In Defense of Some `Cartesian' Assumptions Concerning the Brain and Its Operation

I argue against a growing radical trend in current theoretical cognitive science that moves from the premises of embedded cognition, embodied cognition, dynamical systems theory and/or situated robotics to conclusions either to the effect that the mind is not in the brain or that cognition does not require representation, or both. I unearth the considerations at the foundation of this view: Haugeland's bandwidth-component argument to the effect that the brain is not a component in cognitive activity, and arguments inspired by dynamical systems theory and situated robotics to the effect that cognitive activity does not involve representations. Both of these strands depend not only on a shift of emphasis from higher cognitive functions to things like sensorimotor processes, but also depend on a certain understanding of how sensorimotor processes are implemented - as closed-loop control systems. I describe a much more sophisticated model of sensorimotor processing that is not only more powerful and robust than simple closed-loop control, but for which there is great evidence that it is implemented in the nervous system. The is the emulation theory of representation, according to which the brain constructs inner dynamical models, or emulators, of the body and environment which are used in parallel with the body and environment to enhance motor control and perception and to provide faster feedback during motor processes, and can be run off-line to produce imagery and evaluate sensorimotor counterfactuals. I then show that the emulation framework is immune to the radical arguments, and makes apparent why the brain is a component in the cognitive activity, and exactly what the representations are in sensorimotor control.     

Visual categorization: accessing abstraction in non-human primates

Evolution might have set the basic foundations for abstract mental representation long ago. Because of language, mental abilities would have reached different degrees of sophistication in mammals and in humans but would be, essentially, of the same nature. Thus, humans and animals might rely on the same basic mechanisms that could be masked in humans by the use of sophisticated strategies. In this paper, monkey and human abilities are compared in a variety of perceptual tasks including visual categorization to assess behavioural similarities and dissimilarities, and to determine the level of abstraction of monkeys' mental representations. The question of how these abstract representations might be encoded in the brain is then addressed. A comparative study of the neural processing underlying abstract cognitive operations in animals and humans might help to understand when abstraction emerged in the phylogenetic scale, and how it increased in complexity.     

Reconciling neuronal representations of schema,abstract task structure,and categorization under cognitive maps in the entorhinal-hippocampal-frontal circuits

Learning leads to a neuronal representation of acquired knowledge. This idea of knowledge representation was traditionally developed as a “cognitive map” of spatial memory represented in the hippocampus. The framework of cognitive mapping has been extended in the past decade to include not only spatial memory, but also non-spatial factual and temporal memory. Following this conceptual advancement, a line of recent neurophysiological research discovered such knowledge representations not only in the hippocampus, but also in the entorhinal cortex and frontal cortex. Although the distinct terms “cognitive map,” “schema,” “abstract task structure” or “categorization” were used in these studies, it is likely that these terms can be reconciled as a common mechanism of learned knowledge representations. Future experimental work will be required to differentiate the parametric nature of knowledge representations across brain areas.     

Specifically Human: Going Beyond Perceptual Syntax

The aim of this paper is to help refine the definition of humans as “linguistic animals” in light of a comparative approach on nonhuman animals’ cognitive systems. As Uexküll & Kriszat (1934/1992) have theorized, the epistemic access to each species-specific environment (Umwelt) is driven by different biocognitive processes. Within this conceptual framework, I identify the salient cognitive process that distinguishes each species typical perception of the world as the faculty of language meant in the following operational definition: the ability to connect different elements according to structural rules. In order to draw some conclusions about humans’ specific faculty of language, I review different empirical studies on nonhuman animals’ ability to recognize formal patterns of tokens. I suggest that what differentiates human language from other animals’ cognitive systems is the ability to categorize the units of a pattern, going beyond its perceptual aspects. In fact, humans are the only species known to be able to combine semantic units within a network of combinatorial logical relationships (Deacon 1997) that can be linked to the state of affairs in the external world (Wittgenstein 1922). I assume that this ability is the core cognitive process underlying a) the capacity to speak (or to reason) in verbal propositions and b) the general human faculty of language expressed, for instance, in the ability to draw visual conceptual maps or to compute mathematical expressions. In light of these considerations, I conclude providing some research questions that could lead to a more detailed comparative exploration of the faculty of language.     

