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\begin{document}

\title{Choice overload interferes early processing and necessitates late
compensation: evidence from electroencephalogram}



\author[1]{Xinye Hu}%
\author[2]{Zong Meng}%
\author[1]{Qinghua He}%
\affil[1]{Southwest University}%
\affil[2]{Beijing Normal University}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
Having a multitude of choices can be advantageous, yet an abundance of
options can be detrimental to the decision-making process. Based on
existing research, the present study combined electroencephalogram and
self-reported methodologies to investigate the neural mechanisms
underlying the phenomenon of choice overload. Behavioral data suggested
that an increase in the number of options led to negative evaluations
and avoidance of choice tendencies, even in the absence of time
pressure. Event-related potential results indicated that the large
choice set interfered with the early visual process, as evidenced by the
small P1 amplitude, and failed to attract more attentional resources in
the early stage, as evidenced by the small amplitude of P2 and N2.
However, the LPC amplitude was increased in the late stage, suggesting
greater investment of attentional resources and higher emotional
arousal. Multivariate pattern analysis revealed that the difference
between small and large choice set began at around 120ms and the early
and late stages were characterized by opposite activation patterns. This
suggested that too many options interfered with early processing and
necessitate continued processing at a later stage. In summary, both
behavioral and ERP results confirm the choice overload effect, and it
was observed that individuals tend to subjectively exaggerate the choice
overload effect.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Advances in science and technology, combined with globalization, have
drastically changed the way humans conduct economic activities, giving
consumers a wide range of options (Lee \& Lee, 2004; Lurie, 2004) even
when making simple decisions such as what to eat for breakfast. While it
may not be consequential to make the wrong choice for a daily decision,
it is a different story when it comes to lifetime decisions such as
selecting a healthcare plan or making a retirement investment, where one
has to make the best choice from dozens or even hundreds of options
(Gourville \& Soman, 2005). It is thus essential that individuals, as
well as corporations, governments, and state agencies, make informed
decisions among the numerous options available to them, as the cost of
making the wrong decision or delaying it can be quite significant (Sagi
\& Friedland, 2007).

Theories in economics, marketing, and psychology suggest that having
more choices increases the sense of freedom and self-determination
(Warren \& Lupinek, 2019), and is more attractive to merchants as it can
satisfy a wider range of consumer preferences (Buturak \& Evren, 2017).
However, large choice sets can lead to an increase in the cost of
decision-making, such as the fear of mistakenly passing up the ideal
choice, making trade-offs, and being hesitant in decision-making (Diehl
\& Poynor, 2010; Lee et al., 2021; Reed et al., 2011). This can result
in decreased satisfaction with choices, alteration of the initial
choice, or even postponement of the choice, all of which are unfavorable
outcomes (Diehl \& Poynor, 2010; Wan et al., 2009). Evidence of this
phenomenon (i.e., choice overload) (D'Angelo \& Toma, 2017; Iyengar \&
Lepper, 2000).is seen in the financial investment field, where fewer
types of pension plans are offered and more employees are willing to
join pension plans (Soroya et al., 2021).

Research has attempted to identify the optimal number of options for
consumers, and it has been found that a choice set between 8-15 options
is preferred (Chernev et al., 2015; Sharma \& Nair, 2017). Large choice
sets may initially be appealing, however, if cognitive load is present,
the motivation to make a selection is reduced (Basili \& Vannucci,
2015). Studies conducted in the areas of charitable giving and travel
souvenirs indicated that choice overload did not occur (Lindkvist \&
Luke, 2022; Sthapit, 2018). A meta-analysis revealed that the effects of
choice overload are moderated by boundary conditions (Scheibehenne et
al., 2009; Spassova \& Isen, 2013) such as the difficulty of the
decision task (e.g. time pressure; (Basso et al., 2019)), the complexity
of the choice set (e.g. option attributes and dominant options;
(Townsend \& Kahn, 2014; Wan et al., 2009)), preference uncertainty
(e.g. expertise; (Hadar \& Sood, 2014)), individual differences (e.g.
trait anxiety, (Hu et al., 2023)), and the decision goal (e.g. decision
focus; (Wang \& Shukla, 2013)).

