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<p>In this thesis, I take up two important issues for understanding neurobiological
systems: normative functions and information. After introducing the topic and my methodology
in chapters 1 and 2, chapter 3 contains an extended critique of the most prominent
theory of biological functions, the selected-effects theory of functions. My arguments
center on the influential recent selected-effects theory arguing that it has trouble
accounting for certain cases and does not seem to capture the sense of malfunction
employed in the neurosciences. Chapter 4 defends an alternative theory of normative
biological functions that I label the statistical fitness theory. Roughly, this theory
holds that tokens of a trait type have the normative function to do something y if
it is typical for tokens of that type of trait to y and their doing y contributes
to the inclusive fitness of the organism that possesses the trait. In turn, this theory
defines malfunctioning trait tokens as those whose effects that typically make positive
contributions to fitness fall below the "normal" range in the population. Chapter
5 argues that several other recently popular theories of normative functions have
significant flaws. </p><p>Chapter 6 takes up the issue of a certain kind of information,
namely natural, propositional information. I provide a general framework that explains
when signals carry this kind of information about their signifieds based upon stable,
perfect correlations holding between the two. Hence, I label this the "stable correlation
theory". I also argue that there are good reasons to think that neurons in our brains
carry natural, propositional information and that their ability to do so is also grounded
in stable correlations.</p>
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