A minimum of 300 words each question and References (questions #1 - 3) KEEP QUESTION WITH ANSWER EACH QUESTIONS NEED TO HAVE A SCHOLARY SOURCE 1. How does an understanding of management and organizational behavior lead to organizational effectiveness and efficiency? Why is the study of management theories (classical, behavioral and modern management) relevant today? 2. What are the four career issues in the new workplace facing managers today? Discuss one of the major challenges, highlighting its importance in the 21st century workplace and how it affects the behavior of people within organizations. 3. What are the three essential managerial skills? Explain how the importance of each skill varies across the typical levels of management in organizations. Don’t forget, the question isn’t just asking you to list the skills, you must also provide a thorough discussion on how they vary across different levels of management– answer the question fully. Villegas 8727 Juniper St. Los Angeles, CA 90002 United States ROSALIE 22900 Grove Ave EASTPOINTE, MI 48021-1536 United States Do not change anything. Include them in your research report submission but ofcourse do not include them in your word count. Method Participants A total of 479 undergraduate students from Western Sydney University were recruited via convenience sampling and participated in a study investigating the effects of age of acquisition and the emotional nature of words in lexical access. Participation was completed voluntarily as part of an assessment task. Data from 104 participants was rejected as they either did not complete the task or their accuracy was less than 80%. Therefore, the final sample size was 375. Materials and Apparatus Two sets of letter strings were used in the experiment: words and nonwords. All the stimuli were 3 to 8 characters long. There were 4 categories of words: early acquiring emotional words (EE), early acquiring non-emotional words (ENE), late acquiring emotional words (LE) and late acquiring non-emotional words (LNE). A total of 40 words in each category was used. Early acquiring words were acquired before 5 years of age and late acquiring words were acquired after 7 years of age. The word stimuli were taken from the normative developmental dataset for emotion vocabulary comprehension (Baron-Cohen, Golan, Wheelwright, Granader, & Hill, 2010). The nonwords were selected from ARC nonword database (Rastle, Harrington, & Coltheart, 2002). A total of 120 nonwords were used. The stimuli were presented in a dual lexical decision task where two letter strings were presented on the screen. For half of the trials (80), both the strings were words and for the remaining half (80) either one or both of the letter strings were nonwords. When both the strings were words, they belonged to the same category of words (EE, ENE, LE, LNE). There were 20 trials for each category of words. Procedure Participants were tested in t.

1. A minimum of 300 words each question and References
(questions #1 - 3) KEEP QUESTION WITH ANSWER EACH
QUESTIONS NEED TO HAVE A SCHOLARY SOURCE
1. How does an understanding of management and
organizational behavior lead to organizational effectiveness and
efficiency? Why is the study of management theories (classical,
behavioral and modern management) relevant today?
2. What are the four career issues in the new workplace facing
managers today? Discuss one of the major challenges,
highlighting its importance in the 21st century workplace and
how it affects the behavior of people within organizations.
3. What are the three essential managerial skills? Explain how
the importance of each skill varies across the typical levels of
management in organizations.
Don’t forget, the question isn’t just asking you to list the skills,
you must also provide a thorough discussion on how they vary
across different levels of management– answer the question
fully.

2. Villegas
8727 Juniper St.
Los Angeles, CA 90002
United States
ROSALIE
22900 Grove Ave
EASTPOINTE, MI 48021-1536
United States
Do not change anything. Include them in your research report
submission but ofcourse do not include them in your word

3. count.
Method
Participants
A total of 479 undergraduate students from Western Sydney
University were recruited via convenience sampling and
participated in a study investigating the effects of age of
acquisition and the emotional nature of words in lexical access.
Participation was completed voluntarily as part of an
assessment task. Data from 104 participants was rejected as
they either did not complete the task or their accuracy was less
than 80%. Therefore, the final sample size was 375.
Materials and Apparatus
Two sets of letter strings were used in the experiment: words
and nonwords. All the stimuli were 3 to 8 characters long. There
were 4 categories of words: early acquiring emotional words
(EE), early acquiring non-emotional words (ENE), late
acquiring emotional words (LE) and late acquiring non-
emotional words (LNE). A total of 40 words in each category
was used. Early acquiring words were acquired before 5 years
of age and late acquiring words were acquired after 7 years of
age. The word stimuli were taken from the normative
developmental dataset for emotion vocabulary comprehension
(Baron-Cohen, Golan, Wheelwright, Granader, & Hill, 2010).
The nonwords were selected from ARC nonword database
(Rastle, Harrington, & Coltheart, 2002). A total of 120
nonwords were used.
The stimuli were presented in a dual lexical decision task
where two letter strings were presented on the screen. For half
of the trials (80), both the strings were words and for the
remaining half (80) either one or both of the letter strings were
nonwords. When both the strings were words, they belonged to
the same category of words (EE, ENE, LE, LNE). There were 20
trials for each category of words.
Procedure
Participants were tested in the classroom during their tutorial.
They were instructed to tap the left side of the screen if both the

4. letter strings they saw were words and to tap the right side of
the screen if any of the letter strings were nonwords. Each trial
started with a fixation cross; 500 ms after the fixation cross two
letter strings were presented on the screen. The trial ended after
the participant made a response. If there was no response made
by the participant within 3 seconds after the stimulus
presentation, the trial was terminated. The inter trial interval
was 1 second. Feedback was provided for incorrect trials. The
trials were presented in a random order for each participant.
Stimulus presentation and response collection was controlled by
Presentation Mobile App (Neurobehavioral Systems Inc., CA,
USA) running on the participants’ mobile device (iOS or
Android). The experiment took 5-10 minutes to complete. The
accuracy and response time were calculated. Reaction time (RT)
data from participants who had an accuracy of 80% or more
were further analysed.
Results
Assumption of normality was met for the reaction time
data. A two-way repeated measures analysis of variance
(ANOVA) was computed with the factors age of acquisition of
words (early, late) and emotional nature of the words
(emotional, non-emotional). The ANOVA revealed a significant
effect of age of acquisition of words F(1, 374) = 669.58, p <
.05. Early acquiring words were accessed significantly faster
(Mean RT = 837.52 ms, SE = 7.36) than late acquiring words
(Mean RT = 961.49 ms, SE = 9.20). The effect of emotional
nature of the words was also significant F(1, 374) = 18.53, p <
.05. Emotional words were accessed significantly faster (Mean
RT = 890.56 ms, SE = 8.44) than non-emotional words (Mean
RT = 908.45 ms, SE = 8.04). The interaction between age of
acquisition and emotional nature of words was not significant
F(1, 374) = 0.40, p > .05, suggesting that the effect of
emotional nature of words was similar for both early and late
acquiring words.
Running head: BEHAVIOURAL SCIENCE CHECKLIST AND

5. TIP SHEET 1
BEHAVIOURAL SCIENCE CHECKLIST AND TIP SHEET
2
Psychology: Behavioural Science: Checklist/Tip Sheet for
writing a Research Report
Wing Hong Fu
Western Sydney University
Abstract
This is an exemplar sentence to illustrate where the first line of
the Abstract should be located (see below for more
information). The abstract is not the space to give lots of details
about each paper you have included in the introduction nor is it
the place to be going in-depth into your mythology/results, but
rather, the Abstract should be giving a general overview of your

6. overall viewpoints, arguments, methodology, and conclusions.
Additionally, in order to preserve the correct layout for a
research report, you will find the Assignment Preparation and
Title Construction sections after the References section—
regarding the chronological order of approaching your
assignment, the aforementioned sections should technically go
before you begin writing any other part of your research report.
Therefore, it is advised that you scroll down (p.12) and read the
assignment preparation and title construction sections before
coming back up here to continue reading.
· Is the abstract section on the second page of your manuscript?
· Is the abstract section headed with “Abstract”, horizontally
centred at the top of the page, double line spaced, and not in
boldface? (see start of this page for an example)
· Is the abstract written in one paragraph without indentation
and double spaced, left justified, beginning directly below
“Abstract” (no extra space/line)?
· Does the abstract summarise the study clearly plus concisely
and contain:
· An overview of the general topic area, study aims, and
hypothesis/hypotheses
· Information on the participants and experiment methodology
· A concise statement of key findings and if
hypothesis/hypotheses were supported
· Brief statement of the key implications of the your
experiment’s findings
· Is the abstract within 150 words and written in past tense?
· Does the entire abstract fit onto the second page?
· General Rule of Thumbs:
· The abstract is usually the last section to be written
· An abstract’s word limit is typically 10% of the assignment’s
word count up until 250 words. (i.e., a PhD Thesis would still
contain a 250 word limit abstract).

7. Psychology: Behavioural Science: Checklist/Tip Sheet for
writing a Research Report
This is an exemplar sentence to illustrate where the first
line of the introduction should be located (see below for more
information).
· Is the Title horizontally centred and repeated in uppercase and
lowercase with no boldface on at the top of the first “proper”
page of the report? (see start of this page for an example)
· Does the first sentence of the introduction section start with
an indent on the line directly below the Title? (see start of this
page for an example)
· Does the introduction section start with a broad statement
about the research topic?
· Is this board statement then followed by another statement
explaining why this research topic is an
interesting/impactful/important aspect to explore? (i.e., what
does your research mean in relation to human behaviour)
· Does this first introduction paragraph end with clearly
defining what your manuscript is about? (i.e., paraphrasing your
aim)
· Does the next introduction paragraph (i.e., second paragraph)

8. explain relevant theories and ideas with definitions of key terms
and concepts?
· Does the following introduction paragraphs (i.e., third
paragraph and onwards) then:
· Examine past research in a concise and critical manner? (i.e.,
looking at both sides—for and against—of all arguments)
· Utilise appropriate research evidence? (e.g., primary sources,
experimental journal articles, most (not all) journal articles are
current—within five years—journal articles, avoid referencing
Wikipedia/Textbooks/Unverified Websites, among others)
· Link previous research to your current research aim? (i.e.,
does the research that you have critically reviewed logically
lead to the aim of your experiment). This is achieved by:
· Critically thinking about how the Independent Variable/s has
an effect on the Dependent Variable/s in a theoretical manner
(i.e., why one level of the IV impacts the participant’s
performance in the task).
· Examining both the experimental and the theoretical support
for your overall viewpoint on the research question. (i.e., you
have made a clear effort to construct an organised review of the
topic with a logical argument throughout the entire manuscript).
· Reviewing any existing evidence (i.e., explaining previous
research findings) that can support the idea of the Independent
Variable/s influencing the Dependent Variable (i.e., in what
ways does prior research demonstrate that the Independent
Variable/s manipulate the Dependent Variable/s).
· Addressing any possible counter arguments and the scholarly
sources supporting that/those counter argument.
· Ensuring that all your points/arguments relate to the overall
research question.
· Supporting every single one of your arguments/points with
scholarly sources.
· Not only considering the results/conclusions of each study but
also critically thinking about both how and why each study
reached their conclusion—displayed by the way you discuss
your chosen journal articles in detail.