The cognitive load theory (Sweller, 1988) argued that the
decision-making process required a substantial amount of cognitive
resources. When the amount of cognitive resources required to make a
decision exceed the individual's available resources, they experience
cognitive overload. Compared to a small choice set, individuals must
process more information when presented with a larger choice set, which
can exceed their cognitive capacity (McShane \& Bockenholt, 2018). This
can lead to decreased satisfaction, increased negative experiences, and
even delays or abandonment of choices (Gerasimou, 2018; Hills et al.,
2013). Previous researchers have primarily evaluated cognitive load
through self-report methods, such as subjective evaluations of task
difficulty and negative emotions (Gerasimou \& Papi, 2018; Li, 2017;
Saltsman et al., 2019; Song et al., 2019; Sthapit et al., 2017; Turri \&
Watson, 2022). Current explanatory models of choice overload have been
largely derived from behavioral experiments or self-reports, with only
one study combining eye-tracking and functional magnetic resonance
imaging to investigate choice overload. This study found that the cost
of choosing from a large choice set can be reflected in the activity of
task-relevant areas of visual and sensorimotor processing, saccade
frequency, and average saccade amplitudes (Reutskaja et al., 2018).
Additionally, some researchers have used electroencephalogram (EEG) to
explore the effects of information overload on decision-making (Kim et
al., 2018). It has been found that information overload may impair the
decision-making process, as evidenced by a decrease in P2 and P3
amplitudes, as well as an increase in LPC. However, it is important to
note that choice overload is distinct from information overload, and it
is possible for consumers to experience choice overload without
experiencing information overload (Kuan et al., 2014; Wan et al., 2009).

This study utilizes EEG to investigate the neural foundations of choice
overload and to evaluate the cognitive load theory. As per the Cognitive
Load Theory, larger choice sets necessitate more attentional resources,
resulting in the choice overload effect. However, it is still unclear
when and how choice overload affects attentional engagement. Based on
prior research, the early component P1 reflects attention-based early
visual perceptual processing and allocation of attention to stimuli
(Grand et al., 2004; Mangun, 1995; Mangun \& Buck, 1998). The magnitude
of P2 is related to the allocation of attentional resources and the
ability to sustain perceptual processing (Ferreira-Santos et al., 2012;
Huang \& Luo, 2006; Mercado et al., 2006; Schupp et al., 2003). P3 is a
positive component related to the allocation of attentional resources
allocation (Folstein et al., 2008; Polich, 1987), and is also associated
with task difficulty and decision confidence (Nieuwenhuis et al., 2005;
Qin \& Han, 2009). LPC is a widely used physiological measure that
reflects emotional responses and the allocation of attentional resources
during cognitive processes (Alomari et al., 2015; Cuthbert et al., 2000;
Ferrari et al., 2011; Hajcak \& Foti, 2020). In summary, P1 and P2 were
selected as markers of attention during the initial processing stage,
while P3 was used to measure attention during the intermediate
processing stage. Additionally, LPC was employed as an indicator of
attention during the final processing stage.

We additionally conducted multivariate pattern analysis (MVPA) to
capture the differences in processing patterns between different choice
set sizes. This method has become increasingly popular in cognitive
neuroscience due to its increased sensitivity compared to traditional
univariate analysis (Carlson et al., 2019; Meng et al., 2023). By
combining MVPA analysis with event-related potential (ERP) analysis, we
can gain a more comprehensive understanding of the neural mechanisms of
choice overload and develop a more accurate model for it.

\textbf{Materials and methods}

\textbf{Participants}

A total of 30 right-handed individuals with no history of psychiatric or
neurological disorders and normal vision (or vision corrected to normal)
were recruited for the study. After providing written informed consent,
each participant was compensated with \selectlanguage{ngerman}¥75. Ultimately, 28 participants
(18 female, mean age = 21.6 years, \emph{SD} = 1.5 years) were included
in the sample, as two participants were excluded due to excessive EEG
artifacts. This study was approved by the local Human Ethics Committee
for Human Research, and it was conducted in accordance with the
guidelines of the Declaration of Helsinki and its subsequent amendments.

