Continuing from my 2017 'Learning to see' series, Layers of perception continues my exploration into using state-of-the-art Machine Learning (AI) technologies to investigate human perception, and more broadly speaking, our self affirming cognitive biases, our inability to see the world from others’ points of view, and the resulting social and political polarization.
We see the world through a very specific lens. A lens shaped by both our evolutionary history and biology, and our upbringing and culture.
We have evolved to perceive space at certain scales, scales relevant to our ancestors, prey, predators and immediate environment. Animals, rocks, trees; ranging from grains of sand to mountains. We do have the incredible capacity to build tools to sense scales much smaller and much larger. But we cannot intuitively comprehend these scales, the quantum weirdness of the subatomic realm, or the dynamics of supermassive galactic structures many light years across.
Similarly, we have evolved to perceive time at certain scales, timescales relevant to our ancestors and their activities. And again, though we are able to build tools to sense timescales much smaller and much larger, we cannot comprehend the femtosecond timescales of subatomic particle dynamics, or the geological timescales of erosion, or the cosmic timescales of star and galaxy formation.
The lens through which we see the world is also shaped by our upbringing and culture. It is a very convincing illusion that the reality which we perceive is the truth, the full truth, and nothing but the truth. This illusion, that our individual perceived 'reality' is the one true reality, is not compatible with the global communities in which we live, and leaves us vulnerable to political manipulation and polarization, distracting us from the crises we need to solve together.
Layers of perception bridges vastly different realities and scales of space and time. For a brief moment, the commonalities between radically different worlds are brought together, and shown in a unified vision. And in doing so, similar to 'Learning to see', the piece works on dual layers. It uses the natural limitations of our neurobiological perception and related biases, as a metaphor to reflect on the limitations of our higher level cognition, how we make meaning, and what we consider to be the truth, and our 'reality'.
Provenance: Exhibited at Noise Art Fair, January 2024 by Kate Vass Galerie