9. · Does your introduction section’s final paragraph:
· Begin with an explicit statement of the aim? (e.g., “The aim of
this research/experiment/thesis/report is...”)
· Followed by a statement of the independent variable and
dependent variable? (e.g., “The independent variable/s is/are…
and the dependent variable/s is/are…)
· Note: typically, you wouldn’t really need to be providing an
explicit statement clearly outlining what the independent
variable/s and dependent variable/s is/are in the introduction as
a good literature review would automatically logically lead to
your experiment’s aim and hypothesis/hypotheses. Instead the
explicit statements of the independent variable and dependent
variable would belong in the “Research Design” segment of the
Method section. However, for the purposes of this assignment,
you are required to include the independent variable and
dependent variable in the last paragraph of your introduction
since the Method and Results section will be provided to you
and you are instructed to simply copy and paste them into your
assignment.
· Finally, this introduction paragraph should finish with an
unambiguous statement of your hypothesis/hypotheses which
must include the independent variable and its relationship with
the dependent variable. (e.g., “It was hypothesised that... [the
independent variable/s] will increase/decrease/equal [the
anticipated outcome of the dependent variable/s])
· Final check up for the introduction section:
· Does your introduction section visibly demonstrate the
importance of the research area being investigated?
· Is the introduction section structured like a funnel (i.e., begins
broadly followed by logically leading the reader through prior
research to converge on the ‘literature gap’ which your
experiment hopes to investigate)?
· Does your introduction section contain:
· A review of existing literature which constructs clear and
sound arguments that concludes with the study aim/s.
· Valid and sound logical justification for your arguments and

10. overall viewpoint.
· Logical justification for the experimental design and both the
independent and dependent variable/s
· The introduction section finishes with a clear statement of
hypothesis/hypotheses.
· Is the literature review segment of the introduction written in
past tense; whereas, the aim and hypothesis/hypotheses in the
last paragraph written in present tense?
Method
This is where you should copy and paste the Method section
provided to you—NOTE: you do not need to paraphrase or edit
this section because you will not be marked on this section.
However, you will still need to understand the experimental
method since your Discussion section should demonstrate your
ability to critically analyse the methodology and then provide
suggestions for future research based on this critical analysis.
Accordingly, please ensure that you have read and understood
the Method section provided to you.
Results
This is where you should copy and paste the Results section
provided to you—NOTE: you do not need to paraphrase or edit
this section because you will not be marked on this section.
However, you will still need to understand what the results
represent since your Discussion should demonstrate your ability
to critically analyse the results and link it back to
existing/previous literature (i.e., the literature reviewed in your
introduction). So, please ensure that you have read the Results
section provided to you.
Discussion
This is an exemplar sentence to illustrate where the first
line of the introduction should be located (see below for more
information).
· The discussion section should commence on the next double-
spaced line below the last line of the Results section. (see above
for an example)
· Is the discussion section headed with “Discussion” and

11. horizontally centred at the top of the page in boldface? (see
above for an example)
· Does the first sentence of the discussion section start with an
indent? (see above for an example)
· Does the discussion section begin with a statement that
restates the aim?
· Is this followed by a statement of your results in respect to
whether or not the hypothesis/hypotheses were supported? (e.g.,
“The results are contrary to/support the hypothesis that...”)
· Finally does this first discussion paragraph conclude with a
statement about what your results imply about human
behaviour? (i.e., relate your results to the statement in your
introduction that explains why this topic is an
interesting/impactful/important aspect to research)
· Does the following discussion paragraphs (i.e., second
paragraph and onwards) then:
· Provide an argument for why your results (for each hypothesis
if you have more than one hypothesis) are important and how
your results advances scientific knowledge?
· Link your results (for each hypothesis if you have more than
one hypothesis) with the previous research that you have
already reviewed in your introduction (i.e., are your results
consistent or inconsistent with previous studies’ results and why
might this be)
· Discuss potential reasons underlying your results—this is
when you provide constructive criticism (limitations) towards
your experiment/methodology followed by suggestions for how
future/follow-up studies might address the issues you
encountered to produce meaningful information about human
behaviour.
· Note: these discussion paragraphs is where critical thinking is
absolutely required and where your marker will be actively
seeking out the signs which indicate that you’ve critically
engaged with both the existing literature and your own
experiment.
· Does your discussion section’s final paragraph:

12. · Reflect on whether the experiment has successfully achieved
the research aim?
· Restate what researchers have learned from this experiment in
relation to general human behaviour?
· Final check up for the discussion section:
· All suggestions for methodological improvements must be
relevant and accompanied by sound reasoning (i.e., what are the
specific issues which may generate confounding variables in
your experiment?).
· There are bound to be numerous specific issues in every
experiment. As the popular idiom goes “hindsight is 20/20”
(i.e., it's easy to know the right thing to do after something has
happened, but it's hard to predict the future) and this is also true
for research/experimental design. So, now that you have the
results and that you’ve experienced the actual experiment, ask
yourself: what could have been done better?
· Therefore, generic limitations like: increasing participant size,
attaining equal male and female distributions, among others are
simplistic and if utilised then they must be intensely explained
in relation to how they are actually a confounding variable.
· Are the suggestions for future research logically justified to
concentrate most heavily on generating potential development
of theory and/or research, and on implications for the real
world?
· Have you discussed the theoretical importance of your results?
· Does your conclusion sum up your results?
· Is the discussion section written in present tense?
References
· Is the reference list on a separate page directly after the
discussion section?
· Is the reference list headed with “References” and horizontally
centred at the top of the page? (see start of this page for an
example)
· Did you cross check the in-text citations to ensure that they
match the reference list entries?

13. · See here for a detailed guide on APA references.
Assignment Preparation
· Is your research topic identified?
· Have you conducted a thorough investigation into the existing
literature on your research topic?
· Have you obtained sufficient references to support your
argument/aim/hypothesis/methodology?
· Have you created a skeleton structure (outline/organisation) of
your manuscript to ensure that your arguments are logically
structured and are not actually a fallacy?
· Finally, you can go to here plus here for more assignment
preparation tips and here plus here for more general academic
writing tips deliberately created by Western Sydney University
to assist assignment preparation.
Title Construction
The format of your title should follow this structure:
“Effects of IV(s) on DV”. However, you will also need to
illustrate to your reader the specifics of this effect. For
example, if I had “Effect of self-affirmation on self-esteem” and

14. my results demonstrate that self-affirmation increases an
individual’s self-esteem then a more specific and appropriate
title would be “Self-affirmation facilitates the development of
increased self-esteem”. It is advised that the title is written at
the same time as your abstract (the title of the last things to
write even though it belongs at the start of your paper).
· Does your Title contain the Independent Variable?
· Does your Title contain the Dependent Variable?
· Does your Title contain the specifics of the effect/relationship
between the variables? (e.g., The IV increases/decreases/has no
effect on the DV)
· Is your Title within 10 to 12 words?
APA Formatting
· Is there a manuscript page number on the top right of every
single page?
· Are all pages numbered consecutively with “1” starting on the
Title Page?
· Is there a running head on the top left of every page?
(including the Title Page)
· Is the Title Page the only header that includes the words:
“Running head:”? (see first page of this manuscript in
comparison to the other pages for an example)
· Is the running head a shortened version of your title?
· Is the running head 50 characters or less including spaces and
punctuation?
· Is the running head in capital letters/uppercase?
· Does your title page include:
· The Title
· The author’s name (your name)
· Name of the education institution (e.g., Western Sydney
University)
· Is your title 12 words or less, vertically and horizontally
centred on the page with all main words capitalised, double line
spaced, and not in boldface?
· Is the author’s name vertically and horizontally centred on the
page, double line spaced, and not in boldface (usually, middle

15. initials are not included)?
· Is the name of the education institution vertically and
horizontally centred on the page with all main words
capitalised, double line spaced, and not in boldface?
· Is the footer/header the same font as the body text and in size
10 font?
· Does your manuscript follow APA format? (click here for an
unofficial but surmised guide)
· Is the manuscript in Times, Times New Roman, Courier, or
Arial?
· Is the manuscript in size 12 font?
· Is the manuscript in double line spacing with no additional
spacing between title and first paragraph, or between
paragraphs, or between references in the reference list?
· Is the text in the manuscript not justified on the right hand
margin? (i.e., left justified texts)
· Have you indented, with one tab space, the first line of each
paragraph in your “proper” report? (i.e., not including the
abstract section)
· Are numbers between 0 and 10 written as words (e.g., zero,
one, two, three...) while numbers over ten are in written as
numerals (e.g., 11, 54, 69, 1234, 54320, 345476...)
· Are all your manuscript’s headings in APA style:
APA Headings
Level
Format
1
Centred, Boldface, Uppercase and Lowercase Headings
2
Left-Aligned, Boldface, Uppercase and Lowercase Heading
3
Indented, boldface, lowercase heading with a period. Begin
body text after the period.
4
Indented, boldface, lowercase heading with a period. Begin
body text after the period.

16. 5
Indented, lowercase heading with a period. Begin body
text after the period.
Final Tips
An easy way to ensure that your assignment is well written
is to ask yourself “Why have I included this sentence here and
what does this sentence tell the reader” for every single
sentence inside your assignment as this will ensure that every
sentence you write has a purpose; thus, tremendously improving
flow within paragraphs. Other easy ways to maintain a well
written manuscript is to (1) try and keep every paragraph to one
theme/point; (2) attempt to limit your paragraphs to typically
only include three to five sentences; (3) include sentences, at
the beginning and at the end of each paragraph, whose sole
purpose is to provide a link between your paragraphs as this
will ensure that there is a logical flow throughout your entire
manuscript; (4) avoiding the usage of derogatory terms, slang
terms, informal language, and first person language (I, me,
etc.); and finally (5) utilise formal language and the passive
voice (e.g., “it was done”, not “I did”) to make your manuscript
appear professional. (These points are assessed on the marking
criteria under “Clarity of Premises and Conclusions”).
Furthermore, to ensure that your manuscript makes
grammatical sense, it is usually highly advised that you
proofread your assignment in your head followed by then
verbally proofreading it out loud (regardless of how silly it
makes you feel—honestly, I still feel silly myself when I do
this, but it is super important); alternatively, the text-to-speech
feature on your computer can be used to accomplish this instead
of your own voice. I would also recommend that you use a
dictionary and a thesaurus while writing: the dictionary should
be used to check words that you’re unfamiliar with or to double
check that you are in fact actually using the word you want
rather than a similarity spelt word; whereas, the thesaurus is
used to enhance your writing by slowly gaining familiarity with
complex words (although, using complex words do not always

