Towards a “Brain-Guided” Cognitive Architecture - Bioinspired Approaches for Human-Centric Technologies - page 224

resonates with the concurrent bottom-up activation (through the perceptual stream),
this is sufficient to lead to the inference that the novel object being perceived is most
probably the one the user is requested to grab (Mohan et al. 2013 ).
This kind of property-specific organization and global integration through hubs
is in line with emerging results from neuroscience (van den Heuvel and Sporns
2013 ; Martin 2009 ; Meyer and Damasio 2009 ) as depicted in Fig. 7.3 . It is also
worth remarking that two important features are made possible by this kind of
architecture:
1. The bottom-up processing leads to a distributed representation of the perceived
objects (in relation to its perceptual properties color, shape, size) in the object
connector hub that identifies the object (in other words coding for “what is it”).
2. Due to reciprocal connectivity between the hubs and property-specific maps, it
becomes possible to go beyond “object-action” and learn things at the level of
“property-action” too: in our embodied framework, “actions” are mediated
through the “body” and directed towards “objects” in the environment,
according to “tasks.”
Playful interactions with objects give rise to sensorimotor experience, learning,
and ability to reason in the future. Thus there is the need to connect “object,”
“action,” and the “body.” Note that there is a subtle separation between represen-
tation of actions at an abstract level (“what all can be done with an object/tool”) and
the memories related to the action and its consequences (“how to do”). While the
former relates to the “affordances” of an object, the latter relates to memories of
motor skills, sensorimotor consequences, and anticipated rewards in relation to the
goal. The abstract layer forms the “connector hub” and consists of single neurons
coding for different action goals like reach, grasp, push, stack, use of different tools,
etc. and grows with time as new skills are learned. Single neurons in the connector
hub in turn have the capability to trigger the subsystems that hold (procedural,
semantic, and episodic) knowledge related to the action (and other actions that may
participate as subcomponents). In this sense neurons in the top-level “action
connector hub” are similar to “canonical neurons” found in the premotor cortex
(Murata et al 1997 ) that are activated at the sight of objects to which specific actions
are applicable. At the same time the detailed knowledge itself is learned/
represented in distributed cortical networks which are activated by the action goal
(may also involve other sub-actions and sensorimotor memories related to them).
7.4.2 Learning to Build the Tallest Stack Given a Random
Set of Objects to Play with
While building the tallest stack, the robot is allowed to explore gradually with a
limited set of objects (two at a time, then add a new object, further add another new
object, present them in different combinations). The role of the teacher is important