language proficiency as a modulator of the … · 2015. 10. 15. · language proficiency as a...

LANGUAGE PROFICIENCY AS A MODULATOR OF

THE PROCESSING OF UNATTENDED TEXT

A THESIS SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERISITY OF HAWAI‘I AT MĀNOA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF ARTS

IN

SECOND LANGUAGE STUDIES

AUGUST 2014

By

Ryan E. Peters

Thesis Committee:

Theres Grüter, Chairperson Thom Hudson Scott Sinnett

Language Proficiency as a Modulator of the Processing of Unattended Text

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Abstract

This thesis investigates the modulatory role of proficiency in implicit attentional

processes that co-occur with noticing (Schmidt, 1990). The primary hypothesis was that

higher Japanese proficiency would go hand in hand with a stronger inhibition of

irrelevant stimuli made of Japanese hiragana characters. A secondary purpose was to

explore a hypothesized link between saliency and proficiency upon which the primary

hypothesis depends.

The findings regarding the inhibitory effect were inconclusive. However, a

significant correlation was found between language proficiency, as measured by a

lexical decision task, and reaction times in a task originally designed to investigate

Inattentional Blindness. This is taken as supporting the hypothesized link between

proficiency and the visual saliency of written text. This thesis makes a first step towards

bringing methods from Inattentional Blindness research into the field of SLA, and it is

hoped that this foundation can be improved and built upon in future research.


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Table of Contents

Title Page i Abstract ii Table of Contents iii List of Tables and Figures iv Acknowledgements v 1. Introduction 1 1.1. Background 4 1.2. Research Questions and Hypotheses 18 2. Methods 20 2.1. Participants 20 2.2. Materials 21 2.3. Procedure 26 2.3.1. Picture repetition detection task 27 2.3.2. Surprise recognition test 29 2.3.3. Language history questionnaire 30 2.3.4. Lexical decision task 30 3. Results 32 3.1. Language History Questionnaire 32 3.2. Lexical Decision Task 33 3.3. Picture Repetition Detection Task 37 3.4. Surprise Recognition Test 38 4. General discussion 42 5. References 46 6. Appendix 52 6.1. Language History Questionnaire 52 6.2. Stimuli Groupings 52 6.3. Japanese Stimuli 54


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List of Tables and Figures

Tables

Table 1: Quantities and Lexical Frequency values for words and 25 non-words used in the Picture Repetition Detection Task, Surprise Recognition Test, and Lexical Decision Task

Table 2: Means and standard deviations for total accuracy, hits, 33 false alarms and corrected score on the Lexical Decision Task

Table 3: Means and SDs for total accuracy and RT for hits on Picture 37 Repetition Detection Task

Table 4: Means and SDs for accuracy on Surprise Recognition Test 40

Table 5: Means and SDs for reaction times on Surprise Recognition Test 40

Table 6: t and p values for the comparisons of the means of accuracy 41 on the Surprise Recognition Test with chance values

Figures

Figure 1: Example of the RSVP used in Dewald et al. (2011) 7

Figure 2: Diagram of experiment procedure 26

Figure 3: Example of the RSVP Picture Repetition Detection Task 28

Figure 4: Scatter plot of mean RT for hits vs. correct score for the Lexical 34 Decision Task

Figure 5: Histogram of combined score on the Lexical Decision Task 36

Figure 6: Scatter plot of combined score on the Lexical Decision Task 37 vs RT for hits on the Picture Repetition Detection Task

Figure 7: Histogram of mean RT for hits in the Picture Repetition Detection 38 Task

Figure 8: Scatter plot of corrected score on Lexical Decision Task vs RT for 39 hits on Picture Repetition Detection Task

Figure 9: Box plot of accuracy on the Surprise Recognition Test 41


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Acknowledgments

This thesis would not have been possible without the encouragement and

extensive support of Dr. Theres Grüter, Dr. Thom Hudson, Dr. Scott Sinnett, Dr. Andrew

Dewald, Maegen Walker, Cooper Hughes, Nicholas Hayes, Fumiko Peters, and Dr.

Richard Schmidt.


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1. Introduction

Richard Schmidt’s Noticing Hypothesis (Schmidt, 1990, 1995, 2001, 2010) has

played a major role in the development of the field of Second Language Acquisition

(SLA) over the past 20 years. Much of the early research and theorizing centered on the

debate as to whether or not noticing was an absolute prerequisite for learning. This

debate was in reaction to Schmidt’s original strong formulation of the hypothesis, which

stated that noticing is the “necessary and sufficient condition for conversion of input to

intake” (Schmidt, 1990, p. 129). In the context of SLA, input refers to linguistic material

that a learner encounters, and intake refers to the mental representation of the input in

long-term memory, with this then being a necessary condition for learning and

subsequent recall (Godfroid, Boers, & Housen, 2013). The primary research question I

address in this paper was inspired by Schmidt’s concept of noticing and the crucial role

in learning the mechanism plays. However, it is somewhat atypical in that, drawing on

findings from work in cognitive science, I am asking why and under what conditions

might the inhibition of a task irrelevant linguistic stimulus be learned as a result of an

instance of noticing a task target.

Much research has demonstrated that stimuli that are irrelevant to a task can

nonetheless be processed. For instance, in Dewald, Sinnett, and Doumas (2011)

participants were asked to watch a sequence of pictures and respond when they saw an

immediate repetition: the task target. Task irrelevant stimuli consisting of words were

overlaid on top of the pictures. In a subsequent surprise recognition test of these


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words, participants scored at chance levels for all words except those that had been

overlaid on top of a task target; for those words only participants scored significantly

below chance levels. If the mental processes performed by participants when detecting

such a task target (i.e., a repeated picture) can be defined as a form of noticing, then

the aforementioned results are an example of the inhibition of an irrelevant linguistic

stimulus being learned as a result of the noticing of a task target.

The fact that it is the inhibition of the irrelevant stimuli that is being learned

makes the result described in Dewald et al. (2011) even more intriguing. The

experimental setup is designed so that although participants will be looking directly at

the irrelevant stimuli, they will be unable to attend to them because of the difficulty of

the main task of detecting immediate picture repetitions. This seems to indicate that

the inhibitory effect is not the result of noticing of the irrelevant stimulus itself, but

instead is due to its temporal and spatial alignment with and relationship to the noticed

task target. In other words, this paradigm appears to offer the opportunity to explore

properties of implicit attentional processes that occur concurrently with and possibly

constructively affect an instance of noticing. This has interesting implications for

second language learning regarding the possibility of the implicit learning of the

immediate contexts of linguistic stimuli learned as a result of noticing. In addition to

investigating these implications, the primary purpose of the experiment described in

this thesis is to further explore the inhibitory mechanism hypothesized in Dewald et al.

(2011) and use it to elucidate the role of language proficiency in implicit attentional

processes co-occurring with an instance of noticing.


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The role of proficiency is probed by using a population of participants with

varying levels of Japanese proficiency, ranging from beginning second language learners

to native speakers of Japanese. The hypothesis is that higher proficiency will go hand in

hand with a stronger inhibition of irrelevant stimuli made up of Japanese hiragana

characters. This hypothesis depends on a theoretical link between saliency and

proficiency and is based on the findings of Watanabe and his colleagues (Seitz &

Watanabe, 2003, 2005; Tsushima, Seitz, & Watanabe, 2008; Watanabe, Nanez, & Sasaki,

2001), as discussed in the background section. A secondary purpose of this thesis is to

explore this theoretical link between saliency and proficiency.

The organization of this thesis is as follows: Beginning with the background, I

will first briefly review the noticing hypothesis and then address the question: What

exactly does it mean to notice something? Second, I will more fully explain the

experiment and results of Dewald et al. (2011), and draw relevant connections to SLA by

repeatedly asking: What is it that participants are noticing in this task? In the process, I

will define two closely related types of noticing that are particularly important for this

paper: deliberate noticing and reinforced deliberate noticing. I will then elaborate on

the research questions and relevant hypotheses that I mentioned above before

explaining the experiment, results, and finishing with general conclusions.


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1.1. Background

The noticing hypothesis has played a major role in the development of the field

of SLA since its debut in 1990, and continues to generate a wide array of experimental

research to this day (e.g., Godfroid et al., 2013). When put into very simple language,

the original strong form of the hypothesis basically stated: People learn about the things

they notice, but do not learn about the things they do not notice (Schmidt, 1990).

However, the methodological problems involved in proving zero-point questions (Baars,

1988), in this case whether all learning requires some form of noticing, in essence

forced Schmidt to fall back to the far weaker claim that, at the very least, noticing

facilitates learning (Schmidt, 1995, 2001, 2010).

Yet what exactly does it mean to notice something? Though it seems like a very

intuitive concept that we can all draw up a rough understanding of from subjective

experience, noticing is in actuality a complex hybrid of both attention and awareness,

which are themselves by no means unitary phenomena (Schmidt, 2001; Posner &

Peterson, 1990). Drawing on work from cognitive psychology, Schmidt himself has

defined noticing in a wide variety of ways, including: focal awareness, episodic

awareness, apperceived input (Schmidt, 1990), “the basic sense in which we commonly

say we are aware of something” (Schmidt, 1990, p. 132), “a low level of awareness …

[that] is nearly isomorphic with attention” (Schmidt, 1995, p. 1), conscious registration,

awareness at a very low level of abstraction (Schmidt, 2001), and phenomenological

awareness (Schmidt, personal communication, September 4th, 2013). Schmidt (2001),


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reflecting an emphasis on attention rather than awareness and consciousness in his

later work, has also equated a base level of noticing to Tomlin and Villa’s (1994)

“detection within selective attention” and Robinson’s (1995) “detection plus rehearsal

in short term memory”. These last two definitions, in particular, provide interesting

perspectives on how exactly certain subcomponents of attention and awareness may

combine to form an instance of noticing.

Tomlin and Villa’s (1994) definition of noticing was housed within a

characterization of the human attention system that heavily drew on work by Posner

(1994; Posner & Peterson, 1990). They identified three distinct yet interconnected

mechanisms or subsystems of attention: alertness, orientation, and detection.

Although Posner (1980) initially described detection as the cognitive act of becoming

“aware or conscious of the stimulus” (p. 2), from Tomlin and Villa’s perspective

detection is not tantamount to awareness. They proposed a distinction between

detection without awareness (i.e., cognitive registration) and detection within focal

attention, focused by the orienting system, coinciding with awareness (i.e., noticing). In

addition to this important distinction, Tomlin and Villa also laid out three crucially

important ideas of detection:

1. Information detected causes great interference with the processing of other information. 2. Information detected (cognitive registration) exhausts more attentional resources than even orientation of attention. 3. Detected information is available for other cognitive processing.

