Using computational neuroscience to investigate the neural correlates of cognitive-affective integration during covert decision making

We presented a proposed neural level mechanism for the integration of cognitive and affective information during covert decision making. The central idea is that the ventromedial prefrontal cortex establishes predicted outcomes of responses through its connections with the amygdala, and that this information is passed through the context-moderated gateway in the nucleus accumbens in order to promote behaviours that are most beneficial to the long term survival of the organism. We then implemented the proposed mechanism in a network of spiking neurons, and tested one of its central claims. Results showed that the model was capable of producing behaviour similar to that observed in normal humans, as well as that exhibited during VMPFC damage.     

Memory systems within a cognitive architecture

This article addresses the division of memory systems in relation to an overall cognitive architecture. As understanding the architecture is essential to understanding the mind, developing computational cognitive architectures is an important enterprise in computational psychology (computational cognitive modeling). The article proposes a set of hypotheses concerning memory systems from the standpoint of a cognitive architecture, in particular, the four-way division of memory (including explicit and implicit procedural memory and explicit and implicit declarative memory). It then discusses in detail how these hypotheses may be validated through examining qualitatively the literature on memory. A quick review follows of computational simulations of a variety of quantitative data (which are not limited to narrowly conceived “memory tasks”). Results of accounting for both qualitative and quantitative data point to the promise of this approach.     

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.     

Communicative Competence and the Architecture of the Mind/Brain

Cognitive pragmatics is concerned with the mental processes involved in intentional communication. I discuss a few issues that may help clarify the relationship between this area and the broader cognitive science and the contribution that they give, or might give, to each other. Rather than dwelling on the many technicalities of the various theories of communication that have been advanced, I focus on the different conceptions of the nature and the architecture of the mind/brain that underlie them. My aims are, first, to introduce and defend mentalist views of communication in general; second, to defend one such view, namely that communication is a cognitive competence, that is, a faculty, and the underlying idea that the architecture of the mind/brain is domain-specific; and, third, to review the (scarce) neuropsychological evidence that bears on these issues.     

RELIGIOUS BELIEF AS ACQUIRED SECOND NATURE

Multiple authors in cognitive science of religion (CSR) argue that there is something about the human mind that disposes it to form religious beliefs. The dispositions would result from the internal architecture of the mind. In this article, I will argue that this disposition can be explained by various forms of (cultural) learning and not by the internal architecture of the mind. For my argument, I draw on new developments in predictive processing. I argue that CSR theories argue for the naturalness of religious belief in at least three ways; religious beliefs are adaptive; religious beliefs are the product of cognitive biases; and religious beliefs are the product of content biases. I argue that all three ideas can be integrated in a predictive coding framework where religious belief is learned and hence not caused by the internal architecture of the mind. I argue that the framework makes it doubtful that there are modular cognitive mechanisms for religious beliefs and that the human mind has a fixed proneness for religious belief. I also argue that a predictive coding framework can incorporate a larger role for cultural processes and allows for more flexibility.     

Cognitive architecture of perceptual organization: from neurons to gnosons

What, if anything, is cognitive architecture and how is it implemented in neural architecture? Focusing on perceptual organization, this question is addressed by way of a pluralist approach which, supported by metatheoretical considerations, combines complementary insights from representational, connectionist, and dynamic systems approaches to cognition. This pluralist approach starts from a representationally inspired model which implements the intertwined but functionally distinguishable subprocesses of feedforward feature encoding, horizontal feature binding, and recurrent feature selection. As sustained by a review of neuroscientific evidence, these are the subprocesses that are believed to take place in the visual hierarchy in the brain. Furthermore, the model employs a special form of processing, called transparallel processing, whose neural signature is proposed to be gamma-band synchronization in transient horizontal neural assemblies. In neuroscience, such assemblies are believed to mediate binding of similar features. Their formal counterparts in the model are special input-dependent distributed representations, called hyperstrings, which allow many similar features to be processed in a transparallel fashion, that is, simultaneously as if only one feature were concerned. This form of processing does justice to both the high combinatorial capacity and the high speed of the perceptual organization process. A naturally following proposal is that those temporarily synchronized neural assemblies are “gnosons”, that is, constituents of flexible self-organizing cognitive architecture in between the relatively rigid level of neurons and the still elusive level of consciousness.     

