Free The Happiness of Pursuit: What Neuroscience Can Teach Us About the Good Life by Shimon Edelman

at, a flying object, I need to represent its possible locations and velocities. If the object is ballistic and therefore travels along a parabola, like a catapulted cow would, learning and subsequently predicting its trajectory is a simple matter of estimating a few parameters. If the object is self-propelled and has a mind of its own, like a hummingbird that flits here and there and then brakes in midair in its approach to a hibiscus flower, I need to learn and represent something of its mind.
    If the object to be tracked is self-propelled as well as articulated (not the same as articulate, an attribute I’ll get to in due time), and if it is acting willfully, as Groucho Marx habitually would, the problem of anticipating its moves—that is, estimating the relative probabilities of various likely future moves, given the past ones—is considerably more complex. The observer working on populating the possibility space with data must in this case learn the likely changes in the object’s location (relatively easy), bodily configuration (tricky, especially if a silly walk is in the works), and mind state (very tricky, but not out of the question if you are at least as clever and observant of your object of attention as Chico Marx appeared to be of his brother).
    All these learned patterns of change are represented as paths through possibility space, which I shall call PaThS (an almost-acronym that is easier to pronounce than the cryptic “PTPS” and more transparent than the pedantic “PaThPoSp”). The idea of a path through a representation space has been with us since the previous chapter, where I invoked it to explain how brains counter the effects of the changes in the visual appearance of objects, such as faces, that are brought about by changes in vantage point. As you will recall, the brain makes sense of a vaguely familiar face seen for the first time from a new angle by first populating a face representation space with tracks that record how familiar faces change appearance with vantage.
    By now, it should be clear to us that the dimensions of the possibility space can stand for anything at all. Suppose that Chico undertakes to represent Groucho’s state of mind by focusing on just two of its qualities: affect and arousal. For this purpose, Chico needs to allocate two dimensions of his possibility representation space—the mood plane, as it were. Within it, various conceivable mood shifts of Groucho’s would be represented by PaThS of appropriate shape and placement. For example, a transition from being quietly happy (low arousal, positive affect) to being violently miserable (high arousal, negative affect) would correspond to a diagonal move from one corner of the mood plane to the opposite one.
    Although most of the things and events that populate the possibility space are quite abstract, the physical environment not only gets represented within it but serves as a scaffolding that supports and structures the rest of the possibility space. The representation of the environment is privileged in this manner for a simple reason: it anchors in place episodes and actions and imposes order on sequences of events, which unfold as the learner experiences his or her or its corner of the world. As the learner gets older, avoiding being buried under a pile of indiscriminate memories becomes more and more of a challenge. If time is nature’s way of keeping everything from happening at once, then space—or rather, the memory of space—is nature’s corkboard, where everything that happens has its place.

Where Was I?
     
    The evolutionary roots of the spatially indexed episodic memory system lie in a very common existential need. How can an animal that must leave its burrow to make a living improve the odds of getting back home at the end of the working day, preferably with provender for the family? By keeping track of what happened where on its last few forays into the wild, with an eye toward drawing generalizations