Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Fixed Action Patterns

Fixed Action Patterns

A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Let us define arthroscopic surgeon's ergonomics! design of visualisation and evaluation approaches of surgical ergonomics.

International journal of computer assisted radiology and surgery·2026

Same author

Objective structured assessment of posterior capsular rupture management.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2026

Same author

Perceived Crises and Preparedness Gaps in Operating Room Nursing: A Qualitative Study of Training Priorities With Nurses and Educators.

Journal of nursing management·2026

Same author

Please follow the rules: surgical workflow recognition constrained by linear temporal logic.

International journal of computer assisted radiology and surgery·2026

Same author

DSTED: decoupling temporal stabilization and discriminative enhancement for surgical workflow recognition.

International journal of computer assisted radiology and surgery·2026

Same author

Evaluation of Graph-Based Algorithms for Early Detection of In-Hospital Mortality.

Studies in health technology and informatics·2026

Same journal

Evaluation of temporal preservation in synthetic longitudinal patient data.

Journal of biomedical informatics·2026

Same journal

ARKE: An ontology-driven framework for automated mapping of local radiology procedure terms to the LOINC-RadLex playbook using large language model.

Journal of biomedical informatics·2026

Same journal

A validation-driven training controller for cross-lingual biomedical NER via reinforcement learning-based adaptive loss weighting.

Journal of biomedical informatics·2026

Same journal

ASP-HR: An Adaptive Spatial Perception and Hierarchical Reasoning mechanism for document-level biomedical relation extraction.

Journal of biomedical informatics·2026

Same journal

Beyond Accuracy: Safety-Centered guidelines for the evaluation of LLM-based therapy recommendation systems for chronic multimorbidity patients.

Journal of biomedical informatics·2026

Same journal

DeepEN: A deep reinforcement learning framework for personalized enteral nutrition in critical care.

Journal of biomedical informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 7, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Distinguishing surgical behavior by sequential pattern discovery.

Arnaud Huaulmé¹, Sandrine Voros², Laurent Riffaud³

¹UGA/ CNRS/ INSERM, TIMC-IMAG UMR 5525, Grenoble F-38041, France; INSERM, UMR 1099, Rennes F-35000, France; Université de Rennes 1, LTSI, Rennes F-35000, France.

Journal of Biomedical Informatics

|February 10, 2017

Summary

This summary is machine-generated.

This study introduces a novel pattern discovery method to differentiate surgical behaviors. The approach accurately distinguishes surgical sites, expertise levels, and individual surgeons, outperforming previous time-analysis methods.

Keywords:

Pattern discovery Surgical procedure Surgical process model Surgical skills

More Related Videos

Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate

Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate

Published on: January 17, 2018

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Related Experiment Videos

Last Updated: Mar 7, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate

Simultaneous Distinction of Monospecific and Mixed DFS70 Patterns During ANA Screening with a Novel HEp-2 ELITE/DFS70 Knockout Substrate

Published on: January 17, 2018

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Surgical analytics
Machine learning in surgery
Process mining

Background:

Surgical procedures exhibit unique characteristics influenced by patient and surgeon factors.
Distinguishing surgical behaviors is crucial for training, quality assessment, and patient safety.

Purpose of the Study:

To develop and validate a novel pattern discovery method for differentiating surgical behaviors.
To enable the clustering of surgical process models based on sequential patterns.

Main Methods:

A new metric, Shared Longest Frequent Sequential Patterns (SLFSP), was developed for clustering surgical process models.
The approach was validated by comparing it with Dynamic Time Warping (DTW) for similarity assessment.

Main Results:

The SLFSP method achieved 100% accuracy in distinguishing surgical sites.
Accuracies exceeding 90% and 85% were obtained for differentiating expertise levels and individual surgeons, respectively.
The proposed method demonstrated superior performance compared to time-analysis-based clustering.

Conclusions:

Clustering surgical process models using SLFSP is effective for distinguishing surgical sites, expertise levels, and individual surgeons.
This method allows for comparison of surgical models based on activity structure, not just duration.
Identified patterns can highlight risky behaviors, aiding surgical training and adverse event prevention.