Cognitive Architectures Overview

Representations and Cognitive Architectures

• Representations are arbitrary labels assigned to nodes defined by the modeller, often challenging to conceptualize regarding weights or thresholds that trigger outputs.

Assumptions of Cognitive Architectures

• Productivity:

  • Infinite complex mental representations can be generated from a finite number of simplex ones.
  • Requires postulating mental representations (MRs) that combine elementary structures into more complex forms.

• Systematicity:

  • Ability to entertain complex MRs is interconnected, allowing the formation of new representations through similar syntactic structures.

• Compositionality:

  • The content of a complex MR derives from its simplex components and their structural organization.

Key Differences Between Symbolic and Connectionist Architectures

• Productivity:

  • Symbolic: Finite symbols can generate infinite expressions due to the constituent structure of MRs.
  • Connectionist: Nodes represent MRs, and adding units alters connectivity and structure of knowledge.

• Systematicity:

  • Symbolic: Structurally allows transformation of thought forms (e.g. P&Q to Q&P).
  • Connectionist: Unique states require separate nodes (e.g. “John loves Mary” vs. “Mary loves John”).

• Compositionality:

  • Symbolic: Thoughts retain properties of contained symbols across structures (e.g. P&Q contains P and Q).
  • Connectionist: The thought emerges from the activation pattern of nodes, without containing individual thoughts.

Compromise in Cognitive Modeling

• Symbolic architectures serve well for higher-level cognitive processes such as language and thought comprehension.
• Connectionist architectures model the physical realization of cognitive processes, examining neural connectivity as vehicles for mental activity.

Historical Definitions of Symbols

• Symbols act as representations of real-world entities (Descartes).
• Symbols are meaning-assigning tokens (Whitehead).
• Symbols are physical patterns with semantic roles, akin to data pointers that retrieve information (Newell & Simon).

Cognitive Architectures Explained

• Define how mental functions manifest in the brain, involving knowledge types, processing steps, and operational principles.
• Detail information flow and storage to realize cognitive processes like thoughts and language comprehension.

Symbolic Architecture Characteristics

• Symbols represent mental units, while cognitive processes follow computational rules over these symbols.
• The architecture operates similar to electrical circuits, representing and processing information via "on" and "off" states.

Feature-Based Approach in Symbolic Architecture

• Features are foundational elements carrying meaning, computed to form concepts.
• Requires binding concepts together through rules for feature compilation.

Limitations of Symbolic Approaches

• Rule-governed behaviors lack the flexibility of human cognition, making them insufficient for modeling complex cognitive processes.

Connectionist Architecture Overview

• Envisions cognitive systems as networks of interconnected units (nodes) mimicking neuron behavior.
• Nodes serve as abstract mental representations, activating based on input values, connection strengths, and threshold levels.

Activation Processes in Connectionist Architecture

• Activation patterns establish connections leading to associative networks.
• Configurations may be feed-forward (one-directional inputs) or recurrent (feedback loops).

Types of Connectionism

• Parallel Distributed Processing (PDP): Multiple nodes correspond to features, facilitating broader representation.
• Local Connectionism: Each node represents major concepts or categories, e.g., words like "DOG."

Limitations of Connectionist Approaches

• Lacks structured rules and imposes (quasi-) unconstrained activation without clear compositional principles.

Cognitive Architectures

• Cognitive architectures explore how mental functions are realized in the brain and their reliance on various types of knowledge.
• Key considerations include the flow of information, storage mechanisms, and how mental representations (concepts) form thoughts, plans, and decisions.

Representationalist Schools

• Symbolic Architecture: Characterizes mental representations as symbols, using computations over these symbols.
  • Symbols act as units of information, while cognitive processes are mechanical devices executing rules.
  • Machines process information like electrical circuits, transitioning between states based on input-output operations.
  • Limited representations and rules can result in infinite computations.

Feature-Based Approach

• Emphasizes features as fundamental elements of meaning, requiring rules for feature compilations to yield concepts.
• Concepts must be bound to create coherent mental structures.

Limitations of Symbolic Architecture

• Often lacks the flexibility (plasticity) seen in human cognition.
• Inadequate for modeling higher cognitive processes.

Connectionist Architecture

• Focuses on cognitive systems embodied in interconnected nodes, resembling neural networks.
• Nodes represent abstract mental states with activation dependent on input weights, total input strength, and threshold levels.
• Offers feed-forward and recurrent processing strategies, emphasizing associative networks.

Types of Connectionism

• Parallel Distributed Processing (PDP): Multiple nodes correspond to features.
• Local Connectionism: Each node aligns with major categories (e.g., "word," "DOG").

Limitations of Connectionist Architecture

• Representations lack specific meanings assigned by the modeler.
• Uncertainty exists around how input weights and thresholds are determined for activation.

Assumptions of Cognitive Architectures

• Productivity: Ability to generate infinite complex mental representations from a finite set of simplex representations, relying on a combinatorial structure.
• Systematicity: Complex mental representations share an intrinsic connection; if one can understand a specific representation, related representations become accessible.
• Compositionality: The meaning of complex mental representations derives from the structure and meaning of their simplex components.

Contrasts Between Architectures

• Productivity:

  • Symbolic: Finite symbols can produce infinite expressions due to constituent structure.
  • Connectionist: Each node is a representation, adding nodes alters the network structure.

• Systematicity:

  • Symbolic: Understanding relationships (e.g., P&Q and Q&P) is facilitated by constituent structure.
  • Connectionist: Different states (e.g., "John loves Mary" vs. "Mary loves John") require distinct nodes.

• Compositionality:

  • Symbolic: Thoughts inherently contain constituent symbols (e.g., P&Q consists of P and Q).
  • Connectionist: Thoughts are products of activation patterns without containing constituent symbols.

Compromise Between Architectures

• Symbolic architectures model higher-level cognitive processes effectively, such as thoughts and language comprehension.
• Connectionist architectures model the physical realization of cognitive processes and the connections of neurons in networks.

Symbols & Symbol Systems

• Historical definitions of symbols: Representations in the mind (Descartes), tokens assigned meanings (Whitehead).
• Symbols function as physical patterns with semantic meaning, facilitating complex expressions and cognitive processes governed by structured sequences rather than intrinsic meanings.

Explore the concepts of representations and cognitive architectures, including productivity, systematicity, and compositionality. Understand the differences between symbolic and connectionist architectures, and how they process mental representations. This quiz will enhance your grasp of cognitive modeling principles.