17. equal better marks and could actually result in a loss of marks if
used improperly) and to remove over repetition of common
words.
A personal recommendation regarding when you should
use quotes in psychology research reports: “only quote if it is
absolutely necessary”. For example, if you want to directly
counteract a claim/statement/result made in a journal article
then it would probably be appropriate to use a direct quote as
that enables the greatest impact for your sequential fault-finding
sentences; otherwise, paraphrasing would probably be best as
paraphrasing demonstrates to your marker that you truly
understand the topic being discussed.
Additionally, a very common question I get asked is “How
many references do I need?” and the answer to this question is:
it depends on what type of argument you’re attempting to make
in your manuscript. For example, if your argument is a critical
evaluation of a study and how their main assertion/conclusion is
flawed then theoretically your paragraph would begin by
explaining (in extreme detail) the original study followed by
logically castigating their study. Accordingly, this argument
would only contain one reference but could still result in a high
distinction mark if done correctly. Alternatively, your argument
might be a compare and contrast styled paragraph in which case
you would begin with explaining one reference (i.e., Author A
introduces “this model”), followed reviewing another reference
(i.e., However, Author B says that Author A is clueless about
the topic and thus Author B proposes “this completely new
model”) which could then be followed by including another set
of references (i.e., Nonetheless, Author C, D, E, and F’s
experimental results support Author A’s original model), then
another reference (i.e., Despite Author C, D, E, and F’s
findings, Author G was able to rationalise both Author A, C, D,
E, and F’s findings to a common unexamined fundamental
assumption and thus Author G attempts to mediate between
Author A’s model and Author B’s model to create another brand
new model—these references would then of course be followed

18. by yourself logically and critically analysing all 3 models (i.e.,
Author A’s, Author B’s, and Author G’s). Accordingly, this
argument would contain seven references but could still result
in a high distinction mark if done correctly. Therefore,
evidently the amount of references do not always equal high
marks and thus the important thing is to make sure that you’re
using your references well rather than making sure that you
have a lot of references.
Methods & Resources to Ease your Western Sydney University
Journey
Accessing the University Library Off-Campus
Please follow the instructions here to gain access to
journal articles on your personal computer when you're off
campus. Once you've followed the instructions, please
remember to click on the "Fulltext @ WestSydU Lib" at the
beginning of your research session and then sign in using your
student number and password to access the journal article
without needing to purchase it—after logging in for the first
time of that research session you can just click on the title of
the subsequent journals like normal and still have free access to
the articles available on the Western Sydney University Library.
Alternatively, if the journal article you’re looking for still
is not available and there is still enough time then you can
always send a quick, but polite, email to the Author of the
Journal Article you’re trying to access since some journal
publishing companies (e.g., Elsevier or Springer) allow authors
to distribute their own articles under certain conditions.
Research Techniques
Part of becoming a good academic/student is your ability
to research a given topic. Accordingly there are several basic
search terms that can be applied to most search engines. For
example: if you were to search "Domestic Violence" in Google
then you are asking the search engine to look for ANY result
with either Domestic OR Violence. If you were to add quote
marks (i.e., "Domestic Violence") then you are asking the

19. search engine to only look for results where the two words
appear together in that configuration. For more refined and
specific searches, you can even use the "+" and "-" sign
modifiers—so, if we only want to see males as the victims of
domestic violence but ensure no results which include children
appear in the results then we would type: "Domestic Violence"
+Male -Children to get an enormously reduced number of
results. For even more advanced searching techniques, you can
click here to see how the basic Boolean Operators (i.e., AND,
OR, NOT) work.
Forward and Backwards Citation Searching
Building upon the “Research Techniques” above, there’s
another strategy that you can administer in order to build upon
your knowledge on any given topic. This strategy is known as
Forward and Backwards Citation Searching (i.e., (1) Tracing
References Backward/Forward, (2) Reference Searching, (3)
Chain Searching, (4) Citation Mining, (5) Treeing
Forward/Treeing Backwards, and finally (6) Pearl Growing) and
requires you to already have located at least one high-impact
article or key publication (i.e., the core reading provided to you
for your assignment) in the area of study you’re interested in as
a starting point for this strategy.
This one high-impact article or key publication should be a
well-respected journal article which establishes the fundamental
technique/theory you are investigating or it should be an
innovative journal article which introduces a breakthrough in
the area of study you are investigating—alternatively, in the
unfortunately event that neither types of journal articles are
available, it is possible to apply this strategy to the “best”
journal articles from a previous search you did as the starting
point of this strategy.
The reason why this strategy is so valuable is because
Citation Searching relies on the expertise of the Authors behind
peer reviewed journal articles to link you to pertinent literature
through their references. Accordingly, you get to take advantage
of a peer reviewed journal article author’s subjective mastery of

20. their topic/literature relating to their field and you get to avoid
the problems associated with having to find/use the “correct”
scientific vocabulary related to that topic to search for journal
articles. Finally, Citation Searching allows you to access what
the scientific/scholarly community deems valuable or useful in
that sense that “the article” has both been originally peer
reviewed then cited in another peer reviewed journal article
(i.e., at the very minimum there is at least 4 independent experts
on that topic who think that the content inside “the article” is
worthwhile discussing). Accordingly, now that you know what
Citation Searching is, you might be asking “well, how does one
actually conduct Forward and Backwards Citation Searching?”
Backwards Citation Searching is good for recent publications
and meta-analyses publications; however, this method is only
useful for finding sources older than your starting article. This
is the Citation Searching method that almost all university
students know how to do—Backwards Citation Searching is
achieved by simply looking at the reference list of the one high-
impact article or key publication to identify other relevant
scholarly sources or by following the pictures below.

21. Backwards Citation Searching is extremely useful for: (1)
understanding the development and origin of a specific
construct/theory/model/method, (2) building your own
understanding and knowledge on the particular area of
study/topic of interest, and (3) identifying key organizations,
institutions, or authors which specialize in your particular area
of study/topic of interest. This method can then be continued
back several generations (or even indefinitely) by also checking
the reference lists of the other relevant scholarly sources for
more useful citations. This continuation is excellent for
identifying inconsistencies in the literature (i.e., when multiple
authors start to regularly paraphrase/interpret the same journal
article differently). However, Backwards Citation Searching
becomes inadequate when the original one high-impact article
or key publication’s release date was a long time ago (e.g., over
5-10 years ago).
Forward Citation Searching is good for old publications.
Forward Citation Searching becomes really useful when the one
high-impact article or key publication’s release date is over 5
years old. Forward Citation Searching is when you go onto
databases/literature search engines/library websites and look for
other journal articles that have cited/referenced the original one
high-impact article or key publication (i.e., finding literature
created after the original one high-impact article or key

22. publication's release date). This method allows you to identify
newer literature which uses your original one high-impact
article or key publication in their research. Forward Citation
Searching can also be continued for several generations (i.e.,
the old original one high-impact article or key publication might
lead you to a newer high-impact article or key publication
which you also conduct a Forward Citation Search on—and so
forth). Forward Citation Searching is extremely useful for: (1)
identifying new developments/findings on the particular area of
study/topic of interest, (2) building upon your knowledge of the
particular area of study/topic of interest by locating follow-up
experiments to the literature that you are already familiar with,
and (3) discovering new techniques of data analysis or
methodologies utilised to examine the particular area of
study/topic of interest. Forward Citation Searching can be
achieved by following the images below.
Google Scholar:

23. Western Sydney University Library:

24. Although Citation Searching might appear to be an
unbeatable research technique, there are still downsides to it—
for example, Citation Searching will fail to locate relevant
literature from other fields of study if the authors from the two
journal articles originate from the communities of two very
distinct fields. Similarity, Citation Searching generally fail to
find “hidden” gems since it relies on the literature that is
commonly utilised in the particular area of study/topic of
interest. Consequently, there are hazards to using only Citation
Searching and thus Keyword Searching (found in “Research
Techniques”) should be used in conjunction with Citation
Searching and Author Searching: reviewing a key author's (in
the particular area of study/topic of interest) previous work and
publications through searching via their name and also looking
into (via networking/emails) the development of their later or
more current research in order to examine new developments.
Referencing Assistance
Western Sydney University's APA Referencing Guide: You can
find the Western Sydney University Library's APA6
Referencing Guide here.
Time Saving Strategies for Referencing: There are a few tips
and tricks that you would normally pick up along your
university undergraduate journey which relate to different
methods/strategies to speeding up your referencing. To get you
started on this, I am going to point you in the direction of one
“beginner” and one “advanced” strategy; however, regardless of
the method you use (even if it’s one you found yourself/one you
made up) you must make sure that you check every single one
of your references against your own universities’ reference
guide (sometimes each university has their own little
“adjustments” to the proper/official referencing style—for
example, almost every university in NSW appears has their own
slightly different version of the “Harvard Referencing Style”).
The beginner strategy is to make use of the fact that almost
every online library system has a button which you can press to
reveal a pre-made reference typed up by a librarian. However,

25. you have to be very careful when using these pre-made
citations—they usually contain all the information, but not
always; therefore, you might be required to find and add the
missing information yourself (nonetheless, these pre-made
citations will still save you typing time); for example, in APA6
style, you rarely find that the pre-made citations include the
DOI even though the DOI is a requirement in APA6 style and
marks could potentially be deducted for not including the DOI
(as a sidenote, DOIs always start with “10.” and thus sometimes
the DOI can be found by simply looking at the article’s URL).
Additionally, these pre-made citations were created by another
human being and therefore are also prone to error which further
reinforces that fact that you should double check every single
one of your references. Look at the images below to find out
where the button is for Western Sydney University Library and
Google Scholar.
Western Sydney University Library:

26. Google Scholar:

27. The advanced strategy is to make use of the various
Referencing Management and Tools Software available on the
internet; nevertheless, as a forewarning: try not to lost down the
rabbit hole of which one is better than which or looking too in-
depth into them and then picking an overly complex one
(remember that you are probably only going to be submitting
1000 to 3000 word psychology assignments in your
undergraduate degree and not a PhD of 80,000+ words so my
advice is to try to keep it simple to start off with). Additionally,
I would suggest that you just rely on editing the pre-made
references if the Referencing Management and Tools Software
become too confusing after you have taken a glance at them.
Considering that there are a lot of different Referencing
Management and Tools Software available on the internet I am
going to direct you to this link here which introduces a wide
range of Referencing Management and Tools Software available
on the internet followed by outlining how to use each one via a
short video. I will also say that typically people go for Zotero
or Mendeley since they’re free (some of the others are not free).
Personally, I mostly use Mendeley myself since it comes with
2GB of free cloud storage compared to the 300MB that Zotero
does—note the word “mostly” because you too will probably
eventually end up using a few of these Referencing Management
and Tools Softwares simultaneously as you learn to master them
(if you choose to use them at all).
Finally, keep in mind that these programs are still prone to error
and thus you should still double check every single one of your
references.
Free Microsoft Office
As a student of Western Sydney University, you actually
have access to FREE copies of Microsoft Office (e.g., Microsoft
Word, Microsoft Excel, Microsoft PowerPoint, etc.) that you
can download and install onto a maximum of five devices
during your enrolment at Western Sydney University. If you are
interested, please click here for more information on this
Western Sydney University incentive. Alternatively, click here

