Role In Inference Research Articles

Not all communication styles are created equal: the differing effects of communicating the family business brand’s roots and virtues

Purpose This study aims to examine the impact of communication style focusing on the family business brand’s roots and virtues on inferences of manipulative intent and willingness to pay a price premium, applying the persuasion knowledge model. Design/methodology/approach This study collects data involving 337 participants from France and applies partial least squares structural equation modeling (PLS-SEM) to test the hypotheses. Findings The results demonstrate that communication style plays an important role in inferences of manipulative intent: focusing on the family business brand’s virtues has a positive effect while focusing on the family business brand’s roots has a nonsignificant effect. In turn, inferences of manipulative intent have a negative and significant effect on willingness to pay a price premium. Furthermore, age does not moderate the effect of communication style focusing on the family restaurant brand’s roots and inferences of manipulative intent but positively moderates the effect of communication style focusing on the family restaurant brand’s virtues on inferences of manipulative intent. Originality/value To the best of the authors’ knowledge, this study is the first to try to unpack the differing effects of communication styles in the context of family business brands. In this vein, it has insightful theoretical and managerial implications for family business brands.

Where you go is who you are: a study on machine learning based semantic privacy attacks

Concerns about data privacy are omnipresent, given the increasing usage of digital applications and their underlying business model that includes selling user data. Location data is particularly sensitive since they allow us to infer activity patterns and interests of users, e.g., by categorizing visited locations based on nearby points of interest (POI). On top of that, machine learning methods provide new powerful tools to interpret big data. In light of these considerations, we raise the following question: What is the actual risk that realistic, machine learning based privacy attacks can obtain meaningful semantic information from raw location data, subject to inaccuracies in the data? In response, we present a systematic analysis of two attack scenarios, namely location categorization and user profiling. Experiments on the Foursquare dataset and tracking data demonstrate the potential for abuse of high-quality spatial information, leading to a significant privacy loss even with location inaccuracy of up to 200 m. With location obfuscation of more than 1 km, spatial information hardly adds any value, but a high privacy risk solely from temporal information remains. The availability of public context data such as POIs plays a key role in inference based on spatial information. Our findings point out the risks of ever-growing databases of tracking data and spatial context data, which policymakers should consider for privacy regulations, and which could guide individuals in their personal location protection measures.

Evading Big Brother: Using visual methods to understand children’s perception of sensors and interest in subverting digital surveillance

In relation to this Special Issue’s focus on ugly information, this article examines children’s perception of the often invisible interactions they have with sensor-enabled digital devices and, when prompted, their interest in subverting or blocking these sensors to evade surveillance. The authors report on a study of 12 children, aged 8–12 years, that investigated their knowledge of the sensing abilities of commonly used digital devices (smart phones, smart watches, smart speakers and games consoles), and their attitudes towards having active agency over sensors. In line with this journal’s readership, visual methods used for data collection and analysis are described. Specifically, within semi-structured focus groups, drawing was used to understand what children thought was inside digital devices and the extent of their awareness of digital sensors. Child participants were invited to model speculative tools for deceiving digital sensors in order to explore their interest in having agency over digital surveillance. Data in the form of drawings, photographs of models and video recordings were analysed using experimental visual methods that included 3D rendering and comics, as well as visual content and thematic analysis. These drew out four key themes: (1) the role of inference in sensor awareness; (2) misunderstanding of device components and sensing capabilities; (3) attitudes to surveillance; and (4) children’s interest in subverting rather than blocking sensors. We discuss how technology companies’ desire to create ‘magical experiences’ may contribute to incorrect inferences about information gathering systems, how this reduces children’s agency over the information they share and how it puts them at greater risk from digital surveillance. The article makes an original contribution to knowledge in this area by calling for a two-pronged approach from technology companies and educators to address these issues by making sensor presence more visible, educating children about the full extent of sensor capability and bringing critical discussion of them into curricula.

