In psychology, as in physics, principles approach universality only if formulated at a sufficiently abstract level. Among the most fundamental principles are those of generalization and inductive inference and those of perceptual and mental transformation. With respect to their appropriate abstract representational space, the former principles are well formulated as (Bayesian) integration over suitable (e.g., connected) subsets of points in the space, and the latter as geodesic (hence, least-time) paths between points in the space. Critics sometimes insufficiently appreciate the following: (a) Generality requires such abstraction. (b) Perceptual principles are not themselves given in sensory input. (c) Principles of learning are not themselves learned. (d) Though all such principles are somehow instantiated in the brain, their ultimate, nonarbitrary source must be sought in the regularities of the world – including those reflecting abstract mathematical principles (e.g., of group theory and symmetry). New light may thus be shed on the cognitive grounds of science and ethics.

How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which simulation models can differ are defined and distinctions between them discussed:

1. Relevance: whether the model tests and generates hypotheses applicable to biology.

2. Level: the elemental units of the model in the hierarchy from atoms to societies.

3. Generality: the range of biological systems the model can represent.

4. Abstraction: the complexity, relative to the target, or amount of detail included in the model.

5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour.

6. Performance match: to what extent the model behaviour matches the target behaviour.

7. Medium: the physical basis by which the model is implemented.

No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments.

Using the concepts of chaotic dynamical systems, we present an interpretation of dynamic neural activity found in cortical and subcortical areas. The discovery of chaotic itinerancy in high-dimensional dynamical systems with and without a noise term has motivated a new interpretation of this dynamic neural activity, cast in terms of the high-dimensional transitory dynamics among “exotic” attractors. This interpretation is quite different from the conventional one, cast in terms of simple behavior on low-dimensional attractors. Skarda and Freeman (1987) presented evidence in support of the conclusion that animals cannot memorize odor without chaotic activity of neuron populations. Following their work, we study the role of chaotic dynamics in biological information processing, perception, and memory. We propose a new coding scheme of information in chaos-driven contracting systems we refer to as Cantor coding. Since these systems are found in the hippocampal formation and also in the olfactory system, the proposed coding scheme should be of biological significance. Based on these intensive studies, a hypothesis regarding the formation of episodic memory is given.

This target article is concerned with the implications of the surprisingly different experimental practices in economics and in areas of psychology relevant to both economists and psychologists, such as behavioral decision making. We consider four features of experimentation in economics, namely, script enactment, repeated trials, performance-based monetary payments, and the proscription against deception, and compare them to experimental practices in psychology, primarily in the area of behavioral decision making. Whereas economists bring a precisely defined “script” to experiments for participants to enact, psychologists often do not provide such a script, leaving participants to infer what choices the situation affords. By often using repeated experimental trials, economists allow participants to learn about the task and the environment; psychologists typically do not. Economists generally pay participants on the basis of clearly defined performance criteria; psychologists usually pay a flat fee or grant a fixed amount of course credit. Economists virtually never deceive participants; psychologists, especially in some areas of inquiry, often do. We argue that experimental standards in economics are regulatory in that they allow for little variation between the experimental practices of individual researchers. The experimental standards in psychology, by contrast, are comparatively laissez-faire. We believe that the wider range of experimental practices in psychology reflects a lack of procedural regularity that may contribute to the variability of empirical findings in the research fields under consideration. We conclude with a call for more research on the consequences of methodological preferences, such as the use on monetary payments, and propose a “do-it-both-ways” rule regarding the enactment of scripts, repetition of trials, and performance-based monetary payments. We also argue, on pragmatic grounds, that the default practice should be not to deceive participants.

