The Recommendation Architecture: Lessons from Large-Scale Electronic Systems Applied to Cognition

A fundamental approach of cognitive science is to understand cognitive systems by separating them into modules. Theoretical reasons are described which force any system which learns to perform a complex combination of real time functions into a modular architecture. Constraints on the way modules divide up functionality are also described. The architecture of such systems, including biological systems, is constrained into a form called the recommendation architecture, with a primary separation between clustering and competition. Clustering is a modular hierarchy which manages the interactions between functions on the basis of detection of functionally ambiguous repetition. Change to previously detected repetitions is limited in order to maintain a meaningful, although partially ambiguous context for all modules which make use of the previously defined repetitions. Competition interprets the repetition conditions detected by clustering as a range of alternative behavioural recommendations, and uses consequence feedback to learn to select the most appropriate recommendation. The requirements imposed by functional complexity result in very specific structures and processes which resemble those of brains. The design of an implemented electronic version of the recommendation architecture is described, and it is demonstrated that the system can heuristically define its own functionality, and learn without disrupting earlier learning. The recommendation architecture is compared with a range of alternative cognitive architectural proposals, and the conclusion reached that it has substantial potential both for understanding brains and for designing systems to perform cognitive functions

A Functional Architecture Approach to Neural Systems

The technology for the design of systems to perform extremely complex combinations of real-time functionality has developed over a long period. This technology is based on the use of a hardware architecture with a physical separation into memory and processing, and a software architecture which divides functionality into a disciplined hierarchy of software components which exchange unambiguous information. This technology experiences difficulty in design of systems to perform parallel processing, and extreme difficulty in design of systems which can heuristically change their own functionality. These limitations derive from the approach to information exchange between functional components. A design approach in which functional components can exchange ambiguous information leads to systems with the recommendation architecture which are less subject to these limitations. Biological brains have been constrained by natural pressures to adopt functional architectures with this different information exchange approach. Neural networks have not made a complete shift to use of ambiguous information, and do not address adequate management of context for ambiguous information exchange between modules. As a result such networks cannot be scaled to complex functionality. Simulations of systems with the recommendation architecture demonstrate the capability to heuristically organize to perform complex functionality

A physiologically based approach to consciousness

The nature of a scientific theory of consciousness is defined by comparison with scientific theories in the physical sciences. The differences between physical, algorithmic and functional complexity are highlighted, and the architecture of a functionally complex electronic system created to relate system operations to device operations is compared with a scientific theory. It is argued that there are two qualitatively different types of functional architecture, and that electronic systems have the instruction architecture based on exchange of unambiguous information between functional components, and biological brains have been constrained by natural selection pressures into the recommendation architecture based on exchange of ambiguous information. The mechanisms by which a recommendation architecture could heuristically define its own functionality are described, and compared with memory in biological brains. Dream sleep is interpreted as the mechanism for minimizing information exchange between functional components in a heuristically defined functional system. The functional role of consciousness of self is discussed, and the route by which the experience of that function described at the psychological level can be related to physiology through a functional architecture is outlined

A Physiologically Based System Theory of Consciousness

A system which uses large numbers of devices to perform a complex functionality is forced to adopt a simple functional architecture by the needs to construct copies of, repair, and modify the system. A simple functional architecture means that functionality is partitioned into relatively equal sized components on many levels of detail down to device level, a mapping exists between the different levels, and exchange of information between components is minimized. In the instruction architecture functionality is partitioned on every level into instructions, which exchange unambiguous system information and therefore output system commands. The von Neumann architecture is a special case of the instruction architecture in which instructions are coded as unambiguous system information. In the recommendation (or pattern extraction) architecture functionality is partitioned on every level into repetition elements, which can freely exchange ambiguous information and therefore output only system action recommendations which must compete for control of system behavior. Partitioning is optimized to the best tradeoff between even partitioning and minimum cost of distributing data. Natural pressures deriving from the need to construct copies under DNA control, recover from errors, failures and damage, and add new functionality derived from random mutations has resulted in biological brains being constrained to adopt the recommendation architecture. The resultant hierarchy of functional separations can be the basis for understanding psychological phenomena in terms of physiology. A theory of consciousness is described based on the recommendation architecture model for biological brains. Consciousness is defined at a high level in terms of sensory independent image sequences including self images with the role of extending the search of records of individual experience for behavioral guidance in complex social situations. Functional components of this definition of consciousness are developed, and it is demonstrated that these components can be translated through subcomponents to descriptions in terms of known and postulated physiological mechanisms

Attempting to Close the Evidence-Practice Gap in Physical Activity Intervention Research: Strategies and Outcomes of a Co-Creative Qualitative Study

