Hypothesis Generation Process Research Articles

Rapid hypothesis generation by combining residual sorting with local constraints

Efficient hypothesis generation plays an important role in robust model fitting. In this study, based on the combination of residual sorting and local constraints, we propose an efficient guided hypothesis generation method, called Rapid Hypothesis Generation (RHG). By exploiting the local constraints to guide the hypothesis generation process, RHG raises the probability of generating promising hypotheses and reduces the computational cost during hypotheses generation. Experimental results on homography and fundamental matrix estimation show that RHG can effectively guide hypothesis generation process and rapidly generate promising hypotheses for heavily contaminated multi-structure data.

Human memory research: Current hypotheses and new perspectives

Research on human memory has increased significantly in the last few decades. Inconsistencies and controversies inherent to such research, however, are rarely articulated on published reports. The goal of the present article is to present and discuss a series of open questions related to major topics on human memory research that can be addressed by future research. The topics covered here are visual working memory, recognition memory, emotion and memory interaction, and methodological issues of false memories studies. Overall, the present work reveals a series of open questions and alternative analysis which could be useful for the process of hypothesis generation, and consequently for the design and implementation of future research on human memory.

Exploring Evolving Media Discourse Through Event Cueing.

Online news, microblogs and other media documents all contain valuable insight regarding events and responses to events. Underlying these documents is the concept of framing, a process in which communicators act (consciously or unconsciously) to construct a point of view that encourages facts to be interpreted by others in a particular manner. As media discourse evolves, how topics and documents are framed can undergo change, shifting the discussion to different viewpoints or rhetoric. What causes these shifts can be difficult to determine directly; however, by linking secondary datasets and enabling visual exploration, we can enhance the hypothesis generation process. In this paper, we present a visual analytics framework for event cueing using media data. As discourse develops over time, our framework applies a time series intervention model which tests to see if the level of framing is different before or after a given date. If the model indicates that the times before and after are statistically significantly different, this cues an analyst to explore related datasets to help enhance their understanding of what (if any) events may have triggered these changes in discourse. Our framework consists of entity extraction and sentiment analysis as lenses for data exploration and uses two different models for intervention analysis. To demonstrate the usage of our framework, we present a case study on exploring potential relationships between climate change framing and conflicts in Africa.

Collective action for knowledge mobilisation: a realist evaluation of the Collaborations for Leadership in Applied Health Research and Care

BackgroundThe establishment of the Collaborations for Leadership in Applied Health Research and Care (CLAHRCs) was the culmination of a number of policy initiatives to bridge the gap between evidence and practice. CLAHRCs were created and funded to facilitate development of partnerships and connect the worlds of academia and practice in an effort to improve patient outcomes through the conduct and application of applied health research.ObjectivesOur starting point was to test the theory that bringing higher education institutions and health-care organisations closer together catalyses knowledge mobilisation. The overall purpose was to develop explanatory theory regarding implementation through CLAHRCs and answer the question ‘what works, for whom, why and in what circumstances?’. The study objectives focused on identifying and tracking implementation mechanisms and processes over time; determining what influences whether or not and how research is used in CLAHRCs; investigating the role played by boundary objects in the success or failure of implementation; and determining whether or not and how CLAHRCs develop and sustain interactions and communities of practice.MethodsThis study was a longitudinal realist evaluation using multiple qualitative case studies, incorporating stakeholder engagement and formative feedback. Three CLAHRCs were studied in depth over four rounds of data collection through a process of hypothesis generation, refining, testing and programme theory specification. Data collection included interviews, observation, documents, feedback sessions and an interpretive forum.FindingsKnowledge mobilisation in CLAHRCs was a function of a number of interconnected issues that provided more or less conducive conditions for collective action. The potential of CLAHRCs to close the metaphorical ‘know–do’ gap was dependent on historical regional relationships, their approach to engaging different communities, their architectures, what priorities were set and how, and providing additional resources for implementation, including investment in roles and activities to bridge and broker boundaries. Additionally, we observed a balance towards conducting research rather than implementing it. Key mechanisms of interpretations of collaborative action, opportunities for connectivity, facilitation, motivation, review and reflection, and unlocking barriers/releasing potential were important to the processes and outcomes of CLAHRCs. These mechanisms operated in different contexts including stakeholders’ positioning, or ‘where they were coming from’, governance arrangements, availability of resources, competing drivers, receptiveness to learning and evaluation, and alignment of structures, positions and resources. Preceding conditions influenced the course and journey of the CLAHRCs in a path-dependent way. We observed them evolving over time and their development led to the accumulation of different types of impacts, from those that were conceptual to, later in their life cycle, those that were more direct.ConclusionsMost studies of implementation focus on researching one-off projects, so a strength of this study was in researching a systems approach to knowledge mobilisation over time. Although CLAHRC-like approaches show promise, realising their full potential will require a longer and more sustained focus on relationship building, resource allocation and, in some cases, culture change. This reinforces the point that research implementation within a CLAHRC model is a long-term investment and one that is set within a life cycle of organisational collaboration.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