Impaired Grid-Like Representations at Theta Frequency in Schizophrenia

AimsSchizophrenia is a chronic brain disorder characterised by distortion of thoughts and perception. Several studies have shown a key role of the hippocampal formation in the pathophysiology of schizophrenia. Patients show impaired theta coherence between medial temporal lobe and medial prefrontal cortex (mPFC), and impairment of knowledge structuring and inferential processes. Both the hippocampal formation and mPFC contain hexadirectional modulation of activity, indicative of grid cell populations. Grid cells play an important role in mapping the environment and are believed to represent the transition structure between task states. With other cell populations in the hippocampal formation, they play a fundamental role in inference, episodic memory, and spatial navigation. Here, we investigate whether schizophrenia is associated with disrupted grid firing patterns.MethodsTo test this hypothesis, we asked 18 participants with diagnoses of schizophrenia and 26 controls (matched for age, sex and IQ) to perform a spatial memory task in magnetoencephalography (MEG), while navigating a virtual reality environment. We first analysed theta (4–10 Hz) power during movement onset compared to stationary periods. We then source-localised the signal and looked for the hexadirectional modulation of theta band oscillatory activity by heading direction during movement onset. We also controlled for other symmetries in theta frequencies (four, five, and eight fold) and hexadirectional modulation in other frequencies. The same participants performed an inference task outside MEG, which we used for correlation analysis.ResultsThe peak of theta power during movement onset was stronger in controls compared to patients (p < 0.05). In the control group, we found hexadirectional modulation of theta power by movement direction in the right entorhinal cortex (p < 0.005). This effect was absent in patients with a significant difference between groups (p < 0.05), suggesting that their entorhinal grid firing patterns may be disrupted. No other symmetry modulated theta power significantly in controls or patients, and hexadirectional modulation during movement onset was found only in theta frequencies in controls. Performance in the inference task was significantly impaired in schizophrenic patients, and spatial memory performance in both controls and patients was positively correlated with their performance in the inference task.ConclusionThese results are consistent with the hypothesis that impairments in knowledge structuring and inference associated with schizophrenia may arise from disrupted grid firing patterns in entorhinal cortex. Although further work is needed to better understand the role of grid cells in health and disease, this work provides new insights into dysfunction of the hippocampal formation in schizophrenia.

Prajñākaragupta on the Role of Inference (I)

Prajñākaragupta on the Role of Inference (I)

The role of inferences in reading comprehension: A critical analysis

The central role attributed to inferences in reading comprehension can be traced back to the Construction-Integration (CI) model, and many of its theoretical assumptions are still shared by current models. This article analyses recent research in terms of how inferences have been conceived, how they relate to comprehension, and how the CI model’s theoretical legacy has been articulated. The main issues found are that the way inferences are currently conceived doesn’t satisfactorily distinguish them from ordinary comprehension and that a series of assumptions which plausibly apply to computational models have been often mistakenly attributed to interpersonal processes. This, added to the widespread usage of lab-created texts in experiments, hinders the faithful capturing of personal comprehension processes. Finally, we propose recommendations for future research based on conceptual clarity, metatheoretical awareness, and a meaning-based approach on language, so as to improve interresearcher communication, theoretical consistency, and ecological validity.

Executive Functioning with the NIH EXAMINER and Inference Making in Struggling Readers

ABSTRACT Executive functioning (EF) is a domain-general process implicated in reading, but there remains lack of clarity within the domain due to varied assessment methods. We investigated the relation of EF (indexed with the NIH EXAMINER) to word reading, reading fluency, and reading comprehension in struggling readers and evaluated the mediational role of inference making for the EF-comprehension link. Analyses revealed an overall effect of EF on reading, with significant differences between fluency and comprehension, and between single word reading and comprehension, but not between fluency and single-word reading. The EF-reading comprehension relation was fully mediated by inference making.

