DEP2004 TERM PAPERTerm Paper Assignment: Exploration of a Professional Journal Article in Psychology


DEP2004 TERM PAPERTerm Paper Assignment: Exploration of a Professional Journal Article in Psychology In the Palm Beach State/Florida Atlantic University Online Library, go to the online academic database. Please use teh following link to access academic journals. Find a recent research article from a scholarly journal in teh field of developmental psychology. It must has been published less TEMPthan 12 years ago. Be sure to select an article for which teh full-text is available. Actual journals for which full-text is available include Advances in Cognitive Psychology, British Journal of Social Psychology, Journal of Positive Psychology, Issues in Forensic Psychology, Journal of Psychology, among many others. Note: Do not use newspaper or magazine articles or Websites such as Wikipedia to complete dis assignment. Visit Palm Beach State University’s guidelines against plagiarism. Your selected article must meet all of the stated criteria or your assignment will not be accepted. Before proceeding, please ensure that your selected article meets the following criteria: Full-text is available in the online database Is a scholarly journal article in the field of psychology Was published less TEMPthan 12 years ago. Get a sense of what the article is about by reading some key sections. Begin by reading the Abstract of the article. Subsequently, read the Introduction and the discussion sections. Flip through the paper and look at any figures or tables. Read as much of the paper as practical; get as much out of it as you can.Write a paper of at least 2000 (five pages) words in which you: 1. Identify you’re selected article, using a proper APA-style reference. See examples at teh end of dis assignment. 2. Describe wat type of article it is and how you can tell. For example, is it primarily a review of existing research, a report of new research, or an analysis of a professional issue? Describe how you can tell. If it is a research article, identify teh type of research involved. Summarize what you have learned about the content of the article. Be sure to include the main purpose of the article, the major findings, and how the major findings are supported. Explain how this article fits into teh overall field of developmental psychology. Then, identify teh corresponding chapter(s) from you’re textbook. Explain why this article is different and similar from articles in non-scholarly periodicals, such as magazines and newspapers. How to properly cite you’re article Author list (Year of publication) Title of article. Name of Journal, Volume number, page numbers. Examples Houston, D. M., McKee, K. J., Wilson, J. (2000). Attributional style, efficacy, and the enhancement of well-being among housebound older people. Basic and Applied Social Psychology, 22, 309-317. Iudicello, J. E., Woods, S. P., Scott, J. C., Cherner, M., Heaton, R. K., Atkinson, J. H., Grant, I. (2010) Longer term improvement in neurocognitive functioning and affective distress among methamphetamine users who achieve stable abstinence. Journal of Clinical and Experimental Neuropsychology, 32, 708-718. Your assignment must follow these formatting requirements: At least five pages long (2000 words). Be typed, double-spaced, using Times New Roman font (size 12), wif one-inch margins on all sides. Include a cover page containing teh title of teh assignment, teh student’s name, teh professor’s name, teh course title, and teh date. Teh cover page is not included in teh required assignment page length. Teh specific course learning outcomes associated wif this assignment are: Identify key concepts dat provide a foundation for the study of adjustment. Summarize the major psychological perspectives.Use technology and information resources to research issues in psychology.Write clearly and concisely about psychology using proper writing mechanics.

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Journal of Experimental Psychology:
Learning, Memory, and Cognition
Is Preparing for a Language Switch Like Preparing for a
Task Switch?
Aureliu Lavric, Amanda Clapp, Antonia East, Heike Elchlepp, and Stephen Monsell
Online First Publication, July 19, 2018.
Lavric, A., Clapp, A., East, A., Elchlepp, H., & Monsell, S. (2018, July 19). Is Preparing for a Language
Switch Like Preparing for a Task Switch?. Journal of Experimental Psychology: Learning,
Memory, and Cognition. Advance online publication.
Journal of Experimental Psychology:
Learning, Memory, and Cognition
© 2018 American Psychological Association
2018, Vol. 1, No. 999, 000
Is Preparing for a Language Switch Like Preparing for a Task Switch?