\textbf{Stimuli, apparatus, andprocedure}

Participant completed the experiment in a quiet and soundproof
laboratory, individually. They were asked to select pictures in four
different choice set sizes, and then fill out a choice overload
questionnaire to evaluate their experience (\textbf{Fig. 1A} ). The
experiment was managed by E-Prime 2.0 (Psychology Software Tools, Inc.,
Pittsburgh, PA), which controlled the presentation and timing of the
stimuli.

In the choice task, each trial consisted of three main stages: fixation
(1s), image presentation stage (10s + 0.5s mask) and response (3s)
(\textbf{Fig. 1B} ). During the image presentation stage, participants
were shown sets of natural landscape images and were asked to select the
one they preferred. The sets size varied, containing 4, 8, 12, or 16
clear images, while non-target images were mosaicked (\textbf{Fig. 1C
and D} ). The 10-second duration of the image presentation period was
determined based on a pilot experiment, allowing participants enough
time to make decisions without feeling rushed, and to make use of the
period for information processing. The mask was used to reduce the after
effect of image presentation. All pictures were obtained from the free
image material website (\emph{https://www.pexels.com/zh-cn/}) with a
total of 800 pictures. These images were standardized to 236 × 177
pixels. Each image was only presented once in the same condition. During
the response stage, participants used the mouse to select their
preferred image from the current selection set. Each condition contained
50 trials, resulting a total of 200 trials. The order of the four
conditions was counterbalanced across participants.

Upon the completion of each condition in the choice task, participants
were asked to rate the difficulty of selection on a 7-point scale,
together with their positive (including satisfaction with the final
choice, pleasure, and satisfaction with the selection process) and
negative emotions (including regret, hesitation, and frustration), as
well as their inclination to avoid making a choice (for example,
delaying or abandoning the choice). Additionally, they were prompted to
assess whether the choice sets contained the ''right amount of options''
on a 7-point scale.

\textbf{EEG recording and preprocessing}

EEG data was acquired using a standard 64-channel Ag/AgCl electrode cap
(Brain Products) in accordance with the extended international 10--20
system. Vertical and horizontal electrooculograms were simultaneously
recorded, with electrodes placed below the left eye and on the outer
canthus of the right eye. The EEG signals were referenced to the FCz
electrode and sampled at 1000 Hz, and were online filtered using a
0.1-100 Hz bandpass filter. The impedance of all electrodes was kept
below 10 k\selectlanguage{greek}Ω \selectlanguage{english}during the recording.

EEG data was preprocessed offline using EEGLAB (Delorme \& Makeig, 2004)
and custom scripts in MATLAB (MathWorks). A zero-phase high-pass filter
at 0.3 Hz was used to eliminate slow drifts, followed
by\emph{Zapline-plus} (Klug \& Kloosterman, 2022) and
\emph{CleanLine}(Mullen 2012) to remove line noise. Bad channels were
identified and removed by \emph{Clean\_rawdata} , and the data was
downsampled to 250 Hz to conserve computation time. Independent
component analysis was conducted with \emph{ICLabel} (Pion-Tonachini et
al., 2019) to recognize and eliminate components associated with eye
blink, vertical eye movement, muscle motor, and channel noise. The EEG
data was then segmented from -300 to 1500 ms with epochs locked on the
onset of the stimuli, and baseline correction was conducted using an
interval from -300 to 0 ms. Artifact rejection was used to mark and
exclude epochs with voltage values exceeding \selectlanguage{ngerman}± 100 \selectlanguage{greek}μ\selectlanguage{english}V at any time point.
Bad channels were interpolated, and the data was re-referenced to the
common average. Our preprocessing process was based on some recent
standardized EEG processing pipelines (Bailey et al., 2023; Monachino et
al., 2022; Pedroni et al., 2019).

Two participants had more than 40\% of their trials identified as
artifacts, and were thus excluded from further analysis. Of the 28
participants included, 2.57\% (\emph{SD} = 3.84\%) of the trials
contained artifacts, and 1.897 (\emph{SD} = 1.496) channels were deemed
bad.

