  Neurocognitive models of higher-level somatosensory processing have
emphasised the role of stored body representations in interpreting real-time
sensory signals coming from the body (Longo, Azanon and Haggard, 2010; Tame,
Azanon and Longo, 2019). The need for such stored representations arises from
the fact that immediate sensory signals coming from the body do not specify
metric details about body size and shape. Several aspects of somatoperception,
therefore, require that immediate sensory signals be combined with stored body
representations. This basic problem is equally true for humanoid robots and,
intriguingly, neurocognitive models developed to explain human perception are
strikingly similar to those developed independently for localizing touch on
humanoid robots, such as the iCub, equipped with artificial electronic skin on
the majority of its body surface (Roncone et al., 2014; Hoffmann, 2021). In
this chapter, we will review the key features of these models, discuss their
similarities and differences to each other, and to other models in the
literature. Using robots as embodied computational models is an example of
synthetic methodology or 'understanding by building' (e.g., Hoffmann and
Pfeifer, 2018), computational embodied neuroscience (Caligiore et al., 2010) or
'synthetic psychology of the self' (Prescott and Camilleri, 2019). Such models
have the advantage that they need to be worked out into every detail, making
any theory explicit and complete. There is also an additional way of
(pre)validating such a theory other than comparing to the biological or
psychological phenomenon studied by simply verifying that a particular
implementation really performs the task: can the robot localize where it is
being touched (see https://youtu.be/pfse424t5mQ)?