Why neuroscience matters to cognitive neuropsychology

The broad issue in this paper is the relationship between cognitive psychology and neuroscience. That issue arises particularly sharply for cognitive neurospsychology, some of whose practitioners claim a methodological autonomy for their discipline. They hold that behavioural data from neuropsychological impairments are sufficient to justify assumptions about the underlying modular structure of human cognitive architecture, as well as to make inferences about its various components. But this claim to methodological autonomy can be challenged on both philosophical and empirical grounds. A priori considerations about (cognitive) multiple realisability challenge the thesis on philosophical grounds, and neuroscientific findings from developmental disorders substantiate that challenge empirically. The conclusion is that behavioural evidence alone is inadequate for scientific progress since appearances of modularity can be thoroughly deceptive, obscuring both the dynamic processes of neural development and the endstate network architecture of real cognitive systems.     

Plasticity during childhood and adolescence: innovative approaches to investigating neurocognitive development

Adolescence is a period of profound change, which holds substantial developmental milestones, but also unique challenges to the individual. In this opinion paper, we highlight the potential of combining two recently developed behavioural and neural training techniques (cognitive bias modification and functional magnetic neuroimaging‐based neurofeedback) into a research approach that could help make the most of increased levels of plasticity during childhood and adolescence. We discuss how this powerful combination could be used to explore changing brain–behaviour relationships throughout development in the context of emotion processing, a cognitive domain that exhibits continuous development throughout the second decade of life. By targeting both behaviour and brain response, we would also be in an excellent position to define sensible time windows for enhancing plasticity, thereby allowing for targeted intervention approaches that can help improve emotion processing in both typically and atypically developing populations.     

Neural blackboard architectures of combinatorial structures in cognition

Human cognition is unique in the way in which it relies on combinatorial (or compositional) structures. Language provides ample evidence for the existence of combinatorial structures, but they can also be found in visual cognition. To understand the neural basis of human cognition, it is therefore essential to understand how combinatorial structures can be instantiated in neural terms. In his recent book on the foundations of language, Jackendoff described four fundamental problems for a neural instantiation of combinatorial structures: the massiveness of the binding problem, the problem of 2, the problem of variables, and the transformation of combinatorial structures from working memory to long-term memory. This paper aims to show that these problems can be solved by means of neural "blackboard" architectures. For this purpose, a neural blackboard architecture for sentence structure is presented. In this architecture, neural structures that encode for words are temporarily bound in a manner that preserves the structure of the sentence. It is shown that the architecture solves the four problems presented by Jackendoff. The ability of the architecture to instantiate sentence structures is illustrated with examples of sentence complexity observed in human language performance. Similarities exist between the architecture for sentence structure and blackboard architectures for combinatorial structures in visual cognition, derived from the structure of the visual cortex. These architectures are briefly discussed, together with an example of a combinatorial structure in which the blackboard architectures for language and vision are combined. In this way, the architecture for language is grounded in perception. Perspectives and potential developments of the architectures are discussed.     

The role of falsification in the development of cognitive architectures: insights from a lakatosian analysis

It has been suggested that the enterprise of developing mechanistic theories of the human cognitive architecture is flawed because the theories produced are not directly falsifiable. Newell attempted to sidestep this criticism by arguing for a Lakatosian model of scientific progress in which cognitive architectures should be understood as theories that develop over time. However, Newell's own candidate cognitive architecture adhered only loosely to Lakatosian principles. This paper reconsiders the role of falsification and the potential utility of Lakatosian principles in the development of cognitive architectures. It is argued that a lack of direct falsifiability need not undermine the scientific development of a cognitive architecture if broadly Lakatosian principles are adopted. Moreover, it is demonstrated that the Lakatosian concepts of positive and negative heuristics for theory development and of general heuristic power offer methods for guiding the development of an architecture and for evaluating the contribution and potential of an architecture's research program.     

智力运动专家领域内知觉与记忆的加工特点及其机制

智力运动是以开发智力为目的且涉及到较多认知活动的竞技运动。研究表明, 长期的智力运动经验会影响专家在领域内任务中知觉及记忆的行为表现及其大脑活动。智力运动经验使专家知觉广度增大的同时, 促进专家对棋子关系进行整体性知觉加工, 且这一过程与颞顶联合区、缘上回、压后皮质、侧副沟、梭状回等区域有关; 在长时记忆中存储的具体(空间位置)及抽象信息(知识、策略、棋子关系等)是专家记忆优势发生的基础, 该过程与内侧颞叶、额叶和顶叶有关。未来研究可以从智力运动类型、创新实验范式, 结合测量设备及认知特点, 深入探讨智力运动专家整体知觉优势及记忆优势的神经机制, 为人工智能和技能训练等提供理论依据。     

