© 2017 Elsevier Inc. All rights reserved. Systems Factorial Technology: A Theory Driven Methodology for the Identification of Perceptual and Cognitive Mechanisms explores the theoretical and methodological tools used to investigate fundamental questions central to basic psychological and perceptual processes. Such processes include detection, identification, classification, recognition, and decision-making. This book collects the tools that allow researchers to deal with the pervasive model mimicry problems which exist in standard experimental and theoretical paradigms and includes novel applications to not only basic psychological questions, but also clinical diagnosis and links to neuroscience. Researchers can use this book to begin using the methodology behind SFT and to get an overview of current uses and future directions. The collected developments and applications of SFT allow us to peer inside the human mind and provide strong constraints on psychological theory. Provides a thorough introduction to the diagnostic tools offered by SFT Includes a tutorial on applying the method to reaction time data from a variety of different situations Introduces novel advances for testing the significance of SFT results Incorporates new measures that allow for the relaxation of the high accuracy criterion Examines tools to expand the scope of SFT analyses Applies SFT to a spectrum of different cognitive domains across different sensory modalities.

With the explosion of “big data,” digital repositories of texts and images are growing rapidly. These datasets present new opportunities for psychological research, but they require new methodologies before researchers can use these datasets to yield insights into human cognition. We present a new method that allows psychological researchers to take advantage of text and image databases: a procedure for measuring human categorical representations over large datasets of items, such as arbitrary words or pictures. We call this method discrete Markov chain Monte Carlo with people (d-MCMCP). We illustrate our method by evaluating the following categories over datasets: emotions as represented by facial images, moral concepts as represented by relevant words, and seasons as represented by images drawn from large online databases. Three experiments demonstrate that d-MCMCP is powerful and flexible enough to work with complex, naturalistic stimuli drawn from large online databases.

While previous studies have shown that human behavior adjusts in response to uncertainty, it is still not well understood how uncertainty is estimated and represented. As probability distributions are high dimensional objects, only constrained families of distributions with a low number of parameters can be specified from finite data. However, it is unknown what the structural assumptions are that the brain uses to estimate them. We introduce a novel paradigm that requires human participants of either sex to explicitly estimate the dispersion of a distribution over future observations. Judgments are based on a very small sample from a centered, normally distributed random variable that was suggested by the framing of the task. This probability density estimation task could optimally be solved by inferring the dispersion parameter of a normal distribution. We find that although behavior closely tracks uncertainty on a trial-by-trial basis and resists an explanation with simple heuristics, it is hardly consistent with parametric inference of a normal distribution. Despite the transparency of the simple generating process, participants estimate a distribution biased towards the observed instances while still strongly generalizing beyond the sample. The inferred internal distributions can be well approximated by a nonparametric mixture of spatially extended basis distributions. Thus, our results suggest that fluctuations have an excessive effect on human uncertainty judgments because of representations that can adapt overly flexibly to the sample. This might be of greater utility in more general conditions in structurally uncertain environments.

When people search a display for a target defined by a unique feature, fast saccades are predominantly stimulus-driven whereas slower saccades are primarily goal-driven. Here we use this dissociative pattern to assess whether feature-based selection in patients with lateralized spatial attention deficits is impaired in stimulus-driven processing, goal-driven processing, or both. A group of patients suffering from extinction or neglect after parietal damage, and a group of healthy, age-matched controls, were instructed to make a saccade to a uniquely oriented target line which was presented simultaneously with a differently oriented distractor line. We systematically varied the salience of the target and distractor by changing the orientation of background elements, and used a time-based model to extract stimulus-driven (salience) and goal-driven (target set) components of selection. The results show that the patients exhibited reduced stimulus-driven processing only in the contralesional hemifield, while goal-driven processing was reduced across both hemifields.

When learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule (“easy” stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule (“difficult” stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared with easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning.

