Biomedical subjects

M S Magnusson

Publications and source records attributed to M S Magnusson.

5 recordsLinked to original sources

PubMed2006

Behavioural comparison of human-animal (dog) and human-robot (AIBO) interactions.

The behavioural analysis of human-robot interactions can help in developing socially interactive robots. The current study analyzes human-robot interaction with Theme software and the corresponding pattern detection algorithm. The method is based on the analysis of the temporal structure of the interactions by detecting T-patterns in the behaviour. We have compared humans' (children and adults) play behaviour interacting either with an AIBO or a living dog puppy. The analysis based on measuring latencies and frequencies of behavioural units suggested limited differences, e.g. the latency of humans touching the dog/AIBO was similar. In addition other differences could be accounted for by the limited abilities of the robot to interact with objects. Although the number of interactive T-patterns did not significantly differ among the groups but the partner's type (whether humans were playing with dog or AIBO) had a significant effect on the structure of the patterns. Both children and adults terminated T-patterns more frequently when playing with AIBO than when playing with the dog puppy, which suggest that the robot has a limited ability to engage in temporally structured behavioural interactions with humans. As other human studies suggest that the temporal complexity of the interaction is good measure of the partner's attitude, we suggest that more attention should be paid in the future to the robots' ability to engage in cooperative interaction with humans.

Adult↗

PubMed2005

Detection of temporal patterns in dog-human interaction.

A new time structure model and pattern detection procedures developed by (Magnusson, M.S., 1996. Hidden real-time patterns in intra- and inter-individual behaviour description and detection. Eur. J. Psychol. Assess. 12, 112-123; Magnusson, M.S., 2000. Discovering hidden time patterns in behaviour: T-patterns and their detection. Behav. Res. Methods, Instrum. Comput. 32, 93-110) enables us to detect complex temporal patterns in behaviour. This method has been used successfully in studying human and neuronal interactions (Anolli, L., Duncan, S. Magnusson, M.S., Riva G. (Eds.), 2005. The Hidden Structure of Interaction, IOS Press, Amsterdam). We assume that similarly to interactions between humans, cooperative and communicative interaction between dogs and humans also consist of patterns in time. We coded and analyzed a cooperative situation when the owner instructs the dog to help build a tower and complete the task. In this situation, a cooperative interaction developed spontaneously, and occurrences of hidden time patterns in behaviour can be expected. We have found such complex temporal patterns (T-patterns) in each pair during the task that cannot be detected by "standard" behaviour analysis. During cooperative interactions the dogs' and humans' behaviour becomes organized into interactive temporal patterns and that dog-human interaction is much more regular than yet has been thought. We have found that communicative behaviour units and action units can be detected in the same T-pattern during cooperative interactions. Comparing the T-patterns detected in the dog-human dyads, we have found a typical sequence emerging during the task, which was the outline of the successfully completed task. Such temporal patterns were conspicuously missing from the "randomized data" that gives additional support to the claim that interactive T-patterns do not occur by chance or arbitrarily but play a functional role during the task.

Animals↗

PubMed2000

Discovering hidden time patterns in behavior: T-patterns and their detection.

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 a T-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. Interindividual 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.

Algorithms↗

PubMed2000

OBSERVE: a multimedia course on the observational analysis of behavior.

OBSERVE is a preliminary release of a multimedia course for teaching undergraduate and graduate students how and why to study behavior by direct observation. The instructional text and commentary and the self-test and examination materials are built around a series of exercises in which the student observes and categorizes film clips of the behavior of several different species in several different ways. Incorporation of elements from The Observer software for computer recording and video analysis implements fully computerized continuous recording. At present, the text, together with check sheets that the program generates, enables a comparison between one-zero, instantaneous, and continuous sampling of the same behavioral excerpts. Matrices are printed out for an exercise in calculating interobserver reliability. Another section supports carrying out and writing up a small observational project on human behavior in the field. Plans for the future development of OBSERVE are briefly described.

Behavioral Sciences↗

PubMed1994

The importance of temporal structure in analyzing schizophrenic behavior: some theoretical and diagnostic implications.

THEME, a new method for analyzing the temporal structure of responding on a two-choice task, is described. This method reveals the time relationships (temporal patterns) between all response events, even those not occurring in direct sequence. It selects those temporal patterns that are significantly different (p < 0.0001) from the patterns found in a random Poisson distribution of the same events. The method was applied to data from Lyon et al. (1986) in which n = 17 outpatient schizophrenics were compared with n = 17 age-, sex-, and education-matched normal control subjects. Results revealed that responding of schizophrenic outpatients, in comparison to control subjects, had a larger number of significant temporal patterns, more different types of patterns, and more branching (connectivity) of patterns at a higher level. The latter indicates a higher degree of internal structure. These results are not predicted by standard (DSM-III-R) diagnostic procedures, but are in agreement with studies of two-choice behavior in schizophrenia based on the Lyon-Robbins (1975) theory of behavioral change, which has possible relationship to dopamine/acetylcholine imbalance in the brain. Diagnostic procedures in schizophrenia might benefit from tests oriented toward these findings, which are also consistent with Bleuler's original descriptions of schizophrenic symptomatology.

Adult↗