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.     

Modeling in the museum: On the role of remnant models in the work of Joseph Grinnell

Accounts of the relation between theories and models in biology concentrate on mathematical models. In this paper I consider the dual role of models as representations of natural systems and as a material basis for theorizing. In order to explicate the dual role, I develop the concept of a remnant model, a material entity made from parts of the natural system(s) under study. I present a case study of an important but neglected naturalist, Joseph Grinnell, to illustrate the extent to which mundane practices in a museum setting constitute theorizing. I speculate that historical and sociological analyses of institutions can play a specific role in the philosophical analysis of model-building strategies.     

The Deep Structure of Literary Representations

The cognitive rhetoricians have introduced the idea of cognitive domains into literary theory, but they have not yet developed a model for a comprehensive, species-typical structure of human motives. Evolutionary psychology can provide this model. Elemental human motives and basic emotions provide the deep structure of literary representations, and this deep structure serves to organize the particularities of circumstance and individual identity. Personal power and reproductive success are governing purposes in life and in literary representations. The concept of individual identity is necessary to literary representation, and a theory of literature based in evolutionary psychology has to incorporate models of personality. Literature and its oral antecedents organize experience in personally meaningful ways. They provide models of behavior and help regulate the complex cognitive machinery through which humans negotiate their social and cultural environments.     

Challenging the classical notion of time in cognition: a quantum perspective

All mental representations change with time. A baseline intuition is that mental representations have specific values at different time points, which may be more or less accessible, depending on noise, forgetting processes, etc. We present a radical alternative, motivated by recent research using the mathematics from quantum theory for cognitive modelling. Such cognitive models raise the possibility that certain possibilities or events may be incompatible, so that perfect knowledge of one necessitates uncertainty for the others. In the context of time-dependence, in physics, this issue is explored with the so-called temporal Bell (TB) or Leggett–Garg inequalities. We consider in detail the theoretical and empirical challenges involved in exploring the TB inequalities in the context of cognitive systems. One interesting conclusion is that we believe the study of the TB inequalities to be empirically more constrained in psychology than in physics. Specifically, we show how the TB inequalities, as applied to cognitive systems, can be derived from two simple assumptions: cognitive realism and cognitive completeness. We discuss possible implications of putative violations of the TB inequalities for cognitive models and our understanding of time in cognition in general. Overall, this paper provides a surprising, novel direction in relation to how time should be conceptualized in cognition.     

From cognitive to neural models of working memory

Working memory refers to the temporary retention of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be stored for longer periods of time through active maintenance or rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behaviour. Empirical studies of working memory using neuroscientific techniques, such as neuronal recordings in monkeys or functional neuroimaging in humans, have advanced our knowledge of the underlying neural mechanisms of working memory. This rich dataset can be reconciled with behavioural findings derived from investigating the cognitive mechanisms underlying working memory. In this paper, I review the progress that has been made towards this effort by illustrating how investigations of the neural mechanisms underlying working memory can be influenced by cognitive models and, in turn, how cognitive models can be shaped and modified by neuroscientific data. One conclusion that arises from this research is that working memory can be viewed as neither a unitary nor a dedicated system. A network of brain regions, including the prefrontal cortex (PFC), is critical for the active maintenance of internal representations that are necessary for goal-directed behaviour. Thus, working memory is not localized to a single brain region but probably is an emergent property of the functional interactions between the PFC and the rest of the brain.     