Inference and preference in intertemporal choice

AbstractWhen choosing between immediate and future rewards, how do people deal with uncertainty about the value of the future outcome or the delay until its occurrence? Skylark et al. (2020) suggested that people employ a delay-reward heuristic: the inferred value of an ambiguous future reward is a function of the stated delay, and vice-versa. The present paper investigates the role of this heuristic in choice behaviour. In Studies 1a–2b, participants inferred the value of an ambiguous future reward or delay before the true value was revealed and a choice made. Preference for the future option was predicted by the discrepancy between the estimated and true values: the more pleasantly surprising the delayed option, the greater the willingness to choose it. Studies 3a–3c examined the association between inference and preference when the ambiguous values remained unknown. As predicted by the use of a delay-reward heuristic, inferred delays and rewards were positively related to stated rewards and delays, respectively. More importantly, choices were associated with inferred rewards and, in some circumstances, delays. Critically, estimates and choices were both order-dependent: when estimates preceded choices, estimates were more optimistic (people inferred smaller delays and larger rewards) and were subsequently more likely to choose the delayed option than when choices were made before estimates. These order effects argue against a simple model in which people deal with ambiguity by first estimating the unknown value and then using their estimate as the basis for decision. Rather, it seems that inferences are partly constructed from choices, and the role of inference in choice depends on whether an explicit estimate is made prior to choosing. Finally, we also find that inferences about ambiguous delays depend on whether the estimate has to be made in “days” or in a self-selected temporal unit, and replicate previous findings that older participants make more pessimistic inferences than younger ones. We discuss the implications and possible mechanisms for these findings.

A starring role for inference in the neurocognition of visual narratives

Research in verbal and visual narratives has often emphasized backward-looking inferences, where absent information is subsequently inferred. However, comics use conventions like star-shaped “action stars” where a reader knows events are undepicted at that moment, rather than omitted entirely. We contrasted the event-related brain potentials (ERPs) to visual narratives depicting an explicit event, an action star, or a “noise” panel of scrambled lines. Both action stars and noise panels evoked large N400s compared to explicit-events (300–500 ms), but action stars and noise panels then differed in their later effects (500–900 ms). Action stars elicited sustained negativities and P600s, which could indicate further interpretive processes and integration of meaning into a mental model, while noise panels evoked late frontal positivities possibly indexing that they were improbable narrative units. Nevertheless, panels following action stars and noise panels both evoked late sustained negativities, implying further inferential processing. Inference in visual narratives thus uses cascading mechanisms resembling those in language processing that differ based on the inferential techniques.

A Penalized h-Likelihood Variable Selection Algorithm for Generalized Linear Regression Models with Random Effects

Reinforcement learning is one of the paradigms and methodologies of machine learning developed in the computational intelligence community. Reinforcement learning algorithms present a major challenge in complex dynamics recently. In the perspective of variable selection, we often come across situations where too many variables are included in the full model at the initial stage of modeling. Due to a high-dimensional and intractable integral of longitudinal data, likelihood inference is computationally challenging. It can be computationally difficult such as very slow convergence or even nonconvergence, for the computationally intensive methods. Recently, hierarchical likelihood (h-likelihood) plays an important role in inferences for models having unobservable or unobserved random variables. This paper focuses linear models with random effects in the mean structure and proposes a penalized h-likelihood algorithm which incorporates variable selection procedures in the setting of mean modeling via h-likelihood. The penalized h-likelihood method avoids the messy integration for the random effects and is computationally efficient. Furthermore, it demonstrates good performance in relevant-variable selection. Throughout theoretical analysis and simulations, it is confirmed that the penalized h-likelihood algorithm produces good fixed effect estimation results and can identify zero regression coefficients in modeling the mean structure.

Communicating Sense: The Role of Inference in Joint Sensemaking in Equivocal Contexts

In this paper, we build a conceptual framework that elaborates the communicative mechanisms by which individuals in high reliability organizations (HROs) jointly make sense in order to coordinate in equivocal contexts. We build on past work in cognitive and discursive sensemaking, while also highlighting that past research tends to conflate cognition and discourse by presupposing cognitive effects of discourse. Instead, we submit that sensemaking research would benefit from a focus on how organizational actors make inferences and build shared understanding in context. We provide this analytical focus on inference by borrowing theoretical principles and assumptions from relevance theory, a theory in the neighboring field of pragmatic linguistics, and illustrating these principles with examples from seminal past cases of sensemaking under pressure. Our conceptual framework exposes the communicative processes of ostension and inference by which actors build a mutual cognitive environment. Crucially, our paper underscores the importance of investigating how failed inferences hinder coordinated action. We elaborate the contributions of our conceptual framework to sensemaking research, reexamining the concepts of equivocality, requisite variety, and double interacts from a communication standpoint.

