65% of Humans Identify as Visual Learners

Posted On 30/09/2025

By Nikki Wordsmith
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WhatsApp: 07905 456704

Prevalence of Visual Learning

The claim that approximately 65% of the population are visual learners is a frequently cited figure in educational psychology and learning style research, often linked to the Visual, Auditory, Read/Write, Kinesthetic model (VARK). This statistic suggests that most people process and retain information more effectively when presented visually (e.g. through images, diagrams, or videos) compared to verbal (text or spoken words) or kinesthetic (hands-on) methods.

Below, the evidence behind this 65% figure is unpacked, its implications, limitations, and supporting data, while addressing the nuances of visual learning as a cognitive preference relevant to other thinking modes.

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Origin and Evidence for the 65% Figure

The 65% visual learner estimate stems from studies and meta-analyses in educational psychology, particularly from the 1980s–2000s, when learning style theories gained traction.

Key sources include:

• Fleming’s VARK Model (Visual, Aural, Read/Write, Kinesthetic): Neil Fleming’s work (1990s–2000s) popularized the idea that most learners prefer visual input. Surveys using the VARK questionnaire (self-reported preferences) consistently find that ~60–70% of respondents identify as visual learners, with auditory and kinesthetic trailing at 20–30% and 10–20%, respectively. For example, a 2009 study of college students (n=1,000) found 64% preferred visual modalities (charts, diagrams) over text-based or auditory input.
• Cognitive Load Theory: Research by John Sweller and others (1980s–present) shows that visual aids reduce cognitive load for complex tasks, enhancing retention. A 1998 meta-analysis of instructional design studies reported that visual-based learning (e.g. diagrams) improved recall by 20–40% compared to text alone for ~60–65% of learners, particularly in STEM subjects.
• Neuroscientific Support: Neuroimaging studies indicate that ~50% of the brain’s cortex is dedicated to visual processing, and humans process images 60,000 times faster than text. A 2014 fMRI study found that visual stimuli (e.g. infographics) activated broader neural networks (visual cortex + prefrontal areas) than verbal stimuli, with 62% of participants showing stronger retention for visual content.

These findings align with the broader cognitive preference for visual thinking noted in Zwaan and Madden’s work (2005 & 2015), where visual imagery often complements or dominates verbal processing during comprehension. The 65% figure is thus a rough average from self-reports and performance-based studies, reflecting a population-level tendency to favor visual input for learning and memory.

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Breakdown of Visual Learning Prevalence

• Population Distribution:
  • Primary Visual Learners (~60–65%): These individuals excel with visual aids like graphs, maps, or videos. Studies, such as a 2016 analysis of 2,000 K-12 students, found 63% performed better on tasks with visual cues (e.g. geometry problems with diagrams) than text-based equivalents.
  • Mixed Learners (~25–30%): Most people blend visual with verbal or kinesthetic modes, depending on the task. For example, a 2010 study of medical students showed 68% preferred multimodal learning (visual + verbal), with visuals dominant for anatomy but verbal for abstract concepts like ethics.
  • Non-Visual Learners (~5–10%): A minority, including those with aphantasia (1–3% of the population), struggle to form mental images and rely on verbal or auditory input. A 2020 survey of 500 adults found 2.5% reported no visual learning preference, aligning with aphantasia estimates.
• Demographic Variations:
  • Age: Children show stronger visual preferences (up to 80% in early education, per a 2015 study), as they rely on imagery before verbal skills mature. Visual preference may decline slightly in adulthood as abstract thinking grows.
  • Gender: Mixed evidence; some studies (e.g. 2007 meta-analysis) suggest males lean slightly more visual (67% vs. 60% for females), possibly due to spatial task advantages, but differences are small.
  • Culture: Visual learning appears consistent across cultures, with studies in Asia, Europe, and the U.S. reporting similar 60–70% ranges. However, cultures emphasizing oral traditions (e.g. some Indigenous groups) may show higher auditory preferences.
• Task-Specificity: Visual learning dominates for spatial or concrete tasks (e.g. 80% better recall for diagrams in science education), but verbal modes take over for abstract or linguistic tasks (e.g. philosophy or law).

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Why Visual Learning Is Prevalent

Several factors explain why visual learning is so common:

1. Evolutionary Advantage: Humans evolved to prioritize visual processing for survival (e.g. spotting predators). The brain’s visual cortex is larger and faster than auditory or linguistic regions, enabling rapid image-based learning.
2. Cognitive Efficiency: Visuals reduce cognitive load by presenting information holistically. A 2018 study found that learners using infographics scored 21% higher on retention tests than those using text, with 65% showing faster comprehension.
3. Memory Enhancement: The picture superiority effect shows that images are recalled better than words (e.g. 90% recall for pictures vs. 60% for text after three days). This effect is consistent across 70% of tested populations.
4. Engagement: Visuals engage attention more than text. A 2021 study on e-learning found that 62% of students preferred video-based courses over text, citing higher motivation and clarity.

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Implications for Learning and Thinking

• Education: The prevalence of visual learning drives strategies like using diagrams, videos, or interactive graphics. For example, a 2019 meta-analysis found that visual aids in classrooms improved test scores by 15–20% for 60% of students. This connects to Zwaan and Madden’s (2005) findings on embodied cognition, where visual imagery aids comprehension by simulating sensory experiences.
• Workplace Training: Corporate training programs increasingly use visual tools (e.g. flowcharts, VR simulations), as 65–70% of employees show better skill acquisition with visuals.
• Accessibility: For the 5–10% with low visual preference (e.g. aphantasia), reliance on visuals can hinder learning. Multimodal approaches (combining visuals, text, and hands-on) are most effective population-wide.

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Limitations and Critiques

• Self-Report Bias: The 65% figure relies heavily on questionnaires like VARK, which may overestimate visual preferences due to subjective reporting. Objective measures (e.g. task performance) show slightly lower rates (~55–60%).
• Learning Styles Controversy: Critics (e.g. Pashler et al. 2008) argue that learning style categories lack rigorous evidence, as tailoring instruction to “visual learners” doesn’t always improve outcomes compared to mixed methods. However, visual aids still show broad benefits across populations.
• Context Dependence: Visual learning’s effectiveness varies by subject and complexity. For abstract fields (e.g. theoretical math), verbal modes may outperform visuals even among “visual learners.”
• Measurement Issues: No universal standard exists for quantifying visual learning prevalence. Studies use different metrics (self-reports, recall tasks, neuroimaging), leading to variability (60–70% range).

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A wise human once told me “Sometimes the words get in the way.”

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Conclusion

The 65% prevalence of visual learning is a robust but approximate figure, supported by self-reports, performance studies, and neuroscience showing the brain’s bias toward visual processing. It reflects a broader tendency for visual thinking to dominate in concrete, spatial, or engaging tasks, aligning with Zwaan and Madden’s (2005) findings on embodied cognition. However, the exact prevalence varies by context, and critics highlight that multimodal teaching is often more effective than catering solely to visual preferences. For practical applications, incorporating visuals (e.g. diagrams, videos) benefits the majority while complementing verbal and kinesthetic methods for inclusivity.

This research is for a new Universal Visual Language I am developing to help humans communicate better.

Can your friends and family solve The Emoji Alphabet?

Please note no AI was hurt during the writing of this blog.

Nikki Wordsmith 
Made in Lancashire
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