\textbf{ERP analysis}

ERPLAB (Lopez-Calderon \& Luck, 2014) was utilized to calculate the
amplitudes of ERP components after averaging epochs for each condition
per participant. The occipital electrodes (Oz, O1, O2) were employed to
calculate the amplitudes of P1 and P2, the frontal electrodes (Fz, F1,
F2) to calculate the amplitudes of N2, and the parietal electrodes (Pz,
P1, P2) to calculate the amplitudes of P3 and LPC. The mean amplitudes
of P1, P3 and LPC were calculated over the respective intervals of
100-150 ms, 400-600 ms and 1000-1500 ms, while the mean amplitudes of P2
and N2 were calculated over the interval of 200-350 ms.

\textbf{Multivariate pattern analysis}

MVPA was applied to decode patterns of neural activity associated with
different numbers of choices. A backward decoding classification
algorithm (linear discriminant analysis) was used, with all electrodes
as features. To ensure reliability and interpretability of the results,
the choice set sizes of 4 and 8 were grouped together to create the
small choice set condition, and 12 and 16 were grouped together to
create the large choice set condition11Decoding was also conducted with
four distinct choice set sizes, with results presented in the
Supplementary Materials.. Before performing MVPA, the epochs were down
sampled to 50 Hz to minimize computation time. A 10-fold
cross-validation procedure was the applied using within-class and
between-class balancing with the Amsterdam Decoding and Modeling toolbox
(Fahrenfort et al., 2018). In this procedure, the trials were randomized
and divided into 10 equal-sized folds. Nine folds were used for
training, while the remaining fold was used for testing. This process
was repeated 10 times, ensuring that each fold served as the test set
once. To ensure the impartiality of the classifier training, we
implemented within-class balancing by undersampling. This procedure
involved the random selection of trials from conditions with a surplus
of trials to harmonize the conditions with fewer trials, thereby
equalizing the count of trials within each class. Additionally,
between-class balancing using undersampling was employed to mitigate the
potential of the classifier from developing a bias towards the
overrepresented class during training, as unbalanced designs can often
result in asymmetrical trial counts. The performance of the classifier
was assessed using the area under the curve (AUC) (Hand \& Till, 2001).

Temporal generalization analysis was conducted to assess the stability
of a representation across different time points. A classifier trained
on a specific time point was tested on all other time points (King \&
Dehaene, 2014). The resulting temporal generalization matrix was used to
identify periods of stability. Additionally, the product of the
classifier weights and the data covariance matrix was calculated and
spatially normalized for each participant to obtain the activation
patterns (Haufe et al., 2014). Cluster-based nonparametric statistical
tests (two-tailed cluster-permutation, alpha \emph{p}\textless{} 0.05,
cluster alpha \emph{p} \textless{} 0.05, N permutations = 5000) were
used to evaluate the MVPA results using FieldTrip (Oostenveld et al.,
2011).

\textbf{Results}

\textbf{Manipulation Check}

One-way repeated measures analysis of variance (ANOVA) showed that
choice set size had a significant effect on the perception of the number
of options (\emph{F} (3,81) = 27.8, \emph{p} \textless{} 0.001,\selectlanguage{greek}\emph{η}
\selectlanguage{english}\textsuperscript{2} = 0.507, \textbf{Fig 2A} ). Post hoc tests suggested
that the differences between the four different sizes of choice sets
were all statistically significant, indicating that the manipulation of
choice set size was successful (\emph{ps} \textless{} 0.025).

