Machine learning has enabled researchers to extract meaningful patterns from datasets of unprecedented size and complexity, revolutionizing fields from protein fold prediction to astronomical classification. Just as microscopes opened up spatial scales inaccessible to the naked eye, machine learning tools can reveal patterns at data scales beyond human cognitive capacity, turning vast collections of measurements into interpretable scientific insights.
In behavioral neuroscience, new automated techniques have made it possible to study animals at a very detailed level. By sifting through terabytes of video and audio data, these techniques can extract not only simple patterns that would take time for a human observer to annotate, but also surface patterns and correlations that are too subtle or complex to capture by the human eye. Their findings are already changing long-held assumptions about behavior, including how and when information is integrated to make decisions. Importantly, these techniques also extend the ability to study naturalistic behavior, allowing researchers to capture the complexity of real-world behavior without sacrificing the control and precision afforded by traditional laboratory studies.
Unsupervised methods appear particularly promising as they may help the field address two persistent challenges. One is a shift from restrictive experiments that have traditionally yielded simple yet easily measurable movements to experiments that more fully capture the repertoire of natural animal behaviors. and second, to define and organize basic units of behavior without importing the categories and intuitions of human observers. In other words, scientists are increasingly optimistic that this machine can carve out nature at its joints.
However, it’s important to remember that algorithms can subtly encode very human assumptions that unsupervised methods are supposedly designed to bypass, and can influence not only the patterns they detect, but also what ultimately counts as “behavior” in the first place. How can researchers define behaviors that are particularly complex or that occur during new tasks or in unfamiliar species in a way that respects both the precision demands of computational analysis? and Human interpretability? We believe the answer lies in treating machine learning tools as interpretive tools whose output reflects the designers’ assumptions rather than the truth.
I
In practice, quantifying behavior using machine learning typically starts with: Pose estimationautomatically extracts the positions of animal body parts from video frames, and then Action segmentationdivides continuous animal poses into discrete behavioral units such as grooming, rearing, and exploration. This computational pipeline promises to perform analyzes at reproducible scales, far beyond the limitations of manual annotation, and reveals temporal structure and behavioral variability that are invisible to researchers watching the videos.
Action segmentation is typically done via supervised classification or unsupervised clustering and appears to be a simple technical procedure. However, this is an important place in scientific judgment, where philosophical assumptions about the nature of behavior are embedded in the code. What features we extract from pose data, how we define temporal boundaries between behaviors, and what constitutes a meaningful behavioral structure all reflect a particular theory of animal behavior. These modeling choices determine not only which patterns the algorithm can detect, but also which aspects of behavior remain systematically hidden from analysis.
The scientific consequences of these design choices become clear when researchers deploy behavioral analysis tools in practice, but it is often not clear which method will perform best for a given data. When comparing different approaches, you may find that some methods have problems with certain aspects of your data, while others work well. These differences are generally attributed to technical factors such as insufficient training data, imprecise hyperparameters, or lack of prior validation against specific model organisms. However, these differences in performance may actually be due to a mismatch between the algorithm’s assumptions and the researchers’ assumptions about the behavior they are measuring.
Rather than seeing the challenge of analyzing behavior as a problem to be solved only by better algorithms, we might recognize it as revealing something essential about the nature of behavioral research itself. Experimenters with hundreds of hours of experience observing animals develop pattern recognition that is fundamentally different from researchers working with large, multispecies datasets processed through standardized pipelines. What an algorithm might flag as noise or error may, to an experienced observer, represent interesting individual differences that open new avenues for investigation.
Resolving these differences may require accepting human observers into the analytical process rather than excluding them from it. It remains important to review the video carefully to understand not only what behavior is being detected, but also when and how segmentation decisions are made. By watching algorithms analyze behavioral sequences, you can learn much more about the nuances of movement and the assumptions embedded in computational models than you can from statistical summaries alone.
The real power of machine learning may lie not in increasing objectivity, as is often claimed, but in clarifying the assumptions and interpretations that have always shaped behavioral research. For example, behavioral analysis tools do more than simply reveal existing structures. These help reframe what behavior means as a scientific category, open new ways of observing animal movement, and generate new empirical questions to explore.
S
Scientists make choices at every stage of behavioral experiments, including designing tasks, choosing recording equipment, developing analytical algorithms, and interpreting results. For example, researchers may express the behavior of a machine learning algorithm as continuous or discrete, and determine when trial-to-trial variation reveals adaptive and flexible behavior and when it reflects true error. The active role of experimenters, even in algorithm design, therefore challenges the belief that machine learning techniques provide theory-independent insights into complex systems, as interpretation plays a key role in both quantifying behavior and understanding it.
However, this limitation is not insurmountable. We see this as an opportunity to develop a more sophisticated understanding of how to incorporate machine learning into scientific practice. As Liam Paninski and John Cunningham argue in a 2018 review, these methods must exist within an experiment-analysis-theory loop, where data analysis not only extracts meaningful information from experimental data, but also helps generate hypotheses, suggest new experiments, and refine theory. As researchers refine each step of this loop, they must explicitly consider the assumptions built into both their experiments and the algorithms they use. Such an approach transforms machine learning from a substitute for human judgment to a reflective vehicle that surfaces human assumptions and explicitly encodes them into algorithmic design to reveal new dimensions of behavior.
This perspective turns potential limitations into scientific assets. Although behavioral research is interdisciplinary in nature, different perspectives and conceptual frameworks on behavior may not be naturally standardized or consistent. However, instead of eliminating their variability, these differences can be exploited to develop a more robust and nuanced understanding of behavior. Recognizing the interpretive nature of behavior analysis will not hinder scientific progress, but rather will make it more meaningful by ensuring that sophisticated analytical capabilities provide, rather than replace, true scientific understanding.
