When do parts form wholes? Integrated information as the restriction on mereological composition.

Under what conditions are material objects, such as particles, parts of a whole object? This is the composition question and is a longstanding open question in philosophy. Existing attempts to specify a non-trivial restriction on composition tend to be vague and face serious counterexamples. Consequently, two extreme answers have become mainstream: composition (the forming of a whole by its parts) happens under no or all conditions. In this paper, we provide a self-contained introduction to the integrated information theory (IIT) of consciousness. We show that IIT specifies a non-trivial restriction on composition: composition happens when integrated information is maximized. We compare the IIT restriction to existing proposals and argue that the IIT restriction has significant advantages, especially in response to the problems of vagueness and counterexamples. An appendix provides an introduction to calculating parts and wholes with a simple system.

Filled/non-filled pairs: An empirical challenge to the integrated information theory of consciousness.

Perceptual filling-in for vision is the insertion of visual properties (e.g., color, contour, luminance, or motion) into one's visual field, when those properties have no corresponding retinal input. This paper introduces and provides preliminary empirical support for filled/non-filled pairs, pairs of images that appear identical, yet differ by amount of filling-in. It is argued that such image pairs are important to the experimental testing of theories of consciousness. We review recent experimental research and conclude that filling-in involves brain activity with relatively high integrated information (Φ) compared to veridical visual perceptions. We then present filled/non-filled pairs as an empirical challenge to the integrated information theory of consciousness, which predicts that phenomenologically identical experiences depend on brain processes with identical Φ.

Future Time Perspective and the Achievement of Optimal Best: Reflections, Conceptualizations, and Future Directions for Development.

Time is an interesting concept. For some cultural groups, time is an entity that exists only in the here and now, whereas for others it can be linear, emphasizing a person's past, present, and future. Many of us, while living in the "present moment," may also anticipate and project future goals, dreams, hopes, and ambitions. Indeed, from a positive point of view, future orientations are healthy and may direct one's focus, instill motivation and persistence, and mobilize the expenditure of effort. Existing research has provided empirical evidence to support the promotion and encouragement of a positive future time orientation. From an educational point of view, the study of time may be useful for calculating achievement, given that a student may use future time orientation to guide and direct his/her academic and/or non-academic future. One notable question for consideration, in this case, relates to the importance of timespan - that is, how far into the future should one project? There may be a significant difference between, say, a timespan that scopes a 6-month period as opposed to a timespan that scopes a 2-year period. By the same token, over the past few years we have delved into an interesting line of inquiry, namely, the nature of optimal best - for example, what facilitates and/or causes a person to achieve an optimal level of best practice in particular subject matter? Our theory of human optimization, consolidated and recently published in Frontiers in Psychology, provides an in-depth theoretical account of an underlying process, which we postulate could help explain the achievement of optimal best. Optimization, in this case, is intimately linked to a person's achievement of optimal best. We rationalize that within the context of academic learning, cognitive complexity of particular subject matter could serve as an important source of motivation in the anticipation and projection a student's extended future timespan. In this analysis, the extremely complex nature of a learning task or a suite of tasks may compel a student to consider a longer future timespan for successful completion. We also argue, in contrast, that the specific duration of a future timespan (for e.g., 6 months vs. 2 years) could play a significant role in the successful optimization of a student's state of cognitive functioning.