A Hierarchical Modular Architecture for Embodied Cognition

in Multisensory Research
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Cognition can appear complex owing to the fact that the brain is capable of an enormous repertoire of behaviors. However, this complexity can be greatly reduced when constraints of time and space are taken into account. The brain is constrained by the body to limit its goal-directed behaviors to just a few independent tasks over the scale of 1–2 min, and can pursue only a very small number of independent agendas. These limitations have been characterized from a number of different vantage points such as attention, working memory and dual task performance. It may be possible that the disparate perspectives of all these methodologies can be unified if behaviors can be seen as modular and hierarchically organized. From this vantage point, cognition can be seen as having a central problem of scheduling behaviors to achieve short term goals. Thus dual-task paradigms can be seen as studying the concurrent management of simultaneous, competing agendas. Attention can be seen as focusing on the decision as to whether to interrupt the current agenda or persevere. Working memory can be seen as the bookkeeping necessary to manage the state of the current active agenda items.

A Hierarchical Modular Architecture for Embodied Cognition

in Multisensory Research

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Figures

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    Four levels of a hierarchical cognitive architecture that operate at different timescales. The central element is the task level, wherein a given task may be described in terms of a module of states and actions. A thread keeps track of the process of execution though the module. The next level down consists of visual and motor routines (arrows) that monitor the status state and action space fidelities respectively. Above the task level is the operating system level, whereby priorities for modules are use to select an appropriate small suite of modules to manage a given real world situation. The topmost level is characterized as an attentional level. If a given module is off the page of its expectations, it may be re-programmed via simulation and modification.

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    In a task-directed module, the maintenance of state information is handled by routines that exhibit agenda-driven control strategies. To get information, a hypothesis to be tested is putatively sent to the thalamus, where it is compared to coded image data. The result is state information that may in addition trigger a gaze change.

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    Human gaze data for the same environment showing striking evidence for visual routines. Humans in a virtual walking environment manipulate gaze location depending on the specific task goal. The small black dots show the location of all fixation points on litter and obstacles. When picking up litter (left) gaze points cluster on the center of the object. When avoiding a similar object (right) gaze points cluster at the edges. From Rothkopf and Ballard (2009). This figure is published in color in the online version.

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    In walking down a sidewalk, the virtual reality scenery was augmented with a dizzying array of distractors e.g. an upside-down cow. Subjects viewed the scene with a binocular HMD which was upadate for head motion. While waiting for a start command, subjects did fixate distractors (items 5 and 6), but when the sidewalk navigation experiment began, subjects fixated the task-relevant objects almost exclusively (items 1, 2 and 3).

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    In any period during behavior there is only a subset of the total module set that is active. We term these periods episodes. In the time course of behavior, modules that are needed become active and those that are no longer needed become inactive. The diagram depicts two sequential episodes of three modules each {3, 4, 7} and {2, 8, 10}. The different modules are denoted with different shadings and numbers. The different lengths indicate that modules can exhibit different numbers of states and finish at different times. The horizontal arrows denote the scheduler’s action in activating and deactivating modules. On the right is the large library of possible modules. Our formal results only depend on each module being chosen sufficiently often and not on the details of the selection strategy. The same module may be selected in sequential episodes.

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    A potential job for an alerting module: Detecting unusual variations in optic flow while driving. (A) Encroaching car produces a pronounced deviation from background radial flow expectation. Radial flow can be dismissed as a normal expectation, but the horizontal flow of a car changing lanes signals an alert. (B) The time line shows that this signal, as measured by a space and time-window integration, is easily detectable. This figure is published in color in the online version.

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    (A) The Sprague model of gaze allocation. Modules compete for gaze in order to update their measurements. The figure shows a caricature of the basic method for a given module. The trajectory through the agent’s state space is estimated using Kalman filter that propagates estimates in the absence of measurements and, as a consequence, build up uncertainty (large shaded area). If the behavior succeeds in obtaining a fixation, state space uncertainty is reduced (dark). The reinforcement learning model allows the value of reducing uncertainty to be calculated. (B) In the side-walking venue, three modules are updated using the Sprague protocol, a sequential protocol and a random protocol (reading from left to right). The Sprague protocol outperforms the other two.

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    A fundamental problem for a biological agent using a modular architecture. At any given instant, shown with dotted lines, when multiple modules are active and only a global reward signal G is available, the modules each have to be able to calculate how much of the rewards is due to their activation. This is known as the credit assignment problem. Our setting simplifies the problem by assuming that individual reinforcement learning modules are independent and communicate only their estimates of their reward values. The modules can be activated and deactivated asynchronously, and may each need different numbers of steps to complete, as suggested by the diagram.

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    (A) Reward calculations for the walkway navigation task for the three component behaviors. Top row: Initial values. Bottom row: Final reward estimates. (B) Time course of learning reward for each of the three component behaviors. RMS error between true and calculated reward as a function of iteration number.

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    Value functions and their associated policies for each of three modules that have been learned by a virtual avatar walking along a sidewalk strewn with litter and obstacles. The red disk marks the state estimate for each of them. The individual states for each module are assumed to be estimated by separate applications of the gaze vector to compute the requisite information. Thus the state for the obstacle is the heading to it, and similarly for the state for a litter object. The state for the sidewalk is a measure of the distance to its edge. In the absence of a gaze update, it is assumed that subjects use vestibular and proprioceptive information to update the individual module states. This figure is published in color in the online version.

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