Wanting,liking and welfare: The role of affective states in proximate control of behaviour in vertebrates

Animals choose a course of action countless times each day. To do so, they need to prioritise their behaviour within a set of alternative actions and decide which of these actions to perform at any one time and for how long, that is, determine when the behaviour has reached its desired effect. This process has classically been called the proximate behavioural control mechanism. Several aspects contribute to this process: internal and external stimuli, the emotions that they elicit, motivation (wants), behavioural goals, valuation, decision‐making and its modulation by mood, and the assessment of behavioural outcomes (liking). I will address all these aspects in the form of an integrated conceptual model. In the process of behavioural control, options need to be valued, and I will refer to evidence showing that an affective hedonic process in respect to (future) reward and punishment heavily affects this value. Moreover, I view motivation, the force that finally drives a specific behavioural output, as being primarily influenced by affective states or even corresponding fully to them. Given the feedback in behavioural control by (dis‐)liking outcomes of behaviour, I reason that in respect to welfare it is more important for animals to reach proximate goals than to avoid negative stimuli. All behavioural choices are modulated, and I show how mood, a long‐term affective state, can cause such modulation. Proximate control of behaviour takes place in the brain, and I will briefly discuss how current and future brain research may elucidate how the brain computes these processes. Furthermore, the inclusion of affective states in the conceptual model raises the question of the subjective experience of animals, and I will address some of the important open questions in this area of research. I will conclude that neural studies cannot currently provide a detailed and general theory on the algorithms of proximate behavioural control. In parallel to further developing these approaches, I propose to strengthen a refined ethological approach with a focus on the states of “wanting” and “liking” in ecologically meaningful circumstances and with a strong ontogenetic (within species) and comparative (between species) component. I consider this ethological approach to be a highly promising step in understanding proximate behavioural control.     

Neurophysiological mechanisms involved in language learning in adults

Little is known about the brain mechanisms involved in word learning during infancy and in second language acquisition and about the way these new words become stable representations that sustain language processing. In several studies we have adopted the human simulation perspective, studying the effects of brain-lesions and combining different neuroimaging techniques such as event-related potentials and functional magnetic resonance imaging in order to examine the language learning (LL) process. In the present article, we review this evidence focusing on how different brain signatures relate to (i) the extraction of words from speech, (ii) the discovery of their embedded grammatical structure, and (iii) how meaning derived from verbal contexts can inform us about the cognitive mechanisms underlying the learning process. We compile these findings and frame them into an integrative neurophysiological model that tries to delineate the major neural networks that might be involved in the initial stages of LL. Finally, we propose that LL simulations can help us to understand natural language processing and how the recovery from language disorders in infants and adults can be accomplished.     

Multiscale representations of community structures in attractor neural networks

Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are poorly understood. In this paper we propose an extended attractor network model for graph-based hierarchical computation which we call the Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by the heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model exhibits chunked sequential activity patterns resembling hippocampal theta sequences. Our model connects graph theory and attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain.     

Quantum-like brain: "Interference of minds"

We present a contextualist statistical realistic model for quantum-like representations in physics, cognitive science, and psychology. We apply this model to describe cognitive experiments to check quantum-like structures of mental processes. The crucial role is played by interference of probabilities for mental observables. Recently one such experiment based on recognition of images was performed. This experiment confirmed our prediction on the quantum-like behavior of mind. In our approach "quantumness of mind" has no direct relation to the fact that the brain (as any physical body) is composed of quantum particles. We invented a new terminology "quantum-like (QL) mind." Cognitive QL-behavior is characterized by a nonzero coefficient of interference lambda. This coefficient can be found on the basis of statistical data. There are predicted not only cos theta-interference of probabilities, but also hyperbolic cosh theta-interference. This interference was never observed for physical systems, but we could not exclude this possibility for cognitive systems. We propose a model of brain functioning as a QL-computer (there is a discussion on the difference between quantum and QL computers).     

