D2.1 Requirements and Specification - CORBYS

D2.1 Requirements and Specification
A notorious example for anticipatory failure is represented by the anticipation of extreme events (Nadin,
2005). Such examples are catastrophic events such as earthquakes or financial collapses for which often
power-law type post-hoc models can be given, but which violate the principle suggested in Nadin (2005) that
“a model unfolding in faster than real time [would appear] to the observer as an informational future”, in that
they screen the future from the present observer. A particular challenge is, as the author argues, that for these
models it is difficult to decide when predictions are appropriate because rare, but large events are
distinguished by the fact that their occurrence is not just not easily predicted according to models valid in
more “stable” phases of temporal development, but that the models do not even provide sufficient
introspection to detect this situation.
Seizures can be considered “extreme” events in the brain. In Mormann et al. (2006), a review is given over
existing seizure anticipation models based on EEG data. Methods that have been reported to be more
successful are based on dynamical systems models such as Lyapunov exponents, accumulated signal energy,
simulated neuronal cell models or phase synchronisation, however, later analysis seems to put these results in
doubt and the current state of knowledge is considered inconclusive.
On the level of intentional actions, however, it has been demonstrated that EMG registers the influence of
anticipation on future events; in particular, it appears in preparatory postures anticipating voluntary movement
(Brown and Frank, 1987). The anticipatory movement takes place in response to an expected task e.g. to
preserve balance in anticipation of a push or pull action. The combination of anticipation with attentional
mechanisms is believed to be controlled by the prefrontal cortex, by modulating sensory pathways in a “topdown”
fashion (Liang and Wang, 2003).
This biological evidence indicates the fundamental relevance of anticipation not only to prepare both active
behaviour which requires suitable alignment activities, but also from the standpoint of the preparation of
cognitive processing resources. This is corroborated by studies using principled information-theoretic
methods (van Dijk et al., 2010, van Dijk and Polani, 2011a). In these, it is assumed that limited informational
resources are allocated for the working memory keeping track of current goals (i.e. one places constraints on
goal-relevant information). This minimal assumption gives rise to salient decision transition points for
behaviour strategies, such as intermediate goals. This model acts as a kind of proto-attentional mechanism.
In addition, it highlights the link between attention, anticipation, and their emergence from constraints in the
available informational resources.
An agent’s actions carried out in the context of goal-directed behaviour must necessarily reveal information
about its goals and/or purposes. This information, called digested information, can be identified by other
agents that have the same goal as the first agent (Salge and Polani, 2011).
For the CORBYS project, the properties discussed in the last two paragraphs are of relevance. The digested
information principle under which actions reveal information about the agent’s intentions indicate that human
actions should provide the SOIAA architecture with clues to the intentions of the human. To handle this
possibly sparse information, it is necessary to impose additional regularisation constraints on its estimation.
For this purpose, the studies of task structuring by constrained goal-relevant information (van Dijk et al.,
2010, van Dijk and Polani, 2011a) offer natural approaches to regularisation.
Generally, anticipatory behaviour requires the combination of abilities to predict causally driven as well as
goal-directed dynamics. The first component aims at predicting a dynamics according to e.g. a “passive”
physical law, the second addresses the fact that agents, such as humans, are not passively following laws but
initiate behaviours with certain purposes. The “least commitment” philosophy described earlier provides a
natural framework to extract information about possible future purposeful trajectories from information about
129