Neural Correlates of Trial-to-Trial Fluctuations in Response Caution

Abstract

Trial-to-trial variability in decision making can be caused by variability in information processing as well as by variability in response caution. In this paper, we study which neural components code for trial-to-trial adjustments in response caution using a new computational approach that quantifies response caution on a single-trial level. We found that the frontostriatal network updates the amount of response caution. In particular, when human participants were required to respond quickly, we found a positive correlation between trial-to-trial fluctuations in response caution and the hemodynamic response in the presupplementary motor area and dorsal anterior cingulate. In contrast, on trials that required a change from a speeded response mode to a more accurate response mode or vice versa, we found a positive correlation between response caution and hemodynamic response in the anterior cingulate proper. These results indicate that for each decision, response caution is set through corticobasal ganglia functioning, but that individual choices differ according to the mechanisms that trigger changes in response caution.

Deciding When to Decide: Time-Variant Sequential Sampling Models Explain the Emergence of Value-Based Decisions in the Human Brain

“…We suggest that these fluctuations reflected the probabilistic nature of value-based decisions (Mosteller and Nogee, 1951;Rieskamp, 2008). Future research should test whether voluntary adjustments and involuntary fluctuations of the decision threshold in value-based decisions recruit overlapping brain circuits as suggested for perceptual decisions (van Maanen et al, 2011).…”

“…This information should be immediately accessible for motor preparation and output regions such that the gradual buildup of DVs will be reflected in the motor system (Donner et al, 2009;Cisek and Kalaska, 2010). Furthermore, based on findings from perceptual decision making, we hypothesized that trial-by-trial fluctuations in the decision threshold are related to fMRI signal variations in the caudate nucleus and the presupplementary motor area (pre-SMA; Bogacz et al, 2010;van Maanen et al, 2011).…”

The neural processes underlying perceptual decision making in humans: Recent progress and future directions

“…Modeling studies indeed show that allowing only boundary separation, or an equivalent distance-to-threshold parameter, to vary across speed/accuracy emphasis conditions provides a good quantitative fit to behavioral data (e.g. Ratcliff and McKoon, 2008) and this principle has been exploited in model-based fMRI analyses that estimate correlations between brain activations and model parameters (Forstmann et al, 2008;van Maanen et al, 2011). However, it remains to be determined what such correlations actually mean -as O'Reilly and Mars (2011) have pointed out, the ''latent'' or abstract parameters of cognitive models are invoked with the primary purpose of accounting for behavior, but it is not necessarily the case that they reflect quantities or mechanisms that are directly implemented in the brain.…”

“…Model-based analyses are now being extended further such that multiple decision model parameters are estimated on a single-trial basis from behavioral data and subsequently used to form regressors in general linear models of the fMRI data (van Maanen et al, 2011), an approach that was championed by investigators of reinforcement learning and value-based action selection (e.g. Daw et al, 2006;O'Doherty et al, 2007).…”

The Parallel Episodic Processing (PEP) model 2.0: A single computational model of stimulus-response binding, contingency learning, power curves, and mixing costs

“…In the ASE model, response time will also be autocorrelated in this manner, but for a more incidental reason: the model aims to initiate responding as quickly as it can without inflating errors. This is related to the notion of response caution (van Maanen et al, 2011). Of course, the PEP model learns both when and what to respond, giving it wider breadth, but the differences in the temporal learning mechanisms in the two models are interesting.…”