Inferential Reasoning Research Articles

Artificial intelligence, Digital Single Market and the proposal of a right to fair and reasonable inferences: a legal issue between ethics and techniques

The article discusses the ethical and technical consequences of Artificial intelligence (hereinafter, A.I) applications and their usage of the European Union data protection legal framework to enable citizens to defend themselves against them. This goal is under the larger European Union Digital Single Market policy, which has concerns about how this subject correlates with personal data protection. The article has four sections. The first one introduces the main issue by describing the importance of AI applications in the contemporary world scenario. The second one describes some fundamental concepts about AI. The third section has an analysis of the ongoing policies for AI in the European Union and the Council of Europe proposal about ethics applicable to AI in the judicial systems. The fourth section is the conclusion, which debates the current legal mechanisms for citizens protection against fully automated decisions, based on European Union Law and in particular the General Data Protection Regulation. The conclusion will be that European Union Law is still under construction when it comes to providing effective protection to its citizens against automated inferences that are unfair or unreasonable.

Hybrid BN Approach to Analyzing Risk in Tunnel-Induced Bridge Damage

Tunnels are generally built under trunk roads (where traffic conditions are complex and heavy) in order to share the load of urban road systems. The construction of tunnels has significant effects on the surrounding environment, especially in connection with nearby bridges, since it may cause soil loosening, ground deformation, and bridge pile settlement. Traditional methods, such as fuzzy set, fault tree analysis (FTA), artificial neural network (ANN), are inefficient and inaccurate when it comes to analyzing risk magnitudes or probabilities in a dynamic environment. To fill this gap, this paper, with the aim of predicting, monitoring, and diagnosing risk factors for tunnel-induced damage, provides a new framework that integrates the advantages of rough set (RS), cloud model (CM), and Bayesian network (BN). A RS approach is used to handle the vagueness and uncertainty and reduce redundancies for improving the efficiency of the evaluation process, CM is employed to discretize continuous attributes without changing their distinguishing characteristics, and BN is utilized to train mess data with a reasonable inference mechanism and update the risk magnitude with given evidence. To model the risk network of the tunnel-induced bridge damage, 14 critical influential factors are identified. A real tunnel project that crosses an overpass of expressways in China is used as a case to prove the applicability and feasibility of the proposed approach. Results verify that the proposed hybrid BN approach can be used to predict, supervise, and diagnose the risk before, during, and after tunnel construction. This research contributes to (a) the state of knowledge by providing a novel risk analysis approach that is capable of handling fuzziness, uncertainty, and dynamics in factor characterization; and (b) the state of the practice by providing insights into a better understanding of how to predict, control, and diagnose risks under given observations.

When Stronger Knowledge Slows You Down: Semantic Relatedness Predicts Children's Co-Activation of Related Items in a Visual Search Paradigm.

A large literature suggests that the organization of words in semantic memory, reflecting meaningful relations among words and the concepts to which they refer, supports many cognitive processes, including memory encoding and retrieval, word learning, and inferential reasoning. The co-activation of related items has been proposed as a mechanism by which semantic knowledge influences cognition, and contemporary accounts of semantic knowledge propose that this co-activation is graded-that it depends on how strongly related the items are in semantic memory. Prior research with adults yielded evidence supporting this prediction; however, there is currently no evidence of graded co-activation early in development. This study provides the first evidence that in children the co-activation of related items depends on their relational strength in semantic memory. Participants (N=84, age range: 3-9years) were asked to identify a target (e.g., bone) amid distractors. Children's responses were slowed down by the presence of a related distractor (e.g., puppy) relative to unrelated distractors (e.g., flower)-suggesting that children co-activated related items upon hearing the name of the target. Importantly, the degree of this co-activation was predicted by the strength of the target-distractor relation, such that distractors more strongly related to the targets slowed down children to a larger extent. These findings have important implications for understanding how organized semantic knowledge affects other cognitive processes across development.

