You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Deciding Not to Decide: Computational and Neural Evidence for Hidden Behavior in Sequential Choice}
Article:
Abstract:
Understanding the cognitive and neural processes that underlie human decision making requires the successful prediction of how, but also of when, people choose. Sequential sampling models( SSMs) have greatly advanced the decision sciences by assuming decisions to emerge from a bounded evidence accumulation process so that response times( RTs) become predictable. Here, we demonstrate a difficulty of SSMs that occurs when people are not forced to respond at once but are allowed to sample information sequentially: The decision maker might decide to delay the choice and terminate the accumulation process temporarily, a scenario not accounted for by the standard SSM approach. We developed several SSMs for predicting RTs from two independent samples of an electroencephalography( EEG) and a functional magnetic resonance imaging( fMRI) study. In these studies, participants bought or rejected fictitious stocks based on sequentially presented cues and were free to respond at any time. Standard SSM implementations did not describe RT distributions adequately. However, by adding a mechanism for postponing decisions to the model we obtained an accurate fit to the data. Time-frequency analysis of EEG data revealed alternating states of de- and increasing oscillatory power in beta-band frequencies( 14–30 Hz), indicating that responses were repeatedly prepared and inhibited and thus lending further support for the existence of a decision not to decide. Finally, the extended model accounted for the results of an adapted version of our paradigm in which participants had to press a button for sampling more information. Our results show how computational modeling of decisions and RTs support a deeper understanding of the hidden dynamics in cognition.
Introduction:
Many decisions are not triggered by a single event but based on multiple sources of information. When purchasing a new computer, for instance, we certainly look at the price, but not without accounting for further aspects like quality and appearance. Usually, these multi-attribute decisions evolve sequentially, that is, as long as the collected evidence is insufficient to motivate a particular choice we search for more information to resolve our uncertainty. Importantly, such “decisions not to decide” are not directly observable but can promote significant changes in behavior( like consulting a salesman or leaving the shop). The temporal emergence of decisions is well captured by sequential sampling models( SSMs), a mathematical approach that allows making inferences on both, how and when people decide[1]–[3]. The core structure of an SSM consists of an evidence accumulation process that proceeds until an internal criterion( a decision threshold) is met and a specific response is elicited. SSMs have a long tradition in research on perceptual decision making[4]–[6], but they also predict accuracy and response times( RTs) of preferential choices[7]–[10]. They are used to model rapid[11],[12] as well as slow decisions, which may last up to several seconds[13],[14]. However, even though the assumption of a time-consuming accumulation process implies that decisions are delayed until a threshold has been reached, an explicit decision not to decide is typically not considered by SSMs. Using functional magnetic resonance imaging( fMRI) and electroencephalography( EEG) in two recent studies[15],[16], we investigated the emergence of value-based decisions in the human brain. Thereto, participants performed a stock-buying paradigm( Figure 1A), in which they sampled probabilistic information( stock ratings) about stock offers in a sequential manner and were free to either buy or reject the offers at any time. We developed an SSM variant that successfully predicted which choice is made but also how many ratings are sampled. Critically, beyond the prediction of the number of sampled ratings, the model did not specify the exact timing of the decisions, that is, when exactly during the presentation of a single rating the response was made. Here, we argue that the standard SSM approach fails to account for the RT pattern in this task( Figure 1B) and presumably in many other sequential choice paradigms, as long as it does not assume the existence of a decision not to decide that can inhibit the accumulation process temporarily. We support the computational results by evidence from time-frequency analyses of the EEG data showing that the decision not to decide is accompanied by an increase in oscillatory power in the beta band( 14–30 Hz), a well-established neural marker of the active inhibition of motor responses[17]–[20]. Finally, we tested our model experimentally in the context of an adapted version of our task in which the decision to sample more information is made explicit.