From Sensory Signals to Modality-Independent Conceptual Representations: A Probabilistic Language of Thought Approach

People learn modality-independent, conceptual representations from modality-specific sensory signals. Here, we hypothesize that any system that accomplishes this feat will include three components: a representational language for characterizing modality-independent representations, a set of sensory-specific forward models for mapping from modality-independent representations to sensory signals, and an inference algorithm for inverting forward models—that is, an algorithm for using sensory signals to infer modality-independent representations. To evaluate this hypothesis, we instantiate it in the form of a computational model that learns object shape representations from visual and/or haptic signals. The model uses a probabilistic grammar to characterize modality-independent representations of object shape, uses a computer graphics toolkit and a human hand simulator to map from object representations to visual and haptic features, respectively, and uses a Bayesian inference algorithm to infer modality-independent object representations from visual and/or haptic signals. Simulation results show that the model infers identical object representations when an object is viewed, grasped, or both. That is, the model’s percepts are modality invariant. We also report the results of an experiment in which different subjects rated the similarity of pairs of objects in different sensory conditions, and show that the model provides a very accurate account of subjects’ ratings. Conceptually, this research significantly contributes to our understanding of modality invariance, an important type of perceptual constancy, by demonstrating how modality-independent representations can be acquired and used. Methodologically, it provides an important contribution to cognitive modeling, particularly an emerging probabilistic language-of-thought approach, by showing how symbolic and statistical approaches can be combined in order to understand aspects of human perception.     

Disentangling the dynamic core: a research program for a neurodynamics at the large-scale

My purpose in this paper is to sketch a research direction based on Francisco Varela's pioneering work in neurodynamics (see also Rudrauf et al. 2003, in this issue). Very early on he argued that the internal coherence of every mental-cognitive state lies in the global self-organization of the brain activities at the large-scale, constituting a fundamental pole of integration called here a "dynamic core". Recent neuroimaging evidence appears to broadly support this hypothesis and suggests that a global brain dynamics emerges at the large scale level from the cooperative interactions among widely distributed neuronal populations. Despite a growing body of evidence supporting this view, our understanding of these large-scale brain processes remains hampered by the lack of a theoretical language for expressing these complex behaviors in dynamical terms. In this paper, I propose a rough cartography of a comprehensive approach that offers a conceptual and mathematical framework to analyze spatio-temporal large-scale brain phenomena. I emphasize how these nonlinear methods can be applied, what property might be inferred from neuronal signals, and where one might productively proceed for the future. This paper is dedicated, with respect and affection, to the memory of Francisco Varela.     

Iconicity as structure mapping

Linguistic and psycholinguistic evidence is presented to support the use of structure-mapping theory as a framework for understanding effects of iconicity on sign language grammar and processing. The existence of structured mappings between phonological form and semantic mental representations has been shown to explain the nature of metaphor and pronominal anaphora in sign languages. With respect to processing, it is argued that psycholinguistic effects of iconicity may only be observed when the task specifically taps into such structured mappings. In addition, language acquisition effects may only be observed when the relevant cognitive abilities are in place (e.g. the ability to make structural comparisons) and when the relevant conceptual knowledge has been acquired (i.e. information key to processing the iconic mapping). Finally, it is suggested that iconicity is better understood as a structured mapping between two mental representations than as a link between linguistic form and human experience.     

3D current-voltage-time surfaces unveil critical repolarization differences underlying similar cardiac action potentials: A model study

