A computational model of aesthetic value

People constantly evaluate what they see, and these evaluations determine their choices from small (Where do I look next?) to large (Where do I want to live?). The pleasure associated with a sensory experience, namely its aesthetic value, is perhaps the most frequent and important evaluation. Yet, we have a poor understanding of how sensory experiences gain aesthetic value. We propose a model of aesthetic value that is based on the premise that observers maintain and adapt the states of their cognitive-sensory system in a way that allows them to process stimuli effectively in both the present and the future. Two interlinked components generate value: stimulus processing fluency and the change in fluency with regard to likely future stimuli. In our model, processing fluency is quantified precisely as the likelihood of the stimulus given an observer's state and constitutes immediate sensory reward. The change in fluency with which likely future stimuli will be processed, quantified by the change in the average likelihood of expected future stimuli, constitutes the reward of learning. Simulations show that a simple version of our model can account for empirical data on the effects of exposure, complexity, and symmetry on aesthetic value judgments. Its application offers insight as to how mechanisms that improve long-term processing efficiency give rise to aesthetic value judgments

Discounting the effect of memory on repeated measures of beauty judgment

The intrinsic variance of beauty judgment is key to modeling beauty ratings. However, in repeated measures of beauty, observers surely make use of what they remember. To test how memory contributes to repeated beauty ratings, we asked participants to rate 75 arbitrarily named images (e.g., Fred). Initially, participants rated (1 to 7) how much beauty they felt from looking at a named image. Then participants completed two conditions. In the memory condition, participants saw only the name of an image and were asked to remember the image corresponding to that name and rate how much beauty they felt. In the repeat condition, they once again rated how much beauty they felt from looking at a named image. Lastly, in a memory check, participants tried to select which image was associated with a name. Only considering the correctly remembered trials (60%), we calculated the distribution of the differences between the initial beauty rating and that from either the memory condition or the repeat condition. The variance for the memory condition was more than double that of the repeat condition. Likewise, the initial beauty ratings predicted 84% of the variance in the repeat ratings but only 30% of the variance in the memory ratings. Cue combination studies report that observers typically combine cues by the optimal Bayesian rule: The combined reliability is the sum of the separate reliabilities for each cue, where reliability is one over variance. Assuming optimal combination of memory and immediate-perception judgment, we can discount the contribution of memory to estimate the variance of the immediate-perception judgment. Thus, in our paradigm the 0.83 variance of the repeated beauty rating corresponds to a 0.97 immediate-perception judgment variance (without memory). Overall, since there also was no significant difference in means, our results indicate that memory contributes little to repeated beauty ratings

Beam Scraping to detect and remove Halo in LHC Injection

Fast scrapers are installed in the SPS to detect and remove beam halo before extraction of beams to the LHC, to minimize the probability for quenching of superconducting magnets in the LHC. We shortly describe the current system and then focus on our recent work, which aims at providing a system which can be used as operational tool for standard LHC injection. A new control application was written and tested with the beam. We describe the current status and results and compare these with detailed simulations

A Comparative Survey of Leguminous Plants as Sources of the Isoflavones, Genistein and Daidzein: Implications for Human Nutrition and Health

Over 80 taxa of mostly agriculturally important legumes were surveyed as sources of the metabolites, genistein and daidzein. Remarkably high concentrations (over 2 g · kg–1 dry weight) of the anticancer metabolite, genistein, were found in the leaves of Psoralea corylifolia (Indian bread root). All other legumes, with the exception of fermented soybean miso, had genistein levels <400 mg · kg–1 dry weight. Concentrations of over 1 g · kg–1 dry weight and 0.95 g · kg–1 dry weight of the anticancer metabolite, daidzein, were found in the stems of the fava bean (Vicia faba) and roots of kudzu vine (Pueraria lobata)' respectively. From this survey, our results indicate that the legumes, lupine (Lupinus spp.), fava bean, (Vicia faha), soybeans (Glycine max), kudzu (Pueraria lobata), and psoralea (Psoralea corylifolia), are excellent food sources for both genistein and daidzein. Miso, a fermented soybean product, is also a rich source of both isoflavones.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63131/1/acm.1997.3.7.pd

Boredom in aesthetic experiences

When was the last time you skipped a song on your playlist or scrolled over an image on your Instagram feed? Why? We wager that aesthetic boredom drove your choice. According to our theory, aesthetic boredom arises when sensory value decreases. We assume that sensory experiences are valuable to the extent that they help increase sensory processing efficiency now and in the future. We distinguish between absolute boredom that arises in response to one experience without need for comparison and relative boredom. Absolute boredom arises when: 1) its continuation leads to a decrease in expected long-term processing efficiency, which 2) outweighs short-term increasing processing efficiency for itself. Relative boredom for a sensory experience arises when other available experiences have higher values. Boredom then serves as a signal for observers that dwelling longer in their current environment is suboptimal in the long-run and motivates them to seek out new sensory stimulation

Empirical Aesthetics

Empirical aesthetics is a research area at the intersection of psychology and neuroscience that aims to understand how people experience, evaluate, and create objects aesthetically. Its central two questions are: How do we experience beauty? How do we experience art? In practice, this means that empirical aesthetics studies (1) prototypically aesthetic responses, such as beauty or chills, and (2) responses to prototypically aesthetic objects, such as paintings and music. Empirical aesthetics also encompasses broader questions about how we experience other aesthetic experiences, such as ugliness and the sublime, and about how we create art. The field of empirical aesthetics aims to understand how such aesthetic experiences and behaviors emerge and unfold. To do so, researchers in the field link the observer’s characteristics to her responses, link the object properties to the observer’s responses, or describe an interaction between them. As a science, empirical aesthetics relies heavily on the analysis and interpretation of data. Data is primarily generated from experiments: Researchers conduct studies in which they manipulate the independent variables to observe the effect of those manipulations on one or more independent variables. In addition, empirical aesthetics relies on observational data, where people’s behavior is observed or surveyed without the introduction of manipulations. Empirical aesthetics is as old as empirical psychology. The first thorough written account dates back to Gustav Fechner, who published Vorschule der Aesthetik in 1876. Nonetheless, the modern field of empirical aesthetics can be considered rather young. Its gain in popularity in the 21st century can be linked to the emergence of neuroaesthetics—the study of brain responses associated with aesthetic experiences—in the late 1990s. Contemporary empirical aesthetics studies aesthetic experiences with a variety of methods, including brain-imaging and measures of other physiological responses, such as the movements of the eyes and facial muscles