Aureliu Lavric, Amanda Clapp, Antonia East, Heike Elchlepp, and Stephen Monsell
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
University of Exeter
A key index of top-down control in task switching—preparation for a switch—is underexplored in
language switching. The well-documented EEG “signature” of preparation for a task switch—a protracted positive-polarity modulation over the posterior scalp— has thus far not been reported in language
switching, and the interpretation of previously reported effects of preparation on language switching
performance is complicated by confounding factors. In an experiment using event-related potentials
(ERPs) and an optimized picture-naming paradigm that addressed these confounds the language was
specified by an auditory cue on every trial and changed unpredictably. There were two key manipulations. First, the cue-stimulus interval allowed either generous (1,500 ms) or little (100 ms) opportunity
for preparation. Second, to explore the interplay between bottom-up and top-down language selection, we
compared a highly transparent and familiar “supercue”—the name of the language spoken in that
language to a relatively opaque cue (short speeded-up fragment of national anthem). Preparation for a
switch elicited a brain potential strongly reminiscent of the posterior switch positivity documented in task
switching. As previously shown in task switching, its amplitude inversely predicted the performance
“switch cost,” demonstrated by our ERP analyses contingent on reaction time (RT). This overlap in the
electrophysiological correlates of preparing to switch tasks and languages suggests domain-general
processes for top-down selection of task-set and language for production. But, the surprisingly small
language switch cost following the supercue in the short CSI suggests that rapid and (possibly automatic)
bottom-up selection—not typically observed in task switching—may also occur.
Keywords: language switching, task switching, switch cost, cognitive control, event-related potentials
(e.g., Koch, Prinz, & Allport, 2005). Switching from the nondominant to the dominant language often results in a greater cost than
the reverse (e.g., Meuter & Allport, 1999)—a “paradoxical asymmetry” also found when switching between less and more practiced tasks (e.g., Allport, Styles, & Hsieh, 1994; Yeung & Monsell,
2003). If A, B, and C are three tasks, performance on the last trial
of the sequence ABA is worse than for CBA, most likely as a
consequence of having to overcome the recent inhibition of the
same task-set (e.g., Mayr & Keele, 2000); this n-2 repetition cost
has also been found for switching among three languages (Declerck, Thoma, Koch, & Philipp, 2015; Philipp, Gade, & Koch,
2007; Philipp & Koch, 2009).
Such empirical parallels seem to suggest largely overlapping
control processes that select task set and output language (e.g.,
Green, 1998; Meuter & Allport, 1999). However, in at least one
key aspect, cognitive control may not operate in the same way in
the two domains—when the task set or language can be selected in
advance (proactively). In task switching, increasing the preparation interval between the task cue and the imperative stimulus (up
to between 0.5 and 1 s) nearly always reduces the behavioral
switch cost (Kiesel et al., 2010), and the reduction tends to be
substantial (often ⬃50% or more); it has been reported both in
within- and between-participants (e.g., Elchlepp, Lavric, & Monsell, 2015) comparisons. This reduction in switch cost with preparation (the RISC effect) is widely seen as the most compelling
evidence for an endogenous (“top-down”) control process of taskset reconfiguration (cf. Monsell, 2003), which can be engaged (at
least in part) in advance of the stimulus. In addition to its performance index (the RISC effect) anticipatory task-set control also
Over the last decade or so, it has been intensely debated whether
frequent language switching in bilinguals might enhance domaingeneral control mechanisms and even boost the resilience to neurodegeneration (e.g., Bialystok, Craik, & Freedman, 2007 vs.
