Information: Theory, brain, and behavior

Greg Jensen, Ryan D. Ward, Peter D. Balsam
2013 Journal of The Experimental Analysis of Behavior  
In the 65 years since its formal specification, information theory has become an established statistical paradigm, providing powerful tools for quantifying probabilistic relationships. Behavior analysis has begun to adopt these tools as a novel means of measuring the interrelations between behavior, stimuli, and contingent outcomes. This approach holds great promise for making more precise determinations about the causes of behavior and the forms in which conditioning may be encoded by
more » ... . In addition to providing an introduction to the basics of information theory, we review some of the ways that information theory has informed the studies of Pavlovian conditioning, operant conditioning, and behavioral neuroscience. In addition to enriching each of these empirical domains, information theory has the potential to act as a common statistical framework by which results from different domains may be integrated, compared, and ultimately unified. Keywords information theory; entropy; probability; behavior analysis The business of the brain is computation, and the task it faces is monumental. It must take sensory inputs from the external world, translate this information into a computationally accessible form, then use the results to make decisions about which course of action is appropriate given the most probable state of the world. It must then communicate this state of affairs to appropriate efferent channels, so that the selected action can take place. Even this description is a gross oversimplification. At any given moment, the brain continuously processes imperfect signals from a noisy world, and initiates and channels activity given the available information, doing so in a distributed fashion. Our knowledge of this computation is incomplete. We know details about transduction of sensory stimulation as well as some aspects of how that signal is transformed as it travels through various stages of processing. How then, does the brain use seemingly binary all-or-nothing action potentials, or "spikes," to manage the information needed for the extremely complicated computations it must carry out? Moreover, how can this information be extracted and these computations be made "on the fly," as they so often must be? Because it is impossible a Another approach that behavior analysts routinely oppose is that of "optimality theories." The common feature of optimality theories is to assert what an organism should do, as opposed to characterizing how organisms actually behave. A wide range of disciplines favor the "normative" approach, most notably classical economics (Herrnstein, 1990) , but normative theories have also recently seen flashes of popularity among psychologists, behavioral ecologists, and evolutionary theorists (Staddon, 2007) . A common criticism of information theory is to assert that it requires that organisms respond in an optimal fashion (e.g. Miller, 2012). Crucially, however, information theory itself is principally concerned with measurement, and although it provides objective metrics of the information that an organism could use, it makes no prescription that this information must be used. Although these metrics could serve as the basis for proposed normative strategies, it can also be used to measure which Jensen et al.
doi:10.1002/jeab.49 pmid:24122456 pmcid:PMC5226236 fatcat:cg2lah2l7fcczgxjdkud66hexi