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Deciding not to decide: computational and neural evidence for hidden behavior in sequential choice.

Gluth S, Rieskamp J, Büchel C - PLoS Comput. Biol. (2013)

Bottom Line: Understanding the cognitive and neural processes that underlie human decision making requires the successful prediction of how, but also of when, people choose.Standard SSM implementations did not describe RT distributions adequately.Our results show how computational modeling of decisions and RTs support a deeper understanding of the hidden dynamics in cognition.

View Article: PubMed Central - PubMed

Affiliation: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany ; Department of Psychology, University of Basel, Basel, Switzerland.

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.

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Related in: MedlinePlus

Relationship between computational and neural variability.(A) Beta-band power averaged over the 37 channels shown in Figure 7B separated for low and high evidence. The recovery of beta power is delayed in high evidence ratings. (B) The threshold parameter for the decision not to decide (according to M1*evidence) was correlated across participants with the averaged peak latencies for the de- and increase in beta power.
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pcbi-1003309-g008: Relationship between computational and neural variability.(A) Beta-band power averaged over the 37 channels shown in Figure 7B separated for low and high evidence. The recovery of beta power is delayed in high evidence ratings. (B) The threshold parameter for the decision not to decide (according to M1*evidence) was correlated across participants with the averaged peak latencies for the de- and increase in beta power.

Mentions: We were further interested in bringing the effects of intra- and inter-individual variability in the computational and neural data together. As stated before, the model comparison supported the M1*evidence model, in which the drift rate of the decision not to decide depends negatively on accumulated evidence. This indicates that higher evidence induces a later inhibition of the choice process (within each rating). Therefore, we tested whether at ratings with high evidence the pattern of de- and increase in beta-band power is delayed. For each rating number (from 1 to 6) we conducted a median-split based on the LE and calculated peak latencies for de- and increase in the beta-band signal for the 37 significant channels (see Figure 7B) separately for low and high evidence ratings. As depicted in Figure 8A, the recovery of beta was indeed delayed for high evidence as compared to low evidence ratings (t(27) = 3.15; p = .004). In addition, the recovery appeared to be reduced in amplitude as well. A similar prediction can be tested across participants: The higher the threshold of the decision not to decide, the longer the choice process (within the time period of one rating). Accordingly, the threshold parameter should be positively correlated with the beta-band peak latencies. This prediction was confirmed by the data (r = .42; p = .028) (Figure 8B).


Deciding not to decide: computational and neural evidence for hidden behavior in sequential choice.

Gluth S, Rieskamp J, Büchel C - PLoS Comput. Biol. (2013)

Relationship between computational and neural variability.(A) Beta-band power averaged over the 37 channels shown in Figure 7B separated for low and high evidence. The recovery of beta power is delayed in high evidence ratings. (B) The threshold parameter for the decision not to decide (according to M1*evidence) was correlated across participants with the averaged peak latencies for the de- and increase in beta power.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814623&req=5

pcbi-1003309-g008: Relationship between computational and neural variability.(A) Beta-band power averaged over the 37 channels shown in Figure 7B separated for low and high evidence. The recovery of beta power is delayed in high evidence ratings. (B) The threshold parameter for the decision not to decide (according to M1*evidence) was correlated across participants with the averaged peak latencies for the de- and increase in beta power.
Mentions: We were further interested in bringing the effects of intra- and inter-individual variability in the computational and neural data together. As stated before, the model comparison supported the M1*evidence model, in which the drift rate of the decision not to decide depends negatively on accumulated evidence. This indicates that higher evidence induces a later inhibition of the choice process (within each rating). Therefore, we tested whether at ratings with high evidence the pattern of de- and increase in beta-band power is delayed. For each rating number (from 1 to 6) we conducted a median-split based on the LE and calculated peak latencies for de- and increase in the beta-band signal for the 37 significant channels (see Figure 7B) separately for low and high evidence ratings. As depicted in Figure 8A, the recovery of beta was indeed delayed for high evidence as compared to low evidence ratings (t(27) = 3.15; p = .004). In addition, the recovery appeared to be reduced in amplitude as well. A similar prediction can be tested across participants: The higher the threshold of the decision not to decide, the longer the choice process (within the time period of one rating). Accordingly, the threshold parameter should be positively correlated with the beta-band peak latencies. This prediction was confirmed by the data (r = .42; p = .028) (Figure 8B).

Bottom Line: Understanding the cognitive and neural processes that underlie human decision making requires the successful prediction of how, but also of when, people choose.Standard SSM implementations did not describe RT distributions adequately.Our results show how computational modeling of decisions and RTs support a deeper understanding of the hidden dynamics in cognition.

View Article: PubMed Central - PubMed

Affiliation: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany ; Department of Psychology, University of Basel, Basel, Switzerland.

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.

Show MeSH
Related in: MedlinePlus