(Tomlin & Villa, 1994, p. 192)


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Though we will return to the first and second points later, for now we will follow

the third point, which nicely leads into Robinson’s (1995, 2003) definition of noticing as

detection plus subsequent rehearsal in short term memory. Though it can take a

variety of forms, rehearsal can generally be seen as additional cognitive processing in

the pursuit of fulfilling task demands, in the process giving rise to awareness

(Robinson, 1995). In turn, “the traces left by this additional processing… are what

constitute long-term memory representations (i.e., intake)” (Godfroid et al., 2013, p.

486). Taken together, we thus far end up with a picture of an instance of noticing as a

progression that starts with detection and then, as a result of further processing,

enters awareness and leaves a representation in long-term memory. However, given

the widespread acceptance in SLA of the importance of a manifold of parallel implicit

and explicit processes in constituting any single cognitive act (Ellis, 2005, 2008; Ortega,

2009; Robinson, 1995; Schmidt, 1990), we are left with the realization that this nice

linear progression is most definitely not the whole story. To shed light on this issue, I

will now return to the experiment of Dewald et al. (2011) briefly described in the

introduction, focusing on the question: What exactly is it that participants are noticing

in such a task?

The experiment described in Dewald et al. (2011) was based on an experimental

setup created by Rees, Russell, Frith, and Driver (1999). In the setup, participants are

asked to watch a sequence of pictures, presented in a Rapid Serial Visual Presentation

(RSVP) paradigm, and respond when they see an immediate picture repetition: the task

target. In figure 1, an example of a portion of the RSVP used in Dewald et al. (2011),


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the picture of the second candle in the bottom right, with the word TYPE overlaid on

top of it, is a task target.

Figure 1. An example of the RSVP used in Dewald et al. (2011, p. 63). Each combined picture-word stimulus was shown for 350 ms, and stimuli were separated in the stream by 150 ms Inter-Stimulus Intervals (ISIs). The second picture of a candle is a task target and thus the word TYPE, overlaid on top of the target, is a Target-Aligned (TA) irrelevant stimulus. The words overlaid on top of the other pictures, which are not task targets, are all Non-Aligned (NA) irrelevant stimuli.

TYPE and the words overlaid on top of the other pictures in figure 1 are all

irrelevant stimuli, and are the key innovation of the task that differentiated it from

other tasks that had been designed to explore the Inattentional Blindness effect (IB)

(Mack & Rock, 1998), a research topic in the field of Cognitive Psychology. The IB

effect can be succinctly defined as "the failure to see highly visible objects we may be

looking at directly when our attention is elsewhere" (Mack, 2003, p. 180). “Elsewhere”

is meant in both the literal sense, involving spatial location in the visual field, and


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metaphorically, for example in the cases of daydreaming or, more generally, when the

stimulus is not included in the perceiver’s attentional set to which they are oriented

(Most, Scholl, Clifford, & Simmons, 2005).

The original IB paradigm, created by Mack and Rock (1998), was designed as a

means to measure the processing of irrelevant, unattended stimuli that are

nonetheless likely perceived at some level in the process of performing another task.

In the case of the setup created by Rees et al. (1999), the experimental task was

designed so that although participants are looking directly at the words they are

unable to attend to them because of the difficulty of the main task of detecting

immediate picture repetitions. Rees et al. (1999) measured the amount of processing

of the irrelevant stimuli using two methods. First, they varied the focus of attention in

the task by instructing participants to either attend to the pictures or to the words, and

then compared the amount of processing of the words when they were the primary

versus irrelevant stimuli using a subsequent surprise recognition test. They found that

participants recognized nearly all of the words when they had attended to them, but

those participants that had not attended to the words were unable to differentiate

between words they had seen and foils, words they had never seen before. The

second method Rees et al. (1999) used to measure the amount of processing of the

irrelevant stimuli in the picture repetition detection task was to vary whether the

irrelevant stimulus was a word or a nonword, and then use fMRI to compare levels of

brain activation. They found no significant difference between words and nonwords

when attention was directed towards the picture stream, and took this, combined with


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the other result, as evidence for a complete lack of processing of the irrelevant stimuli

regardless of the fact that they were presumably being perceived.

Though the experiment in Rees et al. (1999) falls within the domain of research

on the IB effect, it also can be considered an example of research on implicit

attentional capture. Research on IB is closely related to research on attentional

capture, and efforts have recently been made in the hopes of combining the two

traditionally separate programs under a shared theoretical framework (Simons, 2000;

Most et al., 2005). Attentional capture is the phenomenon of attention being diverted

away from a primary task or goal by unexpected or irrelevant stimuli. In explicit

attentional capture, the unexpected stimuli reach the level of consciousness (i.e. they

are noticed) regardless of one’s efforts to attend to the primary task. On the other

hand, in the case of implicit attentional capture, though the irrelevant stimuli are

detected, they do not necessarily enter conscious awareness. However, in accordance

with Tomlin and Villa’s (1994) first important point, the detection of the irrelevant

stimuli nonetheless has effects on measures such as response times (Most & Simons,

2001). Other types of evidence that show some level of processing of irrelevant stimuli

as a result of implicit attentional capture include measures of brain activity (e.g., Rees

et al. 1999) and separate tasks that directly test some form of learning of the irrelevant

stimuli (e.g., Dewald et al., 2001; Sinnett & Soto-Faraco, 2006; Watanabe, Nanez, &

Sasaki, 2001).


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Before continuing on to the results of Dewald et al. (2011), let us stop and make

our first attempt at answering the question: What exactly is it that participants are

noticing in the task created by Rees et al. (1999)? Given the primary task is to respond

to the immediate picture repetitions, thus reducing the words to irrelevant stimuli, the

answer seems to be quite obvious. The participants are first and foremost noticing each

individual picture. Indeed, following Tomlin and Villa’s (1994) first and second

important ideas of detection, the evidence presented by Rees et al. (1999) clearly

suggests that participants are unable to notice the words because of their exhausted

attentional resources and interference resulting from noticing the primary stimuli.

However, in the case of task targets, in order to successfully follow the task instructions,

the participants must also be noticing that the picture is the same as the one that

immediately preceded it, albeit slightly rotated and with a different word overlaid on

top of it. Interestingly, rather than being equivalent to the base level of noticing, this

seems like a case that is somewhat closer to Schmidt and Frota’s (1986) concept of

“noticing the gap”, the subjective noticing by a second language learner of the

difference between what they can or do produce and what they need to or should be

producing. The relationship between these two seemingly very different situations is

that both can be seen as involving a higher level of noticing influenced by top down

processes: the noticing of the similarities and differences between the stimulus

currently being processed and a previously attended stimulus held in working memory

(R. Schmidt, personal communication, September 4th, 2013). Because this level of

noticing largely results from top-down, non-automatic processes (Moors & De Houwer,


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2006; Segalowitz & Hulstijn, 2005), in this thesis I will henceforth refer to it as deliberate

noticing. We will come back to the possible importance of deliberate noticing shortly,

but first we will now turn to the intriguing results of Dewald et al. (2011).

The experiment described in Dewald et al. (2011) was for the most part an exact

replication of Rees et al. (1999), but with one very important difference. In the

analysis of the data from the surprise recognition test of words that had appeared in

the picture repetition detection task and foils, Dewald et al. performed the additional

step of breaking up the words that had appeared into two groups. Target-Aligned (TA)

words were those that had appeared overlaid on top of a task target (TYPE in figure 1),

and Non-Aligned (NA) words were the remaining words that had appeared overlaid on

top of pictures that were not task targets (BLUE, DARK, TABLE and WORK in figure 1).

While participants scored at chance for NA words (and foils), participants scored at

well below chance for TA words. This led Dewald et al. (2011) to hypothesize an

inhibitory mechanism for unattended irrelevant stimuli that were presented

concurrently with an attended task target from a different task. The resulting

inhibition can then be revealed in later recognition tests.

Now that we have some additional and quite surprising evidence, once again, let

us ask the question: What exactly is it that participants are noticing in the task

described in Dewald et al. (2011)? As before, in each trial the participants must be

noticing the picture itself in addition to the deliberate noticing of whether or not the

current picture is the same as the one that preceded it. But what is leading to the


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inhibition of the TA irrelevant stimuli? Since such an inhibition is, in effect, being

learned during the picture repetition detection task, does this imply that the stimuli

themselves have been learned? In turn, does that mean the TA irrelevant stimuli are

actually at some level being noticed whereas the NA irrelevant stimuli are not?

One possible solution to this conundrum lies in the work of Watanabe and

colleagues (Seitz & Watanabe, 2003, 2005; Tsushima, Seitz, & Watanabe, 2008;

Watanabe et al., 2001). Watanabe et al. (2001) began an innovative line of research

using an experimental setup with many parallels to the one created by Rees et al.

(1999). Their experimental setup consists of a pretest, an “exposure” stage, and a

posttest. The exposure stage entails a dual-task setup with primary and irrelevant

stimuli. The primary task is to identify white letters embedded in an RSVP consisting of

otherwise all black letters. In the process, the participants are exposed to irrelevant

stimuli consisting of a background of moving dots. In each trial, corresponding to a

different letter in the RSVP, a certain percentage of the dots move coherently in the

same direction, while all of the other dots move in random directions. Importantly, the

direction of coherent motion for task targets, the white letters, was always the same for

a given participant and not repeated in other trials.

The percentage of coherent movement of the dots is positively correlated with

the level of explicitness with which they can be perceived (with the threshold between

implicit and explicit somewhere between 10 and 15 percent in the designs used by

Watanabe and colleagues; Tsushima, Sasaki, & Watanabe, 2006; Tsushima et al., 2008).


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For percentages of coherent motion below the threshold, participants were only able to

answer correctly at chance levels when asked the direction of motion. However, for

percentages of coherence above the threshold, participants were able to answer at

above chance levels, which was taken as evidence that such percentages of coherent

motion could be explicitly perceived. In earlier experiments (Seitz & Watanabe, 2003;

Watanabe et al., 2001), the percentage of coherently moving dots in the exposure stage

was kept at 5 percent. However, Tsushima et al. (2008) varied the percentage of

coherence from 3 percent to 50 percent, with interesting results that we will turn to

shortly. In the pretest and posttest, the moving dots were the primary stimuli.

Participants were shown the moving dots, with either 5 or 10 percent moving

coherently, and then asked to determine what direction, out of eight possibilities, the

coherently moving dots were moving.

