Have you ever had that feeling of seeing something out of the corner of your eye, then turned to look but it’s gone? We’re left feeling cheated, as if some significant event has eluded our attention. But the reality is more prosaic: cells in the human retina are arranged so that movement and contrast are better perceived around the periphery, with the central region better suited to colour and detail. The result is that peripheral vision perceives things that the central region disregards. It’s a simple explanation, but one that reminds us that in order to understand the bigger picture, we sometimes need to see in different ways.

In many ways, searching for information presents a similar challenge: in order to satisfy complex information needs, we must articulate those needs faithfully and then perceive their effect in the form of a response from the environment. We become partners in this exchange: a dialogue between user and search system that can be every bit as rich as human conversation. Crucially, the better we can articulate our own needs, the more trust we can place in the response.

Nowhere is this truer than for structured searching, where the goals of accuracy transparency and reproducibility are at their most acute. In healthcare, for example, it is vitally important that all relevant sources of evidence be considered in developing policy, guidance and interventions. This is especially true during a global pandemic, and healthcare research needs to build on scientific evidence gathered in a systematic manner as part of its due diligence. Systematic literature reviews play a key role in this by synthesising the complex, incomplete and at times conflicting findings of biomedical research into a form that can readily inform healthcare decision making. And the cornerstone of systematic literature reviews is a systematic, structured search strategy.

To illustrate this, let’s take a familiar example: a complex search on the subject of ‘Galactomannan detection for invasive aspergillosis in immunocompromised patients’.

In its traditional form, this would be articulated via a form-based query builder as a series of interconnected Boolean expressions:

1 “Aspergillus”[MeSH]
2 “Aspergillosis”[MeSH]
3 “Pulmonary Aspergillosis”[MeSH]
4 aspergill*[tiab]
5 fungal infection[tw]
6 (invasive[tiab] AND fungal[tiab])
7 1 OR 2 OR 3 OR 4 OR 5 OR 6
8 “Serology”[MeSH]
9 Serology”[MeSH]
10 (serology[tiab] OR serodiagnosis[tiab] OR serologic[tiab])
11 8 OR 9 OR 10
12 “Immunoassay”[MeSH]
13 (immunoassay[tiab] OR immunoassays[tiab])
14 (immuno assay[tiab] OR immuno assays[tiab])
15 (ELISA[tiab] OR ELISAs[tiab] OR EIA[tiab] OR EIAs[tiab])
16 immunosorbent[tiab]
17 12 OR 13 OR 14 OR 15 OR 16
18 Platelia[tw]
19 “Mannans”[MeSH]
20 galactomannan[tw]
21 18 OR 19 OR 20
22 11 OR 17 OR 21
23 7 AND 22 

Each line consists of a series of keywords, operators and controlled vocabulary terms, which are connected via logical operators and Boolean expressions. The glue that binds all this together is the line numbering (a mechanism not entirely dissimilar to that used in early programming languages such as Unstructured BASIC).

Now, here is the test. If you were asked to describe how this search is structured, what would you say? How many conceptual elements does it contain? How are they related?

Clearly all these questions are answerable, albeit more so to the trained eye. But the point is that the answers are not directly visible. Instead, we must proceed through a sequence of steps: we must first retrieve from memory a method for interpreting line-by-line searches, and then implement it. In doing so we must hold data in our short term memory, and keep track of where we are and where we are going, while holding onto any intermediate results. The process is mental work: deliberate, effortful, and laborious: a prototype of slow thinking.

And this is precisely where existing formalisms fall short. Just when we most need an effective way of seeing, we are left with words, lines and numbers. Instead of using perception to understand the structure of our information needs, we are forced to rely on cognition, with its associated human costs of effort and error. Instead of using approaches that allow us to think fast, we rely on formalisms that force us to think slow. Does it have to be this way? In what follows, we challenge this status quo.

Let’s examine three alternative visions that are motivated by the principle of migrating thinking from slow to fast; from cognition to perception. We’ll start with what we call the ‘Nested view’. This view and those that follow can all be invoked by opening traditional, text based search strategies using 2Dsearch.

Nested view

We’ve introduced this view before, so will review it only briefly here. In short, it provides a view which maps hierarchical structure onto a series of nested containers. The benefit is that the grouping and containment become immediately apparent:

This visualisation reveals that our search strategy from earlier consists of a conjunction of two disjunctions (lines 7 and 22), the first of which articulates variations on the fungal infection concept, while the latter contains various nested disjunctions to capture the diagnostic test (serology) and associated procedures. By displaying them as nested groups with transparent structure, it offers support for abstraction, whereby lower level details can be hidden on demand. In addition, it is now possible to give meaningful names to sub-elements, so that they can be reused as modular components.

However, the Nested view has its drawbacks. Although it provides a fine degree of control over the arrangement and layout of the groups, the fact that they are rendered as blocks with operators in their headers isn’t for everyone. Let’s examine an alternative.

Tree view

Another way to understand the hierarchy embodied in complex searches is to apply a metaphor that is almost universally understood: the family tree.  In this view, the search is represented as a visual hierarchy, with the root node (Line 23 in this example) at the top, and each level below represented as successive generations of children:

In this example, we have displayed the search in its entirety, and shrunk it to fit across the page. But it’s easy enough to zoom in and out, and reveal just the higher levels:

Or to close branches on demand, and focus on one particular region of the tree:

Like the Nested view, the Tree view maps conceptual hierarchy onto physical hierarchy, but in a manner that emphasises branching over containment.  But is hierarchy the most important aspect of a search? With that in mind let’s examine a third view.

Inline view

The use of Boolean strings to represent complex searches may be inefficient and error-prone, but it does offer one key benefit: the ability to be read in a left to right manner. Of course, this attribute may reflect nothing more than the inertia of decades of convention, but there remains something useful about being able to read searches as a series of statements or commands. Is of possible to support this principle in a visualisation? This is where the Inline view comes in.

Like the Nested view, the Inline view maps conceptual hierarchy onto physical containment, but this time in a manner that aligns groups along a common midline, giving rise to a natural left-to-right reading:

Notice that in this view we elevate the operators to the same level as content items so that they appear in sequence within the left to right reading. This means that we can also reduce some of the chrome around groups, leading to a ‘cleaner’ layout. Again, we’ve shrunk the above image to fit across a single page. But as before it’s trivially easy to zoom in and out, e.g. to reveal just the higher levels:

Or to close branches on demand, and focus on one particular region of the search:

Of course, the Inline View has its strengths and weaknesses too. However, it’s important to recognise that while this article focuses on new ways of seeing, the real benefit is in the interaction: to modify a search, you can simply move terms from one block to another, or create new groups by combining terms. You can also cut, copy, delete, and lasso multiple objects. If you want to understand the effect of one block in isolation, you can execute it individually. Conversely, if you want to exclude one element, you can temporarily disable it.

In closing

In this article we’ve explored three different ways to visualise complex searches. In each case we’ve shown that it is possible to represent complex logic in a manner that supports both fast and slow thinking. Each view has its own strengths and weaknesses. Indeed, none of them is a silver bullet: the point is that they all reveal different aspects of a search strategy, and offer different insights and ways to understand them. It is through their collective diversity and flexibility that we discover new ways of seeing.

A picture is indeed worth a thousand words. To see for yourself visit 2Dsearch and let us know what you think.

This chapter was originally published in Search Insights 2021. Download the report for free here: