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.