Handwriting generates variable visual output to facilitate symbol learning

Abstract

Recent research has demonstrated that handwriting practice facilitates letter categorization in young children. The present experiments investigated why handwriting practice facilitates visual categorization by comparing 2 hypotheses: that handwriting exerts its facilitative effect because of the visual-motor production of forms, resulting in a direct link between motor and perceptual systems, or because handwriting produces variable visual instances of a named category in the environment that then changes neural systems. We addressed these issues by measuring performance of 5-year-old children on a categorization task involving novel, Greek symbols across 6 different types of learning conditions: 3 involving visual-motor practice (copying typed symbols independently, tracing typed symbols, tracing handwritten symbols) and 3 involving visual-auditory practice (seeing and saying typed symbols of a single typed font, of variable typed fonts, and of handwritten examples). We could therefore compare visual-motor production with visual perception both of variable and similar forms. Comparisons across the 6 conditions (N = 72) demonstrated that all conditions that involved studying highly variable instances of a symbol facilitated symbol categorization relative to conditions where similar instances of a symbol were learned, regardless of visual-motor production. Therefore, learning perceptually variable instances of a category enhanced performance, suggesting that handwriting facilitates symbol understanding by virtue of its environmental output: supporting the notion of developmental change though brain-body-environment interactions. (PsycINFO Database Record

Figure 1

Examples of 4-year olds tracing and copying letters. Top row: Tracing the letter ‘S’. Middle row: A single 4-year old producing the letter ‘E’ three times through copying an example. Bottom row: Three different 4-year olds copying the letter ‘S’ from an example.

Figure 2

Top left corner: warm-up trial example for 4AFC task. Remainder slides are examples of the 4AFC task trials (see text for details).

Figure 3

A. The visual examples that were used in the copying, tracing-typed, and single fonts conditions. B. An example of the tracing-typed cardstock guides. C. Examples of the tracing-handwritten cardstock guides.

Figure 4

Duration of symbol learning phase across conditions. Participants in the symbol production groups had significantly longer learning phase durations than those in the visual only learning groups. All errors bars in all figures depict 95% confidence intervals.

Figure 5

Proportion correct (selection of the upright, learned symbol across all trials) for the Initial and Final 4AFC task across conditions. Significant improvement in symbol recognition for all conditions post card-sorting except Tracing-handwritten and Handwritten-font.

Figure 6

Recognition errors produced in the Initial and Final 4AFC tasks. Majority of errors involved participants selecting the misoriented symbols.

Figure 7

Differences in correct categorization across training conditions in the card-sorting task (combined typed and handwritten flashcards). Tracing-typed and single-font conditions were least successful at correctly categorizing variable symbol examples.

Figure 8a

Categorization errors across conditions for handwritten symbol examples. Tracing-typed and single-font conditions made the most categorization errors.

Figure 8b

Categorization errors across conditions for typed symbol examples. Tracing-typed and single-font conditions made the most categorization errors.

Figure 9a

Anchor points used to measure variability in formation of symbols produced by the Copying and Tracing-typed learning conditions. Anchor points were used for post-processing data analyses only.

Figure 9b

Anchor points used to measure variability in formation of symbols produced by the Tracing-handwritten condition. Anchor points were used for post-processing data analyses only.