Simple games as dynamic, coupled systems: randomness and other emergent properties

From a game theory perspective the ability to generate random behaviors is critical. However, psychological studies have consistently found that individuals are poor at behaving randomly. In this paper we investigated the possibility that the randomness mechanism lies not within the individual players but in the interaction between the players. Provided that players are influenced by their opponent’s past behavior, their relationship may constitute a state of reciprocal causation [Cognitive Science 21 (1998) 461], in which each player simultaneously affects and is affected by the other player. The result of this would be a dynamic, coupled system. Using neural networks to represent the individual players in a game of paper, rock, and scissors, a model of this process was developed and shown to be capable of generating chaos-like behaviors as an emergent property. In addition, it was found that by manipulating the control parameters of the model, corresponding to the amount of working memory and the perceived values of different outcomes, that the game could be biased in favor of one player over the other, an outcome not predicted by game theory. Human data was collected and the results show that the model accurately describes human behavior. The results and the model are discussed in light of recent theoretical advances in dynamic systems theory and cognition.     

Moving to higher ground: The dynamic field theory and the dynamics of visual cognition

In the present report, we describe a new dynamic field theory that captures the dynamics of visuo-spatial cognition. This theory grew out of the dynamic systems approach to motor control and development, and is grounded in neural principles. The initial application of dynamic field theory to issues in visuo-spatial cognition extended concepts of the motor approach to decision making in a sensori-motor context, and, more recently, to the dynamics of spatial cognition. Here we extend these concepts still further to address topics in visual cognition, including visual working memory for non-spatial object properties, the processes that underlie change detection, and the ‘binding problem’ in vision. In each case, we demonstrate that the general principles of the dynamic field approach can unify findings in the literature and generate novel predictions. We contend that the application of these concepts to visual cognition avoids the pitfalls of reductionist approaches in cognitive science, and points toward a formal integration of brains, bodies, and behavior.     

Explaining after by before: Basic aspects of a dynamic systems approach to the study of development

The basic properties of a dynamic systems approach of development are illustrated by contrasting two simple equations. One, y_t+1 = f (y_t), is characteristic of dynamic systems models. The other, y_i = f (x_i), refers to what, for the sake of simplicity, is referred to as the standard developmental approach. We give illustrations from cognitive, language and social development to show the characteristic differences of these two types of models and show their complementarity. The article further compares the “Bloomington” with the “Groningen” approach to dynamic systems theorizing in developmental psychology. It continues with a discussion of two important questions. One involves the issue of measurement and the nature of developmental variables from the viewpoint of dynamic systems. The second concerns the question of short- and long-term time scales in developmental models, which is discussed on the basis of an example, namely dyadic interaction of young children in the context of different social statuses.     

Motivated learning for the development of autonomous systems

A new machine learning approach known as motivated learning (ML) is presented in this work. Motivated learning drives a machine to develop abstract motivations and choose its own goals. ML also provides a self-organizing system that controls a machine’s behavior based on competition between dynamically-changing pain signals. This provides an interplay of externally driven and internally generated control signals. It is demonstrated that ML not only yields a more sophisticated learning mechanism and system of values than reinforcement learning (RL), but is also more efficient in learning complex relations and delivers better performance than RL in dynamically-changing environments. In addition, this paper shows the basic neural network structures used to create abstract motivations, higher level goals, and subgoals. Finally, simulation results show comparisons between ML and RL in environments of gradually increasing sophistication and levels of difficulty.     

Visual working memory for dynamic objects: Impaired binding between object feature and location

In daily life, people frequently need to observe dynamic objects and temporarily maintain their representations in visual working memory (VWM). The present study explored the mechanism underlying the binding between perceptual features and locations of dynamic objects in VWM. In three experiments, we measured and compared the memory performance for feature-location binding of multiple dynamic and static objects. The results showed that the feature-location binding was impaired for the dynamic objects compared with the static objects. The impairment persisted when the global spatial configuration of the objects remained intact during the motion, as well as when the binding task was relatively easy, such as binding between single-feature objects and coarse locations. The results indicate that object features and locations are not maintained in VWM as well-integrated object files; rather, the formation of feature-location binding may require additional processes, which are disrupted by the constant change of locations in dynamic circumstances. We propose a consolidation process as possible underlying mechanism, and discuss factors that may influence the strength of feature-location binding in dynamic circumstances.     

The personality systems framework: Current theory and development

The personality systems framework is a fieldwide outline for organizing the contemporary science of personality. I examine the theoretical impact of systems thinking on the discipline and, drawing on ideas from general systems theory, argue that personality psychologists understand individuals’ personalities by studying four topics: (a) personality’s definition, (b) personality’s parts (e.g., traits, schemas, etc.), (c) its organization and (d) development. This framework draws on theories from the field to create a global view of personality including its position and major areas of function. The global view gives rise to new theories such as personal intelligence—the idea that people guide themselves with a broad intelligence they use to reason about personalities.     

