# HG changeset patch
# User Robert McIntyre <rlm@mit.edu>
# Date 1398645573 14400
# Node ID 0b0fef5e817bfb482f150e962ffb44a3438f3cff
# Parent  8a5abd51cd4f172381e1e82bf2a1864ec055370b
more clarification.

diff -r 8a5abd51cd4f -r 0b0fef5e817b thesis/cortex.org
--- a/thesis/cortex.org	Sun Apr 27 20:25:22 2014 -0400
+++ b/thesis/cortex.org	Sun Apr 27 20:39:33 2014 -0400
@@ -2946,7 +2946,7 @@
    #+end_listing
 
    #+caption: =longest-thread= finds the longest path of consecutive 
-   #+caption: experiences to explain proprioceptive worm data from 
+   #+caption: past experiences to explain proprioceptive worm data from 
    #+caption: previous data. Here, the film strip represents the  
    #+caption: creature's previous experience. Sort sequences of
    #+caption: memories are spliced together to match the
@@ -2967,13 +2967,13 @@
    =longest-thread= takes time proportional to the average number of
    entries in a proprioceptive bin, because for each element in the
    starting bin it performs a series of set lookups in the preceding
-   bins. If the total history is limited, then this is only a constant
-   multiple times the number of entries in the starting bin. This
-   analysis also applies even if the action requires multiple longest
-   chains -- it's still the average number of entries in a
-   proprioceptive bin times the desired chain length. Because
-   =longest-thread= is so efficient and simple, I can interpret
-   worm-actions in real time.
+   bins. If the total history is limited, then this takes time
+   proprotional to a only a constant multiple of the number of entries
+   in the starting bin. This analysis also applies, even if the action
+   requires multiple longest chains -- it's still the average number
+   of entries in a proprioceptive bin times the desired chain length.
+   Because =longest-thread= is so efficient and simple, I can
+   interpret worm-actions in real time.
 
    #+caption: Program to calculate empathy by tracing though \Phi-space
    #+caption: and finding the longest (ie. most coherent) interpretation
@@ -3015,8 +3015,13 @@
    using a gradient over the closest known sensory data points,
    averages can be misleading. It is certainly possible to create an
    impossible sensory state by averaging two possible sensory states.
-   Therefore, I simply replicate the most recent sensory experience to
-   fill in the gaps.
+   For example, consider moving your hand in an arc over your head. If
+   for some reason you only have the initial and final positions of
+   this movement in your \Phi-space, averaging them together will
+   produce the proprioceptive sensation of having your hand /inside/
+   your head, which is physically impossible to ever experience
+   (barring motor adaption illusions). Therefore I simply replicate
+   the most recent sensory experience to fill in the gaps.
 
    #+caption: Fill in blanks in sensory experience by replicating the most 
    #+caption: recent experience.
@@ -3079,7 +3084,7 @@
    #+end_src
    #+end_listing
 
-   #+caption: Use longest thread and a phi-space generated from a short
+   #+caption: Use =longest-thread= and a \Phi-space generated from a short
    #+caption: exercise routine to interpret actions during free play.
    #+name: empathy-debug
 #+begin_listing clojure
@@ -3102,13 +3107,25 @@
            (curled? empathy)       (.setText text "Curled")
            (wiggling? empathy)     (.setText text "Wiggling")
            (resting? empathy)      (.setText text "Resting")
-           :else                       (.setText text "Unknown")))))))
+           :else                   (.setText text "Unknown")))))))
 
 (defn empathy-experiment [record]
   (.start (worm-world :experience-watch (debug-experience-phi)
                       :record record :worm worm*)))
    #+end_src
    #+end_listing
+
+   These programs create a test for the empathy system. First, the
+   worm's \Phi-space is generated from a simple motor script. Then the
+   worm is re-created in an environment almost exactly identical to
+   the testing environment for the action-predicates, with one major
+   difference : the only sensory information available to the system
+   is proprioception. From just the proprioception data and
+   \Phi-space, =longest-thread= synthesises a complete record the last
+   300 sensory experiences of the worm. These synthesized experiences
+   are fed directly into the action predicates =grand-circle?=,
+   =curled?=, =wiggling?=, and =resting?= from before and their output
+   is printed to the screen at each frame.
    
    The result of running =empathy-experiment= is that the system is
    generally able to interpret worm actions using the action-predicates
@@ -3192,9 +3209,11 @@
   boundaries of transitioning from one type of action to another.
   During these transitions the exact label for the action is more open
   to interpretation, and disagreement between empathy and experience
-  is more excusable.
-
-** COMMENT Digression: Learn touch sensor layout through free play
+  is essentially irrelevant at this point, giving a practical
+  identification accuracy of even higher than 95%. When I watch this
+  system myself, I generally see no errors in action identification.
+
+** COMMENT Digression: Learning touch sensor layout through free play
 
    In the previous section I showed how to compute actions in terms of
    body-centered predicates which relied on the average touch