In this target article we question the assumption that perception is divided into separate domains of vision, hearing, touch, taste, and smell. We review implications of this assumption for theories of perception and for our understanding of ambient energy arrays (e.g., the optic and acoustic arrays) that are available to perceptual systems. We analyze three hypotheses about relations between ambient arrays and physical reality: (1) that there is an ambiguous relation between ambient energy arrays and physical reality, (2) that there is a unique relation between individual energy arrays and physical reality, and (3) that there is a redundant but unambiguous relation, within or across arrays, between energy arrays and physical reality. This is followed by a review of the physics of motion, focusing on the existence and status of referents for physical motion. Our review indicates that it is not possible, in principle, for there to be a unique relation between physical motion and the structure of individual energy arrays. We argue that physical motion relative to different referents is specified only in the global array, which consists of higher-order relations across different forms of energy. The existence of specificity in the global array is consistent with the idea of direct perception, and so poses a challenge to traditional, inference-based theories of perception and cognition. However, it also presents a challenge to much of the ecological approach to perception and action, which has accepted the assumption of separate senses.

The overall goal of this target article is to demonstrate a mechanism for an embodied cognition. The particular vehicle is a much-studied, but still widely debated phenomenon seen in 7–12 month-old-infants. In Piaget's classic “A-not-B error,” infants who have successfully uncovered a toy at location “A” continue to reach to that location even after they watch the toy hidden in a nearby location “B.” Here, we question the traditional explanations of the error as an indicator of infants' concepts of objects or other static mental structures. Instead, we demonstrate that the A-not-B error and its previously puzzling contextual variations can be understood by the coupled dynamics of the ordinary processes of goal-directed actions: looking, planning, reaching, and remembering. We offer a formal dynamic theory and model based on cognitive embodiment that both simulates the known A-not-B effects and offers novel predictions that match new experimental results. The demonstration supports an embodied view by casting the mental events involved in perception, planning, deciding, and remembering in the same analogic dynamic language as that used to describe bodily movement, so that they may be continuously meshed. We maintain that this mesh is a pre-eminently cognitive act of “knowing” not only in infancy but also in everyday activities throughout the life span.

Although the dynamic field model predicts infants' perseverative behavior in the context of the A-not-B manual search task, it does not account for infant perseveration in other contexts. An alternative cognitive capacity explanation for perseveration is more parsimonious. It accounts for the graded nature of perseverative responses and perseveration in different contexts.

The universality, invariance, and elegance of principles governing the universe may be reflected in principles of the minds that have evolved in that universe – provided that the mental principles are formulated with respect to the abstract spaces appropriate for the representation of biologically significant objects and their properties. (1) Positions and motions of objects conserve their shapes in the geometrically fullest and simplest way when represented as points and connecting geodesic paths in the six-dimensional manifold jointly determined by the Euclidean group of three-dimensional space and the symmetry group of each object. (2) Colors of objects attain constancy when represented as points in a three-dimensional vector space in which each variation in natural illumination is canceled by application of its inverse from the three-dimensional linear group of terrestrial transformations of the invariant solar source. (3) Kinds of objects support optimal generalization and categorization when represented, in an evolutionarily-shaped space of possible objects, as connected regions with associated weights determined by Bayesian revision of maximum-entropy priors.

It is proposed here that Webb's ideas about robots as possible models of animals need some rethinking. In our view, even though widely used biorobotics strategies are fairly successful at reproducing the macroscopic behavior of biological systems, there are still several problems unresolved on the side of robotics as well as biology. Both mathematical and hardware-like robotics models should be feasible physiologically. Control principles elaborated in robotics are not necessarily applied to biological control systems. Although observations of flying birds inspired aerodynamics and thus modern airplanes, little knowledge has been added to the neurophysiological principles underlying flight in birds. Chess playing computers might outperform most chess players, but they cannot be considered as physiologically feasible models of human thinking.

The global array may be a useful concept in studying behavior in a complex environment, especially in the context of dynamical systems theory. However, Stoffregen & Bardy's arguments are weakened by the conflation of sensation and perception, and by the lack of evidence for synergy between stimulus energy arrays; strong evidence places the convergence of sensory stimuli inside the head.

