CCRG

The Mind According to LIDA is a collection of descriptions, of increasing length and detail, designed to give the reader a quick overview of the LIDA model. Immediately below is a short Abstract, followed by a 2-page Executive Summary, and finally by a Brief Description document.

The Mind According to LIDA – Abstract

How do minds work? Human minds, animal minds, artificial minds? What are their underlying mechanisms? What would answers to such questions even look like? We claim it's best to look for answers not in terms of neurons or cell assemblies, or in terms of algorithms, but in terms of systems-level cognitive models that attempt to conceptually account for everything mental that occurs between an incoming sensory stimulus and a resulting outgoing motor action. Our LIDA systems-level cognitive model, as yet partly computational, maintains that all of our ongoing mental activity is composed of an overlapping sequence of extraordinarily rapid cognitive moments that we call cognitive cycles. Each such cognitive cycle, a mental building block, is exceeding complex in itself, consisting of multiple memory systems and executive systems, together with a host of processes acting upon each of them. External or internal stimuli are first interpreted so as to update LIDA's ongoing understanding of its current situation, not always an easy task. The most salient portions of this understanding come to consciousness to enable learnings in multiple modes, along with the selection and execution of an appropriate mental or motor response, thus completing the cycle. Higher level cognitive processes, reasoning, planning, imagining, etc. are accomplished by cascading these cognitive cycles. An appropriate fleshing out of this brief description may begin to answer the question "what are the mechanisms of minds?" But, what fleshing out?

This abstract is intended as an introduction, and an enticement, to a following two page executive summary of the mind according to LIDA. That summary is itself an introduction and enticement to a six page brief description of the LIDA conceptual model. Finally, a guide to the LIDA literature endeavors to lead the reader through articles and presentations describing the latest intricacies of the LIDA model, its computational architecture, and its implemented LIDA-based software agents and robots.

The LIDA cognitive model attempts to answer the question, "How do minds work, be they human, animal, or artificial?" Conceived of as a cognitive prosthesis, the LIDA model is a conceptual theory intended to facilitate thinking about thinking. It describes conceptually mechanisms underlying mental activities. As a systems-level model, LIDA attempts to account for a full range of mental processes from sensing incoming external or internal stimuli, to producing outgoing motor responses, and every sort of mental activity in between.

LIDA's basic building block is the cognitive cycle, its version of the action-perception cycle of the psychologists and the neuroscientists (Cutsuridis, Hussain, & Taylor, 2011; Dijkstra, Schoner, & Gielen, 1994; Freeman, 2002; Neisser, 1976). Emerging asynchronously from the actions of its processes, each LIDA cognitive cycle consists of three phases. Its understanding phase interprets the incoming stimuli in light of the current situation and its past memories, and updates its current situational model. The following attending phase chooses the most salient portion of the updated current situation, the conscious content, to be broadcast globally (Baars, Franklin, & Ramsoy, 2013; Baars, 1988). This broadcast content enables the final acting and learning phase during which memories of several sorts are encoded or reinforced, and a suitable sensory-motor response is selected and executed. Though complex, each cognitive cycle is quite brief, say 300-600 ms in humans (Koivisto & Revonsuo, 2010; Madl, Baars, & Franklin, 2011).

The LIDA model asserts that all cognitive processing consists of an overlapping sequence of such cognitive cycles, with different parts of overlapping cycles running simultaneously in parallel. Though the model is asynchronous, seriality is maintained by the conscious broadcasts of the cycles effectively producing the illusion of continuity of consciousness. The model further asserts that all higher level cognitive processing, for example, deliberating, reasoning, planning, etc., is implemented via multiple cognitive cycles acting as cognitive "atoms."