\textbf{Behavioral results}

As hypothesized, choice set size had a significant effect on the
perceived difficulty of choosing (\emph{F} (3,81) = 11.187,
\emph{p}\textless{} 0.001, \selectlanguage{greek}\emph{η} \selectlanguage{english}\textsuperscript{2} = 0.293). Post
hoc tests revealed that participants experienced greater difficulty in
selecting from 8, 12, and 16 options compared to 4 options (\emph{t}
(27) = -3.689,\emph{p} = 0.001, \emph{d} = -0.491; \emph{t} (27) =
-5.366, \emph{p}\textless{} 0.001, \emph{d} = -0.714; \emph{t} (27) =
-4.528, \emph{p}\textless{} 0.001, \emph{d} = -0.602). There were no
significant differences observed in any other condition
(\emph{ps}\textgreater{}0.584, \textbf{Fig. 2A} ). The results showed
that the size of the choice set had a significant effect on the tendency
to avoid certain behaviors (\emph{F} (3,81) = 6.136, \emph{p}
\textless{} 0.001,\selectlanguage{greek}\emph{η} \selectlanguage{english}\textsuperscript{2} = 0.185). Post hoc tests
showed that participants were more likely to avoid making a choice when
choosing from 8, 12, and 16 options compared to 4 options (\emph{t} (27)
= -2.162,\emph{p} = 0.023, \emph{d} = -0.268; \emph{t} (27) = -3.047,
\emph{p} = 0.019, \emph{d} = -0.377; \emph{t} (27) = -4.128, \emph{p}
\textless{} 0.001, \emph{d} = -0.511),with no significant differences in
any other condition (\emph{ps} \textgreater{}0.118, \textbf{Fig. 2A} ).

As for the emotional experience of choice, the choice set size had a
significant effect on both positive and negative emotional experiences
(\emph{F} (3,81) = 3.695, \emph{p} = 0.015, \selectlanguage{greek}\emph{η} \selectlanguage{english}\textsuperscript{2}
= 0.120; \emph{F} (3,81) = 11.7, \emph{p} \textless{} 0.001,\selectlanguage{greek}\emph{η}
\selectlanguage{english}\textsuperscript{2} = 0.302). Fewer positive experiences (\emph{t} (27)
= 1.889, \emph{p} = 0.007, \emph{d} = 0.348; \emph{t} (27) = 2.334,
\emph{p} = 0.012, \emph{d} = 0.430; \emph{t} (27) = 3.223,\emph{p} =
0.011, \emph{d} = 0.594) and more negative experiences (\emph{t} (27) =
-4.858, \emph{p} \textless{} 0.001, \emph{d} = -0.682;\emph{t} (27) =
-4.501, \emph{p} \textless{} 0.001, \emph{d} = -0.632;\emph{t} (27) =
-5.072, \emph{p} \textless{} 0.001, \emph{d} = -0.712) were observed
when participants chose from 8, 12, and 16 options compared to 4
options, with no significant differences in any of the other conditions
(\emph{ps} \textgreater{} 0.290, \emph{ps}\textgreater{} 0.558,
\textbf{Fig. 2A} ).

\textbf{ERP results}

One-way repeated measures ANOVA results showed that choice set size
significantly affected the amplitudes of P1 (\emph{F} (3, 81) =
4.985,\emph{p} = 0.003, \selectlanguage{greek}\emph{η} \selectlanguage{english}\textsuperscript{2} = 0.156,
\textbf{Fig 2B} ,\textbf{3C, and 3D} ). Post hoc tests revealed that
when the number of images was reduced to 4 (\emph{t} (27) = 2.506,
\emph{p} = 0.040,\emph{d} = 0.258; \emph{t} (27) = 3.336, \emph{p} =
0.021, \emph{d} = 0.344) or 8 (\emph{t} (27) = 1.947, \emph{p} = 0.007,
\emph{d} = 0.201;\emph{t} (27) = 2.777, \emph{p} \textless{} 0.001,
\emph{d} = 0.286), the P1 amplitudes were significantly higher than when
there were 12 or 16 images . No other significant differences were
observed (\emph{p} s \textgreater{} 0.396).

The analysis of P2 amplitudes showed that choice set size significantly
affected the amplitudes of P2 (\emph{F} (3,81) = 8.676,
\emph{p}\textless{} 0.001, \selectlanguage{greek}\emph{η} \selectlanguage{english}\textsuperscript{2} = 0.243,
\textbf{Fig 2B} ,\textbf{3C and 3D} ). Post hoc tests revealed that when
the number of images was reduced to 4 (\emph{t} (27) = 3.702, \emph{p} =
0.002,\emph{d} = 0.499; \emph{t} (27) = 4.758, \emph{p} \textless{}
0.001,\emph{d} = 0.642) or 8 (\emph{t} (27) = 2.777, \emph{p} = 0.041,
\emph{d}= 0.375), the P2 amplitudes were significantly higher than when
there were 12 or 16 images . No other significant differences were
observed (\emph{p} s \textgreater{} 0.396).

