A Lab Report from a Cognitive Approach 2022

Running head: A LAB REPORT FROM A COGNITIVE APPROACH 1
A Lab Report from a Cognitive Approach
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Abstract
The study explores the “Lexical Decision Task” (LDT), and “Animal Decision Task”
(ADT), which are assessed whether they may alter how emotion is processed with various
stimuli under three situations (negative, positive, or neutral). Importantly, the three groups were
based on two psycholinguistics features; that is, log frequency and word length. The results of
the LDT suggest that asking participants to examine a word’s negativity, or positivity should
influence the same outcome. As a result, the negative and positive word ratings should link with
each other leading to the small margin (t< 1). In the ADT, the word must be categorized
according to some semantic norm; whether it is a name of an animal. The results obtained show
that there was a significant margin between neutral and negative words, and negative and
positive words. To observe the connection between animal and non-animal words in each
situation, the information was assessed based on the response time for both animal and non-
animal words distinctly.

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A Lab Report from a Cognitive Approach
1.1 Introduction
Cognitive psychology is a scientific research of the mind as a data processor. Normally, it
creates cognitive models of data processing, which take place inside a person’s mind such as
language, memory, consciousness, attention, and perception. Majority of the lexical decision
assessments examine the response of participants by pressing a button of single words put in
segregation. Due to the fact, the LD undertaking depends on participant’s reaction time to correct
word trials. Contrary, the AD task is an example of a semantic categorization task, where
decisions must be based on instigation at a semantic level. In spite of multiple studies focusing
on how resemblance influences retorts to animal words, very little is understood on how linked
items in a respondent’s memory affect memorial decisions on non-animal words.
1.2 Purpose Statement
The purpose of this article is to assess the over-all effect various forms of errands have on the
handling of positive, negative, and neutral data. As a result, the “lexical decision task” and an
“animal categorization task” have been utilized to alter the type of processing individuals utilize,
and how they may change emotion being processed with several forms of stimuli under distinct
conditions.
1.3 Research Questions
1. Is the lexical decision approach effective in changing the type of processing humans use?
2. Is the animal decision perspective appropriate in changing the way emotions get processed
with various forms of stimuli under dissimilar conditions?

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3. Using the lexical decision technique, why is there a significant variance between neutral and
positive words, and no margin between unrelated and related words?
4. Using the animal decision approach, why is there a significant margin between neutral fear-
related words, and no such variance in happy and neutral words?
Summary of the Scatterplot
The scatterplot has been used to analyze the connection between two distinct quantitative
variables for the same persons. Normally, the values of one variable appear on a horizontal axis,
while the values of the other variable are placed on a vertical axis. Notably, each individual in
the data is represented by a point on the graph. Thus, the scatterplot evaluates data depending on
its distribution and size, as indicated in Figure 2. Alternatively, the strongest linear connection
happens when one variable elevates by the same amount of the other variable. Alternatively, the
link between two variables is strong when the value of r is greater than 0.7, and is lowest the
value of r is less than 0.3 (Mindrila, and Balentyne, 2017). According to linear lines on the
lexical decision data, the participants were highly anxious in both SSTAI data compared to their
happiness level in both the OHQ data. In the animal decision data, the linear lines indicated that
for Neu-Neg vs. Anxiety” the relationships between the variables was strong compared to that of
“Neu-Pos vs. Anxiety.” As a result, the linear line rose and fell respectively. Contrary, in the
OHQ data of the animal decision approach, there was a strong relationship between “Neu-Pos vs.
happiness” compared to “Neu-Neg vs. Happiness.”
Discussion
Throughout the paper, it is evident that both the lexical and animal decision experiments
have a significant impact changing to the form of processing individuals utilize, and the way it

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may change emotion being processed with multiple forms of stimuli under distinct conditions.
Notably, all the participants of the experiment were original English speakers, which means their
responses to the key stimuli was not attributed to lack of knowledge of the English language.
Overview of SSTAI and OHQ
The SSTAI contains at least 20 statements and the trait item for this study was the anxiety
scale, which assesses how the participants felt at a certain time recently, and how they anticipate
feeling in a particular situation that may be encountered in future. Importantly, there are two
types of SSTAIs that measure anxiety; STAI S-Anxiety and STAI T-Anxiety. The S-Anxiety
scale is commonly used as a sensitive change indicator in transitory anxiety experienced by
people in behavior-adaptation initiatives, while the T-Anxiety scale identifies people with high
neurotic anxiety levels.
Alternatively, the 29-item OHQ scale was also presented to the 124 participants, which
they were required to answer based on their first intuition. Occasionally, these items can be
incorporated into larger quizzes in a random manner, and this helps in reducing the probability of
complaint and contextual answering. In spite of the OHQ scale, assessing someone’s happiness
level currently, it may be used as a determinant of happiness soon.
Lexical Decision Task (LDT)
The first section of the study was the experimental task, which comprised the lexical and
animal decision experiments. In the LDT, the respondents were required to press the ‘q key if a
stimulus was a word, and ‘p’ key if it was not. Before the test began, 12 words were issued for
practice. When the test began, the stimuli were placed at the center of the screen until the latter
responded, and it comprised 120 words (40 negative, 40 positive, and 40 neutral). The three

