The supramodal brain: implications for auditory perception

ABSTRACT

The perceptual brain is designed around multisensory input. Areas once thought dedicated to a single sense are now known to work with multiple senses. It has been argued that the multisensory nature of the brain reflects a cortical architecture for which task, rather than sensory system, is the primary design principle. This supramodal thesis is supported by recent research on human echolocation and multisensory speech perception. In this review, we discuss the behavioural implications of a supramodal architecture, especially as they pertain to auditory perception. We suggest that the architecture implies a degree of perceptual parity between the senses and that cross-sensory integration occurs early and completely. We also argue that a supramodal architecture implies that perceptual experience can be shared across modalities and that this sharing should occur even without bimodal experience. We finish by briefly suggesting areas of future research.     

A neurally plausible parallel distributed processing model of event-related potential word reading data

The Parallel Distributed Processing (PDP) framework has significant potential for producing models of cognitive tasks that approximate how the brain performs the same tasks. To date, however, there has been relatively little contact between PDP modeling and data from cognitive neuroscience. In an attempt to advance the relationship between explicit, computational models and physiological data collected during the performance of cognitive tasks, we developed a PDP model of visual word recognition which simulates key results from the ERP reading literature, while simultaneously being able to successfully perform lexical decision—a benchmark task for reading models. Simulations reveal that the model’s success depends on the implementation of several neurally plausible features in its architecture which are sufficiently domain-general to be relevant to cognitive modeling more generally.     

ECNN: Enhanced convolutional neural network for efficient diagnosis of autism spectrum disorder

This paper aims to apply deep learning to identify autism spectrum disorder (ASD) patients from a large brain imaging dataset based on the patients’ brain activation patterns. The brain images are collected from the ABIDE (Autism Brain Imaging Data Exchange) database. The proposed convolutional neural network (CNN) architecture investigates functional connectivity patterns between different brain areas to identify specifics patterns to diagnose ASD. The enhanced CNN uses blocks of temporal convolutional layers that employ casual convolutions and dilations; hence, it is suitable for sequential data with temporality large receptive fields. Experimental results show that the proposed ECNN achieves an accuracy of up to 80% accuracy. These patterns show an anticorrelation of brain function between anterior and posterior areas of the brain; that is, the disruption in brain connectivity is one primary evidence of ASD.     

The development of delayed response: Parallel distributed processing lacks neural plausibility

Munakata's model of the A-not-B task provides an excellent fit to behavioral data from human infants. From a neuropsychological standpoint, however, its architecture is not very plausible. Dehaene and Changeux's (1989) neuronal model of delayed response tasks, while admittedly very simple, relies on identified features of brain architecture such as multiple hierarchical pathways, bistable clusters with sustained activity, and diffuse reward systems. Ways in which the insights provided by both models might be combined are discussed.     

A physiologically based approach to consciousness

The nature of a scientific theory of consciousness is defined by comparison with scientific theories in the physical sciences. The differences between physical, algorithmic and functional complexity are highlighted, and the architecture of a functionally complex electronic system created to relate system operations to device operations is compared with a scientific theory. It is argued that there are two qualitatively different types of functional architecture: (a) electronic systems have the instruction architecture based on exchange of unambiguous information between functional components, and (b) biological brains have been constrained by natural selection pressures into the recommendation architecture based on exchange of ambiguous information. The mechanisms by which a recommendation architecture could heuristically define its own functionality are described, and compared with memory in biological brains. Dream sleep is interpreted as the mechanism for minimizing information exchange between functional components in a heuristically defined functional system. The functional role of consciousness of self is discussed, and the route by which the experience of that function described at the psychological level can be related to physiology through a functional architecture is outlined.     

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.     

Children's academic attainment is linked to the global organization of the white matter connectome

Literacy and numeracy are important skills that are typically learned during childhood, a time that coincides with considerable shifts in large‐scale brain organization. However, most studies emphasize focal brain contributions to literacy and numeracy development by employing case‐control designs and voxel‐by‐voxel statistical comparisons. This approach has been valuable, but may underestimate the contribution of overall brain network organization. The current study includes children (N = 133 children; 86 male; mean age = 9.42, SD = 1.715; age range = 5.92–13.75y) with a broad range of abilities, and uses whole‐brain structural connectomics based on diffusion‐weighted MRI data. The results indicate that academic attainment is associated with differences in structural brain organization, something not seen when focusing on the integrity of specific regions. Furthermore, simulated disruption of highly‐connected brain regions known as hubs suggests that the role of these regions for maintaining the architecture of the network may be more important than specific aspects of processing. Our findings indicate that distributed brain systems contribute to the etiology of difficulties with academic learning, which cannot be captured using a more traditional voxel‐wise statistical approach.     