Automatic 3D tracking system for large swarm of moving objects

Natural systems such as bird flocks, fish schools and insect swarms consist of a large group of moving individuals. For many years, scientists have been interested in the complex 3D motion patterns and dynamics they exhibit, trying to discover enlightening rules and causes behind them. Unfortunately, the lack of effective techniques to accurately measure the real 3D trajectories of the individuals had limited the quantitative study on these systems. We propose in this paper an automatic tracking system which is able to track a large number of tiny animals in a 3D volume with multiple cameras. Most visual details of such targets are lost in the captured images because of limited image resolution, and the remainder can be easily corrupted due to frequent occlusion or motion blur, which makes it difficult to establish cross-view and cross-frame correspondences. We formulate the problem as a repeated process of hypothesis generation and verification. Hypotheses are generated when cross-view matching ambiguities occur and are verified at an efficient 3D tracking stage where targets are modeled in 3D space and weak yet existing visual information from multi-view video streams are furthest collected. The whole system is fully automatic in dealing with variable number of targets and robust against detection and matching errors.

Commentary: Exploratory data analysis.

Commentary: Exploratory data analysis.

Selecting clinical diagnoses: logical strategies informed by experience.

This article describes reasoning strategies used by clinicians in different diagnostic circumstances and how these modes of inquiry may allow further insight into the evaluation and treatment of patients. Specifically, it aims to make explicit the implicit logical considerations that guide a variety of strategies in the diagnostic process, as exemplified in specific clinical cases. It focuses, in particular, in strategies that clinicians use to move from a large set of possible diagnoses initially suggested by abductive inferences - the process of hypothesis generation that creates a diagnostic space - to a narrower set or even to a single 'best' diagnosis, where the criteria to determine what is 'best' may differ according to different strategies. Experienced clinicians should have a diversified kit of strategies - for example, Bayesian probability or inference to a lovely explanation - to select from among previously generated hypotheses, rather than rely on any one approach every time.

Evaluating the inverse reasoning account of object discovery

People routinely make inferences about unobserved objects. A hotel guest with welts on his arms, for example, will often worry about bed bugs. The discovery of unobserved objects almost always involves a backward inference from some observed effects (e.g., welts) to unobserved causes (e.g., bed bugs). The inverse reasoning account, which is typically formalized as Bayesian inference, posits that the strength of a backward inference is closely connected to the strength of the corresponding forward inference from the unobserved causes to the observed effects. We evaluated the inverse reasoning account of object discovery in three experiments where participants were asked to discover the unobserved “attractors” and “repellers” that controlled a “particle” moving within an arena. Experiments 1 and 2 showed that participants often failed to provide the best explanations for various particle motions, even when the best explanations were simple and when participants enthusiastically endorsed these explanations when presented with them. This failure demonstrates that object discovery is critically dependent on the processes that support hypothesis generation—processes that the inverse reasoning account does not explain. Experiment 3 demonstrated that people sometimes generate explanations that are invalid even according to their own forward inferences, suggesting that the psychological processes that support forward and backward inference are less intertwined than the inverse reasoning account suggests. The experimental findings support an alternative account of object discovery in which people rely on heuristics to generate possible explanations.

ProKinO: a unified resource for mining the cancer kinome.

Protein kinases represent a large and diverse family of evolutionarily related proteins that are abnormally regulated in human cancers. Although genome sequencing studies have revealed thousands of variants in protein kinases, translating “big” genomic data into biological knowledge remains a challenge. Here, we describe an ontological framework for integrating and conceptualizing diverse forms of information related to kinase activation and regulatory mechanisms in a machine readable, human understandable form. We demonstrate the utility of this framework in analyzing the cancer kinome, and in generating testable hypotheses for experimental studies. Through the iterative process of aggregate ontology querying, hypothesis generation and experimental validation, we identify a novel mutational hotspot in the αC-β4 loop of the kinase domain and demonstrate the functional impact of the identified variants in epidermal growth factor receptor (EGFR) constitutive activity and inhibitor sensitivity. We provide a unified resource for the kinase and cancer community, ProKinO, housed at http://vulcan.cs.uga.edu/prokino.

