Individual differences in online spoken word recognition: Implications for SLI

Abstract

Thirty years of research has uncovered the broad principles that characterize spoken word processing across listeners. However, there have been few systematic investigations of individual differences. Such an investigation could help refine models of word recognition by indicating which processing parameters are likely to vary, and could also have important implications for work on language impairment. The present study begins to fill this gap by relating individual differences in overall language ability to variation in online word recognition processes. Using the visual world paradigm, we evaluated online spoken word recognition in adolescents who varied in both basic language abilities and non-verbal cognitive abilities. Eye movements to target, cohort and rhyme objects were monitored during spoken word recognition, as an index of lexical activation. Adolescents with poor language skills showed fewer looks to the target and more fixations to the cohort and rhyme competitors. These results were compared to a number of variants of the TRACE model (McClelland & Elman, 1986) that were constructed to test a range of theoretical approaches to language impairment: impairments at sensory and phonological levels; vocabulary size, and generalized slowing. None of the existing approaches were strongly supported, and variation in lexical decay offered the best fit. Thus, basic word recognition processes like lexical decay may offer a new way to characterize processing differences in language impairment.

Figure 1

Individual differences in language and cognitive ability. A) Scatter plot showing the relationship between language and non-verbal cognitive abilities in 527 children taken from Tomblin and colleagues (1997). B) Diagnostic criteria for specific language impairment (SLI), non-specific language impairment (NLI), specific cognitive impairment (SCI) and normal (N). C) Scatter plot showing the relationship between language and non-verbal cognitive abilities in the present sample.

Figure 2

Some possible patterns in the pattern of fixations to the target over time. A) Variation in the delay before the function begins climbing; B) Variation in the peak amount of fixations; C) variation in slope.

Figure 3

Some possible patterns in the pattern of fixations to the cohort and rhyme competitors over time. A) Variation in the delay before the function begins climbing; B) Variation in the slope as the function falls; C) Variation in the baseline it falls to; D) Variation in the peak fixations.

Figure 4

Looks to the target, cohort, rhyme and unrelated objects as a function of time for each of the four groups.

Figure 5

Looks to the target (panel A) as a function of time and group membership. B) Looks to the cohort as a function of time and group membership. C) Looks to the rhyme as a function of time and group membership.

Figure 6

The functions and parameters used for curvefitting. A) To fit looks to the target, a 4-parameter logistic function was used. This function is defined by upper and lower asymptotes, the cross-over point, and the slope of the transition. B) To fit looks to the cohort and rhymes, an asymmetric Gaussian was used. This function is defined by the location and height of the peak, and the slope and asymptotes at the onset and offset components.

Figure 7

Looks to the target as a function of time when time is normalized to the reaction time on each trial. A) Fixations broken down by each of the four diagnostic groups. B) The first 10 participants' individual functions.

Figure 8

Model fit. A) Predicted fixations to the target, cohort, rhyme and unrelated objects as a function of time after the linking hypothesis had been fit to the non-language-impaired participants. B) The corresponding empirical data.

Figure 9

A summary of the results of the simulations. Shown is RMS error (smaller error means better fits) for each simulation when fit to individual participants (Panel A) or the mean of the language-impaired group (Panel B). See Table 5 for numerical values.

Figure 10

Results from the top three fitting models. Note the default value of each parameter is shown in the heavy lines; dashed lines show lower values; and thin solid lines show values higher than the default. The top row shows results from variation in lexical decay. A) Predicted looks to the target; B) Cohort. C) Rhymes. The second row shows results from varying the rate at which phonemes acquire activation. Note that rhymes were similar and are not displayed. D) Predicted fixations to the target; E) Cohort. The third row shows results from manipulations of lexical inhibition. F) Predicted looks to the target; G) Cohort.

Figure 11

Representative results from simulations that failed to fit the data. A) Vocabulary size had almost no discernable effect on lexical activation. B) Phoneme inhibition could only create a small range of values. C,D) Lexical inhibition created the right pattern, but could not reach the full range of values necessary to account for the data. E) Feature spread affected largely the timing of the function; F) Feature decay caused target activation to decrease; G, H) Input noise created asymptotic differences in the target and cohort, but also shifted the peak fixations to the cohort.

Figure 12

Raw activations for manipulations of lexical decay. A) Target. B) Cohort.