The document compares several cognitive architectures - ACT-R/PM, SOAR, EPIC, CHREST, and ICARUS - in terms of their approaches to perceptual processing. It analyzes factors like whether initial perceptual information needs to be programmed or learned, the modularity of perception, how expectations are handled, and the granularity of visual representations. While the architectures include some perceptual abilities, the document argues they need to more fully incorporate object-level perception, depth perception, scene perception based on distributed attention theories, and the effects of emotion on perception and attention. More learning from experience is also needed to better ground cognition in perception.

2. Cognitive Architectures A cognitive architecture specifies the underlying infrastructure for an intelligent system. The specification of a cognitive architecture consists of its representational assumptions, the characteristics of its memories, and the processes that operate on those memories. Research on cognitive architectures is important because it supports a central goal of artificial intelligence and cognitive science: the creation and understanding of synthetic agents that support the same capabilities as humans. Unlike expert systems, cognitive architectures aim for breadth of coverage across a diverse set of tasks and domains. More important, they offer accounts of intelligent behavior at the systems level, rather than at the level of component methods designed for specialized tasks. Def: A Cognitive architecture can be defined simply as the portion of a system that provides and manages the primitive resources of an agent.

5. ACT-R/PM ACT-R's main components are: modules, buffers, pattern matcher. There are two types of modules: perceptual-motor modules: takes care of the interface with the real world (i.e., with a simulation of the real world)‏ memory modules There are two kinds of memory modules: declarative memory and procedural memory Buffers: ACT-R accesses its modules (except for the procedural-memory module) through buffers. http://act-r.psy.cmu.edu/about/

6. SOAR Hypothesis : All deliberate goal -oriented behavior can be cast as the selection and application of operators to a state . A state is a representation of the current problem-solving situation; an operator transforms a state (makes changes to the representation); a goal is a desired outcome of the problem-solving activity. As Soar runs, it is continually trying to apply the current operator and select the next operator (a state can have only one operator at a time), until the goal has been achieved. Soar has separate memories (and different representations) for descriptions of its current situation and its long-term knowledge. In Soar, the current situation, including data from sensors, results of intermediate inferences, active goals, and active operators is held in working memory .( organized as objects). Objects are described in terms of their attributes ; the values of the attributes may correspond to sub-objects, so the description of the state can have a hierarchical organization. The Soar architecture cannot solve any problems without the addition of long-term knowledge. ( Note the distinction between the “Soar architecture” and the “Soar program”): Soar architecture” refers to the system while the “Soar program” refers to knowledge added to the architecture. Soar execution ..select-> apply..

7. EPIC Human performance in a task is simulated by programming the cognitive processor with production rules organized as methods for accomplishing task goals. The EPIC model then is run in interaction with a simulation of the external system and performs the same task as the human operator would. The model generates events (e.g. eye movements, key strokes, vocal utterances) whose timing is accurately predictive of human performance. Multi-task performance and its simulation in EPIC is one of the core research focus.

8. CHREST The model combines low-level aspects of cognition (e.g., mechanisms monitoring information in short-term memory) with high-level aspects of cognition (e.g., use of strategies) . It consists of perception facilities for interacting with the external world, short-term memory stores (in particular, visual and verbal memory stores), a long-term memory store, and associated mechanisms for problem solving. Short-term memory in CHREST contains references to chunks held in long-term memory, which are recognized through the discrimination network from information acquired by the perception system.

9. ICARUS The basic Icarus interpreter operates on a recognize-act cycle but, unlike many architectures, focuses on reactive execution of existing skills rather than on problem-space search. A skill consists of three elements stated in terms of logical expressions: a set of objectives, a set of requirements or preconditions, and a set of alternate means for accomplishing the objective under those conditions. Each objective, requirement, or means can refer to primitive actions/sensors or to other Icarus skills, thus imposing a hierarchical organization on long-term memory. Each skill also has an associated utility cast as a linear function of sensory attributes. Given a top-level skill to pursue, on each cycle the system first checks the objective field for that skill. If the objectives are true , nothing further needs to be done, but, if not, the interpreter examines the requirements to determine if the preconditions for action are met. If not , Icarus invokes a subskill associated with the failed requirement in an effort to satisfy it; otherwise, it selects one of the alternate means and calls on the primitive action or subskill associated with it. The architecture selects the alternative with the highest expected utility as predicted by a linear function associated with each skill.

10. Comparing Cog Arch