Hohwy et al.’s (2008) ‘epistemological’ explanation of binocular rivalry is taken as a classic illustration of predictive coding’s ubiquity and explanatory power. I revisit the account and show that it cannot explain a core feature of binocular rivalry, namely, perceptual dominance in rewarded conditions. A more recent version of Bayesian model averaging, known as Variational Bayes, can account for the role of reward in rivalry by recasting it as a form of optimism bias. However, I argue that if we accept this modified account, we must revise our understanding of perception as a neutral, informational or ‘theoretical’ process in the mind.
Optogenetics makes possible the control of neural activity with light. In this paper, I explore how the development of this experimental tool has brought about methodological and theoretical advances in the neurobiological study of memory. I begin with Semon’s (1921) distinction between the engram and the ecphory, explaining how these concepts present a methodological challenge to investigating memory. Optogenetics provides a way to intervene into the engram without the ecphory that, in turn, opens up new means for testing theories of memory error. I focus on a series of experiments where optogenetics is used to study false memory and forgetting. I conclude with discussion of the recent discovery of “silent engrams” (e.g., Roy, Muralidhar, Smith, & Tonegawa, 2017) using optogenetics and the way in which these results create further opportunities and challenges for engram theory.
Integrated Information Theory (IIT) (Oizumi, et al., 2014; Tononi, et al., 2016) attempts to account for both the quantitative and the phenomenal aspects of consciousness, and in taking consciousness as fundamental and widespread it bears similarities to panpsychist Russellian monism (RM). In this paper I compare IIT’s and RM’s (in its categoricalist version) response to the conceivability argument, and their metaphysical account of conscious experience. I start by claiming that RM neutralizes the conceivability argument, but that by virtue of its commitment to categoricalism it doesn’t exclude fickle qualia scenarios (e.g. inverted or changing qualia). I argue that IIT’s core notion of intrinsic cause-effect power makes it incompatible with categoricalist versions of RM (Chalmers, 2013; Alter & Nagasawa, 2015) and, to the contrary, best understood as entailing pandispositionalism, the view for which all properties are powers. I show that, thus construed, IIT can cope with both the conceivability and with the fickle qualia arguments, offers a promising way to account for the content of experience, and hence is preferable to categoricalist RM.
What is the relationship between brain and behavior? The answer to this question necessitates characterizing the mapping between structure and function. I will discuss broad issues surrounding the link between structure and function in the brain that will motivate a network perspective to understanding this question. As others in the past, I argue that a network perspective should supplant the common strategy of understanding the brain in terms of individual regions. Whereas this perspective is needed for a fuller characterization of the mind-brain, it should not be viewed as panacea. For one, the challenges posed by the many-to-many mapping between regions and functions is not dissolved by the network perspective. Although the problem is ameliorated, one should not anticipate a one -to- one mapping when the network approach is adopted. Furthermore, decomposition of the brain network in terms of meaningful clusters of regions, such as the ones generated by community-ﬁnding algorithms, does not by itself reveal “true” subnetworks. Given the hierarchical and multi-relational relationship between regions, multiple decompositions will offer different “slices” of a broader landscape of networks within the brain. Finally, I described how the function of brain regions can be characterized in a multidimensional manner via the idea of diversity proﬁles. The concept can also be used to describe the way different brain regions participate in networks.