Reasoning across continuous landscapes: A nonlinear dynamical systems theory approach to reasoning

Dual-processing theories of reasoning have gained renewed attention in recent years, particularly in the fields of decision-making under uncertainty, learning, and social judgment. Although the various accounts differ, the common thread is the distinction between two qualitatively different reasoning processes, such as automatic/controlled, fast/slow, and unconscious/conscious. Accordingly, much research is focused on elucidating the nature of the two processes in terms of the kinds of information they process and respond to. Less extensive are attempts to identify mediators that underlie changes between the two reasoning strategies. We argue that nonlinear dynamical systems theory may be able to provide a fresh perspective on reasoning. Nonlinear dynamical systems theory allows us to shift the perspective to the dynamic interactions and transitions among continuous yet qualitatively different reasoning processes. We apply the approach to reasoning in decision-making and judgment under uncertainty. Our primary claim is that using different reasoning strategies is better understood as phase transitions among a landscape of continuous reasoning capacities.     

The relationship between the perception of axes of symmetry and spatial memory during early childhood

Early in development, there is a transition in spatial working memory (SWM). When remembering a location in a homogeneous space (e.g., in a sandbox), young children are biased toward the midline symmetry axis of the space. Over development, a transition occurs that leads to older children being biased away from midline. The dynamic field theory (DFT) explains this transition in biases as being caused by a change in the precision of neural interaction in SWM and improvements in the perception of midline. According to the DFT, young children perceive midline, but there is a quantitative improvement in the perception of midline over development. In the experiment reported here, children and adults needed to determine on which half of a large monitor a target was located. In support of the DFT, even the youngest children performed above chance at most locations, but performance also improved gradually with age.     

The thalamic dynamic core theory of conscious experience

I propose that primary conscious awareness arises from synchronized activity in dendrites of neurons in dorsal thalamic nuclei, mediated particularly by inhibitory interactions with thalamic reticular neurons. In support, I offer four evidential pillars: (1) consciousness is restricted to the results of cortical computations; (2) thalamus is the common locus of action of brain injury in vegetative state and of general anesthetics; (3) the anatomy and physiology of the thalamus imply a central role in consciousness; (4) neural synchronization is a neural correlate of consciousness.     

Applications of social cognitive theory to the career development of women of color

This article provides a review of the literature on the educational and career development of women of color within the context of social cognitive career theory (Lent, et al, 1994 and Lent, et al, 1996). Specifically, possible gender, ethnic, racial, and cultural influences on career self-efficacy and outcome expectations are explored to delinate the potential mechanisms whereby ethnicity, culture, and ethnic and racial identity development, in interaction with gender, affect career choice and adjustment. Suggestions for future research on social cognitive career theory and the career development of women of color are presented.     

Applications of social cognitive theory to the career development of women of color

This article provides a review of the literature on the educational and career development of women of color within the context of social cognitive career theory (Lent, et al, 1994 and Lent, et al, 1996). Specifically, possible gender, ethnic, racial, and cultural influences on career self-efficacy and outcome expectations are explored to delinate the potential mechanisms whereby ethnicity, culture, and ethnic and racial identity development, in interaction with gender, affect career choice and adjustment. Suggestions for future research on social cognitive career theory and the career development of women of color are presented.     

Life-long environmental enrichment differentially affects the mnemonic response to estrogen in young, middle-aged, and aged female mice

The present study was designed to examine whether life-long exposure to standard or enriched housing affects the ability of estrogen to improve spatial and object memory throughout the lifespan. Three-week-old female mice were maintained in standard or enriched housing up to and through ovariectomy and behavioral testing at 5, 17, or 22 months of age. Spatial memory was tested in the Morris water maze and object memory was tested using an object recognition task. Immediately after training each day, mice were injected intraperitoneally with vehicle or 0.2 mg/kg 17beta-estradiol. Among young females, object recognition was enhanced by estradiol alone, an effect that was reduced by enrichment. In contrast, spatial water maze performance was impaired by estradiol alone, but improved by the combination of both estradiol and enrichment. At middle-age, object recognition was enhanced by estradiol or enrichment alone, and the combination of both treatments. Spatial memory in the water maze was also improved by both treatments at middle-age, but the beneficial effects of estradiol were limited to standard-housed females. Finally, whereas enrichment in aged females significantly enhanced performance in both tasks, estradiol had no effect at this age in either task. In total, the data indicate that life-long enrichment can significantly alter the extent to which estradiol affects memory in mice throughout the lifespan. Importantly, the interaction between these treatments is highly dependent on age and type of memory tested.     

