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The Dance Language and Orientation of Bees

The hippocampus as a cognitive map

In this paper we present a broad overview of the last 40 years of research on cognitive architectures To date, the number of existing architectures has reached several hundred, but most of the existing surveys do not reflect this growth and instead focus on a handful of well-established architectures In this survey we aim to provide a more inclusive and high-level overview of the research on cognitive architectures Our final set of 84 architectures includes 49 that are still actively developed, and borrow from a diverse set of disciplines, spanning areas from psychoanalysis to neuroscience To keep the length of this paper within reasonable limits we discuss only the core cognitive abilities, such as perception, attention mechanisms, action selection, memory, learning, reasoning and metareasoning In order to assess the breadth of practical applications of cognitive architectures we present information on over 900 practical projects implemented using the cognitive architectures in our list We use various visualization techniques to highlight the overall trends in the development of the field In addition to summarizing the current state-of-the-art in the cognitive architecture research, this survey describes a variety of methods and ideas that have been tried and their relative success in modeling human cognitive abilities, as well as which aspects of cognitive behavior need more research with respect to their mechanistic counterparts and thus can further inform how cognitive science might progress

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40 years of cognitive architectures: core cognitive abilities and practical applications

In this paper we present a broad overview of the last 40 years of research on cognitive architectures. Although the number of existing architectures is nearing several hundred, most of the existing surveys do not reflect this growth and focus on a handful of well-established architectures. While their contributions are undeniable, they represent only a part of the research in the field. Thus, in this survey we wanted to shift the focus towards a more inclusive and high-level overview of the research in cognitive architectures. Our final set of 86 architectures includes 55 that are still actively developed, and borrow from a diverse set of disciplines, spanning areas from psychoanalysis to neuroscience. To keep the length of this paper within reasonable limits we discuss only the core cognitive abilities, such as perception, attention mechanisms, learning and memory structure. To assess the breadth of practical applications of cognitive architectures we gathered information on over 700 practical projects implemented using the cognitive architectures in our list. We use various visualization techniques to highlight overall trends in the development of the field. For instance, our data confirms that the hybrid approach to cognitive modeling already dominates the field and will likely continue to do so in the future. Our analysis of practical applications shows that most architectures are very narrowly focused on a particular application domain. Furthermore, there is an apparent gap between general research in robotics and computer vision and research in these areas within the cognitive architectures field. It is very clear that biologically inspired models do not have the same range and efficiency compared to the systems based on engineering principles and heuristics. Another observation is related to a general lack of collaboration, which is surprising to see in the inherently interdisciplinary area of cognitive architectures. Several factors hinder communication, among which are the closed nature of the individual projects (only one-third of the reviewed here architectures are open-source) and terminological differences.

A Review of 40 Years of Cognitive Architecture Research: Focus on Perception, Attention, Learning and Applications.

Understanding the nature of consciousness is one of the grand outstanding scientific challenges. The fundamental methodological problem is how phenomenal first person experience can be accounted for in a third person verifiable form, while the conceptual challenge is to both define its function and physical realization. The distributed adaptive control theory of consciousness (DACtoc) proposes answers to these three challenges. The methodological challenge is answered relative to the hard problem and DACtoc proposes that it can be addressed using a convergent synthetic methodology using the analysis of synthetic biologically grounded agents, or quale parsing. DACtoc hypothesizes that consciousness in both its primary and secondary forms serves the ability to deal with the hidden states of the world and emerged during the Cambrian period, affording stable multi-agent environments to emerge. The process of consciousness is an autonomous virtualization memory, which serializes and unifies the parallel and subconscious simulations of the hidden states of the world that are largely due to other agents and the self with the objective to extract norms. These norms are in turn projected as value onto the parallel simulation and control systems that are driving action. This functional hypothesis is mapped onto the brainstem, midbrain and the thalamo-cortical and cortico-cortical systems and analysed with respect to our understanding of deficits of consciousness. Subsequently, some of the implications and predictions of DACtoc are outlined, in particular, the prediction that normative bootstrapping of conscious agents is predicated on an intentionality prior. In the view advanced here, human consciousness constitutes the ultimate evolutionary transition by allowing agents to become autonomous with respect to their evolutionary priors leading to a post-biological Anthropocene.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

https://royalsocietypublishing.org/doi/pdf/10.1098/rstb.2015.0448

Synthetic consciousness: the distributed adaptive control perspective.

