Perspective piece on the concept of opioid addiction

This perspective piece on opioid addiction resulted from a workshop on enactive approaches to psychopathology our group organized last year. The science of addiction is in desperate need of a better theoretical framework, and we hope to be able to contribute to its development in the coming years.

The Clinical Concept of Opioid Addiction Since 1877: Still Wanting After All These Years

Christian G. Schütz, Susana Ramírez-Vizcaya, and Tom Froese

In 1877, the psychiatrist Edward Levinstein authored the first monograph on opioid addiction. The prevalence of opioid addiction prior to his publication had risen in several countries including England, France and Germany. He was the first to call it an illness, but doubted that it was a mental illness because the impairment of volition appeared to be restricted to opioid use: it was not pervasive, since it did not extend to other aspects of the individuals’ life. While there has been huge progress in understanding the underlying neurobiological mechanisms, there has been little progress in the clinical psychopathology of addiction and in understanding how it relates to these neurobiological mechanisms. A focus on cravings has limited the exploration of other important aspects such as anosognosia and addiction-related behaviors like smuggling opioids into treatment and supporting the continued provision of co-patients. These behaviors are usually considered secondary reactions, but in clinical practice they appear to be central to addiction, indicating that an improved understanding of the complexity of the disorder is needed. We propose to consider an approach that takes into account the embodied, situated, dynamic, and phenomenological aspects of mental processes. Addiction in this context can be conceptualized as a habit, understood as a distributed network of mental, behavioral, and social processes, which not only shapes the addict’s perceptions and actions, but also has a tendency to self-maintain. Such an approach may help to develop and integrate psychopathological and neurobiological research and practice of addictions.

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Editorial introduction to 4E cognition research in Mexico

With the aim of promoting and raising awareness about embodied, embedded, extended, and enactive cognition (4EC) research here in Mexico, Ximena and I organized a special issue on this theme.

In our editorial introduction we show that 4EC research in Mexico has fertile ground to build on, as there are several local traditions that are sympathetic to its core principles:

Grounding 4E Cognition in Mexico: introduction to special issue on spotlight on 4E Cognition research in Mexico

Ximena Gonzalez-Grandón and Tom Froese

Embodied, embedded, extended and enactive (4EC) perspectives on cognition have gained epistemic legitimacy during the last 25 years in the international arena. They have encouraged new ways to understand the mind. Mexico has not been an exception; rather, it has the potential to provide a fertile ground for the development of 4EC perspectives, as shown by the variety of contributions in this special issue. In this editorial introduction, we discuss recent concerns about a lack of coherence in the inter-relations between these perspectives, and we propose that it is more appropriate to view 4EC as an emerging pluralistic research tradition that shares crucial commitments. Furthermore, we show that this pluralistic tradition has been gaining ground in the specific research context of Mexico, because of the country’s distinctive historical, scientific and philosophical development. We finish by describing the promising research potential of the current heterogeneous explanations as evidenced by the papers in this issue.

Paper on how communal ritual makes social hierarchy more effective

Our contribution to the “Special Issue on Social Learning and Cultural Evolution with Cognitive Systems“, edited by Peter Andras and James Borg, has been accepted for publication in the journal Cognitive Systems Research.

Here is the title and abstract. Clicking the title will open a pre-print version.

Modeling collective rule at ancient Teotihuacan as a complex adaptive system: Communal ritual makes social hierarchy more effective

Tom Froese and Linda R. Manzanilla

Experts remain divided about the nature of the sociopolitical system of ancient Teotihuacan, which was one of the earliest and largest urban civilizations of the Americas. Excavations hoping to find compelling evidence of powerful rulers, such as a royal tomb, keep coming away empty-handed. But the alternative possibility of collective rule still remains poorly understood as well. Previously we used a computational model of this city’s hypothetical sociopolitical network to show that in principle collective rule based on communal ritual could be an effective strategy of ensuring widespread social coordination, as long as we assume that the network’s structure could be transformed via social learning and local leaders were not strongly subdivided. Here we extended this model to investigate whether increased social hierarchy could mitigate the negative effects of such strong divisions. We found a special synergy between social hierarchy and communal ritual: only their combination improved the extent of social coordination, whereas the introduction of centralization and top-down influence by themselves had no effect. This finding is consistent with portrayals of the Teotihuacan elite as religious specialists serving the public good, in particular by synchronizing the city’s ritual calendar with the rhythms of the stars.

New paper: Self-Optimization in Continuous-Time Recurrent Neural Networks

We were able to generalize the powerful self-optimization process to continuous-time neural networks, the class of neural networks most used by evolutionary robotics.

