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Journal articles on the topic 'Inference'
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1
Barreyro, Juan Pablo, Jazmín Cevasco, Débora Burín, and Carlos Molinari Marotto. "Working Memory Capacity and Individual Differences in the Making of Reinstatement and Elaborative Inferences." Spanish journal of psychology 15, no. 2 (July 2012): 471–79. http://dx.doi.org/10.5209/rev_sjop.2012.v15.n2.38857.
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This study investigated the role of working memory capacity on the making of reinstatement and causal elaborative inferences during the reading of natural texts. In order to determine participants' working memory capacity, they were asked to take the reading span task before they took part in the study. Those participants that were identified as high or low working memory capacity readers were asked to perform a lexical decision task in two conditions: pre-inference and inference. In the pre-inference condition, target words representing reinstatement or causal elaborative inferences were presented immediately before the sentences that were predicted to prompt them. In the inference condition, the target words were presented immediately after the sentences that were predicted to prompt the inferences. Results indicated that, for the high working memory capacity readers, lexical decision times were faster at the inference compared to the pre-inference locations for both types of inferences. In the case of low working capacity readers, lexical decision times were faster at the inference compared to the pre-inference locations only for reinstatement inferences. These findings suggest that working memory capacity plays a role in the making of causal inferences during the comprehension of natural texts.
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Wang, Yingxu. "Inference Algebra (IA)." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 1 (January 2012): 21–47. http://dx.doi.org/10.4018/jcini.2012010102.
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Inference as the basic mechanism of thought is abilities gifted to human beings, which is a cognitive process that creates rational causations between a pair of cause and effect based on empirical arguments, formal reasoning, and/or statistical norms. It’s recognized that a coherent theory and mathematical means are needed for dealing with formal causal inferences. Presented is a novel denotational mathematical means for formal inferences known as Inference Algebra (IA) and structured as a set of algebraic operators on a set of formal causations. The taxonomy and framework of formal causal inferences of IA are explored in three categories: a) Logical inferences; b) Analytic inferences; and c) Hybrid inferences. IA introduces the calculus of discrete causal differential and formal models of causations. IA enables artificial intelligence and computational intelligent systems to mimic human inference abilities by cognitive computing. A wide range of applications of IA are identified and demonstrated in cognitive informatics and computational intelligence towards novel theories and technologies for machine-enabled inferences and reasoning. This work is presented in two parts. The inference operators of IA as well as their extensions and applications will be presented in this paper; while the structure of formal inference, the framework of IA, and the mathematical models of formal causations has been published in the first part of the paper in IJCINI 5(4).
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Wang, Yingxu. "Inference Algebra (IA)." International Journal of Cognitive Informatics and Natural Intelligence 5, no. 4 (October 2011): 61–82. http://dx.doi.org/10.4018/jcini.2011100105.
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Inference as the basic mechanism of thought is one of the gifted abilities of human beings. It is recognized that a coherent theory and mathematical means are needed for dealing with formal causal inferences. This paper presents a novel denotational mathematical means for formal inferences known as Inference Algebra (IA). IA is structured as a set of algebraic operators on a set of formal causations. The taxonomy and framework of formal causal inferences of IA are explored in three categories: a) Logical inferences on Boolean, fuzzy, and general logic causations; b) Analytic inferences on general functional, correlative, linear regression, and nonlinear regression causations; and c) Hybrid inferences on qualification and quantification causations. IA introduces a calculus of discrete causal differential and formal models of causations; based on them nine algebraic inference operators of IA are created for manipulating the formal causations. IA is one of the basic studies towards the next generation of intelligent computers known as cognitive computers. A wide range of applications of IA are identified and demonstrated in cognitive informatics and computational intelligence towards novel theories and technologies for machine-enabled inferences and reasoning.
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Wilhelm, Marco, and Gabriele Kern-Isberner. "Focused Inference and System P." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 7 (May 18, 2021): 6522–29. http://dx.doi.org/10.1609/aaai.v35i7.16808.
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We bring in the concept of focused inference into the field of qualitative nonmonotonic reasoning by applying focused inference to System P. The idea behind drawing focused inferences is to concentrate on knowledge which seems to be relevant for answering a query while completely disregarding the remaining knowledge even at the risk of missing some meaningful information. Focused inference is motivated by mimicking snap decisions of human reasoners and aims on rapidly drawing still reasonable inferences from large sets of knowledge. In this paper, we define a series of query-dependent, syntactically-driven focused inference relations, elaborate on their formal properties, and show that the series converges against System P. We take advantage of this result in form of an anytime algorithm for drawing inferences which is accompanied by a thorough complexity analysis.
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George, Marie St, Suzanne Mannes, and James E. Hoffman. "Individual Differences in Inference Generation: An ERP Analysis." Journal of Cognitive Neuroscience 9, no. 6 (November 1997): 776–87. http://dx.doi.org/10.1162/jocn.1997.9.6.776.
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Readers routinely draw inferences with remarkable efficiency and seemingly little cognitive effort. The present study was designed to explore different types of inferences during the course of reading, and the potential effects of differing levels of working memory capacity on the likelihood that inferences would be made. The electroencephalogram (EEG) was recorded from five scalp sites while participants read 90 paragraphs, composed of 60 experimental paragraphs and 30 filler paragraphs. Each experimental paragraph was four sentences long, and the final sentence stated explicitly the inference that readers did or did not make. There were four types of experimental paragraphs: (1) Bridging inference, (2) Elaborative inference, (3) Word-Based Priming control, and (4) No Inference control. Participants were tested using the Daneman and Carpenter (1980) Reading Span Task and categorized as having low or high working memory capacity. The average peaks of the N400 component of the event-related brain potential (EM) were used as a measure of semantic priming and integration, such that the lower the N400 was in response to the explicitly stated inference concept, the more likely it was that the reader made the inference. Results indicate that readers with high working memory capacity made both bridging (necessary) and elaborative (optional) inferences during reading, whereas readers with low working memory capacity made only bridging inferences during reading. We interpret the findings within the framework of the Capacity Constrained Comprehension model of Just and Carpenter (1992).
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Starns, Jeffrey J., Andrea M. Cataldo, Caren M. Rotello, Jeffrey Annis, Andrew Aschenbrenner, Arndt Bröder, Gregory Cox, et al. "Assessing Theoretical Conclusions With Blinded Inference to Investigate a Potential Inference Crisis." Advances in Methods and Practices in Psychological Science 2, no. 4 (September 17, 2019): 335–49. http://dx.doi.org/10.1177/2515245919869583.
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Scientific advances across a range of disciplines hinge on the ability to make inferences about unobservable theoretical entities on the basis of empirical data patterns. Accurate inferences rely on both discovering valid, replicable data patterns and accurately interpreting those patterns in terms of their implications for theoretical constructs. The replication crisis in science has led to widespread efforts to improve the reliability of research findings, but comparatively little attention has been devoted to the validity of inferences based on those findings. Using an example from cognitive psychology, we demonstrate a blinded-inference paradigm for assessing the quality of theoretical inferences from data. Our results reveal substantial variability in experts’ judgments on the very same data, hinting at a possible inference crisis.