Typical of cognitive models, LIDA is composed of various modules each with its own processes. LIDA is not only asynchronous, it is almost entirely local in its processing, with the conscious broadcast being the only almost global process. Most of the model’s modules are memory systems, including sensory, perceptual (recognition), spatial, episodic, attentional, procedural, and sensory motor memories. A preconscious workspace houses LIDA's current situational model, the global workspace hosts the competition for consciousness, and there are modules for action selection and motor plan execution. Even at the so fleeting cognitive cycle level, the LIDA model is quite complex, with each of these modules claiming its own inner structure and sophisticated processing.

Currently, the internal structures of several of the LIDA modules are conceptually and computationally constructed as directed graphs similar to semantic nets, but embodied. Nodes in the graph represent entities such as objects, categories, actions, feelings, events, etc., while their links represent various relationships among these entities. Other LIDA modules are composed of more complex structures formed from subgraphs of these same graphs. For mostly computational reasons, there is now a movement afoot to move from nodes and links representations to sparse distributed vector representations (Snaider & Franklin, 2012).

The LIDA Computational Framework is a generic and customizable computational implementation of, as yet, much of the LIDA model, programmed in Java (Snaider, McCall, & Franklin, 2011). The primary goal of the Framework is to provide a generic implementation of the LIDA model, easily customizable for specific problem domains, so as to allow for the relatively rapid development of LIDA controlled software agents and/or robots. The Framework permits a declarative description of the specific implementation in which the architecture of the software agent is specified using an XML formatted file. In this way, the developer need not define the entire agent in Java, but can simply specify it using this XML file.

The Framework is intended to be ready customizable at several levels depending upon the required functionality. At the most basic level, developers can use the XML file to customize their applications. Within the Framework, several small data structures can also be customized by adding one’s own versions of them. Finally, more advanced users can also customize and change the internal implementation of whole modules. For each module, the Framework provides default implementations that greatly simplify the customization process.

In order to show how the computational LIDA architecture can model human cognition in basic psychological tasks, we have used the LIDA Framework to develop several cognitive software agents that replicate experimental data from human subjects (Faghihi, McCall, & Franklin, 2012; Madl, et al., 2011; Madl & Franklin, 2012). Our main goals with these agents were to substantiate some of the claims of the LIDA model, and to move towards identifying a set of internal parameters. Ideally, these internal parameters will remain constant when disparate datasets from different experiments conducted on human subjects are reproduced with LIDA agents. Finding such a set of parameters would provide empirical evidence of the accuracy and usefulness of the conceptual cognitive model.

As demonstrated by the real world problem solving of LIDA's predecessor IDA (Franklin, 2003; McCauley & Franklin, 2002), the LIDA model is quite capable of controlling real-world software agents or robots (Franklin, 2001; Franklin & Jones, 2004)

Every comprehensive model of cognition must be grounded in the underlying neuroscience. How is this grounding in neuroscience to be accomplished in the LIDA Model? Perceptual symbols (Barsalou, 1999) in the form of nodes and links in LIDA's perceptual memory comprise the common representational currency of the LIDA Model. To ground these perceptual symbols in the underlying neuroscience, we think of them as representing not neurons or cell assemblies, but rather wings of chaotic attractors in an attractor landscape (Franklin, Strain, Snaider, McCall, & Faghihi, 2012; Freeman, 1999; Harter, Graesser, & Franklin, 2001; Skarda & Freeman, 1987). When perturbed by a previously learned exogenous stimulus such as one that may result from an inhalation, the spiking trajectory of a cell assembly, such as an olfactory bulb, falls into a wing of an attractor, and so recognizes an odor. Thus we postulate non-linear dynamics as an intermediate theory serving to ground comprehensive cognitive models such as LIDA in the underlying neuroscience.

Baars, B., Franklin, S., & Ramsoy, T. (2013). Global workspace dynamics: Cortical "binding and propagation" enables conscious contents. Frontiers in Consciousness Research, 4, 200. doi: 10.3389/fpsyg.2013.00200

Baars, Bernard J. (1988). A Cognitive Theory of Consciousness. Cambridge: Cambridge University Press.