Self-Optimization in Continuous-Time Recurrent Neural Networks

Mario Zarco and Tom Froese

A recent advance in complex adaptive systems has revealed a new unsupervised learning technique called self-modeling or self-optimization. Basically, a complex network that can form an associative memory of the state configurations of the attractors on which it converges will optimize its structure: it will spontaneously generalize over these typically suboptimal attractors and thereby also reinforce more optimal attractors—even if these better solutions are normally so hard to find that they have never been previously visited. Ideally, after sufficient self-optimization the most optimal attractor dominates the state space, and the network will converge on it from any initial condition. This technique has been applied to social networks, gene regulatory networks, and neural networks, but its application to less restricted neural controllers, as typically used in evolutionary robotics, has not yet been attempted. Here we show for the first time that the self-optimization process can be implemented in a continuous-time recurrent neural network with asymmetrical connections. We discuss several open challenges that must still be addressed before this technique could be applied in actual robotic scenarios.

Ritual anti-structure as an alternate pathway to social complexity

I was invited to contribute a short opinion piece to the “In Conversation” section of Material Religion regarding recent insights of cognitive science.

Ritual anti-structure as an alternate pathway to social complexity? The case of ancient Teotihuacan, Central Mexico

Tom Froese

There is growing dissatisfaction with the traditional approach to the evolution of complex societies, which treated it principally as a sequence of transformations toward political centralization driven by the construction of increasingly vertical hierarchies by a powerful elite. In Mesoamerica the evidence is more consistent with a variety of alternative pathways to social complexity, and these are fruitfully approached from theoretical perspectives based on social heterarchy (Crumley 2003), collective action (Fargher et al. 2011), and, so I will suggest, ritual anti-structure (Turner 1969).

New article on entraining chaotic dynamics

We show that it is possible for a participant to interactively control a chaotic system by entraining with its dynamics, with the effect that they become more regular while the participant becomes more chaotic.

This has implications both for researchers interested in controlling chaotic systems, and also for practitioners in movement rehabilitation.

Entraining chaotic dynamics: A novel movement sonification paradigm could promote generalization

Dobromir Dotov and Tom Froese

Tasks encountered in daily living may have instabilities and more dimensions than are sampled by the senses such as when carrying a cup of coffee and only the surface motion and overall momentum are sensed, not the fluid dynamics. Anticipating non-periodic dynamics is difficult but not impossible because mutual coordination allows for chaotic processes to synchronize to each other and become periodic. A chaotic oscillator with random period and amplitude affords being stabilized onto a periodic trajectory by a weak input if the driver incorporates information about the oscillator. We studied synchronization with predictable and unpredictable stimuli where the unpredictable stimuli could be non-interactive or interactive. The latter condition required learning to control a chaotic system. We expected better overall performance with the predictable but more learning and generalization with unpredictable interactive stimuli. Participants practiced an auditory-motor synchronization task by matching their sonified hand movements to sonified tutors: the Non-Interactive Predictable tutor (NI-P) was a sinusoid, the Non-Interactive Unpredictable (NI-U) was a chaotic system, the Interactive Unpredictable (I-U) was the same chaotic system with an added weak input from the participant’s movement. Different pre/post-practice stimuli evaluated generalization. Quick improvement was seen in NI-P. Synchronization, dynamic similarity, and causal interaction increased with practice in I-U but not in NI-U. Generalization was seen for few pre-post stimuli in NI-P, none in NI-U, and most stimuli in I-U. Synchronization with novel chaotic dynamics is challenging but mutual interaction enables the behavioral control of such dynamics and the practice of complex motor skills.

The standard genetic code can evolve from a two-letter GC code

The model of an iterated learning approach the origins of the genetic code inspired this related hypothesis about a simplified precursor to the standard four-letter genetic code, which will be released in Origins of Life and Evolution of Biospheres:

The standard genetic code can evolve from a two-letter GC code without information loss or costly reassignments

Alejandro Frank and Tom Froese

It is widely agreed that the standard genetic code must have been preceded by a simpler code that encoded fewer amino acids. How this simpler code could have expanded into the standard genetic code is not well understood because most changes to the code are costly. Taking inspiration from the recently synthesized six-letter code, we propose a novel hypothesis: the initial genetic code consisted of only two letters, G and C, and then expanded the number of available codons via the introduction of an additional pair of letters, A and U. Various lines of evidence, including the relative prebiotic abundance of the earliest assigned amino acids, the balance of their hydrophobicity, and the higher GC content in genome coding regions, indicate that the original two nucleotides were indeed G and C. This process of code expansion probably started with the third base, continued with the second base, and ended up as the standard genetic code when the second pair of letters was introduced into the first base. The proposed process is consistent with the available empirical evidence, and it uniquely avoids the problem of costly code changes by positing instead that the code expanded its capacity via the creation of new codons with extra letters.