Contributions of longitudinal research to a cognitive theory of adjustment to aging

In agreement with Costa and McCrae (1992), the principle of ‘multiple uses’ of longitudinal studies is stressed based on a comparative review of the history of the Berkeley longitudinal studies and their different uses, and of those of the Bonn Longitudinal Study on Aging (BOLSA). Of the many uses of BOLSA, its contribution to the testing of a cognitive theory of aging is discussed. A selection of the findings of BOLSA and other studies of the Bonn Department of Psychology and the Institute of Gerontology, University of Heidelberg, is reported. These findings support three postulates of this theory: ( 1 ) the perceived rather than the objective situation directs behaviour; (2) perceptions of situations are related to dominant concerns of the person; and (3) adjustment to aging is achieved when a balance between the cognitive and motivational systems of the person has been attained.     

Applying linear systems analysis to dynamic behavior

In this paper we present an abbreviated discussion of the linear systems analysis in the time domain. We then consider the qualitative character of the behavioral dynamics predicted using the linear form of the analysis. The analysis is then extended to a second-order form. We illustrate some relevant new features introduced by the second-order form with a special case example.     

A category theory approach to cognitive development

The category theory concept of a commutative diagram is used to construct a model of the way in which symbolic processes are applied to problem solving. The model provides for a relationship between symbolic processes and the problem which depends on structural isomorphism and consistency, but is independent of similarity between symbol elements and problem elements. It is then shown that several different levels of thought can be distinguished within the basic model. More information is needed to assign symbolic processes to a problem in a consistent way with higher-level thought processes than with lower-level processes. These information-processing requirements permit the approximate age of mastery of each level to be predicted, thereby offering an alternate theory of cognitive developmental stages. Two experiments designed to test the theory are reported.     

Learning representations in a gated prefrontal cortex model of dynamic task switching

The prefrontal cortex is widely believed to play an important role in facilitating people's ability to switch performance between different tasks. We present a biologically‐based computational model of prefrontal cortex (PFC) that explains its role in task switching in terms of the greater flexibility conferred by activation‐based working memory representations in PFC, as compared with more slowly adapting weight‐based memory mechanisms. Specifically we show that PFC representations can be rapidly updated when a task switches via a dynamic gating mechanism based on a temporal‐differences reward‐prediction learning mechanism. Unlike prior models of this type, the present model develops all of its internal representations via learning mechanisms as shaped by the demands of continuous periodic task switching. This advance opens up a new domain of research into the interactions between working memory task demands and the representations that develop to meet them. Results on a version of the Wisconsin card sorting task are presented for the full model and a number of comparison networks that test the importance of various model features. Furthermore, we show that a lesioned model produces perseverative errors like those seen in frontal patients.     

Doing cognitive neuroscience: a third way

The “top-down” and “bottom-up” approaches have been thought to exhaust the possibilities for doing cognitive neuroscience. We argue that neither approach is likely to succeed in providing a theory that enables us to understand how cognition is achieved in biological creatures like ourselves. We consider a promising third way of doing cognitive neuroscience, what might be called the “neural dynamic systems” approach, that construes cognitive neuroscience as an autonomous explanatory endeavor, aiming to characterize in its own terms the states and processes responsible for brain-based cognition. We sketch the basic motivation for the approach, describe a particular version of the approach, so-called ‘Dynamic Causal Modeling’ (DCM), and consider a concrete example of DCM. This third way, we argue, has the potential to avoid the problems that afflict the other two approaches.     

Independent operation of implicit working memory under cognitive load

Implicit working memory (WM) has been known to operate non-consciously and unintentionally. The current study investigated whether implicit WM is a discrete mechanism from explicit WM in terms of cognitive resource. To induce cognitive resource competition, we used a conjunction search task (Experiment 1) and imposed spatial WM load (Experiment 2a and 2b). Each trial was composed of a set of five consecutive search displays. The location of the first four displays appeared as per pre-determined patterns, but the fifth display could follow the same pattern or not. If implicit WM can extract the moving pattern of stimuli, response times for the fifth target would be faster when it followed the pattern compared to when it did not. Our results showed implicit WM can operate when participants are searching for the conjunction target and even while maintaining spatial WM information. These results suggest that implicit WM is independent from explicit spatial WM.     

A complex dynamic systems approach to lasting positive change: The Synergistic Change Model

This paper argues that psychological and social functioning is fundamentally complex, and that this complexity is critical to understanding how lasting positive changes can be achieved. Principles from complex systems theory are integrated into an empirical positive psychological framework to propose a domain-based systems model of positive change called the Synergistic Change Model. This model proposes that enduring positive change depends on the formation of mutually supportive interactions across multiple domains of psychological and social functioning. The paper uses the model to explore three types of response to intervention – relapse, spill-over, and synergy – which have been supported in the existing positive psychology literature. Three practical intervention strategies arising from the model are outlined to inform the design of future positive interventions. The model challenges reductionistic approaches to positive change and offers several approaches to embrace the complexities of lasting positive change in future theory and research.