The number of mathematical models of cardiac cellular excitability is rapidly growing, and compact graphical representations of their properties can make new acquisitions available for a broader range of scientists in cardiac field. Particularly, the intrinsic over-determination of the model equations systems when fitted only to action potential (AP) waveform and the fact that they are frequently tuned on data covering only a relatively narrow range of dynamic conditions, often lead modellers to compare very similar AP profiles, which underlie though quite different excitable properties. In this study I discuss a novel compact 3D representation of the cardiac cellular AP, where the third dimension represents the instantaneous current–voltage profile of the membrane, measured as repolarization proceeds. Measurements of this type have been used previously for in vivo experiments, and are adopted here iteratively at a very high time, voltage, current-resolution on (i) the same human ventricular model, endowed with two different parameters sets which generate the same AP waveform, and on (ii) three different models of the same human ventricular cell type. In these 3D representations, the AP waveforms lie at the intersection between instantaneous time–voltage–current surfaces and the zero-current plane. Different surfaces can share the same intersection and therefore the same AP; in these cases, the morphology of the current surface provides a compact view of important differences within corresponding repolarization dynamics.Refractory period, supernormal excitability window, and extent of repolarization reserve can be visualized at once. Two pivotal dynamical properties can be precisely assessed, i.e. all-or-nothing repolarization window and membrane resistance during recovery. I discuss differences in these properties among the membranes under study, and show relevant implications for cardiac cellular repolarization.     

Concurrent word generation and motor performance: further evidence for language-motor interaction

Embodied/modality-specific theories of semantic memory propose that sensorimotor representations play an important role in perception and action. A large body of evidence supports the notion that concepts involving human motor action (i.e., semantic-motor representations) are processed in both language and motor regions of the brain. However, most studies have focused on perceptual tasks, leaving unanswered questions about language-motor interaction during production tasks. Thus, we investigated the effects of shared semantic-motor representations on concurrent language and motor production tasks in healthy young adults, manipulating the semantic task (motor-related vs. nonmotor-related words) and the motor task (i.e., standing still and finger-tapping). In Experiment 1 (n = 20), we demonstrated that motor-related word generation was sufficient to affect postural control. In Experiment 2 (n = 40), we demonstrated that motor-related word generation was sufficient to facilitate word generation and finger tapping. We conclude that engaging semantic-motor representations can have a reciprocal influence on motor and language production. Our study provides additional support for functional language-motor interaction, as well as embodied/modality-specific theories.     

Human brain evolution: harnessing the genomics (r)evolution to link genes, cognition, and behavior

The evolution of the human brain has resulted in numerous specialized features including higher cognitive processes such as language. Knowledge of whole-genome sequence and structural variation via high-throughput sequencing technology provides an unprecedented opportunity to view human evolution at high resolution. However, phenotype discovery is a critical component of these endeavors and the use of nontraditional model organisms will also be critical for piecing together a complete picture. Ultimately, the union of developmental studies of the brain with studies of unique phenotypes in a myriad of species will result in a more thorough model of the groundwork the human brain was built upon. Furthermore, these integrative approaches should provide important insights into human diseases.     

A Goldilocks theory of cognitive control: Balancing precision and efficiency with low-dimensional control states

Cognitive control orchestrates interactions between brain regions, guiding the transformation of information to support contextually appropriate and goal-directed behaviors. In this review, we propose a hierarchical model of cognitive control where low-dimensional control states direct the flow of high-dimensional representations between regions. This allows cognitive control to flexibly adapt to new environments and maintain the representational capacity to capture the richness of the world.     

Symbols as Self-emergent Entities in an Optimization Process of Feature Extraction and Predictions

In the mammalian cortex the early sensory processing can be characterized as feature extraction resulting in local and analogue low-level representations. As a direct consequence, these map directly to the environment, but interpretation under natural conditions is ambiguous. In contrast, high-level representations for cognitive processing, e.g. language, require symbolic representations characterized by expression and syntax. The representations are binary, structured and disambiguated. However, do these fundamental functional distinctions translate into a fundamental distinction of the respective brain areas and their anatomical and physiological properties? Here we argue that the distinction between early sensory processing and higher cognitive functions may not be based on structural differences of cortical areas; instead similar learning principles acting on input signals with different statistics give rise to the observed variations of function. Firstly, we give an account of present research describing neuronal properties at early stages of sensory systems as a consequence of an optimization process over the set of natural stimuli. Secondly, addressing a stage following early visual processing we suggest to extend the unsupervised learning scheme by including predictive processes. These contain the widely used objective of temporal coherence as a special case and are a powerful approach to resolve ambiguities. Furthermore, in combination with a prior on the bandwidth of information exchange between units it leads to a condensation of information. Thirdly, as a crucial step, not only are predictive units optimized, but the selectivity of the feature extractors are adapted to allow optimal predictability. Thus, over and beyond making useful predictions, we propose that the predictability of a stimulus be in itself a selection criterion for further processing. In a hierarchical system the combined optimization process leads to entities that represent condensed pieces of knowledge and that are not analogue anymore. Instead, these entities work as arguments in a framework of transformations that realize predictions. Thus, the criteria of predictability and condensation in an optimization of sensory representations relate directly to the two defining properties of symbols of expression and syntax. In this paper, we sketch an unsupervised learning process that gradually transforms analogue local representations into discrete binary representations by means of four hypotheses. We propose that in this optimization process acting in a hierarchical system, entities emerge at, higher levels that fulfil the criteria defining symbols, instantiating qualitatively different representations at similarly structured low and high levels.     

