What Is Cognitive Psychology?

What Is Cognitive Psychology? identifies the theoretical foundations of cognitive psychology—foundations which have received very little attention in modern textbooks. Beginning with the basics of information processing, Michael R. W. Dawson explores what experimental psychologists infer about these processes and considers what scientific explanations are required when we assume cognition is rule-governed symbol manipulation. From these foundations, psychologists can identify the architecture of cognition and better understand its role in debates about its true nature. This volume offers a deeper understanding of cognitive psychology and presents ideas for integrating traditional cognitive psychology with more modern fields like cognitive neuroscience.Publishe

Design cognition for conceptual design

Creative competences are the major currency in 21st century product development domain. Consequentially, the number of new methodologies and working styles targeted for the fuzzy front-end constantly increases. In response, those responsible for managing the early phases of product design need to repeatedly question methods, the organization of work, and the constitution of a design team. In this dissertation I argue that the development of design practices can benefit from a science of design that can provide robust evidence about successful and purposeful ways of working. I particularly endorse a psychological science of design which concerns mental constructs that underlie design work regardless of the utilized tools and methods. This dissertation is motivated by an urge to understand the generation of ideas in conceptual design. By idea generation, I refer to the rapid production of seeds for future products or services, not necessarily very clever or unique yet. The goal is to develop a psychologically plausible view on how this kind of idea generation unfolds and particularly how human memory processes underlie this activity. This kind of cognitive, descriptive account is considered necessary to highlight the reality of design work in contrast to the prevalent prescriptive models of design. This thesis reviews the existing literature on design idea generation and general theories of idea generation. Based on several empirical studies and cycles of theoretical development, I develop a heuristic model of conceptual design; an example of design cognition for conceptual design. The model is utilized as an explanatory framework to argue why certain phenomena (e.g. incubation, inspiration, and fixation) arise and influence design idea generation as they do. The model emphasizes memory search, recognition, and analogical transfer as essential cognitive processes in idea generation. As a critical reflection of the work, I come to acknowledge the need to expand the cognitive theory beyond its present scope. I see that the design cognition needs to give up the problem solving perspective and consider new discourses to keep up with the accounts based on social constructivism. In future, I believe that establishing a science of design should help both educators and practitioners to advance their skills and help developing more effective and designer-centered practices

Proceedings of the 11th international Conference on Cognitive Modeling : ICCM 2012

The International Conference on Cognitive Modeling (ICCM) is the premier conference for research on computational models and computation-based theories of human behavior. ICCM is a forum for presenting, discussing, and evaluating the complete spectrum of cognitive modeling approaches, including connectionism, symbolic modeling, dynamical systems, Bayesian modeling, and cognitive architectures. ICCM includes basic and applied research, across a wide variety of domains, ranging from low-level perception and attention to higher-level problem-solving and learning. Online-Version published by Universitätsverlag der TU Berlin (www.univerlag.tu-berlin.de

Attention Restraint, Working Memory Capacity, and Mind Wandering: Do Emotional Valence or Intentionality Matter?

Attention restraint appears to mediate the relationship between working memory capacity (WMC) and mind wandering (Kane et al., 2016). Prior work has identifed two dimensions of mind wandering—emotional valence and intentionality. However, less is known about how WMC and attention restraint correlate with these dimensions. Te current study examined the relationship between WMC, attention restraint, and mind wandering by emotional valence and intentionality. A confrmatory factor analysis demonstrated that WMC and attention restraint were strongly correlated, but only attention restraint was related to overall mind wandering, consistent with prior fndings. However, when examining the emotional valence of mind wandering, attention restraint and WMC were related to negatively and positively valenced, but not neutral, mind wandering. Attention restraint was also related to intentional but not unintentional mind wandering. Tese results suggest that WMC and attention restraint predict some, but not all, types of mind wandering