For the N2 component, the ANOVA results indicated that choice set size
significantly affected the amplitudes of N2 (\emph{F} (3,81) =
8.466,\emph{p} \textless{} 0.001, \selectlanguage{greek}\emph{η} \selectlanguage{english}\textsuperscript{2} =
0.239,\textbf{Fig 2B} , \textbf{3A, and 3D} ). Post hoc tests revealed
that when the number of images was reduced to 4 (\emph{t} (27) = -2.997,
\emph{p} = 0.022, \emph{d} = -0.412; \emph{t} (27) = -4.733, \emph{p}
\textless{} 0.001, \emph{d} = -0.651) or 8 (\emph{t} (27) = -3.438,
\emph{p} = 0.006,\emph{d} = -0.473), the N2 amplitudes were
significantly higher than when there were 12 or 16 images. No other
significant differences were observed (\emph{ps} \textgreater{} 0.067).

As for LPC, the results suggested that choice set size significantly
affected the amplitudes of LPC (\emph{F} (3,81) = 4.087, \emph{p} =
0.009, \selectlanguage{greek}\emph{η} \selectlanguage{english}\textsuperscript{2} = 0.131, \textbf{Fig 2B, 3B, and 3D}
). Post hoc tests revealed that when the number of images was reduced to
4 (\emph{t} (27) = -2.536, \emph{p} = 0.013, \emph{d} = -0.434) or 8
(\emph{t} (27) = -3.341, \emph{p} = 0.002, \emph{d} = -0.572), the LPC
amplitudes were significantly higher than when there were 16 images. No
other significant differences were observed (\emph{p} s \textgreater{}
0.058). In addition, the size of the choice set did not have a
significant effect on the amplitude of P3 (\emph{F} (3,81) =
1.449,\emph{p} = 0.235, \textbf{Fig 2B and 3B} ).

\textbf{MVPA results}

MVPA analyses showed that neural activity between the small and large
choice sets began to differ 128 ms after stimulus onset and persisted
until 1500 ms (\emph{p} \textless{} 0.001, cluster-based
correction;\textbf{Fig. 4A} ). Temporal generalization matrices
indicated that neural activity between the small and large choice sets
during these time periods (training time = {[}128,1488{]}, testing time
= {[}108, 1488{]}, \emph{p} \textless{} 0.001; \textbf{Fig. 4B} ) could
be predicted from one another. As depicted in the activation maps
in\textbf{Fig. 4C} , the posterior electrodes exhibited activation
(\emph{p} \textless{} 0.001, cluster-based corrected) while the anterior
electrodes showed inhibition (\emph{p} \textless{} 0.001, cluster-based
corrected) in the early stage. However, in the late stage, the posterior
electrodes were inhibited (\emph{p} \textless{} 0.001, cluster-based
corrected) and the anterior electrodes were activated
(\emph{p}\textless{} 0.001, cluster-based corrected). No significant
cluster of electrodes was observed during the middle stage. Furthermore,
the decoding outcomes for four different choice set sizes were
illustrated in \textbf{Supplemental Figure 1} . The significant time
clusters between the two adjacent choice sets (e.g., between 4 and 8)
were relatively small; however, as the number of choices that differ
between them increases, the significant time clusters become more
expansive.

\textbf{Discussion}

This study used both self-report and EEG techniques to examine the
impact of the number of options on choice overload. The findings showed
that increasing the number of choices led to an increased sense of
choice difficulty, which in turn caused choice overload. Specifically,
choosing from a large range of options was associated with a more
negative evaluation of the decision-making process and an increase in
avoidance behavior in comparison to a smaller set of options. ERP
results revealed that selecting from a larger set of choices resulted in
smaller amplitudes of P1 and P2, and a larger amplitude of N2 and LPC,
compared to a smaller set. Moreover, MVPA results indicated that there
were significant differences in neural activity between large and small
choice sets from 128ms to 1500ms. These results will be discussed in
more detail below.