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Murza, Kimberly A., Chad Nye, Jamie B. Schwartz, Barbara J. Ehren, and Debbie L. Hahs-Vaughn. "A Randomized Controlled Trial of an Inference Generation Strategy Intervention for Adults With High-Functioning Autism Spectrum Disorder." American Journal of Speech-Language Pathology 23, no. 3 (August 2014): 461–73. http://dx.doi.org/10.1044/2014_ajslp-13-0012.
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PurposeThe present intervention study investigated the efficacy of the ACT & Check Strategy intervention to improve inference generation when reading, metacognitive ability, general reading comprehension, and social inference ability in adults with high-functioning autism spectrum disorder (HF-ASD).MethodTwenty-five adults with HF-ASD were randomly assigned to either a treatment or a control group. Treatment sessions were conducted in 1-hr sessions, twice a week, for a total of 6 weeks. Treatment focused on explicit instruction of components of inference generation, categories of inferences, and increasingly independent strategy use.ResultsThe treatment group demonstrated significantly superior performance on 1 of 2 measures of inference generation in reading and 1 measure of metacognitive ability compared with the control group. Significant differences between groups were not found on measures of reading comprehension or social inference ability.ConclusionThese findings suggest that the ACT & Check Strategy was effective in improving participants' ability to generate inferences in reading and certain metacognitive abilities, but the skills do not appear to generalize to other social communication contexts, such as social inference generation. This research provides a measure of support for explicitly teaching inference generation to address a reading inference deficit in adults with HF-ASD.
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Krishna Raju, Rama. "Dynamic Memory Inference Network for Natural Language Inference (2017)." International Journal of Science and Research (IJSR) 6, no. 2 (February 5, 2017): 2219–30. http://dx.doi.org/10.21275/sr24926091431.
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Bahri, Toufik, and Abdulqader A. Al Hussain. "Question Type and Order of Inference in Inferential Processes during Reading Comprehension." Perceptual and Motor Skills 85, no. 2 (October 1997): 655–64. http://dx.doi.org/10.2466/pms.1997.85.2.655.
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For three groups of 20 subjects each who participated reading time was examined when stories suggested goal and state inferences which could be made by readers when asked state questions, goal questions, or no questions at all. Order of inference statement was also used as a variable. In addition, inferable statements were either left in or out of the text. Subjects read an equal number (12) of stories. Analysis showed that state inference took longer time than goal inference. Also, it took longer for subjects to draw inferences when the inferrable statement was absent than when it was present in the text. The effect of inference type, and condition on reading comprehension is discussed.
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Bar-Haim, Roy, Ido Dagan, and Jonathan Berant. "Knowledge-Based Textual Inference via Parse-Tree Transformations." Journal of Artificial Intelligence Research 54 (September 9, 2015): 1–57. http://dx.doi.org/10.1613/jair.4584.
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Textual inference is an important component in many applications for understanding natural language. Classical approaches to textual inference rely on logical representations for meaning, which may be regarded as "external" to the natural language itself. However, practical applications usually adopt shallower lexical or lexical-syntactic representations, which correspond closely to language structure. In many cases, such approaches lack a principled meaning representation and inference framework. We describe an inference formalism that operates directly on language-based structures, particularly syntactic parse trees. New trees are generated by applying inference rules, which provide a unified representation for varying types of inferences. We use manual and automatic methods to generate these rules, which cover generic linguistic structures as well as specific lexical-based inferences. We also present a novel packed data-structure and a corresponding inference algorithm that allows efficient implementation of this formalism. We proved the correctness of the new algorithm and established its efficiency analytically and empirically. The utility of our approach was illustrated on two tasks: unsupervised relation extraction from a large corpus, and the Recognizing Textual Entailment (RTE) benchmarks.
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Fu, Xiyan, and Anette Frank. "Exploring Continual Learning of Compositional Generalization in NLI." Transactions of the Association for Computational Linguistics 12 (2024): 912–32. http://dx.doi.org/10.1162/tacl_a_00680.
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Abstract Compositional Natural Language Inference (NLI) has been explored to assess the true abilities of neural models to perform NLI. Yet, current evaluations assume models to have full access to all primitive inferences in advance, in contrast to humans that continuously acquire inference knowledge. In this paper, we introduce the Continual Compositional Generalization in Inference (C2Gen NLI) challenge, where a model continuously acquires knowledge of constituting primitive inference tasks as a basis for compositional inferences. We explore how continual learning affects compositional generalization in NLI, by designing a continual learning setup for compositional NLI inference tasks. Our experiments demonstrate that models fail to compositionally generalize in a continual scenario. To address this problem, we first benchmark various continual learning algorithms and verify their efficacy. We then further analyze C2Gen, focusing on how to order primitives and compositional inference types, and examining correlations between subtasks. Our analyses show that by learning subtasks continuously while observing their dependencies and increasing degrees of difficulty, continual learning can enhance composition generalization ability.1
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Landis, Christopher B., and Joshua A. Kroll. "Mitigating Inference Risks with the NIST Privacy Framework." Proceedings on Privacy Enhancing Technologies 2024, no. 1 (January 2024): 217–31. http://dx.doi.org/10.56553/popets-2024-0013.
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The NIST Privacy Framework describes itself as a comprehensive approach to organization-wide privacy program management. However, inferences can yield sensitive information of identities or attributes from nonsensitive information. Privacy governance must protect this information. Although many people and organizations are expanding their privacy definitions to include inferences, our gap analysis reveals that the framework's mapped controls are insufficient for managing inference-driven risk. The framework does not attend organizational focus to privacy inference risk sufficiently to support its stated claim of comprehensive risk management. Applying the framework to past incidents where ostensibly protected information was re-inferred, we analyze how organizations can better mitigate inference-based privacy violations. Finally, we recommend detailed improvements to the framework's controls to account better for inferences. Our recommendations encompass augmenting and mapping additional privacy risk controls to increase implementing organizations' awareness of inference risks, updating controls that depend on protecting specific PII categories, and enhancing organizations' proficiency in translating legal and policy requirements into technical implementations.
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De Cooman, Gert, Jasper De Bock, and Márcio Alves Diniz. "Coherent Predictive Inference under Exchangeability with Imprecise Probabilities." Journal of Artificial Intelligence Research 52 (January 10, 2015): 1–95. http://dx.doi.org/10.1613/jair.4490.
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Coherent reasoning under uncertainty can be represented in a very general manner by coherent sets of desirable gambles. In a context that does not allow for indecision, this leads to an approach that is mathematically equivalent to working with coherent conditional probabilities. If we do allow for indecision, this leads to a more general foundation for coherent (imprecise-)probabilistic inference. In this framework, and for a given finite category set, coherent predictive inference under exchangeability can be represented using Bernstein coherent cones of multivariate polynomials on the simplex generated by this category set. This is a powerful generalisation of de Finetti's Representation Theorem allowing for both imprecision and indecision. We define an inference system as a map that associates a Bernstein coherent cone of polynomials with every finite category set. Many inference principles encountered in the literature can then be interpreted, and represented mathematically, as restrictions on such maps. We discuss, as particular examples, two important inference principles: representation insensitivitya strengthened version of Walley's representation invarianceand specificity. We show that there is an infinity of inference systems that satisfy these two principles, amongst which we discuss in particular the skeptically cautious inference system, the inference systems corresponding to (a modified version of) Walley and Bernard's Imprecise Dirichlet Multinomial Models (IDMM), the skeptical IDMM inference systems, and the Haldane inference system. We also prove that the latter produces the same posterior inferences as would be obtained using Haldane's improper prior, implying that there is an infinity of proper priors that produce the same coherent posterior inferences as Haldane's improper one. Finally, we impose an additional inference principle that allows us to characterise uniquely the immediate predictions for the IDMM inference systems.
