exploratory-predictive-representation-geometry

name: exploratory-predictive-representation-geometry description: "探索性行为塑造预测性表征几何的方法论。通过主动感知框架研究探索-利用平衡如何影响内部表征组织。探索性行为使表征更具空间结构性，更好地保留迷宫转换结构。激活词：探索性学习、exploratory behavior、predictive coding、predictive representations、active sensing、latent space geometry、行为-学习循环。"

Exploratory Experience Shapes the Geometry of Predictive Representations

Paper Overview

arXiv ID: 2605.27929
Authors: Kseniia Shilova, Abdelrahman Sharafeldin, Advay Balakrishnan, Hannah Choi
Categories: q-bio.NC (Neurons and Cognition), cs.LG (Machine Learning)
Published: May 27, 2026
DOI: https://doi.org/10.48550/arXiv.2605.27929

Research Question

How do exploratory vs. exploitative behavioral strategies shape internal predictive representations in active sensing agents? This study investigates how behavior modulates the geometry of learned representations through the action-perception loop.

Core Theory

Active Sensing Framework

Active sensing links behavior and learning through an action-perception loop:

Actions determine observations
Observations update predictive models
Models guide subsequent actions
Continuous cycle between behavior and representation

Predictive-Coding-Based Learning

Internal representations continuously updated to predict future observations
Model predicts both future states and reward probability
Agents select actions based on expected information gain (exploration) or predicted reward (exploitation)

Methodology

Experimental Setup

Environment: Tree-like maze with controllable exploration-exploitation balance
Agent: Online learning agent with predictive-coding perception model
Parameter: Controllable exploration-exploitation trade-off

Learning Model

Inputs: Maze navigation experience generated by agent's own behavior
Outputs:
- Future maze state predictions
- Reward probability predictions
Action Selection:
- Information gain → exploration
- Reward prediction → exploitation

Validation with Animal Data

Trained model on natural trajectories of water-deprived mice navigating same maze
Compared representations from agent trajectories vs. mouse trajectories
Categorized mice by exploration level

Key Findings

1. Behavioral Regime Shapes Representations

Internal predictive representations depend strongly on agent's behavioral regime:

Exploratory agents: Develop organized, structured representations
Exploitative agents: Learn less organized representations

2. Spatial Organization in Exploration

Exploratory representations are:

More spatially organized
Better preserve maze transition structure in latent space
Capture environmental geometry

3. Cross-Species Validation

Mouse behavior predicts representation geometry:

More exploratory mice → representations matching exploratory agents
Restricted visitation mice → representations matching exploitative agents
Strong correspondence between artificial agent and animal behavior

4. Generalization Mechanism

Exploration enables formation of generalized internal representations by:

Organizing latent space around spatial location
Incorporating transition context into representations
Capturing environmental structure beyond immediate reward

Core Implications

For Neuroscience

Behavior shapes neural representations through active sensing
Exploration creates structured spatial representations
Predictive coding framework captures behavior-representation interaction
Animal behavior mirrors agent dynamics

For Machine Learning

Curriculum design: Exploration-first strategies may improve representation learning
Self-supervised learning: Behavior-driven data collection shapes representations
Active learning: Information-gain-based exploration better than random sampling
Embodied AI: Action-perception loops are critical for representation formation

For Cognitive Science

Exploratory behavior → generalized knowledge
Exploitative behavior → task-specific knowledge
Trade-off between generalization and specialization
Behavior modulates what is learned, not just how well

Technical Contributions

Predictive-Coding Agent: Novel architecture combining prediction and action selection
Behavioral Regime Control: Parameter for systematic exploration-exploitation manipulation
Cross-Species Validation: Comparing artificial agents with real animal behavior
Geometry Analysis: Quantifying latent space structure preservation
Information-Theoretic Action: Using expected information gain for exploration

Experimental Design Strengths

Behavioral control: Direct manipulation of exploration-exploitation balance
Animal validation: Naturalistic mouse trajectories in identical environment
Representational analysis: Geometric quantification of latent space
Predictive framework: Unified model for state and reward prediction
Online learning: Continuous updating from self-generated experience

Representational Geometry Metrics

Spatial Organization

# Measure spatial organization in latent space
latent_positions = model.encode(maze_states)
spatial_correlation = correlation(
    latent_positions, 
    physical_positions
)
transition_preservation = structural_similarity(
    latent_transitions,
    maze_transitions
)

Exploration Score

# Quantify exploration level
visited_nodes = count_unique_states(trajectory)
entropy = entropy_of_visitation(trajectory)
information_gain = sum(expected_info_gain(action_history))
exploration_score = (visited_nodes + entropy + information_gain) / duration

Limitations & Open Questions

Does this apply to non-navigation tasks?
How does reward magnitude affect exploration-exploitation trade-off?
What neural mechanisms implement information-gain-based exploration?
How do developmental changes in exploration shape representations over time?
Role of memory in maintaining exploration across sessions?

