A bio-inspired model of behavior considering decision-making and planning,spatial attention and basic motor commands processes

Cognitive architectures (CA) are an IA approach to implement computer systems with human-like behavior. Fundamental exhibited human capabilities include planning and decision-making. In that regard, numerous AI systems successfully exhibit human-like behavior but are limited to either achieving specific objectives or are restrained to too heavily constrained environments, which makes them unsuitable in the presence of unforeseen situations where autonomy is required. To try to alleviate the problem, we present a bio-inspired computational model to solve the autonomous navigation problem of a computational entity in a controlled context. This proposal is the result of the interaction between planning and decision-making, spatial attention and the motor cognitive functions. The proposed model is based on neuroscientific evidence concerning the involved cognitive functions and is part of a more general cognitive architecture. In the case study developed to validate our idea, we can see that the processes previously identified play an important role to accomplish spatial navigation. In the case study presented, an agent achieves the navigation over an unexplored maze from an initial to a final position successfully. The reunited results motivate us to continue improving our model considering attentional information to influence the agent’s motor behavior.     

Attentional sensitization of unconscious visual processing: Top-down influences on masked priming

Classical theories of automaticity assume that automatic processes elicited by unconscious stimuli are autonomous and independent of higher-level cognitive influences. In contrast to these classical conceptions, we argue that automatic processing depends on attentional amplification of task-congruent processing pathways and propose an attentional sensitization model of unconscious visual processing: According to this model, unconscious visual processing is automatic in the sense that it is initiated without deliberate intention. However, unconscious visual processing is susceptible to attentional top-down control and is only elicited if the cognitive system is configured accordingly. In this article, we describe our attentional sensitization model and review recent evidence demonstrating attentional influences on subliminal priming, a prototypical example of an automatic process. We show that subliminal priming (a) depends on attentional resources, (b) is susceptible to stimulus expectations, (c) is influenced by action intentions, and (d) is modulated by task sets. These data suggest that attention enhances or attenuates unconscious visual processes in congruency with attentional task representations similar to conscious perception. We argue that seemingly paradoxical, hitherto unexplained findings regarding the automaticity of the underlying processes in many cognitive domains can be easily accommodated by our attentional sensitization model. We conclude this review with a discussion of future research questions regar-ding the nature of attentional control of unconscious visual processing.     

Integrating a cognitive computational model of planning and decision-making considering affective information

Planning and decision-making are two of the cognitive functions involved in the solution of problems. These functions, among others, have been studied from the point of view of a new field known as cognitive informatics focused on the development of cognitive architectures, autonomous agents, and human robots that are capable of showing human-like behavior. We present an exhaustive study of current biological and computational models proposed in the fields of neuroscience, psychology, and cognitive informatics. Also, we present a deep review of the brain areas involved in planning, decision-making, and affection. However, the majority of the proposed computational models are seeking to mimic human external behavior. This paper aims to contribute to the cognitive informatics field with an innovative cognitive computational model of planning and decision-making. The two main differences of our model with respect to the current models in the literature are: (i) our model considers affective and motivational information as a basic and essential trigger in planning and decision-making processes; (ii) our model attempts to mimic both the internal human brain as well as the external human behavior. We developed a computational model capable of offering a direct mapping from human brain areas to computational modules of our model. Thus, in this paper we present our model from a conceptual, formal, and computational approach in order to show how our proposal must be implemented. Finally, a set of tests were conducted in order to validate our proposal. These tests show an interesting comparison between the behavior of our prototype and the behavior exhibited by some people involved in a case study.     

An embodied model for sensorimotor grounding and grounding transfer: experiments with epigenetic robots

The grounding of symbols in computational models of linguistic abilities is one of the fundamental properties of psychologically plausible cognitive models. In this article, we present an embodied model for the grounding of language in action based on epigenetic robots. Epigenetic robotics is one of the new cognitive modeling approaches to modeling autonomous mental development. The robot model is based on an integrative vision of language in which linguistic abilities are strictly dependent on and grounded in other behaviors and skills. It uses simulated robots that learn through imitation the names of basic actions. Robots also learn higher order action concepts through the process of grounding transfer. The simulation demonstrates how new, higher order behavioral abilities can be autonomously built on previously grounded basic action categories following linguistic interaction with human users.     

