience. In other words, grasping active inference through autoethnographic research. In this short communication paper, accessing AIT through autoethnography is explained in terms of active inference in existing organizational practice (implicit active inference), new organizational methodologies that are informed by AIT (deliberative active inference), and combining implicit and deliberative active inference. In addition, these autoethnographic options for grasping AIT are related to generative learning.

Introducing Statistical Inference: Design of a Theoretically and Empirically Based Learning Trajectory

This paper comprises the results of a design study that aims at developing a theoretically and empirically based learning trajectory on statistical inference for 9th-grade students. Based on theories of informal statistical inference, an 8-step learning trajectory was designed. The trajectory consisted of two similar four step sequences: (1) experimenting with a physical black box, (2) visualizing distributions, (3) examining sampling distributions using simulation software, and (4) interpreting sampling distributions to make inferences in real -life contexts. Sequence I included only categorical data and Sequence II regarded numerical data. The learning trajectory was implemented in an intervention among 267 students. To examine the effects of the trajectory on students’ understanding of statistical inference, we analyzed their posttest results after the intervention. To investigate how the stepwise trajectory fostered the learning process, students’ worksheets during each learning step were analyzed. The posttest results showed that students who followed the learning trajectory scored significantly higher on statistical inference and on concepts related to each step than students of a comparison group (n = 217) who followed the regular curriculum. Worksheet analysis demonstrated that the 8-step trajectory was beneficial to students’ learning processes. We conclude that ideas of repeated sampling with a black box and statistical modeling seem fruitful for introducing statistical inference. Both ideas invite more advanced follow-up activities, such as hypothesis testing and comparing groups. This suggests that statistics curricula with a descriptive focus can be transformed to a more inferential focus, to anticipate on subsequent steps in students’ statistics education.

Establishing a role of the semantic control network in social cognitive processing: A meta-analysis of functional neuroimaging studies

The contribution and neural basis of cognitive control is under-specified in many prominent models of socio-cognitive processing. Important outstanding questions include whether there are multiple, distinguishable systems underpinning control and whether control is ubiquitously or selectively engaged across different social behaviours and task demands. Recently, it has been proposed that the regulation of social behaviours could rely on brain regions specialised in the controlled retrieval of semantic information, namely the anterior inferior frontal gyrus (IFG) and posterior middle temporal gyrus. Accordingly, we investigated for the first time whether the neural activation commonly found in social functional neuroimaging studies extends to these ‘semantic control’ regions. We conducted five coordinate-based meta-analyses to combine results of 499 fMRI/PET experiments and identified the brain regions consistently involved in semantic control, as well as four social abilities: theory of mind, trait inference, empathy and moral reasoning. This allowed an unprecedented parallel review of the neural networks associated with each of these cognitive domains. The results confirmed that the anterior left IFG region involved in semantic control is reliably engaged in all four social domains. This supports the hypothesis that social cognition is partly regulated by the neurocognitive system underpinning semantic control.

The decision support system for conducting fire-prevention measures in woodlands

Purpose of the work is to propose provisions for the aggregation of parameters of a climatic and anthropogenic nature. Regulations are necessary to determine the rating of forest areas in terms of fire hazard level. As a result of the study, theoretical provisions for the aggregation of variables have been proposed. Variables are described on different metric scales and affect the final result in different ways. The provisions are based on the methods of the theory of fuzzy sets and fuzzy inference. The computational complex for determining the rating of the forest areas is implemented in the form of a hierarchical system of fuzzy inference. It is based on the Takagi-Sugeno algorithm. Simulation of the complex operation was carried out. As a result, it was concluded that the generated output pattern has a stepped nature. This makes it possible to classify the analyzed elements into state groups. This allows you to solve the problems of planning work on the implementation of preventive and/or preparatory measures to eliminate or prevent forest fires. The prospect of further research is the formation of decision support systems in the control processes organization of forest areas.

