In the attention schema theory, the value of explaining the feature of awareness and attributing it to a person is to gain a useful predictive model of that persons attentional processing. Attention is a style of information processing in which a brain focuses its resources on a limited set of interrelated signals. Awareness, in this theory, is a useful, simplified schema that represents attentional states. To be aware of X is explained by constructing a model of ones attentional focus on X. In 2013, the perturbational complexity index (PCI) was proposed, a measure of the algorithmic complexity of the electrophysiological response of the cortex to transcranial magnetic stimulation. This measure was shown to be higher in individuals that are awake, in REM sleep or in a locked-in state than in those who are in deep sleep or in a vegetative state, making it potentially useful as a quantitative assessment of consciousness states. In 2017, work by David Rudrauf and colleagues, including Karl Friston, applied the active inference paradigm to consciousness, a model of how sensory data is integrated with priors in a process of projective transformation. The authors argue that, while their model identifies a key relationship between computation and phenomenology, it does not completely solve the hard problem of consciousness or completely close the explanatory gap.Assuming that not only humans but even some non-mammalian species are conscious, a number of evolutionary approaches to the problem of neural correlates of consciousness open up.
Searle himself was vague about what extra ingredients it would take to make a machine conscious: all he proposed was that what was needed was "causal powers" of the sort that the brain has and that computers lack. But other thinkers sympathetic to his basic argument have suggested that the necessary (though perhaps still not sufficient) extra conditions may include the ability to pass not just the verbal version of the Turing test, but the robotic version, which requires grounding the robots words in the robots sensorimotor capacity to categorize and interact with the things in the world that its words are about, Turing-indistinguishably from a real person. Turing-scale robotics is an empirical branch of research on embodied cognition and situated cognition.In 2014, Victor Argonov has suggested a non-Turing test for machine consciousness based on machines ability to produce philosophical judgments. He argues that a deterministic machine must be regarded as conscious if it is able to produce judgments on all problematic properties of consciousness (such as qualia or binding) having no innate (preloaded) philosophical knowledge on these issues, no philosophical discussions while learning, and no informational models of other creatures in its memory (such models may implicitly or explicitly contain knowledge about these creatures consciousness). However, this test can be used only to detect, but not refute the existence of consciousness. A positive result proves that machine is conscious but a negative result proves nothing. For example, absence of philosophical judgments may be caused by lack of the machines intellect, not by absence of consciousness.