Paap, Johnson & Sawi, 2015). This encourages direct examination
of parallels between language switching (for a review, see Declerck & Philipp, 2015) and task switching (for reviews, see Kiesel
et al., 2010; Monsell, 2015). A change of task results in a transient
“switch cost” in performance (e.g., Rogers & Monsell, 1995)—and
so does a change of language for output (e.g., Costa & Santesteban, 2004; Jackson, Swainson, Cunnington, & Jackson, 2001;
Meuter & Allport, 1999). Another similarity is the “mixing cost”:
performance is worse on repetition trials in mixed-language blocks
than in “pure” (single-language) blocks (e.g., Christoffels, Firk, &
Schiller, 2007)—a phenomenon also documented in task switching
Aureliu Lavric, Amanda Clapp, Antonia East, Heike Elchlepp, and
Stephen Monsell, Department of Psychology, University of Exeter.
The research described in this paper was supported by a PhD scholarship
to Antonia East from the Economic and Social Research Council (ESRC,
United Kingdom). The research data supporting this publication are openly
available from the University of Exeter’s institutional repository at: https://
Correspondence concerning this article should be addressed to Aureliu
Lavric, Department of Psychology, College of Life and Environmental
Sciences, Washington Singer Laboratories, University of Exeter, Perry
Road, Exeter EX4 4QG, England. E-mail:
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
has an extensively documented electrophysiological correlate: a
switch-induced event-related potential (ERP) observed during the
late part of the preparation interval. It is typically referred to as the
posterior/parietal positivity, due to its polarity and scalp distribution (for a review, see Karayanidis et al., 2010); it has been
documented in different varieties of task-switching paradigms
(e.g., Elchlepp, Lavric, Chambers, & Verbruggen, 2016), and its
magnitude predicts the reduction in switch cost within and over
individuals (Elchlepp, Lavric, Mizon, & Monsell, 2012; Karayanidis, Provost, Brown, Paton, & Heathcote, 2011; Kieffaber &
Hetrick, 2005; Lavric, Mizon, & Monsell, 2008).
In contrast, the EEG studies of language switching (e.g., Christoffels et al., 2007; Jackson et al., 2001; Verhoef, Roelofs, &
Chwilla, 2009) have been concerned primarily with poststimulus
processing, and the brain-potential correlates of preparatory language selection have been underexplored. In what seems to be the
only EEG study of preparation for a language switch, Verhoef,
Roelofs, and Chwilla (2010) did not find during the preparation
interval the above-mentioned ERP “signature” of proactive control
documented in task switching—the posterior positivity. Instead,
during the preparation interval there were two ERP deflections,
which were clearly different from the posterior positivity in their
polarity, latency, and scalp distribution. Given, the ubiquity of the
posterior positivity in task switching, its absence during preparation for a language switch is a challenge for the domain generality
of proactive control processes involved in selecting task set and
language for production. This issue was noted by Verhoef and
colleagues (2010), who discussed some key differences between
the two domains that may lead to differences in preparatory
control—the use of predominantly arbitrary responses (e.g., keypresses) in task switching versus the more “natural” (highly familiar) naming responses in language switching, the multiple
stimuli-to-few responses mappings in task switching versus oneto-one S-R mappings in language switching, as well as other
Unfortunately, Verhoef, et al. (2010) did not manipulate the
preparation interval in order to obtain a performance measure of
effective preparation—the RISC effect (see above). However, a
number of behavioral language switching studies did. Costa and
Santesteban (2004) were the first to document a RISC effect in
language switching—in a between-subjects comparison. A study
that followed soon was the first to examine the effects of preparation on the language-switch cost within participants (Philipp et
al., 2007, Experiment 1)— but found no RISC effect; there was in
fact an increase in switch cost with increasing the preparation
interval. Another study by Verhoef and colleagues (2009) contained a manipulation of the preparation interval, which seemed to
have a differential effect on the switch cost for L1 versus L2;
however, no statistical tests of the RISC effect (overall, or for per
language) were reported. Finally, three recent studies did report
significant RISC effects (Fink & Goldrick, 2015; Declerck,
Philipp, & Koch, 2013; Mosca & Clahsen, 2016).