28. to go straight to the download/installation instructions.
It is also worth noting that you have access to Microsoft
Office Online via OneDrive if you do not wish to download and
install the software onto your computer. Please click the first
hyperlink for detailed information on how to access Microsoft
Office Online or go to your Campus’ IT desk for detailed
assistance.
Finally, a pretty decent template for APA 6 formatting
style can be found and downloaded from here. It is worth noting
that this is originally designed for a Research Report or a
Thesis in APA 6 Format Style.
By Wing Hong Fu
By Wing Hong Fu By Wing Hong Fu
Contents lists available at ScienceDirect
Cognition
journal homepage: www.elsevier.com/locate/cognit
Original Articles
Sensitivity to emotion information in children’s lexical
processing
Tatiana C. Lund, David M. Sidhu, Penny M. Pexman⁎
University of Calgary, Canada
A R T I C L E I N F O
Keywords:
Emotion
Word valence

29. Lexical processing
Affective embodiment
Auditory lexical decision
Concreteness
A B S T R A C T
We tested predictions of multiple representation accounts of
conceptual processing, including the proposal that
emotion information may provide a bootstrapping mechanism
for vocabulary acquisition. We investigated the
influence of word valence on children’s lexical processing,
presenting 40 positive words, 40 neutral words, and
40 negative words in an auditory lexical decision task (ALDT),
along with 120 nonwords. We tested 99 children
across three age groups: 5, 6, or 7 years. There were no
significant effects of valence on the ALDT responses of 5-
year-old children. The 6-year-old children, however, were faster
to respond to negative words than to neutral
words and, for more abstract words, faster to respond to
positive words than to neutral words. The 7-year-old
children were faster for positive words than for neutral words,
regardless of concreteness. As such, children
showed sensitivity to word valence in lexical processing, at a
younger age than had been established in previous
research. In addition, children’s language skills were related to
their improved processing of more abstract
neutral words between 6 and 7 years of age. These results are
consistent with multimodal accounts of word
meaning and lexical development.
1. Introduction
According to several recent proposals, conceptual knowledge is
acquired and represented in multimodal systems (Barsalou,
Santos,

30. Simmons, & Wilson, 2008; Borghi et al., 2017; Dove, 2011,
2018; Thill
& Twomey, 2016). That is, word meanings are represented in
sensory,
motor, emotion, and language systems, and different systems
are rela-
tively more important for the representation of different kinds
of con-
cepts. These multiple representation views stand in contrast to
tradi-
tional views which assumed a single system of representation;
for
instance, that word knowledge is represented in symbolic,
amodal
format (e.g., Collins & Loftus, 1975) or, alternatively, could be
re-
presented in the statistical relationships between words, as
captured in
lexical co-occurrence (e.g., Lund & Burgess, 1996). The
multiple re-
presentation views also stand in contrast to strongly embodied
ac-
counts, by which it is assumed that all word meanings are
grounded in
sensorimotor and emotion systems (e.g., Glenberg, 2015;
Glenberg &
Gallese, 2012).
The multimodal accounts are supported by the results of recent
studies with adults, which have shown that responses in simple
lexical
tasks are influenced by variables that capture the extent to
which lin-
guistic, sensory, motor, and/or emotion information is
associated with
word referents (Moffat, Siakaluk, Sidhu, & Pexman, 2015; Yap,

31. Pexman,
Wellsby, Hargreaves, & Huff, 2012). For instance, Yap and
Seow (2014;
also Kousta, Vinson, & Vigliocco, 2009; Vinson, Ponari, &
Vigliocco,
2014) showed that adult participants’ responses in a visual
lexical de-
cision task (LDT; is the letter string a real word?) were affected
by word
valence, with faster responses for words with positive or
negative
meanings than for words with neutral meanings. One
explanation for
facilitatory effects of emotion information is that the emotion
in-
formation associated with valenced words affords richer
semantic re-
presentations and thus speeds lexical decisions (Pexman, 2012;
Siakaluk et al., 2016).
Some adult studies have shown a somewhat different pattern of
valence effects. For instance, in a large-scale analysis of adult
LDT re-
sponses Kuperman, Estes, Brysbaert, and Warriner (2014) found
that
responses were fastest to positive words and slowest to negative
words,
with responses to neutral words falling in between (see also
Estes &
Adelman, 2008). Although somewhat different to that described
above,
this pattern still demonstrates sensitivity to emotion information
in
lexical processing, and has been taken as evidence for automatic
vigi-

32. lance to negative stimuli (Pratto & John, 1991). There is
speculation
that the particular pattern of valence effects observed in adult
studies
may depend on stimulus list, small effect sizes, frequency
confounds,
and other factors that are not yet understood (Kuperman, 2015).
As highlighted in a handful of recent reviews (Marshall, 2016;
Pexman, 2018; Wellsby & Pexman, 2014), embodied and
multimodal
accounts both raise interesting questions about development of
word
https://doi.org/10.1016/j.cognition.2019.04.017
Received 27 June 2018; Received in revised form 10 January
2019; Accepted 17 April 2019
⁎ Corresponding author at: Department of Psychology,
University of Calgary, 2500 University Drive NW, Calgary,
Alberta T2N1N4, Canada.
E-mail address: [email protected] (P.M. Pexman).
Cognition 190 (2019) 61–71
Available online 23 April 2019
0010-0277/ © 2019 Elsevier B.V. All rights reserved.
T
http://www.sciencedirect.com/science/journal/00100277
https://www.elsevier.com/locate/cognit
https://doi.org/10.1016/j.cognition.2019.04.017
https://doi.org/10.1016/j.cognition.2019.04.017
mailto:[email protected]
https://doi.org/10.1016/j.cognition.2019.04.017

33. http://crossmark.crossref.org/dialog/?doi=10.1016/j.cognition.2
019.04.017&domain=pdf
meanings, but their predictions have rarely been tested with
children.
In the present study, we tested developmental predictions of
those ac-
counts by investigating three proposed mechanisms for learning
word
meanings: (1) the affective embodiment account, (2) the
language
competence hypothesis, and (3) the nimble-hands, nimble-minds
hy-
pothesis.
It has been argued that emotion information might provide an
im-
portant mechanism for learning and grounding word meaning,
parti-
cularly for words with less concrete meanings (Barsalou &
Wiemer-
Hastings, 2005; Glenberg & Gallese, 2012; Kousta, Vigliocco,
Vinson,
Andrews, & Del Campo, 2011). Word concreteness is generally
defined
as the degree to which a word’s referent can be experienced
through the
senses (Brysbaert, Warriner, & Kuperman, 2014), with all words
falling
somewhere along a continuum from concrete (e.g., truck) to
abstract
(e.g., truth). Explaining how we learn and represent the
meanings of
words that don’t have tangible referents has become a central
concern

34. in semantic research.
Kousta et al. (2011; also Ponari, Norbury, & Vigliocco, 2017)
argued
that emotion provides a bootstrapping mechanism for
vocabulary ac-
quisition, mapping language to felt experience, because
understanding
of emotion terms is grounded in bodily experience
(Winkielman,
Coulson, & Niedenthal, 2018). By this view, valence provides
an em-
bodied learning experience for word meanings, especially for
abstract
word meanings. Borghi et al. (2017) referred to this as the
Affective
Embodiment Account. One way this proposal has been tested is
by ex-
amining words’ age of acquisition (AoA), either from child
production
norms or from adult ratings of AoA, as an index of concept
learning.
Results have been mixed, however, with some evidence that
words with
emotional meanings are acquired earlier (Kousta et al., 2011;
Moors
et al., 2013) and another study showing that word valence is not
related
to AoA (Thill & Twomey, 2016).
An alternative strategy for evaluating the Affective Embodiment
Account is to directly test whether children’s early lexical
processing is
influenced by word valence. This was the strategy adopted in
the pre-
sent study. To our knowledge, this has been tested in only one

35. previous
study. Ponari et al. (2017; also summarized in Vigliocco,
Ponari, &
Norbury, 2018) conducted an auditory lexical decision task
(ALDT)
with children aged 6–12 years. A total of 48 word stimuli were
selected
to achieve a factorial manipulation of valence and concreteness
(8
words of each type: concrete positive, concrete neutral, etc.).
Children’s
ALDT reaction times were not measured but analyses of their
response
accuracy showed that effects of valence were significant only
for the
8–9 year old age group, and only for abstract words (effects of
valence
were marginal for concrete words). Ponari et al. inferred that
only
children aged 8–9 years were are able to derive significant
benefit from
valence, but noted that it was difficult to make inferences from
the
results of the younger children because those children had very
low
accuracy in the ALDT (< 70%). The word stimuli selected by
Ponari
et al. had quite high mean AoA (in years; M = 7.80), suggesting
that the
younger children may not have known as many of the words’
meanings
as did the older children. As such, Ponari et al. concluded that
floor
effects might have been an issue in this younger age group.
Thus, to
evaluate the Affective Embodiment Account, there is a need to

36. test
younger children with age-appropriate words to determine
whether
they are sensitive to valence information in lexical processing.
Another explanation for children’s word learning is that they
are
able to derive meanings from their experience with language,
including
their knowledge of co-occurrence information in linguistic input
(Vigliocco et al., 2018). That is, aspects of a word’s meaning
can be
extracted from the linguistic context in which it is used.
Vigliocco et al.
(2018) argued that while this information would be relevant for
learning both concrete and abstract words, it would be a
particularly
important mechanism for learning abstract words, for which
meanings
cannot be mapped to referents in the physical environment (also
Vigliocco, Meteyard, Andrews, & Kousta, 2009; Vigliocco,
Ponari, &
Norbury, 2017). Similarly, it has been argued that the meanings
of
neutral words, particularly neutral words without concrete
referents,
could be acquired through language, by their use in the context
of other
words (Howell, Jankowicz, & Becker, 2005).
As such, linguistic experience should be important to children’s
acquisition of abstract word meanings, and we refer to this here
as the
language competence hypothesis. To our knowledge, this
possibility has

37. been tested in only one previous study. Ponari, Norbury, Rotaru,
Lenci,
and Vigliocco (2018; also reported in Vigliocco et al., 2017)
examined
whether children with developmental language disorder (DLD)
found
abstract words to be particularly challenging. Despite having
the ex-
pected language deficits, the children with DLD did not show
dis-
proportionately poor performance for abstract words relative to
con-
crete words. The authors took these findings as evidence that
language
competence is not the primary driver of abstract word learning.
Instead,
they inferred that both typically developing children and
children with
DLD are likely able to learn abstract and concrete meanings
through
affective and sensorimotor associations.
Vigliocco et al. (2018) argued, further, that the Affective
Embodi-
ment Account could be complementary with a role for language
com-
petence, for development of an abstract vocabulary. In
particular, “A
likely scenario is one in which while emotional grounding plays
a key
role early in development, linguistic information becomes
essential
later on” (p. 537). The suggestion is that emotion information
might
help children to begin to represent the differences between
abstract and