It is argued that a gap exists between research evidence and “real-world” physical activity (PA) intervention practice. One potential way to aid the translatability of evidence in this field is for researchers to work actively with the public health practitioners and organisations that run PA interventions to engage in co-creative research. This paper reports the process and strategies used to underpin research co-creation during a recent qualitative PA intervention study, and the outcomes of the co-creative approach from the perspective of the public health organisation involved in the research in terms of providing them with translatable evidence. A range of strategies were reported to facilitate co-creation in the study, such as engaging the public health organisation in the identification of the research question and development of the research protocol and involving them in participant recruitment. The co-creative research approach resulted in timely, relevant, and understandable research evidence for the organisation, which was translatable to their real-world PA intervention practice. The evidence provided them with clear actions and information to plan their future work and objectives. This paper demonstrates how a co-creative research approach can potentially help to close the evidence-practice gap in the PA intervention field

Using the Change Manager Model for the Hippocampal System to Predict Connectivity and Neurophysiological Parameters in the Perirhinal Cortex

Theoretical arguments demonstrate that practical considerations, including the needs to limit physiological resources and to learn without interference with prior learning, severely constrain the anatomical architecture of the brain. These arguments identify the hippocampal system as the change manager for the cortex, with the role of selecting the most appropriate locations for cortical receptive field changes at each point in time and driving those changes. This role results in the hippocampal system recording the identities of groups of cortical receptive fields that changed at the same time. These types of records can also be used to reactivate the receptive fields active during individual unique past events, providing mechanisms for episodic memory retrieval. Our theoretical arguments identify the perirhinal cortex as one important focal point both for driving changes and for recording and retrieving episodic memories. The retrieval of episodic memories must not drive unnecessary receptive field changes, and this consideration places strong constraints on neuron properties and connectivity within and between the perirhinal cortex and regular cortex. Hence the model predicts a number of such properties and connectivity. Experimental test of these falsifiable predictions would clarify how change is managed in the cortex and how episodic memories are retrieved

A comparison of ocean model data and satellite observations of features affecting the growth of the North Equatorial Counter Current during the strong 1997-1998 El Nino

Descriptions of the ocean's role in the El Niño usually focus on equatorial Kelvin waves and the ability of such waves to change the mean thermocline depth and the sea surface temperature (SST) in the central and eastern Pacific. In contrast, starting from a study of the transport of water with temperatures greater than 28 ∘C, sufficient to trigger deep atmospheric convection, Webb (2018) found that, during the strong El Niños of 1983–1984 and 1997–1998, advection by the North Equatorial Counter Current (NECC) had a much greater impact on sea surface temperatures than processes occurring near the Equator. Webb's analysis, which supports the scheme proposed by Wyrtki (1973, 1974), made use of archived data from a high-resolution ocean model. Previously the model had been checked in a preliminary comparison against SST observations in the equatorial Pacific, but, given the contentious nature of the new analysis, the model's behaviour in key areas needs to be checked further against observations. In this paper this is done for the 1987–1988 El Niño, making use of satellite observations of SST and sea level. SST is used to check the movement of warm water near the Equator and at the latitudes of the NECC. Sea level is used to check the model results at the Equator and at 6∘ N in the North Equatorial Trough. Sea level differences between these latitudes affect the transport of the NECC, the increased transport at the start of each strong El Niño being associated with a drop in sea level at 6∘ N in the western Pacific. Later rises in sea level at the Equator increase the transport of the NECC in mid-ocean. The variability of sea level at 6∘ N is also used to compare the strength of tropical instability waves in the model and in the observations. The model showed that in a normal year these act to dilute the temperature in the core of the NECC. However their strength declined during the development of the strong El Niños, allowing the NECC to carry warm water much further than normal across the Pacific. The results of this paper should not be taken as providing proof of the hypotheses of Wyrtki (1973, 1974) or Webb (2018) but instead as a failure of a targeted study, using satellite observations, to disprove the hypotheses

A window on the deep ocean: the special value of ocean bottom pressure for monitoring the large-scale, deep-ocean circulation

We show how, by focusing on bottom pressure measurements particularly on the global continental slope, it is possible to avoid the “fog” of mesoscale variability which dominates most observables in the deep ocean. This makes it possible to monitor those aspects of the ocean circulation which are most important for global scale ocean variability and climate. We therefore argue that such measurements should be considered an important future component of the Global Ocean Observing System, to complement the present open-ocean and coastal elements. Our conclusions are founded on both theoretical arguments, and diagnostics from a fine-resolution ocean model that has realistic amplitudes and spectra of mesoscale variability. These show that boundary pressure variations are coherent over along-slope distances of tens of thousands of kilometres, for several vertical modes. We illustrate the value of this in the model Atlantic, by determining the time for boundary and equatorial waves to complete a circuit of the northern basin (115 and 205 days for the first and second vertical modes), showing how the boundary features compare with basin-scale theoretical models, and demonstrating the ability to monitor the meridional overturning circulation using these boundary measurements. Finally, we discuss applicability to the real ocean and make recommendations on how to make such measurements without contamination from instrumental drift