Aesthetic appreciation is a complex cognitive processing with inherent aspects of cold as well as hot cognition. Research
from the last decades of empirical has shown that evaluations of aesthetic appreciation are highly reliable. Most
frequently, facial attractiveness was used as the corner case for investigating aesthetic appreciation. Evaluating facial
attractiveness shows indeed high internal consistencies and impressively high inter-rater reliabilities, even across
cultures. Although this indicates general and stable mechanisms underlying aesthetic appreciation, it is also obvious that
our taste for specific objects changes dynamically. Aesthetic appreciation on artificial object categories, such as fashion,
design or art is inherently very dynamic. Gaining insights into the cognitive mechanisms that trigger and enable
corresponding changes of aesthetic appreciation is of particular interest for research as this will provide possibilities to
modeling aesthetic appreciation for longer durations and from a dynamic perspective. The present paper refers to a recent
two-step model ("the dynamical two-step-model of aesthetic appreciation"), dynamically adapting itself, which accounts
for typical dynamics of aesthetic appreciation found in different research areas such as art history, philosophy and
psychology. The first step assumes singular creative sources creating and establishing innovative material towards
which, in a second step, people adapt by integrating it into their visual habits. This inherently leads to dynamic changes
of the beholders' aesthetic appreciation.
We present a framework for cognitive modeling of esthetic decision making based on dynamic prototypes. Starting point of our work
is empirical evidence which shows that subjects’ initial ratings of attractiveness of objects can be influenced by adapting them to new,
typically more innovative objects. The framework consists of three steps: (1) Estimating an initial prototype from the ratings, (2) adapting
the prototype due to the impact of the new objects, and (3) predicting the attractiveness ratings for subsequently presented object by
their similarity to the adapted prototype. The framework allows representation of prototypes and objects as feature vectors containing
metrical or categorial attributes or as structural representations. Within the framework, a variety of similarity measures and similarity-torating
mappings can be explored to gain more precise insight into the cognitive processes underlying esthetical appreciations. We instantiated
the framework for a first set of data obtained in a psychological experiment. In this experiment subjects rated the attractiveness of
an initial set of chairs which varied in length of the backrest and the saturation of the color. Subjects then were adapted to a new set of
chairs with extreme values on both dimensions. Finally, subjects again rated the initial objects. The framework was instantiated with an
e-function to model the non-linear effects of variations in length and saturation on the judgements. Although there were only 25 data
points per subject, we got satisfying results in predicting the shift of esthetical judgements due to adaptation to novel stimuli.
Jeffery et al. extensively and thoroughly describe how different species navigate through a three-dimensional environment. Undeniably, the world offers numerous three-dimensional opportunities. For most navigation tasks, we argue, a two-dimensional representation is nevertheless sufficient, as physical conditions and limitations such as gravity, thermoclines, or layers of earth encountered in a specific situation provide the very elevation data the navigating individual needs.
Are challenging stimuli appreciated due to perceptual insights during elaboration? Drawing on the literature
regarding aesthetic appreciation, several approaches can be identified. For instance, fluency of processing as
well as perceptual challenge are supposed to increase appreciation: One group (Reber, Schwarz, & Winkielman,
2004) claims that fluency of processing increases appreciation. Others link aesthetics to engagement: Creation
and manipulation of sense itself should be rewarding (Ramachandran & Hirstein, 1999). We experimentally
tested the influence of insights during elaboration on liking. Pairs of stimuli – hardly detectable two-tone images
including a face (Mooney face) and meaningless stimuli matched for complexity – were presented repeatedly.
Having an insight as well as the intensity of the insight predicted subsequent gains in liking. This paper qualifies
the role of insight (—aha!) on aesthetic appreciation through the effects of elaboration and problem-solving on
understanding the processing of modern art.
Tobler (1965) introduced bidimensional regression to the research � eld of geography in
1965 to provide a method for estimating mapping relations between two planes on the basis
of regression modeling. The bidimensional regression method has been widely used within
geographical research. However, the applicability in assessing the degree of similarity of
two-dimensional patterns has not much explored in the area of psychological research,
particularly in the domains of cognitive maps, face research and comparison of 2D-data
patterns. Describing Tobler's method in detail, Friedman and Kohler (2003) made an
attempt to bridge the gulf between geographical methodological knowledge and psychological
research practice. Still, the method has not been incorporated into psychologists'
standard methodical repertoire to date. The present paper aims to make bidimensional
regression applicable also for researchers and users unfamiliar with its theoretical basis.
The BiDimRegression function provides a manageable computing option for bidimensional
regression models with a� ne and Euclidean transformation, which makes it easy
to assess the similarity of any planar con� guration of points. Typical applications are,
for instance, assessments of the similarity of facial images de� ned by discrete features
or of (cognitive) maps characterized by landmarks. BiDimRegression can be a valuable
tool since it provides estimation, statistical inference, and goodness-of-� t measures for