A tutorial introduction to Bayesian models of cognitive development

We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.     

Toward integrating cognitive components with computational models of emotion using software design patterns

Computational models of emotion (CMEs) are software systems designed to imitate particular aspects of human emotions. The main purpose of this type of computational model is to capture the complexity of the human emotion process in a software system that is incorporated into a cognitive agent architecture. However, creating a CME that closely imitates the actual functioning of emotions demands to address some challenges such as (i) sharing information among independently developed cognitive and affective components, and (ii) interconnecting complex cognitive and affective components that must interact with one another in order to generate realistic emotions, which may even affect agents’ decision making. This paper proposes an architectural pattern aimed at cataloging and describing fundamental components of CMEs and their interrelationships with cognitive components. In this architectural pattern, external cognitive components and internal affective components of CMEs do not interact directly but are extended by including message exchange methods in order to use a publish-subscribe channel, which enables their intercommunication, thus attenuating issues such as software heterogeneity. This structural approach centralizes communication management and separates the inherent complexity of the cognitive and affective processes from the complexity of their interaction mechanisms. In so doing, it enables the design of CMEs’ architectures composed of independently developed affective and cognitive components. The proposed architectural pattern attempts to make progress in capturing the complex process of human emotions in a software system that adheres to software engineering best practices and that incorporates quality attributes such as flexibility and interoperability.     

Ability, breadth, and parsimony in computational models of higher-order cognition

Computational models will play an important role in our understanding of human higher‐order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher‐order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern‐day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher‐order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a model's ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model.     

Vocational maturity: Its past,present, and future development

A review and critical analysis of various approaches used in studying vocational maturity revealed that its viability was generally supported, yet remained questionable because of the conflicting results of previous research and the lack of applying these results to counseling. This analysis provided the basis for conceptualizing vocational maturity development as being analogous to and not separate from personality development. Proposed research studies were mentioned which would test basic relationships between vocational maturity development and various aspects of personality development.     

Hippocampus, cortex, and basal ganglia: insights from computational models of complementary learning systems

We present a framework for understanding how the hippocampus, neocortex, and basal ganglia work together to support cognitive and behavioral function in the mammalian brain. This framework is based on computational tradeoffs that arise in neural network models, where achieving one type of learning function requires very different parameters from those necessary to achieve another form of learning. For example, we dissociate the hippocampus from cortex with respect to general levels of activity, learning rate, and level of overlap between activation patterns. Similarly, the frontal cortex and associated basal ganglia system have important neural specializations not required of the posterior cortex system. Taken together, this overall cognitive architecture, which has been implemented in functioning computational models, provides a rich and often subtle means of explaining a wide range of behavioral and cognitive neuroscience data. Here, we summarize recent results in the domains of recognition memory, contextual fear conditioning, effects of basal ganglia lesions on stimulus-response and place learning, and flexible responding.     

A computational cognitive framework of spatial memory in brains and robots

Computational cognitive models of spatial memory often neglect difficulties posed by the real world, such as sensory noise, uncertainty, and high spatial complexity. On the other hand, robotics is unconcerned with understanding biological cognition. Here, we describe a computational framework for robotic architectures aiming to function in realistic environments, as well as to be cognitively plausible.We motivate and describe several mechanisms towards achieving this despite the sensory noise and spatial complexity inherent in the physical world. We tackle error accumulation during path integration by means of Bayesian localization, and loop closing with sequential gradient descent. Finally, we outline a method for structuring spatial representations using metric learning and clustering. Crucially, unlike the algorithms of traditional robotics, we show that these mechanisms can be implemented in neuronal or cognitive models.We briefly outline a concrete implementation of the proposed framework as part of the LIDA cognitive architecture, and argue that this kind of probabilistic framework is well-suited for use in cognitive robotic architectures aiming to combine spatial functionality and psychological plausibility.     

A computational analysis of general intelligence tests for evaluating cognitive development

The progression in several cognitive tests for the same subjects at different ages provides valuable information about their cognitive development. One question that has caught recent interest is whether the same approach can be used to assess the cognitive development of artificial systems. In particular, can we assess whether the ‘fluid’ or ‘crystallised’ intelligence of an artificial cognitive system is changing during its cognitive development as a result of acquiring more concepts? In this paper, we address several IQ tests problems (odd-one-out problems, Raven’s Progressive Matrices and Thurstone’s letter series) with a general learning system that is not particularly designed on purpose to solve intelligence tests. The goal is to better understand the role of the basic cognitive operational constructs (such as identity, difference, order, counting, logic, etc.) that are needed to solve these intelligence test problems and serve as a proof-of-concept for evaluation in other developmental problems. From here, we gain some insights into the characteristics and usefulness of these tests and how careful we need to be when applying human test problems to assess the abilities and cognitive development of robots and other artificial cognitive systems.     