Barsalou, L. W. (1999). Perceptual symbol systems. Behavioral and Brain Sciences, 22, 577–609.

Cutsuridis, V., Hussain, A., & Taylor, J. G. (2011). Perception-Action Cycle: Models, Architectures, and Hardware (Vol. 1): Springer.

Dijkstra, T. M. H., Schoner, G., & Gielen, C. C. A. M. (1994). Temporal stability of the action-perception cycle for postural control in a moving visual environment. Experimental Brain Research, 97(3), 477-486.

Faghihi, U., McCall, R., & Franklin, S. (2012). A Computational Model of Attentional Learning in a Cognitive Agent. Biologically Inspired Cognitive Architectures, 2, 25-36.

Franklin, S. (2001). Automating Human Information Agents. In Z. Chen & L. C. Jain (Eds.), Practical Applications of Intelligent Agents (pp. 27–58 ). Berlin: Springer-Verlag.

Franklin, S. (2003). An Autonomous Software Agent for Navy Personnel Work: A Case Study. In D. Kortenkamp & M. Freed (Eds.), Human Interaction with Autonomous Systems in Complex Environments: Papers from 2003 AAAI Spring Symposium (pp. 60–65). Palo Alto: AAAI.

Franklin, S., & Jones, D. (2004, October 22-24, 2004). A Triage Information Agent (TIA) based on the IDA Technology Paper presented at the AAAI Fall Symposium on Dialogue Systems for Health Communication Washington, DC, USA.

Franklin, S., Strain, S., Snaider, J., McCall, R., & Faghihi, U. (2012). Global Workspace Theory, its LIDA model and the underlying neuroscience. Biologically Inspired Cognitive Architectures, 1, 32-43. doi: 10.1016/j.bica.2012.04.001

Freeman, Walter J. (1999). Consciousness, Intentionality and Causality. In R. Nunez & W. J. O. R. o. E. e. Freeman (Eds.), Reclaiming Cognition (pp. 143–172). Exeter: Imprint Academic.

Freeman, W. J. (2002). The limbic action-perception cycle controlling goal-directed animal behavior. Neural Networks, 3, 2249-2254.

Harter, D., Graesser, Arthur C., & Franklin, S. (2001). Bridging the gap: Dynamics as a unified view of cognition. Behavioral and Brain Sciences, 24, 45–46.

Koivisto, M., & Revonsuo, A. (2010). Event-related brain potential correlates of visual awareness. Neurosci Biobehav Rev, 34(6), 922-934. doi: 10.1016/j.neubiorev.2009.12.002

Madl, T., Baars, B. J., & Franklin, S. (2011). The Timing of the Cognitive Cycle. PLoS ONE, 6(4), e14803. doi: 10.1371/journal.pone.0014803

Madl, T., & Franklin, S. (2012). A LIDA-based Model of the Attentional Blink. Paper presented at the International Conference on Cognitive Modelling.

McCauley, L., & Franklin, S. (2002). A Large-Scale Multi-Agent System for Navy Personnel Distribution. Connection Science, 14, 371–385 %O Comments: special issue on agent autonomy and groups.

Neisser, U. (1976). Cognition and Reality: Principles and Implications of Cognitive Psychology San Francisco: W. H. Freeman.

Skarda, C., & Freeman, Walter J. (1987). How Brains Make Chaos in Order to Make Sense of the World. Behavioral and Brain Sciences, 10, 161–195.

Snaider, J., & Franklin, S. (2012). Extended Sparse Distributed Memory and Sequence Storage. Cognitive Computation, 4(2), 172-180. doi: 10.1007/s12559-012-9125-8

Snaider, J., McCall, R., & Franklin, S. (2011). The LIDA Framework as a General Tool for AGI. Paper presented at the The Fourth Conference on Artificial General Intelligence (Springer Lecture Notes in Artificial Intelligence), Mountain View, California, USA.