Fuzzy cognitive modeling of agricultural land productivity in the context of food security

The article deals with the problems of fuzzy cognitive modeling and evaluation of the productivity of reclaimed soils, taking into account the combination of natural-climatic, soil and environmental factors. To construct a fuzzy model, the parameters to be modeled were the coefficient of bioclimatic productivity with a range of variation of 0.5-1.5, and the yield of grain crops, which varied within 10…45 dt/ha for different natural and climatic zones. The theoretical basis for the development of a model of land productivity is the theory of fuzzy inference based on the fuzzy-multiple approach. The main stages of fuzzy modeling using the Mamdani algorithm in interactive mode are presented. An algorithmic representation of the dependence of the integral indicator of the productivity of agricultural land on the value of the yield of grain crops, and the coefficient of bioclimatic productivity is obtained. The constructed fuzzy model allows to obtain estimates of the generalized indicator of agricultural land productivity based on the yield values for the range of values of the bioclimatic coefficient. The constructed model can be used as a part of a system for predicting the level of food security.

The Contextual Theory of Explanation and Inference to the Best Explanation

Van Fraassen explains rejections and asymmetries in science in terms of his contextual theory of explanation in the same way that scientists explain observable phenomena in the world in terms of scientific theories. I object that van Fraassen’s skeptical view regarding inference to the best explanation together with the English view of rationality jointly imply that the contextual theory is not rationally compelling, so van Fraassen and his epistemic colleagues can rationally disbelieve it. Prasetya replies that the truth of the contextual theory coincides with its empirical adequacy, so the contextual theory is compelling. He also replies that the contextual theory is compelling, provided that van Fraassen’s argument for it instantiates a compelling subset of IBE. I argue that Prasetya’s replies either fail or help scientific realism.

Should the Coercion Thesis really be defended as a conceptual claim?

Kenneth Himma’s Coercion and the Nature of Law offers a forceful response to the claim that coercion is an ancillary, rather than a conceptually necessary, property of law. It matters for Himma that the Coercion Thesis is defended as a conceptual claim, although his metaphysics purports to be a “modest” conceptual analysis in Frank Jackson’s sense. In this comment I have no argument against the Coercion Thesis, and I grant that Himma provides a successful reply to the skepticism about coercion that permeates the jurisprudence of H.L.A. Hart and Joseph Raz. Nonetheless, I believe that this thesis cannot be defended with the methodology that Himma suggests. I challenge Himma’s interpretation of Jackson (which claims that it requires one to start with lexical meanings and “canons” of ordinary language) and argue that his argument for the coercive character of international law only works because it is based on a pragmatic argument about empirical features of social practices. After explaining this objection, I hold that other methodologies, like Dworkin’s interpretivism (if interpreted as endorsing an inferentialist theory of meaning), can provide a more plausible account of the coercive character of law.

La gènesi dels somatismes: accions humanes fossilitzades

Somatic idioms – those including a part of the body – have been traditionally studied from a synchronic perspective, yielding different explanations for their semantic value. The main objective of this paper is to highlight the diachronic origin of idiomatic meaning, by illustrating the process of phraseologization from a historical, usage-based perspective. As the first step, we will reflect on the general nature of phraseological meaning, and then on the semantic particularities of somatic idioms. Secondly, we will carryout a corpus-based diachronic analysis of the Catalan idiom tapar-se el nas (to hold one’s nose) within the framework of the Invited Inference Theory of Semantic Change. The different stages of the process will be exemplified and discussed. As a result, a new notion of somatic idioms as frozen human actions will be presented.