The crucial result from the initial experiments using 5 percent coherent motion

during the exposure stage was that between the pretest and posttest, participants

showed improvement in being able to identify the direction of coherently moving dots

only for the direction that had been associated with the task targets in the exposure

stage (Watanabe et al., 2001). This led Seitz and Watanabe (2005), drawing extensively

on work by Posner and colleagues (Fan, McCandliss, Sommer, Raz, & Posner, 2002;

Posner & Peterson, 1990) to create a model capable of explaining both task relevant

and task irrelevant learning. For task irrelevant learning to occur, they hypothesized the

key is temporal alignment of stimulus signals from the irrelevant stimulus with

“diffusive reinforcement signals” that result from attentional processing of the task


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target and successful task performance. They further hypothesized that the limitation

of irrelevant learning to those stimuli that occur concurrently with task targets suggests

the reinforcement signals must be temporally precise, but “featurally non-specific.”

Interestingly, the signal associated with Posner’s (Posner & Peterson, 1990) alerting

attentional system, a “phasic but non-specific signal [that] increases general processing

based on the time during which important stimuli are thought to be present” (Seitz &

Watanabe, 2005, p. 4), closely matches these criteria.

Now, for the final and most relevant piece of the picture, let us return to the

interesting results from Tsushima et al. (2008) that I briefly alluded to earlier. Although

in prior experiments (Seitz & Watanabe, 2003; Watanabe et al., 2001) the percentage of

coherently moving dots in the exposure stage was kept at 5 percent, Tsushima et al.

(2008) varied the percentage of coherence from 3 percent to 50 percent. As before,

those participants who were exposed to values of coherent motion near the threshold

between implicit and explicit, saw a marked improvement between the pretest and

posttest. However, participants who were exposed to the lower 3 percent level

coherence saw no improvement, and participants who were exposed to 50 percent

level of coherence actually saw a decrease in their performance between the pretest

and posttest. To account for this additional evidence, Tsushima et al. hypothesized that

in the case of the 50 percent level of coherence, the irrelevant signals were salient

enough to be detected, deemed irrelevant, and inhibited. However, learning occurred

for those levels of coherence around the threshold level because they were too weak to

be detected by the inhibitory system and thus were learned via the previously


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mentioned method. Finally, in the case of the 3 percent level of coherence, such a low

level was hypothesized to simply be too low to be detected even implicitly.

Before returning to Dewald et al. (2011), let us first take one more look at the

results and hypotheses of Watanabe and colleagues and try to translate their ideas into

the language of noticing by once again asking: What exactly is it that participants are

noticing in the exposure stage? As before, in each trial the participants must first and

foremost be noticing the letter, the stimulus from the primary task. In addition, in

order to successfully complete the task, there must also be deliberate noticing of

whether or not the letter on each trial matches the criteria of the task targets kept in

working memory. In the case of successful deliberate noticing of task targets, there are

accompanying diffuse reinforcement signals originating from the alerting attentional

system. Due to the crucial importance of the combination of these reinforcement

signals and deliberate noticing for learning, in addition to ease of reference, I will

henceforth refer to this combination simply as reinforced deliberate noticing. When the

level of coherence of the irrelevant stimuli is low enough not to be detected by the

inhibitory mechanism but just high enough to be implicitly detected, an instance of

reinforced deliberate noticing results in the implicit learning of the stimuli. However,

when the level of coherence of the irrelevant stimuli is high enough to be explicitly

perceived, it is detected and inhibited, and thus an instance of reinforced deliberate

noticing results in the implicit learning of the inhibition of the irrelevant stimulus. We

now have the tools to attempt a full explanation of the results of Dewald et al.


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In our last attempt at tackling the results of Dewald et al. (2011), we were left

with the following unanswered questions. What is leading to the inhibition of the TA

irrelevant stimuli? Since such an inhibition is, in effect, being learned during the picture

repetition detection task, does this imply that the stimuli themselves have been

learned? In turn, does that mean the TA irrelevant stimuli are actually at some level

being noticed whereas the NA irrelevant stimuli are not? Starting with the first

question, just as with the 50 percent level of coherently moving dots from Tsushima et

al. (2008), the learning of the inhibition of the TA irrelevant stimuli results from their

temporal alignment with an act of reinforced deliberate noticing and the fact that they

are salient enough to be detected and inhibited. Next, according to the data of Dewald

et al. it seems that the TA irrelevant stimuli have indeed, in a sense been learned.

However, whether this learning is simply at the perceptual level, or something deeper

involving some degree of lexical access, is yet to be determined. Finally, since the

detection of the irrelevant stimulus is not entering awareness, neither the TA or NA

stimuli are technically being noticed. Instead, the crucial difference is that only the TA

stimuli are temporally aligned with, though not the focus of, an instance of reinforced

deliberate noticing. Although deliberate noticing is sufficient for the learning of task

relevant stimuli, for irrelevant stimuli that are themselves not the target of noticing only

temporal alignment with an instance of successful reinforced deliberate noticing

appears to offer the opportunity for implicit learning. Taken all together, the

experimental setup created by Rees et al. (1999) and further developed by Dewald et

al., appears to offer the opportunity to explore properties of implicit attentional


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processes occurring concurrently with and seemingly constructively affecting an

instance of reinforced deliberate noticing.

To sum up, the noticing of any one stimulus typically does not happen in a

vacuum. Usually there will be a host of other stimuli that are simultaneously perceived,

but, particularly in the case of deliberate noticing, deemed irrelevant to the task at hand

by implicit processes influenced by top-down goals. However, in the case of reinforced

deliberate noticing, whether it be inhibitory for particularly salient stimuli or facilitatory

for those that fall in a sweet spot near the threshold between implicit and explicit

perceivability, implicit learning of some kind seems to be a possibility. This has

interesting implications for second language learning. When a linguistic stimulus is the

target of reinforced deliberate noticing there is the possibility for implicit learning of the

full array of stimuli, particularly those that fall in the sweet spot, which have been

deemed irrelevant but nonetheless make up the context within which the primary

stimulus is being learned. Interestingly, proficiency is one crucial factor that may decide

whether an irrelevant linguistic stimulus falls within an individual’s sweet spot, resulting

in implicit learning, or is salient enough to be inhibited. The purpose of this thesis is to

use the experimental setup of Dewald et al. (2011) to explore the possible modulatory

role of language proficiency in the implicit attentional processes that co-occur with and

shape an instance of deliberate noticing.


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1.2. Research Question and Hypotheses

In order to probe the role of proficiency, we recruited participants with varying

levels of Japanese proficiency, ranging from beginning second language learners to

native speakers of Japanese. Along with the goal of testing whether the results of

Dewald et al. (2011) could be replicated in a language using a completely different

script, Japanese was chosen as an ideal language for the irrelevant stimuli precisely

because of the possibility of recruiting participants with a wide range of ability levels. In

addition to a language history questionnaire, the proficiency of the participants was

separately checked using a lexical decision task (Harrington, 2006).

The main research question this experiment was designed to address is: Will the

magnitude of the inhibitory effect described in Dewald et al. (2011) show a relationship

with participants’ Japanese language proficiency as determined by the language history

questionnaire and lexical decision task? The primary hypothesis is that higher

proficiency will lead to a stronger inhibition of the irrelevant stimuli. This hypothesis is

based on the findings and ideas of Watanabe and his colleagues (Seitz & Watanabe,

2003, 2005; Tsushima et al., 2008; Watanabe et al., 2001), but depends on a theoretical

link between saliency and proficiency. Specifically, in the case of Japanese native

speakers it is hypothesized that the Japanese irrelevant stimuli will be highly salient,

and thus detected and inhibited during instances of deliberate noticing. In cases of

reinforced deliberate noticing (i.e., in trials with successful deliberate noticing of task

targets), the inhibition of the words (TA irrelevant stimuli) will be learned, and thus


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result in below chance performance on the subsequent surprise recognition test for

those words. However, for those participants with lower Japanese proficiency, if there

is indeed a link between proficiency and saliency, it is hypothesized that the irrelevant

stimuli will be less salient and therefore less likely to be detected and inhibited. This

will result in higher accuracy, relative to participants with higher proficiency, for TA

irrelevant stimuli on the subsequent surprise recognition test.

In order to explore the aforementioned theoretical link between saliency and

proficiency, the second research question asks: Will reaction times in the picture

repetition detection task show a relationship with Japanese language proficiency as

determined by the language history questionnaire and lexical decision task? The

hypothesis is that reaction time will positively correlate with language proficiency. This

hypothesis is based on research demonstrating longer response times as a result of the

implicit attentional capture (i.e., detection without awareness) of task irrelevant stimuli,

with the likelihood of capture being determined by the perceived salience of the stimuli

in comparison to other concurrently displayed stimuli (Most & Simmons, 2001;

Theeuwes, 1992; Theeuwes, 1994). To elaborate, as in the main hypothesis, in the case

of Japanese native speakers and highly proficient second language learners it is

hypothesized that the Japanese irrelevant stimuli will be highly salient and thus more

likely to be implicitly detected. In turn, this higher rate of detection and subsequent

processing will result in longer average reaction times.


20

2. Method

2.1. Participants

The participants were mainly students recruited from the University of Hawai’i

at Manoa and the Hawai’i English Language Program. They either received course

credit or were paid $10 for their participation. Participants all belonged to one of two

target populations: native speakers of Japanese who speak English as a second language

(Japanese L1 users) and native speakers of English who have in the past or currently are

studying Japanese as a second language (Japanese L2 users). As part of the recruitment

process, the Japanese L2 users were asked about their Japanese language proficiency to

ensure that at the very least they were able to read the irrelevant stimuli made up of

hiragana (a Japanese phonetic alphabet). During the experiment, their proficiency was

again checked via self-report in a questionnaire and with a Lexical Decision Task.

There were 10 (9 female) Japanese L1 users, ages 24 – 52 years (M = 29.7).

There were 10 (4 female) Japanese L2 users, ages 18 – 33 years (M = 22.7). The

participants all had normal or corrected to normal vision. Due to the fact that

participants were recruited specifically because of their L1 or L2 being Japanese, they

cannot be said to have been completely naïve to the importance of the linguistic stimuli

in the first task.


21

2.2. Materials

There were two main types of stimuli used for this experiment: pictures and

Japanese words.

The pictures consisted of 63 black and white simple cartoon pictures taken from

the Snodgrass and Vanderwart (1980) picture database. Following the examples of

Rees et al. (1999) and Dewald et al. (2011), the pictures were rotated +/- 30 degrees in

order to achieve a proper level of task difficulty (see Figure 3 for an example). The color

of the pictures was inverted and they were shown on a black background so as to

stimulate larger pupil size for maximum eye-tracking accuracy.