Taste perceptual circuit for the generation of autonomous responses in virtual creatures

BraLearning is one of the most complex cognitive processes that living beings have. This process is distributed within the brain and each independent cognitive function contributes to a small or large extent to it. Particularly, affective learning is a process that allows us to identify stimuli in the environment and discriminate them based on our tastes or preferences. Within this work we will address a type of affective evaluation generated by the perceptual circuit of taste that allows us to determine flavors from the environment and how this information becomes part of the perceptual affective evaluation. We will start this work by identifying other computational works that address affective processes oriented to taste or that involve some other affective evaluation. Subsequently, we analyze of neuroscientific information that allows us to build an informal model of the perceptual circuit of taste. Finally, we propose a cognitive architecture for the generation of autonomous responses oriented to taste in virtual creatures and we will demonstrate its functionality through an implementation of this model in some case studies through distributed modules and discuss the results obtained.     

From intention to action: The role of presence

Recent research in neuroscience has tried to understand human action from two different but converging perspectives: the cognitive and the volitional. On one side, cognitive studies analyze how action is planned and controlled in response to environmental conditions. On the other side, volitional studies analyze how action is planned and controlled by a subject's needs, motives and goals. In this paper we suggest that the notion of presence may be the missing link between these two approaches, explaining how can we differentiate between perception, action and concepts.In particular, a consideration of presence can explain how can we distinguish between a perceived action, a planned or an executed one. We argue that the evolutionary role of presence is the control of agency through the unconscious separation of “internal” and “external” and the enaction/reenaction of intentions.The model makes sense in terms of evolutionary psychology and is beginning to be supported by evidence of the neural and other physical correlates of action, imitation and self-monitoring. Another strength of this model is that it provides testable predictions about how to improve the experience of presence in media: maximal presence in a mediated experience arises from an optimal combination of form and content, able to support the intentions of the user.     

Human bounds: rationality for our species

Is there such a thing as bounded rationality? I first try to make sense of the question, and then to suggest which of the disambiguated versions might have answers. We need an account of bounded rationality that takes account of detailed contingent facts about the ways in which human beings fail to perform as we might ideally want to. But we should not think in terms of rules or norms which define good responses to an individual’s limitations, but rather in terms of desiderata, situations that limited agents can hope to achieve, and corresponding virtues of achieving them. We should not take formal theories defining optimal behavior in watered-down bounded form, even though they can impose enormous computational or cognitive demands.     

Analogy as relational priming: a developmental and computational perspective on the origins of a complex cognitive skill

The development of analogical reasoning has traditionally been understood in terms of theories of adult competence. This approach emphasizes structured representations and structure mapping. In contrast, we argue that by taking a developmental perspective, analogical reasoning can be viewed as the product of a substantially different cognitive ability - relational priming. To illustrate this, we present a computational (here connectionist) account where analogy arises gradually as a by-product of pattern completion in a recurrent network. Initial exposure to a situation primes a relation that can then be applied to a novel situation to make an analogy. Relations are represented as transformations between states. The network exhibits behaviors consistent with a broad range of key phenomena from the developmental literature, lending support to the appropriateness of this approach (using low-level cognitive mechanisms) for investigating a domain that has normally been the preserve of high-level models. Furthermore, we present an additional simulation that integrates the relational priming mechanism with deliberative controlled use of inhibition to demonstrate how the framework can be extended to complex analogical reasoning, such as the data from explicit mapping studies in the literature on adults. This account highlights how taking a developmental perspective constrains the theory construction and cognitive modeling processes in a way that differs substantially from that based purely on adult studies, and illustrates how a putative complex cognitive skill can emerge out of a simple mechanism.     

The computational implementation of the landscape model: Modeling inferential processes and memory representations of text comprehension

The complexity of text comprehension demands a computational approach to describe the cognitive processes involved. In this article, we present the computational implementation of the landscape model of reading. This model captures both on-line comprehension processes during reading and the off-line memory representation after reading is completed, incorporating both memory-based and coherence-based mechanisms of comprehension. The overall architecture and specific parameters of the program are described, and a running example is provided. Several studies comparing computational and behavioral data indicate that the implemented model is able to account for cycle-by-cycle comprehension processes and memory for a variety of text types and reading situations.     