ASSESSMENT OF INFORMAL AND FORMAL INFERENTIAL REASONING: A CRITICAL RESEARCH REVIEW

As a contribution to the discussion on the assessment of informal inferential reasoning (IIR) and the transition from this to formal inferential reasoning (FIR), we present a review of research on how these two types of inferential reasoning have been conceptualized and assessed. Based on our review, we discuss the need to redefine the conceptions of IIR and FIR in order to create an integrated description of inferential reasoning that includes not only ideas of IIR and FIR, but also the whole activity of argumentation, which involves the production of both statistical and contextual reasons. Current descriptions of IIR and FIR list the facts that might be brought from data analysis to the process of inferential reasoning. The approach we propose considers how the facts, both statistical and contextual, can be used as arguments, leading to assessments of students’ inferential reasoning focusing on articulating the statistical and contextual reasons students present to support an inference. First published May 2019 at Statistics Education Research Journal Archives

A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI

Big Data analytics and artificial intelligence (AI) draw non-intuitive and unverifiable inferences and predictions about the behaviors, preferences, and private lives of individuals. These inferences draw on highly diverse and feature-rich data of unpredictable value, and create new opportunities for discriminatory, biased, and invasive decision-making. Data protection law is meant to protect people’s privacy, identity, reputation, and autonomy, but is currently failing to protect data subjects from the novel risks of inferential analytics. The legal status of inferences is heavily disputed in legal scholarship, and marked by inconsistencies and contradictions within and between the views of the Article 29 Working Party and the European Court of Justice (ECJ). This Article shows that individuals are granted little control or oversight over how their personal data is used to draw inferences about them. Compared to other types of personal data, inferences are effectively “economy class” personal data in the General Data Protection Regulation (GDPR). Data subjects’ rights to know about (Articles 13–15), rectify (Article 16), delete (Article 17), object to (Article 21), or port (Article 20) personal data are significantly curtailed for inferences. The GDPR also provides insufficient protection against sensitive inferences (Article 9) or remedies to challenge inferences or important decisions based on them (Article 22(3)). This situation is not accidental. In standing jurisprudence the ECJ has consistently restricted the remit of data protection law to assessing the legitimacy of input personal data undergoing processing, and to rectify, block, or erase it. Critically, the ECJ has likewise made clear that data protection law is not intended to ensure the accuracy of decisions and decision-making processes involving personal data, or to make these processes fully transparent. Current policy proposals addressing privacy protection (the ePrivacy Regulation and the EU Digital Content Directive) and Europe’s new Copyright Directive and Trade Secrets Directive also fail to close the GDPR’s accountability gaps concerning inferences. This Article argues that a new data protection right, the “right to reasonable inferences,” is needed to help close the accountability gap currently posed by “high risk inferences,” meaning inferences drawn from Big Data analytics that damage privacy or reputation, or have low verifiability in the sense of being predictive or opinion-based while being used in important decisions. This right would require ex-ante justification to be given by the data controller to establish whether an inference is reasonable. This disclosure would address (1) why certain data form a normatively acceptable basis from which to draw inferences; (2) why these inferences are relevant and normatively acceptable for the chosen processing purpose or type of automated decision; and (3) whether the data and methods used to draw the inferences are accurate and statistically reliable. The ex-ante justification is bolstered by an additional ex-post mechanism enabling unreasonable inferences to be challenged.

Highly polymorphic mitochondrial DNA and deceiving haplotypic differentiation: implications for assessing population genetic differentiation and connectivity

BackgroundHyperdiverse mtDNA with more than 5% of variable synonymous nucleotide sites can lead to erroneous interpretations of population genetic differentiation patterns and parameters (φST, DEST). We illustrate this by using hyperdiverse mtDNA markers to infer population genetic differentiation and connectivity in Melarhaphe neritoides, a NE Atlantic (NEA) gastropod with a high dispersal potential. We also provide a recent literature example of how mtDNA hyperdiversity may have misguided the interpretation of genetic connectivity in the crab Opecarcinus hypostegus.ResultsmtDNA variation surveyed throughout the NEA showed that nearly all M. neritoides specimens had haplotypes private to populations, suggesting at first glance a lack of gene flow and thus a strong population genetic differentiation. Yet, the bush-like haplotype network, though visually misleading, showed no signs of phylogeographic or other haplotype structuring. Coalescent-based gene flow estimates were high throughout the NEA, irrespective of whether or not mtDNA hyperdiversity was reduced by removing hypervariable sites.ConclusionsMelarhaphe neritoides seems to be panmictic over the entire NEA, which is consistent with its long-lived pelagic larval stage. With hyperdiverse mtDNA, the apparent lack of shared haplotypes among populations does not necessarily reflect a lack of gene flow and/or population genetic differentiation by fixation of alternative haplotypes (DEST ≈ 1 does not a fortiori imply φST ≈ 1), but may be due to (1) a too low sampling effort to detect shared haplotypes and/or (2) a very high mutation rate that may conceal the signal of gene flow. Hyperdiverse mtDNA can be used to assess connectivity by coalescent-based methods. Yet, the combined use of φST and DEST can provide a reasonable inference of connectivity patterns from hyperdiverse mtDNA, too.