Imagination and the generation of new ideas

A variety of theories have been put forth to explain the function of imagination, most notably that imagination engages and develops children's theory of mind and counterfactual reasoning. Here, we propose that a primary role for imagination is as a cognitive mechanism for efficiently generating new ideas without observing new evidence. Learners must generate hypotheses before they can assess the truth of these hypotheses. Given infinite possibilities, how do learners constrain the process of hypothesis generation? We suggest that learners represent abstract criteria for the solution to a problem and generate solutions that, if true, would solve the problem. As a preliminary test of this idea, we show that, in the absence of any fact of the matter (i.e., when neither prior knowledge nor statistical data distinguishes competing hypotheses), 4–6-year-olds (mean: 63 months) systematically converge on solutions to problems, consistent with an ability to imagine the abstract properties of causal problems and their solutions.

Quantitative Systems Pharmacology can reduce attrition and improve productivity in pharmaceutical research and development.

The empirical hypothesis generation and testing approach to pharmaceutical research and development (R&D), and biomedical research has proven very effective over the last half-century; resulting in tremendous increases productivity and the rates of approval for new drug applications at the Food and Drug Administration (FDA). However, as discovery of new therapeutic approaches for diseases with unmet medical need becomes more challenging, the productivity and efficiency of the traditional approach to drug discovery and development is diminishing. Innovative approaches are needed, such as those offered by Quantitative Systems Pharmacology (QSP) modeling and simulation. This “systems” approach to modeling and simulation can be used to guide the hypothesis generation and testing process in pharmaceutical R&D, in a manner similar to its adoption in other industries in the past. Embedding QSP into the existing processes within pharmaceutical discovery and development will be required in order to realize the full beneficial impact of this innovative approach.

No need to match: a comment on Bach, Nicholson and Hudson's "Affordance-Matching Hypothesis".

No need to match: a comment on Bach, Nicholson and Hudson's "Affordance-Matching Hypothesis".

‘A Picture Is Worth 10,000 Words’: A Module to Test the ‘Visualization Hypothesis’ in Quantitative Methods Teaching

AbstractInadequate quantitative methods (QM) training provision for undergraduate social science students in the United Kingdom is a well-known problem. This paper reports on the design, implementation and assessment of an induction module created to test the hypothesis that visualization helps students learn key statistical concepts. The induction module is a twelve-week compulsory unit taught to first year social science students at a UK university, which they complete prior to a more traditional statistical, workshop-based QM module. A component of the induction module focuses on the use of visualization through Geographic Information Systems (GIS), to teach the process of hypothesis generation to students while they also are introduced to the basics of QM research design and univariate and bivariate forms of data analysis. Self-reflexive evaluation indicates that visualization could assist students with more advanced QM statistical skills.

A concept analysis of abductive reasoning

To describe an analysis of the concept of abductive reasoning. In the discipline of nursing, abductive reasoning has received only philosophical attention and remains a vague concept. In addition to deductive and inductive reasoning, abductive reasoning is not recognized even in prominent nursing knowledge development literature. Therefore, what abductive reasoning is and how it can inform nursing practice and education was explored. Concept analysis. Combinations of specific keywords were searched in Web of Science, CINAHL, PsychINFO, PubMed, Medline and EMBASE. The analysis was conducted in June 2012 and only literature before this period was included. No time limits were set. Rodger's evolutionary method for conducting concept analysis was used. Twelve records were included in the analysis. The most common surrogate term was retroduction, whereas related terms included intuition and pattern and similarity recognition. Antecedents consisted of a complex, puzzling situation and a clinician with creativity, experience and knowledge. Consequences included the formation of broad hypotheses that enhance understanding of care situations. Overall, abductive reasoning was described as the process of hypothesis or theory generation and evaluation. It was also viewed as inference to the best explanation. As a new approach, abductive reasoning could enhance reasoning abilities of novice clinicians. It can not only incorporate various ways of knowing but also its holistic approach to learning appears to be promising in problem-based learning. As nursing literature on abductive reasoning is predominantly philosophical, practical consequences of abductive reasoning warrant further research.