The methods of experiments in the social sciences should depend on their purposes. To support this claim, I attempt to state some general principles relating method to purpose for three of the issues addressed. (I do not understand what is not a script, so I will omit that issue.) I illustrate my outline with examples from psychological research on judgment and decision making (JDM).

Chaotic dynamics can be related to analog computation. A possibility of electronically implementing the chaos-driven contracting system in the target article is explored with an analog electronic circuit with inevitable noise from the viewpoint of analog computation with chaotic neurons.

As models of living beings acting in a real world biorobots undergo an accelerated “philogenic” complexification. The first efficient robots performed simple animal behaviours (e.g., those of ants, crickets) and later on isolated elementary behaviours of complex beings. The increasing complexity of the tasks robots are dedicated to is matched by an increasing complexity and versatility of the architectures now supporting conditioning or even elementary planning.

In the dynamic field model, parametric variations of the same general processes predict how infants reach for a goal. Animal learning investigators argue that locating a goal is the product of qualitatively different mechanisms (response learning and place learning) Response versus place learning experiments suggest limitations to the dynamic field model hut where those limitations begin or end is unclear.

Hertwig and Ortmann suggest paying participants contingent upon performance in order to increase the thoroughness they devote to a decision task. We argue that monetary incentives can yield a number of unintended effects including distortions of the subjective representation of the task and impaired performance. Therefore, we conclude that performance-contingent payment should not be generally employed in judgment and decision research.

The framework within which Tsuda proposes his solution for transitory dynamics between attractor states is flawed from a neurological perspective. We present a more genuine framework and discuss the roles that external input and synaptic modulations play in the evolution of the dynamics of neuronal systems. Chaotic itinerancy, it is argued, is not necessary for transitory dynamics.

Statistical regularities of the environment are important for learning, memory, intelligence, inductive inference, and in fact, for any area of cognitive science where an information-processing brain promotes survival by exploiting them. This has been recognised by many of those interested in cognitive function, starting with Helmholtz, Mach, and Pearson, and continuing through Craik, Tolman, Attneave, and Brunswik. In the current era, many of us have begun to show how neural mechanisms exploit the regular statistical properties of natural images. Shepard proposed that the apparent trajectory of an object when seen successively at two positions results from internalising the rules of kinematic geometry, and although kinematic geometry is not statistical in nature, this is clearly a related idea. Here it is argued that Shepard's term, “internalisation,” is insufficient because it is also necessary to derive an advantage from the process. Having mechanisms selectively sensitive to the spatio-temporal patterns of excitation commonly experienced when viewing moving objects would facilitate the detection, interpolation, and extrapolation of such motions, and might explain the twisting motions that are experienced. Although Shepard's explanation in terms of Chasles' rule seems doubtful, his theory and experiments illustrate that local twisting motions are needed for the analysis of moving objects and provoke thoughts about how they might be detected.

Based on studies with infants, we expand on Stoffregen & Bardy's explanation of perceptual motor errors, given the global array. Information pick-up from the global array is not sufficient without adequate exploratory movements and learning to support perceptually guided activity.

Stoffregen & Bardy's target article is based on the assumption that our senses' ultimate purpose is to provide us with perfect information about the outside world. We argue that it is often more important that information be available quickly than that it be perfect. Consequently our nervous system processes different aspects of information about our surrounding as separately as possible. The separation is not between the senses, but between separate aspects of our surrounding. This results in inconsistencies between judgments: sometimes because different frames of reference are used. Such inconsistencies are fundamental to the way the information is picked up, however, and hence cannot be avoided with clearer instructions to the subjects.

While correctly describing the differences in current practices between experimental psychologists and economists, Hertwig and Ortmann do not provide a compelling explanation for these differences. Our explanation focuses on the fact that psychologists view the world as composed of categories and types. This discrete organizational scheme results in merely testing nulls and wider variation in observed practices in experimental psychology.