This article deals with the definition and detection of particular kinds of temporal patterns in behavior, which are sometimes obvious or well known, but other times difficult to detect, either directly or with standard statistical methods. Characteristics of well-known behavior patterns were abstracted and combined in order to define a scale-independent, hierarchical time pattern type, called aT-pattern. A corresponding detection algorithm was developed and implemented in a computer program, called Theme. The proposed pattern typology and detection algorithm are based on the definition and detection of a particular relationship between pairs of events in a time series, called a critical interval relation. The proposed bottom-up, level-by-level (or breadth-first) search algorithm is based on a binary tree of such relations. The algorithm first detects simpler patterns. Then, more complex and complete patterns evolve through the connection of simpler ones, pattern completeness competition, and pattern selection. lnterindividual T-patterns in a quarter-hour interaction between two children are presented, showing that complex hidden T-patterns may be found by Theme in such behavioral streams. Finally, implications for studies of complexity, self-organization, and dynamic patterns are discussed.Hidden or nonobvious temporal patterns in behavior are oflong-standing interest in various areas ofbehavioral research: "Behavior consists ofpatterns in time. Investigations of behavior deal with sequences that, in contrast to bodily characteristics, are not always visible" (EiblEibesfeldt, 1970, p. I, emphasis added). Integrated studies of the structure ofverbal and nonverbal behavior have been repeatedly proposed (Pike, 1960; Skinner, 1957): "The activity of man constitutes a structural whole, in such a way that it cannot be subdivided into neat "parts" or "levels" or "compartments" insulated in character, content, and organization from other behavior. Verbal and nonverbal activity is a unified whole, and theory and methodology should be organized or created to treat it as such" (Pike, 1960, p. 2).Such an approach requires sufficient knowledge about recurrent patterns in observable behavior. However, these patterns may be hidden and difficult to detect without adequate tools. This paper concerns some ofthese methodological difficulties and proposes some solutions. hi.is/-msm).butatthetimeofthiswriting.itis hoped that a more powerful commercial version will soon be available from a company (PaUernScope) that is now being created by the author and Icelandic Venture Funds (see www.hi.is/r-rnsrn for up-to-date information).

It is widely believed that human interaction is much more regular than has yet been detected. What kinds of hidden regularities exist is, however, unsettled. In this paper a structural hypothesis is proposed where each continuous human interaction is seen as the performance of a set of a particular type of temporal patterns. Some of these repeated intra- and inter-individual real-time behavior patterns may be mutually exclusive in time while others may overlap in various ways. Perceptual limitations making such patterns hidden to the naked eye are illustrated. A computerized detection method and illustrative empirical findings from various types of face-to-face interactions in children and adults are presented. The specially developed pattern detection and analysis software, THEME, is also shortly described.

Quantitative analysis of sports performance has been shown to produce information that coaches can use within the coaching process to enhance performance. Traditional methods for quantifying sport performances are limited in their capacity to describe the complex interactions of events that occur within a performance over time. In this paper, we outline a new approach to the analysis of time-based event records and real-time behaviour records on sport performance known as T-pattern detection. The relevant elements of the T-pattern detection process are explained and exemplar data from the analysis of 13 soccer matches are presented to highlight the potential of this form of analysis. The results from soccer suggest that it is possible to identify new profiles for both individuals and teams based on the analysis of temporal behavioural patterns detected within the performances.

A basic tenet in the realm of modern behavioral sciences is that behavior consists of patterns in time. For this reason, investigations of behavior deal with sequences that are not easily perceivable by the unaided observer. This problem calls for improved means of detection, data handling and analysis. This review focuses on the analysis of the temporal structure of behavior carried out by means of a multivariate approach known as T-pattern analysis. Using this technique, recurring sequences of behavioral events, usually hard to detect, can be unveiled and carefully described. T-pattern analysis has been successfully applied in the study of various aspects of human or animal behavior such as behavioral modifications in neuro-psychiatric diseases, route-tracing stereotypy in mice, interaction between human subjects and animal or artificial agents, hormonal–behavioral interactions, patterns of behavior associated with emesis and, in our laboratories, exploration and anxiety-related behaviors in rodents. After describing the theory and concepts of T-pattern analysis, this review will focus on the application of the analysis to the study of the temporal characteristics of behavior in different species from rodents to human beings. This work could represent a useful background for researchers who intend to employ such a refined multivariate approach to the study of behavior

The “dilution effect” implies that where species vary in susceptibility to infection by a pathogen, higher diversity often leads to lower infection prevalence in hosts. For directly transmitted pathogens, non-host species may “dilute” infection directly (1) and indirectly (2). Competitors and predators may (1) alter host behavior to reduce pathogen transmission or (2) reduce host density. In a well-studied system, we tested the dilution of the zoonotic Puumala hantavirus (PUUV) in bank voles (Myodes glareolus) by two competitors and a predator. Our study was based on long-term PUUV infection data (2003–2013) in northern Sweden. The field vole (Microtus agrestis) and the common shrew (Sorex araneus) are bank vole competitors and Tengmalm’s owl (Aegolius funereus) is a main predator of bank voles. Infection probability in bank voles decreased when common shrew density increased, suggesting that common shrews reduced PUUV transmission. Field voles suppressed bank vole density in meadows and clear-cuts and indirectly diluted PUUV infection. Further, Tengmalm’s owl decline in 1980–2013 may have contributed to higher PUUV infection rates in bank voles in 2003–2013 compared to 1979–1986. Our study provides further evidence for dilution effect and suggests that owls may have an important role in reducing disease risk.