New paper on iterated learning at the origins of life

Jorge, Nathaniel and I have published an extension of our iterated learning approach to the origins of the genetic code in the Proceedings of the Artificial Life Conference 2018. We unexpectedly found that the most likely sequences in which amino acids get incorporated into the emerging genetic codes in our simulation model exhibit a remarkable overlap with the sequence predicted in the literature based on empirical considerations.

We will present this work at the ALIFE conference in Tokyo as part of the special session on “Hybrid Life: Approaches to integrate biological, artificial and cognitive systems”.

An iterated learning approach to the origins of the standard genetic code can help to explain its sequence of amino acid assignments

Tom Froese, Jorge I. Campos, and Nathaniel Virgo

Artificial life has been developing a behavior-based perspective on the origins of life, which emphasizes the adaptive potential of agent-environment interaction even at that initial stage. So far this perspective has been closely aligned to metabolism-first theories, while most researchers who study life’s origins tend to assign an essential role to RNA. An outstanding challenge is to show that a behavior-based perspective can also address open questions related to the genetic system. Accordingly, we have recently applied this perspective to one of science’s most fascinating mysteries: the origins of the standard genetic code. We modeled horizontal transfer of cellular components in a population of protocells using an iterated learning approach and found that it can account for the emergence of several key properties of the standard code. Here we further investigated the diachronic emergence of artificial codes and discovered that the model’s most frequent sequence of amino acid assignments overlaps significantly with the predictions in the literature. Our explorations of the factors that favor early incorporation into an emerging artificial code revealed two aspects: an amino acid’s relative probability of horizontal transfer, and its relative ease of discriminability in chemical space.

Figure 2

Illustration of the architecture of the genetic system of one of our hypothetical protocells.

Psychological study on chaos control

Dobri Dotov and I have published an extended abstract in the Proceedings of the Artificial Life Conference 2018 about the study that he realized at UNAM. We suggest that the results have implications for how we should think about how to stabilize the behavior of complex adaptive systems with which we can interact.

We will present this work at the ALIFE conference in Tokyo as part of the special session on “ALife and Society: Transcending the artificial-natural divide”.

Mutual synchronization and control between artificial chaotic system and human

Dobromir Dotov and Tom Froese

Dexterous assistive devices constitute one of the frontiers for hybrid human-machine systems. Manipulating unstable systems requires task-specific anticipatory dynamics. Learning this dynamics is more difficult when tasks, such as carrying liquid or riding a horse, produce unpredictable, irregular patterns of feedback and have hidden dimensions not projected as sensory feedback. We addressed the issue of coordination with complex systems producing irregular behaviour, with the assumption that mutual coordination allows for non-periodic processes to synchronize and in doing so to become regular. Chaos control gives formal expression to this: chaos can be stabilized onto periodic trajectories provided that the structure of the driving input takes into account the causal structure of the controlled system.

Can we learn chaos control in a sensorimotor task? Three groups practiced an auditory-motor synchronization task by matching their continuously sonified hand movements to sonified tutors: a sinusoid served as a Non-Interactive Predictable tutor (NIP), a chaotic system stood for a Non-Interactive Unpredictable tutor (NI-U), and the same system weakly driven by the participant’s movement stood for an Interactive Unpredictable tutor (I-U). We found that synchronization, dynamic similarity, and causal interaction increased with practice in I-U. Our findings have implications for current efforts to find more adequate ways of controlling complex adaptive systems.

UNISON

A dynamical approach to the phenomenology of body memory

A special issue of the Journal of Consciousness Studies is in the final stages of preparation, following on from last year’s conference on the “Formation of Embodied Memory” in Heidelberg.

I teamed up with my old colleague Eduardo to put together the following article:

A dynamical approach to the phenomenology of body memory: Past interactions can shape present capacities without neuroplasticity

Tom Froese and Eduardo J. Izquierdo

Body memory comprises the acquired dispositions that constitute an individual’s present capacities and experiences. Phenomenological accounts of body memory describe its effects using dynamical metaphors: it is conceived of as curvatures in an agent-environment relational field, leading to attracting and repelling forces that shape ongoing sensorimotor interaction. This relational perspective stands in tension with traditional cognitive science, which conceives of the underlying basis of memory in representational-internal terms: it is the encoding and storing of informational content via structural changes inside the brain. We propose that this tension can be resolved by replacing the traditional approach with the dynamical approach to cognitive science. Specifically, we present three of our simulation models of embodied cognition that can help us to rethink the basis of several types of body memory. The upshot is that, at least in principle, there is no need to explain their basis in terms of content or to restrict their basis to neuroplasticity alone. Instead these models support the perspective developed by phenomenology: body memory is a relational property of a whole brain-body-environment system that emerges out of its history of interactions.

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