Pathway Activity Profiling (PAPi): a tool for metabolic pathway analysis.

Pathway Activity Profiling (PAPi) is a method developed to correlate levels of metabolites to the activity of metabolic pathways operating within biological systems. Based solely on a metabolomics data set and the Kyoto Encyclopedia of Genes and Genomes, PAPi predicts and compares the activity of metabolic pathways across experimental conditions, which considerably improves the hypothesis generation process for achieving the biological interpretation of biological studies. In this chapter, we describe how to apply PAPi to a metabolomics data set using the R-software.

Changes in geographic variation in the uptake of cervical cancer screening in Taiwan: Possible effects of “leadership style factor”?

ObjectivesWennberg proposed the “practice style factor” to explain the large variations in the use of medical care. As a corollary, we propose the “leadership style factor” of the director of the city/county bureau of public health to explain changes in geographic variation in the uptake of cervical cancer screening. MethodsWe first calculated the triennial Pap smear rates for women aged 30–69 years from 1997 through 2010 for each city/county in Taiwan and the rate difference and rate ratio between the highest and the lowest city/county to illustrate the geographic variation in the uptake of cervical cancer screening. We then created an expert panel to conduct a hypothesis generation process to examine the possible effects of “leadership style factors” in explaining the changes. ResultsThe Pap smear rate in Taiwan as a whole was 35% in 1997 and increased to 56% in 2001, and was then stable until 2010 (55%). In 2002, the geographic variation in the Pap smear rate was the smallest, ranging from 49% in Penghu County to 63% in I-lan County, with a rate ratio of 1.28. Unfortunately, the rate ratio increased to 1.49 in 2010, the rate being lowest in Penghu County (42%) and highest in Tainan City (63%). We identified four cities/counties with unique patterns of change in Pap smear rates, which were highly associated with the leadership style of the director of the city/county bureau of public health. ConclusionsDespite the launch of an organized cancer screening program in Taiwan, geographic variation in the uptake of cervical cancer screening still exists and has increased during the past decade. The “leadership style factor” of the director of the city/county bureau of public health might play a plausible role in explaining the pattern of change in geographic variation in the use of cervical cancer screening in Taiwan.

Text detection in street level images

Text detection system for natural images is a very challenging task in Computer Vision. Image acquisition introduces distortion in terms of perspective, blurring, illumination, and characters which may have very different shape, size, and color. We introduce in this article a full text detection scheme. Our architecture is based on a new process to combine a hypothesis generation step to get potential boxes of text and a hypothesis validation step to filter false detections. The hypothesis generation process relies on a new efficient segmentation method based on a morphological operator. Regions are then filtered and classified using shape descriptors based on Fourier, Pseudo Zernike moments and an original polar descriptor, which is invariant to rotation. Classification process relies on three SVM classifiers combined in a late fusion scheme. Detected characters are finally grouped to generate our text box hypotheses. Validation step is based on a global SVM classification of the box content using dedicated descriptors adapted from the HOG approach. Results on the well-known ICDAR database are reported showing that our method is competitive. Evaluation protocol and metrics are deeply discussed and results on a very challenging street-level database are also proposed.

Temporal Dynamics of Hypothesis Generation: The Influences of Data Serial Order, Data Consistency, and Elicitation Timing

The pre-decisional process of hypothesis generation is a ubiquitous cognitive faculty that we continually employ in an effort to understand our environment and thereby support appropriate judgments and decisions. Although we are beginning to understand the fundamental processes underlying hypothesis generation, little is known about how various temporal dynamics, inherent in real world generation tasks, influence the retrieval of hypotheses from long-term memory. This paper presents two experiments investigating three data acquisition dynamics in a simulated medical diagnosis task. The results indicate that the mere serial order of data, data consistency (with previously generated hypotheses), and mode of responding influence the hypothesis generation process. An extension of the HyGene computational model endowed with dynamic data acquisition processes is forwarded and explored to provide an account of the present data.

The effects of the Recognition of Scientific concepts in Question Situation on the Recognition of a Prior Knowledge in a Hypothesis Generation Process

The effects of the Recognition of Scientific concepts in Question Situation on the Recognition of a Prior Knowledge in a Hypothesis Generation Process

Bringing Clinicians to Bear in the Hypothesis Generation Process

Bringing Clinicians to Bear in the Hypothesis Generation Process