A Hartian Account of Genuine Theoretical Disagreement

In Law’s Empire, Ronald Dworkin raised what he called a new objection to Hart’s positivist theory of law. Dworkin contended that Hartian legal positivism cannot account for the genuine possibility of theoretical disagreement in the law, because, according to the positivists, law reduces to a question about social facts. This means that if there is a question about what the criteria of legal validity are, it must be resolved by answers to empirical questions, like how in fact the officials are acting, and not theoretical questions. In response, Leiter and Shapiro proffer ways of defusing the problem for the positivist. Leiter questions the face value of theoretical disagreements by showing that the prototypical examples of theoretical disagreement are disingenuous or erroneous. Shapiro shows that the positivist can account for theoretical disagreement, by looking at competing interpretive methodologies, but answering the question this way requires sacrificing the conventionality thesis of Hartian legal positivism. In this piece, I set forth a new response on behalf of the Hartian legal positivist. Specifically, I contend that the Hartian legal positivist can respond to the problem of theoretical disagreement in a way that both vindicates the face value of theoretical disagreement and maintains its critical commitments, specifically the separability thesis, the social facts thesis, and the conventionality thesis. To do so, I contend that we must attend to the role of inference and the norms of reasoning in legal discourse. Consistent with the Hartian picture, participants in a theoretical disagreement can agree about all the ground facts about the law, but disagree about the grounds of the law because they arrive at their positions by differing legal inferences and reasoning.

More Serious Harm than Good? An Empirical Observation and Analysis of the Effects of the Serious Harm Requirement in Section 1(1) of the Defamation Act 2013

This thesis empirically investigates the extent to which section 1(1) of the Defamation Act 2013 has transformed defamation law, seeking to uncover the most appropriate construction of the serious reputational harm threshold and analysing consequent shifts in the balance between freedom of expression and the protection of reputation. To this pursuit, each judgment determining serious harm has been carefully examined with a qualitative analysis approach, with prominent themes and factors extracted and discussed. Subsequently, this analysis pursues the evolution of judicial opinion in the construction of section 1(1) and exhibits the merits of a multi-circumstantial approach in assessing the existence or likelihood of serious reputational harm, in line with the recent Supreme Court Lachaux judgment. Thus, although there are ongoing difficulties with costs, uncertainty and the role of inferences, the prevailing interpretation of the serious harm test represents a constructive development in safeguarding free speech as well as reputation.

Optimal or not; depends on the task

Decision-making involves a tradeoff between pressures for caution and urgency. Previous research has investigated how well humans optimize this tradeoff, and mostly concluded that people adopt a sub-optimal strategy that over-emphasizes caution. This emphasis reduces how many decisions can be made in a fixed time, which reduces the “reward rate”. However, the strategy that is optimal depends critically on the timing properties of the experiment design: the slower the rate of decision opportunities, the more cautious the optimal strategy. Previous studies have almost uniformly adopted very fast designs, which favor very urgent decision-making. This raises the possibility that previous findings—that humans adopt strategies that are too cautious—could either be ascribed to human caution, or to the experiments’ design. To test this, we used a slowed-down decision-making task in which the optimal strategy was quite cautious. With this task, and in contrast to previous findings, the average strategy adopted across participants was very close to optimal, with about equally many participants adopting too-cautious as too-urgent strategies. Our findings suggest that task design can play a role in inferences about optimality, and that previous conclusions regarding human sub-optimality are conditional on the task settings. This limits claims about human optimality that can be supported by the available evidence.