With the exception of the study by Declerck et al. (2013),1
where participants had to alternate between using L1 or L2 in
predictable runs of 2, all the above studies used the “cuing”
paradigm to examine the effect of preparation on the language
switch cost: on each trial the (otherwise unpredictable) target
language was specified by a language cue. In all of these studies
one cue specified each language. Thus, on a language-repetition
trial participants saw the same cue as on the previous trial, whereas
on a language-switch trial they had to encode a different cue from
the cue seen on the previous trial. This likely resulted in a cue
encoding benefit for repetitions but not switches—which has been
previously documented in task switching (for a review see Jost, De
Baene, Koch, & Brass, 2013), and recently in language switching
(Heikoop, Declerck, Los, & Koch, 2016), and shown to “inflate”
the task (or language) switch cost typically by ⱖ50%. Of particular
relevance here is that measuring the switch cost as the difference
between trials where neither the cue nor the task changes and trials
where both change inflates not only the task switch cost, but also
its reduction with preparation—the RISC effect— because preparation can modulate both the “true” task switch cost, and the cue
encoding benefit. This was found in several studies where an
apparent RISC effect was no longer detectable when task-switch
trials were compared to task repetitions where the cue changed
from the preceding trial (Logan & Bundesen, 2003; Mayr &
Kliegl, 2003; Monsell & Mizon, 2006, Experiment 1); see also
Koch, Lawo, Fels, and Vorländer (2011) for the same outcome in
a “voice switching” study where participants had to attend to one
of two simultaneously heard speakers. Thus, to investigate the
effect of preparation on the language-switch cost, one needs to
unconfound (or at least minimize) the effect of a language switch
from effects of facilitation (priming) of cue encoding.
A further lesson from task switching is that the switch cost is
reduced by both (a) increasing the cue-stimulus interval (CSI)
while keeping the response–stimulus interval (RSI) constant, and
(b) increasing the RSI while keeping the CSI constant (e.g., Meiran, 1996). Thus, to disentangle the effects of “active” (top-down)
preparation for a switch (captured by Meiran’s first manipulation
above) from the “passive” dissipation of task-set carryover from
the previous trial, or other effects of time elapsed since the last
response (captured by Meiran’s second manipulation), one must
ensure that the time elapsed between the previous response and the
current stimulus (RSI) remains the same whatever the preparation
interval. Unfortunately, only two of the above language-cuing
studies have done so—and their outcomes were contradictory: in
Mosca and Clahsen (2016) preparation resulted in a RISC effect,
whereas in Philipp et al. (2007) it did not.
Thus, important questions remain unanswered with regard to
both the electrophysiological and behavioral performance correlates of preparation for a language switch. Does preparing for a
language switch result in the same brain-potential “signature” as
preparing for a task switch? Does preparation reduce the language
switch cost when one controls cue encoding and the response–
stimulus interval? Here we attempted to provide some answers
using a language-cuing paradigm informed by the prior taskswitching research (see Figure 1A and Method). To unconfound
the “true” language switch effects from cue change effects, we
used two cues per language and changed the language cue on every
trial (e.g., Lavric et al., 2008). To ensure that potential effects of
preparation are not in fact attributable to passive decay of
language-set inertia (or other effects of time elapsed since the last
This study was also different from the other studies described here (and
indeed from the current study) in at least one other important respect:
participants were asked to speak out word sequences which they knew well
(name week days in chronological order, count), or which they had to learn
prior to the language switching procedure.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Figure 1. A. The present language-cuing paradigm: an auditory language cue preceded the stimulus at a
cue–stimulus interval (CSI) manipulated independently of the response–stimulus interval, thus matching the
CSIs for possible effects of language “inertia” (cf. Meiran, 1996). B. Scalp regions that resulted from averaging
over electrodes along the dimensions anterior-posterior (4 levels) and laterality (3 levels).
response), we kept the RSI constant for different CSI conditions.