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groups were based on two psycholinguistics features; log frequency and word length. An
additional 120 non-words were issued to the participants. Although most studies are based on the
lexical decision task focus on word processing, some characteristics influence non-word lexical
decision performance such as base-word properties, and the length of syllables.
Most lexical decision evaluations examine the response of participants by pressing a
button of single words put in isolation. Contrary, in the actual world, words are mostly
encountered when one reads, and successful word recognition is identified by the movement of
participant's eyes from one word to the next. The main issue that arises is whether similar
semantic priming procedures noted in button press tasks with secluded words can be applied to
contextual reading. Importantly, most studies on visual word recognition have focused on how
the orthographic neighborhood of a word impacts its perception.
Due to the fact, the lexical decision undertaking depends on participant’s reaction time to
correct word trials. They were provided with a 1000ms blank screen prior to the next stimuli
appearing. The essence of this was to ensure that accurate decision were made by the participants
to prevent the collection of incorrect responses. Unfortunately, this approach focuses more on the
reaction times rather than decision accuracies; it focuses on the speed of word recognition
instead of the precision of the procedure.
The results obtained from this experiment indicate that there were major differences
between neutral and positive words, and the neutral and negative words. Ironically, there was
small margin between the negative and positive words. According to the lexical decision task,
affective processing assumes that words are either negative, neutral, or positive, but not at the
same time. According to Briesemeister, Kuchinke, and Jacobs (2012, p.4), “Using a standard
manipulation with positive, negative, and neutral words and an additional emotionally

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ambivalent stimulus category, faster responses to positive and slowed responses to negative
words,” the latter further suggested, “but no difference between neutral and emotionally
ambivalent words, were expected according to the unidimensional valence model.” The
negativity and positivity assessments are linked with dissimilar cortical networks. As a result, it
is assumed that sovereign neutral processing of negative and positive valuations cause the
witnessed inconsistency effects.
According to the above experiment, asking respondents to independently evaluate a
word’s negativity or positivity should influence the same outcome. Hence, the negative and
positive word ratings should highly relate to each other, which led to a small margin (t< 1).
Nevertheless, when words are neither negative nor positive, they are described as neutral words.
When stimuli are rated as neutral on either “one or two-dimensional valence scales”, they get
processed slower compared to stimuli regarded as neutral on “unidimensional valence,” but as
indecisive on two distinct scales. Therefore, words in the neutral range of the lexical decision
experiment have a massive difference on the two distinct dimensions (t (59) = 2.85, p=.0060),
and t (59) =2.67, p=.0097).
The LDT expects that the behavioral effects with faster reaction times for happiness
compared to the anxiety and neutral words are replicated. Thus, processing of positive words
such as happy words results in a higher drift compared to fear and neutral related words. 59
participants had a faster response to happy words than neutral words, while the same number had
a faster response rate to anxiety words than neutral words. Consequently, the difference in the
response rate of the 59 participants to positive words (happiness) and negative words (anxiety)
was less than 1.

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A positive effect compared to negative and neutral words in the LDT can be replicated
for pleasant words. Besides, a speed merit of positive items can be expressed concerning a higher
density, and a greater number of connections among positive stimuli. As a result, the similarity
and density of valenced items may not be sufficient in explaining the positivity advantage (Koch,
Alves, Krüger, and Unkelbach, 2016). In contrast, an emotion effect can be evident in error rates.
Due to this fact, there is a larger error rate for anxiety and fear associated words compared to
neutral and happy words.
Notably, the attention of the participants is usually disengaged more slowly from stimuli
of a negative emotion compared to stimuli of positive and neutral emotions. Thus, the latter may
have tried to avoid slowing down their responses, which resulted in a greater error rate. Based on
the results of the drift rates, it is evident that word positivity causes the effect toward a
processing merit, instead of a reduced reaction time of words with a negative undertone. Hence,
a positivity merit with a higher drift rate that indicates a higher rate of evidence accumulation in
happy words compared to fear and neutral connected words.
Animal Decision Task
The experiment was presented just like the LDT, only that an additional 120 animal
words were incorporated to the set, instead of 120 non-words. The participants were required to
press the ‘q’ button if a stimulus was an animal, and the ‘p’ button if the stimuli was not an
animal. In this section, the experiment was also based on two psycholinguistics features; log
frequency and word length. The semantic approach of emotional ratings is utilized to quantify
opinions and attitudes of participants towards psychological phenomena (Warriner, Kuperman,
and Brysbaert, 2013). Thus, any non-animal word is bound to evoke a negative feeling, or high
arousal rate.