A Jumpstart Framework for Semantically Enhanced OPC-UA

Decentralization is the norm of future smart production as it assists in contextual dynamic decision-making and thereby increases the flexibility required to produce highly customized products. When manufacturing business software is operated as a cloud based solution, it experiences network latency and connectivity issues. To overcome these problems, the production control should be delegated to the manufacturing edge layer and hence, the argument of decentralization is even more applicable to this narrative. In order to accomplish the assigned manufacturing task effectively, the edge layer is required to possess contextual awareness to make run-time decisions in production. Semantic technologies, on the other hand, assist in discerning the meaning, reasoning and drawing inferences from the data. There are several specifications and frameworks to automate the discovery, orchestration and invocation of web services; the prominent are OWL-S, SAWSDL and WSMO. This paper derives a hybrid approach that integrates OWL-S and SAWSDL specifications to overcome the downsides, yet retain the benefits of both approaches to the OPC-UA application methods. Consequently, the proposed semantically enriched OPC-UA concept enables the edge layer to create flexible production orchestration plans in a manufacturing scenario controlled by cloud MES. Furthermore, the derived hybrid approach is applied to a real use case to demonstrate its feasibility in industrial environments.

통계적 추리에서 표집 분포와 신뢰구간에 대한 이해 조사

This study is a part of the design research project for the development of informal inferential reasoning. With informal inferential reasoning, students can better learn formal statistical inference, which have been shown in recent statistical education researches. The goal of this study is to investigate how the undergraduates who have not completed basic statistics course understand the sampling distribution and the confidence interval, and to find which misconceptions they have. For this purpose, we developed tasks related to sampling distribution and confidence interval, and conducted the surveys and follow-up interviews with 27 participants selected as the sample. The results were analyzed quantitatively and qualitatively. From the analysis, many participants did not understand the concept of sampling distribution and the confidence interval, and had various misconceptions. The participants showed a tendency to choose a shape similar to the population distribution instead of correct sampling distribution and did not recognize the effect of sample size. They also did not understand that the mean of the sample mean is the same as the mean of the population. In addition, the participants showed errors in interpreting the confidence interval such as a fixed interval without variability, or an interval containing a proportion of elements of the population as much as the confidence level, or an interval containing a proportion of the sample as much as the confidence level. With these findings, implications for the teaching of statistical inference were derived.

Toddlers’ understanding and use of verbal negation in inferential reasoning search tasks

Past research has demonstrated that young children and nonhuman animals are able to reason by elimination (“If not A, then B”) by relying on visual cues such as seeing that one container is empty. Other research has shown that young children can solve similar, simple inferential reasoning tasks where “emptiness” is conveyed verbally through negation (e.g., “The toy is not in the box”). However, it is unclear whether these tasks involved reasoning through the disjunctive syllogism, which requires the representation of logical negation (NOT A) and disjunction (A OR B) or simpler, nondeductive strategies. In Study 1, we extended this work by investigating whether 2-year-olds can infer the location of a toy in typical two-location elimination trials, when given both affirmative and negative sentences, and more complex three-location trials, when information about emptiness was conveyed verbally and visually. Younger 2-year-olds performed significantly better on the search task when hearing affirmative than negative sentences, whereas older 2-year-olds were equally successful with both types of sentences. Study 2 examined children’s ability to use verbal negation to solve a more complex deductive task involving disjunctive syllogism. Results showed that, in this linguistic version of the disjunctive reasoning task, both 2.5- and 3-year-olds made accurate inferences about the location of a reward, unlike prior (nonlinguistic) evidence that demonstrated this ability in 3-year-olds but not in younger children. We conclude that by the end of their second year of life, children have a robust understanding of negation which they can apply in abstract reasoning.

Kritische Metaphysik oder Analytische Hermeneutik?

