The compassion of Easter's tears

There’s complexity and beauty behind crying.

Simon Cansdale

Simon is a vicar in Winchester, Hampshire.

A stone statue's face depicts a falling tear.

Ohlsdorf Cemetery, Hamburg.

Marek Studzinski on Unsplash.

The great English metaphysical playboy poet, John Donne, became Dean of St. Paul’s Cathedral in 1621. During Lent a year later he preached a majestic sermon entitled ‘To speake of Tears’. I first read it 30 years ago and it has prodded and challenged me ever since. This hyper-bright poet and reformed Lothario brought to the pulpit all his astonishing rhetorical skill, and a deep learning, combined with an overriding sense of God’s mercy and the wonder of new beginnings. His sermons were as thick as treacle and as rich as chocolate mousse, but built on a profound religious sympathy and a pastor’s ear for the yearnings of his listeners.

In his 1622 sermon, Donne highlights the different kinds of tears shed by Jesus in the last weeks of his life.

He speaks of Jesus’ ‘humane tears’ - tears he shed alongside Mary and Martha at the grave of his dear friend Lazarus - so surprising, Donne suggests, that the scholars charged with the chapter and verse divisions of the New Testament stopped in wonder at the two words ‘Jesus wept’ and made it a complete (and the shortest) verse in the Bible.

He speaks of Jesus’ ‘prophetic tears’ on Palm Sunday, as Jesus looks down over the city of Jerusalem, foreseeing the people’s rejection of God and the judgement that would come upon this city he loved. These tears are again surprising - Jesus had been borne into the city on the excited adulation of the crowds - so why does he weep?

Donne speaks of Jesus’ ‘pontifical’ or ‘sacrificial tears’ on the Cross - forsaken, despairing tears, encapsulated in Jesus’ agonisingly seizing a line of dereliction from the Psalms and hurling it at the dark sky - ‘My God, my God, why have you forsaken me?’

Donne was hardly the first theologian to wonder at these tears. But he is compelling in separating them out, wondering how different they are, and plotting the complexity of Jesus becoming a Man of Sorrows, for people who know so much sorrow. And he has the pastor’s touch as well as the preacher’s flourish to help us understand that we see ourselves most clearly through the tears of Jesus, or as C.S. Lewis would put it in the Problem of Pain, ‘the tears of God are the meaning of history.’

Tears, like snowflakes, are unique. Donne started to tease them apart 400 years ago, and we can see this even more clearly today, though it is always a challenge to do so because of the emotional intensity and maelstrom they spring from.

We now know there are physically three kinds of tears; basal tears, which lubricate the eye, irritant tears, which flush out bugs or specks of dirt and emotional tears, agreed by most to be unique to humans (though newborn babies don’t normally cry tears for the first month or more). Rose-Lynn Fisher poignantly deepened this understanding of different kinds of tears in her ground-breaking work on The Topography of Tears. As an artist, she captured some of her own tears and placed them on a microscope slide. She then took close-up pictures of the tears with a digital microscopy camera mounted on a 1960’s Zeiss standard light microscope;

‘The microscope provided the means to examine my tears and visually evoke the unseen realm of my emotions.’

She discovered that no two tears look the same, much as another hero of mine, Snowflake Bentley, had discovered, using a similar method in a frostier setting, the same is true for snowflakes. Tears of grief, even if shed at the same time, are all uniquely different; each one subtly changed by air temperature, and the proteins, minerals, hormones, antibodies and enzymes in an individual tear.

This knowledge brings a new weight to Jesus’ searching question to Mary on Easter morning - ‘Woman, why are you crying?’ These tears that I’m shedding, today, what kind of tears are these? Angry, grieving, frustrated, fearful? Fisher gives astonishing names to her close-ups of tears - ‘Compassion’, ‘Tears of Change’, ‘Overwhelm’, ‘Redemption.’ And it opens up the question of what tears am I not shedding? If there are so many different kinds of tears, are there some I am avoiding, or closing my heart to?

Richard Rohr has just published a long-awaited book on the Minor Prophets called The Tears of Things. I cannot possibly summarise it here, but Rohr includes an argument for the necessity of tears to soften our anger and outrage, the defining emotions of our age. He charts the prophet’s journey from outrage at the lawlessness of the world, through tears for the greed and cruelty of the world, to a settled but fiercer love and mercy. The prophetic tears of Jesus - tears of love, not for ourselves, but an expression of compassion for others - are the ultimate expression of this. This is a compelling vision - I would prefer the people who mould our world to be less shouty and angry, and more tearful and compassionate, people who live near enough to the pain of others to have cried with them and for them before making a plan.

The Psalms offer us a second discipline for our tears. As well as knowing them, that is understanding them, naming them, placing them, we can sow them:

‘Those who sow in tears

Will reap with songs of joy.’

This is an ancient invitation to give weight to our tears. To take them to God, to share them with others, and not just to see them as a way to get things of our chest.