38. concrete words. In particular, the fact that abstract emotion
terms refer
to internal states may help children to create a framework for
abstract
concepts (Ponari et al., 2018). Once this framework is
established,
language competence becomes the key factor to help children
derive
word meanings from patterns in language use (Vigliocco et al.,
2017).
As yet, there is no empirical evidence for this proposed tradeoff
in
abstract word learning, wherein there is early reliance on
emotion in-
formation and later support from language skills, but we tested
this
proposal in the present study, by investigating the relationship
of
children’s language skills to their reliance on emotion
information in
lexical processing of abstract words.
In addition to linguistic and emotion information, multiple re-
presentation views of conceptual knowledge propose that
sensorimotor
information is important to understanding word meanings (e.g.,
Barsalou et al., 2008; Dove, 2018). In support of this position,
there is
evidence that children’s early fine motor or manual dexterity
skills are
related to their language skills (e.g., Grissmer, Grimm, Aiyer,
Murrah, &
Steele, 2010; Pexman & Wellsby, 2016). Two recent studies
have pro-
vided more specific support for the role of sensorimotor
information in

39. vocabulary acquisition. Suggate and Stoeger (2014) tested the
nimble-
hands, nimble-minds hypothesis: the notion that children who
have more
advanced fine motor skills will have richer sensorimotor
interactions
with objects, and thus will show more advanced understanding
for
terms referring to objects that afford easy interaction. To
quantify this
aspect of object information, Suggate and Stoeger used the
body-object-
interaction (BOI) dimension (Siakaluk, Pexman, Aguilera,
Owen, &
Sears, 2008). High BOI words refer to objects with which the
human
body can easily interact, whereas low BOI words refer to
objects with
which the human body can less easily interact. In a sample of 3–
6 year
old children, Suggate and Stoeger found that children’s fine
motor skills
were more strongly related to their accuracy on a receptive
vocabulary
test for high BOI words than for low BOI words. In a
subsequent study,
Suggate and Stoeger (2017) also found that children’s fine
motor skills
were related to their speed to point to pictures of high BOI
objects, in
that children with more advanced fine motor skills tended to
point to
pictures of named high BOI objects more quickly than did
children with
less advanced fine motor skills. They concluded that children’s
voca-

40. bulary development is initially grounded in their sensorimotor
experi-
ence before it becomes more abstract. While these relationships
be-
tween fine motor skills and knowledge of high BOI words have
been
observed by Suggate and Stoeger in receptive vocabulary
accuracy and
pointing latencies, they have not been tested in the conventional
lexical
T.C. Lund, et al. Cognition 190 (2019) 61–71
62
processing task (lexical decision, or auditory lexical decision as
in the
Ponari et al., 2017, study) but we did so here.
In the present study we examined children’s lexical processing,
measuring their responses in an ALDT. We focused on younger
children
than in the Ponari et al. (2017) study (groups of 5-, 6-, and 7-
year-olds),
using a large set of words (40 positive words, 40 neutral words,
40
negative words) that were chosen to be familiar for children in
this age
range (AoA M = 5.35). With the larger word set, we hoped to
have
enough correct ALDT responses to permit analyses of both
reaction
times (the primary behavioral measure in the lexical processing
lit-

41. erature) and accuracy. The words within each valence type were
mat-
ched for frequency and several other lexical dimensions and
varied in
concreteness values. We did not manipulate concreteness
categorically,
in the way that Ponari et al. did, because we found this wasn’t
feasible
given the younger age range we wanted to test, the large number
of
items we wanted to include, and the lexical factors we wanted
to match
across word types. It is estimated that the average 5-year-old
vocabu-
lary contains less than 20% abstract words, and that children of
this age
are just beginning to develop their knowledge of abstract word
mean-
ings (Ponari et al., 2017). This limits the number of familiar
words with
very abstract meanings to choose from. In addition, we wanted
to
match our stimuli for AoA, yet AoA tends to be higher for
abstract
words than for concrete words (Ponari et al., 2017; Thill &
Twomey,
2016), and our preliminary checks on the potential item set
suggested
that the matching of AoA for concrete and abstract words would
not be
possible. As such, we elected to examine the effects of valence
on
children’s lexical processing across positive, neutral, and
negative word
types, and included both concreteness and AoA as continuous
variables

42. in our analyses. We did not include frequency and other lexical
vari-
ables as predictors in the analyses because these variables were
not
significantly different for more concrete and more abstract
words (all
p > .27). Given the age-sensitive nature of the valence effects
observed
in Ponari et al. (recall that they found valence effects in only
one in-
termediate age group), we expected that valence effects might
be
somewhat different in the three age groups tested. To fully
evaluate this
possibility, we planned analyses of each age group (5-, 6-, and
7-year-
olds) separately for effects of word valence. We also tested the
inter-
action of age and valence in the overall analyses. Further, based
on the
Affective Embodiment Account, we hypothesized that there
might be an
interaction of valence and concreteness. Indeed, Ponari et al.
found
somewhat different valence effects for concrete and abstract
words.
Finally, we assessed children’s language and fine motor skills,
and these
measures allowed us to test additional theoretical predictions,
outlined
above, related to the language competence hypothesis and the
nimble-
hands, nimble-minds hypothesis.
2. Method

43. 2.1. Participants
A total of 99 children participated in the study, including 35 5-
year-
old children (M = 5;6, SD = 0;2, 21 female), 34 6-year-old
children
(M = 6;5, SD = 0;3, 15 female), and 30 7-year-old children (M
= 7;5,
SD = 0;3, 15 female). All children were recruited through our
partici-
pant database and received a small toy and a pencil case for
partici-
pating. Two participants (one 5-year-old female and one 6-year-
old
female) were excluded from the analyses for failing to score
above
chance for ALDT accuracy (chance performance was 50%
accuracy).
Ten additional participants were excluded by a response
criterion de-
scribed in the Results section. Thus, 87 participants were
included in
the analyses.
2.2. Stimuli
Word stimuli for the ALDT were 40 positive words (e.g., cake,
heart),
40 neutral words (e.g., map, nest), and 40 negative words (e.g.,
jail,
trash). Word valence was determined based on published norms
(Warriner, Kuperman, & Brysbaert, 2013). All words were
mono-
syllabic. Words were selected such that the three word sets
differed in

44. mean valence, but were not significantly different on a number
of other
factors that might influence ALDT performance (Table 1):
number of
phonemes, phonological Levenshtein distance (PLD, a measure
of
words’ phonological similarity or confusability, Yarkoni,
Balota, & Yap,
2008), children’s spoken frequency for the 72–83 month age
range
(from the ChildFreq norms, which are extracted from the
CHILDES
database, as described in Bääth, 2010), Grade 2 print frequency
(Zeno,
Ivens, Millard, & Duvvuri, 1995), adult log SUBTLEXWF word
frequency
(Brysbaert & New, 2009), age of acquisition (Kuperman,
Stadthagen-
Gonzalez, & Brysbaert, 2012), imageability (Cortese & Fugett,
2004),
and concreteness (Brysbaert et al., 2014).
In addition, we selected a set of 120 monosyllabic nonwords
(e.g.,
darb). We chose nonwords from the English Lexicon Project
(Balota
et al., 2007) such that they were matched to the word stimuli for
onset
phoneme and number of phonemes, so that these factors could
not be
used as a cue to the decision.
A female speaker recorded word and nonword ALDT stimuli in
a
sound attenuated chamber. Once recorded, sound files were
edited with

45. the program Praat (Boersma, 2001) to ensure that there were no
sig-
nificant differences in sound file length across word types, or
between
words and nonwords. Three adult listeners who were naïve to
the study
purpose listened to the sound files and reported what they heard
to
ensure that each file was interpreted as intended.
2.3. Procedure
Participants were tested in our university laboratory. They sat in
front of a computer wearing headphones. The experimenter sat
beside
the participant, and also wore headphones. Sound files were
presented
one at a time through both the participant’s and the
experimenter’s
headphones using E-Prime presentation software (Schneider,
Eschman,
& Zuccolotto, 2001). Sound files were presented in a different
random
order for each participant. The computer screen was blank
except for a
small central fixation cross. Participants were told that they
were
playing a game in which they were word detectives. A response
box
with red and green response buttons was placed in front of
participants.
Participants were instructed to place their index fingers on the
red and
green buttons, and to press the red button when they heard a
fake word
and the green button when they heard a real word, pressing the

46. button
as soon as they decided. To ensure understanding of the game,
parti-
cipants were first presented with a practice block consisting of
ten trials
(five words and five nonwords). After each response, the
experimenter
Table 1
Mean characteristics of word stimuli, as a function of word type
(standard
deviations in parentheses).
Word type
Positive Neutral Negative p for effect of
word type
Word characteristics
Valence 7.18 (0.43) 5.47 (0.87) 3.14 (0.68) < .001
Number of phonemes 3.33 (0.62) 3.50 (0.68) 3.35 (0.62) .42
PLD 1.26 (0.28) 1.29 (0.32) 1.22 (0.27) .56
Child spoken
frequency
76.25
(104.97)
108.63
(171.24)
71.98
(90.73)
.38

47. Grade 2 print
frequency
85.55
(60.44)
87.73
(72.61)
81.18
(61.37)
.90
Adult word
frequency
3.58 (0.52) 3.45 (0.45) 3.55 (0.50) .49
Age of acquisition 5.16 (1.18) 5.62 (1.57) 5.26 (1.23) .27
Imageability 4.64 (1.48) 4.67 (1.45) 4.64 (1.21) .99
Concreteness 3.45 (1.20) 3.72 (1.05) 3.78 (0.86) .34
Note. PLD = phonological Levenshtein distance.
T.C. Lund, et al. Cognition 190 (2019) 61–71
63
pressed a button on a standard keyboard to proceed to the next
item.
Following the ALDT, children completed the Peabody Picture
Vocabulary Test (PPVT-4; Dunn & Dunn, 2007) and manual

48. dexterity
subscales of the Bruininks-Oseretsky Test of Motor Profiency-
2nd Edi-
tion (BOT-2; Bruininks & Bruininks, 2005). The BOT includes
five
manual dexterity subscales: drawing dots in circles (BOT-Dots),
picking
up pennies and transferring them into a box (BOT-Pennies),
placing
small pegs into a board (BOT-Pegs), sorting cards (BOT-Cards),
and
stringing blocks on a shoelace (BOT-Blocks). Each subscale
task is
performed in two 15-second trials (excluding dots which is
performed
once). Parents completed the Children’s Communication
Checklist-2
(CCC-2; Bishop, 2006). This caregiver-report measure is
comprised of
70 items divided into 10 scales (A: Speech, B: Syntax, C:
Semantics, D:
Coherence, E: Initiation, F: Scripted language, G: Context, H:
Nonverbal
communication, I: Social relations, J: Interests) to assess
children’s
communication difficulties and pragmatic language. Caregivers
are
asked to rate the frequency with which their child displays each
item’s
statement, ranging from 0 (less than once a week, or never) to 3
(sev-
eral times i.e. more than twice a day, or always). A General
Commu-
nication Composite (GCC) score is calculated by summing
scaled scores
of scales A-H. Four participants’ CCC-2 Checklists (data for

