Knowledge Discovery as an Aid to Organizational Creativity

Computers can play an important role in the creative process. With the abundance of data and increasing speed of computers, creativity can now be stimulated and enhanced with knowledge mined from available data. The process is known as knowledge discovery or data mining. Knowledge discovery is the process of discovering interesting associations among data in the database. Users in the creativity process can feed on the discovered associations to generate creative solutions. The objective of this paper is to present knowledge discovery as an aid to creativity. The paper first presents the concept of knowledge discovery and then discusses the various techniques in knowledge discovery. Mednick's associative theory of creative thought serves as the theoretical foundation for this research.     

Treatment of Missing Data: Beyond Ends and Means

The ethical decision making process behind the treatment of missing data has yet to be examined in the research literature in any discipline. The purpose of the current paper is to begin to discuss this decision-making process in view of a Foucauldian framework. The paper suggests how the ethical treatment of missing data should be considered from the adoption of this theoretical framework.     

A computer program for determining optimal data transformations minimizing skew

A program is presented that solves for an optimal transformation of data that minimizes skew. Various possible transformations are represented by a function that depends upon a single parameter, λ. A process of iterative approximations is employed to find a value of λ that will transform the data so as to produce a third moment equal to zero. The transformation found by this process may be used as computed or used to indicate which of the commonly employed transformations will most adequately counteract skew. The use of this program in processing data before analysis of variance is discussed.     

Linking cognitive and reaching trajectories via intermittent movement control

Theories of decision-making have traditionally been constrained by reaction time data. A limitation of reaction time data, particularly for studying the temporal dynamics of cognitive processing, is that they index only the endpoint of the decision making process. Recently, physical reaching trajectories have been used as proxies for underlying mental trajectories through decision space. We suggest that this approach has been oversimplified: while it is possible for the motor control system to access the current state of the evidence accumulation process, this access is intermittent. Instead, we demonstrate how a model of arm movements that assumes intermittent, not continuous, access to the decision process is sufficient to describe the effects of stimulus quality and viewing time in curved reaching movements.     

Intentional and identity processes: A social constructionist investigation using student journals

In contrast to traditional theories of career development that focus on decision-making processes in relation to market work, [Richardson, M. S. (2004). The emergence of new intentions in subjective experience: A social/personal constructionist and relational understanding. Journal of Vocational Behavior, 64, 485-498], informed by social constructionism and considering both work and relationships, suggests that the process of emerging intentional states or intentional process is central to the broader task of constructing a life. This study was designed to investigate this process. The research situation consisted of three structured group discussions with students in a graduate class. Journals written after the group discussions constituted the data for the study. Based on the first stage of data analysis, the scope of investigation was expanded to include emerging identity states or identity process and the emotional experience of the group discussions. In the second phase of data analysis, intentional process and identity process data were analyzed for themes and the emotional experience data were coded. Results of both phases of data analysis are discussed in relation to future research and implications for practice.     

VideoNoter: A productivity tool for video data analysis

The software described in this paper, VideoNoter, addresses the need for tools that support annotation and retrieval of video data and organize the presentation of multiple analyses of the same data. Video is widely perceived as an important medium for psychological research, because video recording makes the fleeting particulars of human interaction available as data for detailed analysis, while retaining much of the context of the event. Though the benefits of using video data are high, the process can be prohibitively time-consuming. We have developed a prototype computer-based video analysis tool that can enhance the productivity of the video analysis process. In this paper, we report on the design and implementation of VideoNoter, and we discuss how it facilitates video data analysis.     

The clinical inference process and the role of theory

Perhaps the most challenging and creative aspect of clinical testing is the clinical inference process, the sequential steps the examiner takes in transforming the raw test data into a clinically relevant testing report. Unfortunately, this part of the testing process has received little attention in the testing literature. In this article, the specific steps in the inferential process are outlined. In addition, the ways in which theory enter into the inferential process are also discussed.     

The Clinical Inference Process and the Role of Theory

Perhaps the most challenging and creative aspect of clinical testing is the clinical inference process, the sequential steps the examiner takes in transforming the raw test data into a clinically relevant testing report. Unfortunately, this part of the testing process has received little attention in the testing literature. In this article, the specific steps in the inferential process are outlined. In addition, the ways in which theory enter into the inferential process are also discussed.     

