The architecture of representation

Causal Reasoning and Event Cognition as Evolutionary Determinants of Language Structure

The aim of this article is to provide an evolutionarily grounded explanation of central aspects of the structure of language. It begins with an account of the evolution of human causal reasoning. A comparison between humans and non-human primates suggests that human causal cognition is based on reasoning about the underlying forces that are involved in events, while other primates hardly understand external forces. This is illustrated by an analysis of the causal cognition required for early hominin tool use. Second, the thinking concerning forces in causation is used to motivate a model of human event cognition. A mental representation of an event contains two vectors representing a cause as well as a result but also entities such as agents, patients, instruments and locations. The fundamental connection between event representations and language is that declarative sentences express events (or states). The event structure also explains why sentences are constituted of noun phrases and verb phrases. Finally, the components of the event representation show up in language, where causes and effects are expressed by verbs, agents and patients by nouns (modified by adjectives), locations by prepositions, etc. Thus, the evolution of the complexity of mental event representations also provides insight into the evolution of the structure of language.

Arthropod Intelligence? The Case for Portia

Macphail’s “null hypothesis,” that there are no differences in intelligence, qualitative, or quantitative, between non-human vertebrates has been controversial. This controversy can be useful if it encourages interest in acquiring a detailed understanding of how non-human animals express flexible problem-solving capacity (“intelligence”), but limiting the discussion to vertebrates is too arbitrary. As an example, we focus here on Portia, a spider with an especially intricate predatory strategy and a preference for other spiders as prey. We review research on pre-planned detours, expectancy violation, and a capacity to solve confinement problems where, in each of these three contexts, there is experimental evidence of innate cognitive capacities and reliance on internal representation. These cognitive capacities are related to, but not identical to, intelligence. When discussing intelligence, as when discussing cognition, it is more useful to envisage a continuum instead of something that is simply present or not; in other words, a continuum pertaining to flexible problem-solving capacity for “intelligence” and a continuum pertaining to reliance on internal representation for “cognition.” When envisaging a continuum pertaining to intelligence, Daniel Dennett’s notion of four Creatures (Darwinian, Skinnerian, Popperian, and Gregorian) is of interest, with the distinction between Skinnerian and Popperian Creatures being especially relevant when considering Portia. When we consider these distinctions, a case can be made for Portia being a Popperian Creature. Like Skinnerian Creatures, Popperian Creatures express flexible problem solving capacity, but the manner in which this capacity is expressed by Popperian Creatures is more distinctively cognitive.

A Detour Task in Four Species of Fishes

Four species of fish (Danio rerio, Xenotoca eiseni, Carassius auratus, and Pterophyllum scalare) were tested in a detour task requiring them to temporarily abandon the view of the goal-object (a group of conspecifics) to circumvent an obstacle. Fishes were placed in the middle of a corridor, at the end of which there was an opaque wall with a small window through which the goal was visible. Midline along the corridor two symmetrical apertures allowed animals to access two compartments for each aperture. After passing the aperture, fishes showed searching behavior in the two correct compartments close to the goal, appearing able to localize it, although they had to temporarily move away from the object's view. Here we provide the first evidence that fishes can solve such a detour task and therefore seem able to represent the "permanence in existence" of objects, which continue to exist even if they are not momentarily visible.

Structural representations: causally relevant and different from detectors

This paper centers around the notion that internal, mental representations are grounded in structural similarity, i.e., that they are so-called S-representations. We show how S-representations may be causally relevant and argue that they are distinct from mere detectors. First, using the neomechanist theory of explanation and the interventionist account of causal relevance, we provide a precise interpretation of the claim that in S-representations, structural similarity serves as a "fuel of success", i.e., a relation that is exploitable for the representation using system. Then, we discuss crucial differences between S-representations and indicators or detectors, showing that-contrary to claims made in the literature-there is an important theoretical distinction to be drawn between the two.

The execution of planned detours by spider-eating predators

Many spiders from the salticid subfamily Spartaeinae specialize at preying on other spiders and they adopt complex strategies when targeting these dangerous prey. We tested 15 of these spider-eating spartaeine species for the capacity to plan detours ahead of time. Each trial began with the test subject on top of a tower from which it could view two boxes: one containing prey and the other not containing prey. The distance between the tower and the boxes was too far to reach by leaping and the tower sat on a platform surrounded by water. As the species studied are known to avoid water, the only way they could reach the prey without getting wet was by taking one of two circuitous walkways from the platform: one leading to the prey ('correct') and one not leading to the prey ('incorrect'). After leaving the tower, the test subject could not see the prey and sometimes it had to walk past the incorrect walkway before reaching the correct walkway. Yet all 15 species chose the correct walkway significantly more often than the incorrect walkway. We propose that these findings exemplify genuine cognition based on representation.