When interacting with categories, representations focused on within-category relationships are often learned, but the conditions promoting within-category representations and their generalizability are unclear. We report the results of three experiments investigating the impact of category structure and training methodology on the learning and generalization of within-category representations (i.e., correlational structure). Participants were trained on either rule-based or information-integration structures using classification (Is the stimulus a member of Category A or Category B?), concept (e.g., Is the stimulus a member of Category A, Yes or No?), or inference (infer the missing component of the stimulus from a given category) and then tested on either an inference task (Experiments 1 and 2) or a classification task (Experiment 3). For the information-integration structure, within-category representations were consistently learned, could be generalized to novel stimuli, and could be generalized to support inference at test. For the rule-based structure, extended inference training resulted in generalization to novel stimuli (Experiment 2) and inference training resulted in generalization to classification (Experiment 3). These data help to clarify the conditions under which within-category representations can be learned. Moreover, these results make an important contribution in highlighting the impact of category structure and training methodology on the generalization of categorical knowledge.

We provide a tutorial on the basic attributes of computational cognitive models—models that are formulated as a set of mathematical equations or as a computer simulation. We first show how models can generate complex behavior and novel insights from very simple underlying assumptions about human cognition. We survey the different classes of models, from description to explanation, and present examples of each class. We then illustrate the reasons why computational models are preferable to purely verbal means of theorizing. For example, we show that computational models help theoreticians overcome the limitations of human cognition, thereby enabling us to create coherent and plausible accounts of how we think or remember and guard against subtle theoretical errors. Models can also measure latent constructs and link them to individual differences, which would escape detection if only the raw data were considered. We conclude by reviewing some open challenges.

We introduce the δ-machine, a statistical learning tool for classification based on (dis)similarities between profiles of the observations to profiles of a representation set consisting of prototypes. In this article, we discuss the properties of the δ-machine, propose an automatic decision rule for deciding on the number of clusters for the K-means method on the predictive perspective, and derive variable importance measures and partial dependence plots for the machine. We performed five simulation studies to investigate the properties of the δ-machine. The first three simulation studies were conducted to investigate selection of prototypes, different (dis)similarity functions, and the definition of representation set. Results indicate that we best use the Lasso to select prototypes, that the Euclidean distance is a good dissimilarity function, and that finding a small representation set of prototypes gives sparse but competitive results. The remaining two simulation studies investigated the performance of the δ-machine with imbalanced classes and with unequal covariance matrices for the two classes. The results obtained show that the δ-machine is robust to class imbalances, and that the four (dis)similarity functions had the same performance regardless of the covariance matrices. We also showed the classification performance of the δ-machine compared with three other classification methods on ten real datasets from UCI database, and discuss two empirical examples in detail.

Visualization is an important tool for capturing the network activities. Effective visualization allows people to gain insights into the data information and discovery of communication patterns of network flows. Such information may be difficult for human to perceive its relationships due to its numeric nature such as time, packet size, inter-packet time, and many other statistical features. Many existing work fail to provide an effective visualization method for big network traffic data. This work proposes a novel and effective method for visualizing network traffic data with statistical features of high dimensions. We combine Principal Component Analysis (PCA) and Mutidimensional Scaling (MDS) to effectively reduce dimensionality and use colormap for enhance visual quality for human beings. We obtain high quality images on a real-world network traffic dataset named ‘ISP’. Comparing with the popular t-SNE method, our visualization method is more flexible and scalable for plotting network traffic data which may require to preserve multi-dimensional information and relationship. Our plots also demonstrate the capability of handling a large amount of data. Using our method, the readers will be able to visualize their network traffic data as an alternative method of t-SNE.

Under the guidance of a formal exemplar model of categorization, we conduct comparisons of natural-science classification learning across four conditions in which the nature of the training examples is manipulated. The specific domain of inquiry is rock classification in the geologic sciences; the goal is to use the model to search for optimal training examples for teaching the rock categories. On the positive side, the model makes a number of successful predictions: Most notably, compared with conditions involving focused training on small sets of training examples, generalization to novel transfer items is significantly enhanced in a condition in which learners experience a broad swath of training examples from each category. Nevertheless, systematic departures from the model predictions are also observed. Further analyses lead us to the hypothesis that the high-dimensional feature-space representation derived for the rock stimuli (to which the exemplar model makes reference) systematically underestimates within-category similarities. We suggest that this limitation is likely to arise in numerous situations in which investigators attempt to build detailed feature-space representations for naturalistic categories. A low-parameter extended version of the model that adjusts for this limitation provides dramatically improved accounts of performance across the four conditions. We outline future steps for enhancing the current feature-space representation and continuing our goal of using formal psychological models to guide the search for effective methods of teaching science categories.