A Constructive Treatment to Elemental Life Forms through Mathematical Philosophy

The quest to understand the natural and the mathematical as well as philosophical principles of dynamics of life forms are ancient in the human history of science. In ancient times, Pythagoras and Plato, and later, Copernicus and Galileo, correctly observed that the grand book of nature is written in the language of mathematics. Platonism, Aristotelian logism, neo-realism, monadism of Leibniz, Hegelian idealism and others have made efforts to understand reasons of existence of life forms in nature and the underlying principles through the lenses of philosophy and mathematics. In this paper, an approach is made to treat the similar question about nature and existential life forms in view of mathematical philosophy. The approach follows constructivism to formulate an abstract model to understand existential life forms in nature and its dynamics by selectively combining the elements of various schools of thoughts. The formalisms of predicate logic, probabilistic inference and homotopy theory of algebraic topology are employed to construct a structure in local time-scale horizon and in cosmological time-scale horizon. It aims to resolve the relative and apparent conflicts present in various thoughts in the process, and it has made an effort to establish a logically coherent interpretation.

Statistical Inference for High-Dimensional Matrix-Variate Factor Models

ABSTRACT This article considers the estimation and inference of the low-rank components in high-dimensional matrix-variate factor models, where each dimension of the matrix-variates (p × q) is comparable to or greater than the number of observations (T). We propose an estimation method called α-PCA that preserves the matrix structure and aggregates mean and contemporary covariance through a hyper-parameter α. We develop an inferential theory, establishing consistency, the rate of convergence, and the limiting distributions, under general conditions that allow for correlations across time, rows, or columns of the noise. We show both theoretical and empirical methods of choosing the best α, depending on the use-case criteria. Simulation results demonstrate the adequacy of the asymptotic results in approximating the finite sample properties. The α-PCA compares favorably with the existing ones. Finally, we illustrate its applications with a real numeric dataset and two real image datasets. In all applications, the proposed estimation procedure outperforms previous methods in the power of variance explanation using out-of-sample 10-fold cross-validation. Supplementary materials for this article are available online.

Johanna Seibt’s Process Ontology of Categorical Inference: On Nomological Axiomatics and Category Projection

Abstract Drawing from a Sellarsian realist-naturalist epistemology, we trace different levels of cognitive hierarchy procedures through which a representational system learns to update its own states and improve its ‘map-making’ capabilities from pre-conscious operations which modulate base-localization functions, to patterns of epistemic revision and integration at the conceptual and theoretical levels, producing a nomological double of its world. We show how ontological theorization becomes diachronically coordinated with and constrained by empirical science, and how the formal-quantitative kernel of scientific theories corresponds to qualitative-conceptual determinations at the structural level. Following Johanna Seibt’s characterization of ontology as a theory of categorial inference, we trace the preservation of inferential semantic structure across ontological theories in relation to model languages and provide provisional indications to coordinate Seibt’s account with a convergent realist assessment of systematic modeling, defining the epistemological conditions for articulating the preservation of formal structure in theories toward a limit-point of enquiry.

Outcome uncertainty influences probability perception and risk attitudes.

Subjective inferences of probability play a critical role in decision-making. How we learn about choice options, through description or experience, influences how we perceive their likelihoods, an effect known as the description–experience (DE) gap. Classically, the DE gap details how low probability described options are perceptually inflated as compared to equiprobable experience ones. However, these studies assessed probability perception relative to a ‘sure-bet’ option, and it remained unclear whether the DE gap occurs when humans directly trade-off equiprobable description and experience options and whether choice patterns are influenced by the prospects of gain and loss. We addressed these questions through two experiments where humans chose between description and experience options with equal probabilities of either winning or losing points. Contrary to early studies, we found that gain-seeking participants preferred experience options across all probability levels and, by contrast, loss-mitigating participants avoided the experience options across all probability levels, with a maximal effect at 50%. Our results suggest that the experience options were perceived as riskier than descriptive options due to the greater uncertainty associated with their outcomes. We conclude by outlining a novel theory of probabilistic inference where outcome uncertainty modulates probability perception and risk attitudes.