Human creativity, evolutionary algorithms, and predictive representations: The mechanics of thought trials

Creative thinking is arguably the pinnacle of cerebral functionality. Like no other mental faculty, it has been omnipotent in transforming human civilizations. Probing the neural basis of this most extraordinary capacity, however, has been doggedly frustrated. Despite a flurry of activity in cognitive neuroscience, recent reviews have shown that there is no coherent picture emerging from the neuroimaging work. Based on this, we take a different route and apply two well established paradigms to the problem. First is the evolutionary framework that, despite being part and parcel of creativity research, has no informed experimental work in cognitive neuroscience. Second is the emerging prediction framework that recognizes predictive representations as an integrating principle of all cognition. We show here how the prediction imperative revealingly synthesizes a host of new insights into the way brains process variation-selection thought trials and present a new neural mechanism for the partial sightedness in human creativity. Our ability to run offline simulations of expected future environments and action outcomes can account for some of the characteristic properties of cultural evolutionary algorithms running in brains, such as degrees of sightedness, the formation of scaffolds to jump over unviable intermediate forms, or how fitness criteria are set for a selection process that is necessarily hypothetical. Prospective processing in the brain also sheds light on how human creating and designing - as opposed to biological creativity - can be accompanied by intentions and foresight. This paper raises questions about the nature of creative thought that, as far as we know, have never been asked before.

Constructing a philosophy of science of cognitive science

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information-processing mechanisms, cognitive science needs an account of how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher-level mechanisms from their components and situating them in their environments.

Entrainment and motor emulation approaches to joint action: Alternatives or complementary approaches?

Joint actions, such as music and dance, rely crucially on the ability of two, or more, agents to align their actions with great temporal precision. Within the literature that seeks to explain how this action alignment is possible, two broad approaches have appeared. The first, what we term the entrainment approach, has sought to explain these alignment phenomena in terms of the behavioral dynamics of the system of two agents. The second, what we term the emulator approach, has sought to explain these alignment phenomena in terms of mechanisms, such as forward and inverse models, that are implemented in the brain. They have often been pitched as alternative explanations of the same phenomena; however, we argue that this view is mistaken, because, as we show, these two approaches are engaged in distinct, and not mutually exclusive, explanatory tasks. While the entrainment approach seeks to uncover the general laws that govern behavior the emulator approach seeks to uncover mechanisms. We argue that is possible to do both and that the entrainment approach must pay greater attention to the mechanisms that support the behavioral dynamics of interest. In short, the entrainment approach must be transformed into a neuroentrainment approach by adopting a mechanistic view of explanation and by seeking mechanisms that are implemented in the brain.

The effect of movement kinematics on predicting the timing of observed actions

The ability to predict the actions of other agents is vital for joint action tasks. Recent theory suggests that action prediction relies on an emulator system that permits observers to use a model of their own movement kinematics to predict the actions of other agents. If this is the case, then people should be more accurate at generating predictions about actions that are similar to their own. We tested this hypothesis in two experiments in which participants were required to predict the occurrence and timing of particular critical points in an observed action. In Experiment 1, we employed a self/other prediction paradigm in which prediction accuracy for recordings of self-generated movements was compared with prediction accuracy for recordings of other-generated movements. As expected, prediction was more accurate for recordings of self-generated actions because in this case the movement kinematics of the observer and observed stimuli are maximally similar. In Experiment 1, people were able to produce actions at their own tempo and, therefore, the results might be explained in terms of self-similarity in action production tempo rather than in terms of movement kinematics. To control for this possibility in Experiment 2, we compared prediction accuracy for stimuli that were matched in tempo but differed only in terms of kinematics. The results showed that participants were more accurate when predicting actions with a human kinematic profile than tempo-matched stimuli that moved with non-human kinematics. Finally, in Experiment 3, we confirmed that the results of Experiment 2 cannot be explained by human-like stimuli containing a slowing down phase before the critical points. Taken together, these findings provide further support for the role of motor emulation in action prediction, and they suggest that the action prediction mechanism produces output that is available rapidly and available to drive action control suggesting that it can plausibly support joint action coordination.

Empirical perspectives from the self-model theory of subjectivity: a brief summary with examples

A concise sketch of the self-model theory of subjectivity (SMT; Metzinger, 2003a), aimed at empirical researchers. Discussion of some candidate mechanisms by which self-awareness could appear in a physically realized information-processing system like the brain, using empirical examples from various scientific disciplines. The paper introduces two core-concepts, the "phenomenal self-model" (PSM) and the "phenomenal model of the intentionality relation" (PMIR), developing a representationalist analysis of the conscious self and the emergence of a first-person perspective.

Self-Representation in Nervous Systems

The brain's earliest self-representational capacities arose as evolution found neural network solutions for coordinating and regulating inner-body signals, thereby improving behavioral strategies. Additional flexibility in organizing coherent behavioral options emerges from neural models that represent some of the brain's inner states as states of its body, while representing other signals as perceptions of the external world. Brains manipulate inner models to predict the distinct consequences in the external world of distinct behavioral options. The self thus turns out to be identifiable not with a nonphysical soul, but rather with a set of representational capacities of the physical brain.

Self-representation in nervous systems

The brain's earliest self-representational capacities arose as evolution found neural network solutions for coordinating and regulating inner-body signals, thereby improving behavioral strategies. Additional flexibility in organizing coherent behavioral options emerges from neural models that represent some of the brain's inner states as states of its body, while representing other signals as perceptions of the external world. Brains manipulate inner models to predict the distinct consequences in the external world of distinct behavioral options. The self thus turns out to be identifiable not with a nonphysical soul, but rather with a set of representational capacities of the physical brain.