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Morgan, Mary S. "Narrative Inference with and without Statistics." History of Political Economy 53, no. 6 (August 26, 2021): 113–38. http://dx.doi.org/10.1215/00182702-9414803.
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This article investigates the role played by narrative in drawing inferences from statistics before the adoption of formal inference regimes in economics. Two well-known, and exemplary, cases of informal inference provide the materials. Nikolai Kondratiev’s struggles to make inferences about the existence of his “long waves” from heaps of statistics in the 1920s contrast sharply with Thomas Robert Malthus’s confident account of demographic-economic oscillations made on the basis of the limited numbers available in the late eighteenth century. Comparison of their inferential reasoning, using detailed textual analysis, casts attention on the important role of narrative. These cases prompt the notion of “narrative inference”: where informal statistical inference depends on narrative accounts—used to make sense of the numbers by Malthus or to add sense onto the numbers by Kondratiev.
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Lehman-Blake, Margaret T., and Connie A. Tompkins. "Predictive Inferencing in Adults With Right Hemisphere Brain Damage." Journal of Speech, Language, and Hearing Research 44, no. 3 (June 2001): 639–54. http://dx.doi.org/10.1044/1092-4388(2001/052).
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Predictive inferencing was evaluated in 13 adults with right hemisphere damage (RHD) and 11 adults without brain damage (NBD). Brief narrative stimuli that strongly suggested a single outcome were constructed to vary recency of mention of inference-related information. Reading times were recorded for narrative-final sentences that disconfirmed the target inferences. Slowed reading time on the final sentences was an indicator of inference generation. Adults with RHD generated target predictive inferences in contexts with recent mention of strongly biasing inference-related information. This group also evidenced maintenance of inferences over time, but to a lesser degree than participants in the NBD group. Overall, individuals with better auditory comprehension or larger estimated working memory capacity tended to maintain inferences better than did the other participants. The results are discussed in relation to current hypotheses of inferencing and discourse comprehension in adults with RHD.
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Gras, Doriane, Hubert Tardieu, and Serge Nicolas. "Predictive Inference Activation." Swiss Journal of Psychology 71, no. 3 (January 2012): 141–48. http://dx.doi.org/10.1024/1421-0185/a000081.
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Predictive inferences are anticipations of what could happen next in the text we are reading. These inferences seem to be activated during reading, but a delay is necessary for their construction. To determine the length of this delay, we first used a classical word-naming task. In the second experiment, we used a Stroop-like task to verify that inference activation was not due to strategies applied during the naming task. The results show that predictive inferences are naturally activated during text reading, after approximately 1 s.
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Kim, Woori, Mikyung Shin, and Yongseok Yoo. "Comparative Analysis of Inferencing in Low-Reading and Average-Reading Comprehenders: Utilizing the Think-Aloud Protocol." Communication Sciences & Disorders 28, no. 3 (September 30, 2023): 480–91. http://dx.doi.org/10.12963/csd.23976.
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Objectives: This is a conceptual replication aiming to investigate the cognitive processes of students with reading comprehension difficulties using the think-aloud protocol. Methods: Among 72 third- and fourth-grade participants, 28 poor comprehenders and 44 average students were identified based on screening criteria and standardized tests. The think-aloud protocol was used to monitor comprehension processes during reading. The participants verbalized their thoughts as they read expository and narrative texts. Those responses were transcribed and coded according to inference rates, correctness, and inferential types (explanation, prediction, or association). Results: First, poor comprehenders showed significantly lower rates and accuracies of inferences for both expository and narrative texts than average students did. Second, there were significant differences between poor comprehenders and average students in the proportions of the three types of inferences. Poor comprehenders generated significantly lower rates of explanatory, predictive, and associative inferences. Third, the inference types differed for different type of texts. Both groups made more predictive inferences when reading the narrative text than when reading the expository text. Conclusion: Differences in inference patterns between poor comprehenders and average students were identified using the think-aloud protocol. Different types of inferences were involved in processing different types of texts. Future research directions for developing learning strategies for encouraging solid inference are discussed.
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Alhaqbani, Bandar, and Colin Fidge. "Probabilistic Inference Channel Detection and Restriction Applied to Patients’ Privacy Assurance." International Journal of Information Security and Privacy 4, no. 4 (October 2010): 35–59. http://dx.doi.org/10.4018/jisp.2010100103.
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Traditional access control models protect sensitive data from unauthorised direct accesses; however, they fail to prevent indirect inferences. Information disclosure via inference channels occurs when secret information is derived from unclassified (non-secure) information and other sources like metadata and public observations. Previously, techniques using precise and fuzzy functional dependencies were proposed to detect inference channels. However, such methods are inappropriate when probabilistic relationships exist among data items that may be used to infer information with a predictable likelihood of accuracy. In this paper, the authors present definitions and algorithms for detecting inference channels in a probabilistic knowledge base and maximising an attacker’s uncertainty by restricting selected inference channels to comply with data confidentiality and privacy requirements. As an illustration, a healthcare scenario is used to show how inference control can be performed on probabilistic relations to address patients’ privacy concerns over Electronic Medical Records. To limit an attacker’s ability to know secret data selected inference channels are restricted by using a Bayesian network that incorporates the information stored within a medical knowledge base to decide which facts must be hidden to limit undesired inferences.
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Din, Muhammad, and Mamuna Ghani. "Evaluating University Students’ Inference Making Ability: A Study at Bachelor Level in Pakistan." International Journal of English Linguistics 9, no. 5 (September 6, 2019): 351. http://dx.doi.org/10.5539/ijel.v9n5p351.
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Inference means the process of deriving a conclusion from a set of premises, including a conclusion that is probably in relation to the premises. This study has aimed to evaluate university students’ inference making ability. To explore this aim, the present study has set five research objectives which include to understand university students’ attitude towards critical thinking subset of making inferences, find out the relationship between university students’ attitude towards making inferences and their ability in making inferences, know whether critical thinking is a positive predictor for the overall score of critical thinking test (CTT), evaluate university students’ performance in exhibiting their attitude towards critical thinking and in critical thinking test (CTT) taken by them and understand the variance based on university students’ gender, location and medium of instruction in their attitude towards critical thinking inference and their ability of making inferences. To achieve these objectives, the researcher has used quantitative research methodology. The participants of this study consist of 550 male and female university students of different state-run colleges of Punjab (Pakistan). Critical thinking inventory (CTI) and Watson-Glaser’s Critical Thinking Appraisal (WGCTA) (2002) have been used to collect data from the subjects of the study. The researcher used SPSS (XX) to analyze the collected data. The findings of this study reveal that the university EFL leaners have highly positive attitude towards making inferences but their inference making ability does not correspond with their attitude toward inference making skill.
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Liptáková, Ľudmila. "On the research of children’s text comprehension." Journal of Linguistics/Jazykovedný casopis 74, no. 2 (December 1, 2023): 577–92. http://dx.doi.org/10.2478/jazcas-2024-0012.