Psychoneuroimmunology: past, present, and future

Provides a brief overview of the history and current status of behavioral immunology research, as well as speculation on likely future directions, and suggests that the field may have broad implications for basic biological sciences and medicine. In addition, the field has clear relevance for health psychology; its relevance to actual health outcomes, however, is not yet known.     

Dev E-R: A computational model of early cognitive development as a creative process

This paper describes a computational model named Dev E-R (Developmental Engagement-Reflection) that, inspired by Piaget’s theory, simulates the assimilation-accommodation adaptation process. It is implemented with a new extended version of the computational model of creativity known as Engagement-Reflection. That is, this model simulates adaptation as a creative activity. We introduce here the implementation of our model on an agent that is initialized with basic reflex conducts and that through the interaction with a 3D virtual world, it is able to build new behaviors autonomously. The new acquired skills, according to Piaget’s theory, are typically observed in children that have reached the second substage of the sensorimotor period.     

Bootstrapping the lexicon: a computational model of infant speech segmentation

Prelinguistic infants must find a way to isolate meaningful chunks from the continuous streams of speech that they hear. BootLex, a new model which uses distributional cues to build a lexicon, demonstrates how much can be accomplished using this single source of information. This conceptually simple probabilistic algorithm achieves significant segmentation results on various kinds of language corpora - English, Japanese, and Spanish; child- and adult-directed speech, and written texts; and several variations in coding structure - and reveals which statistical characteristics of the input have an influence on segmentation performance. BootLex is then compared, quantitatively and qualitatively, with three other groups of computational models of the same infant segmentation process, paying particular attention to functional characteristics of the models and their similarity to human cognition. Commonalities and contrasts among the models are discussed, as well as their implications both for theories of the cognitive problem of segmentation itself, and for the general enterprise of computational cognitive modeling.     

Reflections on dynamics,adaptation and control: A cognitive architecture for mental models

In this paper, an overview of the wide variety of occurrences of mental models in the literature is discussed. They are classified according to two dimensions obtaining four categories of them: static-dynamic and world-mental, where static refers to mental models for static world states or for static mental states and dynamic refers to mental models for world processes or for mental processes. In addition, distinctions are made for what can be done by mental models: they can, for example, be (1) used for internal simulation, they can be (2) adapted, and these processes can be (3) controlled. This leads to a global three-level cognitive architecture covering these three ways of handling mental models. It is discussed that in this cognitive architecture reflection principles play an important role to define the interactions between the different levels.     

Pastoral research: past, present, and future

In healthcare environments of excellence, clinically trained chaplains are valued members of the medical treatment team. There are skills and values they hold in common with medicine and allied health disciplines: enhancing the health and well-being of the patient as a unity of body, mind, and spirit within a unique family and cultural system. This article examines the past, present, and future of pastoral research, including the chaplain's moral imperative to examine practice and to share what is found.     

Change blindness: past, present, and future

Change blindness is the striking failure to see large changes that normally would be noticed easily. Over the past decade this phenomenon has greatly contributed to our understanding of attention, perception, and even consciousness. The surprising extent of change blindness explains its broad appeal, but its counterintuitive nature has also engendered confusions about the kinds of inferences that legitimately follow from it. Here we discuss the legitimate and the erroneous inferences that have been drawn, and offer a set of requirements to help separate them. In doing so, we clarify the genuine contributions of change blindness research to our understanding of visual perception and awareness, and provide a glimpse of some ways in which change blindness might shape future research.     

Body image: past, present, and future

This brief editorial article introduces the new scientific journal, Body Image: An International Journal of Research, and describes its rationale and mission in relation to the history and future of the study of body image and human appearance.     

Hippocampal sequences link past, present, and future

Disrupting the reactivation of hippocampal neurons during sleep impairs memory consolidation in rats. However, the functional importance of reactivation during awake states is unknown. An experiment in which awake reactivation was disrupted suggests that this phenomenon could adaptively guide behavior by linking previous learning with the current state of the world.     

Evaluating the reliance on past choices in adaptive learning models

Adaptive learning models are used to predict behavior in repeated choice tasks. Predictions can be based on previous payoffs or previous choices of the player. The current paper proposes a new method for evaluating the degree of reliance on past choices, called equal payoff series extraction (EPSE). Under this method a simulated player has the same exact choices as the player but receives equal constant payoffs from all of the alternatives. Success in predicting the next choice ahead for this simulated player therefore relies strictly on mimicry of previous choices of the actual player. This allows determining the marginal fit of predictions that are not based on the actual task payoffs. To evaluate the reliance on past choices under different models, an experiment was conducted in which 48 participants completed a three-alternative choice task in four task conditions. Two different learning rules were evaluated: an interference rule and a decay rule. The results showed that while the predictions of the decay rule relied more on past choices, only the reliance on past payoffs was associated with improved parameter generality. Moreover, we show that the Equal Payoff Series can be used as a criterion for optimizing parameters resulting in better parameter generalizability.