The Emergence of the Modern Mind: An Evolutionary Perspective on Aesthetic Experience

On the basis of archaeological data and cognitive research, this article proposes an evolutionary story about aesthetic experience, arguing three intertwined theses. Aesthetic experience is adaptive; that is, it represents a specific implementation of the epistemic goal of knowing. It refunctionalizes antecedents and precursors: play and dreaming, technology and the ability to manipulate, and proto‐aesthetic elements and aesthetic preferences. Mind and aesthetic experience co‐evolve; that is, aesthetic experience requires mind reading and metacognition, and it helps them to reach their advanced metarepresentational architecture.     

Reading other people's mind: Insights from neuropsychology

When trying to make sense of other people's behaviour we usually invoke their mental states, such as their intentions, beliefs or emotions. This mind reading ability has been traditionally investigated in developmental psychology and comparative psychology but is now receiving increasing attention from the cognitive neurosciences. I will show the important role that neuropsychology plays in unravelling the cognitive and neural basis of our mind reading abilities. I will illustrate this by showing how cases of adults with acquired brain lesions can help us tease apart the different mechanisms that underlie mind reading abilities and can help us understand the nature of these mechanisms, especially their relation to language and executive function.     

Brain-inspired distributed cognitive architecture

In this paper we present a brain-inspired cognitive architecture that incorporates sensory processing, classification, contextual prediction, and emotional tagging. The cognitive architecture is implemented as three modular web-servers, meaning that it can be deployed centrally or across a network for servers. The experiments reveal two distinct operations of behaviour, namely high- and low-salience modes of operations, which closely model attention in the brain. In addition to modelling the cortex, we have demonstrated that a bio-inspired architecture introduced processing efficiencies. The software has been published as an open source platform, and can be easily extended by future research teams. This research lays the foundations for bio-realistic attention direction and sensory selection, and we believe that it is a key step towards achieving a bio-realistic artificial intelligent system.     

Could positive affect help engineer robot control systems?

Emotions have long been seen as counteracting rational thought, but over the last decades, they have been viewed as adaptive processes to optimize human (but also animal) behaviour. In particular, positive affect appears to be a functional aspect of emotions closely related to that. We argue that positive affect as understood in Kuhl’s PSI model of the human cognitive architecture appears to have an interpretation in state-of-the-art hybrid robot control architectures, which might help tackle some open questions in the field.     

The convergent evolution of neural substrates for cognition

This review describes a case of convergence in the evolution of brain and cognition. Both mammals and birds can organize their behavior flexibly over time and evolved similar cognitive skills. The avian forebrain displays no lamination that corresponds to the mammalian neocortex; hence, lamination does not seem to be a requirement for higher cognitive functions. In mammals, executive functions are associated with the prefrontal cortex. The corresponding structure in birds is the nidopallium caudolaterale. Anatomic, neurochemical, electrophysiologic and behavioral studies show these structures to be highly similar, but not homologous. Thus, despite the presence (mammals) or the absence (birds) of a laminated forebrain, ‘prefrontal’ areas in mammals and birds converged over evolutionary time into a highly similar neural architecture. The neuroarchitectonic degrees of freedom to create different neural architectures that generate identical prefrontal functions seem to be very limited.     

The neurocognitive basis of autism

The cognitive study of the underlying mental abnormalities in autism has advanced rapidly, while the biological study of the underlying brain abnormalities and of putative genetic mechanisms is lagging somewhat behind. However, the linking of cognitive and biological studies has become a real possibility. Developmental cognitive neuroscience has transformed our understanding of this enigmatic disorder, which was once misguidedly thought to be caused by maternal rejection. The hypothesis of a specific theory of mind deficit was a crucial step in this process. It explains the puzzle of the characteristic social and communication impairments of autism and allows for the fact that they can coexist with good general abilities. This hypothesis has been widely accepted and a start has been made at pinpointing the brain basis of theory of mind. The non-social impairments of autism have now become a major focus for cognitive research. One theory proposes dysfunction in executive processes, in an attempt to explain repetitive behaviour and inflexibility. Another theory proposes weak information integration, in an attempt to explain narrow interests and special talents. Autism research has thus stimulated ideas on important mind-brain systems that may be dedicated to the development of social awareness, executive functions and integrative processing.     

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.