A total of 345 two mora Japanese words (e.g., ねこ, はし, しろ, etc.) were

selected from a randomly generated list based on lexical frequency values calculated

using the NTT Japanese Psycholinguistic Database (Amano & Kondo, 2003a, 2003b), and

were separately checked by two Japanese L1 users (see Appendix 6.3. for the full list of

stimuli with lexical frequency values).

One major complication with using Japanese words as stimuli is that Japanese

words can be written in three different alphabets: hiragana, katakana and kanji. The

kana (hiragana and katakana) are 48 character syllabic scripts, with each character

equating to one mora (Hino, Miyamura, & Lupker, 2011; “Hiragana,” 2014). Kanji, on

the other hand, are logographic characters of Chinese origin. There are around 2,000 to

3,000 kanji characters in common use in Japan, most of which have multiple different

pronunciations (Hino et al., 2011; “Kanji,” 2014). Katakana are used mainly for foreign


22

loan words, whereas hiragana are used for Japanese words, often in combination with

kanji.

The Japanese words used as stimuli were all shown in the form of two hiragana

characters. This was done for two major reasons. First, both second language learners

of Japanese and children learning Japanese as their native language typically start

learning hiragana well before they learn katakana or kanji. Second, all Japanese words,

including those typically written with kanji or katakana, can be written phonetically

using hiragana, and indeed this is often done in novels, magazines, newspapers and

other printed media for difficult or rare kanji or katakana words. In a sense, hiragana

can be considered the base alphabet upon which both native speakers and second

language learners build the other two alphabets. Using hiragana stimuli also opens up

the experiment to the widest possible range of second language learners, since even

many relatively common kanji characters are only learned in advanced Japanese

language courses, long after students have learned the actual words themselves.

Though these benefits make hiragana the seemingly ideal choice for linguistic

stimuli that will be used in an experiment for both a wide range of proficiency levels of

second language learners and native speakers of Japanese, there are also major

downsides to using hiragana stimuli. The first problem is that since basically all

Japanese words have multiple different forms in two or sometimes all three of the

alphabets, it is difficult to determine the true frequency of any written word. One way

to circumvent this problem is to use each word’s "lexical frequency" (Twomeny et al.


23

2012). The lexical frequency of a word is calculated by simply summing the frequency

of its hiragana, katakana and kanji word forms. The example from Twomeny et al.

(2012) is for the Japanese word pronounced /tembo:/, which they assign the lexical

frequency value of 6984 (instances in the NTT psycholinguistic database). This is

because its hiragana form (てんぼう) has a frequency of 5, and its kanji form (展望) has

a frequency of 6979. For frequency values of the stimuli used in this experiment, since

the NTT frequency values are simply the number of instances within their corpus, the

values had to be normalized to instances per million (ipm) by dividing by 287,792,797

(the total number of instances in the NTT corpus; Hino et al., 2011), and then

multiplying by 1 million.

As can be seen from the above example, the hiragana forms are often far less

frequent than their kanji counterparts. This is in part because the NTT database is a

corpus made up solely of Japanese newspaper articles, a form of media with a

dramatically higher percentage of kanji forms than those encountered by second

language learners of Japanese. Indeed, even though child native speakers and

beginning and intermediate level second language learners of Japanese often see words

written solely in hiragana, for adult native speakers and advanced second language

learners it is a rarity. This difference in actual frequency values for the different forms

means that the lexical frequency values used to select stimuli for this experiment do not

match the levels of visual familiarity with the stimuli, particularly for the Japanese

native speakers. This may or may not be a fatal flaw, but it is something worth

exploring in future research.


24

One additional confounding factor is that many two character hiragana words

have multiple different possible kanji forms, and likewise multiple possible meanings.

In order to account for this, for any hiragana word that had multiple possible kanji or

katakana forms, all of these were summed together to make a total lexical frequency

for each word, compounding the problem described above.

Table 1 presents an overview of the quantities and lexical frequency values for

all words and non-words used in the experiment. The overall average lexical frequency

of all 345 words used in the experiment is 243.43, ranging between 0.62 and 6167.92

(all frequency values listed here are in ipm). These 345 words were then divided into 6

different groups. The frequencies for group 1, group 2 and group 3 are all comparable

to those in Dewald et al. (2011). The words from group 1 were used as irrelevant

stimuli in the initial practice session for the picture repetition detection task, while the

words from group 2 were used in the main portion of the task. The words from group 3

were used as foils in the surprise recognition task. The words from groups 4, 5 and 6

were all used in the lexical decision task, and consisted of mid, high and low frequency

words, respectively; with a total of 75 words, an average lexical frequency of 724.35,

and ranging between 0.62 and 6167.92. Finally, group 7 consisted of 75 non-words.

These were initially selected due to their lexical frequencies of zero, and verified to be

non-words by a native speaker of Japanese.


25

Table 1: Quantities and Lexical Frequency values for all words and non-words used in the Picture Repetition Detection Task (PRDT), Surprise Recognition Test (SRT), and Lexical Decision Task (LDT).

Group Use Quantity Lexical Frequency

Average Range

1 PRDT: practice 20 94.75 10.82 – 576.69

2 PRDT: irrelevant stimuli 200 111.90 7.40 – 757.38

3 SRT: foils 50 107.61 10.92 – 624.31

4 LDT: mid frequency 35 59.73 7.14 – 338.79

5 LDT: high frequency 20 2607.41 788.50 – 6167.92

6 LDT: low frequency 20 4.38 0.62 – 7.10

7 LDT: non-words 75 0 0 – 0

For the first task, word stimuli were overlaid on top of picture stimuli to make

combined picture-word stimuli. To make the combined stimuli, first, 50 of the pictures

were duplicated, then one of each pair was randomly selected and rotated + 30 degrees

and the other – 30 degrees (these steps were also followed to make the stimuli for the

practice session, using the other 13 pictures and words from group 1). These pictures

were then duplicated again, making 2 sets of 50 pairs, for a total of 200 pictures. These

pictures were then organized and labeled so that aStim1-aStim100 were used for block

1, with aStim1-aStim50 being pairs with aStim51-aStim100, and bStim1-bStim100

(which were the same pictures in the same order as aStim1-aStim100) were used for

block 2, with bStim1-bStim50 being pairs with bStim51-bStim100. Next, the 200

Japanese words from group 2 were overlaid on top of the stimuli, with care taken to

make certain that none of the picture-word combinations had any kind of semantic

relationship. Finally, groupings were made so that half of the pairs would appear

consecutively in the concatenated stream of stimuli as repeats (repetition sets) and the

other half would appear separately. The non-repeats were also organized and grouped


26

together in non-repetition sets so that no accidental repeats would occur when using

the randomized presentation capabilities of the SMI software (see Appendix 6.2. for the

full list of stimuli groupings). In total, each picture was presented four times across

both blocks, once as a repetition set in one block and then two other times as non-

repeats in the other block. The words all only appeared once.

2.3. Procedure

Figure 2: Diagram of experiment procedure.

As illustrated in figure 2, the experiment consisted of a language history

questionnaire (see Appendix 6.1. for the full questionnaire) and three experimental

tasks: first an RSVP picture repetition detection task, then a surprise recognition test,

and finally a lexical decision task. The language history questionnaire was administered

after the first two tasks for two main reasons. First, so as to draw as little attention as

possible to the role of the Japanese stimuli in Task 1. Second, as a rest to allow

participants to recover from experiment fatigue before continuing on to the final task.

Tasks 1 and 2 were created and presented using SMI Experiment Center, whereas the


27

Language History Questionnaire and the Lexical Decision Task were created and

presented using Ibex.

2.3.1. Picture repetition detection task

This was a dual task in which participants saw a concatenated stream of

combination stimuli made up of the pictures from the Snodgrass and Vanderwart

(1980) picture database with Japanese words from group 2 overlaid on top (as seen in

figure 3). The stimuli were presented in two blocks of 100, for a total of 200 trials (not

including the initial practice session).

Participants sat approximately 24 in. from a 22-in. monitor. Stimuli were

presented (and randomized) using SMI Experiment Center. Although the eye-gaze data

is not presented in this thesis, an SMI RED250 tabletop remote eye-tracker system was

used to collect and store eye-tracking data, which consisted of the participants’ eye

position sampled at 250 Hz (approximately 4 ms intervals). Before starting the task, the

eye-tracker was calibrated for each participant, and, if needed, a second calibration was

performed between the two blocks. Though the combined picture-word stimuli varied

to some degree in size, they did not exceed 4.5 in. horizontally or vertically, with the

words not exceeding 1.5 in. horizontally and 1 in. vertically.


28

Figure 3: Example of the RSVP Picture Repetition Detection Task

As shown in figure 3, the picture-word combination stimuli were shown for 500

ms each. This presentation time was 150 ms longer than the one used in Dewald et al.

(2011), and was chosen so as to provide enough time for meaningful eye-tracking data

(which is not presented in this thesis), with 500 ms providing enough time for at least

one saccade and one fixation. The stimuli were separated in the sequence by Inter-

Stimulus Intervals (ISI), blank black screens that were presented for 150 ms each.

Participants were instructed to press a green key when they saw an immediate

picture repetition (c and d in figure 3). Thus, the second stimulus of a repetition set (d

in figure 3) is a task Target. Though there were no directions regarding the Japanese


29

stimuli, if participants asked about them, they were told that the words were distractors

and that they should be ignored, hence the label Irrelevant Stimulus. The Japanese

words that were overlaid on top of a task target, temporally and spatially aligned with

it, will be referred to as Target-Aligned (TA) irrelevant stimuli (ふた on d, in figure 3).

Whereas the words overlaying all non-Targets (a, b and c in figure 4) will be referred to

as Non-Aligned (NA) irrelevant stimuli.

After reading the instructions, participants completed a brief practice session

and were given the opportunity to ask the experimenter any questions to ensure that

they fully understood the task before starting.

2.3.2. Surprise recognition test

Following the picture repetition detection task, all participants were asked to do

a surprise recognition test. Participants saw a series of Japanese words, one at a time in

a fully randomized series, and were asked to determine (as quickly as possible) whether

they had seen the word during the previous task (overlaid on top of a picture) or not.

The words were displayed individually in the center of the screen in the same size and

font as in the picture repetition detection task. This task was also presented (and

randomized) using SMI Experiment Center.

Three different types of words appeared in the task: the 50 words that had been

Target-Aligned in the previous task, 50 words that had been Non-Aligned, and 50 words

that the participants had not seen before (foils, group 3), for a total of 150 trials.


