Metaphorical motion in mathematical reasoning: further evidence for pre-motor implementation of structure mapping in abstract domains

Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing Cognition in Diverse Embodiments

One of the most salient features of life is its capacity to handle novelty and namely to thrive and adapt to new circumstances and changes in both the environment and internal components. An understanding of this capacity is central to several fields: the evolution of form and function, the design of effective strategies for biomedicine, and the creation of novel life forms via chimeric and bioengineering technologies. Here, we review instructive examples of living organisms solving diverse problems and propose competent navigation in arbitrary spaces as an invariant for thinking about the scaling of cognition during evolution. We argue that our innate capacity to recognize agency and intelligence in unfamiliar guises lags far behind our ability to detect it in familiar behavioral contexts. The multi-scale competency of life is essential to adaptive function, potentiating evolution and providing strategies for top-down control (not micromanagement) to address complex disease and injury. We propose an observer-focused viewpoint that is agnostic about scale and implementation, illustrating how evolution pivoted similar strategies to explore and exploit metabolic, transcriptional, morphological, and finally 3D motion spaces. By generalizing the concept of behavior, we gain novel perspectives on evolution, strategies for system-level biomedical interventions, and the construction of bioengineered intelligences. This framework is a first step toward relating to intelligence in highly unfamiliar embodiments, which will be essential for progress in artificial intelligence and regenerative medicine and for thriving in a world increasingly populated by synthetic, bio-robotic, and hybrid beings.

How Do Living Systems Create Meaning?

Meaning has traditionally been regarded as a problem for philosophers and psychologists. Advances in cognitive science since the early 1960s, however, broadened discussions of meaning, or more technically, the semantics of perceptions, representations, and/or actions, into biology and computer science. Here, we review the notion of “meaning” as it applies to living systems, and argue that the question of how living systems create meaning unifies the biological and cognitive sciences across both organizational and temporal scales.

Do Process-1 simulations generate the epistemic feelings that drive Process-2 decision making?

We apply previously developed Chu space and Channel Theory methods, focusing on the construction of Cone-Cocone Diagrams (CCCDs), to study the role of epistemic feelings, particularly feelings of confidence, in dual process models of problem solving. We specifically consider "Bayesian brain" models of probabilistic inference within a global neuronal workspace architecture. We develop a formal representation of Process-1 problem solving in which a solution is reached if and only if a CCCD is completed. We show that in this representation, Process-2 problem solving can be represented as multiply iterated Process-1 problem solving and has the same formal solution conditions. We then model the generation of explicit, reportable subjective probabilities from implicit, experienced confidence as a simulation-based, reverse engineering process and show that this process can also be modeled as a CCCD construction.

Sciences of Observation

Multiple sciences have converged, in the past two decades, on a hitherto mostly unremarked question: what is observation? Here, I examine this evolution, focusing on three sciences: physics, especially quantum information theory, developmental biology, especially its molecular and “evo-devo” branches, and cognitive science, especially perceptual psychology and robotics. I trace the history of this question to the late 19th century, and through the conceptual revolutions of the 20th century. I show how the increasing interdisciplinary focus on the process of extracting information from an environment provides an opportunity for conceptual unification, and sketch an outline of what such a unification might look like.

Suppression of somatosensory stimuli during motor planning may explain levels of balance and mobility after stroke

The ability to actively suppress, or gate, irrelevant sensory information is required for safe and efficient walking in sensory-rich environments. Both motor attention and motor planning alter somatosensory evoked potentials (SEPs) in healthy adults. This study's aim was to examine the effect of motor attention on processing of irrelevant somatosensory information during plantar flexion motor planning after stroke. Thirteen healthy older adults and 11 individuals with stroke participated. Irrelevant tibial nerve stimulation was delivered while SEPs were recorded over Cz, overlaying the leg portion of the sensorimotor cortex at the vertex of the head. Three conditions were tested in both legs: (1) Rest, (2) Attend To, and (3) Attend Away from the stimulated limb. In conditions 2 and 3, relevant vibration cued voluntary plantar flexion movements of the stimulated (Attend To) or non-stimulated (Attend Away) leg. SEP amplitudes were averaged during motor planning per condition. Individuals with stroke did not show attention-mediated gating of the N40 component associated with irrelevant somatosensory information during motor planning. It may be that dysfunction in pathways connecting to area 3b explains the lack of attention-mediated gating of the N40. Also, attention-mediated gating during motor planning explained significant and unique variance in a measure of community balance and mobility combined with response time. Thus, the ability to gate irrelevant somatosensory information appears important for stepping in both older adults and after stroke. Our data suggest that therapies that direct motor attention could positively impact walking after stroke.

A structural motor network correlates with motor function and not impairment post stroke

Combining structural and functional magnetic resonance imaging may provide insight into how residual motor networks contribute to motor outcomes post-stroke. The purpose of this study was to examine whether a structural motor network (SMN), generated with fMRI guided diffusion-based tractography, relates to motor function post-stroke. Twenty-seven individuals with mild to moderate upper limb impairment post stroke underwent diffusion magnetic resonance imaging. A bilateral motor network mask guided white matter tractography for each participant. Fractional anisotrophy (FA) was calculated for the SMN and corticospinal tracts (CST). The Wolf Motor Function Test (WMFT) rate and Fugl-Meyer Upper Limb (FM) tests characterized arm function and impairment respectively. The SMN and ipsilesional CST together explained approximately 35% of the variance in paretic arm function (WMFT-rate p=0.006). This study demonstrates that a broader motor network, like the SMN, is functionally meaningful. Given that the motor network is widely distributed, the proposed SMN warrants further investigation as a potential adjunct biomarker to characterize recovery potential after stroke.

Motion, identity and the bias toward agency

The well-documented human bias toward agency as a cause and therefore an explanation of observed events is typically attributed to evolutionary selection for a "social brain". Based on a review of developmental and adult behavioral and neurocognitive data, it is argued that the bias toward agency is a result of the default human solution, developed during infancy, to the computational requirements of object re-identification over gaps in observation of more than a few seconds. If this model is correct, overriding the bias toward agency to construct mechanistic explanations of observed events requires structure-mapping inferences, implemented by the pre-motor action planning system, that replace agents with mechanisms as causes of unobserved changes in contextual or featural properties of objects. Experiments that would test this model are discussed.