Multiple time-scales and the developmental dynamics of social systems

To build a theory of social complexity, we need to understand how aggregate social properties arise from individual interaction rules. Here, I review a body of work on the developmental dynamics of pigtailed macaque social organization and conflict management that provides insight into the mechanistic causes of multi-scale social systems. In this model system coarse-grained, statistical representations of collective dynamics are more predictive of the future state of the system than the constantly in-flux behavioural patterns at the individual level. The data suggest that individuals can perceive and use these representations for strategical decision-making. As an interaction history accumulates the coarse-grained representations consolidate. This constrains individual behaviour and provides the foundations for new levels of organization. The time-scales on which these representations change impact whether the consolidating higher-levels can be modified by individuals and collectively. The time-scales appear to be a function of the 'coarseness' of the representations and the character of the collective dynamics over which they are averages. The data suggest that an advantage of multiple timescales is that they allow social systems to balance tradeoffs between predictability and adaptability. I briefly discuss the implications of these findings for cognition, social niche construction and the evolution of new levels of organization in biological systems.     

Espaces de représentation et psychopathologie

A subjective world can be conceptualized as a “representational space” — that is, as a universe displayed from memory, in which the subject “is sailing”. Recent cognitive theory provides some conceptual tools for describing such a space in a relevant way for clinical purposes. The construction of a representational space is based on mental representations: (a) analogical representations, which display a content in working memory; (b) symbolic representations, which code and link up analogical representations to form a represented world. The dynamics of representations and affects is ruled by a principle of unification of representational space. The topology of a representational space depends on objective and subjective constraints which cause some “folds” and limit the display of such a space. The interaction between an event and a subjective memory can be analyzed within this framework, so that the concepts of trauma and defence processes can be defined. Clinical syndromes are defensive configurations that tend to close the representational space.     

Intensity generalization: physiology and modelling of a neglected topic

I briefly review empirical data about the generalization of acquired behaviour to novel stimuli, showing that variations in stimulus intensity affect behaviour differently from variations in characteristics such as, for instance, visual shape or sound frequency. I argue that such differences can be seen already in how the sense organs react to changes in intensity compared to changes in other stimulus characteristics. I then evaluate a number of models of generalization with respect to their ability to reproduce intensity generalization. I reach three main conclusions. First, realistic stimulus representations, based on knowledge of the sense organs, are necessary to account for intensity effects. Models employing stimulus representations too remote from the sense organs are unable to reproduce the data. Second, the intuitive notion that generalization is based on similarities between stimuli, possibly modelled as distances in an appropriate representation space, is difficult to reconcile with data about intensity generalization. Third, several simple models, in conjunction with realistic stimulus representations, can account for a wide array of generalization phenomena along both intensity and non-intensity stimulus dimensions. The paper also introduces concepts which may be generally useful to evaluate and compare different models of behaviour.     

Categorial Compositionality: A Category Theory Explanation for the Systematicity of Human Cognition

Classical and Connectionist theories of cognitive architecture seek to explain systematicity (i.e., the property of human cognition whereby cognitive capacity comes in groups of related behaviours) as a consequence of syntactically and functionally compositional representations, respectively. However, both theories depend on ad hoc assumptions to exclude specific instances of these forms of compositionality (e.g. grammars, networks) that do not account for systematicity. By analogy with the Ptolemaic (i.e. geocentric) theory of planetary motion, although either theory can be made to be consistent with the data, both nonetheless fail to fully explain it. Category theory, a branch of mathematics, provides an alternative explanation based on the formal concept of adjunction, which relates a pair of structure-preserving maps, called functors. A functor generalizes the notion of a map between representational states to include a map between state transformations (or processes). In a formal sense, systematicity is a necessary consequence of a higher-order theory of cognitive architecture, in contrast to the first-order theories derived from Classicism or Connectionism. Category theory offers a re-conceptualization for cognitive science, analogous to the one that Copernicus provided for astronomy, where representational states are no longer the center of the cognitive universe—replaced by the relationships between the maps that transform them.     

Associative learning and animal cognition

Associative learning plays a variety of roles in the study of animal cognition from a core theoretical component to a null hypothesis against which the contribution of cognitive processes is assessed. Two developments in contemporary associative learning have enhanced its relevance to animal cognition. The first concerns the role of associatively activated representations, whereas the second is the development of hybrid theories in which learning is determined by prediction errors, both directly and indirectly through associability processes. However, it remains unclear whether these developments allow associative theory to capture the psychological rationality of cognition. I argue that embodying associative processes within specific processing architectures provides mechanisms that can mediate psychological rationality and illustrate such embodiment by discussing the relationship between practical reasoning and the associative-cybernetic model of goal-directed action.     