Searching through functional space reveals distributed visual,auditory, and semantic coding in the human brain

The extent to which brain functions are localized or distributed is a foundational question in neuroscience. In the human brain, common fMRI methods such as cluster correction, atlas parcellation, and anatomical searchlight are biased by design toward finding localized representations. Here we introduce the functional searchlight approach as an alternative to anatomical searchlight analysis, the most commonly used exploratory multivariate fMRI technique. Functional searchlight removes any anatomical bias by grouping voxels based only on functional similarity and ignoring anatomical proximity. We report evidence that visual and auditory features from deep neural networks and semantic features from a natural language processing model, as well as object representations, are more widely distributed across the brain than previously acknowledged and that functional searchlight can improve model-based similarity and decoding accuracy. This approach provides a new way to evaluate and constrain computational models with brain activity and pushes our understanding of human brain function further along the spectrum from strict modularity toward distributed representation.     

Conservation of endangered species and the patterns and propensities of biodiversity

It is commonly asserted in the ecological and economic literature that habitat loss is the main cause of loss of imperiled species. The evidence clearly shows that habitat loss is a common contributing factor, but there is little evidence that it is the most important factor. Studies that have focused on the mechanisms of species loss have failed to produce models capable of predicting patterns of loss as a function of human activities. I propose that this is because ecologists have employed an unrealistic conceptual model of the functioning of natural systems. Karl Popper's construct of the propensities of natural systems provides a more realistic view, and better potential to yield predictive models. I provide two examples of patterns of biodiversity and species loss in Canada where mechanistic reasoning is inconsistent with the observed propensities of species loss.     

Neural networks for perceptual processing: from simulation tools to theories

Neural networks are modelling tools that are, in principle, able to capture the input-output behaviour of arbitrary systems that may include the dynamics of animal populations or brain circuits. While a neural network model is useful if it captures phenomenologically the behaviour of the target system in this way, its utility is amplified if key mechanisms of the model can be discovered, and identified with those of the underlying system. In this review, we first describe, at a fairly high level with minimal mathematics, some of the tools used in constructing neural network models. We then go on to discuss the implications of network models for our understanding of the system they are supposed to describe, paying special attention to those models that deal with neural circuits and brain systems. We propose that neural nets are useful for brain modelling if they are viewed in a wider computational framework originally devised by Marr. Here, neural networks are viewed as an intermediate mechanistic abstraction between 'algorithm' and 'implementation', which can provide insights into biological neural representations and their putative supporting architectures.     

Abstraction from a sensori-motor perspective: can we get a quick hold on simple perception?

Two main types of dissociation can be considered in order to articulate action and abstraction. Vision for action and vision for perception are often described as dissociated systems at both anatomical and functional levels. Within this framework, abstraction should be specific to perceptual representation, whereas the action system would simply analyse the objective metric of space. By contrast, one may focus on dissociations within the action system. In this case, one will accept that action may involve abstract representations, at least during movement preparation. But a specific visuomotor level of processing can be described that appears to comply with the spatial properties of the relationship between the actor and the environment. This system would be specialized for fast movement guidance towards pre-defined goals. Such an automatic piloting system would thus be free of abstraction.