Our human tears can deepen our sense of frailty and dependence on others and God.

Our prophetic tears can invigorate our fight for justice and peace, without destroying our spirit or making us worse than the people we criticise.

Our forsaken tears, the ones shed quietly, without hope, without even the hope that God sees them, can prepare the way for God’s new beginnings.

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21 June 2024

4 min read

It's our mistakes that make us human

What we learn distinguishes us from tech.

Silvianne Aspray

Silvianne Aspray is a theologian and postdoctoral fellow at the University of Cambridge.

A man staring at a laptop grimmaces and holds his hands to his head.

Francisco De Legarreta C. on Unsplash.

The distinction between technology and human beings has become blurry: AI seems to be able to listen, answer our questions, even respond to our feelings. It becomes increasingly easy to confuse machines with humans. In this situation, it is increasingly important to ask: What makes us human, in distinction from machines? There are many answers to this question, but for now I would like to focus on just one aspect of what I think is distinctively human: As human beings, we live and learn in time.

To be human means to be intrinsically temporal. We live in time and are oriented towards a future good. We are learning animals, and our learning is bound up with the taking of time. When we learn to know or to do something, we necessarily make mistakes, and we take practice. But keeping in view something we desire – a future good – we keep going.

Let’s take the example of language. We acquire language in community over time. Toddlers make all sorts of hilarious mistakes when they first try to talk, and it takes them a long time even to get single words right, let alone to try and form sentences. But they keep trying, and they eventually learn. The same goes with love: Knowing how to love our family or our neighbours near and far is not something we are good at instantly. It is not the sort of learning where you absorb a piece of information and then you ‘get’ it. No, we learn it over time, we imitate others, we practice and even when we have learned, in the abstract, what it is to be loving, we keep getting it wrong.

This, too, is part of what it means to be human: to make mistakes. Not the sort of mistakes machines make, when they classify some information wrongly, for instance, but the very human mistake of falling short of your own ideal. Of striving towards something you desire – happiness, in the broadest of terms – and yet falling short, in your actions, of that very goal. But there’s another very human thing right here: Human beings can also change. They – we – can have a change of heart, be transformed, and at some point in time, actually start to do the right thing – even against all the odds. Statistics of past behaviours, do not always correctly predict future outcomes. Part of being human means that we can be transformed.

Transformation sometimes comes suddenly, when an overwhelming, awe-inspiring experience changes somebody’s life as by a bolt of lightning. Much more commonly, though, such transformation takes time. Through taking up small practices, we can form new habits, gradually acquire virtue, and do the right thing more often than not. This is so human: We are anything but perfect. As Christians would say: We have a tendency to entangle ourselves in the mess of sin and guilt. But we also bear the image of the Holy One who made us, and by the grace and favour of that One, we are not forever stuck in the mess. We are redeemed: are given the strength to keep trying, despite the mistakes we make, and given the grace to acquire virtue and become better people over time. All of this to say that being human means to live in time, and to learn in time.

So, this is a real difference between human beings and machines: Human beings can, and do strive toward a future good.

Now compare this to the most complex of machines. We say that AI is able to “learn”. But what does it mean to learn, for AI? Machine learning is usually categorized into supervised learning, unsupervised and self-supervised learning. Supervised learning means that a model is trained for a specific task based on correctly labelled data. For instance, if a model is to predict whether a mammogram image contains a cancerous tumour, it is given many example images which are correctly classed as ‘contains cancer’ or ‘does not contain cancer’. That way, it is “taught” to recognise cancer in unlabelled mammograms. Unsupervised learning is different. Here, the system looks for patterns in the dataset it is given. It clusters and groups data without relying on predefined labels. Self-supervised learning uses both methods: Here, the system uses parts of the data itself as a kind of label – such as, for instance, predicting the upper half of an image from its lower half, or the next word in a given text. This is the predominant paradigm for how contemporary large-scale AI models “learn”.

In each case, AI’s learning is necessarily based on data sets. Learning happens with reference to pre-given data, and in that sense with reference to the past. It may look like such models can consider the future, and have future goals, but only insofar as they have picked up patterns in past data, which they use to predict future patterns – as if the future was nothing but a repetition of the past.

So this is a real difference between human beings and machines: Human beings can, and do strive toward a future good. Machines, by contrast, are always oriented towards the past of the data that was fed to them. Human beings are intrinsically temporal beings, whereas machines are defined by temporality only in a very limited sense: it takes time to upload data, and for the data to be processed, for instance. Time, for machines, is nothing but an extension of the past, whereas for human beings, it is an invitation to and the possibility for being transformed for the sake of a future good. We, human beings, are intrinsically temporal, living in time towards a future good – which machines do not.

In the face of new technologies we need a sharpened sense for the strange and awe-inspiring species that is the human race, and cultivate a new sense of wonder about humanity itself.