Surprisal, the negative log-probability a language model assigns to a word given its preceding context, reliably predicts adult reading times. Does it contribute as much to explaining when children acquire individual words? Frequency reflects a learner's cumulative exposure to a word, whereas surprisal reflects how predictable a single occurrence is given its context. We investigate this question across two corpus-based studies of Spanish. In Study 1, we modeled age of acquisition (AoA) for 225 Spanish nouns using lexical frequency and contextual diversity from child-directed speech, plus surprisal from three language models differing in architecture and training language (BETO, BERTIN, mGPT). Frequency strongly predicted AoA (r=-.597, p<.001); surprisal added little beyond frequency and word length, including in a naturalistic-context analysis. In Study 2, we modeled adult fixation durations in the Chilean Spanish subsample of the Multilingual Eye-movement Corpus (MECO Wave 2), using mGPT surprisal alongside two independent frequency measures. Surprisal robustly predicted longer fixation durations after controlling for frequency and word length, consistent across both frequency sources. A matched word-type-level comparison showed the surprisal-behavior association was stronger in reading than in acquisition (z=3.63, p<.001). The findings suggest cumulative lexical exposure and contextual predictability play different roles across the language trajectory: frequency is particularly informative about when early lexical representations are acquired, whereas surprisal captures moment-to-moment processing difficulty in an already-established linguistic system. We discuss this pattern in relation to usage-based and entrenchment-based accounts of lexical development and to the evaluation of language models as models of human language behavior.
Figures & tables
Predictor
ρ
p
Context
Frequency (CDS)
−.597
<.001
Corpus
Contextual diversity
−.564
<.001
Corpus
Contextual diversity, partialled for frequency
.028
.680
Corpus
BETO surprisal
−.036
.590
Standardized frames
BERTIN surprisal
.102
.128
Standardized frames
mGPT surprisal
−.048
.469
Standardized frames
Table 1: Spearman correlations between distributional predictors and age of acquisition in Study 1. Sample sizes vary slightly across analyses because of missing corpus or surprisal estimates. The frequency and contextual-diversity analyses use N=224 , whereas standardized-context LLM analyses use N=225 and the natural-context mGPT analysis uses N=224 .
Figure 1: Frequency in child-directed speech predicts age of acquisition for 225 Spanish nouns. The association was negative and significant (Spearman r=−.597 , p<.001 ). Selected early- and late-acquired nouns are labeled for illustration.
Figure 2: Corpus-based measures and LLM-derived surprisal as predictors of age of acquisition in Study 1. Dashed lines represent linear trends.
Model
R2
ΔR2
βsurprisal
p
Frequency + word length
.379
–
–
–
+ BETO surprisal
.381
.0023
.048
.368
+ BERTIN surprisal
.385
.0067
.082
.124
+ mGPT surprisal
.380
.0006
−.024
.653
Table 2: Incremental variance in age of acquisition explained by standardized-context LLM surprisal beyond CDS frequency and word length in Study 1.
Sample
N
Baseline R2
Full R2
ΔR2
βmGPT
p
All valid natural contexts
224
.3677
.3685
.0008
−.0209
.595
≥5 contexts per noun
154
.3135
.3211
.0076
−.0737
.197
Table 3: Incremental predictive value of natural-context mGPT surprisal for age of acquisition. Models include CDS frequency and word length as baseline predictors.
Figure 3: Standardized fixed effects predicting log total fixation duration in the MECO Wave 2 Chilean Spanish sample. Error bars show 95% confidence intervals.
Predictor
Model A (EsPal)
Model B (SUBTLEX-ESP)
Surprisal (mGPT)
0.0114∗∗∗ (SE =.00067 )
0.0114∗∗∗ (SE =.00066 )
Lexical frequency
−0.0226∗∗ (SE =.0070 )
−0.0294∗∗∗ (SE =.0068 )
Word length
0.0581∗∗∗ (SE =.0025 )
0.0540∗∗∗ (SE =.0028 )
Intercept
5.349∗∗∗ (SE =.055 )
5.359∗∗∗ (SE =.048 )
Item variance (word type)
0.0200 (SD =.142 )
0.0198 (SD =.141 )
Participant variance
0.0354 (SD =.188 )
0.0354 (SD =.188 )
Table 4: Mixed-effects model coefficients predicting log total fixation duration in MECO Wave 2 Chilean Spanish, with crossed random intercepts for participant and word type (item).
Figure 4: Graphical summary of the main findings from Studies 1 and 2. Study 1 shows that lexical frequency in child-directed speech strongly predicts age of acquisition (AoA) for Spanish nouns, whereas LLM-derived surprisal provides little additional predictive value beyond frequency and word length. Study 2 shows that mGPT surprisal predicts longer adult fixation durations during reading after controlling for lexical frequency and word length. Together, the findings illustrate different predictive roles for cumulative lexical exposure and contextual predictability across the Spanish language trajectory.
Department of Linguistics, UC San Diego · School of Foreign Languages, Shanghai Jiao Tong University · School of Computer Science, Shanghai Jiao Tong University +3