49. two 5-year-
old and two 6-year-old participants) were not scored due to
incomplete
or missing parent responses. Mean scores for each task are
included in
Table 2.
2.4. Supplementary material
The data analyzed in this study are available here: https://osf.io/
9a5ng/.
3. Results
Analyses consisted of mixed effects regression models. Models
were
computed using the “lme4” package (Bates, Maechler, Bolker,
&
Walker, 2015) in the statistical software R (R Core Team,
2017). In each
model, we took a confirmatory approach, and fit all fixed
effects of
interest. We developed each model’s random effects structure
using the
approach suggested by Bates, Kliegl, Vasishth, and Baayen
(2015). This
was as follows:
1. We began with a model containing all possible random
effects. For
cases in which this did not converge, we fit a simpler model that
omitted correlations among random effects. This was done using
the
“afex” package in R (Singmann, Bolker, & Westfall, 2015).
2. We then used the “RePsychLing” package in R (Baayen,

50. Bates,
Kliegle, & Vasishth, 2015) to perform a principal components
ana-
lysis on this random effects structure to determine the number
of
random effects that could be specified (i.e., the number of
compo-
nents explaining > 1% of variance) while achieving model
identi-
fication. Beginning with the highest order random effect with
the
least amount of variance, we removed random slope effects until
we
reached a model that contained the number suggested by the
prin-
cipal components analysis.
3. If correlations among random effects were retained up to this
point,
we then compared models with and without correlations among
random effects using likelihood ratio tests (LRTs) to determine
if
they were warranted.
4. We then tested the inclusion of every remaining random slope
effect,
beginning with the highest order effect with the least amount of
variance, using LRTs.
5. Finally, if there were no correlations among random effects,
we
tested whether the model could be improved by their inclusion
using
LRTs.

51. Code for this entire process, for each model, is available here:
https://osf.io/9a5ng/. We only report the results of the model
con-
taining the final random effects structure in the text. Note that
models
always included random subject and item intercepts to deal with
non-
independence. Continuous predictors were always mean-
centered.
Analyses were only carried out on real word trials as these
involve
the manipulation of word valence. Nonword response data were
not
analyzed further, but mean latencies and accuracy for nonword
re-
sponses are presented in Table 2. We removed three words with
an
overall accuracy below 50%: rise (49.48%), tramp (42.27%),
and whip
(38.14%), from all analyses. In addition, as in Ponari et al.
(2017), we
conducted a signal detection analysis to determine whether any
of the
participants had a bias towards responding either “word” or
“non-
word”. We computed a Criterion C for each participant, using
“word”
responses to real words as hits, and “word” responses to
nonwords as
false alarms. We excluded participants with a Criterion C value
greater
or less than 1.5 standard deviations from the mean of their age
group.
This led to the exclusion of four 5-year-olds, four 6-year-olds,
and two

52. 7-year-olds. Lastly, we removed all trials with a reaction time
less than
200 ms (0.12% of remaining trials) or greater than 3000 ms
(5.46% of
remaining trials) as we judged these to be outliers.
3.1. Reaction times
We used mixed effects linear regression models to analyze
children’s
reaction times. These analyses were only carried out on trials
that re-
sulted in a correct response. In addition, after removing
incorrect trials,
we removed trials with latencies greater than 2.5 standard
deviations
from each participant’s mean (2.73% of remaining trials). We
used the
“lmerTest” package (Kuznetsova, Brockhoff, & Christensen,
2017) to
generate p-values for models’ fixed effects.
3.1.1. Affective Embodiment Account
Our first set of analyses explored the relationship between
valence
and concreteness in reaction times.
3.1.1.1. All ages. We began with an analysis of reaction times
for all
age groups. See Table 2 for average reaction times by age group
and
Table 2
Mean participant characteristics and ALDT responses (standard
deviations in parentheses), as a function of age group (N = 87).

53. 5-year-olds (n = 30) 6-year-olds (n = 29) 7-year-olds (n = 28)
PPVT4 (Raw Score) 117.60 (15.53) 132.59 (15.77) 145.11
(16.13)
BOT2 (Point Score) 16.47 (3.08) 19.76 (2.50) 22.46 (3.65)
CCC2 GCC 82.36 (11.31) 81.70 (15.07) 85.79 (9.99)
ALDT word accuracy (%) 78.47 (14.65) 90.33 (5.93) 94.32
(3.61)
ALDT word reaction time (ms) 1473.33 (235.31) 1364.88
(133.47) 1329.40 (181.29)
ALDT nonword accuracy (%) 72.45 (18.80) 87.25 (11.70) 86.97
(10.99)
ALDT nonword reaction time (ms) 1605.23 (306.62) 1618.63
(205.80) 1601.95 (201.78)
Note. PPVT4 = Peabody Picture Vocabulary Test-4th Edition;
BOT2 = Bruininks-Oseretsky Test of Motor Proficiency-2nd
Edition; CCC2 GCC = Children’s
Communication Checklist-2 General Communication
Composite; ALDT = Auditory Lexical Decision Task. Means
and standard deviations calculated for trials and
participants included in the analyses. Note that nonword trials
were cleaned in the same manner described below for real word
trials.
T.C. Lund, et al. Cognition 190 (2019) 61–71
64
https://osf.io/9a5ng/
https://osf.io/9a5ng/
https://osf.io/9a5ng/
Fig. 1 for overall patterns. Age of acquisition (Kuperman et al.,

54. 2012)
and uniqueness point (Luce, 1986) were included as control
variables.
We also coded the onset phoneme of each word, following
Balota,
Cortese, Sergent-Marshall, Spieler, and Yap (2004): we dummy
coded
variables for voicing, place of articulation (i.e., one each coding
for
whether the onset phoneme was a bilabial, labiodental, alveolar,
palatal, velar or glottal) and manner of articulation (i.e., one
each
coding for whether the phoneme was a stop, fricative, affricate,
nasal or
liquid/glide). Note that glottal and liquid/glide terms were
excluded
automatically due to rank deficiency. Our variables of interest
were:
valence (neutral, negative or positive; dummy coded using
neutral
words as a reference category), concreteness (Brysbaert et al.,
2014),
and age (5-, 6- or 7-year-olds; successive difference coded
comparing
successive ages). Our a priori hypotheses led us to also include
an
interaction between concreteness and valence, and between age
and
valence. This analysis revealed a significant interaction between
concreteness and positive (vs. neutral) valence (p = .02). No
other
interactions reached statistical significance (all p > .09), see
Table 3.
Plotting this interaction suggests that positive valence is
facilitatory for
more abstract words, but not for more concrete words (see Fig.

55. 2). This
was confirmed by follow up analyses which split items based on
median
concreteness. We built a model in each group of items including
all
previously mentioned control variables, as well as age, with the
predictor of interest being valence. These analyses revealed a
significant effect of positive (vs. negative) valence for more
abstract
words (p = .02), but not for more concrete words (p = .62)
Next, the interaction terms were removed to allow interpretation
of
individual valence, concreteness, and age predictors. This
revealed a
significant effect of age (6 vs. 5) in which 6-year-olds
responded faster
than 5-year-olds (p = .03). There was also a significant effect of
nega-
tive (vs. neutral) valence, in which responses were faster to
negative
words than to neutral words (p = .045). While there was also a
sig-
nificant effect of positive (vs. neutral) valence, this will not be
inter-
preted as it was previously shown to interact with concreteness.
See
Table 4.
3.1.1.2. 5-year-olds. Planned analyses for each age group
included all
previously mentioned control variables. Predictors of interest
were
valence, concreteness, and their interaction. For 5-year-olds, no
interactions reached statistical significance (all p > .14). Next,
the

56. Fig. 1. Marginal plots for relationships of standardized
concreteness and valence to reaction time in each age group.
Table 3
Linear mixed effects regression model predicting reaction time
for all ages,
including interactions.
Fixed Effect B S.E. t p
Intercept 1354.10 45.22 29.94 < .001***
Control Variables
Age of acquisition 13.52 6.41 2.11 .04*
Voicing −13.31 16.95 −0.79 .43
Bilabial 67.30 35.49 1.90 .06
Labiodental 97.63 31.21 3.13 .002**
Alveolar 87.03 30.56 2.85 .005**
Palatal 109.75 42.08 2.61 .01*
Velar 77.09 36.85 2.09 .04*
Stop −31.63 23.13 −1.37 .17
Fricative 14.70 32.39 0.45 .65
Affricate −100.17 30.00 −3.34 .001**
Nasal 12.29 25.01 0.49 .62
Uniqueness Point 10.83 6.28 1.73 .09
Predictor Variables
Concreteness −25.34 10.94 −2.32 .02*

57. Age (6) −80.74 50.95 −1.59 .12
Age (7) −45.80 50.42 −0.91 .37
Valence (Negative) −29.64 14.18 −2.09 .04*
Valence (Positive) −27.19 14.03 −1.94 .06
Concreteness × Valence (Negative) 8.75 16.71 0.52 .60
Concreteness × Valence (Positive) 31.57 13.52 2.34 .02*
Age (6) × Valence (Negative) −46.18 26.89 −1.72 .09
Age (7) × Valence (Negative) 22.04 21.29 1.04 .30
Age (6) × Valence (Positive) −37.53 26.56 −1.41 .16
Age (7) × Valence (Positive) 1.59 21.27 0.08 .94
Random Effect s2
Item Intercept 2055.33
Item Age (6) Slope 4249.20
Item Age (7) Slope 0
Subject Intercept 32923.83
Residual 107213.22
Note. Observations = 8200; Items = 117; Subjects = 87.
* p < .05.
** p < .01.
*** p < .001.
T.C. Lund, et al. Cognition 190 (2019) 61–71
65
interaction term was removed to allow interpretation of
individual
valence and concreteness predictors. None of these predictors
reached

58. statistical significance (all p > .07).
3.1.1.3. 6-year-olds. The same analysis was conducted on
reaction
times for children in the 6-year-old group. This analysis
revealed an
interaction between concreteness and positive (vs. neutral)
valence
(p = .02). Plotting this interaction suggests that positive valence
is
facilitatory for more abstract words, but not for more concrete
words
(see Fig. 3). This was confirmed by follow up analyses which
split items
based on median concreteness. We built a model in each group
of items
including all previously mentioned control variables, with the
predictor
of interest being valence. These analyses revealed a significant
effect of
positive (vs. neutral) valence for more abstract words (p = .01),
but not
for more concrete words (p = .57). The interaction between
concreteness and negative (vs. neutral) valence did not reach
statistical significance (p = .39). In addition, this analysis
revealed a
simple effect (i.e., at mean levels of concreteness) of negative
(vs.
neutral) valence, in which children responded faster to negative
words
than to neutral words (p = .01). No other predictors reached
statistical
significance (all p > .08), see Table 5.
3.1.1.4. 7-year-olds. The same analysis was conducted on

59. reaction
times for children in the 7-year-old group. No interactions in
this
model reached statistical significance (all p > .39). Next, the
interaction terms were removed to allow interpretation of
individual
valence and concreteness predictors. This revealed a significant
effect of
positive (vs. neutral) valence in which children responded faster
to
positive words than neutral words (p = .02). No other predictors
reached statistical significance (all p > .10), see Table 6.
3.1.2. Language competence hypothesis
We next conducted analyses including only the more abstract of
our
items, based on a median split of concreteness ratings. These
analyses
included data for children in the 6- and 7-year-old groups, as
there was
no evidence of valence playing a role for 5-year-olds. We
examined
whether language competence contributed to differences in the
pro-
cessing of abstract items of different valences. As a first step,
to identify
a dimension of language competence, we performed a principal
com-
ponents analysis of scores on the PPVT and each of the
component
subscales of the CCC. We used an oblimin rotation as we
expected
components to be correlated with one another. We extracted
compo-
nents until a component had an Eigenvalue lower than 1.00.

