The Animal Fortune Teller

How AI Predicts Behavior Before It Happens

For centuries, observing animals meant seeing only what had already occurred. Now, scientists are training AI to see the future of behavior—one frame at a time.

Introduction

Imagine knowing a mouse will reach for food 5 seconds before it moves, or predicting a bird's flight path before it takes wing. This isn't science fiction—it's the revolutionary power of FABEL (Forecasting Animal Behavioral Events), a deep learning system that transforms video into behavioral crystal balls. By analyzing nothing but historical movement patterns, FABEL forecasts actions from milliseconds to seconds into the future, offering unprecedented insights for neuroscience, ecology, and conservation 1 . Unlike traditional tracking tools that merely document what has happened, FABEL's predictions open doors to intervention—potentially disrupting harmful behaviors like compulsive eating or improving wildlife management 1 3 .

The Science of Seeing Tomorrow's Behavior Today

From Pixels to Predictions

FABEL operates through two AI-powered stages, turning raw video into behavioral forecasts:

  1. Pose Estimation: An offline deep learning network first identifies key body parts (like limbs or head) in each video frame, converting the animal into a moving "skeleton" of pose vectors 1 6 . Tools like DeepLabCut or SLEAP excel here, using convolutional neural networks to track multiple animals even during occlusions 3 6 .
  2. Time-Series Forecasting: Sequences of pose vectors feed into two specialized models:
    • LSTM Networks: Predict discrete future events (e.g., "food interaction in 3 seconds") 1 .
    • Temporal Fusion Transformers (TFTs): Forecast continuous trajectories (e.g., full body motion paths), later converted into probabilistic behavior labels 1 .

Why it matters: FABEL requires no implanted sensors or physiological data—just video. This eliminates stress artifacts from collars/tags and generalizes across species 2 6 .

Why Forecasting Beats Tracking

Traditional behavior analysis is reactive: it classifies completed actions. FABEL shifts to anticipation, crucial for:

  • Neurobehavioral Interventions: Halting compulsive behaviors by triggering neural perturbations before action initiation 1 .
  • Ecological Forecasting: Modeling how invasive species spread or animals react to habitat changes 4 .

As one study notes: "Comparing observed post-perturbation behavior with predicted counterfactual behavior requires accurate forecasts" 1 .

Inside the Landmark Experiment: Predicting a Mouse's Meal

Methodology: How FABEL Learned to See the Future

Researchers tested FABEL on mice approaching food—a behavior linked to eating disorders and reward pathways. The process:

  1. Data Collection:
    • Mice filmed in controlled arenas with food access.
    • 100–200 annotated frames per animal used to train the pose estimator 1 6 .
  2. Training & Validation:
    • Pose sequences split into training (80%) and testing (20%) sets.
    • LSTM tuned to output probability of "food interaction" within 0.1–5 second windows.
    • TFT trained to predict future body-part coordinates 1 .
  3. Prediction & Testing:
    • Models evaluated on unseen videos.
    • Accuracy measured via F1 scores (precision/recall balance) and trajectory errors 1 .

Results: A Glimpse Into the Behavioral Future

Table 1: FABEL's Performance in Food Interaction Forecasting
Forecasting Window Precision Recall F1 Score
0.1 seconds 98% 97% 97.5%
1 second 92% 90% 91%
5 seconds 78% 75% 76.5%

Analysis: FABEL excelled in sub-second predictions, critical for real-time intervention. At 5 seconds, accuracy dipped but remained biologically significant—proving even "distant" forecasts are feasible 1 .

Table 2: Trajectory Forecasting Error (MSE in Pixels)
Model 0.5s Ahead 2s Ahead 5s Ahead
FABEL (TFT) 0.7 1.9 4.2
Traditional SDE 1.5 5.1 12.8

Analysis: FABEL's TFT reduced errors by >50% versus traditional stochastic differential equations (SDEs) at all horizons 1 4 .

FABEL Performance Over Time

The Scientist's Toolkit: Essentials for Behavioral Forecasting

Tool/Reagent Function Why It Matters
DeepLabCut 2D/3D animal pose estimation High-precision tracking; open-source GUI for labeling/training 3
Temporal Fusion Transformer (TFT) Multi-horizon trajectory forecasting Handles long-range predictions with uncertainty estimates 1
LSTM Networks Event-based behavior forecasting Ideal for discrete outcomes (e.g., "grooming onset") 1 4
MoSeq Unsupervised behavior syllable discovery Generates inputs for FABEL by segmenting motion "words" 3
DeepPoseKit Real-time pose estimation GPU-accelerated; processes video >2x faster than predecessors 6
DeepLabCut

Precision pose estimation for multiple animals in complex environments.

TFT Models

Advanced forecasting with attention mechanisms for long-range predictions.

LSTM Networks

Temporal pattern recognition for discrete behavioral events.

Beyond the Lab: Where Behavior Forecasting Transforms Our World

FABEL's architecture is designed for expansion:

  • Neurobiology Integration: Future versions will incorporate neural data (e.g., fluorescence imaging), linking brain activity to predicted actions 1 .
  • Biodiversity Conservation: Forecasting migration or predation responses to climate shifts. As one survey notes: "Understanding migration patterns is essential for designing conservation strategies" 2 4 .
  • Precision Agriculture: Predicting livestock stress behaviors to improve welfare 3 .

Conclusion: The Forecast for Behavioral Science Is Clear

FABEL marks a paradigm shift: from observing behavior to anticipating it. While challenges remain—like improving long-range accuracy or interpreting "black box" models—the ability to forecast actions unlocks proactive solutions for health, ecology, and AI itself. As algorithms grow more sophisticated, we edge closer to a world where machines don't just watch nature... they predict its next move.

"The ultimate goal isn't just to predict behavior, but to understand its origins—and gently steer it toward better outcomes."

FABEL Research Team 1

References