This thesis aims to understand visual style in the context of computer science, using traditionally intangible artistic properties to enhance existing content manipulation algorithms and develop new content creation methods. The developed algorithms can be used to apply extracted properties to other drawings automatically; transfer a selected style; categorise images based upon perceived style; build 3D models using style features from concept artwork; and other style-based actions that change our perception of an object without changing our ability to recognise it. The research in this thesis aims to provide the style manipulation abilities that are missing from modern digital art creation pipelines.

This special section brings together behavioral, computational, mathematical, and neuroimaging approaches to understand the processes underlying category learning. Over the past decade, there has been growing convergence in research on categorization, with computational-mathematical models influencing the interpretation of brain imaging and neuropsychological data, and with cognitive neuroscience findings influencing the development and refinement of models. Classic debates between single-system and multiple-memory-system theories have become more nuanced and focused. Multiple brain areas and cognitive processes contribute to categorization, but theories differ markedly in whether and when those neurocognitive components are recruited for different aspects of categorization. The articles in this special section approach this issue from several diverse angles.

This paper provides a critical examination of the current state and future possibility of formal cognitive theory for insight problem solving and its associated “aha!” experience. Insight problems are contrasted with move problems, which have been formally defined and studied extensively by cognitive psychologists since the pioneering work of Alan Newell and Herbert Simon. To facilitate our discussion, a number of classical brainteasers are presented along with their solutions and some conclusions derived from observing the behavior of many students trying to solve them. Some of these problems are interesting in their own right, and many of them have not been discussed before in the psychological literature. The main purpose of presenting the brainteasers is to assist in discussing the status of formal cognitive theory for insight problem solving, which is argued to be considerably weaker than that found in other areas of higher cognition such as human memory, decision-making, categorization, and perception. We discuss theoretical barriers that have plagued the development of successful formal theory for insight problem solving. A few suggestions are made that might serve to advance the field.

This article reports data sets aimed at the development of a detailed feature-space representation for a complex natural category domain, namely 30 common subtypes of the categories of igneous, metamorphic, and sedimentary rocks. We conducted web searches to develop a library of 12 tokens each of the 30 subtypes, for a total of 360 rock pictures. In one study, subjects provided ratings along a set of 18 hypothesized primary dimensions involving visual characteristics of the rocks. In other studies, subjects provided similarity judgments among pairs of the rock tokens. Analyses are reported to validate the regularity and information value of the dimension ratings. In addition, analyses are reported that derive psychological scaling solutions from the similarity-ratings data and that interrelate the derived dimensions of the scaling solutions with the directly rated dimensions of the rocks. The stimulus set and various forms of ratings data, as well as the psychological scaling solutions, are made available on an online website (https://osf.io/w64fv/) associated with the article. The study provides a fundamental data set that should be of value for a wide variety of research purposes, including: (1) probing the statistical and psychological structure of a complex natural category domain, (2) testing models of similarity judgment, and (3) developing a feature-space representation that can be used in combination with formal models of category learning to predict classification performance in this complex natural category domain.

The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similarity mainly quantify it as a numerical score, map‐based methods also define (dense) shape correspondences. After presenting in detail the theoretical foundations underlying these approaches, we classify them by looking at their most salient features, including the kind of structure and invariance properties they capture, as well as the distances and the output modalities according to which the similarity between shapes is assessed and returned. We also review the usage of these methods in a number of 3D shape application domains, ranging from matching and retrieval to annotation and segmentation. Finally, the most promising directions for future research developments are discussed.

The present study proposes and examines the multidimensional vector (MDV) model framework as a modeling schema for choice response times. MDV extends the Thurstonian model, as well as signal detection theory, to classification tasks by taking into account the influence of response properties on stimulus discrimination. It is capable of accounting for stimulus-response compatibility, which is known to be an influential task variable determining choice-reaction performance but has not been considered in previous mathematical modeling efforts. Specific MDV models were developed for 5 experiments using the Simon task, for which stimulus location is task irrelevant, to examine the validity of model assumptions and illustrate characteristic behaviors of model parameters. The MDV models accounted for the experimental data to a remarkable degree, demonstrating the adequacy of the framework as a general schema for modeling the latency of choice performance. Some modeling issues involved in the MDV model framework are discussed.