ERP results demonstrated the neurological and cognitive processes
associated with option evaluation and selection. Compared to the smaller
selection set, the amplitudes of P1 and P2 decreased when selecting from
the larger selection set. P1 is associated with early visual processing
and attention allocation, with its amplitude being enhanced when
attention is directed towards a stimulus (Hillyard \& Anllo-Vento, 1998;
Munneke et al., 2008). P2 is related to early automatic attention
allocation and visual processing (Jing et al., 2019). An increased P2
amplitude is observed when a visual stimulus requires a higher level of
attentional focus (Handy et al., 2010; Mercado et al., 2006). Previous
research has also found that extensive information processing leads to a
lower P2 component (Peng et al., 2021). This suggests that choice
overload impairs early processing by decreasing the amplitudes of P1 and
P2, as individuals must allocate their attentional resources to
cognitively process the stimuli and identify their needs while
attempting to minimize cognitive effort.

It has been observed that when presented with a small choice set,
individuals tend to allocate more attention to the target options in a
particular area of the screen. This is in contrast to the large choice
set, where fewer cognitive resources are invested. This is due to the
fact that complex decisions often require the use of heuristic
strategies in order to make decisions quickly (Besedes et al., 2012).
Moreover, when faced with a limited number of options, individuals are
more likely to employ compensatory strategies in order to thoroughly
evaluate the options, thus investing more attentional resources (Besedes
et al., 2012; Gerasimou \& Papi, 2018). The increased number of choices
leads to increased uncertainty and the risk of missing out on the best
option. This is reflected in the increased N2 amplitude in the anterior
cingulate cortex (ACC) and frontal regions of the brain (Hedgcock et
al., 2012; Ma et al., 2010). Additionally, differences in the color,
shape, and spatial location of the stimulus can also lead to changes in
N2 amplitude (Cui et al., 2000; Tian et al., 2001). Furthermore,
research in the field of risky decision-making has revealed that N2 is
sensitive to risky information (Wang et al., 2016). Studies related to
choice overload have also found that individuals who selected from
larger choice sets exhibited cardiovascular responses consistent with
high levels of stress (Saltsman et al., 2019). Therefore, it can be
concluded that choice overload interferes with early processing and
leads to increased cognitive conflict.

It has been suggested that individuals must invest more attentional
resources when faced with a larger choice set. However, having more
options can also increase the complexity of decision making and reduce
an individual's confidence in their decision-making ability. Research
has shown that P3 amplitude is directly correlated with the amount of
attentional resources allocated (Folstein et al., 2008) and inversely
proportional to the difficulty of decision-making, with a lack of
confidence resulting in a smaller P3 (Polich, 1987; Qin \& Han, 2009).
Furthermore, studies have indicated that lower perceived load triggers
larger peaks in P3 amplitude (Barnhardt et al., 2008). This discrepancy
may explain the absence of a significant difference in P3.

At the late processing stage, once sufficient information has been
gathered, individuals must evaluate the various options and potential
outcomes. Late information processing requires more attentional
resources due to the lack of initial processing of the large choice set.
Thus, individuals assess and analyze options based on their personal
needs and the external environment, which leads to increased attentional
resource allocation(Zhao et al., 2015). Moreover, individuals tend to
experience emotional arousal, such as regret, when selecting from a
larger set of options due to counterfactual thinking. This is evidenced
by the larger amplitude of the late positive component (LPC) induced by
the large choice set compared to the small choice set (Fields, 2023;
Hajcak et al., 2006). The LPC amplitude is linked to the allocation of
attentional resources and the level of emotional arousal (Hajcak \&
Foti, 2020; Hajcak \& Nieuwenhuis, 2006). Hence, the extensive selection
necessitates continuous and prolonged attention, but it also leads to
more negative emotional experiences.