Ecological frames of mind: the role of cognition in behavioral ecology.

Cognitive psychology is the study of how information, from the senses and from memory, is used in the production of behavior. Investigation of the specifics of behavioral adaptation has already led some behavioral ecologists into the domain of animal cognition. I make several arguments for the benefits and the necessity of a sophisticated assessment by ecologists of the cognitive aspects of behavioral adaptation. First, because cognition typically serves to produce adaptive behavior, cognitive structure and function should reflect ecological demands; studies of cognition in ecological contexts are opportunities to understand adaptation. Furthermore, constraints on cognitive properties may help determine how behavior meets the environment. Studies of spatial memory in food-caching corvids exemplify how cognitive aspects of behavior may both reflect and determine specifics of adaptation. Second, many models in behavioral ecology assume certain cognitive abilities, such as timing or counting. Cognitive theory and methodology should be used to determine whether animals possess these abilities. I have provided examples. Third, consideration of cognitive function can lead to original ideas about the details of behavioral adaptation. Without a thorough integration of cognitive psychology with behavioral ecology, our understanding of the relation between behavior and selective pressures will be compromised.     

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.     

A cortical-hippocampal system for declarative memory

Recent neurobiological studies have begun to reveal the cognitive and neural coding mechanisms that underlie declarative memory--our ability to recollect everyday events and factual knowledge. These studies indicate that the critical circuitry involves bidirectional connections between the neocortex, the parahippocampal region and the hippocampus. Each of these areas makes a unique contribution to memory processing. Widespread high-order neocortical areas provide dedicated processors for perceptual, motor or cognitive information that is influenced by other components of the system. The parahippocampal region mediates convergence of this information and extends the persistence of neocortical memory representations. The hippocampus encodes the sequences of places and events that compose episodic memories, and links them together through their common elements. Here I describe how these mechanisms work together to create and re-create fully networked representations of previous experiences and knowledge about the world.     

A neuroscientific grasp of concepts: from control to representation

Abstraction denotes the cognitive process by means of which general concepts are formed. The dominant view of abstraction considers it not only as a complex and sophisticated cognitive activity, but also as a distinctive hallmark of mankind. The distinctiveness of abstract thought has indeed been closely related to another feature peculiar to our species: language. Following this perspective, the possibility to entertain conceptual representations is thus precluded to animals devoid of full-blown language. I challenge this view and propose that the representational dynamic of the brain is conceivable as a type of self-organization, in which action plays a crucial part. My aim will be to investigate whether, and to what extent, conceptual knowledge can be attributed to non-linguistic animal species, with particular emphasis on nonhuman primates. I therefore introduce the notion of semantic content as a type of 'relational specification'. A review of recent neurophysiological data on the neural underpinnings of action end-states in the macaque monkey brain is presented. On the basis of this evidence, I propose that conceptual representations can be conceived as the expression of a coherent internal world model. This model decomposes the 'outer' space inhabited by things in a meaningful way only to the extent that it accords to biologically constrained, embodied invariance. Finally, I discuss how the 'comparative' neuroscientific approach to abstraction proposed here may shed some light on its nature and its evolutionary origin.     