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Abstract The paper presents theoretical background and empirical research on text comprehension processes of a junior school-age child. The first part of the paper discusses the most influential models of text comprehension and inference making as centrally important component of comprehension. The research findings concerning children’s text comprehension are discussed as well. Further, we present our ongoing empirical research, which focuses on children’s inference making when listening to and reading an informational, narrative, and multimodal text. We focus both on the online and offline inferences, i.e., on the inferences during and after listening to and reading the text. The aim of the research is to find out how children make different types of inferences and to compare their individual skills in inference making. The last part of the paper outlines preliminary data collected via retelling technique after reading the informational text. The data indicate individual differences between children in processing and verbalising information from the text and beyond the text. However, to draw complex conclusions about individual inference making profiles of children, further research is needed.
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Waking, Douglas A., and Christy L. Kluttz. "Effects of Task on the Activation of Predictive Inferences." Psychological Reports 83, no. 3_suppl (December 1998): 1287–96. http://dx.doi.org/10.2466/pr0.1998.83.3f.1287.
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Research on the activation of predictive inferences has provided inconsistent results that may be explained within a contextual view of reading. The present study tested whether the type of test for explicit memory would affect the activation of knowledge-based predictive inferences. The information necessary for the activation of a predictive inference was provided to readers in four different conditions (no inference, local processing, global processing, coherence). Manipulation was accomplished by varying the type of question asked after reading the passage (verbatim, factual, or inference). Analysis suggests predictive inferences are automatically activated and not affected by contextual factors such as the question. Consequently, the current data do not provide clear support for a contextual view of comprehension. These conclusions are supported by a two-stage view of elaborative processing.
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León, Jose A., María T. Dávalos, Inmaculada Escudero, Ricardo Olmos, Yurena Morera, and Manuel Froufe. "Procesamiento de inferencias emocionales. Un análisis sobre el efecto de la valencia y la dirección causal mediante una tarea de decisión léxica." Anales de Psicología 31, no. 2 (April 25, 2015): 677. http://dx.doi.org/10.6018/analesps.31.2.167391.
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In two experiments we investigated the role that activation of emotional inferences when readers represent fictional characters' emotional states using an affective lexical decision task. Subjects read short stories that described concrete actions. In the first experiment, we analyzed whether the valence (positive or negative) was an important factor of inference´s activation. The results showed that valence was determinant factor in the moment that emotional inference was generated, being the positive valence faster than negative. In the second experiment we studied whether the emotion inference activation was influenced by the causal direction of the story, where the causal direction of the text was manipulated in order to induce towards an emotional inference predictive (the reader looking for a consequence that promote a particular emotion) or inducing an explanatory inference (reader looking for a cause that “explain” a particular emotion). The results suggest that emotional inferences are made online, and that valence and causal directions are two decisive components of emotional trait, but only positive valence increase their processing.
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Zhang, Sheng, Rachel Rudinger, Kevin Duh, and Benjamin Van Durme. "Ordinal Common-sense Inference." Transactions of the Association for Computational Linguistics 5 (December 2017): 379–95. http://dx.doi.org/10.1162/tacl_a_00068.
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Humans have the capacity to draw common-sense inferences from natural language: various things that are likely but not certain to hold based on established discourse, and are rarely stated explicitly. We propose an evaluation of automated common-sense inference based on an extension of recognizing textual entailment: predicting ordinal human responses on the subjective likelihood of an inference holding in a given context. We describe a framework for extracting common-sense knowledge from corpora, which is then used to construct a dataset for this ordinal entailment task. We train a neural sequence-to-sequence model on this dataset, which we use to score and generate possible inferences. Further, we annotate subsets of previously established datasets via our ordinal annotation protocol in order to then analyze the distinctions between these and what we have constructed.
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Yoo, Yongseok. "On the Dynamics of Inferential Behavior while Reading Expository and Narrative Texts." Brain Sciences 14, no. 5 (April 26, 2024): 428. http://dx.doi.org/10.3390/brainsci14050428.
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Inference plays a key role in reading comprehension. This study examines changes in inferential behavior while reading different genres. The inferential behavior of 28 students with reading disabilities (RDs) and 44 students without RDs was quantified while they read expository and narrative texts. First, the average rates of inference attempts and correct inferences were measured during reading. Then, the same rates were measured separately during early and late reading to see if there was a change in inferential behavior. The results show that the change in inferential behavior depends on the genre. While reading the expository text, both groups showed no significant change in their inference making. In contrast, while reading the narrative text, both groups showed higher rates of inference attempts, and only the students without RD showed a significant increase in correct inferences. The implications of these findings for the design of more engaging and effective reading programs are discussed.
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Chen, Dingding, Yanchen Deng, Ziyu Chen, Wenxing Zhang, and Zhongshi He. "HS-CAI: A Hybrid DCOP Algorithm via Combining Search with Context-Based Inference." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7087–94. http://dx.doi.org/10.1609/aaai.v34i05.6195.
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Search and inference are two main strategies for optimally solving Distributed Constraint Optimization Problems (DCOPs). Recently, several algorithms were proposed to combine their advantages. Unfortunately, such algorithms only use an approximated inference as a one-shot preprocessing phase to construct the initial lower bounds which lead to inefficient pruning under the limited memory budget. On the other hand, iterative inference algorithms (e.g., MB-DPOP) perform a context-based complete inference for all possible contexts but suffer from tremendous traffic overheads. In this paper, (i) hybridizing search with context-based inference, we propose a complete algorithm for DCOPs, named HS-CAI where the inference utilizes the contexts derived from the search process to establish tight lower bounds while the search uses such bounds for efficient pruning and thereby reduces contexts for the inference. Furthermore, (ii) we introduce a context evaluation mechanism to select the context patterns for the inference to further reduce the overheads incurred by iterative inferences. Finally, (iii) we prove the correctness of our algorithm and the experimental results demonstrate its superiority over the state-of-the-art.
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Bill, Cory, Elena Pagliarini, Jacopo Romoli, Lyn Tieu, and Stephen Crain. "Children’s Interpretation of Sentences Containing Multiple Scalar Terms." Journal of Semantics 38, no. 4 (November 2021): 601–37. http://dx.doi.org/10.1093/jos/ffab016.
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Abstract Sentences containing the scalar term “some”, such as “The pig carried some of his rocks”, are usually interpreted as conveying the scalar inference that the pig did not carry all of his rocks. Previous research has reported that when interpreting such sentences, children tend to derive fewer of these scalar inferences than adults ( Noveck (2001); Papafragou & Musolino (2003); Guasti et al. (2005), among others). One approach to explaining these results contends that children have difficulties accessing the alternative sentences involved in the derivation of such scalar inferences. This ‘Alternatives-based’ approach raises the possibility that children’s performance may improve if certain scalar terms are presented together in the same sentence, for example, if a sentence contains both an existential quantifier and a universal quantifier, as in “Every pig carried some of his rocks”. Such ‘EverySome’ sentences have been associated with the inference that not every pig carried all of his rocks, as well as the stronger inference that none of the pigs carried all of his rocks (see Chemla & Spector (2011), among others). We present two experiments that explore the possibility that children might more readily derive scalar inferences from sentences containing such a combination of scalar terms. Experiment 1 investigates children’s interpretation of sentences containing only the quantifier some and replicates the previously established finding of fewer inference-based interpretations by children compared to adults. Experiment 2 explores children’s interpretation of sentences in which “some” is embedded under “every”, and reveals that adults and children access inference-based interpretations of such sentences at similar rates. Moreover, adults and children appear to differ with regards to which of the two possible inferences their interpretations are based on. We discuss the implications of the experimental results for our understanding of children’s acquisition of scalar inferences and for proposals that attempt to capture differences between adults’ and children’s interpretive preferences.
