CHAPTER 4 RESULTS AND DISCUSSIONS
4.5. Summary: Reflections on Lexical Process Models
Table 4-19. Summary of Phonological Units
Units Criteria Effect
Onset
Bare Tone Error N Facilitation
RT Inhabitation
Syllable Error N Facilitation
RT Facilitation
Tonal Syllable
Error N Facilitation
RT Facilitation
4.5. Summary: Reflections on Lexical Process Models
The traditional serial account, such as in Fromkin (1971), single input is the central issue on lexical process. The input could go on to next linguistic section after is processed in former linguistic generator. If the process brings out some problems at certain stage, speech errors will be generated. Serial model provides a reasonable space for linguistic rules to be operated in individual stages, even if the speech errors are generated. However, Fromkin’s model is not sufficient enough to explain the errors form dual external inputs and the way that speech errors are retrieved. The input of each linguistic department is always from single source, including the first stage of meaning to be conveyed.
Levelt’s model (1989, 1999) provided serial model a better explanation for the way that speakers retrieved speech errors, by means of the nodes, links, activation and spreading within lemma level. The neural-linguistic approach provided a psychological account for the wrong retrieving. In order to explain the lexical error with phonological similarity, Levelt’s model explained that nodes with more
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similarities could have higher chance to be altered and activated. Levelt’s model provided a possible basis for accounting for the dual inputs, such as Stroop technique in this study, because the external inputs could have competition after they are activated and spread. The competition could tell us that the target color or speech error would be generated in the end. However, the model could not have enough explanation for why color term and homophone could induce significant difference in error distribution and RT. It is difficult to explain the competition of semantic and phonological levels occurred in task 3 and 4 without interaction between linguistic layers, as depicted in figure 4-1.
With a view to the interactive models, Stemberger (1985) proposed that the linguistic departments should be interactive, and the activation is transported through units and links. It still lacked sufficient account for the operation of dual inputs during lexical process.
Connectionist model, especially the model of Dell & O’Seaghdha (1991), provided enough explanation for dual external inputs. The model also accounted for the interaction of semantic, lexicon and phonological layers, as shown in figure 4-1, and the way how task 3 and 4 induced difference in error amount and response time.
Based on the results in linguistic effects and shared unit test, this model still provided limited space for the reason why certain units induced facilitative effect (tonal syllable, and syllable), and why errors always share phonological structure and tone with visual targets. However, this model really helps explain how Stroop effect operates in lexical network and how it generates speech errors.
It shows that when a certain lexical domain (color in this study) is activated, and all the lexicon and phonological lines and nodes are prepared to be activated, which forms a strong network for further lexical processing. Therefore, it is scarce to see any error occurring without meaningful or phonological relation in this study. Beside
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activation of forward spreading (cascading from lexicon to lexeme layer), backward spreading (feedback from lexeme to lexicon layer) also works to retrieve sound-similar color in error generation. With the interactive account, discrete departments could communicate among layers, so that prominent phonological information is traceable in color substitution errors. Therefore, we could see that both semantic and phonological effects are equally crucial in visual tasks. Phonological effect suggests that when the semantic domain is specified and gets ready for activation, all the network of relevant semantic nodes, lexicon nodes, and phonological nodes would get ready at the same time (or maybe gradually). These relevant nodes form a stronger network before specific activation, and relevant semantic and phonological nodes are sensitive to be activated. By means of observing the linguistic effects in speech errors and the shared unit test, it appeared that dual visual inputs could have co-effect in generating speech errors and encoding speed, especially within the network between the semantic and phonological layers. The hierarchies in (1) and (2) show the hierarchies of phonological sensitivity of error number and RT among these linguistic layers, which means that some of phonological units could have different sensitivity (or dominance) when lexical encoding in interactive manner.
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Chapter 5 Conclusion
With the result of speech error and response time, we could respond to the research questions proposed in charter 2. First, by means of controlling phonological similarity, the speed of response displayed diverse patterns when we compared these tasks in pairs, even though the phonological similarity did not induce significant difference within each task. Dell’s model provided a theoretical basis for dual inputs and why the results were different when subjects received different visual tasks.
Second, with regard to linguistic effects, we found that Stroop effect, syllable structure, phonotactic regularity, and tone effects were significant in the generation of speech errors. These effects seem to impact on the lexical process apparently in these tests.
Third, concerning the issue of advance planning unit, we concluded that the unit of tonal syllable and syllable could serve as possible units in lexical planning, while the units of onset, vowel, and rhyme might be not significant enough to reduce speech errors or process faster in lexical production.