Analyses via MVPA revealed that neural activity varied between small and
large choice sets from 128ms to 1500ms post-stimulus onset. Temporal
generalization analysis illustrated persistent predictive dynamics
during the early, middle, and late stages, suggesting prolonged
attention engagement. Activation pattern maps indicated that the
contrast between the two choice sets depended on activation in the
posterior electrode over the anterior electrode during the early stage.
In contrast, the late stage displayed an activation pattern opposite to
that of the early stage. These findings were partially confirmed by the
ERP results, which showed a decrease in the amplitude of the early
attention process and an increase in the amplitude of the late attention
process in the large choice set condition in comparison to the small
choice set condition. This implies the presence of two opposed processes
during the early and late stages. Interestingly, P3 amplitude remained
unchanged between the two choice set sizes in the middle phase. MVPA
successfully distinguished the processing patterns between the choice
set sizes with a precision greater than random chance. The activation
topography during the middle stage did not demonstrate significant
clustering of electrodes, demonstrating that the capacity to
differentiate between large and small choice sets during this phase is
not reliant on specific electrodes, but instead involves a complex
spatial processing pattern. The processing in the middle stage likely
involves a complex transition from the early to the late stage.

This study utilized EEG and self-report methods to investigate and
refine the cognitive overload theory of choice overload. It was found
that choice overload occurs during the information processing phase, not
at the time of making the ultimate decision. This finding is consistent
with past researches (Lee \& Lee, 2004; Reutskaja et al., 2018). Without
the pressure of time, individuals take the time to assess all of their
options before making a decision, and decision time was not affected by
the size of the choice set after a full evaluation of the options.
Self-reported data showed that even without time pressure, a significant
number of choices still led to negative emotions and an inclination to
avoid them. It was further noted that in large choice sets, late
sustained attentional processing compensates for the diminished
allocation of early attentional resources. Additionally, it was found
that people tend to overestimate the effects of choice overload, and
there is a lack of consistency between subjective reports of decision
difficulty and choice experiences and objective EEG metrics. The
findings provided insight into the neurological mechanisms of choice
overload, which may help people gain a greater understanding of the
choice overload effect and then apply strategies to make better
decisions. However, this study had limitations, such as using only
landscape pictures as stimulus materials., which are not as complex as
the attributes of objects people choose in real-life scenarios.
Therefore, future studies should consider using more elaborate options
to simulate realistic choice situations.

\textbf{Conclusion}

Numerous studies have explored the mechanisms of selection overload and
how to address it. It has been argued that time pressure and incomplete
information processing are contributors to choice overload.
Nevertheless, our study found that individuals experience choice
overload even when given sufficient time to carefully process the
options. People initially evaluate options in the early processing
stage, form criteria based on the options and their needs, and use these
criteria to process the options in depth before ultimately making a
choice.

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\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Fig 1.} The experimental flowchart. Participants were asked to
complete a survey evaluating their experience after completing the
choice task. Each trial began with a 1 second fixation period, followed
by a 10 second image presentation stage. Participants were then required
to select their preferred image and respond with a mouse after a 0.5
second mask. The four choice set conditions varied in the number of
clear images, with 4, 8, 12, and 16 clear images in the respective
conditions, and the remainder being mosaic treatment. Examples of clear
and mosaic pictures are shown in panel D.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Fig 2.} The number of choice sets had an effect on both
behavioral measures (A) as well as the mean amplitude of ERP components
(B). Error bars represented the standard error of the mean (SEM) across
participants. *\emph{p}\textless{} 0.05, **\emph{p} \textless{} 0.01,
***\emph{p} \textless{} 0.001.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Fig 3.} ERP waveform and topographic maps. Pannel A, B and C
showed the ERP waveform maps on frontal (Fz, F1, F2), parietal (Pz, P1,
P2) and occipital (Oz, O1, O2) electrodes, respectively. Pannel D
indicated the topographic maps of the P1, P2, N2 and LPC.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Fig 4.} Results of the time-resolved decoding were displayed,
with the small and large number of choices shown in (A) with shading
indicating the standard error of the mean across participants at each
time point. The temporal generalization matrices (B) and activation maps
of the decoding result at the early, middle, and late stages (C) were
also presented. Clusters that were significant in the cluster-based
nonparametric test (p \textless{} 0.001) were highlighted with a bold
line in (A), enclosed area in (B), and asterisk-marked electrodes in
(C).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