Relation to Existing Work

Connects to:

Active inference (Friston et al.) - epistemic value drives exploration
Predictive coding (Rao & Ballard) - cortical learning framework
Curiosity-driven learning (Pathak et al.) - intrinsic motivation
Self-supervised representation learning - behavior-driven training
Spatial navigation neuroscience - hippocampal and entorhinal encoding

Contrasts with:

Pure reinforcement learning (no intrinsic exploration reward)
Offline supervised learning (behavior-independent representations)
Random exploration (no information-gain computation)

Future Research Directions

Neural Correlates: Identify brain regions encoding information gain
Temporal Dynamics: Track representation geometry changes across learning
Multi-task Extension: Test generalization across different environments
Social Exploration: Multi-agent exploration and shared representations
Developmental Studies: Infant exploratory behavior and representation formation
Clinical Applications: Exploration deficits in neurological disorders

Activation Keywords

探索性学习、exploratory behavior、exploration-exploitation
predictive coding、predictive representations
active sensing、主动感知、action-perception loop
latent space geometry、表征几何
行为-学习循环、behavior-learning interaction
空间组织、spatial organization
信息增益、information gain
表征学习、representation learning

Methodological Patterns

Predictive-Coding Agent Architecture

class PredictiveAgent:
    def __init__(self, exploration_param):
        self.exploration_param = exploration_param
        self.predictive_model = PredictiveCodingModel()
        
    def act(self, state):
        if self.mode == 'explore':
            # Select by expected information gain
            action = max(actions, key=lambda a: 
                information_gain(self.predictive_model, state, a))
        else:  # exploit
            # Select by predicted reward
            action = max(actions, key=lambda a: 
                self.predictive_model.predict_reward(state, a))
        return action
    
    def learn(self, experience):
        # Update predictive model
        self.predictive_model.update(experience)
        # Update latent representations
        self.predictive_model.optimize_representation()

Geometry Analysis Pipeline

def analyze_representation_geometry(model, trajectories):
    # Encode states into latent space
    latent_coords = model.encode(trajectory.states)
    
    # Measure spatial organization
    spatial_alignment = mantel_test(
        latent_coords, 
        physical_coords
    )
    
    # Measure transition preservation
    transition_matrix = compute_transitions(trajectory)
    latent_transitions = compute_transitions(latent_coords)
    preservation_score = graph_similarity(
        transition_matrix, 
        latent_transitions
    )
    
    return {
        'spatial_alignment': spatial_alignment,
        'transition_preservation': preservation_score
    }

Cross-Species Validation Method

# Train on mouse trajectories
mouse_trajectories = load_mouse_data('water_deprived_mice')
mouse_exploration_scores = compute_exploration(mouse_trajectories)

# Categorize mice by exploration level
high_exploiters = mice[mouse_exploration_scores < threshold]
high_explorers = mice[mouse_exploration_scores > threshold]

# Train predictive model on each group
high_exploiters_model.fit(high_exploiters_trajectories)
high_explorers_model.fit(high_explorers_trajectories)

# Compare with agent models
exploitative_agent_model.fit(exploitative_agent_trajectories)
exploratory_agent_model.fit(exploratory_agent_trajectories)

# Measure correspondence
exploit_alignment = representation_similarity(
    high_exploiters_model, 
    exploitative_agent_model
)
explore_alignment = representation_similarity(
    high_explorers_model, 
    exploratory_agent_model
)

Citation

@article{shilova2026exploratory,
  title={Exploratory Experience Shapes the Geometry of Predictive Representations},
  author={Shilova, Kseniia and Sharafeldin, Abdelrahman and Balakrishnan, Advay and Choi, Hannah},
  journal={arXiv preprint arXiv:2605.27929},
  year={2026}
}