A Robotic Approach to Understanding the Role and the Mechanism of Vicarious Trial-And-Error in a T-Maze Task

Abstract

Vicarious trial-and-error (VTE) is a behavior observed in rat experiments that seems to suggest self-conflict. This behavior is seen mainly when the rats are uncertain about making a decision. The presence of VTE is regarded as an indicator of a deliberative decision-making process, that is, searching, predicting, and evaluating outcomes. This process is slower than automated decision-making processes, such as reflex or habituation, but it allows for flexible and ongoing control of behavior. In this study, we propose for the first time a robotic model of VTE to see if VTE can emerge just from a body-environment interaction and to show the underlying mechanism responsible for the observation of VTE and the advantages provided by it. We tried several robots with different parameters, and we have found that they showed three different types of VTE: high numbers of VTE at the beginning of learning, decreasing numbers afterward (similar VTE pattern to experiments with rats), low during the whole learning period, and high numbers all the time. Therefore, we were able to reproduce the phenomenon of VTE in a model robot using only a simple dynamical neural network with Hebbian learning, which suggests that VTE is an emergent property of a plastic and embodied neural network. From a comparison of the three types of VTE, we demonstrated that 1) VTE is associated with chaotic activity of neurons in our model and 2) VTE-showing robots were robust to environmental perturbations. We suggest that the instability of neuronal activity found in VTE allows ongoing learning to rebuild its strategy continuously, which creates robust behavior. Based on these results, we suggest that VTE is caused by a similar mechanism in biology and leads to robust decision making in an analogous way.

Long-Term Homeostatic Properties Complementary to Hebbian Rules in CuPc-Based Multifunctional Memristor

“…Otherwise, failed habituation/sensitization may lead to the exorbitantly high/low level neural activities. These mechanisms are also missing in Hebbian rules in addition to the homeostatic plasticity 19 30 34 . Although the relevant accurate algorithms of the inter-regulatory mechanisms have not been developed, the plasticity can be implemented in thicker CuPc memristors.…”

“…Therefore, in the electronics, it is necessary to create an memristive element with homeostatic plasticity to enhance the long-term stability of the overall conductivity under excessive stimuli through self-adjustment 28 29 . In another situation for the long-term homeostasis of neural activities, during learning processes the overall response in our brain is gradually weakened under repeated moderate signals and recovered by intense signals, termed as habituation and dishabituation (sensitization) which are also neglected by Hebbian rules 30 . They regulate each other to contribute to achieving the neural homeostasis.…”

Evolutionary Robotics: Model or Design?

“…The importance of ER models of adaptive behavior is revealed also by an attentive analysis beyond qualitative observation. For instance, Matsuda et al (2014) explore deliberative decision-making in an ER model, showing that robust deliberation is linked to the coupled effects of continuous learning abilities and rich sensorimotor experiences. Finally, Sellers and colleagues exploited the strengths of ER to model plausible gaits of extinct species, from the Australopithecus afarensis (Sellers et al, 2005) to sauropod dinosaurs (Sellers et al, 2013).…”

Algebraic path tracking to aid the manual harvesting of olives using an automated service unit

“…Over the last two decades, the robots have shown to be efficient tools for improving agricultural tasks developing what is known as precision agriculture (Lee et al (1999); Perez-Ruiz et al (2011); Malinowski & Yu (2011), Wood et al (2014); Matsuda et al (2014)).…”