Building Cognition: The Construction of Computational Representations for Scientific Discovery

Novel computational representations, such as simulation models of complex systems and video games for scientific discovery (Foldit, EteRNA etc.), are dramatically changing the way discoveries emerge in science and engineering. The cognitive roles played by such computational representations in discovery are not well understood. We present a theoretical analysis of the cognitive roles such representations play, based on an ethnographic study of the building of computational models in a systems biology laboratory. Specifically, we focus on a case of model‐building by an engineer that led to a remarkable discovery in basic bioscience. Accounting for such discoveries requires a distributed cognition (DC) analysis, as DC focuses on the roles played by external representations in cognitive processes. However, DC analyses by and large have not examined scientific discovery, and they mostly focus on memory offloading, particularly how the use of existing external representations changes the nature of cognitive tasks. In contrast, we study discovery processes and argue that discoveries emerge from the processes of building the computational representation. The building process integrates manipulations in imagination and in the representation, creating a coupled cognitive system of model and modeler, where the model is incorporated into the modeler's imagination. This account extends DC significantly, and we present some of the theoretical and application implications of this extended account.     

Metastable attunement and real-life skilled behavior

In everyday situations, and particularly in some sport and working contexts, humans face an inherently unpredictable and uncertain environment. All sorts of unpredictable and unexpected things happen but typically people are able to skillfully adapt. In this paper, we address two key questions in cognitive science. First, how is an agent able to bring its previously learned skill to bear on a novel situation? Second, how can an agent be both sensitive to the particularity of a given situation, while remaining flexibly poised for many other possibilities for action? We will argue that both the sensitivity to novel situations and the sensitivity to a multiplicity of action possibilities are enabled by the property of skilled agency that we will call metastable attunement. We characterize a skilled agent’s flexible interactions with a dynamically changing environment in terms of metastable dynamics in agent-environment systems. What we find in metastability is the realization of two competing tendencies: the tendency of the agent to express their intrinsic dynamics and the tendency to search for new possibilities. Metastably attuned agents are ready to engage with a multiplicity of affordances, allowing for a balance between stability and flexibility. On the one hand, agents are able to exploit affordances they are attuned to, while at the same time being ready to flexibly explore for other affordances. Metastable attunement allows agents to smoothly transition between these possible configurations so as to adapt their behaviour to what the particular situation requires. We go on to describe the role metastability plays in learning of new skills, and in skilful behaviour more generally. Finally, drawing upon work in art, architecture and sports science, we develop a number of perspectives on how to investigate metastable attunement in real life situations.

     

Simplicity: a unifying principle in cognitive science?

Much of perception, learning and high-level cognition involves finding patterns in data. But there are always infinitely many patterns compatible with any finite amount of data. How does the cognitive system choose 'sensible' patterns? A long tradition in epistemology, philosophy of science, and mathematical and computational theories of learning argues that patterns 'should' be chosen according to how simply they explain the data. This article reviews research exploring the idea that simplicity drives a wide range of cognitive processes. We outline mathematical theory, computational results and empirical data that underpin this viewpoint.     

Beyond subgoaling: A dynamic knowledge generation framework for creative problem solving in cognitive architectures

In this paper we propose a computational framework aimed at extending the problem solving capabilities of cognitive artificial agents through the introduction of a novel, goal-directed, dynamic knowledge generation mechanism obtained via a non monotonic reasoning procedure. In particular, the proposed framework relies on the assumption that certain classes of problems cannot be solved by simply learning or injecting new external knowledge in the declarative memory of a cognitive artificial agent but, on the other hand, require a mechanism for the automatic and creative re-framing, or re-formulation, of the available knowledge. We show how such mechanism can be obtained trough a framework of dynamic knowledge generation that is able to tackle the problem of commonsense concept combination. In addition, we show how such a framework can be employed in the field of cognitive architectures in order to overcome situations like the impasse in SOAR by extending the possible options of its subgoaling procedures.     

Deciding, Planning, and Practical Reasoning: Elements towards a Cognitive Architecture

I intend to show some of the limits of the decision-theoretic model in connection with the analysis of cognitive agency. Although the concept of maximum expected utility can be helpful for explaining the decision-making process, it is certainly not the primary motor that moves agents to action. Moreover, it has been noticed elsewhere that this model is inadequate to the analysis of single cases of practical reasoning. A theory is proposed that introduces a plan-structure as a basic idea. In order to know its very conceptual scope, we need to accommodate that theory to a cognitive architecture whereby the rational and autonomous agent makes choices and deliberates between courses of action to achieve specific goals that are of its interest. The paper endeavours also to clarify the relationship between the different modules that should constitute the architecture of an agent to accomplish the requisites of efficiency and autonomy in a changeable and dynamic world.     

