Abstract reasoning is exercised whenever you identify a pattern or rule from visual information without being told what to look for. Noticing that the traffic lights at a particular junction follow an unusual phasing sequence and predicting when your lane will get a green signal is an abstract reasoning task: you observe the pattern, form a hypothesis about the rule, and apply it. Assembling a jigsaw puzzle requires a form of abstract reasoning too: you must identify how shapes, colours and edge patterns relate to each other to determine where each piece belongs.

More demanding examples include interpreting an unfamiliar dashboard or control panel for the first time. If you sit in a hire car you have never driven before, you must quickly identify which symbols on the instrument cluster represent which functions, recognise which warning lights share a common colour-coding logic (red for critical, amber for caution), and work out the layout of controls without a manual. You are decoding an unfamiliar visual system by identifying its governing rules. This is the same cognitive process assessed in abstract reasoning tasks, where the candidate must decode an unfamiliar set of visual elements by identifying the rules that connect them.

• System state pattern recognition:
• Modern aircraft present the pilot with large quantities of information across multiple displays: engine parameters, flight instruments, system synoptic pages and navigation data. Effective monitoring requires the pilot to recognise normal patterns (the "expected picture") and detect deviations from those patterns. This is fundamentally an abstract reasoning task: the pilot learns what the normal state looks like, establishes the rules that define it, and then identifies when an element breaks those rules. A parameter that is trending differently from the others, a system indication that does not match the current phase of flight, or a display element that has changed state unexpectedly all represent pattern violations that the pilot must detect and interpret.
• Weather pattern interpretation:
• Interpreting weather radar displays, satellite imagery and prognostic charts requires the pilot to identify patterns in visual data and extrapolate them forward in time. Recognising that a line of convective activity is moving in a particular direction and predicting where it will be in 30 minutes requires the pilot to identify the governing rule (the movement vector) from the visual pattern (the sequence of radar images) and apply it to a future state. This is the same cognitive process as identifying the next element in an abstract sequence.
• Procedure and checklist logic:
• Standard operating procedures, MEL dispatch conditions, and performance limitation tables all contain conditional logic: "if X, then Y; if not X, then Z." The pilot must identify the structure of the rule, determine which conditions apply, and follow the correct branch. Although this is often presented in text, the underlying reasoning demand is abstract: the pilot must decode the logical structure of the rule system and apply it correctly to the current situation.

• Tactical pattern recognition:
• In operations such as search and rescue, law enforcement support or military tasking, the helicopter pilot must identify patterns in the operational environment: the search area geometry, the behaviour of a moving target, the pattern of weather movement across the operating area, or the relationship between terrain features and wind behaviour. Recognising these patterns and predicting how they will develop requires the same hypothesis-and-test reasoning assessed in abstract reasoning tasks.
• System trend monitoring:
• Helicopter systems (engines, transmissions, hydraulics, electrical) produce continuous parameter data that the pilot must monitor for trends. Detecting that a transmission oil temperature is rising at a rate that differs from the normal pattern for the current power setting, or noticing that two engine parameters are diverging when they should be tracking together, requires the pilot to identify the expected pattern and detect when the current data violates it. The ability to spot the "odd one out" in a set of indications is directly analogous to the odd-one-out identification assessed in abstract reasoning modules.
• Dynamic environment assessment:
• Low-level helicopter operations require rapid assessment of an environment that is continuously changing as the aircraft moves through it. The pilot must identify patterns in terrain, obstacles, and wind indicators, and predict what the environment will look like in the next few seconds. This continuous cycle of pattern recognition, rule extraction, and forward prediction is the operational expression of the abstract reasoning ability assessed in aptitude tests.

Pilot training requires candidates to learn large volumes of unfamiliar material across diverse subjects, from aerodynamics and meteorology to aircraft systems and operational procedures. Abstract reasoning ability predicts how efficiently a candidate will acquire this new knowledge because it measures the capacity to identify structure and rules in unfamiliar information, which is precisely what learning demands ^[2].

Candidates with stronger abstract reasoning can identify the underlying principles in new material more quickly, connect new information to existing knowledge more effectively, and transfer understanding from one context to another with less instruction. This learning efficiency is particularly valuable in pilot training, where the pace is high and the subject matter is diverse.

Many of the problems a pilot encounters are essentially visual and spatial: interpreting displays, reading charts, understanding system schematics, and monitoring the external environment. Abstract reasoning tests assess the ability to solve problems using visual information alone, without the support of language or numerical data. This non-verbal reasoning capacity underpins the pilot's ability to process the visual information that dominates the cockpit environment.

The visual, non-verbal nature of abstract reasoning tests also makes them relatively culture-fair, which is relevant to the international nature of pilot recruitment. Performance on abstract reasoning tests is less influenced by language proficiency, educational background, or cultural factors than performance on verbal or knowledge-based tests ^[3].

Airline and flying school assessments that incorporate testing of abstract reasoning will typically extend to testing of other reasoning abilities. The assessment may also include verbal reasoning (evaluating statements against written information), numerical reasoning (interpreting and drawing conclusions from data), and inductive or deductive reasoning tasks. Abstract reasoning forms the non-verbal component of this broader reasoning assessment, and strong performance across all reasoning types indicates a well-rounded cognitive profile suited to the demands of pilot training.