A Biologically Plausible Action Selection System for Cognitive Architectures: Implications of Basal Ganglia Anatomy for Learning and Decision‐Making Models

Several attempts have been made previously to provide a biological grounding for cognitive architectures by relating their components to the computations of specific brain circuits. Often, the architecture's action selection system is identified with the basal ganglia. However, this identification overlooks one of the most important features of the basal ganglia—the existence of a direct and an indirect pathway that compete against each other. This characteristic has important consequences in decision‐making tasks, which are brought to light by Parkinson's disease as well as genetic differences in dopamine receptors. This paper shows that a standard model of action selection in a cognitive architecture (ACT‐R) cannot replicate any of these findings, details an alternative solution that reconciles action selection in the architecture with the physiology of the basal ganglia, and extends the domain of application of cognitive architectures. The implication of this solution for other architectures and existing models are discussed.     

Models of visuospatial and verbal memory across the adult life span

The authors investigated the distinctiveness and interrelationships among visuospatial and verbal memory processes in short-term, working, and long-term memories in 345 adults. Beginning in the 20s, a continuous, regular decline occurs for processing-intensive tasks (e.g., speed of processing, working memory, and long-term memory), whereas verbal knowledge increases across the life span. There is little differentiation in the cognitive architecture of memory across the life span. Visuospatial and verbal working memory are distinct but highly interrelated systems with domain-specific short-term memory subsystems. In contrast to recent neuroimaging data, there is little evidence for dedifferentiation of function at the behavioral level in old compared with young adults. The authors conclude that efforts to connect behavioral and brain data yield a more complete understanding of the aging mind.     

A cognitive architecture that combines internal simulation with a global workspace

This paper proposes a brain-inspired cognitive architecture that incorporates approximations to the concepts of consciousness, imagination, and emotion. To emulate the empirically established cognitive efficacy of conscious as opposed to non-conscious information processing in the mammalian brain, the architecture adopts a model of information flow from global workspace theory. Cognitive functions such as anticipation and planning are realised through internal simulation of interaction with the environment. Action selection, in both actual and internally simulated interaction with the environment, is mediated by affect. An implementation of the architecture is described which is based on weightless neurons and is used to control a simulated robot.     

Ontogenetic constraints on neural and behavioral plasticity: evidence from imprinting and face processing.

This paper addresses the extent and limits on brain plasticity during development through the detailed study of imprinting in the domestic chick and the development of face processing in human infants. In both of these systems, evidence for constraints on plasticity is reviewed. The first source of constraint comes from the basic architecture of learning mechanisms that support plasticity. With regard to the chick, a specific "Hebbian" model based on the known neural circuitry of the region of the brain involved is presented and discussed. In human infants, a more abstract model inspired by cortical circuitry is mentioned. The second source of constraint comes from biases on the nature of the stimuli selected for attention by the young organism. Both in the chick and the human there is evidence for a subcortical brain system which orients their attention toward conspecifics, and particularly to their faces. It is argued that these systems tutor, or bias the input to, the more plastic learning systems.     

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.     

Bio-inspired cognitive architecture of episodic memory

Memory and learning are essential functions in human beings as they allow us to acquire and store in the brain representations of thoughts, experiences, and behaviors, which are required for problem-solving in our daily life and mainly for survival. Episodic memory is a type of memory that provides the ability to re-experience events in one’s life, and it is associated with their conscious recollection. Since episodic memory can represent our experiences about the environment, similar to a mental journey, it is desired in systems that attempt to create human-like behavior. Currently, the main problem is that state of the art proposals do not consider neuroscientific evidence like memory dynamics for forgetting or bottom-up inputs, and most of them do not consider episodic memory as a different memory but as part of general declarative memory. We consider these omissions to limit the generation of human-like behavior. In this work, we propose a bio-inspired cognitive architecture of episodic memory. Neuroscientific evidence provides us with the brain structures associated with this type of memory, the connections, and the operations these areas perform. We hypothesize that virtual entities endowed with our episodic memory cognitive architecture will plan and make decisions in a more human-like fashion. To test the capabilities of the proposal, we endowed a virtual creature with a distributed and concurrent implementation of our architecture, and it was given two tasks. The first task validated the functions of the memory independently, and in the second task, the creature used episodic memory to solve a planning problem. From the results of these experiments, we validate our proposal and show that it is possible to create a system that behaves as the human brain does.     

A neural cognitive architecture

It is difficult to study the mind, but cognitive architectures are one tool. As the mind emerges from the behaviour of the brain, neuropsychological methods are another method to study the mind, though a rather indirect method. A cognitive architecture that is implemented in spiking neurons is a method of studying the mind that can use neuropsychological evidence directly. A neural cognitive architecture, based on rule based systems and associative memory, can be readily implemented, and would provide a good bridge between standard cognitive architectures, such as Soar, and neuropsychology. This architecture could be implemented in spiking neurons, and made available via the Human Brain Project, which provides a good collaborative environment. The architecture could be readily extended to use spiking neurons for subsystems, such as spatial reasoning, and could evolve over time toward a complete architecture. The theory behind this architecture could evolve over time. Simplifying assumptions, made explicit, such as those behind the rule based system, could gradually be replaced by more neuropsychologically accurate behaviour. The overall task of collaborative architecture development would be eased by direct evidence of the actual neural cognitive architectures in human brains. While the initial architecture is biologically inspired, the ultimate goal is a biological cognitive architecture.     

Large-scale brain networks and psychopathology: a unifying triple network model

The science of large-scale brain networks offers a powerful paradigm for investigating cognitive and affective dysfunction in psychiatric and neurological disorders. This review examines recent conceptual and methodological developments which are contributing to a paradigm shift in the study of psychopathology. I summarize methods for characterizing aberrant brain networks and demonstrate how network analysis provides novel insights into dysfunctional brain architecture. Deficits in access, engagement and disengagement of large-scale neurocognitive networks are shown to play a prominent role in several disorders including schizophrenia, depression, anxiety, dementia and autism. Synthesizing recent research, I propose a triple network model of aberrant saliency mapping and cognitive dysfunction in psychopathology, emphasizing the surprising parallels that are beginning to emerge across psychiatric and neurological disorders.     

A proposal of bioinspired motor-system cognitive architecture focused on feed-forward-control movements

The objective of this article is to present a conceptual motor-system cognitive architecture inspired in the human nervous system, and a cognitive architecture focused on the voluntary movement controlled by feed-forward. The article first focuses on describing the brain cortex areas that make up the motor system, presenting the supplementary motor area (SMA), premotor cortex and primary cortex, the tasks that these cortices do, and how they work together. Then, a cognitive architecture based on the information presented is shown. The areas in a computational level (functions, and algorithms used) are then described. Finally, a case study is presented, where the cognitive architecture proposed is used to execute a voluntary-movement-by-feed-forward-control task.     

Should Psychology Ignore the Language of the Brain?

ABSTRACT— Claims that neuroscientific data do not contribute to our understanding of psychological functions have been made recently. Here I argue that these criticisms are solely based on an analysis of functional magnetic resonance imaging (fMRI) studies. However, fMRI is only one of the methods in the toolkit of cognitive neuroscience. I provide examples from research on event-related brain potentials (ERPs) that have contributed to our understanding of the cognitive architecture of human language functions. In addition, I provide evidence of (possible) contributions from fMRI measurements to our understanding of the functional architecture of language processing. Finally, I argue that a neurobiology of human language that integrates information about the necessary genetic and neural infrastructures will allow us to answer certain questions that are not answerable if all we have is evidence from behavior.     

Bayes,predictive processing,and the cognitive architecture of motor control

Despite their popularity, relatively scant attention has been paid to the upshot of Bayesian and predictive processing models of cognition for views of overall cognitive architecture. Many of these models are hierarchical; they posit generative models at multiple distinct “levels,” whose job is to predict the consequences of sensory input at lower levels. I articulate one possible position that could be implied by these models, namely, that there is a continuous hierarchy of perception, cognition, and action control comprising levels of generative models. I argue that this view is not entailed by a general Bayesian/predictive processing outlook. Bayesian approaches are compatible with distinct formats of mental representation. Focusing on Bayesian approaches to motor control, I argue that the junctures between different types of mental representation are places where the transitivity of hierarchical prediction may be broken, and I consider the upshot of this conclusion for broader discussions of cognitive architecture.