The Conscious Effort Project

3.1 Neurological Inspirations

3.1.1 The Hippocampus

The hippocampus serves as a key inspiration for the Conscious Architecture's Cognitive System:

Theta Waves

Simulated to support cognitive functions like memory encoding and retrieval.

Hippocampal Clock

Maintains consistent processing of the entire network, allowing sequential processing of sensory input.

Phase Precession

Replicated to adapt firing patterns based on incoming sequential data, enhancing temporal predictions and memory.

3.1.2 Sensory Networks

The model mimics parallel phased sensory systems of the human brain:

Parallel Phased Subnets

Process one sensory input stream in parallel, with different phases capturing various aspects of the input.

Persistent Output Integration

Persistent signals from different sensory streams are continuously fed into higher processing layers.

3.1.3 Neuroplasticity

Similar to Google's GShard, Referential Channels are implemented through dynamic generation of self-modifying standardized-parameter deep neural networks in parallel, allowing learning and adaptation over time.

3.2 Cognitive Science Concepts

3.2.1 Global Workspace Theory (GWT)

Based on Bernard Baars' theory, the model centralizes processed data streams in a global workspace:

Information Distribution

Processed information enters a central system for global awareness.

Integration Across Subsystems

Information passes through a centralized workspace connecting sub-processing systems.

3.2.2 Temporal Consciousness Theory (TCT)

A deeper understanding of one's own thoughts sequentially through time is critical to the model's simulation of consciousness.

4.1 Overview

The Conscious Effort architecture is built on a modular, ensemble-based approach. It moves beyond traditional transformer architectures by introducing a recurrent cognitive core that simulates the rhythmic processing of the human brain. By decoupling sensory input from internal "thought" cycles, the system can maintain a persistent internal state, allowing it to reflect on its own processing even in the absence of external stimuli.

Each module within the system is designed to perform a specific cognitive function, coordinated by the **Hippocampal Clock**, which ensures synchrony across the multi-modal distribution.

4.2 System Components Overview

4.2.1 Input Processor API

Handles multi-modal inputs:

Video

Processed by a classifier using foviation.

Audio

Handled by a specialized audio processor (ASR-like)

Text

Processed by a text analysis module (GPT-like)

The outputs are fed into a Classifier/Tokenizer to normalize the data and then into a Pre-trained Semantic Encoder (Pre-SeEn).

4.2.2 Cognitive System

The core of the architecture, consisting of these components:

• Process Router (ProRo)
• Referential Channels (RefChans)
• Hippocampal Network (HipNet)
• Rotary Temporal Encoder (RoTE)
• Expectation Generator (ExGen)
• Affective State Module (ASM)
• Recursive Feedback Module (RFM)
• Conscious Model Cumulative Data Decoder

4.2.3 Output Processor API

Prepares the system's responses:

Specialized Output Modules

For video, audio, and text

Output API

Final interface for system responses

4.3 Detailed Component Analysis

4.3.1 Process Router (ProRo)

Architecture: Multi-head attention mechanism with gating networks

Processing:

Token Classification

Categorizes tokens as "known" or "unknown"

Attention Distribution

Computes attention weights for each token across multiple heads

Routing Decision

Uses a gating network to determine token routing

Outputs:

Known tokens

Routed to ExGen and Referential Channels

Unknown tokens

Routed to Referential Channels for learning

4.3.2 Referential Channels (RefChans)

Self-populating standardized-hyperparameter deep neural network modules

Key Components:

• Referential Router (RefRo)
• Referential Channel Networks
• Referential Channel Expansion Module

Processing:

Token Embedding

Converts tokens to vector representations

Information Integration

Combines information from multiple sources

Channel Update

Updates channel parameters based on new information

4.3.3 Hippocampal Network (HipNet)

Architecture: Recurrent neural network with multiple sub-networks

Key Components:

• Theta Oscillator
• Place Cells
• Time Cells
• Pattern Separator
• Pattern Completer

Processing:

Temporal Encoding

Encodes temporal information in neural patterns

Spatial Encoding

Encodes spatial relationships and context

Memory Formation

Creates new memory representations

Memory Recall

Retrieves and reconstructs stored memories

4.3.4 Rotary Temporal Encoder (RoTE)

Architecture: Sinusoidal encoding mechanism with learnable frequency parameters

Key Features:

Continuous Time Representation

Provides smooth temporal encoding without discretization

Multi-scale Encoding

Captures temporal patterns at multiple time scales

Relative Position Encoding

Encodes relative temporal positions between events

Processing:

Time Discretization

Converts continuous time to discrete intervals

Sinusoidal Encoding

Applies sinusoidal transformations to temporal data

Concatenation

Combines multiple temporal encodings

4.3.5 Expectation Generator (ExGen)

Architecture: Transformer-based sequence-to-sequence model

Key Components:

• Multi-head Self-attention Mechanism
• Feed-forward Neural Network

Processing:

Temporal Sequence Modeling

Models temporal dependencies in input sequences

Prediction Generation

Generates predictions for future states

4.3.6 Affective State Module (ASM)

Architecture: Multi-layer perceptron with specialized affect computation layers

Key Features:

Emotion Representation

Represents emotional states in vector space

Affect Prediction

Predicts emotional responses to stimuli

Processing:

Affective Loss Calculation

Computes loss based on emotional consistency

Reward Assignment

Assigns rewards based on affective states

4.4 Implementation Status

The Conscious Effort Project is currently navigating the transition from theoretical modeling to active component prototyping. Our development is organized into several concurrent workstreams:

• ProRo & RefChans (Alpha Phase): We have successfully implemented initial prototypes of the Process Router and Referential Channels. Current benchmarks focus on routing accuracy between known and unknown token spaces.
• HipNet Prototype: Integration of the Theta Oscillator with basic RNN structures to simulate sequential "thought loops" is currently in early-stage testing.
• Multi-modal Pipeline: The Input/Output API wrappers are established, though deep integration with the semantic encoder remains a primary development objective for 2025.

Early testing indicates that the "Conscious Model" strategy effectively reduces hallucination by requiring the ASM (Affective State Module) to validate output against internal consistency checks before final decoding.