30

Participants were asked to press a green key if they had seen the word during the

repetition detection task or press a red key if they had not seen the word before. The

words remained on the screen until the participant made a response. There was no

practice session for this task, so as to ensure that participants fully understood what

they were supposed to do, the experimenter verbally checked their understanding of

the instructions and answered any questions they had before allowing them to start.

2.3.3. Language history questionnaire

After completing the surprise recognition test participants were moved to a

different computer in the same lab to complete the language history questionnaire and

lexical decision task, both of which were created and presented using Ibex software.

Though Ibex experiments are stored online (at http://spellout.net/ibexfarm/ ) and run

in a browser (for this experiment the google Chrome browser was used), all participants

completed this portion of the experiment using the exact same computer, minimizing

the issue of response time inaccuracy that still plagues most online experiments.

2.3.4. Lexical decision task

The lexical decision task was chosen as an appropriate way to measure the

Japanese proficiency of participants due to the central role of visual word processing in

the task, and the fact that the characteristics of participants’ visual processing

capabilities of both unattended and attended Japanese words is of central importance

http://spellout.net/ibexfarm/


31

for both the primary and secondary hypotheses. In other words, the participants’

familiarity with printed Japanese words, and their resulting processing capabilities of

those words, is of key importance, and the lexical decision task is the most efficient way

to effectively measure such familiarity (Harrington, 2006; Mochida & Harrington, 2006).

For the lexical decision task, participants saw a series of linguistic stimuli and

were asked to decide if what they were presented with was a word or not as quickly as

they could. If they thought it was a word they were asked to answer “Yes” by pressing

the “1” key, and if they thought it was not a word they were asked to answer “No” by

pressing the “2” key. Trials timed-out after 5000 ms and automatically continued on to

the next trial. The stimuli, which did not appear in any other tasks, all consisted of a

combination of 2 hiragana characters, including the 75 Japanese words from groups 4, 5

and 6, and the 75 non-words from group 7. The stimuli were displayed in large font (a

different font from the first two tasks) individually in the upper-center of the screen,

with the possible answers (1. Yes 2. No) displayed in the lower-center of the screen.

There was no practice session for this task; so as to ensure that participants fully

understood what they were supposed to do, the experimenter verbally checked their

understanding of the instructions and answered any questions they had before allowing

them to start.


32

3. Results

3.1. Language History Questionnaire

Participants were asked to provide a self-rating of their second language

comprehension proficiency on a scale from 1 (beginner) to 10 (fluent). The self-ratings

of Japanese L2 proficiency for native speakers of English ranged from 2 to 8, with a

mean of 4.8 (SD = 2.04).

In response to the final question (‘What did you think of the first 2 tasks? Any

problems?’), many participants provided useful answers demonstrating that they were

indeed focusing on the pictures and not the words in the picture repetition detection

task. Here are two representative answers: “I didn't pay attention to the words on the

first task so that I answered randomly to the second task questions...”, “Since I did not

care about the words in the first task at all, I had no idea in the second task.” These

answers also reflect that participants felt they were randomly guessing on the surprise

recognition test. However, there was one interesting exception. In response to the

aforementioned question, one participant answered “I have no idea about how

accurate I was in the second task, except for [participant’s own name]. I'm 99% sure I

saw the word in the first task.” This participant was a Japanese native speaker whose

name is also a Japanese word that happened to be one of the words in the first and

second tasks. Interestingly, this was an accidental replication of Mack, Pappas,

Silverman, and Gay (2002), a replication of Rees et al. (1999) in which for each

participant one of the irrelevant stimuli in the picture repetition detection task was


33

their own name. Many of the participants saw their names, and Mack et al. took this as

counter evidence to the claim of Rees et al. that participants were not processing the

unattended irrelevant stimuli.

3.2. Lexical Decision Task

Table 2: Means and standard deviations for total accuracy, hits, false alarms and corrected score on the lexical decision task by group.

Group Accuracy Hits False Alarms Corrected Score RT for Hits (ms)

M (SD) M (SD) M (SD) M (SD) M (SD)

Both 0.85 (.11) 0.88 (.1) 0.17 (.17) 0.7 (.22) 1035.88 (422.29)

Japanese L1 0.94 (.03) 0.92 (.07) 0.05 (.04) 0.87 (.07) 719.75 (131.21)

English L1 0.77 (.09) 0.83 (.11) 0.3 (.15) 0.53 (.19) 1352.01 (370.36)

Table 2 shows participants’ total accuracy in addition to values for hits (Yes

responses to words), false alarms (Yes responses to non-words), and a corrected score

that accounts for guessing. The corrected score was calculated following the method

described by Huibregtse, Admiraal and Meara (2002), subtracting each participant’s

mean for false alarms from their mean for hits. In comparison to the English L1 group,

the Japanese L1 group had significantly greater accuracy, t(18) = 5.34, p < .001, d = 2.52,

rate of hits, t(18) = 2.21, p = .04, d = 1.04, and corrected score, t(18) = 5.34, p < .001, d

=2.52. The Japanese L1 group had a significantly lower rate of false alarms, t(18) = -

5.06, p < .001, d = 2.39, and significantly shorter reaction times for hits, t(18) = -5.39, p

< .001, d = 2.54.


34

Figure 4 shows a scatter plot comparing participants’ mean reaction times for

hits (measured from when the word first appeared on the screen) to their corrected

scores. There is a significant negative correlation between the two values, r(18) = -.60,

p = .005. The reaction times were cleaned of outliers by replacing times greater than

two standard deviations above the mean with a value equal to two standard deviations

above the mean. Reaction times less than 200 ms were replaced with the mean. All

calculations were performed with the specific means and standard deviations calculated

for each individual, and affected 4.1 percent of the data.

Figure 4: Scatter plot of mean reaction time for hits versus corrected score for the lexical decision task. For the English L1 group (circles), the points are accompanied by their respective Japanese L2 proficiency self-ratings from the language history questionnaire.


35

The two groups are visibly separated with the exception of one of the English

native speakers that gave himself a Japanese L2 proficiency self-rating of 7. Perhaps

unsurprisingly, this participant also happens to be the most experienced and proficient

out of all of the Japanese second language learners. In his response on the language

history questionnaire, he professed over 6 years of intensive self-study of the Japanese

language, over a year living in Japan, and the impressive achievement of passing the

highest level of the Japanese Language Proficiency Test (JLPT), the JLPT N1.

Interestingly, the English native speaker who gave himself a self-rating of 8 is also quite

proficient. In his answer to the language history questionnaire, he professed to have

completed a Bachelor’s degree in Japanese, traveled in Japan, and passed the JLPT N2,

the second highest level. Importantly, the self-rating scores of the other English native

speakers also appear to be correlated with their corrected scores and mean reaction

times.

In order to create a single score for proficiency that could be compared with

findings from other tasks the mean reaction time and corrected score were reduced

into a single combined score of Japanese proficiency. The score was calculated by

dividing each participant’s corrected score by their mean reaction time and then

multiplying by 1000. The histogram for these combined scores is shown in figure 5.


36

Figure 5: Histogram of combined scores on the lexical decision task.

Scores were significantly different between groups, t(18) = 9.26, p < .001, d =

4.37, and also once again visibly separated. This result validates the use of L1 as a

grouping factor.

Figure 6 shows a scatter plot comparing the combined scores for the English

native speaker group to their Japanese L2 proficiency self-ratings. In line with what

would be expected if both the self-ratings and lexical decision task are accurately

assessing the Japanese proficiency of the participants, there is a significant positive

correlation between the two values, r(8) = 0.79, p = .006. In general, these results seem

to confirm that performance on a lexical decision task is a surprisingly good indicator of

proficiency.


37

Figure 6: Scatter plot with a trend line of the combined score on the lexical decision task for English native speakers versus their Japanese L2 proficiency self-ratings from the language history questionnaire.

3.3. Picture Repetition Detection Task

Table 3: Means and standard deviations for total accuracy and reaction time for hits on the picture repetition detection task by group.

Group Accuracy Reaction time for hits (msec)

M SD M SD

Both 0.89 0.1 470.02 34.41

Japanese L1 0.9 0.1 484.04 29.98

English L1 0.88 0.11 456 34.12

Table 3 shows the values for accuracy and reaction time for hits for the picture

repetition detection task by group. The reaction times were cleaned of outliers (4.7

percent of the data) by replacing times greater than two standard deviations above the

mean with a value equal to two standard deviations above the mean, and times less


38

than 100 ms with the mean. Values for false alarms are not reported due to their

extreme rarity. Though the accuracy scores do not appear to differ by group, the

difference in mean reaction times between the two groups is approaching significance,

t(18) = 1.95, p = .067, d = .92. Figure 7 shows the histogram of mean reaction times.

Figure 7: Histogram of mean reaction times for hits (msec) in the picture repetition detection task. Areas of overlap are displayed in the middle shade of gray.

This result appears to broadly confirm the hypothesis for the second research

question that reaction time on the picture repetition detection task will positively

correlate with language proficiency, indicating higher levels of implicit attentional

capture by the Japanese stimuli for participants with higher Japanese proficiency. Even

stronger supportive evidence comes from a correlational analysis, which shows a

significant positive relation between participants’ corrected scores on the lexical

decision task and their mean reaction time on the picture repetition detection task,


39

r(18) = 0.60, p < .001. Figure 8 shows a scatter plot comparing the two variables.

Corrected scores were used instead of combined scores in order to remove the reaction

time component of the lexical decision score, thus avoiding a possible hidden factor

underlying both reaction time scores.

Figure 8: Scatter plot with a trend line of corrected score on the lexical decision task versus reaction time for hits on the picture repetition detection task. For native speakers of English (circles), the points are accompanied by their respective Japanese L2 proficiency self-ratings from the language history questionnaire.

3.4. Surprise Recognition Test

Tables 4 and 5show the accuracy and reaction time results respectively from the

surprise recognition test. In order to determine whether the hypothesis for the first

research question that higher Japanese proficiency will lead to a stronger inhibition of

the irrelevant stimuli held true, first the mean accuracy for Target-Aligned stimuli for


40

Japanese native speakers (.46) and English native speakers (.58) was compared. The

difference was not significant, t(18) = -1.25, p = 0.23, d = .59. In addition, the values for

all relevant accuracy means (TA, NA and Foils for both groups) were compared to

chance (.5). None of the means are were significantly different from chance for an

adjusted p value of .05/6. See table 6 for the actual t and p values.

Table 4: Means and standard deviations for accuracy on surprise recognition test, by group and picture repetition detection task condition.