Fourth, the status of tone seems to be a lexicon-like unit because it did not bring out any facilitative effect in processing speed but helped reduce the error amount of speech errors. The possible account is that tone might be a lexical organization and encoded in phonological representation, not a pure phonological tone.
Fifth, under the visual competition of color term and visual concept, the dual inputs and the diverse results among different visual tasks seem to support interactive account of lexical access, because interactive account provides a flexible and theoretical basis to explain Stroop effect in current study.
For future study, we could combine Stroop technique, and controlling of
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phonological similarity as well as the semantic domain to examine the lexical access in aphasic speech or in children’s language development. Phonological similarity might induce different patterns of speech errors and response time in aphasic lexical network or the one which is developing language. By means of Stroop technique, we could also extend the domain outside the color, such as naming of number system to testify operation of processing model. However, we still need more psychological evidence to distinguish the processes of naming and reading task, even though the data collected so far could imply the different mechanisms between them. We need more direct evidence, such as ERP or MEG tests, to help us explain the operation between linguistic mechanism and the visual tasks.
At the end of this study, we supposed that classical experiment of psychological technique could provide linguistics more evidence and insights on the interaction among linguistic representations, as well as the lexical structure in mental process.
Stroop technique is an ideal case to open the cross-field view of linguistics and psychology.
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Appendix 1: Stimuli Design
Trials design in Task 1Color Naming Test
H1 黑灰 黃紅 灰紅 黃灰 ■■ ■■ ■■ ■■ L1 綠白 黃紫 綠黃 橙綠 ■□ ■■ ■■ ■■
H2 黃紅 灰棕 紅灰 黃棕 ■■ ■■ ■■ ■■ L2 紫白 灰紫 白綠 橙灰 ■□ ■■ □■ ■■
H3 棕紅 灰黑 紅黃 灰黃 ■■ ■■ ■■ ■■ L3 黑藍 橙紫 綠黑 黃藍 ■■ ■■ ■■ ■■
