In the context of the question of the emergence of mind in evolution the present paper argues that the concept of linguistic motivation, through the theory of embodiment in cognitive semantics, can be connected with the concept of motivation in ethology. This connection is established through Lakoff and Johnson’s embodied cognitive semantics on the one hand and on the other hand through the theory of biosemiotics. The biosemiotics used is based on C. S. Peirce´s semiotics and the work of J. von Uexkull. Motivation will in this context be understood as a decisive factor in determining which kind of interpretant a living system constructs when perturbed by a significant disturbance in its signification sphere. From this basis the concept of sign stimuli in Ethology, based on the concept of innate release response mechanism (IRM,) is paralleled with the concept of embodied metaphorical categorization based on the concept of idealized cognitive models (ICM). It is realized that we are dealing with motivation on two different levels, that of the linguistic and that of the perceptual-behavioral level. The connection is made through pragmatic language and sign theory viewing language as getting its meaning through language games integrated in cultural life forms and animals signs to get their meaning through sign games and natural life forms. Further connection is made through the common insight of the significant role of embodiment to create signification through the construction of a signification sphere. The later concept is a Peircian biosemiotic conceptualization of von Uexkull’s orginal Umwelt concept.

According to the sensorimotor approach, perception is a form of embodied know-how, constituted by lawful regularities in the sensorimotor flow or in sensorimotor contingencies (SMCs) in an active and situated agent. Despite the attention that this approach has attracted, there have been few attempts to define its core concepts formally. In this paper, we examine the idea of SMCs and argue that its use involves notions that need to be distinguished. We introduce four distinct kinds of SMCs, which we define operationally. These are the notions of sensorimotor environment (open-loop motor-induced sensory variations), sensorimotor habitat (closed-loop sensorimotor trajectories), sensorimotor coordination (reliable sensorimotor patterns playing a functional role), and sensorimotor strategy (normative organization of sensorimotor coordinations). We make use of a minimal dynamical model of visually guided categorization to test the explanatory value of the different kinds of SMCs. Finally, we discuss the impact of our definitions on the conceptual development and empirical as well as model-based testing of the claims of the sensorimotor approach.

Key words: embodied cognition, sensorimotor contingencies, dynamical systems, sensorimotor approach to perception, minimal cognition

This article focuses on an artificial life approach to some important problems in machine learning such as statistical discrimination, curve approximation, and pattern recognition. We describe a family of models, collectively referred to as semi-algebraic networks (SAN). These models are strongly inspired by two complementary lines of thought: the biological concept of autopoiesis and morphodynamical notions in mathematics. Mathematically defined as semi-algebraic sets, SANs involve geometric components that are submitted to two coupled processes: (a) the adjustment of the components (under the action of the learning examples), and (b) the regeneration of new components. Several examples of SANs are described, using different types of components. The geometric nature of SANs gives new possibilities for solving the bias/variance dilemma in discrimination or curve approximation problems. The question of building multilevel semi-algebraic networks is also addressed, as they are related to cognitive problems such as memory and morphological categorization. We describe an example of such multilevel models.

A goal of perception is to estimate true properties of the world. A goal of categorization is to classify its structure. Aeons of evolution have shaped our senses to this end. These three assumptions motivate much work on human perception. I here argue, on evolutionary grounds, that all three are false. Instead, our perceptions constitute a species-specific user interface that guides behavior in a niche. Just as the icons of a PC’s interface hide the complexity of the computer, so our perceptions usefully hide the complexity of the world, and guide adaptive behavior. This interface theory of perception offers a framework, motivated by evolution, to guide research in object categorization. This framework informs a new class of evolutionary games, called interface games, in which pithy perceptions often drive true perceptions to extinction.

We propose that selection favors nonveridical perceptions that are tuned to fitness. Current textbooks assert, to the contrary, that perception is useful because, in the normal case, it is veridical. Intuition, both lay and expert, clearly sides with the textbooks. We thus expected that some commentators would reject our proposal and provide counterarguments that could stimulate a productive debate. We are pleased that several commentators did indeed rise to the occasion and have argued against our proposal. We are also pleased that several others found our proposal worth exploring and have offered ways to test it, develop it, and link it more deeply to the history of ideas in the science and philosophy of perception. To both groups of commentators: thank you. Point and counterpoint, backed by data and theory, is the essence of science. We hope that the exchange recorded here will advance the scientific understanding of perception and its evolution. In what follows, we respond to the commentaries in alphabetical order.

