Victor S. Johnston

Is beauty in the eye of the beholder

Abstract Theories of beauty were evaluated by requiring subjects to “evolve” a beautiful female face using a Genetic Algorithm. In this procedure, a computer program generated a small population of faces (first generation of phenotypes) from a set of random binary strings (genotypes). Genotypes specified the shapes and soft tissue anthropometrics of facial features. Each of the first generation of faces was rated by a subject (relative fitness measure) for beauty. The fittest genotypes then bred in proportion to their fitness, with crossover and mutation of the binary strings, to produce offspring which were again rated by the subject. This process continued until the most beautiful face, for that subject, was evolved. Forty Caucasian subjects (20 M, 20 F) were required to evolve their idealized beautiful female face using this procedure. The features and soft tissue anthropometrics of their final composites were compared to population norms. Also, the final composites, and different faces generated from the same data base, were rated for beauty by independent judges. The results support the conclusion that the concept of facial beauty is the result of sexual selection, and a beautiful female face has features and proportions indicative on high fertility.

Gender differences in late positive components evoked by human faces

Event-related potentials (ERPs) were recorded in male and female participants in response to 32 male and 32 female faces. Participants were instructed to simply look carefully at each face; after ERP collection they were asked to rate each face on a 5-point attractiveness scale. A positive correlation between average rating and average P300 scores to opposite sex faces was observed in male (r = .40) and in preovulatory (r = .41) and postovulatory (r = .44) female subjects. Correlations to same sex faces were only found in postovulatory females (r = .61). Male participants showed a much larger average P300 than did female participants, and the P300 evoked in female participants was unexpectedly larger to female than to male faces. Neither task relevance nor stimulus probability is a plausible explanations for these findings because they were experimentally controlled. These results support the emotional value hypothesis, according to which classical P300 processes reflect an affective evaluation of the stimulus, which in turn produces context updating.

Facial beauty and the late positive component of event‐related potentials

Research on facial attractiveness provided a method for changing the affect elicited by computer‐generated facial images by manipulating facial features and proportions. Twenty‐five male volunteers were individually exposed to a sequence of male and female computer‐generated faces, presented in a random order, during three experimental sessions. Event‐related potentials (ERPs), the electroencephalographic activity immediately following each stimulus presentation, were recorded from each participant as he viewed the stimulus material. During a final experimental session, participants were required to rate each face using a five‐point scale of physical attractiveness. Based on prior research, it was hypothesized that a late positive component (LPC) of the ERPs elicited by facial images would increase with the physical attractiveness of the face. The results indicated that (a) the LPC, with a parietal greater than frontal scalp distribution, was correlated with the beauty rating of female faces; (b) modified...

The origin and function of pleasure

Currently, most cognitive scientists view the brain as a general-purpose computer and the processes of mind as software algorithms running on this neural architecture. From this perspective, conscious feelings, like pleasure, play no functional role in controlling human behaviour. This paper proposes that such computational theories are based on a false premise; namely, that the external world is full of light, sounds, smells, and tastes that can be detected through our senses. An alternative viewpoint, evolutionary functionalism, considers the world to be composed of energy/matter and views conscious experiences, like pleasure, as evolved emergent properties of biological tissue. From this perspective, natural selection has favoured conscious experiences that serve as evaluations of (feelings), or discriminations among (sensations) those aspects of the physical and social world that are biologically relevant. Over generations, it is the functional usefulness of these emergent properties that has shaped the neural architecture that underlies them.

Electrophysiological Correlates of Meaning

The use of context-sensitive symbols offers an appropriate methodology for investigating the representation of meaning in the brain. This approach revealed that late components of frontal, but not occipital, evoked potentials reflect the change of meaning of a symbolic stimulus when it appears in different temporal contexts.

