Michel Kreutzer

Bio: Michel Kreutzer is an academic researcher from University of Paris. The author has contributed to research in topics: Canto & Psittacus erithacus. The author has an hindex of 24, co-authored 62 publications receiving 2298 citations. Previous affiliations of Michel Kreutzer include Max Planck Society & Paris West University Nanterre La Défense.

Directional female preference for an exaggerated male trait in canary (Serinus canaria) song.

Abstract: Motor constraints on vocal production impose a trade-off between trill rate and frequency bandwidth within birdsong. We tested whether domesticated canary (Serinus canaria) females, reared either in acoustic isolation or in aviary conditions, had a preference for broad bandwidth songs with artificially increased syllable rates. The copulation solicitation display (CSD) was used as an index of female preference. As predicted, both naive and experienced females were especially responsive to syllables with a broad bandwidth emitted at an artificially increased rate. Female preference for supernormal stimuli provide support for the honest-signalling hypothesis and our results are consistent with recent findings indicating that production of song phrases maximizing both bandwidth and syllable rate may be a reliable indicator of male physical or behavioural qualities. We suggest that female preference for vocal emissions, which simultaneously maximize these two parameters, could be a widespread pattern within songbirds.

Women use voice parameters to assess men's characteristics

Abstract: The purpose of this study was: (i) to provide additional evidence regarding the existence of human voice parameters, which could be reliable indicators of a speaker's physical characteristics and (ii) to examine the ability of listeners to judge voice pleasantness and a speaker's characteristics from speech samples. We recorded 26 men enunciating five vowels. Voices were played to 102 female judges who were asked to assess vocal attractiveness and speakers' age, height and weight. Statistical analyses were used to determine: (i) which physical component predicted which vocal component and (ii) which vocal component predicted which judgment. We found that men with low-frequency formants and small formant dispersion tended to be older, taller and tended to have a high level of testosterone. Female listeners were consistent in their pleasantness judgment and in their height, weight and age estimates. Pleasantness judgments were based mainly on intonation. Female listeners were able to correctly estimate age by using formant components. They were able to estimate weight but we could not explain which acoustic parameters they used. However, female listeners were not able to estimate height, possibly because they used intonation incorrectly. Our study confirms that in all mammal species examined thus far, including humans, formant components can provide a relatively accurate indication of a vocalizing individual's characteristics. Human listeners have the necessary information at their disposal; however, they do not necessarily use it.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Complex signal function: developing a framework of testable hypotheses

Abstract: The basic building blocks of communication are signals, assembled in various sequences and combinations, and used in virtually all inter- and intra-specific interactions. While signal evolution has long been a focus of study, there has been a recent resurgence of interest and research in the complexity of animal displays. Much past research on signal evolution has focused on sensory specialists, or on single signals in isolation, but many animal displays involve complex signaling, or the combination of more than one signal or related component, often serially and overlapping, frequently across multiple sensory modalities. Here, we build a framework of functional hypotheses of complex signal evolution based on content-driven (ultimate) and efficacy-driven (proximate) selection pressures (sensu Guilford and Dawkins 1991). We point out key predictions for various hypotheses and discuss different approaches to uncovering complex signal function. We also differentiate a category of hypotheses based on inter-signal interactions. Throughout our review, we hope to make three points: (1) a complex signal is a functional unit upon which selection can act, (2) both content and efficacy-driven selection pressures must be considered when studying the evolution of complex signaling, and (3) individual signals or components do not necessarily contribute to complex signal function independently, but may interact in a functional way.

Phenotypic integration: studying the ecology and evolution of complex phenotypes

Abstract: Phenotypic integration refers to the study of complex patterns of covariation among functionally related traits in a given organism. It has been investigated throughout the 20th century, but has only recently risen to the forefront of evolutionary ecological research. In this essay, I identify the reasons for this late flourishing of studies on integration, and discuss some of the major areas of current endeavour: the interplay of adaptation and constraints, the genetic and molecular bases of integration, the role of phenotypic plasticity, macroevolutionary studies of integration, and statistical and conceptual issues in the study of the evolution of complex phenotypes. I then conclude with a brief discussion of what I see as the major future directions of research on phenotypic integration and how they relate to our more general quest for the understanding of phenotypic evolution within the neo-Darwinian framework. I suggest that studying integration provides a particularly stimulating and truly interdisciplinary convergence of researchers from fields as disparate as molecular genetics, developmental biology, evolutionary ecology, palaeontology and even philosophy of science.