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HAMAMI, YACIN. "MATHEMATICAL INFERENCE AND LOGICAL INFERENCE." Review of Symbolic Logic 11, no. 4 (January 8, 2018): 665–704. http://dx.doi.org/10.1017/s1755020317000326.
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AbstractThe deviation of mathematical proof—proof in mathematical practice—from the ideal of formal proof—proof in formal logic—has led many philosophers of mathematics to reconsider the commonly accepted view according to which the notion of formal proof provides an accurate descriptive account of mathematical proof. This, in turn, has motivated a search for alternative accounts of mathematical proof purporting to be more faithful to the reality of mathematical practice. Yet, in order to develop and evaluate such alternative accounts, it appears as a necessary prerequisite to first possess a clear picture of what the deviation of mathematical proof from formal proof consists in. The present work aims to contribute building such a picture by investigating the relation between the elementary steps of deduction constituting the two types of proofs—mathematical inference and logical inference. Many claims have been made in the literature regarding the relation between mathematical inference and logical inference, most of them stating that the former is lacking properties that are constitutive of the latter. Such differentiating claims are, however, usually put forward without a clear conception of the properties occurring in them, and are generally considered to be immediately justified by our direct acquaintance, or phenomenological experience, with the two types of inferences. The present study purports to advance our understanding of the relation between mathematical inference and logical inference by developing a detailed philosophical analysis of the differentiating claims, that is, an analysis of the meaning of the differentiating claims—through the properties that occur in them—as well as the reasons that support them. To this end, we provide at the outset a representative list of the different properties of logical inference that have occurred in the differentiating claims, and we notice that they all boil down to the three properties of formality, generality, and mechanicality. For each one of these properties, our analysis proceeds in two steps: we first provide precise conceptual characterizations of the different ways logical inference has been said to be formal, general, and mechanical, in the philosophical and logical literature on formal proof; we then examine why mathematical inference does not appear to be formal, general, and mechanical, for the different variations of these notions identified. Our study results in a precise conceptual apparatus for expressing and discussing the properties differentiating mathematical inference from logical inference, and provides a first inventory of the various reasons supporting the observations of those differences. The differentiating claims constitute thus a set of data that any philosophical account of mathematical inference and proof purporting to be more faithful to mathematical practice ought to be able to accommodate and explain.
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Molinari Marotto, Carlos, Juan Pablo Barreyro, Jazmín Cevasco, and Paul Van den Broek. "Generation of Emotional Inferences during Text Comprehension: Behavioral Data and Implementation through the Landscape Model." Escritos de Psicología - Psychological Writings 4, no. 1 (May 1, 2011): 9–17. http://dx.doi.org/10.24310/espsiescpsi.v4i1.13308.
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This study investigated the generation of emotional inferences during the reading and recall of narrative texts. Experiment 1 compared the fit of two simulations of text comprehension to the recall data. One simulation examined causal and referential inferences, while the other examined causal, referential and emotional inferences. We found that the simulation that involved emotional inferences provided a better fit to the human data than the other simulation. Experiment 2 tested whether emotional inferences are generated online by recording lexical decision times at preinference and inference locations. Lexical decision times were faster at the inference than the preinference locations. These findings suggest that emotional inferences play a role in the understanding of natural texts, and that they require the reader to establish connections between text segments.
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Markovits, Henry, Cécile Saelen, and Hugues Lortie Forgues. "An Inverse Belief–Bias Effect." Experimental Psychology 56, no. 2 (January 2009): 112–20. http://dx.doi.org/10.1027/1618-3169.56.2.112.
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Two studies examined the hypothesis that accepting false premises as true in order to make the modus ponens (MP) inference requires inhibition of contradictory knowledge. Study 1 presented both MP and affirmation of the consequent (AC) inferences using either false, but plausible premises or completely unbelievable premises, with standard logical constructions using either an evaluation or a production paradigm. The rate of acceptance of the MP inferences was significantly greater with unbelievable premises than with plausible premises, in both evaluation and production, while no such effect was observed with the AC inferences. Study 2 used a computer-generated presentation allowing for measures of response times. Participants who tended to accept the MP inference with unbelievable premises took longer to do so with plausible premises than with unbelievable premises. Participants who tended to reject the MP inference showed an opposite pattern. In both studies, the observed effects were not shown for the AC inferences. The overall pattern of results was consistent with the hypothesis that inhibition is a key component of logical reasoning with false premises.
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Kane, Benjamin, Will Gantt, and Aaron Steven White. "Intensional Gaps: Relating veridicality, factivity, doxasticity, bouleticity, and neg-raising." Semantics and Linguistic Theory 31 (January 5, 2022): 570. http://dx.doi.org/10.3765/salt.v31i0.5137.
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We investigate which patterns of lexically triggered doxastic, bouletic, neg(ation)-raising, and veridicality inferences are (un)attested across clause-embedding verbs in English. To carry out this investigation, we use a multiview mixed effects mixture model to discover the inference patterns captured in three lexicon-scale inference judgment datasets: two existing datasets, MegaVeridicality and MegaNegRaising, which capture veridicality and neg-raising inferences across a wide swath of the English clause-embedding lexicon, and a new dataset, MegaIntensionality, which similarly captures doxastic and bouletic inferences. We focus in particular on inference patterns that are correlated with morphosyntactic distribution, as determined by how well those patterns predict the acceptability judgments in the MegaAcceptability dataset. We find that there are 15 such patterns attested. Similarities among these patterns suggest the possibility of underlying lexical semantic components that give rise to them. We use principal component analysis to discover these components and suggest generalizations that can be derived from them.
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Bowles, C. Travis, Arthur D. Fisk, and Wendy A. Rogers. "Inference and the Use of Similes and Metaphors in Warnings." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 46, no. 19 (September 2002): 1703–7. http://dx.doi.org/10.1177/154193120204601903.
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Warning content must be written to assure that the user knows how to apply the information at the appropriate time. Since the warning cannot provide information about all possible circumstances, in some situations individuals must make inferences about safe behavior. This paper contains two experiments that investigated compliance with warning information in situations that do and situations that do not require inferences. The first experiment, with 39 undergraduate students, tested simile use in warnings to improve compliance in inference situations. The second experiment, with 44 undergraduate students, tested metaphors in the place of the similes. In both experiments, participants were presented with products and warnings, followed by situations testing their intention to comply. Normal warnings were compared with similes (Experiment 1) and metaphors (Experiment 2). In each study, the tests required either matching information or drawing inferences. Compliance was significantly lower for situations requiring inference, for both experiments. Use of metaphors and similes to reduce inference requirements is discussed.
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Luo, Yu, and Jiaying Zhao. "Statistical Learning Creates Novel Object Associations via Transitive Relations." Psychological Science 29, no. 8 (May 22, 2018): 1207–20. http://dx.doi.org/10.1177/0956797618762400.