Abstract Kant in his critical metaphysics, as one might call his transcendental philosophy, proceeds from the syncategorematic, subject-sided forms of thinking, which are revealed by general logic qua doctrine of the inferences of reason (i. e. syllogistics), and assigns to them one-to-one categorematic, object-sided forms of thinking: the categories qua pure, non-empirical predicates of things. Kant then shows in his transcendental deduction that the categories are objectively, – i. e. without our invasive intervention – valid of all things in space-time. In the present essay, philosophy is understood not so much as critical metaphysics in a narrow sense of “metaphysics”, but rather as the a priori hermeneutic science; and the transcendental deduction of the categories is replaced by arguments for (1) a readability thesis and (2) a theory of the a priori presuppositions of referencing things in space and time. The readability thesis states that things can be read (1) as world-sided primal tokens (ur-tokens) of proper names of themselves and also (2) as world-sided primal tokens (ur-tokens) of elementary propositions about them. The theory of the a priori presuppositions clarifies the conditions of the possibility of subjects orienting themselves in space and time and being able to refer, first, to themselves qua embodied thinkers and then as well to arbitrary individual items.

Biased political reasoning and relational inferences in a small-group deliberative context

Theorists of deliberative democracy envision a citizenry engaged in collective reasoning about public issues on the merits of rival arguments. Partisanship undermines this ideal when it causes people to discount counter-attitudinal arguments, independent of their quality. Empirical deliberative theory lacks an account for what mechanism mitigates this bias in small-group settings. To close that theoretical gap, this study draws on Relational Framing Theory and identifies a relational component of the reasoning process. Participants rated the relevance of dominance/submission and affiliation/disaffiliation relational frames after a small-group deliberation. This perception influenced participants’ decisions to endorse arguments as legitimate public reasons. Implications for deliberative theory, research, and practice are discussed.

Exploring informal inferential reasoning: the case of comparing two data sets problem

Informal inferential reasoning as part of statistical reasoning is extremely important in learning. However, this reasoning has not been widely studied in Indonesia. This article explores informal inferential reasoning of students in solving comparing two data sets problem. This qualitative research was involved 118 junior high school students in Mataram West Nusa Tenggara. Instrument in this study is Assessment of Informal Inferential Reasoning (AIIR) which was modified and validated by mathematics and mathematics education experts. Student responses are classified based hierarchical cognitive framework. The findings showed that the existing levels was still relevant for the students of junior high school in Mataram. However, there were subjects which could not be classified in the levelling. The characteristic reasoning of the subjects are tends to ignore the variability and distribution aspects of the data in making predictions, but they can indicate significant value differences between two groups of data. They encountered an error in certain aspects, but they are able to produce an almost right conclusions. This study recommends the need for a new levelling to assess informal inferential reasoning of junior high school students by considering the ability of local and global view of data.

Are mnemonic failures and benefits two sides of the same coin?: Investigating the real-world consequences of individual differences in memory integration.

Theories of reconstructive memory have long been influenced by investigations of false recognition errors, in which old/new judgements are compromised by spontaneous activation of associated but nonpresented concepts. Recent evidence similarly suggests that reconstructive memory processes (so-called memory integration) also support positive learning behaviors, such as inferential reasoning. Despite prevailing hypotheses, the question of whether a common integration process underlies these seemingly disparate mnemonic outcomes is not well understood. To address this question, young adults, recruited from two institutions, completed the Deese-Roediger-McDermott (Deese, Journal of Experimental Psychology, 58, 17-22, 1959; Roediger & McDermott, Journal of Experimental Psychology: Learning, Memory, and Cognition, 21(4), 803-814, 1995) and Bransford and Franks (Cognitive Psychology, 2, 331-350, 1971) false recognition paradigms, as well as an inferential paradigm (Varga & Bauer, Memory & Cognition, 45, 1014-1027, 2017b), all of which depend on integration of related information in memory. Across two experiments, the well-established tasks were adapted such that successful memory integration resulted in the same negative outcome (i.e., false recognition; Experiment 1) or positive outcome (i.e., inferential reasoning; Experiment 2). By capturing variability in item-to-item responding within and among tasks for each person, a common memory integration process was found to elicit positive and negative consequences in paradigms that required the combination of individual units to construct a composite understanding, but only when memory for directly learned and novel, integrated items were modeled together. Furthermore, linking task-related behavior to academic performance revealed that a greater propensity to integrate factual information (but not arbitrary materials) was related to higher SAT scores. Together, these results provide evidence for domain-general and domain-specific reconstructive mechanisms and their role in supporting educational success beyond the laboratory.