Evidence accumulation modelling in the wild: understanding safety-critical decisions

Evidence accumulation models (EAMs) are a class of computational cognitive model used to understand the latent cognitive processes that underlie human decisions and response times (RTs). They have seen widespread application in cognitive psychology and neuroscience. However, historically, the application of these models was limited to simple decision tasks. Recently, researchers have applied these models to gain insight into the cognitive processes that underlie observed behaviour in applied domains, such as air-traffic control (ATC), driving, forensic and medical image discrimination, and maritime surveillance. Here, we discuss how this modelling approach helps researchers understand how the cognitive system adapts to task demands and interventions, such as task automation. We also discuss future directions and argue for wider adoption of cognitive modelling in Human Factors research.     

The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation

The informativity of a computational model of language acquisition is directly related to how closely it approximates the actual acquisition task, sometimes referred to as the model's cognitive plausibility. We suggest that though every computational model necessarily idealizes the modeled task, an informative language acquisition model can aim to be cognitively plausible in multiple ways. We discuss these cognitive plausibility checkpoints generally and then apply them to a case study in word segmentation, investigating a promising Bayesian segmentation strategy. We incorporate cognitive plausibility by using an age‐appropriate unit of perceptual representation, evaluating the model output in terms of its utility, and incorporating cognitive constraints into the inference process. Our more cognitively plausible model shows a beneficial effect of cognitive constraints on segmentation performance. One interpretation of this effect is as a synergy between the naive theories of language structure that infants may have and the cognitive constraints that limit the fidelity of their inference processes, where less accurate inference approximations are better when the underlying assumptions about how words are generated are less accurate. More generally, these results highlight the utility of incorporating cognitive plausibility more fully into computational models of language acquisition.     

Agent communication pragmatics: the cognitive coherence approach

Different approaches have investigated the syntax and semantics of agent communication languages. However, these approaches have not indicated how agents should dynamically use communications. Instead of filling this pragmatics gap, most approaches have mainly focused on the ‘structure’ of dialogues even though developers are more interested in agents’ capabilities of having ‘useful’ automated conversations with respect to their goals rather than in their abilities to structure dialogues. This led us to work on a theory of the use of conversations between agents. In this paper, we propose a pragmatics theory which extends and adapts the cognitive dissonance theory (a major theory of social psychology) to multi-agent systems by unifying it with the theory of coherence in thought and action that issues from computational philosophy of mind. Precisely, we show how this theory allows us to provide generic conceptual tools for the automation of both agent communicational behavior and attitude change processes. This new motivational model is formulated in terms of constraints and elements of cognition and allows us to define cognitive incoherences and dialogue utility measures. We show how these measures could be used to solve common problems and answer some critical questions concerning agent communication frameworks use. Finally, our exploration in applying the cognitive coherence pragmatics theory as a new communication layer over classical BDI agents is presented. It relies on our dialogue games based agent communication language (DIAGAL) and our dialogue games simulator toolbox (DGS). The resulting framework provides the necessary theoretical and practical elements for implementing our theory. In doing so, it brings in a general scheme for automatizing agents’ communicational behavior as it is exemplified in this article.     

Conflict monitoring and cognitive control

A neglected question regarding cognitive control is how control processes might detect situations calling for their involvement. The authors propose here that the demand for control may be evaluated in part by monitoring for conflicts in information processing. This hypothesis is supported by data concerning the anterior cingulate cortex, a brain area involved in cognitive control, which also appears to respond to the occurrence of conflict. The present article reports two computational modeling studies, serving to articulate the conflict monitoring hypothesis and examine its implications. The first study tests the sufficiency of the hypothesis to account for brain activation data, applying a measure of conflict to existing models of tasks shown to engage the anterior cingulate. The second study implements a feedback loop connecting conflict monitoring to cognitive control, using this to simulate a number of important behavioral phenomena.     

Can cognitive psychological research on reasoning enhance the discussion around moral judgments?

In this article we will demonstrate how cognitive psychological research on reasoning and decision making could enhance discussions and theories of moral judgments. In the first part, we will present recent dual-process models of moral judgments and describe selected studies which support these approaches. However, we will also present data that contradict the model predictions, suggesting that approaches to moral judgment might be more complex. In the second part, we will show how cognitive psychological research on reasoning might be helpful in understanding moral judgments. Specifically, we will highlight approaches addressing the interaction between intuition and reflection. Our data suggest that a sequential model of engaging in deliberation might have to be revised. Therefore, we will present an approach based on Signal Detection Theory and on intuitive conflict detection. We predict that individuals arrive at the moral decisions by comparing potential action outcomes (e.g., harm caused and utilitarian gain) simultaneously. The response criterion can be influenced by intuitive processes, such as heuristic moral value processing, or considerations of harm caused.