Irrational time allocation in decision-making

Oud, Bastiaan; Krajbich, Ian; Miller, Kevin J.; Cheong, Jin Hyun; Botvinick, Matthew; Fehr, Ernst

doi:10.1098/rspb.2015.1439

Cited by 48 publications

(51 citation statements)

References 72 publications

(80 reference statements)

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“…According to this framework, decision difficulty, and in turn response time (RT), is proportional 48 to the relative difference in the evidence supporting each option, consistent with results from 49 perceptual (Ditterich et al, 2003) and value-based (Oud et al, 2016) decisions. 50…”

Section: Introduction 41supporting

confidence: 55%

Reward certainty and preference bias selectively shape voluntary decisions

Zajkowski

Krzemiński

Barone

et al. 2019

Preprint

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19Choosing between equally valued options can be a conundrum, for which classical decision 20 theories predicted a prolonged response time (RT). Paradoxically, a rational decision-maker 21 would need no deliberative thinking in this scenario, as outcomes of alternatives are indifferent. 22How individuals choose between equal options remain unclear. Here, we characterized the 23 neurocognitive processes underlying such voluntary decisions, by integrating advanced cognitive 24 modelling and EEG recording in a probabilistic reward task, in which human participants chose 25 between pairs of cues associated with identical reward probabilities at different levels. We 26 showed that higher reward certainty accelerated RT. At each certainty level, participants 27 preferred to choose one cue faster and more frequently over the other. The behavioral effects on 28 RT persisted in simple reactions to reward cues. By using hierarchical Bayesian parameter 29 estimation for an accumulator model, we showed that the certainty and preference effects were 30 independently associated with the rate of evidence accumulation during decisions, but not with 31 visual encoding or motor execution latencies. Time-resolved multivariate pattern classification of 32 EEG evoked response identified significant representations of reward certainty and preference 33 choices as early as 120 ms after stimulus onset, with spatial relevance patterns maximal in 34 middle central and parietal electrodes. Furthermore, EEG-informed computational modelling 35 showed that the rate of change between N100 and P300 event-related potentials reflected 36 changes in the model-derived rate of evidence accumulation on a trial-by-trial basis. Our findings 37 suggested that reward certainty and preference collectively shaped voluntary decisions between 38 equal options, providing a mechanism to prevent indecision or random behavior. 39Keywords: decision making, reward certainty, preference bias, EEG, cognitive modelling 40 suggesting a possible bias between equal options, rendering some options more preferred than 63 others. 64Previous research on equal choices raises three further unresolved issues. First, it is unclear how 65 reward certainty and preference bias impact on different sub-components of the decision-making 66 process. Second, functional imaging studies have localized the mesocorticolimbic dopaminergic 67 network to be involved in both reward certainty and preference processing (Tobler et al., 2007; 68 Abler et al., 2009). Less is known about how macroscopic brain activities relating to these effects 69 unfold in time. Third, conventional equal-choice paradigms commonly use subjective ratings to 70 quantify and equate values of options. This design has been shown to be vulnerable to value 71 fluctuations (Chen and Risen, 2010; Izuma and Murayama, 2013) that originate from voluntary 72 choices themselves (Festinger, 1957;Bem, 1967). 73Here, we addressed these questions by combining advanced computational modelling and EEG 74 in a probabilistic reward t...

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Section: Introduction 41supporting

confidence: 55%

Reward certainty and preference bias selectively shape voluntary decisions

Zajkowski

Krzemiński

Barone

et al. 2019

Preprint

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“…While we were writing this paper, another group of researchers, independently, reported their results on two experiments similar to what we reported here Oud et al (2016). Specifically, in study 2 in that paper, participants performed a perceptual decision making task in which the hard and easy trials were presented randomly in blocks with fixed duration.…”

Section: Allocation Of Limited Time To Decisions With Different Outcomementioning

confidence: 55%

Learning to allocate limited time to decisions with different expected outcomes

Khodadadi

Fakhari

Busemeyer

2017

Cognitive Psychology

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The goal of this article is to investigate how human participants allocate their limited time to decisions with different properties. We report the results of two behavioral experiments. In each trial of the experiments, the participant must accumulate noisy information to make a decision. The participants received positive and negative rewards for their correct and incorrect decisions, respectively. The stimulus was designed such that decisions based on more accumulated information were more accurate but took longer. Therefore, the total outcome that a participant could achieve during the limited experiments' time depended on her "decision threshold", the amount of information she needed to make a decision. In the first experiment, two types of trials were intermixed randomly: hard and easy. Crucially, the hard trials were associated with smaller positive and negative rewards than the easy trials. A cue presented at the beginning of each trial would indicate the type of the upcoming trial. The optimal strategy was to adopt a small decision threshold for hard trials. The results showed that several of the participants did not learn this simple strategy. We then investigated how the participants adjusted their decision threshold based on the feedback they received in each trial. To this end, we developed and compared 10 computational models for adjusting the decision threshold. The models differ in their assumptions on the shape of the decision thresholds and the way the feedback is used to adjust the decision thresholds. The results of Bayesian model comparison showed that a model with time-varying thresholds whose parameters are updated by a reinforcement learning algorithm is the most likely model. In the second experiment, the cues were not presented. We showed that the optimal strategy is to use a single time-decreasing decision threshold for all trials. The results of the computational modeling showed that the participants did not use this optimal strategy. Instead, they attempted to detect the difficulty of the trial first and then set their decision threshold accordingly.