Dynamic dispatch algorithm proposal for last-mile delivery vehicle

The complex urban scenario demands decisions which needs to be taken not only fast, but considering a high level of uncertainty. In such context, this study proposes an algorithm to address the vehicle allocation scheduling with a request list unknown a priori, and is revealed along the operation horizon. Based on the OSCO (online stochastic combinatorial optimization) problem, it is presented a framework to operate such problem in a last-mile delivery system where customers requests from e-commerce (or food delivery) along an operations day. The deterministic calculation was realized using a permutational flowshop scheduling problem with the objective of minimizing makespan. Experiments indicated convergence in the algorithm along the planning trials, and a satisfactory makespan prediction, before the orders are revealed, with accuracy. The probability distribution adheres with a bell-shaped (normal) distribution with its population parameters estimated using classical statistics inference theory. Incorporating uncertain in such type of mathematical modeling could open doors for a new era of systems in current days with cloud computing, big data, smart cities, and artificial intelligence.

Future-Proofing Startups: Stress Management Principles Based on Adaptive Calibration Model and Active Inference Theory.

In this paper, the Adaptive Calibration Model (ACM) and Active Inference Theory (AIT) are related to future-proofing startups. ACM encompasses the allocation of energy by the stress response system to alternative options for action, depending upon individuals’ life histories and changing external contexts. More broadly, within AIT, it is posited that humans survive by taking action to align their internal generative models with sensory inputs from external states. The first contribution of the paper is to address the need for future-proofing methods for startups by providing eight stress management principles based on ACM and AIT. Future-proofing methods are needed because, typically, nine out of ten startups do not survive. A second contribution is to relate ACM and AIT to startup life cycle stages. The third contribution is to provide practical examples that show the broader relevance ACM and AIT to organizational practice. These contributions go beyond previous literature concerned with entrepreneurial stress and organizational stress. In particular, rather than focusing on particular stressors, this paper is focused on the recalibrating/updating of startups’ stress responsivity patterns in relation to changes in the internal state of the startup and/or changes in the external state. Overall, the paper makes a contribution to relating physics of life constructs concerned with energy, action and ecological fitness to human organizations.

Human Theory of Mind Inference in Search and Rescue Tasks

The ability to make inferences about other’s mental state is referred to as having a Theory of Mind (ToM). Such ability is the foundation of many human social interactions such as empathy, teamwork, and communication. As intelligent agents being involved in diverse human-agent teams, they are also expected to be socially intelligent to become effective teammates. To provide a feasible baseline for future social intelligent agents, this paper presents a experimental study on the process of human ToM reference. Human observers’ inferences are compared with participants’ verbally reported mental state in a simulated search and rescue task. Results show that ToM inference is a challenging task even for experienced human observers.

Consciousness in active inference: Deep self-models, other minds, and the challenge of psychedelic-induced ego-dissolution.

Predictive processing approaches to brain function are increasingly delivering promise for illuminating the computational underpinnings of a wide range of phenomenological states. It remains unclear, however, whether predictive processing is equipped to accommodate a theory of consciousness itself. Furthermore, objectors have argued that without specification of the core computational mechanisms of consciousness, predictive processing is unable to inform the attribution of consciousness to other non-human (biological and artificial) systems. In this paper, I argue that an account of consciousness in the predictive brain is within reach via recent accounts of phenomenal self-modelling in the active inference framework. The central claim here is that phenomenal consciousness is underpinned by ‘subjective valuation’—a deep inference about the precision or ‘predictability’ of the self-evidencing (‘fitness-promoting’) outcomes of action. Based on this account, I argue that this approach can critically inform the distribution of experience in other systems, paying particular attention to the complex sensory attenuation mechanisms associated with deep self-models. I then consider an objection to the account: several recent papers argue that theories of consciousness that invoke self-consciousness as constitutive or necessary for consciousness are undermined by states (or traits) of ‘selflessness’; in particular the ‘totally selfless’ states of ego-dissolution occasioned by psychedelic drugs. Drawing on existing work that accounts for psychedelic-induced ego-dissolution in the active inference framework, I argue that these states do not threaten to undermine an active inference theory of consciousness. Instead, these accounts corroborate the view that subjective valuation is the constitutive facet of experience, and they highlight the potential of psychedelic research to inform consciousness science, computational psychiatry and computational phenomenology.