Symbolic vs Connectionist
A symbolic model is based on concrete algorithm for the situation. A symbolic model may have mapped this task to a number of if-then statements which tell the robot how to act given a situation e.g. If there is an enemy ahead with a better gun, then run away. In a symbolic model, the robot explicitly tries to figure out what next to do based on the determined algorithm.
On the other hand, if we use the connectionist model to approach this task, we would have a lot of connection weights between the layers which would determine the actions that we take and learning can take place by reinforcement based adjustment of these weights and this can happen without any explicit algorithm to play quake.
I think that the connectionist model more closely resembles the processes in the mind because
- There is no definite optimal way (concrete algorithm) to do the things that we do in our daily life.
- the neurons form the basic components of the brain and when we gain implicit knowledge, it is in the form of connections between them (according to ‘My Brilliant Brain’) and this closely resembles the mechanism by learning by adjusting weights in connectivist model.

Also, if a robot is to become an expert in a field, since experts use implicit representations and intutions which are better modelled by the connectionist model, using a connectionist model may be more apt for this scenario.

Embodiment

A Robot with a mind but without a body is essentially like a Blackbox which takes input and returns an output without any realisation of the external environment or the physical constraints.

In a disembodied/disjoint system, the brain is essentially a function of its own parameters however for learning, an interaction between the mind and body is almost essential. Various aspects of the cognition such as thoughts, ideas and concepts are indirectly influenced by the body.
Any implemented, interactive system is embedded in the world in at least three ways: temporally, physically and socially [R. Picard. Affective Computing. MIT Press, Cambridge, MA, 1998 ]. It is observed that a robot while trying to learn instructions is dependent on time which it utilizes, the space which it occupies and the objects it physically interacts with.

Some examples :

Space utilization

1. In order to adjust with the difficulty level of the game, the Robot can switch to a different position of its fingers in an orientation that brings out more swift movements.

2. A Robot can adjust his orientation relative to the position of the Laptop or for that matter a keypad.
But when is this possible????
Only when there is an external medium( a Body with hands and fingers in this case)which interacts with the space around and helps in improving his actions.

Time utilization

1. Perceiving relative moments of objects and reacting differently in various situations requires a ‘sense of time’ which can be not be alone perceived by the mind.

Viewing from the perspective of neuroscience, the phantom sensations perceived by amputated humans how integral body is to mind. This shows that the learning may go beyond the mind and it is the whole system (Mind and Body) which learns.

Though the ‘unused’ feedback and sensory data perceived by the mind through this external body in which it is embedded may not play a ‘direct’ role in ‘task execution’ however they definitely contribute to attaining expertise over the task by providing an ‘intrinsic feedback’.

Our thought process is not an abstract entity that is distinct from perception and action. It exists conjointly with the material aspects of the physical realities surrounding us. The instructions provided to the Robot form a part of the ‘explicit memory’ in it. However while trying to utilize space and time, the Robot is essentially learning instructions ( of which he is not consciously aware of ) implicitly. These adjustments ( stored in his implicit memory ) complement his external input and help him master the game.

Thus, it can be concluded that apart from the basic input given to a robot to perform the task, following two aspects of cognition play a vital role in learning :

1. Embodiment with the outer space and time through a materialistic entity.

2. Formation of Mental Representations and Patters( in chunks ) stored in the implicit memory that help him quickly identify the best possible ‘move’