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.