The challenge of designing a human-level learner is central to creating a computational equivalent of the human mind. It demands the level of robustness and flexibility of learning that is still only available in biological systems. Therefore, it is essential that we better understand at a computational level how biological systems naturally develop their cognitive and learning functions. In recent years, biologically inspired cognitive architectures (BICA) have emerged as a powerful new approach toward gaining this kind of understanding. The impressive success of BICA-2008 was clear evidence of this trend. As the second event in the series, BICA-2009 continues our attack on the challenge, with the overall atmosphere of excitement and promise, brainstorming, and collaboration.

A self-organizing neural network is described that can associate between different modalities and also has the ability to learn perceptual sequences. This architecture is a step towards the development of a complete agent containing simplified versions of all major neural subsystems in a mammal. It aims at exploring as well as takes inspiration from the idea that cognitive function involves an internal simulation of perception and movement. We have tested the architecture in simulations as well as together with real sensors with very encouraging results.

In this paper, we present a cognitive model of frequency-based decision-making applied to the task of landmine detection. The model is implemented in the ACT-R cognitive architecture and is strongly constrained by the cognitive primitives of the architecture. We then generalize the model to another task in the domain of macroeconomic decision-making using the same architecture, pursuing theoretical parsimony. We describe each model's representation requirements, assess their fits to the data, and analyze their performance scaling as a function of task and architectural parameters. Efforts to generalize the landmine detection model to macroeconomic decision making showed that reasonable fits to the macro-economic performance data could be achieved by models based either on procedural knowledge or declarative knowledge. This finding underscores the importance of distinguishing between processing strategies employed to execute tasks. Such detail appears needed to understand the neural foundations of frequency-based decision-making.

Memory retrieval is a spontaneous process difficult to measure in naturalistic settings. By adapting an automated paging process, we measured spontaneous autobiographical and prospective memory retrieval probability, and found the derived frequency of recall in a given time period to be significantly higher than expected. Altogether, this research provides a quantitative characterization of human memory.

A state is not alive, yet it performs many of the central enjoys few bonds of kinship: and residence depends upon functions of life like replication and adaptation to new conditions occupational specialization rather than blood relations. A to balance social protection and opportunity. As a modern state can declare war on behalf of the entire collectivity, lifelike system the rise of the modern state raises four sets reserving the right to declare mandatory participation of fundamental questions about its evolutionary design. A and to contract the area of private vengeance. They proclaim first set concerns how it became a sustainable, autonomously a monopoly of force and of law, while requiring citizens to replicating system, capable of evolution. All non-state agglomerations forgo violence; vengeance is not the responsibility of the offended such as empires or chiefdoms eventually stagnate party. Almost any crime against one member is a because they are closed systems that break down over crime against the state. Subgroups seeking vengeance are time (Weber). A state is an open system that must able to viewed as threatening to the order of the state.

Consciousness is a major feature of mammalian nervous significant "news" is demanding to be heard by the systems. Recent evidence indicates it may extend from established powers in the society of mind. The only known mammals to birds and even cephalopods (Edelman & Seth, function of slow-wave sleep, for example, is to consolidate 2009). Since all major biological adaptations are episodic memories (i.e., memories of conscious events that functional, or sequelae of biofunctions, and since brains took place during the conscious period before sleep). They be to "reapportion" cortical functions based on the are based on biochemistry and eukaryotic cells, which previous day's experiences, consistent with the last two impose narrow limits on such features as the peak rate of decades of work on cortical plasticity.

A human subject experiment was conducted to investigate caffeine’s effect on appraisal and performance of a mental serial subtraction task. Serial subtraction performance data was collected from three treatment groups: placebo, 200, and 400 mg caffeine. The data were analyzed by caffeine treat ment group and how subjects appraised the task (as challenging or threatening). A cognitive model of the serial subtraction task was developed. The model was fit to the human performance data using a parallel genetic algorithm. How the model’s parameters change to fit the data suggest how cognition changes due to caffeine and appraisal. Over all, the cognitive modeling and optimization results suggest that the speed of vocalization varies the most along with changes to declarative memory. This approach provides a way to compute how cognitive mechanisms change due to moderators.

We describe some experiments which simulate a grounded approach to the acquisition of the cognitive and linguistic competence required to communicate propositional logic sentences. This encompasses both the construction of a conceptualisation of its environment by each individual agent and of a shared language by the population. The processes of conceptualisation and language acquisition in each individual agent are based on general purpose cognitive capacities, such as categorisation, discrimination, invention, adoption and induction. The construction of a shared language by the population is achieved using a particular type of linguistic interaction, known as the evaluation game, which gives rise to a common set of linguistic conventions through a process of self-organisation. This work addresses the problem of the acquisition of both the semantics and the syntax of propositional logic. Trying to learn these two aspects at the same time is more difficult than learning the semantics or the syntax of propositional logic separately. Because the agents must coordinate their linguistic behaviour taking into account only the subset of objects which constitutes the topic of a particular linguistic interaction. This means that a pair of agents can communicate successfully about a particular subset of objects (a topic) even if they use different conceptualisations (formulas) in order to identify the same topic. And this introduces a high degree of ambiguity in the interpretation process the agents have to deal with when they try to construct a shared communication language. In spite of this, the results of the experiments show that at the end of the simulation runs the individual agents build different conceptualisations and grammars, but that the conceptualisations and grammars of the agents in the population are compatible in the sense that they guarantee the unambiguous communication of propositional logic sentences.

Dramatic advances in the last six months in High Definition Fiber Tracking (HDFT) make it possible to image the fiber connectivity from source to destination mapping hundreds of thousands fiber tracks with sufficient resolution to identify the cable level circuit diagram of the human brain. Brain activity imaging studies using functional Magnetic Resonance Imagining (fMRI) identify differential activation patterns as a function of task and level of practice. These data show subnetworks with communication of high bandwidth vector associations, scalar priority and control signals, and interactions with control and meta cognition. The connectivity and activity data support a triarchic cognitive architecture. Processing is the synergistic interaction of three interlinked cognitive computational systems with differential computation role and evolutionary history. These data provided a detailed diagram to guide reverse engineering of the systems levels of the human brain.

Decades ago, Alan Turing proposed a test to show if a machine has intelligence, a test that has yet to be replaced by a more comprehensive theory. The same test however, says nothing about what is intelligence. This paper proposes a definition based on a system ability to deal with uncertainty, which is the main attribute of our intelligence. It introduces a new adaptive system theory and the Viable Complex System (VCS), concept that is applied to organisms, social organizations, and to the design and architecture of IT systems. All VCSs share a dual structure built on two function types: operations (i.e. resource processing) and change (adaptability). A system adapts by learning from the interactions with environment on how to improve its chances to survive. All systems sharing common operations are part of a realm. Obviously, we may have systems which could live in two realms at the same time. In conclusion, we define information as the interaction between two similar VCSs, and intelligence as a property of adaptive systems which exist in the context of two realms (i.e. humans being biological organisms and members of the society). We extend the model to quantify intelligence through the use of a new term called information density. This concept associates complexity of the logic embedded in a message, especially the one related to changes, with the system ability to process that logic in its quest to survive. The more intelligent the system, the better it is at extracting information towards higher efficiency and higher viability. We are closing the paper with the presentation of two case studies from our practice that shows how this model can be applied in the IT when designing enterprise systems.