Investigating the concept of representation in the neural and psychological sciences

Are mental dysfunctions autonomous from brain dysfunctions? A perspective from the personal/subpersonal distinction

Despite many authors in psychiatry endorsing a naturalist view of the mind, many still consider that mental dysfunctions cannot be reduced to brain dysfunctions. This paper investigates the main reasons for this view. Some arguments rely on the analogy that the mind is like software while the brain is like hardware. The analogy suggests that just as software can malfunction independently of hardware malfunctions, similarly the mind can malfunction independently of any brain malfunction. This view has been critically examined in recent literature. However, other less discussed reasons suggest that mental dysfunctions cannot be reduced to brain dysfunctions because mental dysfunctions are appropriately ascribed at the level of intentional mental states, while brain dysfunctions are solely related to abnormalities in anatomy and physiological processes. This paper questions why such a view would be upheld. The discussion is framed within the interface problem in the philosophy of cognitive science, which concerns the relationship between personal and subpersonal levels of explanation. The paper examines the view that an autonomist perspective on the personal/subpersonal distinction could justify the separation of mental dysfunctions, described in intentional terms, from brain dysfunctions, described in anatomical or physiological terms. Ultimately, the paper argues that the autonomist view cannot be upheld in psychiatry and, consequently, does not provide a principled justification for rejecting the reduction of mental dysfunctions to brain dysfunctions.

Formalising the role of behaviour in neuroscience

We develop a mathematical approach to formally proving that certain neural computations and representations exist based on patterns observed in an organism's behaviour. To illustrate, we provide a simple set of conditions under which an ant's ability to determine how far it is from its nest would logically imply neural structures isomorphic to the natural numbers ℕ . We generalise these results to arbitrary behaviours and representations and show what mathematical characterisation of neural computation and representation is simplest while being maximally predictive of behaviour. We develop this framework in detail using a path integration example, where an organism's ability to search for its nest in the correct location implies representational structures isomorphic to two-dimensional coordinates under addition. We also study a system for processinga n b n strings common in comparative work. Our approach provides an objective way to determine what theory of a physical system is best, addressing a fundamental challenge in neuroscientific inference. These results motivate considering which neurobiological structures have the requisite formal structure and are otherwise physically plausible given relevant physical considerations such as generalisability, information density, thermodynamic stability and energetic cost.

Synergy Makes Direct Perception Inefficient

A typical claim in anti-representationalist approaches to cognition such as ecological psychology or radical embodied cognitive science is that ecological information is sufficient for guiding behavior. According to this view, affordances are immediately perceptually available to the agent (in the so-called "ambient energy array"), so sensory data does not require much further inner processing. As a consequence, mental representations are explanatorily idle: perception is immediate and direct. Here we offer one way to formalize this direct-perception claim and identify some important limits to it. We argue that the claim should be read as saying that successful behavior just implies picking out affordance-related information from the ambient energy array. By relying on the Partial Information Decomposition framework, and more concretely on its development of the notion of synergy, we show that in multimodal perception, where various energy arrays carry affordance-related information, the "just pick out affordance-related information" approach is very inefficient, as it is bound to miss all synergistic components. Efficient multimodal information combination requires transmitting sensory-specific (and not affordance-specific) information to wherever it is that the various information streams are combined. The upshot is that some amount of computation is necessary for efficient affordance reconstruction.

Embodied essentialism in the reconstruction of the animal sign in robot animal design

Robot animals are important for the interpretation of the biological world. In this paper, I show that specific design solutions for robot animal signs usually privilege the overall perception of biological systems (and animal signs in general) as machinic entities, which ignores the role of identity self-generation and sustainability, the hallmark of biological signs. Animal signs are semiotic systems that operate roughly on three subsystems: affordance mapping (related with niche construction and embeddedness), essence categorization, displaying co-enabling relations within the animal system, and sensorimotor autonomy. Of these interrelated systems, the first two are commonly associated with robot animal design and conceptualization, whereas the third one suffers from the unsolved AI design problem of engineering true context-dependent sensitivity. As a result, a semiotic blindness toward biological organisms has derived in dissociated perceptions of robot animals as objects that emulate their biological counterparts through both biomorphic affordance design (biomorphism) and bioinspired task completion. Robot signs are thus confined to the realm of techno myths populating experimental settings narratives, which contributes to a diminishing awareness of biological diversity and animal uniqueness for the construction and preservation of the Umwelt.

The Wire Is Not the Territory: Understanding Representational Drift in Olfaction With Dynamical Systems Theory

Representational drift is a phenomenon of increasing interest in the cognitive and neural sciences. While investigations are ongoing for other sensory cortices, recent research has demonstrated the pervasiveness in which it occurs in the piriform cortex for olfaction. This gradual weakening and shifting of stimulus-responsive cells has critical implications for sensory stimulus-response models and perceptual decision-making. While representational drift may complicate traditional sensory processing models, it could be seen as an advantage in olfaction, as animals live in environments with constantly changing and unpredictable chemical information. Non-topographical encoding in the olfactory system may aid in contextualizing reactions to promiscuous odor stimuli, facilitating adaptive animal behavior and survival. This article suggests that traditional models of stimulus-(neural) response mapping in olfaction may need to be reevaluated and instead motivates the use of dynamical systems theory as a methodology and conceptual framework.