Infotropism as the underlying principle of perceptual organization

Whether perceptual organization favors the simplest or most likely interpretation of a distal stimulus has long been debated. An unbridgeable gulf has seemed to separate these, the Gestalt and Helmholtzian viewpoints. But in recent decades, the proposal that likelihood and simplicity are two sides of the same coin has been gaining ground, to the extent that their equivalence is now widely assumed. What then arises is a desire to know whether the two principles can be reduced to one. Applying Occam's Razor in this way is particularly desirable given that, as things stand, an account referencing one principle alone cannot be completely satisfactory. The present paper argues that unification of the two principles is possible, and that it can be achieved in terms of an incremental notion of `information seeking' (infotropism). Perceptual processing that is infotropic can be shown to target both simplicity and likelihood. The ability to see perceptual organization as governed by either objective can then be explained in terms of it being an infotropic process. Infotropism can be identified as the principle which underlies, and thus generalizes the principles of likelihood and simplicity

The Dissolution of Bar-Hillel-Carnap Paradox by Semantic Information Theory Based on a Paraconsistent Logic

Several logical puzzles, riddles and problems are defined based on the notion of games in informative contexts. Hintikka argues that epistemology or the theory of knowledge must be considered from the notion of information. So, knowledge cannot just be based on the notions of belief and justification. The present proposal will focus on the logical structure of information, and not only on the quantification of information as suggested by Claude A. Shannon (1916-2001) (Shannon 1948). In many cases, the information bits, although seemingly or factually contradictory, are quite relevant. The paraconsistent systems of logic offer a formalization of reasoning that can support certain contradictions. The well-known “Bar-Hillel–Carnap Paradox” (Bar-Hillel, 1964) causes embarrassment when it concludes that the informational content of a contradiction would be maximum, opposing the traditional notion that the semantic information must be true, and that contradictions are necessarily false

Biproportional Techniques in Input-Output Analysis: Table Updating and Structural Analysis

This paper is dedicated to the contributions of Sir Richard Stone, Michael Bacharach, and Philip Israilevich. It starts out with a brief history of biproportional techniques and related matrix balancing algorithms. We then discuss the RAS algorithm developed by Sir Richard Stone and others. We follow that by evaluating the interpretability of the product of the adjustment parameters, generally known as R and S. We then move on to discuss the various formal formulations of other biproportional approaches and discuss what defines an algorithm as “biproportionalâ€. After mentioning a number of competing optimization algorithms that cannot fall under the rubric of being biproportional, we reflect upon how some of their features have been included into the biproportional setting (the ability to fix the value of interior cells of the matrix being adjusted and of incorporating data reliability into the algorithm). We wind up the paper by pointing out some areas that could use further investigation.Input-Output Economics; RAS; data raking; iterative proportional fitting; estimating missing data

Contributions of Signal-detection Mechanisms and Semantic Memory Representations to Famous Name Recognition

In past research, investigators have often used the recognition memory paradigm to study the cognitive and neural processes that permit the ability to accurately assess whether or not stimuli are familiar. This paradigm involves presenting stimuli to participants in a study phase, and examining their later recognition of them when these stimuli are subsequently presented again in a later test phase. It is not well understood, however, whether the same mechanisms that support familiarity assessment in recognition memory also support familiarity based on general life experience (e.g., recognizing a famous celebrity in daily life). To address this, I implemented modified recognition memory paradigms for the purpose of better understanding the processes that support famous name recognition. In Chapter 2, I developed a signal-detection model that describes how people discriminate between famous and fictional names. I found that similarly to recognition memory, famous name recognition relies on graded evidence that can be modeled successfully with Gaussian distributions. In Chapter 3, I studied the contributions of semantic knowledge to famous name familiarity, with a focus on recognition experiences in which ‘names ring a bell’. I revealed that despite the fact that participants understand this recognition experience to reflect situations where names are familiar but do not provoke retrieval of any related semantic details, they still achieve above-chance performance on an occupation forced-choice task for the same names. Based on these results, I investigated in Chapter 4 whether ‘name rings a bell’ experiences engage the same brain regions as those that also support the ability to successfully retrieve semantic knowledge about famous names. Using functional magnetic resonance imaging, I examined whether the brain regions that support ‘name rings a bell’ experiences overlap with those that support successful identification and correct occupation forced-choice decisions. Two brain areas that I found to be engaged during ‘name rings a bell’ responses were also engaged while participant’s successfully retrieved semantic knowledge for names, which included the left posterior middle temporal gyrus and an inferior aspect of the left ventrolateral prefrontal cortex. Overall, my thesis advances our knowledge of how feelings of familiarity for famous names relate to underlying semantic representations about them

Short and long-term vocabulary learning and retention through multimedia glossing: A mixed methods research

Vocabulary development is significant for mastering a second/foreign language. There are several approaches for vocabulary instruction, including glossing. Glossing is a context-based technique that represents information on target words via definitions, explanations, synonyms, pictures, sounds, and videos. Glossing can also be used electronically through texts, pictures, audios, and video/animations. Studies on different gloss combinations in multimedia learning environments have led investigators to inconclusive findings. The present study examined which mode(s) of gloss presentation (L2 definition, aural, and video/animation) is effective for learners’ short and long-term vocabulary learning and retention. Utilizing a mixed methods approach, 132 intermediate language learners formed one control and three experimental groups. The experimental groups received target words in different glossing modes; the control group received no glossing instruction. ANCOVA and paired samples t-test were used to analyze the pre/post-test data. Learners’ attitudes and perceptions towards glossing modes were also examined through a questionnaire and interviews. The results showed that glossing was significantly more effective than non-glossing strategy for participants’ short-term retention in both productive recall and multiple-choice productive recognition tests; and partially effective for their long-term retention. Additionally, in both vocabulary measurements, L2 definition and video/animation glossing as well as L2 definition and audio glossing were more effective than L2 definition alone for most test sessions; but since L2 definition alone was also effective for few test sessions, the findings cannot be generalized largely. The results of the questionnaire and interviews showed that the participants preferred L2 definition and video/animation glossing over the two other modes

New Axioms for Probability and Likelihood Ratio Measures

ABSTRACT Probability ratio and likelihood ratio measures of inductive support and related notions have appeared as theoretical tools for probabilistic approaches in the philosophy of science, the psychology of reasoning, and artificial intelligence. In an effort of conceptual clarification, several authors have pursued axiomatic foundations for these two families of measures. Such results have been criticized, however, as relying on unduly demanding or poorly motivated mathematical assumptions. We provide two novel theorems showing that probability ratio and likelihood ratio measures can be axiomatized in a way that overcomes these difficulties