Lessons from the Large Hadron Collider for model-based experimentation: the concept of a model of data acquisition and the scope of the hierarchy of models

According to the hierarchy of models (HoM) account of scientific experimentation developed by Patrick Suppes and elaborated by Deborah Mayo, theoretical considerations about the phenomena of interest are involved in an experiment through theoretical models that in turn relate to experimental data through data models, via the linkage of experimental models. In this paper, I dispute the HoM account in the context of present-day high-energy physics (HEP) experiments. I argue that even though the HoM account aims to characterize experimentation as a model-based activity, it does not involve a modeling concept for the process of data acquisition, and it thus fails to provide a model-based characterization of the theory-experiment relationship underlying this process. In order to characterize the foregoing relationship, I propose the concept of a model of data acquisition and illustrate it in the case of the ATLAS experiment at CERN’s Large Hadron Collider, where the Higgs boson was discovered in 2012. I show that the process of data acquisition in the ATLAS experiment is performed according to a model of data acquisition that specifies and organizes the experimental procedures necessary to select the data according to a predetermined set of selection criteria. I also point out that this data acquisition model is theory-laden, in the sense that the underlying data selection criteria are determined by considering the testable predictions of the theoretical models that the ATLAS experiment is aimed to test. I take this sense of theory-ladenness to indicate that the relationship between the procedures of the ATLAS experiment and the theoretical models of the phenomena of interest is first established, prior to the formation of data models, through the data acquisition model of the experiment, thus not requiring the intermediary of other types of models as suggested by the HoM account. I therefore conclude that in the context of present-day HEP experiments, the HoM account does not consistently extend to the process of data acquisition so as to include models of data acquisition.     

A method for sensation scaling based on an analogy between perception and judgment

Two techniques for studying judgment are discussed. In the first of these one assumes invariance of perception over different judgment processes and conceives of the properties of one judgment process as known. Then, perceptions may be estimated under this process and utilized in the study of other judgment processes of interest. A second technique disposes of the reliance up on a basic, “known” process and instead suggests that perception be treated as free parameters to be estimated from data. The paper then proceeds to show how this second technique may be used under certain conditions on perception themselves. A concept of sensation is introduced which is thought of as generating percepts in formally the same manner as percepts are thought of as generating judgments. The idea is tried out on the perception of movement time. A simple perception process appears to give a good fit to data.     

The what,why, and how of born-open data

Although many researchers agree that scientific data should be open to scrutiny to ferret out poor analyses and outright fraud, most raw data sets are not available on demand. There are many reasons researchers do not open their data, and one is technical. It is often time consuming to prepare and archive data. In response, my laboratory has automated the process such that our data are archived the night they are created without any human approval or action. All data are versioned, logged, time stamped, and uploaded including aborted runs and data from pilot subjects. The archive is GitHub, github.com, the world’s largest collection of open-source materials. Data archived in this manner are called born open. In this paper, I discuss the benefits of born-open data and provide a brief technical overview of the process. I also address some of the common concerns about opening data before publication.     

BOOK REVIEWS:

Both the Piagetian theory of the learning process and a psycholinguistic interpretation of the reading process are integrated in and reflected by the reading program that is presented in this paper. Because of this, a brief interpretation of the reading process and of Piaget's theory is necessary to understand and appreciate the reading program. Each component of the program will be explained and interpreted from Piagetian perspectives. Finally, evaluative data attesting to the success of the program will be presented.     

Spline smoothing with model-based penalties

Nonparametric regression techniques, which estimate functions directly from noisy data rather than relying on specific parametric models, now play a central role in statistical analysis. We can improve the efficiency and other aspects of a nonparametric curve estimate by using prior knowledge about general features of the curve in the smoothing process. Spline smoothing is extended in this paper to express this prior knowledge in the form of a linear differential operator that annihilates a specified parametric model for the data. Roughness in the fitted function is defined in terms of the integrated square of this operator applied to the fitted function. A fastO(n) algorithm is outlined for this smart smoothing process. Illustrations are provided of where this technique proves useful.     