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A remarkable ability of the cognitive system is to make novel inferences on the basis of prior experiences. What mechanism supports such inferences? We propose that statistical learning is a process through which transitive inferences of new associations are made between objects that have never been directly associated. After viewing a continuous sequence containing two base pairs (e.g., A–B, B–C), participants automatically inferred a transitive pair (e.g., A–C) where the two objects had never co-occurred before (Experiment 1). This transitive inference occurred in the absence of explicit awareness of the base pairs. However, participants failed to infer the transitive pair from three base pairs (Experiment 2), showing the limits of the transitive inference (Experiment 3). We further demonstrated that this transitive inference can operate across the categorical hierarchy (Experiments 4–7). The findings revealed a novel consequence of statistical learning in which new transitive associations between objects are implicitly inferred.
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Johar, Gita Venkataramani, Jaideep Sengupta, and Jennifer L. Aaker. "Two Roads to Updating Brand Personality Impressions: Trait versus Evaluative Inferencing." Journal of Marketing Research 42, no. 4 (November 2005): 458–69. http://dx.doi.org/10.1509/jmkr.2005.42.4.458.
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This research examines the dynamic process of inference updating. The authors present a framework that delineates two mechanisms that guide the updating of personality trait inferences about brands. The results of three experiments show that chronics (those for whom the trait is accessible) update their initial inferences on the basis of the trait implications of new information. Notably, nonchronics (those for whom the trait is not accessible) also update their initial inferences, but they do so on the basis of the evaluative implications of new information. The framework adds to the inference-making literature by uncovering two distinct paths of inference updating and by emphasizing the moderating role of trait accessibility. The findings have direct implications for marketers attempting to understand the construction of brand personality, and they emphasize the constantly evolving nature of brand perceptions and the notion that both the consumer and the marketer have important roles to play in this process.
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Romoli, Jacopo, and Agata Renans. "Multiplicity and Modifiers." Journal of Semantics 37, no. 3 (June 30, 2020): 455–74. http://dx.doi.org/10.1093/jos/ffaa005.
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Abstract A sentence with an adverbial modifier under negation like Mike didn’t wash the window with soap gives rise to an inference that Mike did wash the window. A sentence with a plural noun like Mike washed windows gives rise to a so-called ‘multiplicity’ inference that Mike washed multiple windows. In this note, we focus on the interaction between these two inferences in sentences containing both an adverbial modifier and a plural noun under negation, like Mike didn’t wash windows with soap. We observe that this sentence has a reading conveying that Mike didn’t wash any window with soap but that he did wash multiple windows (albeit not with soap). As we discuss, this reading is not predicted by any version of the implicature approach to the multiplicity inference, in combination with the implicature treatment of the inference of adverbial modifiers. We sketch two solutions for this problem. The first keeps the implicature approach to adverbial modifiers but adopts a non-implicature approach to multiplicity based on homogeneity. The second solution holds on to the implicature approach to the multiplicity inference but accounts for the inference of adverbial modifiers as a presupposition. In addition, it adopts the idea that presuppositions can be strengthened via implicatures, as proposed recently in the literature. Either way, the interaction between multiplicity and the inference of adverbial modifiers suggests that we cannot treat both as implicatures: if we want to treat either one as an implicature, we need to do something different for the other. We end by comparing the case above to analogous cases involving different scalar inferences and showing that the ambiguity approach to the multiplicity inference does not provide a solution to our problem.
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Franks, Bridget A. "Deductive Reasoning with Prose Passages: Effects of Age, Inference Form, Prior Knowledge, and Reading Skill." International Journal of Behavioral Development 21, no. 3 (October 1997): 501–35. http://dx.doi.org/10.1080/016502597384767.
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This study applied knowledge about inference-making from the deductive reasoning literature to the drawing of specific inferences from prose passages. It explored the effects of age, inference form, prior knowledge, and reading skill on inferential comprehension. In Experiment 1, fourth-grade, seventh-grade, and college students read three prose passages, each containing six inferential questions based on premises expressed in the passages. Premise information was either true, false, or neutral with regard to subjects’ prior knowledge. To answer the questions correctly, subjects were required to make deductive inferences with six different inference forms. Content (true, false, or neutral) and form interacted differently depending on the age of subjects, but content affected performance with at least some forms for all age groups. When reasoning with conditional forms, subjects’ use of more advanced reasoning patterns with true content decreased with false and neutral content, where less advanced reasoning patterns were shown. In Experiment 2, the relationships among reading skill, inference form, and content were explored with seventh-grade and college students. For college students, reading skill had a positive main effect, but did not interact with form or content. For seventh-graders, skilled readers were better able than less skilled readers to reason from false and neutral premises with determinate inference forms.
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Vázquez-Polo, Francisco-José, Miguel-Ángel Negrín-Hernández, and María Martel-Escobar. "Meta-Analysis with Few Studies and Binary Data: A Bayesian Model Averaging Approach." Mathematics 8, no. 12 (December 4, 2020): 2159. http://dx.doi.org/10.3390/math8122159.
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In meta-analysis, the existence of between-sample heterogeneity introduces model uncertainty, which must be incorporated into the inference. We argue that an alternative way to measure this heterogeneity is by clustering the samples and then determining the posterior probability of the cluster models. The meta-inference is obtained as a mixture of all the meta-inferences for the cluster models, where the mixing distribution is the posterior model probabilities. When there are few studies, the number of cluster configurations is manageable, and the meta-inferences can be drawn with BMA techniques. Although this topic has been relatively neglected in the meta-analysis literature, the inference thus obtained accurately reflects the cluster structure of the samples used. In this paper, illustrative examples are given and analysed, using real binary data.
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Wang, Yingxu. "Formal Rules for Fuzzy Causal Analyses and Fuzzy Inferences." International Journal of Software Science and Computational Intelligence 4, no. 4 (October 2012): 70–86. http://dx.doi.org/10.4018/jssci.2012100105.
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Causal inference is one of the central capabilities of the natural intelligence that plays a crucial role in thinking, perception, and problem solving. Fuzzy inferences are an extended form of formal inferences that provide a denotational mathematical means for rigorously dealing with degrees of matters, uncertainties, and vague semantics of linguistic variables, as well as for rational reasoning the semantics of fuzzy causalities. This paper presents a set of formal rules for causal analyses and fuzzy inferences such as those of deductive, inductive, abductive, and analogical inferences. Rules and methodologies for each of the fuzzy inferences are formally modeled and illustrated with real-world examples and cases of applications. The formalization of fuzzy inference methodologies enables machines to mimic complex human reasoning mechanisms in cognitive informatics, cognitive computing, soft computing, abstract intelligence, and computational intelligence.
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Ramsey, Charles A. "Considerations for Inference to Decision Units." Journal of AOAC INTERNATIONAL 98, no. 2 (March 1, 2015): 288–94. http://dx.doi.org/10.5740/jaoacint.14-292.
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Abstract The goal of sampling is to take a small portion of a target material for analysis instead of collecting all the material. If sampling is done following certain principles, then inference can be made from analytical results of the portion taken back to the entire target material (Decision Unit). There are different sampling strategies that are dependent on theproperties of the material being sampled as well as different methods for making inferences from analytical results to the Decision Unit (DU). A thorough understanding of material properties and methods for inference is therefore critical for the development of proper sampling protocols. This paper addresses inferences from analytical test results to DUs and theimplications to the development of a sampling protocol.