Transitive inference in non-human animals: an empirical and theoretical analysis

Transitive inference has long been considered one of the hallmarks of human deductive reasoning. Recent reports of transitive-like behaviors in non-human animals have prompted a flourishing empirical and theoretical search for the mechanism(s) that may mediate this ability in non-humans. In this paper, I begin by describing the transitive inference tasks customarily used with non-human animals and then review the empirical findings. Transitive inference has been demonstrated in a wide variety of species, and the signature effects that usually accompany transitive inference in humans (the serial position effect and the symbolic distance effect) have also been found in non-humans. I then critically analyze the most prominent models of this ability in non-human animals. Some models are cognitive, proposing for instance that animals use the rules of formal logic or form mental representations of the premises to solve the task, others are based on associative mechanisms such as value transfer and reinforcement and non-reinforcement. Overall, I argue that the reinforcement-based models are in a much better empirical and theoretical position. Hence, transitive inference in non-human animals should be considered a property of reinforcement history rather than of inferential processes. I finalize by shedding some light on some promising lines of research.     

The evolution of costly displays,cooperation and religion: credibility enhancing displays and their implications for cultural evolution

This paper lays out an evolutionary theory for the cognitive foundations and cultural emergence of the extravagant displays (e.g., ritual mutilation, animal sacrifice and martyrdom) that have so tantalized social scientists, as well as more mundane actions that influence cultural learning and historical processes. In Part I, I use the logic of natural selection to build a theory for how and why seemingly costly displays influence the cognitive processes associated with cultural learning — why do “actions speak louder than words?” The core idea is that cultural learners can both avoid being manipulated by their models (those they are inclined to learn from) and more accurately assess their belief commitment by attending to displays or actions by the model that would seem costly to the model if he held beliefs different from those he expresses verbally. Part II examines the implications for cultural evolution of this learning bias in a simple evolutionary model. The model reveals the conditions under which this evolved bias can create stable sets of interlocking beliefs and practices, including quite costly practices. Part III explores how cultural evolution, driven by competition among groups or institutions stabilized at alternative sets of these interlocking belief-practice combinations, has led to the association of costly acts, often in the form of rituals, with deeper commitments to group beneficial ideologies, higher levels of cooperation within groups, and greater success in competition with other groups or institutions. I close by discussing the broader implications of these ideas for understanding various aspects of religious phenomena.     

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.     

From theory to data: Representing neurons in the 1940s

Recent literature on the role of pictorial representation in the life sciences has focused on the relationship between detailed representations of empirical data and more abstract, formal representations of theory. The standard argument is that in both a historical and epistemic sense, this relationship is a directional one: beginning with raw, unmediated images and moving towards diagrams that are more interpreted and more theoretically rich. Using the neural network diagrams of Warren McCulloch and Walter Pitts as a case study, I argue that while in the empirical sciences, pictorial representation tends to move from data to theory, in areas of the life sciences that are predominantly theoretical, when abstraction occurs at the outset, the relationship between detail and abstraction in pictorial representations can be of a different character.     

Categorial compositionality II: universal constructions and a general theory of (quasi-)systematicity in human cognition

A complete theory of cognitive architecture (i.e., the basic processes and modes of composition that together constitute cognitive behaviour) must explain the systematicity property--why our cognitive capacities are organized into particular groups of capacities, rather than some other, arbitrary collection. The classical account supposes: (1) syntactically compositional representations; and (2) processes that are sensitive to--compatible with--their structure. Classical compositionality, however, does not explain why these two components must be compatible; they are only compatible by the ad hoc assumption (convention) of employing the same mode of (concatenative) compositionality (e.g., prefix/postfix, where a relation symbol is always prepended/appended to the symbols for the related entities). Architectures employing mixed modes do not support systematicity. Recently, we proposed an alternative explanation without ad hoc assumptions, using category theory. Here, we extend our explanation to domains that are quasi-systematic (e.g., aspects of most languages), where the domain includes some but not all possible combinations of constituents. The central category-theoretic construct is an adjunction involving pullbacks, where the primary focus is on the relationship between processes modelled as functors, rather than the representations. A functor is a structure-preserving map (or construction, for our purposes). An adjunction guarantees that the only pairings of functors are the systematic ones. Thus, (quasi-)systematicity is a necessary consequence of a categorial cognitive architecture whose basic processes are functors that participate in adjunctions.