Stimuli with Biological Significance

Life lives on negative entropy (6). It is only by the exploitation of the spatial and temporal orderliness in their environment that organisms can survive in a world governed by the Second Law of Thermodynamics. Lorenz (6) has eloquently stated that [a living system] „very much like a prairie fire greedily gathers energy and, in a positive feedback cycle, becomes able to gather more energy, and to do so the quicker, the more it has already acquired.“ Thus, the biological luxury of animal life can only exist at the expense of those heterotrophs who, until recently, were the only life forms to make efficient use of the virtually limitless and distributed energy source supplied by the sun.

Changes in Cognitive and Emotional Processing with Reproductive Status

Behavioral ratings on several affective scales (non-erotic/erotic, unpleasant/pleasant, simple/complex and low arousal/high arousal), and electrophysiological responses (event-related brain potentials) to emotional pictures, were collected from 30 female subjects, at different phases of their menstrual cycle. The pictures belonged to 5 emotional categories, whose content was babies, dermatological cases, ordinary people, male models and female models. The subjects were grouped into hormone defined phases, according to their expected levels of androgens, estrogen or progesterone. The data were analyzed to determine if emotional or cognitive processing was sensitive to the reproductive status, as indicated by menstrual phase. Only one component of event-related potentials, the P3 component, varied with menstrual phase. Baby and male model pictures elicited larger P3 waves when progesterone level was high. High progesterone was also associated with a decrease in complexity and eroticism to all picture categories. An increase in the pleasantness of all categories was evident when estrogen levels were high. The results are interpreted as support for a general proximal design, whereby emotional and cognitive processes are adaptively regulated by reproductive status.

Computer programs as theories in biology

Complex theories in biology may be developed, refined, and tested by the use of computer programmed simulations. The computer is recognized as a powerful tool for theory development; it is, in fact, the only means of thoroughly testing and examining a large and intricate theory. A program as a text is a statement of a theory and when run on the computer it is model of that theory. As the programs behavior is then the major argument for the credibility of a large and complex theory, the program itself is the only irrefutable statement of the theory. Bur programs written in the currently available programming languages tend to be incomprehensible. We argue that the program should be the definitive statement of the theory. In addition, the program plus a series of abstractions is a vehicle for effective communication of complex theories in biology. Several techniques of computer science are borrowed, for the purpose of developing a methodology for abstraction and a language for representing abstractions. The arguments are fully illustrated with a recently published biological theory.

A neural theory of cognitive development

A physiologically based model of the neocortex has been developed in an attempt to elucidate possible structural and functional mechanisms of the mammalian cortex and account for a wide range of low level cognitive behavior. The model has been constrained by diverse empirical data. At the level of structural details, neuroanatomical and neurophysiological data have been considered and at the level of gross behavior, psychological data has been used. From the theory that groups of reverberating neurons provide a short term memory mechanism and that primary drive reduction triggers consolidation of a memory, a mechanism for selective learning has been developed. Fundamental to the model is the postulate of a novelty drive mechanism that functions in a manner analogous to the more widely accepted primary drives (e.g. hunger and fear). This paper examines the novelty drive mechanism and demonstrates its utility in accounting for a wide range of habituation behaviors. The success of the model is evaluated by comparing its behavior to appropriate empirical data. Finally, it is argued that a computer program is both a theory and a model, and that important advantages accrue from such a viewpoint.

Experiments with a cognitive industrial robot

Abstract Input—expectation discrepancy reduction is a ubiquitous mechanism; it permeates the human nervous system. This mechanism thus appears to be a generic strategy underlying many aspects of intelligent behavior. We have applied this paradigm to the domain of industrial robotics. In addition, we have explored some applications of human perceptual mechanisms in the visual system of the robot; the general strategy employed yielded a trade-off between efficient, intelligent decisions and errors. The result is a cognitive industrial robot that exemplifies a novel view of the industrial robotics field and serves to cast some fundamental problems, of AI as well as of robotics, in a new light. In particular, we describe a concrete application of our ideas which can be contrasted with most AI projects, functioning as they do in purely abstract domains. The concrete application introduces subproblems such as inexact matching and uncertainty with respect to all interactions with the real world, problems that abstract applications of AI theories can, and often do, avoid.