Parametric schema inference for massive JSON datasets

In recent years, JSON established itself as a very popular data format for representing massive data collections. JSON data collections are usually schemaless. While this ensures several advantages, the absence of schema information has important negative consequences as well: Data analysts and programmers cannot exploit a schema for a reliable description of the structure of the dataset, the correctness of complex queries and programs cannot be statically checked, and many schema-based optimizations are not possible. In this paper, we deal with the problem of inferring a schema from massive JSON datasets. We first identify a JSON type language which is simple and, at the same time, expressive enough to capture irregularities and to give complete structural information about input data. We then present our contributions, which are the design of a parametric and parallelizable schema inference algorithm, its theoretical study, and its implementation based on Spark, enabling reasonable schema inference time for massive collections. Our algorithm is parametric as the analyst can specify a parameter determining the level of precision and conciseness of the inferred schema. Finally, we report about an experimental analysis showing the effectiveness of our approach in terms of execution time, conciseness of inferred schemas, and scalability.

Reasonable inferences from quantum mechanics: A response to “Quantum misuse in psychic literature”.

Reasonable inferences from quantum mechanics: A response to “Quantum misuse in psychic literature”.

Reasonable inferences from quantum mechanics: A response to “Quantum misuse in psychic literature”.

This invited article is a response to the paper "Quantum Misuse in Psychic Literature," by Jack A. Mroczkowski and Alexis P. Malozemoff, published in this issue of the Journal of Near-Death Studies. Whereas I sympathize with Mroczkowski's and Malozemoff's cause and goals, and I recognize the problem that they attempted to tackle, I argue that their criticisms often overshot the mark and end up adding to the confusion. I address nince specific technical points that Mroczkowski and Malozemoff accused popular writers in the fields of health care and parapsychology of misunderstanding and misrepresenting. I argue that, by and large--and contrary to Mroczkowski's and Malozemoff's claims--the statements made by these writers are often reasonable and generally consistent with the current state of play in foundations of quantum mechanics.

Supplemental Material for Inferential Reasoning in the Visual and Auditory Modalities by Cotton-Top Tamarins (Saguinus oedipus)

Supplemental Material for Inferential Reasoning in the Visual and Auditory Modalities by Cotton-Top Tamarins (Saguinus oedipus)

Performance of a 270 MW Gas Power Plant Using Exergy and Heat Rate

The performance of a 270 MW (9 × 30 MW) AES Corporation barge mounted gas turbine power plant in Nigeria is evaluated using the heat rate and entropy generation by the components of the plant to characterize the irreversibility in each component when operating at different loads between 90% and 25%. The power plants have the peculiarity that three of the plants were supplied by three (3) different Original Equipment Manufacturers (OEM); A, B and C. This study is sequel to the fact that the gas turbines were the first independent power plants in the country and after more than fifteen years of operation, it is reasonable to evaluate the performance of the major components. By analyzing the thermodynamic performance of these components, the study demonstrates the utility value of exergy efficiency as an important parameter in the evaluation of major components in a gas power plant. Exergy efficiency is shown to be an important parameter in ranking the power plant components, identifying and quantifying the possible areas of reduction in thermodynamic losses and improvement in efficiencies. A new relationship is derived to demonstrate the correlation between the exergy efficiency and the heat rate of a 30 MW gas power plant. The prediction of the derived relationship correlates well with the observed operational performance of the 30 MW power plants. The combustion chamber in each of the plants provides the maximum exergy destruction during operation. Its exergy efficiency is shown to exhibit good correlation with its energy efficiency and the plant rational exergy. The implication is that from an operational and component selection viewpoint in the specifications of a gas power plant, knowledge of the Heat Rate which is usually provided by the OEM is adequate to make a reasonable inference on the performance of some critical components of the plant.

Conditionally autoregressive models improve occupancy analyses of autocorrelated data: An example with environmental DNA.

Site occupancy-detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false-positive and false-negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true-/false-positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true-/false-positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs.

How Do Actions Influence Attitudes? An Inferential Account of the Impact of Action Performance on Stimulus Evaluation

Over the past decade, an increasing number of studies have shown that the performance of specific actions (e.g., approach and avoidance) in response to a stimulus can lead to changes in how that stimulus is evaluated. In contrast to the reigning idea that these effects are mediated by the automatic formation and activation of associations in memory, we describe an inferential account that specifies the inferences underlying the effects and how these inferences are formed. We draw on predictive processing theories to explain the basic processes underlying inferential reasoning and their main characteristics. Our inferential account accommodates past findings, is supported by new findings, and leads to novel predictions as well as concrete recommendations for how action performance can be used to influence real-world behavior.