Cognitive Biology: Dealing with Information from Bacteria to Minds

Providing a new conceptual scaffold for further research in biology and cognition, this text introduces the new field of cognitive biology, treating developing organisms as information processors which use cognition to control and modify their environments

Natural Language Syntax Complies with the Free-Energy Principle

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating syntactic objects. We argue that recently proposed principles of economy in language design - such as "minimal search" criteria from theoretical syntax - adhere to the FEP. This affords a greater degree of explanatory power to the FEP - with respect to higher language functions - and offers linguistics a grounding in first principles with respect to computability. We show how both tree-geometric depth and a Kolmogorov complexity estimate (recruiting a Lempel-Ziv compression algorithm) can be used to accurately predict legal operations on syntactic workspaces, directly in line with formulations of variational free energy minimization. This is used to motivate a general principle of language design that we term Turing-Chomsky Compression (TCC). We use TCC to align concerns of linguists with the normative account of self-organization furnished by the FEP, by marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference

Apperceptive patterning: Artefaction, extensional beliefs and cognitive scaffolding

In “Psychopower and Ordinary Madness” my ambition, as it relates to Bernard Stiegler’s recent literature, was twofold: 1) critiquing Stiegler’s work on exosomatization and artefactual posthumanism—or, more specifically, nonhumanism—to problematize approaches to media archaeology that rely upon technical exteriorization; 2) challenging how Stiegler engages with Giuseppe Longo and Francis Bailly’s conception of negative entropy. These efforts were directed by a prevalent techno-cultural qualifier: the rise of Synthetic Intelligence (including neural nets, deep learning, predictive processing and Bayesian models of cognition). This paper continues this project but first directs a critical analytic lens at the Derridean practice of the ontologization of grammatization from which Stiegler emerges while also distinguishing how metalanguages operate in relation to object-oriented environmental interaction by way of inferentialism. Stalking continental (Kapp, Simondon, Leroi-Gourhan, etc.) and analytic traditions (e.g., Carnap, Chalmers, Clark, Sutton, Novaes, etc.), we move from artefacts to AI and Predictive Processing so as to link theories related to technicity with philosophy of mind. Simultaneously drawing forth Robert Brandom’s conceptualization of the roles that commitments play in retrospectively reconstructing the social experiences that lead to our endorsement(s) of norms, we compliment this account with Reza Negarestani’s deprivatized account of intelligence while analyzing the equipollent role between language and media (both digital and analog)

Continuity in categorization and theoretical implications

sorry for the silly error. hopefully this'll do the trick. --rickTraditional theories of cognition assume that motor action is executed in an all-or-none fashion, and has little importance for understanding cognitive representation and processing. A series of experiments and simulations presented here challenges this assumption. A relatively higher-order cognitive process, categorization, is shown to have graded effects that are reflected in manual motor output, measured through streaming x-y coordinates from mouse trajectories. Two simulations show that these effects are likely generated from a system in which cognition and action interact fluidly. Finally, theoretical implications of these experiments are drawn out. Symbolic dynamics is introduced, a potential means for reconciling both traditional and continuous accounts of cognition. A broad philosophical discussion follows, in which an integrative and pluralistic approach to cognition is proposed and briefly discussed

How hand movements and speech tip the balance in cognitive development:A story about children, complexity, coordination, and affordances

When someone asks us to explain something, such as how a lever or balance scale works, we spontaneously move our hands and gesture. This is also true for children. Furthermore, children use their hands to discover things and to find out how something works. Previous research has shown that children’s hand movements hereby are ahead of speech, and play a leading role in cognitive development. Explanations for this assumed that cognitive understanding takes place in one’s head, and that hand movements and speech (only) reflect this. However, cognitive understanding arises and consists of the constant interplay between (hand) movements and speech, and someone’s physical and social environment. The physical environment includes task properties, for example, and the social environment includes other people. Therefore, I focused on this constant interplay between hand movements, speech, and the environment, to better understand hand movements’ role in cognitive development. Using science and technology tasks, we found that children’s speech affects hand movements more than the other way around. During difficult tasks the coupling between hand movements and speech becomes even stronger than in easy tasks. Interim changes in task properties differently affect hand movements and speech. Collaborating children coordinate their hand movements and speech, and even their head movements together. The coupling between hand movements and speech is related to age and (school) performance. It is important that teachers attend to children’s hand movements and speech, and arrange their lessons and classrooms such that there is room for both

Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

The Semiotic Nature of Power in Social-Ecological Systems

abstract: Anderies (2015); Anderies et al. (2016), informed by Ostrom (2005), aim to employ robust feedback control models of social-ecological systems (SESs), to inform policy and the design of institutions guiding resilient resource use. Cote and Nightingale (2012) note that the main assumptions of resilience research downplay culture and social power. Addressing the epistemic gap between positivism and interpretation (Rosenberg 2016), this dissertation argues that power and culture indeed are of primary interest in SES research. Human use of symbols is seen as an evolved semiotic capacity. First, representation is argued to arise as matter achieves semiotic closure (Pattee 1969; Rocha 2001) at the onset of natural selection. Guided by models by Kauffman (1993), the evolution of a symbolic code in genes is examined, and thereon the origin of representations other than genetic in evolutionary transitions (Maynard Smith and Szathmáry 1995; Beach 2003). Human symbolic interaction is proposed as one that can support its own evolutionary dynamics. The model offered for wider dynamics in society are “flywheels,” mutually reinforcing networks of relations. They arise as interactions in a domain of social activity intensify, e.g. due to interplay of infrastructures, mediating built, social, and ecological affordances (An- deries et al. 2016). Flywheels manifest as entities facilitated by the simplified interactions (e.g. organizations) and as cycles maintaining the infrastructures (e.g. supply chains). They manifest internal specialization as well as distributed intention, and so can favor certain groups’ interests, and reinforce cultural blind spots to social exclusion (Mills 2007). The perspective is applied to research of resilience in SESs, considering flywheels a semiotic extension of feedback control. Closer attention to representations of potentially excluded groups is justified on epistemic in addition to ethical grounds, as patterns in cul- tural text and social relations reflect the functioning of wider social processes. Participatory methods are suggested to aid in building capacity for institutional learning.Dissertation/ThesisDoctoral Dissertation Anthropology 201