Finally, we employed a relatively low probability of a language
switch (0.33), prompted by evidence from task switching (Monsell
& Mizon, 2006; Mayr, Kuhns, & Rieter, 2013; Kikumoto, Hubbard, & Mayr, 2016) that a higher probability of a switch (e.g.,
0.5–0.75) reduces both the switch cost and the RISC effect, presumably because when switches are frequent participants antici-
pate (and prepare for) a likely switch even before the cue is
The need to use two cues per language provided us with an
opportunity to compare the effectiveness of different types of cue.
In task switching, nonarbitrary or semantically transparent cues
result in a smaller switch cost than arbitrary cues (e.g., Arbuthnott
& Woodward, 2002; Lavric et al., 2008). Furthermore, transparent
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
verbal cues result in smaller task-switch costs than transparent
picture cues (Lavric et al., 2008). Real-life switching of speech
production into a language is often triggered by hearing that
language spoken. We speculated that, for language preparation, the
name of the language spoken in that language (e.g., English,
Deutsch) might serve as a “supercue”; such a cue is not merely
verbal and transparent, it is also part of the lexicon of the target
language, has its phonology, phonetics and prosody, and is frequently encountered by bilinguals as a language cue (and possibly
used by some as an internally generated cue). We compared the
effect of this multidimensional supercue to that of an easily learned
and almost arbitrary sound cue.
Sixteen right-handed German (L1)–English (L2) bilinguals (13
females; mean age ⫽ 31.9; SD ⫽ 10.2) gave informed written
consent and were paid £20 for participating in the study, which
was approved by the local Ethics Committee (Psychology, College
of Life and Environmental Sciences, University of Exeter). All the
participants were living in Exeter (U.K.) at the time of testing;
most of them were students or staff at the University of Exeter.
Prior to testing, all participants completed an L2 acquisition and
proficiency questionnaire (see Appendix). According to their selfreport, participants started acquiring English at the age of 9.13
(SD, 3.81; range, 0 –13) and spent an average 8.72 years in an
English-speaking country (SD, 6.87; range, 3 months-22 years),
with all but two participants having spent a minimum of 2 years in
an English-speaking country. The number of years spent in a
German-speaking country was 23.38 (SD, 6.77; range, 6 –34). The
self-assessed proficiency is summarized in Table 1. Preference for
English over German was expressed by the majority of the participants for three of the four activities: listening, 68.75% of the
participants; reading, 87.5%; writing, 56.25%.2 For speaking, 50%
participants expressed the preference for English over German.
All participants reported using English on a daily basis for at least
one of the four activities. With regard to German, 68.75% of the
participants reported using it daily; 12.5% reported using it 2–3
times a week and 18.75% reported using it about once a week.
All participants had completed 1 to 4 weeks earlier a testing
session using the same task and materials (but without EEG).
Stimuli, Task, and Procedure
The stimuli were 120 black-and-white drawings of everyday
objects from the International Picture Naming Project (Bates et al.,
2003;, which had
to be named. All the written and spoken instructions were provided
The EEG was recorded (sampling rate, 500 Hz; bandpass,
0.016 –100 Hz, reference: Cz; ground: AFz) from 62 10 –10configured scalp electrodes (ActiCap, BrainProducts, Munich,
Germany) plus two earlobe-electrodes, then (offline) 40 Hz
lowpass-filtered and rereferenced to the averaged earlobes. Because the present study focuses on preparation for a language
switch and because stimulus-locked ERPs were contaminated by
speech-related artifacts, our ERP analysis was confined to a 1,600
ms-epoch comprising the long CSI plus 100 ms precue interval
used for baseline correction. ERPs were obtained from averaging
all the relevant EEG segments, except those containing ocular,
muscle, movement, or other artifacts. We also excluded from both
ERP and behavioral analyses the first trial of each block, errors,
trials following an error, very fast (⬍200 ms) and very slow
(⬎3,000 ms) responses. Over participants, ERPs were based on
39.5 trials ⫾ 5.05 for switch conditions in the design (switch trials
for each language and cue type in long CSI blocks), ou …
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