In the Abstract activity, candidates are presented with sequences of diagrams containing geometric elements, symbols and binary states. They must understand how components interact and transform, identify the rules governing the progression of the sequence, and then apply those rules to determine what comes next. The rules can operate on multiple dimensions simultaneously: shape, colour, position, orientation, size and quantity may all change according to different patterns within the same sequence.

This format is the most complex of the abstract reasoning tasks because the candidate must track multiple rules operating in parallel. A sequence might involve shapes that rotate clockwise by 45 degrees in each step, whilst simultaneously changing colour in a repeating cycle and alternating between filled and unfilled states. The candidate must identify all of these concurrent rules and apply them together to select the correct next element.

In the Sets activity, candidates examine sets of images and must identify the image within each set that does not follow the rule governing the others. Though this "odd-one-out" format appears simple, the rule governing the images displayed can be tricky to identify. The difficulty lies not in the visual complexity of each individual image but in determining what property or relationship connects the majority and which element violates it.

The rules can be based on obvious visual features (shape, colour, number of sides) or on more subtle properties (symmetry, the relationship between internal and external elements, the direction of a pattern, or a mathematical property such as odd or even counts). The candidate must consider multiple possible rules, test each against the full set, and determine which rule is broken by exactly one element. This process of systematic hypothesis testing is a direct measure of inductive reasoning applied to visual information.

In the Match activity, candidates examine a series of pairs of images and must identify the rule governing the change that occurs between them. Having identified the transformation rule, they must apply the same rule to further pairs of images to determine the correct output.

This format is distinctive because the candidate must identify the transformation (what changes between image A and image B) rather than a static property. The transformation might involve rotation, reflection, colour inversion, element addition or removal, scaling, or a combination of operations. The candidate must determine the rule from the example pairs and then predict the result of applying the same transformation to a new input image. This tests the ability to extract a process (a rule about change) rather than a property (a rule about appearance).

In the Patterns activity, candidates undertake grid-based challenges, each containing diverse visual elements. The task is to decode the positioning and transformative relationships within the grid: how elements relate to each other across rows, columns, or diagonals. Candidates must identify governing rules that vary significantly between questions whilst working within consistent grid formats.

The grid format introduces a structural dimension that the other formats do not: the candidate must determine whether the rules operate across rows, down columns, diagonally, or in some combination. A grid might contain shapes where the rule governing each row is different from the rule governing each column, and the candidate must identify both to determine the missing element. This multi-directional analysis makes grid-based abstract reasoning one of the more demanding formats, requiring the candidate to hold multiple rule hypotheses simultaneously.

Airline and flying school assessments that incorporate abstract reasoning will typically extend to testing of verbal, numerical and other reasoning abilities. Our software includes three additional activities that provide preparation across these broader reasoning areas, each generating infinite questioning:

Mental Ability provides a comprehensive assessment of combined verbal reasoning and numerical reasoning aptitude. Across sixteen question categories (including vocabulary, analogies, number sequences, word problems, and pattern recognition), candidates must demonstrate inductive reasoning and cognitive flexibility by alternating between linguistic and mathematical challenges within a time-constrained environment.

Numerical Ability assesses numerical reasoning and mental arithmetic proficiency through eleven categories of mathematical challenge. Candidates must perform calculations involving percentages, fractions, ratios, and unit conversions, whilst also demonstrating competence in applied scenarios such as speed-distance-time problems and timezone calculations, reflecting the quantitative demands encountered in aviation operations.

Verbal Ability evaluates a candidate's capacity for deductive reasoning and logical analysis. Presented with passages containing multiple constraints and conditions, candidates must draw valid conclusions, evaluate statement veracity, interpret conditional logic, and solve spatial arrangements, developing skills essential for processing complex operational information and making sound judgements under pressure.

Abstract Reasoning appears within pilot aptitude assessments that test broader cognitive and reasoning abilities. The Sova assessment, for example, includes Logical Reasoning as one component of its blended assessment, directly assessing abstract reasoning through visual pattern and sequence tasks. Other test systems assess related abilities through their Inductive Reasoning and Logic modules.

If your assessment includes any form of non-verbal, pattern-based or logical reasoning, preparing for Abstract Reasoning will be beneficial.

The table below outlines the activities available for Abstract Reasoning preparation, organised by reasoning type.

Having identified the activities relevant to your assessment, you can navigate directly to them within our software.

Our software organises activities by the type of assessment you are preparing for, the skill being evaluated, and the specific airline, flying school or cadet scheme you are applying to. This means the relevant Abstract Reasoning activities will already be included in your tailored preparation.

To find the activities relevant to you, navigate to one of the following within the software:

• Activities by Aptitude Test
• If you know which test system your assessment uses. Navigate to Activities by Aptitude Test and select your assessment to see which Abstract Reasoning activities are included.
• Activities by Skill
• If you want to focus specifically on Abstract Reasoning. Navigate to Activities by Skill and select Abstract Reasoning to see every relevant activity.
• Activities by Airline, Flying School or Cadet Scheme
• If you know where you are applying but not which test system is used. Navigate to Activities by Airline or Activities by Flying School and select your chosen organisation. The software will include the appropriate Abstract Reasoning activities alongside all other relevant preparation.

If you have created a Preparation Strategy, the relevant Abstract Reasoning activities will already appear in your Focus Activities; no additional navigation is required.