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Although multiple studies pay attention to how similarity influences responses to animal
words, very little are understood on how related items in a participant’s memory affect memorial
decisions on non-animal words. The false recognition for animal words is initiated when
participants may think of an associated non-animal word (Olszewska, and Ulatowska, 2013).
Thus, if respondents are handed a non-animal word as a distraction, they may claim to recognize
it because of the previously understood associative response (Curl, 2016). Contrary, it is not
clear whether the participants know about these association during the study, or if there was an
unattended implicit connection being made through some associative networks. For instance,
studying lions, buffalos, and zebras may influence a strong African wildlife associative
indoctrination, and a minimal encoding for certain features that are distinct to any one animal.
In the animal decision undertaking, which is a semantic categorization task, the word
must be categorized according to some semantic norm; whether it is a name of an animal. The
decisions now must be based on instigation at a semantic level. The results obtained indicate that
there was a significant margin between neutral and negative words, and negative and positive
words. Astonishingly, there was no difference between positive and neutral words. To observe
the connection between animal and non-animal words in each condition (either negative,
positive, or neutral), the data were evaluated based on the response time for both animal and non-
animal words in each situation distinctly.
Importantly, the ‘q’ button represented related words to animals, while the ‘p’ button
represented unrelated words to animals. The primary proportional effect of related animal words
to q was different between circumstances (t (63) = 2.56, p = .013), while the main effect of
related animal words to q was not different between situations (t <1). Therefore, the related word
q changes greatly as the proportion of an associated word increases. On the other hand, there is a

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decline in associated word q when the related word induces a negative emotion opposed to
animal words.
The connection between related (positive words) and unrelated (negative words) response
rates suggest that there can be a relation between the memory strength of each type of word. As a
result, a sophisticated interaction with the decision criterion is produced. Hence, the memory
strength for associated animal words could be elevated compared to the memory strength for
unassociated words. According to the results obtained, the neutral and positive words related to
animals had no difference (t <1), while the negative and positive words differed greatly (t (63) =
2.56, p = .013). This suggests that what is encoded is applicable for all associated words, and
because these words were studied and evaluated together, they were subjected to the same
decision criterion. This may lead to respondents having a decision criterion that is liberal for the
associated words, and conservative for the unlinked words.
Conclusion
The study examines the inclusive effect that various types of tasks have on the processing
of neutral, fear-related, and positive information. Due to this fact, the research uses a “lexical
decision task”, and an “animal categorization task” which is a type of “semantic categorization
task.” Additionally, the study evaluates whether individuals’ differences in anxiety and
happiness influences their processing in these tasks. Therefore, the main idea is to change the
form of processing individuals utilize, and assess whether it may alter how emotion is processed
with various stimuli under several situations. According to linear lines on the LD data, the
respondents were highly anxious in both SSTAI data than their happiness level in both the OHQ
data. In the AD data, the linear lines showed that for Neu-Neg vs. Anxiety” the connections
between the variables was strong compared to that of “Neu-Pos vs. Anxiety.”

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References
Briesemeister, B. B., Kuchinke, L., & Jacobs, A. M. (2012). Emotional valence: A bipolar
continuum or two independent dimensions?. SAGE Open, 2(4), 2158244012466558.
Curl, R. A. (2016). Characterizing the Relationship Between Related and Unrelated Items in
Recognition Memory.
Koch, A., Alves, H., Krüger, T., & Unkelbach, C. (2016). A general valence asymmetry in
similarity: Good is more alike than bad. Journal of Experimental Psychology: Learning,
Memory, and Cognition, 42(8), 1171.
Mindrila, D., & Balentyne, P. (2017). Scatterplots and correlation. Retrieved from
https://www.westga.edu/academics/research/vrc/assets/docs/scatterplots_and_correlation
_notes.pdf
Olszewska, J., & Ulatowska, J. (2013). Encoding strategy affects false recall and recognition:
Evidence from categorical study material. Advances in cognitive psychology, 9(1), 44.
Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013). Norms of valence, arousal, and
dominance for 13,915 English lemmas. Behavior research methods, 45(4), 1191-1207.