Groups: Both Japanese L1 English L1

Accuracy: Total M 0.53 0.51 0.55

SD 0.08 0.1 0.06

Accuracy: Foils M 0.55 0.61 * 0.49 *

SD 0.2 0.26 0.11

Accuracy: NA M 0.51 0.47 * 0.56 *

SD 0.21 0.26 0.15

Accuracy: TA M 0.52 0.46 * 0.58 *

SD 0.23 0.28 0.14

Note. The values marked with * are those relevant to hypothesis 1. They were found to be not significantly different from chance values. See table 6 for actual t and p values.

Table 5: Means and standard deviations for reaction times on surprise recognition test.

Groups Both Japanese L1 English L1

RT (msec): Total M 1340.35 1163.53 1517.18

SD 505.07 475.85 492.55

RT (msec): Foils M 1358.42 1166.62 1550.22

SD 518.87 498.86 487.6

RT (msec): NA M 1307.58 1153.14 1462.03

SD 494.15 481.99 479.76

RT (msec): TA M 1355.37 1171.19 1539.54

SD 520.69 453.81 539.48


41

Table 6: t and p values for the comparisons of means of accuracy on the surprise recognition test with chance values for Foils, NA and TA conditions for both groups.

Group Foils NA TA

Japanese L1 t(9) = 1.2818 p = 0.2319

t(9) = -0.3949 p = 0.7021

t(9) = -0.4414 p = 0.6694

English L1 t(9) = -0.1627 p = 0.8744

t(9) = 1.1793 p = 0.2685

t(9) = 1.952 p = 0.08269

There are a number of factors that could have contributed to this null result,

including the low number of participants, outliers among the Japanese native speakers

(that are apparent in figure 9), and the issues with the Japanese hiragana stimuli

described in the methods section. In regards to the outliers, it appears that some

participants were heavily biased towards answering either yes or no. Considering this

phenomenon was limited to the Japanese L1 group, this may be due to higher chances

of misunderstanding instructions in an L2 or cultural differences in regards to the

participants’ willingness to guess.

Figure 9: Box plot of accuracy on the surprise recognition test by condition of the stimuli as they were shown (or not shown in the case of foils) in the picture repetition detection task, separated by L1. The boxes contain the interquartile range. The dark lines inside are the median value. The individual points are possible outliers.


42

4. General Discussion

The primary purpose of this thesis was to use the experimental setup of Dewald

et al. (2011) to explore the possible modulatory role of language proficiency in the

implicit attentional processes that co-occur with and shape an instance of deliberate

noticing. A secondary purpose was to explore the theoretical link between language

proficiency and saliency assumed in the primary hypothesis.

The primary hypothesis was that higher Japanese language proficiency would

lead to a stronger inhibition of the irrelevant stimuli. Specifically, in the case of

Japanese native speakers it was hypothesized that in cases of reinforced deliberate

noticing (i.e., in trials with successful deliberate noticing of task targets), the inhibition

of the words (TA irrelevant stimuli) would be learned, and thus result in below chance

performance on the subsequent surprise recognition test for those words. However,

for those participants with lower Japanese proficiency, it was hypothesized that the

irrelevant stimuli would be less salient and therefore less likely to be detected and

inhibited. This would result in higher accuracy, relative to participants with higher

proficiency, for TA irrelevant stimuli on the subsequent surprise recognition test.

As a result of the inability to replicate the inhibitory effect seen in Dewald et al.

(2011), the primary hypothesis was not supported by the data, leaving us unable to

further explore the characteristics of an instance of reinforced deliberate noticing.

However, as mentioned above, there were a number of complicating factors adding

noise to the data that could be hiding evidence of the expected inhibitory effect. These


43

complicating factors include the low number of participants, outliers among the

Japanese native speakers that are apparent in figure 9, and the issues with the Japanese

hiragana stimuli described in the methods section. The first two factors will be resolved

by running more participants, but the issue with the hiragana stimuli is something that

might be worth addressing in future research.

The secondary hypothesis was that reaction times in the picture repetition

detection task would positively correlate with Japanese language proficiency. In detail,

in the case of Japanese native speakers and highly proficient second language learners it

was hypothesized that the Japanese irrelevant stimuli would be highly salient and thus

more likely to be implicitly detected. In turn, this higher rate of detection and

subsequent processing would interfere with processing of the main task and result in

longer average reaction times in comparison to participants with lower Japanese

proficiency. This hypothesis was based on research demonstrating longer reaction

times as a result of the implicit attentional capture (i.e., detection without awareness)

of task irrelevant stimuli, with the likelihood of capture being determined by the

perceived salience of the stimuli in comparison to other concurrently displayed stimuli

(Most & Simmons, 2001; Theeuwes, 1992, 1994).

The key finding of this thesis is that the secondary hypothesis was supported by

the data, indicating the likelihood of an identifiable link between language proficiency

and the subjective visual salience of written linguistic stimuli. A crucial point was that

not only was there an apparent difference between Japanese native speakers and


44

second language learners, but also that as the second language learners became more

proficient their performance began to resemble that of the native speakers. Indeed,

the strongest support for this hypothesis was the significant correlation between

participants’ corrected scores on the lexical decision task and their mean reaction times

on the picture repetition detection task.

Another important finding was the validity of using a lexical decision task to

measure language proficiency of both native speakers and second language learners,

confirming the work of Harrington (2006) using a different language and orthography.

The fact that the use of only hiragana, rather than more typical kanji and hiragana

combinations, did not have any obvious negative impact on the results is particularly

important in regards to the potential use of the task in Japanese SLA research, allowing

for participants with a wider range of proficiency levels to be tested using the same

task.

In regards to the possible wider implications of the results of this thesis for the

field of SLA, the key finding is that there seems to be an identifiable link between

language proficiency and the visual salience of text. Interestingly, many world travelers

have likely experienced the results of this link. For example, when looking at a sign with

the message written in a variety of languages, perhaps in a train station or airport, we

seem to have no trouble whatsoever in immediately spotting the section written in our

native language and ignoring all of the rest. Indeed, for second language learners

looking for the opportunity to practice, the existence of adjacent text written in their


45

native language can be an annoying and difficult to ignore distraction. This thesis has

shown that experimental paradigms employed to explore the IB effect and implicit

attentional capture are useful for exploring this link. In regards to the specific method

described in this thesis, possible ways to improve and build upon it in future studies

include replicating the experiment using a different language without the orthographic

complexities of Japanese and extending the range of participant proficiency levels to

include those with no exposure to the language.

In conclusion, although the inhibitory effect seen in the data of Dewald et al.

(2011) was not replicated, the significant correlation between participants’ corrected

scores on the lexical decision task and their mean reaction times on the picture

repetition detection task is an important result that provides crucial support for the

theoretical link between language proficiency and the visual salience of text. In regards

to the processing of unattended text, these results indicate that language proficiency

indeed plays a modulatory role, with higher proficiency going hand in hand with

additional processing. While, as argued by Mack et al. (2002), the one participant who

saw her name raises the possibility that this processing includes some degree of lexical

access that in certain cases leads to noticing, overall the results do not allow for the

determination of whether the processing is simply at the perceptual level, or something

deeper.


46

5. References

Amano, S., Kondo, T., (2003a). Nihongo-no Goi-tokusei [Lexical properties of Japanese].

Vol. 7. Tokyo: Sanseido, CD-ROM version (in Japanese).

Amano, S., Kondo, T., (2003b). Nihongo-no Goi-tokusei [Lexical properties of Japanese].

Vols. 1–6. Tokyo: Sanseido, CD-ROM version (in Japanese).

Baars, B. J. (1988). A cognitive theory of consciousness. New York: Cambridge

University Press.

Dewald, A. D., Sinnett, S., & Doumas, L. A. A. (2011). Conditions of directed attention

inhibit recognition performance for explicitly presented target-aligned irrelevant

stimuli. Acta Psychologica, 138(1), 60-67.

Ellis, N. C. (2005). At the interface: dynamic interactions of explicit and implicit

language knowledge. Studies in Second Language Acquisition, 27(2), 305-352.

Ellis, N. C. (2008). Implicit and explicit knowledge about language. In N. Hornberger

(Ed.), Encyclopedia of language and education (pp. 1878-1890). New York:

Springer US.

Fan, J., McCandliss, B. D., Sommer, T., Raz, A., & Posner, M. I. (2002). Testing the

efficiency and independence of attentional networks. Journal of Cognitive

Neuroscience, 14(3), 340-347.


47

Godfroid, A., Boers, F., & Housen, A. (2013). An eye for words: gauging the role of

attention in incidental L2 vocabulary acquisition by means of eye-

tracking. Studies in Second Language Acquisition, 35(3), 483-517.

Harrington, M. (2006). The lexical decision task as a measure of L2 lexical

proficiency. EUROSLA Yearbook, 6(1), 147-168.

Hino, Y., Miyamura, S., & Lupker, S. J. (2011). The nature of orthographic–phonological

and orthographic–semantic relationships for Japanese kana and kanji

words. Behavior research methods, 43(4), 1110-1151.

Hiragana. (2014, January 8). In Wikipedia, the free encyclopedia. Retrieved from

http://en.wikipedia.org/w/index.php?title=Hiragana&oldid=589769488.

Huibregtse, I., Admiraal, W., & Meara, P. (2002). Scores on a yes-no vocabulary test:

Correction for guessing and response style. Language testing, 19(3), 227-245.

Kanji. (2014, January 5). In Wikipedia, the free encyclopedia. Retrieved from

http://en.wikipedia.org/w/index.php?title=Kanji&oldid=589284587.

Mack, A., (2003). Inattentional Blindness: Looking without seeing. Current Directions in

Psychological Science, 12 (5): 179–184.

Mack, A., Pappas, Z., Silverman, M., & Gay, R. (2002). What we see: Inattention and the

capture of attention by meaning. Consciousness and cognition, 11(4), 488-506.

Mack, A., & Rock, I. (1998). Inattentional blindness. Cambridge: MIT Press.


48

Mochida, A., & Harrington, M. W. (2006). The Yes-No test as a measure of receptive

vocabulary knowledge. Language Testing, 23(1), 73-98.

Moors, A., & De Houwer, J. (2006). Automaticity: a theoretical and conceptual

analysis. Psychological bulletin, 132(2), 297.

Most, S. B., Scholl, B., Clifford, E., & Simons, D. (2005). What you see is what you set:

Sustained inattentional blindness and the capture of awareness. Psychology

Review, 112(1), 217-242.

Most, S. B., & Simons, D. J. (2001). Attention capture, orienting, and awareness. In C.

Folk & B. Gibson (Eds.), Attraction, distraction, and action: Multiple perspectives

on attentional capture (pp. 151-173). Amsterdam: Elsevier Science.