H4 藍白 黑灰 黑紅 棕黃 ■□ ■■ ■■ ■■ L4 紫橙 黃綠 黑藍 橙黑 ■■ ■■ ■■ ■■
H5 黃紅 灰黃 藍白 黑紅 ■■ ■■ ■□ ■■ L5 黃藍 紫橙 綠黃 橙綠 ■■ ■■ ■■ ■■
H6 紅黃 黃棕 灰黑 紅灰 ■■ ■■ ■■ ■■ L6 綠黃 橙綠 白紫 黃綠 ■■ ■■ □■ ■■
H7 紅灰 灰棕 黃紅 棕灰 ■■ ■■ ■■ ■■ L7 紫藍 黃橙 綠紫 藍黑 ■■ ■■ ■■ ■■
H8 黑紅 黃灰 紅灰 灰棕 ■■ ■■ ■■ ■■ L8 黑橙 綠黃 藍紫 黑藍 ■■ ■■ ■■ ■■
H9 棕黃 黑灰 紅棕 白藍 ■■ ■■ ■■ □■ L9 紫黃 黑橙 綠橙 藍黑 ■■ ■■ ■■ ■■
H10 灰黃 灰棕 紅灰 黃黑 ■■ ■■ ■■ ■■ L10 黃紫 綠橙 藍橙 黃綠 ■■ ■■ ■■ ■■
H11 灰黃 紅灰 紅黃 灰黑 ■■ ■■ ■■ ■■ L11 綠橙 黃綠 紫黃 白綠 ■■ ■■ ■■ □■
H12 棕黃 灰紅 棕灰 紅黃 ■■ ■■ ■■ ■■ L12 灰橙 綠白 紫灰 白紫 ■■ ■□ ■■ □■
H13 黃灰 黃紅 黑灰 紅棕 ■■ ■■ ■■ ■■ L13 藍黃 黑綠 紫橙 藍黑 ■■ ■■ ■■ ■■
H14 黃棕 紅黑 灰黑 白藍 ■■ ■■ ■■ □■ L14 黑橙 藍黑 綠黃 橙紫 ■■ ■■ ■■ ■■
H15 紅黑 白藍 黃灰 紅黃 ■■ □■ ■■ ■■ L15 橙紫 綠橙 黃綠 藍黃 ■■ ■■ ■■ ■■
H16 灰紅 黑灰 棕黃 黃紅 ■■ ■■ ■■ ■■ L16 綠黃 紫白 綠橙 黃綠 ■■ ■□ ■■ ■■
H17 灰棕 紅黃 棕灰 灰紅 ■■ ■■ ■■ ■■ L17 黑藍 紫綠 橙黃 藍紫 ■■ ■■ ■■ ■■
H18 棕灰 灰紅 灰黃 紅黑 ■■ ■■ ■■ ■■ L18 藍黑 紫藍 黃綠 橙黑 ■■ ■■ ■■ ■■
H19 藍白 棕紅 灰黑 黃棕 ■□ ■■ ■■ ■■ L19 黑藍 橙綠 橙黑 黃紫 ■■ ■■ ■■ ■■
H20 紅黃 灰紅 棕灰 黃灰 ■■ ■■ ■■ ■■ L20 綠黃 橙藍 橙綠 紫黃 ■■ ■■ ■■ ■■
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Trials design in Task 2
Color Reading Test
H1 黑灰 黃紅 灰紅 黃灰 L1 綠白 黃紫 綠黃 橙綠 H2 黃紅 灰棕 紅灰 黃棕 L2 紫白 灰紫 白綠 橙灰 H3 棕紅 灰黑 紅黃 灰黃 L3 黑藍 橙紫 綠黑 黃藍 H4 藍白 黑灰 黑紅 棕黃 L4 紫橙 黃綠 黑藍 橙黑 H5 黃紅 灰黃 藍白 黑紅 L5 黃藍 紫橙 綠黃 橙綠 H6 紅黃 黃棕 灰黑 紅灰 L6 綠黃 橙綠 白紫 黃綠 H7 紅灰 灰棕 黃紅 棕灰 L7 紫藍 黃橙 綠紫 藍黑 H8 黑紅 黃灰 紅灰 灰棕 L8 黑橙 綠黃 藍紫 黑藍 H9 棕黃 黑灰 紅棕 白藍 L9 紫黃 黑橙 綠橙 藍黑 H10 灰黃 灰棕 紅灰 黃紅 L10 黃紫 綠橙 藍橙 黃綠 H11 灰黃 紅灰 紅黃 灰黑 L11 綠橙 黃綠 紫黃 白綠 H12 棕黃 灰紅 棕灰 紅黃 L12 灰橙 綠白 紫灰 白紫 H13 黃灰 黃紅 黑灰 紅棕 L13 藍黃 黑綠 紫橙 藍黑 H14 黃棕 紅黑 灰黑 白藍 L14 黑橙 藍黑 綠黃 橙紫 H15 紅黑 白藍 黃灰 紅黃 L15 橙紫 綠橙 黃綠 藍黃 H16 灰紅 黑灰 棕黃 黃紅 L16 綠黃 紫白 綠橙 黃綠 H17 灰棕 紅黃 棕灰 灰紅 L17 黑藍 紫綠 橙黃 藍紫 H18 棕灰 灰紅 灰黃 紅黑 L18 藍黑 紫藍 黃綠 橙黑 H19 藍白 棕紅 灰黑 黃棕 L19 黑藍 橙綠 橙黑 黃紫 H20 紅黃 灰紅 棕灰 黃灰 L20 綠黃 橙藍 橙綠 紫黃
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Trials design in Task 3
Stroop Naming Test
H1 黑黑黑灰灰 灰黃黃黃紅紅紅 灰灰灰紅紅紅 黃黃黃灰灰 灰 L1 綠綠綠白白白 黃黃黃紫紫紫 綠綠綠黃黃黃 橙橙橙綠綠 綠 H2 黃黃黃紅紅紅 灰灰灰棕棕棕 紅紅紅灰灰灰 黃黃黃棕棕棕 L2 紫紫紫白白白 灰灰灰紫紫紫 白白白綠綠綠 橙橙橙灰灰灰 H3 棕棕紅棕紅紅 灰灰灰黑黑黑 紅紅紅黃黃黃 灰灰灰黃黃黃 L3 黑黑藍黑藍藍 橙橙橙紫紫紫 綠綠綠黑黑黑 黃黃黃藍藍藍
H1 黑黑黑灰灰 灰黃黃黃紅紅紅 灰灰灰紅紅紅 黃黃黃灰灰 灰 L1 綠綠綠白白白 黃黃黃紫紫紫 綠綠綠黃黃黃 橙橙橙綠綠 綠 H2 黃黃黃紅紅紅 灰灰灰棕棕棕 紅紅紅灰灰灰 黃黃黃棕棕棕 L2 紫紫紫白白白 灰灰灰紫紫紫 白白白綠綠綠 橙橙橙灰灰灰 H3 棕棕紅棕紅紅 灰灰灰黑黑黑 紅紅紅黃黃黃 灰灰灰黃黃黃 L3 黑黑藍黑藍藍 橙橙橙紫紫紫 綠綠綠黑黑黑 黃黃黃藍藍藍