60. This re-
sulted in three components being extracted (see Table 7 for the
pattern
matrix).
We used component 3 to quantify language competence, as it in-
cluded the child’s syntactic skills, their capacity to produce
fluent and
Fig. 2. Marginal plot for interaction between standardized
concreteness and positive (vs. neutral) valence in the prediction
of reaction time. Negative (vs. neutral)
valence also shown for reference, but in dashed form as not part
of significant interaction.
Table 4
Linear mixed effects regression model predicting reaction time
for all ages,
without interactions.
Fixed Effect B S.E. t p
Intercept 1346.61 45.72 29.45 < .001***
Control Variables
Age of acquisition 14.66 6.23 2.35 .02*
Voicing −14.90 17.32 −0.86 .39
Bilabial 73.94 36.25 2.04 .04*
Labiodental 102.55 31.88 3.22 .002**
Alveolar 91.99 31.22 2.95 .004**
Palatal 123.11 42.69 2.88 .005**

61. Velar 81.92 37.63 2.18 .03*
Stop −28.86 23.45 −1.23 .22
Fricative 14.94 32.84 0.46 .65
Affricate −105.18 30.44 −3.46 .001**
Nasal 11.64 25.57 0.46 .65
Uniqueness Point 12.47 6.38 1.95 .05
Predictor Variables
Concreteness −8.88 6.35 −1.40 .17
Age (6) −108.90 48.51 −2.25 .03*
Age (7) −37.81 48.83 −0.77 .44
Valence (Negative) −29.08 14.34 −2.03 .045*
Valence (Positive) −28.72 14.28 −2.01 .047*
Random Effect s2
Item Intercept 2228.64
Item Age (6) Slope 4529.14
Item Age (7) Slope 0
Subject Intercept 32896.64
Residual 107240.84
Note. Observations = 8200; Items = 117; Subjects = 87.
* p < .05.
** p < .01.
*** p < .001.
T.C. Lund, et al. Cognition 190 (2019) 61–71
66

62. coherent expressions, and to engage in meaningful conversation
(Bishop, 1998).1 The analysis also included all previously
mentioned
control variables, as well as Age (effects coded, age 6 = −1, 7 =
1)
which served as a control variable in the present analysis.
Predictors of
interest were component 3 scores (henceforth language
competence),
valence, and their interaction. Note that two children who did
not have
subscale scores on the CCC were not included in this analysis.
This
analysis revealed a significant interaction between language
compe-
tence and negative (vs. neutral) valence (p = .006). There was
not a
significant interaction between language competence and
positive (vs.
neutral) valence (p = .70), see Table 8. Plotting this interaction
sug-
gests that language competence is facilitatory for neutral
abstract words
but not for negatively valenced abstract words (see Fig. 4). We
used the
“jtools” package (Long, 2018) in R to conduct simple slope
analyses for
language competence, in neutral and negatively valenced words.
This
revealed a significant facilitatory effect of language competence
for
neutral items (p < .001), and a significant inhibitory effect of
language
competence for negative items (p < .001).

63. Fig. 3. Marginal plot for interaction between standardized
concreteness and positive (vs. neutral) valence in the prediction
of reaction time for 6-year-olds. Negative
(vs. neutral) valence also shown for reference, but in dashed
form as not part of significant interaction.
Table 5
Linear mixed effects regression model predicting reaction time
for 6-year-olds.
Fixed Effect B S.E. t p
Intercept 1382.22 60.62 22.80 < .001***
Control Variables
Age of acquisition 20.52 8.86 2.32 .02*
Voicing 1.85 23.42 0.08 .94
Bilabial 18.25 48.40 0.38 .71
Labiodental 47.85 42.58 1.12 .26
Alveolar 40.99 41.55 0.99 .33
Palatal 97.31 57.72 1.69 .10
Velar 17.15 50.34 0.34 .73
Stop −28.22 31.85 −0.89 .38
Fricative −2.30 44.54 −0.05 .96
Affricate −132.79 41.33 −3.21 .002**
Nasal −11.09 34.42 −0.32 .75
Uniqueness Point 4.73 8.61 0.55 .58
Predictor Variables
Concreteness −20.93 15.16 −1.38 .17
Valence (Negative) −50.36 19.50 −2.58 .01*
Valence (Positive) −34.26 19.36 −1.77 .08

64. Concreteness × Valence (Negative) 19.75 23.03 0.86 .39
Concreteness × Valence (Positive) 42.78 18.70 2.29 .02*
Random Effect s2
Item Intercept 3056.10
Subject Intercept 16091.36
Residual 90922.77
Note. Observations = 2857; Items = 117; Subjects = 29.
* p < .05.
** p < .01.
*** p < .001.
Table 6
Linear mixed effects regression model predicting reaction time
for 7-year-olds,
without interactions.
Fixed Effect B S.E. t p
Intercept 1291.17 59.13 21.84 < .001***
Control Variables
Age of acquisition 13.60 7.38 1.84 .07
Voicing –23.76 20.56 −1.16 .25
Bilabial 56.32 42.80 1.32 .19
Labiodental 89.36 37.72 2.37 .02*
Alveolar 87.35 36.88 2.37 .02*
Palatal 121.03 50.34 2.40 .02*
Velar 70.50 44.51 1.58 .12
Stop −16.78 27.64 −0.61 .55
Fricative 18.71 38.73 0.48 .63

65. Affricate −69.21 36.08 −1.92 .06
Nasal 20.83 30.29 0.69 .49
Uniqueness Point 9.09 7.58 1.20 .23
Predictor Variables
Concreteness −10.49 7.52 −1.40 .17
Valence (Negative) −27.87 16.90 −1.65 .10
Valence (Positive) −41.51 16.93 −2.45 .02*
Random Effect s2
Item Intercept 2019.78
Subject Intercept 30865.36
Note. **p < .01. Observations = 2881; Items = 117; Subjects =
28.
* p < .05.
*** p < .001.
1 Supplementary analyses using the first and second
components found that
they did not interact with valence.
T.C. Lund, et al. Cognition 190 (2019) 61–71
67
3.1.3. Nimble-hands nimble-minds
We next conducted an analysis that included all items for which
body-object interaction ratings (BOI; Pexman, Muraki, Sidhu,
Siakaluk,
& Yap, 2019) were available (104 words). The model included
all

66. previously mentioned control variables as well as Age.
Predictors of
interest were BOI rating, BOT score, and their interaction. The
analysis
revealed that the interaction was not significant (p = .80). The
main
effects of BOI (p = .58) and BOT (p = .19) were also not
significant.2
3.2. Response accuracy
We used mixed effects logistic regression models to analyze
chil-
dren’s response accuracy. We removed trials with latency
greater than
2.5 SD from each participant’s mean (2.65% of remaining
trials). In the
7-year-old age group, 14 of the 28 participants had mean
accuracy
greater than 95% (compared to two 5-year-olds and five 6-year-
olds).
Given this ceiling effect in accuracy for the 7-year-olds we did
not in-
clude their accuracy data in the analyses.
3.2.1. Affective Embodiment
Our first set of accuracy analyses explored the relationship
between
valence and concreteness in response accuracy.
3.2.1.1. Both ages. We began with an analysis of accuracy data
for 5-
and 6 year-old children. See Table 2 for average response
accuracy by

67. age group and Fig. 5 for overall patterns. The model included
all
previously mentioned control variables. Our variables of
interest were:
valence, concreteness (Brysbaert et al., 2014), and age (effects
coded,
age 5 = −1, 6 = 1). Our a priori hypotheses led us to also
include an
interaction between concreteness and valence, and between age
and
valence. This analysis revealed a marginally significant
interaction
between age and negative (vs. neutral) valence (p = .07). No
other
interactions reached statistical significance (all p > .46). Next,
the
interaction terms were removed to allow interpretation of
individual
valence, concreteness and age predictors. This revealed a
significant
effect of age, in which 6-year-olds were more accurate than 5-
year-olds
(p < .001). In addition, there was a marginal effect of negative
(vs.
neutral) valence, in which children were marginally more
accurate in
their responses to negative than neutral words (p = .06).
3.2.1.2. 5-year-olds. Planned analyses for each age group
included all
previously mentioned control variables. Predictors of interest
were
valence, concreteness, and their interaction. No interactions
reached
statistical significance (all p > .53). Next, the interaction term
was

68. removed to allow interpretation of individual valence and
concreteness
predictors. None of these predictors reached statistical
significance (all
p > .18).
3.2.1.3. 6-year-olds. The same analysis was conducted on
accuracy for
the 6-year-old group. No interactions reached statistical
significance
(all p > .16). Next, the interaction term was removed to allow
interpretation of individual valence and concreteness predictors.
This
revealed a marginally significant effect of negative (vs. neutral)
valence, in which 6-year-olds responded marginally more
accurately
to negative than neutral words (p = .06).
3.2.2. Language competence hypothesis
As in the reaction time analyses, this set of analyses did not
include
5-year olds. In addition the accuracy analyses excluded 7-year-
olds. As
such, our analysis of the language competence hypothesis in
response
accuracy only included data from 6-year-old children. The
analysis
included all previously mentioned control variables. Predictors
of in-
terest were language competence, valence, and their interaction.
The
analysis revealed that the interaction was not significant for
negative
(vs. neutral) valence (p = .88), nor for positive (vs. neutral)
valence