Discussion:
In the present study, we developed a computational model that allows making inferences on response times for a sequential choice paradigm, in which people are free to collect multiple pieces of information before they decide. The model comparison demonstrated the superiority of assuming that people actively terminate the decision process to wait for more information in this task. The time-frequency EEG analysis revealed alternating states of decreases and increases in oscillatory beta-band power supporting the view that responses are considered but then actively inhibited if the current information is insufficient to motivate a choice. The difficulty of the standard SSM approaches in predicting the RT distribution originated from the repeated declines in choice frequency at the end of every rating presentation. In other words, the RT distribution within a rating resembled the RT pattern commonly found in non-sequential tasks but the model without a decision not to decide( M1standard) and the conventional random walk alternatives( M2walk, M2leak) were unable to capture the distribution over multiple ratings as a whole. An alternative solution could be to first assign the probabilities of choosing at each rating( e. g., on the basis of our model proposed in[15]) and then to model how these probabilities are distributed within ratings. However, such an approach appears psychologically implausible as it would require people to specify their choice probabilities at the beginning of each rating. But the likelihood of a choice is not simply set after 100 or 200 ms; it emerges over time, and this is what SSMs aim to model. As stated, our SSM adaptation is just one out of many possible solutions for sequential choice tasks. However, the simulation results demonstrate that our conclusions are not restricted to the particular implementation that we chose, which is in line with the notion that different SSM approaches lead to equivalent predictions under many circumstances[3],[28]. Among the four random walk models we tested, only M2stop provided an acceptable fit to the observed RT distributions. Even though this model does not include a separate accumulator for the decision not to decide, the model's concept that the influence of evidence on the DV is temporally restricted is very similar to the idea that the choice process is terminated at some point( and does not naturally follow from a standard random walk or diffusion model). In contrast to M1*evidence, however, M2stop does not answer the question of why the drift rate changes during each rating. Accordingly, for tasks in which the decision to sample more information is explicitly made by the participant( as in our additional behavioral study), M2stop does not provide a prediction whether and when a new rating will be sampled. In contrast, M1*evidence is applicable to both scenarios and makes clear predictions that were consistent with the observed behavior. Moreover, only the accumulator models correctly predicted decisions that were made after switches in evidence. Finally, the higher mathematical feasibility of our approach, which offers a closed-form solution to modeling RT distributions in sequential choice paradigms, is a strong advantage. Under which circumstances is it necessary to extend an SSM by including a decision not to decide? Certainly, the majority of( non-sequential) decision-making paradigms with a continual input of information can be successfully modeled without this component. However, in many decision problems faced in everyday life, people do not receive information automatically but actively and explicitly search for information. This implies that people have to decide whether the information acquired so far is sufficient to reach a decision or whether additional information should be searched for. Accordingly, there are many experiments using sequential delivery of information, particularly in the emerging field of decision neuroscience[14]–[16],[26],[27],[29]–[34]( methods such as fMRI and EEG also require well-controlled stimulus presentations). Accurate computational modeling of the decision mechanisms in sequential choice is therefore highly desirable. In addition, there is a substantial body of research on choice deferral[35],[36] where decision makers are explicitly provided with the option to refuse a choice( similar to our behavioral study). Here, the decision not to decide is relevant as it is defined as an explicit option in the decision process. Interestingly, the assumption of a decision not to decide seems to improve the model fit in the fMRI data set even more than in the EEG data set( see Table S1 in Text S1). This could be related to the longer presentation times of ratings( 2–4 s instead of 1. 5 s), such that the periods are prolonged, in which participants terminate the choice process to wait for more information. Future studies should identify the conditions, under which a decision not to decide becomes particularly relevant to account for RT distributions in sequential choice. We propose that factors like the number of cues( and its variability) and the presentation time of cues( and its variability) influence for how long people postpone their decision process. In a previous paper that was based on the same EEG data set[16], we looked at event-related potentials( ERPs) in the motor system, such as the readiness potential, to investigate the emergence of value-based decision in the human brain. The ERP analyses already pointed to the existence of a decision not to decide, insofar as the readiness potential declined after 700 ms within every rating. However, this effect provides only indirect evidence as compared to the increase in beta-band power, which has been directly linked to active response inhibition[17]–[20]. Furthermore, unlike the latency of de- and increases in the beta-band( Figure 8), we did not find any relationship between the latency of the readiness potential and intra- or inter-individual variability of the decision not to decide. A probable reason for this difference is that in contrast to ERPs, induced oscillatory signals do not have to be phase-locked to the onset of stimulus events so that they are more likely to reveal the existence and the temporal extent of endogenous brain activity[37]. In line with this, the alternating pattern in the beta-band could not be observed in the phase-locked time-frequency analysis. Finally, it should be noted that our conclusion about the existence of a decision not to decide does not rest upon reverse inference[38]: Instead of first finding the beta synchronization and then speculating about inhibition processes, we first established computational evidence for a hidden choice mechanism and then hypothesized( and corroborated) that this mechanism is reflected in a specific oscillatory EEG pattern. Our findings demonstrate that a thorough modeling of behavioral data can reveal hidden dynamics in cognition that would remain hidden if only observable decisions are taken into account or if models are fitted against too coarsely averaged data[39]. Whereas comparing the SSM variants only on the basis of how many ratings are sampled failed to reveal the necessity of assuming a decision not to decide, the comparison based on RT distributions was decisive. Moreover, we were able to specify the decision not to decide in more detail by showing that people are faster in reaching this decision as long as evidence for either choice option is low. Our study further advocates combining computational modeling with brain imaging techniques, as both methodologies attempt to look into the “black box” of the human mind. In this context, the temporal precision of EEG nicely dovetailed with the fine-grained RT modeling allowing us to discover a decision that would otherwise not have been observable.
When decisions are made under uncertainty , we often decide not to choose immediately but to search for more information that reduces the uncertainty . In most psychological experiments , however , participants are forced to choose at once and cognitive models do not account for the possibility of deliberately delaying decisions . By modeling RT distributions in a sequential choice paradigm , we demonstrate that people decide not to decide when given the opportunity to sample more information . Importantly , this explicit decision to wait is distinguishable from an implicit delay in ongoing decisions as it actively inhibits this ongoing process . We then looked at EEG spectral power in the beta-band ( frequencies from 14 to 30 Hz ) , which is known to reflect both the preparation and inhibition of responses . The obtained pattern is consistent with our proposal that participants repeatedly alternated between considering and postponing their decision in the sequential task . In an additional behavioral experiment , we show that our model also predicts RTs of the decision to sample more information . Hence , our combination of cognitive modeling and EEG provides converging evidence for the existence of a decision that is usually not directly observable .