An embodied biologically constrained model of foraging

When learning new environments, rats often pause at decision points and look back and forth over their possible trajectories as if they were imagining the future outcome of their actions, a behavior termed "Vicarious trial and error" (VTE). As the animal learns the environmental configuration, rats change from deliberative to habitual behavior, and VTE tends to disappear, suggesting a functional relevance in the early stages of learning. Despite the extensive research on spatial navigation, learning and VTE in the rat model, fewer studies have focused on humans. Here, we tested whether head-scanning behaviors that humans typically exhibit during spatial navigation are as predictive of spatial learning as in the rat. Subjects performed a goal-oriented virtual navigation task in a symmetric environment. Spatial learning was assessed through the analysis of trajectories, timings, and head orientations, under habitual and deliberative spatial navigation conditions. As expected, we found that trajectory length and duration decreased with the trial number, implying that subjects learned the spatial configuration of the environment over trials. Interestingly, IdPhi (a standard metric of VTE) also decreased with the trial number, suggesting that humans benefit from the same head-orientation scanning behavior as rats at spatial decision-points. Moreover, IdPhi captured exclusively at the first decision-point of each trial, was correlated with trial trajectory duration and length. Our findings demonstrate that in VTE is a signature of the stage of spatial learning in humans, and can be used to predict performance in navigation tasks with high accuracy.

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Human Vicarious Trial and Error Is Predictive of Spatial Navigation Performance.

Many hippocampal cell types are characterized by a progressive increase in scale along the dorsal-to-ventral axis, such as in the cases of head-direction, grid and place cells. Also located in the medial entorhinal cortex (MEC), border cells would be expected to benefit from such scale modulations. However, this phenomenon has not been experimentally observed. Grid cells in the MEC of mammals integrate velocity related signals to map the environment with characteristic hexagonal tessellation patterns. Due to the noisy nature of these input signals, path integration processes tend to accumulate errors as animals explore the environment, leading to a loss of grid-like activity. It has been suggested that border-to-grid cells' associations minimize the accumulated grid cells' error when rodents explore enclosures. Thus, the border-grid interaction for error minimization is a suitable scenario to study the effects of border cell scaling within the context of spatial representation. In this study, we computationally address the question of i) border cells' scale from the perspective of their role in maintaining the regularity of grid cells' firing fields, as well as ii) what are the underlying mechanisms of grid-border associations relative to the scales of both grid and border cells. Our results suggest that for optimal contribution to grid cells' error minimization, border cells should express smaller firing fields relative to those of the associated grid cells, which is consistent with the hypothesis of border cells functioning as spatial anchoring signals.

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Size Matters: How Scaling Affects the Interaction between Grid and Border Cells.

Insects are great explorers, able to navigate through long-distance trajectories and successfully find their way back. Their navigational routes cross dynamic environments suggesting adaptation to novel configurations. Arthropods and vertebrates share neural organizational principles and it has been shown that rodents modulate their neural spatial representation accordingly with environmental changes. However, it is unclear whether insects reflexively adapt to environmental changes or retain memory traces of previously explored situations. We sought to disambiguate between insect behavior in environmental novel situations and reconfiguration conditions. An immersive mixed-reality multi-sensory setup was built to replicate multi-sensory cues. We have designed an experimental setup where female crickets Gryllus Bimaculatus were trained to move towards paired auditory and visual cues during primarily phonotactic driven behavior. We hypothesized that insects were capable of identifying sensory modifications in known environments. Our results show that, regardless of the animal’s history, novel situation conditions did not compromise the animals performance and navigational directionality towards a new target location. However, in trials where visual and auditory stimuli were spatially decoupled, the animals heading variability towards a previously known position significantly increased. Our findings showed that crickets can behaviorally manifest environmental reconfiguration, suggesting the encoding for spatial representation.

Insect Behavioral Evidence of Spatial Memories During Environmental Reconfiguration

Rodents are able to navigate within dynamic environments by constantly adapting to their surroundings. Hippocampal place-cells encode the animals current location and fire in sequences during path planning events. Place-cells receive excitatory inputs from grid-cells whose metric system constitute a powerful mechanism for vector based navigation for both known and unexplored locations. However, neither the purpose or the behavioral consequences of such mechanism are fully understood. During early exploration of a maze with multiple discrimination points, rodents typically manifest a conflict-like behavior consisting of alternating head movements from one arm of the maze to the other be- fore making a choice, a behavior which is called vicarious trial and error (VTE). Here, we suggest that VTE is modulated by the learning process between spatial- and reward-tuned neuronal populations. We present a hippocampal model of place- and grid-cells for both space representation and mental travel that we used to control a robot solving a foraging task. We show that place-cells are able to represent the agents current location, whereas grid-cells encode the robots movement in space and project their activity over unexplored paths. Our results suggest a tight interaction between spatial and reward related neuronal activity in defining VTE behavior.

Diogo Santos-Pata

Papers

An embodied biologically constrained model of foraging

Human Vicarious Trial and Error Is Predictive of Spatial Navigation Performance.

Size Matters: How Scaling Affects the Interaction between Grid and Border Cells.

Insect Behavioral Evidence of Spatial Memories During Environmental Reconfiguration

Navigate the Unknown: Implications of Grid-Cells “Mental Travel” in Vicarious Trial and Error