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Goodwin, Chris, and Enrique Ortiz. "It's a Girl! Random Numbers, Simulations, and the Law of Large Numbers." Mathematics Teaching in the Middle School 20, no. 9 (May 2015): 561–64. http://dx.doi.org/10.5951/mathteacmiddscho.20.9.0561.
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Modeling using mathematics and making inferences about mathematical situations are becoming more and more prevalent in most fields of study. When we want to generalize about a population or make predictions of what could occur, we cannot use descriptive statistics. Instead, we turn to inference. Simulation and sampling are essential in building a foundation for statistical inference.
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Chernev, Alexander, and Gregory S. Carpenter. "The Role of Market Efficiency Intuitions in Consumer Choice: A Case of Compensatory Inferences." Journal of Marketing Research 38, no. 3 (August 2001): 349–61. http://dx.doi.org/10.1509/jmkr.38.3.349.18865.
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The authors examine consumer inferences about product attributes that are unobservable at the time of the decision. Extant research predicts that in the absence of an explicit correlation between product attributes, consumers will infer that the brand that is superior on the observable attributes is also superior on the unobservable attributes. The authors propose an alternative inference strategy that makes the counterintuitive prediction that the apparently superior brand is inferior on the unobservable attributes. The authors refer to these inferences as “compensatory inferences” and assert that they are associated with consumers' intuitive theories about the competitive nature of a market. In a series of four experiments, the authors examine the occurrence of compensatory inferences and compare them with other inference strategies.
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Akagi, Yasunori, Takuya Nishimura, Yusuke Tanaka, Takeshi Kurashima, and Hiroyuki Toda. "Exact and Efficient Inference for Collective Flow Diffusion Model via Minimum Convex Cost Flow Algorithm." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3163–70. http://dx.doi.org/10.1609/aaai.v34i04.5713.
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Collective Flow Diffusion Model (CFDM) is a general framework to find the hidden movements underlying aggregated population data. The key procedure in CFDM analysis is MAP inference of hidden variables. Unfortunately, existing approaches fail to offer exact MAP inferences, only approximate versions, and take a lot of computation time when applied to large scale problems. In this paper, we propose an exact and efficient method for MAP inference in CFDM. Our key idea is formulating the MAP inference problem as a combinatorial optimization problem called Minimum Convex Cost Flow Problem (C-MCFP) with no approximation or continuous relaxation. On the basis of this formulation, we propose an efficient inference method that employs the C-MCFP algorithm as a subroutine. Our experiments on synthetic and real datasets show that the proposed method is effective both in single MAP inference and people flow estimation with EM algorithm.
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Nishiyama, Yu, Motonobu Kanagawa, Arthur Gretton, and Kenji Fukumizu. "Model-based kernel sum rule: kernel Bayesian inference with probabilistic models." Machine Learning 109, no. 5 (January 2, 2020): 939–72. http://dx.doi.org/10.1007/s10994-019-05852-9.
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AbstractKernel Bayesian inference is a principled approach to nonparametric inference in probabilistic graphical models, where probabilistic relationships between variables are learned from data in a nonparametric manner. Various algorithms of kernel Bayesian inference have been developed by combining kernelized basic probabilistic operations such as the kernel sum rule and kernel Bayes’ rule. However, the current framework is fully nonparametric, and it does not allow a user to flexibly combine nonparametric and model-based inferences. This is inefficient when there are good probabilistic models (or simulation models) available for some parts of a graphical model; this is in particular true in scientific fields where “models” are the central topic of study. Our contribution in this paper is to introduce a novel approach, termed the model-based kernel sum rule (Mb-KSR), to combine a probabilistic model and kernel Bayesian inference. By combining the Mb-KSR with the existing kernelized probabilistic rules, one can develop various algorithms for hybrid (i.e., nonparametric and model-based) inferences. As an illustrative example, we consider Bayesian filtering in a state space model, where typically there exists an accurate probabilistic model for the state transition process. We propose a novel filtering method that combines model-based inference for the state transition process and data-driven, nonparametric inference for the observation generating process. We empirically validate our approach with synthetic and real-data experiments, the latter being the problem of vision-based mobile robot localization in robotics, which illustrates the effectiveness of the proposed hybrid approach.
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Charles, Cara, Ra’Shida Rockette, and Caitlin Bowman. "MEMORY CHANGES IN COGNITIVELY HEALTHY OLDER ADULTS: AGE EFFECTS IN MEMORY-BASED INFERENCE." Innovation in Aging 8, Supplement_1 (December 2024): 690–91. https://doi.org/10.1093/geroni/igae098.2257.
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Abstract Memory changes in older age are well documented, even among cognitively healthy older adults. Most prior research has focused on age-related declines in remembering details of individual experiences. However, memory-based inference is another important memory ability that allows us to make connections across separate experiences to make novel decisions. Little is known about the extent of age differences in inference abilities. In the present experiment, we administered an inference task to young and cognitively healthy older adults that asked them to both remember pairs of items that were presented directly (e.g., apple – truck, truck - clock) and that could be combined to form an indirect inference association that was not shown (e.g., apple-clock). Test results showed above-chance performance in older adults for both remembering direct pairs and making inferences. However, older adults performed worse than young adults overall, with a larger age deficit in inference compared to direct memory. We also tested for age differences in the ability to remember which context the associations were learned in (direct vs. indirect) because a loss of context memory can indicate separate experiences have been combined in memory. Successful inference was more likely to lead to a loss of context memory in older adults compared to young adults, but further analyses indicate that strategic responding in older adults could account for this difference. Overall, we found that older adults had an additional cost to forming inferences compared to remembering direct items, but the underlying mechanisms of that difference will require future investigation.
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Melgarejo, Teófilo Félix Valentín, Pablo Lenin La Madrid Vivar, Clodoaldo Ramos Pando, Pablo Lolo Valentín Melgarejo, and Agustín Arturo Aguirre Adauto. "Inference and reading comprehension in university students." Nurture 18, no. 4 (September 18, 2024): 785–94. http://dx.doi.org/10.55951/nurture.v18i4.846.
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Purpose: The objective of this research was to determine the relationship between inference and reading comprehension. We sought to verify the relationship between inductive and deductive inferences in the comprehensive reading of Daniel Alcides Carrión National University Peru students majoring in communication and literature. Design/Methodology/Approach: The correlational-explanatory research design was used since the correlation of the study variables was sought through scientific and specifically analytical, deductive and interpretive methodology on a population of 104 and the probabilistic sample of 83 students. Similarly, a 20-item questionnaire was used through survey, observation and recording techniques to collect the information. Therefore, the research was developed using a basic quantitative approach. Findings: The result of the study allowed us to obtain a correlation coefficient of 0.215 which was positive but moderate according to Spearman's Rho statistical statistician. Conclusion: The process of inductive and deductive inferences is related to the reading comprehension of university students. Therefore, reading strategies must be implemented in university training institutions to develop inferences in a relevant way. Practical Implications: The result of the study will allow university training centers to develop the inference process in the academic training of students. Contribution to the Literature: Information is contributed about the theoretical underpinnings of inference and reading comprehension in this study.
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Al-Sulaimaan, Misbah M. D., and Teeba N. T. Al-Khaffaf. "Rationality and Gricean Inference with Reference to English-Arabic Translation." International Journal of Social Sciences & Humanities (IJSSH) 1, no. 1 (October 13, 2016): 138–76. http://dx.doi.org/10.58885/ijssh.v1i1.138.ma.