The Role of Randomization in Bayesian and Frequentist Design of Clinical Trial

A key role in inference is played by randomization, which has been extensively used in clinical trials designs. Randomization is primarily intended to prevent the source of bias in treatment allocation by producing comparable groups. In the frequentist framework of inference, randomization allows also for the use of probability theory to express the likelihood of chance as a source for the difference of end outcome. In the Bayesian framework, its role is more nuanced. The Bayesian analysis of clinical trials can afford a valid rationale for selective controls, pointing out a more limited role for randomization than it is generally accorded. This paper is aimed to offer a view of randomization from the perspective of both frequentist and Bayesian statistics and discussing the role of randomization also in theoretical decision models.

A Context-Enriched Neural Network Method for Recognizing Lexical Entailment

Recognizing lexical entailment (RLE) always plays an important role in inference of natural language, i.e., identifying whether one word entails another, for example, fox entails animal. In the literature, automatically recognizing lexical entailment for word pairs deeply relies on words' contextual representations. However, as a "prototype" vector, a single representation cannot reveal multifaceted aspects of the words due to their homonymy and polysemy. In this paper, we propose a supervised Context-Enriched Neural Network (CENN) method for recognizing lexical entailment. To be specific, we first utilize multiple embedding vectors from different contexts to represent the input word pairs. Then, through different combination methods and attention mechanism, we integrate different embedding vectors and optimize their weights to predict whether there are entailment relations in word pairs. Moreover, our proposed framework is flexible and open to handle different word contexts and entailment perspectives in the text corpus. Extensive experiments on five datasets show that our approach significantly improves the performance of automatic RLE in comparison with several state-of-the-art methods.

Reanalyses and Observations: What’s the Difference?

Abstract Are there important differences between reanalysis data and familiar observations and measurements? If so, what are they? This essay evaluates four possible answers that relate to: the role of inference, reliance on forecasts, the need to solve an ill-posed inverse problem, and understanding of errors and uncertainties. The last of these is argued to be most significant. The importance of characterizing uncertainties associated with results—whether those results are observations or measurements, analyses or reanalyses, or forecasts—is emphasized.

An Appraisal of Robert Brandom's Making it Explicit

This paper attends to the moral thought of Robert Brandom as it appears in his 1994 magnum opus Making It Explicit. Insofar that it is necessarily to outline Brandom’s thought the presentation will refer to the conception of deontic commitments as providing a basis for inference and entitlements for the purposes of meaning making. Accepting these remarks as sound enough, the paper directs attention at the role of inference in moral-decision making. Finally, it offers an appraisal of Brandom’s moral thought system.

The role of inference in food safety

Author Summary: Concerned individuals have been trying to determine the safety of their food since ancient times. In ancient times, people themselves were the ultimate test of food safety, but as human evolution progressed, other techniques such as sensory perception and experimentation on animals were used. Today sophisticated analytical techniques and models are available to measure and predict food safety. These sophisticated techniques and models are dependent not only on the quality of samples that are collected and analyzed but also on how inferences are made from the analytical results to the food being sampled. Unfortunately, the Theory of Sampling and the role of inference have not been fully integrated into prediction of food safety.The basis for many "modern" food sampling protocols was developed prior to the development of the Theory of Sampling. Many of these sampling protocols were based on concepts of acceptance sampling procedures and associated inference. The Theory of Sampling enables the representative sampling of bulk materials and eliminates the reliance of acceptance sampling as the only method for the characterization of food and utilizes a different type of inference than for acceptance sampling. This contribution addresses the differences between inference for acceptance sampling and inference for the sampling of bulk materials and the implications of these differences for food safety.

Inference and Association in Children's Early Numerical Estimation

How do children map number words to the numerical magnitudes they represent? Recent work in adults has shown that two distinct mechanisms-structure mapping and associative mapping-connect number words to nonlinguistic numerical representations (Sullivan & Barner, ). This study investigated the development of number word mappings, and the roles of inference and association in children's estimation. Fifty-eight 5- to 7-year-olds participated, and results showed that at both ages, children possess strong item-based associative mappings for numbers up to around six, but rely primarily on structure mapping-an inferential process-for larger quantities. These findings suggest that children rely primarily on an inferential mechanism to construct and deploy mappings between number words and large approximate magnitudes.