69. (p = .82).3
3.2.3. Nimble-hands, nimble-minds
We again included all previously mentioned control variables as
well as Age, which served as a control variable in the present
analysis.
Predictors of interest were mean-centered BOI rating, BOT
score, and
their interaction. The analysis revealed that the interaction was
not
significant (p = .72), nor were the main effects of BOI (p = .70)
or BOT
(p = .87).4
Table 7
Resulting pattern matrix of the PCA.
Variable Component 1 Component 2 Component 3
PPVT 0.59
CCC: Speech −0.96
CCC: Syntax −0.84
CCC: Semantics
CCC: Coherence −0.55
CCC: Initiation 0.52
CCC: Scripted Language
CCC: Context 0.52
CCC: Nonverbal Communication 0.71
CCC: Social Relations 0.94
CCC: Interests −0.86
Note: Only loadings > 0.5 are shown.
Table 8
Linear mixed effects regression model predicting reaction time

70. for abstract
items, for 6- and 7-year-olds.
Fixed Effect B S.E. t p
Intercept 1319.61 62.97 20.96 < .001***
Control Variables
Age of acquisition 18.23 9.10 2.00 .049*
Voicing 1.33 27.57 0.05 .96
Bilabial 29.77 50.93 0.59 .56
Labiodental 56.85 44.13 1.29 .20
Alveolar 53.75 44.43 1.21 .23
Palatal 126.01 56.32 2.24 .03*
Velar 112.78 56.59 1.99 .05*
Stop −8.98 31.49 −0.29 .78
Fricative 54.15 45.68 1.19 .24
Affricate −81.52 41.89 −1.95 .06
Nasal 6.04 34.54 0.18 .86
Uniqueness Point −10.79 9.37 −1.15 .25
Age −13.42 21.59 −0.62 .54
Predictor Variables
Language Competence −35.01 22.71 −1.54 .13
Valence (Negative) −34.43 21.80 −1.58 .12
Valence (Positive) −54.72 20.71 −2.64 .01*
Language Competence × Valence
(Negative)
37.47 13.67 2.74 .006***
Language Competence × Valence

71. (Positive)
5.16 13.28 0.39 .70
Random Effect s2
Item Intercept 1977
Subject Intercept 23,611
Note. **p < .01. Observations = 2813; Item = 60; Subject = 55.
* p < .05.
*** p < .001.
2 Note that we also ran this analysis only including five year-
olds (the age
group examined by Suggate & Stoeger, 2017) and also found no
significant
effects.
3 Supplementary analyses using the first and second
components found that
they also did not interact with valence.
4 Note that we also ran this analysis only including five year-
olds (the age
T.C. Lund, et al. Cognition 190 (2019) 61–71
68
4. Discussion
The purpose of the present study was to test three proposals for
vocabulary acquisition, derived from current theories of

72. conceptual
knowledge. The first proposal is that emotion provides a
bootstrapping
mechanism for vocabulary acquisition. Recent results (Ponari et
al.,
2017) suggested that around 8–9 years of age children show
sensitivity
to word valence in their ALDT responses. Interpreting the
results for
younger children in that study was complicated, however, by the
low
rate of ALDT accuracy among the 6–7 year olds tested. In the
present
study we also examined effects of valence on lexical
processing, in
younger groups of children. We used a large set of words that
were, on
average, acquired earlier than those presented in the previous
study.
With these more familiar words, children in the present study
had
higher accuracy in ALDT responses and we were able to
examine
children’s reaction times. This is an important advance; reaction
times
are the primary source of evidence about underlying processes
in the
adult literature because they are less susceptible to floor and
ceiling
effects than are accuracy data.
We tested for valence effects in each of our 5-, 6-, and 7-year-
old age
groups and found that 6-year-old and 7-year-old children’s
ALDT re-

73. action times were influenced by word valence. This sensitivity
to
emotion information was not present in the 5-year-old children
we
tested and was not significant in the accuracy analyses. Our
reaction
time findings provide evidence that at 6–7 years of age children
ground
word meanings via emotion systems (Kousta et al., 2011),
suggesting
that sensitivity to emotion information in lexical processing can
be
observed at a younger age than that inferred from previous
research.
In the Ponari et al. (2017) study, the only significant valence
effect
was for abstract words in their intermediate (8–9 year old) age
group,
Fig. 4. Marginal plot for interaction between standardized
language competence and valence in the prediction of reaction
time. Positive (vs. neutral) valence also
shown for reference, but in dashed form as not part of
significant interaction.
Fig. 5. Marginal plots for relationships of standardized
concreteness and valence to response accuracy in each age
group.
(footnote continued)
group examined by Suggate & Stoeger, 2017) and also found no
significant
effects.
T.C. Lund, et al. Cognition 190 (2019) 61–71

74. 69
where accuracy was higher for positive words than for neutral
words.
There is little overlap between the stimuli used by Ponari et al.
and
those used in the present study (only 2 words in common), and
our
effects were observed in reaction times, yet we would argue that
there
is similarity in the basic pattern of sensitivity. In particular, we
found
faster reaction times for positive abstract words than for neutral
ab-
stract words in the present 6-year-old age group, and this is
analogous
to the accuracy advantage observed by Ponari et al. for positive
abstract
words vs neutral abstract words. While this pattern in similar
across the
two studies, we also found some differences. In the present
study, 6-
year-olds were faster (and tended to be more accurate, although
not
significantly so) for negative words than for neutral words. This
did not
interact with concreteness. This processing advantage for
negative
words is in keeping with some findings with adults: in visual
lexical
decision tasks adults have responded more quickly to negative
words
than neutral words (Kousta et al., 2009; Vinson et al., 2014;

75. Yap &
Seow, 2014). To our knowledge this has not previously been
reported
for child participants. In another departure from the Ponari et
al. re-
sults, the 7-year-olds we tested showed faster responses to
positively
valenced words, and this too was not significantly modulated by
con-
creteness.
One difference between the present study and that of Ponari et
al.
(2017) that may be important to explaining the different results
is that
the abstract items in Ponari et al. were numerically somewhat
lower in
mean concreteness rating (M = 2.51, SD = 0.68) than those we
con-
sidered more abstract by our median split (M = 2.77, SD =
0.69). As
such, the present manipulation of concreteness was likely
weaker than
that in the Ponari et al. study and this might have influenced the
strength of the concreteness by valence interactions, rendering
them
nonsignificant in some cases. It is also true that the present
participants
were younger than those in the Ponari et al. study, so another
ex-
planation is that in this younger age group valence effects tend
to be
more generalized, whereas those observed in older children
(e.g., the
8–9 year olds in Ponari et al.) tend to be more limited to
abstract word

76. stimuli. Future research will be required to adjudicate between
these
possibilities.
The valence effects that we observed in children’s ALDT
responses
involved faster reaction times for positive and negative words
than for
neutral words. While this is consistent with many of the
findings from
the adult literature (e.g., Kousta et al., 2009; Siakaluk et al.,
2016;
Vinson et al., 2014; Yap & Seow, 2014), we noted in the
Introduction
that there is considerable variability in the particular ways in
which
valence influences adult lexical processing measures, and the
reasons
for this variability have not been established (Kuperman, 2015).
It also
seems possible that the particular effects of valence change
across de-
velopment, in the years after those tested in the present study.
This will
be an important issue for future research, but testing that issue
will
require different items than those used here. Since the current
stimuli
were selected for the age range tested (5–7 years), these items
would be
less suitable for older child and adult participants.
We also tested predictions of the language competence
hypothesis,
that language experience may be important to acquisition of
abstract

77. word meanings, particularly abstract neutral word meanings.
Such
words do not enjoy the benefit of valence information to ground
meanings and must be learned via other mechanisms. We tested
the
prediction that language competence is important for acquisition
of
these word meanings once emotion information has already been
re-
cruited to capture differences between abstract and concrete
word
meanings (Vigliocco et al., 2018). Our results were consistent
with this
prediction: children with a greater degree of language
competence re-
sponded faster to abstract items that were neutral in valence, but
not
those that were negative in valence. Further, our results suggest
that the
aspects of language competence that are related to processing of
ab-
stract neutral word meanings are those captured by the speech,
syntax
and coherence (i.e., producing easy to understand sentences)
subscales
of the CCC.
We also found an inhibitory relationship between language com-
petence and processing of words of negative valence. Although
highly
speculative, we note that this could be consistent with more
advanced
language users beginning to transition to the alternative pattern
that is
sometimes observed for words of negative valence. That is,
some adult

78. studies have shown that responses are slower to negative words
than to
neutral words. As mentioned, the reasons for this alternative
pattern are
not well understood, and the pattern is usually attributed to
vigilance to
negative stimuli (Pratto & John, 1991). Our results suggest that
one
factor to consider in future research on valence effects for
negative
stimuli is language competence. We would suggest that related
di-
mensions like executive function skills would also need to be
con-
sidered, but the present findings may be useful as researchers
work to
understand the conditions under which effects of positive
valence are
distinct from effects of negative valence.
Finally, we tested predictions of the “nimble hands, nimble
minds”
proposal (Suggate & Stoeger, 2014, 2017) and found no
evidence that
children with better fine motor skills were better able to recruit
sen-
sorimotor information for word stimuli in the ALDT. The fact
that we
did not find support for the nimble hands, nimble minds
hypothesis is
problematic for a strong embodied account, as our findings
suggests
that embodied information is not always recruited in children’s
lexical
processing. Although problematic for a strong embodied
account (e.g.,

79. Glenberg & Gallese, 2012) our findings could be explained by a
mul-
timodal or multiple representations account, since those
frameworks
allow that there are multiple sources of information that support
word
knowledge (e.g., Borghi et al., 2017; Howell et al., 2005);
embodied
information is just one such source and its relevance to
performance
depends on the task and the type of concept.
4.1. Conclusions
In the present study we found that even 5-year-old children de-
monstrated reasonable rates of accuracy in the ALDT. As such,
the task
shows promise as a tool for exploring children’s lexical-
semantic de-
velopment in future research, if the items used are familiar for
children
of the age tested. The results for the present version of the task
provide
new evidence that children recruit valence information in the
process of
word recognition, consistent with proposals like the Affective
Embodiment Account (Borghi et al., 2017; Kousta et al., 2011)
and also
with broader proposals about grounded lexical development
(Dove,
2011, 2018; Howell et al., 2005; Thill & Twomey, 2016) and
multi-
modal semantic models (e.g., Andrews, Vigliocco, & Vinson,
2009;
Barsalou et al., 2008; Borghi et al., 2017).

80. Declarations of interest
None.
Acknowledgements
This work was supported by the Natural Sciences and
Engineering
Research Council (NSERC) of Canada through a Discovery
Grant to PMP
(Grant number RGPIN/217309-2013). We thank Sophia van
Hees for
assistance with experiment programming.
Appendix A. Supplementary material
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.cognition.2019.04.017.
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