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“…Previous research has identified a number of parallels between behavioral and neural patterns evoked by perceptual and value-based decisions [54][55][56] . Both have been well described by similar classes of evidence accumulation models 8,57,58 .…”

Section: Discussionmentioning

confidence: 99%

Dissociable neural mechanisms track evidence accumulation for selection of attention versus action

Shenhav

Straccia

Cohen

et al. 2017

Preprint

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Decision-making is typically studied as a sequential process from the selection of what to attend (e.g., between possible tasks, stimuli, or stimulus attributes) to the selection of which actions to take based on the attended information. However, people often gather information across these levels in parallel. For instance, even as they choose their actions, they may continue to evaluate how much to attend other tasks or dimensions of information within a task. We scanned participants while they made such parallel evaluations, simultaneously weighing how much to attend two dynamic stimulus attributes and which response to give based on the attended information. Regions of prefrontal cortex tracked information about the stimulus attributes in dissociable ways, related to either the predicted reward (ventromedial prefrontal cortex) or the degree to which that attribute was being attended (dorsal anterior cingulate, dACC). Within dACC, adjacent regions tracked uncertainty at different levels of the decision, regarding what to attend versus how to respond. These findings bridge research on perceptual and value-based decision-making, demonstrating that people dynamically integrate information in parallel across different levels of decision making.Naturalistic decisions allow an individual to weigh their options within a particular task (e.g., how best to word the introduction to a paper) while also weighing how much to attend other tasks (e.g., responding to e-mails). These different types of decision-making have a hierarchical but reciprocal relationship: Decisions at higher levels inform the focus of attention at lower levels (e.g., whether to select between citations or email addresses) while, at the same time, information at lower levels (e.g., the salience of an incoming email) informs decisions regarding which task to attend. Critically, recent studies suggest that decisions across these levels may occur in parallel, continuously informed by information that is integrated from the environment and from one’s internal milieu1,2.Research on cognitive control and perceptual decision-making has examined how responses are selected when attentional targets are clearly defined (e.g., based on instruction to attend a stimulus dimension), including cases in which responding requires accumulating information regarding a noisy percept (e.g., evidence favoring a left or right response)3-7. Separate research on value-based decision-making has examined how individuals select which stimulus dimension(s) to attend in order to maximize their expected rewards8-11. However, it remains unclear how the accumulation of evidence to select high-level goals and/or attentional targets interacts with the simultaneous accumulation of evidence to select responses according to those goals (e.g., based on the perceptual properties of the stimuli). Recent work has highlighted the importance of such interactions to understanding task selection12-15, multi-attribute decision-making16-18, foraging behavior19-21, cognitive effort22,23, and self-control24-27.While these interactions remain poorly understood, previous research has identified candidate neural mechanisms associated with multi-attribute value-based decision-making11,28,29 and with selecting a response based on noisy information from an instructed attentional target3–5. These research areas have implicated the ventromedial prefrontal cortex (vmPFC) in tracking the value of potential targets of attention (e.g., stimulus attributes)8,11 and the dorsal anterior cingulate cortex (dACC) in tracking an individual’s uncertainty regarding which response to select30–32. It has been further proposed that dACC may differentiate between uncertainty at each of these parallel levels of decision-making (e.g., at the level of task goals or strategies vs. specific motor actions), and that these may be separately encoded at different locations along the dACC’s rostrocaudal axis32,33. However, neural activity within and across these prefrontal regions has not yet been examined in a setting in which information is weighed at both levels within and across trials.Here we use a value-based perceptual decision-making task to examine how people integrate different dynamic sources of information to decide (a) which perceptual attribute to attend and (b) how to respond based on the evidence for that attribute. Participants performed a task in which they regularly faced a conflict between attending the stimulus attribute that offered the greater reward or the attribute that was more perceptually salient (akin to persevering in writing one’s paper when an enticing email awaits). We demonstrate that dACC and vmPFC track evidence for the two attributes in dissociable ways. Across these regions, vmPFC weighs attribute evidence by the reward it predicts and dACC weighs it by its attentional priority (i.e., the degree to which that attribute drives choice). Within dACC, adjacent regions differentiated between uncertainty at the two levels of the decision, regarding what to attend (rostral dACC) versus how to respond (caudal dACC).

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Irrational time allocation in decision-making

Cited by 48 publications

References 72 publications

Reward certainty and preference bias selectively shape voluntary decisions

Reward certainty and preference bias selectively shape voluntary decisions

Learning to allocate limited time to decisions with different expected outcomes

Dissociable neural mechanisms track evidence accumulation for selection of attention versus action

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