MULTIVARIATE MODELING OF COGNITIVE PERFORMANCE AND CATEGORICAL PERCEPTION FROM NEUROIMAGING DATA

State-of-the-art cognitive-neuroscience mainly uses hypothesis-driven statistical testing to characterize and model neural disorders and diseases. While such techniques have proven to be powerful in understanding diseases and disorders, they are inadequate in explaining causal relationships as well as individuality and variations. In this study, we proposed multivariate data-driven approaches for predictive modeling of cognitive events and disorders. We developed network descriptions of both structural and functional connectivities that are critical in multivariate modeling of cognitive performance (i.e., fluency, attention, and working memory) and categorical perceptions (i.e., emotion, speech perception). We also performed dynamic network analysis on brain connectivity measures to determine the role of different functional areas in relation to categorical perceptions and cognitive events. Our empirical studies of structural connectivity were performed using Diffusion Tensor Imaging (DTI). The main objective was to discover the role of structural connectivity in selecting clinically interpretable features that are consistent over a large range of model parameters in classifying cognitive performances in relation to Acute Lymphoblastic Leukemia (ALL). The proposed approach substantially improved accuracy (13% - 26%) over existing models and also selected a relevant, small subset of features that were verified by domain experts. In summary, the proposed approach produced interpretable models with better generalization.Functional connectivity is related to similar patterns of activation in different brain regions regardless of the apparent physical connectedness of the regions. The proposed data-driven approach to the source localized electroencephalogram (EEG) data includes an array of tools such as graph mining, feature selection, and multivariate analysis to determine the functional connectivity in categorical perceptions. We used the network description to correctly classify listeners behavioral responses with an accuracy over 92% on 35 participants. State-of-the-art network description of human brain assumes static connectivities. However, brain networks in relation to perception and cognition are complex and dynamic. Analysis of transient functional networks with spatiotemporal variations to understand cognitive functions remains challenging. One of the critical missing links is the lack of sophisticated methodologies in understanding dynamics neural activity patterns. We proposed a clustering-based complex dynamic network analysis on source localized EEG data to understand the commonality and differences in gender-specific emotion processing. Besides, we also adopted Bayesian nonparametric framework for segmentation neural activity with a finite number of microstates. This approach enabled us to find the default network and transient pattern of the underlying neural mechanism in relation to categorical perception. In summary, multivariate and dynamic network analysis methods developed in this dissertation to analyze structural and functional connectivities will have a far-reaching impact on computational neuroscience to identify meaningful changes in spatiotemporal brain activities

Mitigating the effects of implicit constraints in verbal insight problem solving through training

The main focus of this thesis was to design training to mitigate the effects of constraints underlying verbal insight problem solving. Concurrent verbal protocols were collected. Experiments 1 and 2 tested training that was specific to solving problems that were exemplars of trained categories. In Experiment 1, heuristic training was provided for two categories of constraint: those with ambiguous words and those with human names that should be associated with animals. Transfer was positive for novel problems within the two trained categories but not for problems outside. Experiment 2 improved performance on problems with ambiguous words once shortcomings in Experiment 1 were addressed. Experiment 3 tested training that was specific to solving functional fixedness verbal insight problems. An iterative process of considering many functions of individual items was successful in facilitating performance but not for problems outside. Experiments 4 and 5 investigated the effectiveness of different types of generic training in facilitating solution of novel verbal insight problems. In Experiment 4, participants were trained to identify assumptions, which they made during problem solving and were inconsistent with the problem statement. In Experiment 5, participants were trained to iteratively consider different parts of the problem specification and to identify inconsistencies as in Experiment 4. Solution rate was improved in both experiments although instruction to explain and justify oneself during problem solving was also sufficient in facilitating performance in Experiment 5. Finally, Experiment 6 tested a novel method for identifying when a participant was constrained by an incorrect representation during verbal insight problem solving. The results supported that there is variability in the nature of stereotypical constraints involved and demonstrated how training can be designed to induce restructuring or a shift in representation

Psychological Engagement in Choice and Judgment Under Risk and Uncertainty

Theories of choice and judgment assume that agents behave rationally, choose the higher expected value option, and evaluate the choice consistently (Expected Utility Theory, Von Neumann, & Morgenstern, 1947). However, researchers in decision-making showed that human behaviour is different in choice and judgement tasks (Slovic & Lichtenstein, 1968; 1971; 1973). In this research, we propose that psychological engagement and control deprivation predict behavioural inconsistencies and utilitarian performance with judgment and choice. Moreover, we explore the influences of engagement and control deprivation on agent’s behaviours, while manipulating content of utility (Kusev et al., 2011, Hertwig & Gigerenzer 1999, Tversky & Khaneman, 1996) and decision reward (Kusev et al, 2013, Shafir et al., 2002)