Grand Challenges in Perception Science: Modeling the Future

Perception science is one of the oldest research domains of psychology, by tradition interdisciplinary. Over the years, perception has evolved to provide what is arguably the most mature test-bed for sophisticated theories. The main reasons are that the research generates complementary data sets from human and animal cognition, psychophysics, electrophysiology, neuroscience and brain imaging, and the strong tradition of perception researchers to interact with communities from statistical modeling, computer vision, philosophy of mind and the arts. Such diversity of complementary perspectives has enriched theories of perception, possibly more so than in any other domain of Psychology. Nowadays, mechanisms of perception can be studied in their true complexity, from their early unfolding during development, maturation in adulthood and decline with age. The research platform is sufficiently mature to start specifying a computational theory (what is the visual information processed? ), infer its abstract mechanisms (i.e. its algorithms, how is information transformed? ) and measure with millisecond precision their neural implementation with brain measurements. Advances in functional neuroimaging provide dimensions of analysis (where and when is information processed? ) that ideally complement the experimental rigor of psychophysics. For example, the researcher can use Magneto-Encephalographic (MEG) activity to reconstruct the oscillatory cortical networks associated with perception, isolate their functional nodes, and further localize network nodes in the brain with fMRI. Then, selective application of TMS can examine the contribution of each node to the overall architecture. Characterization of such information processing networks can be extended across age groups (to examine effects of development, maturation and decline), in the normal vs. impaired brain (to confirm the functional role of brain regions) and across species (to refine specificity of e.g. primate vs. non-primate perception mechanisms, from complex to simpler brain organizations). In the context of such a sophisticated research platform, what are, then, the Grand Challenges of perception science? The most obvious challenge is not grand, but it is a challenge nevertheless. A cursory glance at the methods section of publications in visual neuroscience, electrophysiology, neuroimaging, psychophysics and visual cognition reveals that we are far from the ideal integration of methods highlighted in the previous paragraph, implying that the research platform is not always fully exploited. The challenge is to integrate different research communities with their own experimental and methodological traditions around the same theme of perception. But another glance this time at the authors list also reveals that the research culture is at an interesting juncture, progressively evolving from individual “Master Craftsman” to interdisciplinary “Team of Experts. ” As this transition happens, and as training and research funding straddle disciplinary boundaries, imports of methods will optimize usage of the platform and enhance research across the field. Though real, the challenges associated with this transition are generic to any interdisciplinary venture. Sections of Biology addressed them a while back, Physics even before, now it is our turn. Perhaps surprisingly, the Grand Challenges of perception science arise from its relative maturity. More than a century of perception research produced an articulated landscape of the main fundamental problems, a good grasp of their performance envelopes, and the development of innovative methodologies. The Grand Challenge is now to integrate behavioral and brain data using the common denominator of formal models of perceptual tasks. That is, models that formally specify input information, model how information is transformed in the brain and predict how parametric variations in the input information modify brain processes and behavioral variables. The Grand Challenge of integrative models comprises two intertwined aspects: (a) to formalize visual information in complex perceptual tasks and (b) to model how the processing of this information in the brain predicts behavioral performance. I sketch the Grand Challenge below.