The Importance of Complexity in Model Selection

Model selection should be based not solely on goodness-of-fit, but must also consider model complexity. While the goal of mathematical modeling in cognitive psychology is to select one model from a set of competing models that best captures the underlying mental process, choosing the model that best fits a particular set of data will not achieve this goal. This is because a highly complex model can provide a good fit without necessarily bearing any interpretable relationship with the underlying process. It is shown that model selection based solely on the fit to observed data will result in the choice of an unnecessarily complex model that overfits the data, and thus generalizes poorly. The effect of over-fitting must be properly offset by model selection methods. An application example of selection methods using artificial data is also presented. Copyright 2000 Academic Press.     

An expectancy model for memory search

A model for memory search is proposed in which S first forms an “expectancy” regarding the item to be tested on the next trial, then carries out a memory search. It is proposed that an expected item is encoded faster (or perhaps responded to more quickly), but the memory scanning process for expected and nonexpected items is otherwise identical. Assuming a serial exhaustive scanning process, we were able to fit much of the data in the literature. In addition, we tested the model by having S give his expectancy aloud before each trial. The data showed about a 100-msec advantage for expected items that did not interact with memory load. The model fit this data reasonably well.     

The Dynamics of Religious Practice in Spain from the Mid‐19th Century to 2010

Like other Western countries, Spain is rapidly becoming more secularized. While there is almost unanimous agreement that this is happening, significant discord exists about the phases of the process and the speed of its development. This is mostly due to the use of partial and sometimes unreliable data. The aim of this study is to remedy these uncertainties. Our work is based on the data provided by time‐use surveys conducted in 2003 and 2010. Using some basic provisional but reasonable assumptions, we demonstrate that it is possible to identify a plausible dynamic of secularization in Spain starting in the second half of the 19th century. We also highlight the fact that the different indicators feature some significant time lags. Indeed, the dating of the start of the secularization process varies depending on whether reference is made to data on birth cohorts, the overall population, or the “visibility” of the phenomenon.     

An exploratory analysis of the latent structure of process data via action sequence autoencoders

Computer simulations have become a popular tool for assessing complex skills such as problem-solving. Log files of computer-based items record the human–computer interactive processes for each respondent in full. The response processes are very diverse, noisy, and of non-standard formats. Few generic methods have been developed to exploit the information contained in process data. In this paper we propose a method to extract latent variables from process data. The method utilizes a sequence-to-sequence autoencoder to compress response processes into standard numerical vectors. It does not require prior knowledge of the specific items and human–computer interaction patterns. The proposed method is applied to both simulated and real process data to demonstrate that the resulting latent variables extract useful information from the response processes.     

A Hierarchical Ornstein–Uhlenbeck Model for Continuous Repeated Measurement Data

In this paper, we present a diffusion model for the analysis of continuous-time change in multivariate longitudinal data. The central idea is to model the data from a single person with an Ornstein–Uhlenbeck diffusion process. We extend it hierarchically by allowing the parameters of the diffusion process to vary randomly over different persons. With this approach, both intra and interindividual differences are analyzed simultaneously. Furthermore, the individual difference parameters can be regressed on covariates, thereby providing an explanation of between-person differences. Unstructured and unbalanced data pose no problem for the model to be applied. We demonstrate the method on data from an experience sampling study to investigate changes in the core affect. It can be concluded that different factors from the five factor model of personality are related to features of the trajectories in the core affect space, such as the cross-correlation and variability of the changes.     

A hierarchical latent stochastic differential equation model for affective dynamics

In this article a continuous-time stochastic model (the Ornstein-Uhlenbeck process) is presented to model the perpetually altering states of the core affect, which is a 2-dimensional concept underlying all our affective experiences. The process model that we propose can account for the temporal changes in core affect on the latent level. The key parameters of the model are the average position (also called home base), the variances and covariances of the process, and the regulatory mechanisms that keep the process in the vicinity of the average position. To account for individual differences, the model is extended hierarchically. A particularly novel contribution is that in principle all parameters of the stochastic process (not only the mean but also its variance and the regulatory parameters) are allowed to differ between individuals. In this way, the aim is to understand the affective dynamics of single individuals and at the same time investigate how these individuals differ from one another. The final model is a continuous-time state-space model for repeated measurement data taken at possibly irregular time points. Both time-invariant and time-varying covariates can be included to investigate sources of individual differences. As an illustration, the model is applied to a diary study measuring core affect repeatedly for several individuals (thereby generating intensive longitudinal data).