Ortega, L. (2009). Understanding second language acquisition. London: Hodder Arnold.

Posner, M. I. (1980). Orienting of attention. Quarterly journal of experimental

psychology, 32(1), 3-25.

Posner, M. I. (1994). Attention: the mechanisms of consciousness. Proceedings of the

National Academy of Sciences, 91(16), 7398-7403.

Posner, M. I., & Petersen, S. E. (1990). The Attention System of the Human Brain.

Annual Review of Neuroscience, 13(1), 25-42.

Rees, G., Russell, C., Frith, C. D., & Driver, J. (1999). Inattentional blindness versus

inattentional amnesia for fixated but ignored words. Science, 286, 2504–2507.


49

Robinson, P. (1995). Attention, memory, and the “noticing” hypothesis. Language

learning, 45(2), 283-331.

Robinson, P. (2003). Attention and memory during SLA. In C. Doughty & M. Long

(Eds.), The handbook of second language acquisition (pp. 631-678). Malden:

Blackwell Publishing Ltd.

Schmidt, R. (1990). The role of consciousness in second language learning. Applied

Linguistics, 11, 129-158.

Schmidt, R. (1995). Consciousness and foreign language learning: A tutorial on attention

and awareness in learning. In R. Schmidt (Ed.), Attention and awareness in

foreign language learning (pp. 1-63). Honolulu: University of Hawai`i, National

Foreign Language Resource Center.

Schmidt, R. W. (2001). Attention. In P. Robinson (Ed.), Cognition and second language

instruction (pp. 3-32). New York: Cambridge University Press.

Schmidt, R. (2010). Attention, awareness, and individual differences in language

learning. In W. M. Chan, S. Chi, K. N. Cin, J. Istanto, M. Nagami, J. W. Sew, T.

Suthiwan, & I. Walker (Eds.), Proceedings of CLaSIC 2010, Singapore, December

2-4 (pp. 721-737). Singapore: National University of Singapore, Centre for

Language Studies.

Schmidt, R., & Frota, S. (1986). Developing basic conversational ability in a second

language: A case study of an adult learner of Portuguese. In R. Day (Ed.), Talking


50

to learn: Conversation in second language acquisition (pp. 237-326). Rowley:

Newbury House.

Segalowitz, N., & Hulstijn, J. (2005). Automaticity in bilingualism and second language

learning. In J. Kroll & A. de Groot (Eds.), Handbook of bilingualism:

Psycholinguistic approaches (pp. 371-388). New York: Oxford University Press.

Seitz, A. R., & Watanabe, T. (2003). Psychophysics: Is subliminal learning really passive?

Nature, 422, 36.

Seitz, A. R., & Watanabe, T. (2005). A unified model for perceptual learning. Trends in

Cognitive Science, 9(7), 329–334.

Simons, D. J. (2000). Attentional capture and inattentional blindness. Trends in Cognitive

Sciences, 4, 147–155.

Sinnett, S., Costa, A., & Soto-Faraco, S. (2006). Manipulating inattentional blindness

within and across sensory modalities. Quarterly Journal of experimental

Psychology, 59(8), 1425–1442.

Snodgrass, J. G., & Vanderwart, M. (1980). A standardized set of 260 pictures: norms for

name agreement, image agreement, familiarity, and visual complexity. Journal

of experimental psychology: Human learning and memory, 6(2), 174.

Theeuwes, J. (1992). Perceptual selectivity for color and form. Perception &

Psychophysics, 51(6), 599-606.


51

Theeuwes, J. (1994). Stimulus-driven capture and attentional set: Selective search for

color and visual abrupt onset. Journal of Experimental Psychology: Human

Perception and Performance, 20(4), 799-806.

Tomlin, R. S., & Villa, V. (1994). Attention in cognitive science and second language

acquisition. Studies in second language acquisition, 16(02), 183-203.

Tsushima, Y., Sasaki, Y., & Watanabe, T. (2006). Greater disruption due to failure of

inhibitory control on an ambiguous distractor. Science, 314, 1786–1788.

Tsushima, Y., Seitz, A. R., & Watanabe, T. (2008). Task-irrelevant learning occurs only

when the irrelevant feature is weak. Current Biology, 18(12), 516–517.

Twomeny, T., Kawabata Duncan, K., Hogan, J., Morita, K., Umeda, K., Sakai, K., Devlin, J.,

(2013) Dissociating visual form from lexical frequency using Japanese. Brain &

Language, 125(2), 184-193.

Watanabe, T., Náñez, Y., & Sasaki, S. (2001). Perceptual learning without perception.

Nature, 413, 844-848.


52

6. Appendix

6.1. Language History Questionnaire

6.2. Stimuli Groupings

In the following tables the stimuli marked in bold italics are immediate picture repetitions, and so are the target stimuli in the picture repetition detection task. The irrelevant stimuli overlaid on these pictures are the Task-Aligned irrelevant stimuli. Those overlaid on all other images are the Non-Aligned irrelevant stimuli.

All groupings in this list were presented randomized in a concatenated stream. The stimuli in the Non-Repetition Sets were organized so as to avoid accidental repetitions without restricting the number of possible non-repetition stimuli occurring sequentially in the stream.


53

Block 1, Repetition Sets Block 1, Non-Repetition Sets

aStim1 aStim51 aStim26 aStim30 aStim38







aStim8 aStim58 aStim80







aStim65 aStim15 aStim42 aStim44











Block 2, Repetition Sets Block 2, Non-Repetition Sets

bStim26 bStim76 bStim1 bStim5 bStim12







bStim33 bStim83 bStim55







bStim40 bStim90 bStim54 bStim65






54







6.3. Japanese Stimuli

The following table contains all of the Japanese stimuli used in the experiment. The hiragana form was used in the experiment. The two frequency values are in instances per million, with that for Hiragana being the frequency of the hiragana form; while that for Total is the frequency for all forms including hiragana, kanji and katakana.

Romanization Hiragana Frequency:

Hiragana (ipm) Frequency: Total (ipm)