The large use of CAD systems in many industrial fields, such as automotive, naval, and aerospace, has generated a number of 3D databases making available a lot of 3D digital models. Within enterprises, which make use of these technologies, it is common practice to access to CAD models of previously developed products. In fact, designing new products often refers to existing models since similar products allow knowing in advance common problems and related solutions. Therefore, it is useful to have technological solutions that are able to evaluate the similarities of different products in such a way that the user can retrieve existing models and thus have access to the associated useful information for the new design.The concept of similarity has been widely studied in literature and it is well known that two objects can be similar under different perspectives. These multiple possibilities make complicate the assessment of the similarity between two objects. So far, many methods are proposed for the recognition of different parts similarities, but few researches address this problem for assembly models. If evaluating the similarity between two parts may be done under different perspectives, considering assemblies, the viewpoints increase considerably since there are more elements playing a meaningful role.Based on these requirements, we propose a system for retrieving similar assemblies according to different similarity criteria. To achieve this goal, it is necessary having an assembly description including all the information required for the characterizations of the possible different similarity criteria between the two assemblies. Therefore, one of the main topics of this work is the definition of a descriptor capable of encoding the data needed for the evaluation of similarity adaptable to different objectives. In addition, some of the information included in the descriptor may be available in CAD models, while other has to be extracted appropriately. Therefore, algorithms are proposed for extracting the necessary information to fill out the descriptor elements. Finally, for the evaluation of assembly similarity, several measures are defined, each of them evaluating a specific aspect of their similarity.

The field of psychological science has seen major advances in the development of formal models of perceptual classification learning; however, little work has tested such models in real-world natural-category domains. In our current project, we aim to fill that gap by testing the ability of a formal exemplar model of classification to predict learning of rock categories in the geologic sciences. As a prerequisite for testing the model in this domain, we have conducted extensive work to derive a high-dimensional feature-space representation for the rock stimuli. An eight-dimensional representation yields good accounts of naive participants’ judgments of similarity among a large battery of rock-picture samples; furthermore, the eight dimensions have natural psychological interpretations. We then use the exemplar model in combination with the derived feature-space representation to successfully predict participants’ learning and generalization of a variety of scientifically defined rock categories. We discuss further steps for making use of the model and its associated feature-space representation to search for effective techniques of teaching categories in the science classroom.

The experimental study of sonic interactions can elucidate one of the most important aspects of the design process: "How should the sonic interaction be structured to produce a target perceptual result or to induce a specic motor behavior of the user?" Very similar questions are the object of basic research on the human processing of sensory events (e.g., "What is the perceptual effect of this sound stimulus?"), and have spurred the development of a large number of experimental methods. This chapter is meant as an introductory guide to the behavioral methods for the experimental study of complex sound events and sonic interactions.

The auditory perception of materials is a popular topic in the study of non-vocal sound-source perception. In this chapter, we review the empirical evidence on the mechanical and acoustical correlates of the perception of impacted stiff materials, and of the state of matter of sound-generating substances (solids, liquids, gases). As a whole, these studies suggest that recognition abilities are only highly accurate when differentiating between widely diverse materials (e.g. liquids vs. solids or plastics vs. metals) and that limitations in the auditory system, along with the possible internalization of biased statistics in the acoustical environment (e.g. clinking-glass sounds tend to be produced by small objects), might account for the less-than-perfect ability to differentiate between mechanically similar materials. This review is complemented by a summary of studies concerning the perception of deformable materials (fabrics and liquids) and the perceptual and motor-behaviour effects of auditory material-related information in audio-haptic contexts. The results of perceptual studies are the starting point for the development of interactive sound synthesis techniques for rendering the main auditory correlates of material properties, starting from physical models of the involved mechanical interactions. We review the recent literature dealing with contact sound synthesis in such fields as sonic interaction design and virtual reality. Special emphasis is given to softness/hardness correlates in impact sounds, associated with solid object resonances excited through impulsive contact. Synthesis methods for less studied sound-generating systems such as deformable objects (e.g. fabrics and liquids) and aggregate materials are also described.