Key words: bayesian inference, categorization, parameter estimation, perceptual categorization and identification, visual perception.

Perception is a product of evolution. Our perceptual systems, like our limbs and livers, have been shaped by natural selection. The effects of selection on perception can be studied using evolutionary games and genetic algorithms. To this end, we define and classify perceptual strategies and allow them to compete in evolutionary games in a variety of worlds with a variety of fitness functions. We find that veridical perceptions – strategies tuned to the true structure of the world – are routinely dominated by nonveridical strategies tuned to fitness. Veridical perceptions escape extinction only if fitness varies monotonically with truth. Thus, a perceptual strategy favored by selection is best thought of not as a window on truth but as akin to a windows interface of a PC. Just as the color and shape of an icon for a text file do not entail that the text file itself has a color or shape, so also our perceptions of space-time and objects do not entail (by the Invention of Space-Time Theorem) that objective reality has the structure of space-time and objects. An interface serves to guide useful actions, not to resemble truth. Indeed, an interface hides the truth; for someone editing a paper or photo, seeing transistors and firmware is an irrelevant hindrance. For the perceptions of H. sapiens, space-time is the desktop and physical objects are the icons. Our perceptions of space-time and objects have been shaped by natural selection to hide the truth and guide adaptive behaviors. Perception is an adaptive interface.

Key words: bayesian inference and parameter estimation, perceptual learning, perceptual categorization and identification, spatial cognition, evolution.

Open peer commentary on the article “An Analysis Procedure for the Micro-Phenomenological Interview” by Camila Valenzuela-Moguillansky & Alejandra Vásquez-Rosati. Abstract: The authors describe how the structures of experience unfold in the course of micro-phenomenological analysis step by step by suggesting iterative interrogation. The proposed abstraction operations ultimately deserve more thorough discussion concerning the categorization process, specifically as to the potential necessity of integrating more hierarchical steps.

Context: Ernst von Glasersfeld collaborated with the Italian Operational School from the early 1960s when the project on the mechanization of higher human activities began. Problem: To analyze the cognitive processes in terms of a mnemonic-attentional dynamic and to study every thought content in light of the interdependence between observer and observed. Method: The project comprised two research areas: the linguistic translation, in which von Glasersfeld participated; and the semantic analysis of words, in which I participated. The common basis was the analysis of attentional dynamisms. This allowed the syntactic complexity of a sentence to be transferred to the correlational structure of the thought. The semantic analysis, especially of the observational words, was based on the attentional dynamisms used for the categorization, perception, and representation processes. Results: The analysis of visual processes led to the “constitutive structures.” These structures allowed me to establish an operative didactic based on the awareness of mental operations. Implications: The comparison between von Glasersfeld’s and my experiences revealed the equivalence of some analyses, which was due to the common presumption that the experiential units depend on the operation performed by the perceiver.

Key words: constitutive structure, attention, vision, experience, cognitive processes, Silvio Ceccato

It is argued that a confusion among correspondence, consensus and coherence truth values is counterproductive, notably in the social field. Refinements of this rough categorization are discussed, as well as novel interpretations like dynamic coherence, akin to ‘hermeneutic’ truth regarded as ‘the iterative refinement of meaning’. It is maintained that dynamic coherence is fundamental to the other forms of truth prior to them, especially in specific domains of interpretation. Societal implications are considered insofar as a democratic organization is intended to exist, notably the difference between ‘unity’ and ‘uniformity’.

In this conceptual paper we propose a shift of perspective for parts of AI – from search to model construction. We claim that humans construct a model of a problem situation consisting of only a few, hierarchically structured elements. A model allows selective exploration of possible continuations and solutions, and for the flexible instantiation and adaptation of concepts. We underpin our claims with results from two protocol studies on problem-solving imagery and on the inductive learning of an algorithmic structure. We suggest that a fresh look into the small-scale construction processes humans execute would further ideas in categorization, analogy, concept formation, conceptual blending, and related fields of AI. Relevance: In accordance with von Glasersfeld’s postulate that knowledge is actively built up by the cognizing subject, the paper emphasizes the constructive nature of human intelligence. While problem space models in AI also partly reflect this, the metaphorical language of problem space search lends itself to epistemological misinterpretations.