Anticipation by multi-modal association through an artificial mental imagery process

Gaona, Wilmer; Escobar, Esaú; Hermosillo, J.; Lara, Bruno

doi:10.1080/09540091.2014.956289

Cited by 10 publications

(5 citation statements)

References 27 publications

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“…It is worth noting that the training data for the sensory predictions is either 1 for collision or 0 for no collision; however, the actual predictions are continuous values in the range [0−1], and these values correspond to the location and distance to obstacles. This activation has been shown previously to not be a mere artifact in several control experiments (Gaona et al, 2014). This result strongly suggests that multimodal associations between visual input and how the world would feel, modeled with internal models and using LTPs, can account as a mechanism that allows the perception of distance.…”

Section: Discussionsupporting

confidence: 74%

Development of body-based spatial knowledge through mental imagery in an artificial agent

et al. 2020

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Distance perception for mobile agents is of great importance for safe navigation in unknown environments. Traditional methods make use of analytical solutions. Yet, according to some research hypothesis, distance perception is not the result of mathematical calculations, but an emergent consequence of an association process, where visual and tactile information acquire a central role. Designing models closer to natural cognition poses paramount challenges to artificial intelligence (AI), which call for a review of some of the foundations of current methods. Our work is framed in the embodied cognition paradigm, which highlights the importance of the body for the development of cognitive processes. We provide theoretical grounds and empirical evidence for an artificial account of distance perception through a multimodal association process. By learning multimodal sensorimotor schemes, an agent is capable of perceiving affordances related to distance perception without any non-body-based geometric knowledge. We let an agent interact with an environment cluttered with objects, while learning multimodal sensorimotor associations. The learned spatial relations are thoroughly characterized to show how the model depends on the agent’s specific sensorimotor capabilities. The system is tested in a passability experiment and a navigation task, showing the agent anticipates undesired situations using the learned model predictions.

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Section: Discussionsupporting

confidence: 74%

Development of body-based spatial knowledge through mental imagery in an artificial agent

et al. 2020

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“…Other investigators have been interested in the use of robotically embodied neural models to investigate mental imagery, and a recent special issue collects many examples from this field [ 26 ]. Additional neurorobotic models have addressed motor imagery [ 27 ], the use of imagery as an aid to learning [ 28 ], and multi-modal associative learning to create imagery [ 29 ]. Recent work has addressed mental rotation in a mean firing rate neural model that controlled a virtual humanoid robot [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Imagery May Arise from Associations Formed through Sensory Experience: A Network of Spiking Neurons Controlling a Robot Learns Visual Sequences in Order to Perform a Mental Rotation Task

et al. 2016

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Mental imagery occurs “when a representation of the type created during the initial phases of perception is present but the stimulus is not actually being perceived.” How does the capability to perform mental imagery arise? Extending the idea that imagery arises from learned associations, we propose that mental rotation, a specific form of imagery, could arise through the mechanism of sequence learning–that is, by learning to regenerate the sequence of mental images perceived while passively observing a rotating object. To demonstrate the feasibility of this proposal, we constructed a simulated nervous system and embedded it within a behaving humanoid robot. By observing a rotating object, the system learns the sequence of neural activity patterns generated by the visual system in response to the object. After learning, it can internally regenerate a similar sequence of neural activations upon briefly viewing the static object. This system learns to perform a mental rotation task in which the subject must determine whether two objects are identical despite differences in orientation. As with human subjects, the time taken to respond is proportional to the angular difference between the two stimuli. Moreover, as reported in humans, the system fills in intermediate angles during the task, and this putative mental rotation activates the same pathways that are activated when the system views physical rotation. This work supports the proposal that mental rotation arises through sequence learning and the idea that mental imagery aids perception through learned associations, and suggests testable predictions for biological experiments.

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“…One could argue that it is a non-grounded decision. To address this issue, Gaona, Escobar, Hermosillo, and Lara (2014) used LTPs to ground the concept of collisions. The system was developed in two stages.…”

Section: Embodied Sensorimotor Schemesmentioning

confidence: 99%

Embodied Cognitive Robotics and the learning of sensorimotor schemes

et al. 2018

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Embodied Cognitive Robotics focuses its attention on the design of artificial agents capable of performing cognitive tasks autonomously. A central issue in this consists in studying process by which agents learn through interaction with their environment. Embodied Cognitive Robotics aims to implement models of cognitive processes coming from Cognitive Sciences. The guidelines in this research area are a direct response to the shortcomings of Classical Artificial Intelligence, where high-level tasks and behaviors were studied. This article describes the work carried out in the Cognitive Robotics Laboratory at the Universidad Autónoma del Estado de Morelos (UAEM). Our work is based on the concept of low-level sensorimotor schemes coded by Internal Models, thus falling as a matter of course within the tenets of Embodied Cognition, particularly with the idea that cognition must be understood as occurring in agents that have a body with which they interact in a specific environment. It is through this interaction that learning emerges laying the ground for cognitive processes. Our research includes theoretical work laying the foundations of Embodied Cognitive Robotics, as well as work with artificial and with natural agents.

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Anticipation by multi-modal association through an artificial mental imagery process

Cited by 10 publications

References 27 publications

Development of body-based spatial knowledge through mental imagery in an artificial agent

Development of body-based spatial knowledge through mental imagery in an artificial agent

Imagery May Arise from Associations Formed through Sensory Experience: A Network of Spiking Neurons Controlling a Robot Learns Visual Sequences in Order to Perform a Mental Rotation Task

Embodied Cognitive Robotics and the learning of sensorimotor schemes

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