Causal role of dorsolateral prefrontal cortex in human perceptual decision making.

Philiastides, M. G., Auksztulewicz, R., Heekeren, H. R., and Blankenburg, F.
Curr Biol, 21:980–983, 2011
DOI, Google Scholar

Abstract

The way that we interpret and interact with the world entails making decisions on the basis of available sensory evidence. Recent primate neurophysiology [1-6], human neuroimaging [7-13], and modeling experiments [14-19] have demonstrated that perceptual decisions are based on an integrative process in which sensory evidence accumulates over time until an internal decision bound is reached. Here we used repetitive transcranial magnetic stimulation (rTMS) to provide causal support for the role of the dorsolateral prefrontal cortex (DLPFC) in this integrative process. Specifically, we used a speeded perceptual categorization task designed to induce a time-dependent accumulation of sensory evidence through rapidly updating dynamic stimuli and found that disruption of the left DLPFC with low-frequency rTMS reduced accuracy and increased response times relative to a sham condition. Importantly, using the drift-diffusion model, we show that these behavioral effects correspond to a decrease in drift rate, a parameter describing the rate and thereby the efficiency of the sensory evidence integration in the decision process. These results provide causal evidence linking the DLPFC to the mechanism of evidence accumulation during perceptual decision making.

Review

They apply repetitive TMS to the dorsolateralprefrontal cortex (DLPFC) assuming that this inhibits the decision making ability of subjects, because DLPFC has been shown to be involved in perceptual decision making. Indeed, they find a significant effect of TMS vs. SHAM on the responses of subjects (after TMS responses of subjects are less accurate and take longer). They also argue that the effect is particular to TMS, because it reduces over time, but I wonder why they did not compute the corresponding interaction (they just report that the effect of TMS vs. SHAM is significant earlier, but not significant later).

Furthermore, they hypothesised that TMS disrupted the accumulation process of noisy evidence over time by decreasing the rate of evidence increase. This is based on the previous finding that the DLPFC has higher BOLD activation for less noisy stimuli which suggests that, when DLPFC is disrupted, the evidence coming from less noisy stimuli cannot be optimally processed anymore.

They investigated the evidence accumulation hypothesis by fitting a drift-diffusion model (DDM) to response data. The DDM has more parameters than are necessary to explain the variations of response data for the different experimental conditions. Hence, they use the Bayesian information criterion (BIC) to select parameters which should be fitted for each experimental condition separately, i.e., to be able to say which parameters are affected by the experimental manipulations. The other parameters are still fitted but to all data across experimental conditions. The problem is that the BIC is a very crude approximation just taking the number of freely varying parameters into account. For example, an assumption underlying the BIC is that the Hessian of the likelihood evaluated at the fitted parameter values has full rank (Bishop, 2006, p. 217), but for highly correlated parameters this may not be the case. The used DMAT fitting toolbox actually approximates the Hessian matrix, checks whether a local minimum has been found (instead of a valley) and computes confidence intervals from the approximated Hessian, but the authors report no results for this apart from error bars on the plot for drift rate and nondecision time.

Anyway, the BIC analysis conveniently indicates that drift rate and nondecision time best explain the variations in response data across conditions. However, it has to be kept in mind that these results have been obtained by (presumably) assuming that the diffusion is fixed across conditions which is the standard when fitting a DDM [private correspondence with Rafal Bogacz, 09/2012], because drift rate, diffusion and threshold are redundant (a change in one of them can be reverted by a suitable change in the others). The interpretation of the BIC analysis probably should be that drift rate and nondecision time are the smallest set of parameters which still allow a good fit of the data given that diffusion is fixed.

You need to be careful when interpreting the fitted parameter values in the different conditions. In particular, fitting a DDM to data assumes that the evidence accumulation still works like a DDM, just with different parameters. However, it is not clear what TMS does to the affected processes in the brain. Hence, we can only say from the fitting results that TMS has an effect which is equivalent to a reduction of the drift rate (no clear effect on nondecision time) in a normally functioning DDM.

Similarly, the interpretation of the results for nondecision time is not straightforward. There, the main finding is that nondecision time decreases for high-evidence stimuli which the authors interpret as a reduced time of low-level sensory processing which provides input to evidence accumulation. However, it should be kept in mind that the total amount of time necessary to make a decision is also reduced for high-evidence stimuli. Also, part of the processes which are collected under ‘nondecision time’ may actually work in parallel to evidence accumulation, e.g., movement preparation. If you look at the percentage of RT that is explained by the nondecision time, then the picture is reversed: for high-evidence stimuli nondecision time explains about 82% of RTs while for low-evidence stimuli it explains only about 75% which is consistent with the basic idea that evidence accumulation takes longer for noisier stimuli. In general, these percentages are surprisingly high. Does the evidence accumulation really only account for about 25% of total RTs? But it’s good that we have a number to compare now.

So what do these findings mean for the DLPFC? We cannot draw any definite conclusions. The hypothesis that TMS over DLPFC affects drift rate is somewhat built into the analysis, because the authors use a DDM to fit the responses. Of course, other parameters could have been affected stronger such that the finding of the BIC analysis that drift rate explains the changes best can indeed be taken as evidence for the drift rate hypothesis. However, it is not possible to exclude other explanations which lie outside the parameter space of the DDM. What, for example, if the DLPFC has indeed a somewhat attentional effect on evidence accumulation in the sense that it not only accumulates evidence, but also modulates how big the individual peaces of evidence are by modulating lower-level sensory processing? Then, interrupting the DLPFC may still have a similar effect as observed here, but the interpretation of the role of the DLPFC would be slightly different. Actually, the authors argue against a role of the DLPFC (at least the part of DLPFC they found) in attentional processing, but I’m not entirely convinced. Their main argument is based on the assumption that a top-down attentional effect of the DLPFC on low-level sensory processing would increase the nondecision time, but this is not necessarily true. A) there is the previously mentioned issue of parallel processing and the general problems of fitting a standard model to a disturbed process which makes me doubt the reliability of the fitted nondecision times and B) I can easily conceive a system in which attentional modulation would not delay low-level sensory processing.

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