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The present study tackles “Rationality and Gricean inference” in English and its translation from English into Arabic. Inference can be defined as a logical conclusion that is drawn from a premise and it is used to describe that process which the reader (hearer) must go through to get from the literal meaning of what is written (or said) to what the writer (speaker) intended to convey. Rationality can be defined as thinking, speaking, reasoning, making a decision, or acting in a way that is generally reliable and efficient for achieving one's goals. This study aims at (1) specifying and studying a number of different patterns of inferences in books of Pragmatics and some Pragmatic periodicals in order to grasp their nature and role in the process of communication, (2) giving a comprehensive coverage of inferences in English and, (3) testing the translatability of the inferences in question which are linguistically, culturally and genetically different and (4) showing the realizations of the inferences in the TL (Arabic) and (5) showing that inferences in English cannot be successfully translated into Arabic without grasping cultural values, and linguistic variation. To achieve the above mentioned aims the study hypothesizes that: (1) inferences in English cannot be successfully translated into Arabic without grasping cultural values and linguistic variations, (2) multiplicity of inferences that can be concluded from every utterance results in different renderings by the subjects, (3) taking Grice's maxims of conversation into consideration enables the translators to arrive at how inferences in the utterances under investigation can be deduced and (4) inference cannot be deduced without the premise. The study is based on a corpus of (16) English examples involving inferences derived from various written speech situations in books of pragmatics. These examples are translated by 5 subjects (M. A students in the Department of Translation /College of Arts/University of Mosul). The utterances involving the respective inferences with their Arabic renderings have been analyzed in terms of “type of inference”, “what is said” (natural meaning to use Grice's 1975 terms), what is inferred (non natural meaning), and method of translation (semantic or communicative). Each text analysis is supplemented by pragmatic interpretation and translational discussion. As for the proposed rendering, the most appropriate one will be chosen. In case of subjects' failure, a new rendering will be suggested. The main findings the study arrived at is that inference is regarded a problem in translation in the SL is different from that of the TL and it depends on many elements such as the context which specifies the situation, the speaker's observance of Grice's Maxims.
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Engberg-Pedersen, Elisabeth. "Inference from academic texts in children with autism." Pragmatics and Cognition 25, no. 2 (December 31, 2018): 363–83. http://dx.doi.org/10.1075/pc.18011.eng.
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Abstract Children and adults with autism do worse on tests of inferences than controls. This fact has been attributed to poor language skills, a tendency to focus on detail, and poor social understanding. This study examines whether children with autism with age-appropriate language and cognitive skills have difficulties drawing inferences from academic, expository texts. Sixteen children with autism and a control group of twenty-four children were matched on language skills, nonverbal cognitive ability, and auditory and nonverbal working memory and compared on their responses to questions that require inferences. The children with autism scored significantly lower on inference questions than the controls. Although language skills explain much of the variance in inference scores, diagnostic background also made a contribution. The results are discussed in the light of theories of suppression of irrelevant information and recognition of text writers’ communicative intention.
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Michalewicz, Zbigniew, and Anthony Yeo. "Multiranges and Multitrackers in Statistical Databases." Fundamenta Informaticae 11, no. 1 (January 1, 1988): 41–48. http://dx.doi.org/10.3233/fi-1988-11104.
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The goal of statistical databases is to provide statistics about groups of individuals while protecting their privacy. Sometimes. by correlating enough statistics, sensitive data about individual can be inferred. The problem of protecting against such indirect disclosures of confidential data is called the inference problem and a protecting mechanism – an inference control. A good inference control mechanism should be effective (it should provide security to a reasonable extent) and feasible (a practical way exists to enforce it). At the same time it should retain the richness of the information revealed to the users. During the last few years several techniques were developed for controlling inferences. One of the earliest inference controls for statistical databases restricts the responses computed over too small or too large query-sets. However, this technique is easily subverted. In this paper we propose a new query-set size inference control which is based on the idea of multiranges and has better performance then the original one.
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Liu, Yu, Anurag Andhare, and Kyoung-Don Kang. "Corun: Concurrent Inference and Continuous Training at the Edge for Cost-Efficient AI-Based Mobile Image Sensing." Sensors 24, no. 16 (August 14, 2024): 5262. http://dx.doi.org/10.3390/s24165262.
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Intelligent mobile image sensing powered by deep learning analyzes images captured by cameras from mobile devices, such as smartphones or smartwatches. It supports numerous mobile applications, such as image classification, face recognition, and camera scene detection. Unfortunately, mobile devices often lack the resources necessary for deep learning, leading to increased inference latency and rapid battery consumption. Moreover, the inference accuracy may decline over time due to potential data drift. To address these issues, we introduce a new cost-efficient framework, called Corun, designed to simultaneously handle multiple inference queries and continual model retraining/fine-tuning of a pre-trained model on a single commodity GPU in an edge server to significantly improve the inference throughput, upholding the inference accuracy. The scheduling method of Corun undertakes offline profiling to find the maximum number of concurrent inferences that can be executed along with a retraining job on a single GPU without incurring an out-of-memory error or significantly increasing the latency. Our evaluation verifies the cost-effectiveness of Corun. The inference throughput provided by Corun scales with the number of concurrent inference queries. However, the latency of inference queries and the length of a retraining epoch increase at substantially lower rates. By concurrently processing multiple inference and retraining tasks on one GPU instead of using a separate GPU for each task, Corun could reduce the number of GPUs and cost required to deploy mobile image sensing applications based on deep learning at the edge.
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Chernyshova, Elizaveta. "Explicitation sequences in conversation: some considerations on formulations, candidate inferences and grounding." Travaux neuchâtelois de linguistique, no. 68 (January 1, 2018): 51–58. http://dx.doi.org/10.26034/tranel.2018.2862.
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This conversation analysis study focuses on sequences where speakers make a piece of information explicit (explicitation sequences). Among others, formulations (Heritage & Watson 1979) and candidate inferences (turns submitting an inference and requiring a confirmation in a second position) can initiate an explicitation. Based on a short analysis of conversational data in French this study shows that, despite their similarities, formulations and candidate inferences have different impacts on the grounding processes in conversation (Clark & Brennan 1991). More generally, this paper is concerned with the questions of inference and information in the co-construction of meaning in interaction.
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MIAO, CHUNYAN, ANGELA GOH, YUAN MIAO, and ZHONGHUA YANG. "A DYNAMIC INFERENCE MODEL FOR INTELLIGENT AGENTS." International Journal of Software Engineering and Knowledge Engineering 11, no. 05 (October 2001): 509–28. http://dx.doi.org/10.1142/s0218194001000669.
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This paper proposes an Agent Inference Model (AIM) for constructing intelligent software agents. AIM has the ability of representing various types of fuzzy concepts, temporal concepts, and dynamic causal relationships between concepts. It also has the ability of handling feedback and analyzing inference patterns over different causal impact models. Based on AIM, a new type of intelligent agent, Dynamic Inference Agent (DIA) is presented. A dynamic inference agent has the ability to model, infer and make decisions on behalf of human beings. It uses numeric representations and computation instead of symbolic representation and logic deduction to represent knowledge and to carry out the inferences respectively. Thus the construction of DIA is simplified and the implementation code is compact. The application of DIA to various areas, especially for electronic commerce over the Internet is exemplified.