Group 1 - Picture Repetition Detection Task: Practice Session

au あう 31.50 175.09

ame あめ 0.99 46.04

iwa いわ 5.25 11.92

oya おや 1.42 89.16

kasa かさ 1.90 10.82

kuni くに 0.00 451.79

kumi くみ 2.87 104.56

shiya しや 0.00 20.05

tsui つい 17.12 35.26

teko てこ 7.67 13.74

nao なお 71.39 72.13

nami なみ 6.50 68.49

niwa にわ 0.00 18.59

nuku ぬく 0.00 22.05

hashi はし 7.79 33.69

hane はね 10.55 18.42

mae まえ 0.00 576.69

mura むら 6.95 70.07

muri むり 0.65 35.44

waru わる 0.00 20.89

mean = 8.63 94.75

Group 2 - Picture Repetition Detection Task

ai あい 11.46 72.94

aka あか 2.77 22.20

aki あき 5.32 90.65

aku あく 9.48 39.59

asa あさ 1.11 90.31

ase あせ 0.81 14.52

ate あて 24.93 30.91


55

ano あの 63.74 63.76

are あれ 25.79 27.01

ie いえ 4.08 124.72

iki いき 5.05 49.33

ishi いし 10.45 189.84

isu いす 11.06 16.10

ichi いち 0.00 462.21

itsu いつ 39.22 39.61

inu いぬ 0.33 19.28

ima いま 270.26 553.43

imi いみ 0.00 151.12

imo いも 3.23 7.69

iya いや 33.95 35.95

iru いる 641.63 649.15

iro いろ 0.72 44.42

ushi うし 7.75 25.16

uta うた 0.00 48.46

uchi うち 371.38 402.07

utsu うつ 9.27 68.77

uni うに 11.36 12.80

uma うま 1.46 19.57

umi うみ 1.07 54.85

ume うめ 0.74 9.91

uru うる 17.18 94.86

eki えき 0.00 134.61

echi えさ 7.12 13.46

eru える 117.26 330.08

oi おい 6.04 13.98

ou おう 0.33 106.32

oki おき 5.63 42.36

oku おく 31.86 639.70

ori おり 18.96 31.19

ore おれ 12.18 21.24

kau かう 9.74 138.28

kake かけ 13.09 16.70

kako かこ 0.00 141.77

kasu かす 1.76 35.19

katsu かつ 27.04 61.62

kana かな 41.61 131.61

kane かね 4.77 55.40

kami かみ 2.91 210.61

kamo かも 42.67 45.84

kare かれ 9.06 109.93

kawa かわ 18.05 73.21

kita きた 34.27 75.50

kimi きみ 1.50 36.04

kimo きも 2.28 16.31

kiru きる 12.50 109.99

kiwa きわ 0.00 105.81


56

kui くい 8.30 14.59

kushi くし 5.09 14.39

kuchi くち 1.96 58.99

kutsu くつ 2.65 17.48

kuma くま 1.82 9.58

kumu くむ 0.00 40.61

kumo くも 3.90 19.09

kura くら 6.26 11.41

kure くれ 6.43 24.86

kuro くろ 0.00 17.16

keru ける 81.61 82.26

koi こい 1.80 347.39

koe こえ 0.00 304.39

koku こく 1.36 215.60

koshi こし 0.00 28.22

komu こむ 20.44 49.19

kome こめ 0.48 182.12

koru こる 18.89 21.59

sai さい 11.72 683.29

sake さけ 0.71 51.36

sasu さす 14.97 45.08

sato さと 1.71 10.99

sama さま 31.11 31.52

sara さら 12.11 20.83

shio しお 0.00 22.26

shiki しき 3.34 115.33

shichi しち 0.00 191.14

shinu しぬ 0.00 74.71

shiru しる 0.00 220.25

suu すう 0.00 413.52

suki すき 4.81 23.28

sushi すし 8.70 9.57

sumi すみ 4.27 23.92

sumu すむ 41.26 166.29

seki せき 6.71 123.53

sou そう 224.13 516.84

sora そら 0.09 45.21

sore それ 577.99 578.50

taki たき 1.45 9.53

take たけ 0.00 16.15

tako たこ 31.15 34.73

tachi たち 658.40 671.47

tatsu たつ 59.61 229.28

tate たて 0.00 18.11

tane たね 1.58 67.08

tama たま 5.75 62.31

taru たる 23.96 28.67

chie ちえ 0.40 22.52

chika ちか 1.14 80.67


57

chiku ちく 0.00 83.41

chiri ちり 1.27 18.60

tsuku つく 540.75 599.24

tsuchi つち 0.00 31.14

tsuma つま 3.33 147.55

tsumi つみ 0.54 42.05

tsuyu つゆ 0.76 12.92

tsuri つり 2.79 11.06

tsuru つる 6.38 15.56

teki てき 0.00 16.36

tetsu てつ 0.00 26.45

tema てま 0.00 8.20

tera てら 0.00 19.70

teru てる 41.67 42.25

toshi とし 136.54 553.88

tomu とむ 0.00 9.80

tora とら 0.00 10.18

tori とり 12.40 51.65

toru とる 296.57 472.13

naki なき 14.44 21.62

nashi なし 61.04 64.90

nasu なす 17.25 25.76

natsu なつ 1.88 116.27

nani なに 6.37 166.48

nama なま 0.00 36.86

nishi にし 0.00 46.22

neko ねこ 0.61 15.98

netsu ねつ 0.00 26.85

nou のう 0.18 46.11

nomu のむ 6.66 43.69

noru のる 0.00 100.53

hai はい 2.43 64.70

haka はか 8.08 24.70

haku はく 0.00 40.49

hata はた 1.65 57.29

hachi はち 0.95 156.97

hato はと 4.12 7.64

hana はな 171.89 251.54

hara はら 0.00 41.58

hari はり 3.59 20.74

haru はる 4.51 120.76

hime ひめ 0.48 7.40

himo ひも 7.00 8.51

futa ふた 9.47 115.85

fuchi ふち 1.58 17.86

futo ふと 8.63 11.82

fune ふね 0.11 37.71

fumu ふむ 0.00 18.21

furi ふり 8.38 20.30


58

furu ふる 0.00 37.92

furo ふろ 9.18 10.79

hei へい 2.07 27.49

heya へや 0.00 61.75

heru へる 6.13 193.32

hou ほう 32.65 471.32

hoka ほか 376.78 403.36

mai まい 39.63 262.24

maki まき 3.88 20.12

maku まく 27.04 57.81

masu ます 0.00 41.03

mata また 590.73 592.53

matsu まつ 3.80 285.34

mari まり 3.80 11.99

mare まれ 8.98 9.75

miki みき 0.70 12.35

mise みせ 0.00 113.35

miso みそ 6.12 8.76

michi みち 0.00 136.06

mina みな 28.11 42.41

muki むき 0.00 17.72

muke むけ 1.00 106.55

mushi むし 2.82 56.68

mune むね 1.05 58.53

mure むれ 0.00 10.50

mochi もち 9.58 24.91

motsu もつ 110.35 484.85

moto もと 112.74 677.80

yatsu やつ 5.19 13.32

yane やね 0.00 15.24

yama やま 7.89 64.25

yamu やむ 12.44 15.90

yari やり 7.89 11.32

yaru やる 237.19 237.23

yuu ゆう 4.94 78.15

yuka ゆか 0.85 18.99

yuki ゆき 3.38 53.50

yuku ゆく 11.31 13.01

yume ゆめ 0.58 67.96

yoso よそ 9.31 9.59

yoru よる 589.52 757.38

raku らく 2.52 13.45

retsu れつ 0.00 19.56

roku ろく 0.00 213.05

waku わく 13.76 91.94

wake わけ 180.77 246.95

mean = 38.72 111.90

Group 3 - Picture Repetition Detection Task: Foils


59

ashi あし 0.00 75.47

asu あす 3.22 10.96

ato あと 157.67 192.38

ane あね 0.00 14.46

ami あみ 0.56 10.92

iku いく 341.77 624.31

ita いた 0.00 20.24

ito いと 0.00 62.17

uso うそ 11.71 20.29

umu うむ 0.19 61.05

ura うら 1.76 32.28

osu おす 0.00 45.55

kaku かく 10.35 608.25

kata かた 6.56 58.25

kachi かち 0.00 77.82

kani かに 10.22 14.71

kari かり 7.93 24.32

kishi きし 0.93 23.35

kesu けす 0.00 23.69

ketsu けつ 0.00 27.35

koro ころ 119.65 121.38

saku さく 5.38 128.71

saru さる 8.36 45.28

shima しま 11.26 52.39

suku すく 8.60 13.18

sune すね 10.69 11.06

sei せい 33.57 569.55

soko そこ 121.05 121.06

sochi そち 0.00 149.30

soto そと 0.33 53.97

toki とき 215.38 534.09

tochi とち 0.00 150.83

nuki ぬき 1.45 20.29

nomi のみ 44.65 51.16

nori のり 4.94 26.61

hako はこ 0.00 22.20

hiki ひき 2.68 30.70

hiku ひく 31.46 77.99

fuyu ふゆ 0.13 35.92

hone ほね 0.02 22.02

make まけ 0.15 14.55

maru まる 7.27 40.73

mei めい 5.01 113.78

more もれ 2.26 14.92

yoi よい 162.63 233.54

yosa よさ 8.59 17.74

yori より 480.19 492.27

rei れい 1.45 145.16

waka わか 1.70 14.11


60

waki わき 25.68 27.97

mean = 37.35 107.61

Group 4 - Lexical Decision Task: Mid

awa あわ 2.97 10.70

ika いか 167.08 259.89

ue うえ 187.37 257.10

uri うり 0.54 32.84

ei えい 8.02 40.81

oka おか 0.00 10.91

oru おる 7.11 16.60

kao かお 0.00 106.93

kaki かき 3.46 21.87

kamu かむ 11.29 12.07

kiri きり 10.20 27.88

kuru くる 10.42 15.33

saki さき 7.20 158.08

satsu さつ 0.46 28.48

shiku しく 0.00 19.62

shiri しり 5.00 9.90

shiro しろ 6.23 28.01

suna すな 0.00 10.41

taka たか 29.26 35.31

taku たく 30.10 72.28

tsuki つき 21.79 220.13

naku なく 3.50 24.60

nuno ぬの 0.00 9.78

nuru ぬる 0.00 7.14

neru ねる 0.00 29.24

hiru ひる 0.00 22.48

fuku ふく 0.00 338.79

hoshi ほし 0.00 26.44

mitsu みつ 4.48 16.76

muku むく 9.10 27.17

mesu めす 0.00 13.61

moshi もし 43.19 43.86

mori もり 5.47 86.95

yoko よこ 0.00 36.82

rika りか 0.50 11.70

mean = 16.42 59.73

Group 5 - Lexical Decision Task: High

aru ある 3498.18 3500.33

iu いう 2226.37 2637.29

kara から 4996.70 5007.03

koto こと 4355.00 4391.48

kono この 1699.07 1699.27

kore これ 831.47 839.28

suru する 4775.73 4777.70


61

sono その 1222.75 1234.18

tame ため 1656.56 1658.56

tou とう 13.20 822.12

nai ない 5635.93 6167.92

naka なか 115.44 788.50

nara なら 236.09 5701.39

naru なる 3527.92 3544.49

nichi にち 0.00 3760.14

hito ひと 38.60 938.21

miru みる 524.92 1059.05

mono もの 1198.78 1322.71

yaku やく 0.00 1259.59

you よう 799.25 1038.97

mean = 1867.60 2607.41

Group 6 - Lexical Decision Task: Low

ana あな 0.03 3.35

eri えり 1.50 4.76

oto おと 0.00 6.39

oni おに 0.47 5.83

kuri くり 1.26 5.62

kechi けさ 1.13 4.07

kechi けち 0.31 1.68

kona こな 0.00 4.31

shita した 0.00 4.00

tani たに 0.00 6.37

chiru ちる 0.00 5.33

nuu ぬう 0.00 4.12

hima ひま 1.99 7.07

heso へそ 0.97 1.41

heta へた 1.14 4.12

hera へら 0.00 0.62

hosu ほす 0.00 2.19

mane まね 6.21 7.10

meshi めし 0.00 3.92

rusu るす 0.11 5.38

mean = 0.76 4.38

Group 7 - Lexical Decision Task: Non-words

amo あも 0.00 0.00

uhe うへ 0.00 0.00

uya うや 0.00 0.00

eka えか 0.00 0.00

kaa かあ 0.00 0.00

kunu くぬ 0.00 0.00

keo けお 0.00 0.00

kehe けへ 0.00 0.00

keyu けゆ 0.00 0.00

koho こほ 0.00 0.00


62

sunu すぬ 0.00 0.00

suhe すへ 0.00 0.00

sea せあ 0.00 0.00

seka せか 0.00 0.00

seso せそ 0.00 0.00

sene せね 0.00 0.00

seha せは 0.00 0.00

sohi そひ 0.00 0.00

somu そむ 0.00 0.00

chini ちに 0.00 0.00

tsuo つお 0.00 0.00

tsuso つそ 0.00 0.00

tsunu つぬ 0.00 0.00

teshi てし 0.00 0.00

techi てち 0.00 0.00

teyo てよ 0.00 0.00

nake なけ 0.00 0.00

nate なて 0.00 0.00

nane なね 0.00 0.00

niha には 0.00 0.00

niyu にゆ 0.00 0.00

nua ぬあ 0.00 0.00

nuto ぬと 0.00 0.00

nuho ぬほ 0.00 0.00

neso ねそ 0.00 0.00

nechi ねち 0.00 0.00

nenu ねぬ 0.00 0.00

nefu ねふ 0.00 0.00

nonu のぬ 0.00 0.00

hao はお 0.00 0.00

hinu ひぬ 0.00 0.00

hiho ひほ 0.00 0.00

heka へか 0.00 0.00

heku へく 0.00 0.00

hese へせ 0.00 0.00

hete へて 0.00 0.00

honi ほに 0.00 0.00

hohi ほひ 0.00 0.00

hoyo ほよ 0.00 0.00

howa ほわ 0.00 0.00

miha みは 0.00 0.00

mihi みひ 0.00 0.00

muto むと 0.00 0.00

mehi めひ 0.00 0.00

mewa めわ 0.00 0.00

moha もは 0.00 0.00

mofu もふ 0.00 0.00

yoto よと 0.00 0.00

raka らか 0.00 0.00


63

ranu らぬ 0.00 0.00

rahi らひ 0.00 0.00

rike りけ 0.00 0.00

rua るあ 0.00 0.00

ruto ると 0.00 0.00

ruha るは 0.00 0.00

ruho るほ 0.00 0.00

rure るれ 0.00 0.00

reta れた 0.00 0.00

rehe れへ 0.00 0.00

reya れや 0.00 0.00

reyu れゆ 0.00 0.00

reyo れよ 0.00 0.00

rone ろね 0.00 0.00

wato わと 0.00 0.00

wahe わへ 0.00 0.00

language proficiency as a modulator of the … · 2015. 10. 15. · language proficiency as a...

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