Why is doug lee a toolbox

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For each participant, we then regressed, at each time point during the decision, pupil size onto effort ratings across trials. Time series of regression coefficients were then reported at the group level, and tested for statistical significance correction for multiple comparison was performed using random field theory 1D-RFT. Appendix 1—figure 8 summarizes this analysis, in terms of the baseline-corrected time series of regression coefficients. Here, epochs are co-registered w.

Right panel: Same, but for epochs co-registered w. We found that the correlation between subjective effort ratings and pupil dilation became significant from ms after stimulus onset onwards. Our eye-tracking data also allowed us to ascertain which item was being gazed at for each point in peristimulus time during decisions.

Using the choice responses, we classified each time point as a gaze at the to be chosen item or at the to be rejected item. The difference between these two gaze ratios measures the overt attentional bias toward the chosen item.

We refer to this as the gaze bias. However, we also found that this effect was in fact limited to low effort choices. Appendix 1—figure 9 shows the gaze bias for low- and high-effort trials, based on a median-split of subjective effort. A potential trivial explanation for the fact that the gaze bias is large for low effort trials is that these are the trials where participants immediately recognize their favorite option, which attracts their attention.

More interesting is the fact that the gaze bias is null for high effort trials. This may be taken as evidence for the fact that, on average, people allocate the same amount of attentional resources to both options. This is important, because we use this simplifying assumption in our MCD model derivations.

In the main text, we evaluate the accuracy of the MCD model predictions, without considering alternative computational scenarios. Here, we report results of a model-based data analysis that relies on the standard drift-diffusion decision or DDM model for value-based decision-making De Martino et al.

In brief, DDMs tie together decision outcomes and response times by assuming that decisions are triggered once the accumulated evidence in favor of a particular option has reached a predefined threshold or bound Ratcliff and McKoon, ; Ratcliff et al.

Importantly here, evidence accumulation has two components: a drift term that quantifies the strength of evidence and a random diffusion term that captures some form of neural perturbation of evidence accumulation. The latter term allows choice outcomes to deviate from otherwise deterministic, evidence-driven, decisions. Importantly, standard DDMs do not predict choice confidence, spreading of alternatives, value certainty gain, or subjective effort ratings.

This is because these concepts have no straightforward definition under the standard DDM. However, DDMs can be used to make out-of-sample trial-by-trial predictions of, for example, decision outcomes, from parameter estimates obtained with response times alone.

Given these model parameters, the expected response time conditional on the decision outcome is given by Srivastava et al. In particular, if one knows how, for example, drift rates vary over trials, then one can predict the ensuing expected RT variations. The variational Laplace treatment of the ensuing generative model then yields estimates of the remaining DDM parameters.

Out-of-sample predictions of change of mind i. Here, we use two modified variants of the standard DDM for value-based decisions. This is done by enabling the decision bound to vary over trials, i. The exponential mapping is used for imposing a positivity constraint on the resulting bound see section 8 above. The two DDM variants then differ in terms of how pre-choice value certainty is taken into account Lee and Usher, :. Here, the strength of evidence in favor of a given alternative option is measured in terms of a signal-to-noise ratio on value.

In this parameterization, value representations that are more certain will be signaled more reliably. For each subject and each DDM variant, we estimate unknown parameters from RT data alone using Equation A27 , and derive out-of-sample predictions for changes of mind using Equation A We also perform the exact same analysis under the MCD model this is slightly different from the analysis reported in the main text, because only RT data is included in model fitting here.

To begin with, we compare the accuracy of RT postdictions, which is summarized in Appendix 1—figure This is because, in DDM2, as value certainty ratings increase and the diffusion standard deviation decreases, the probability that DDM bounds are hit sooner decreases hence prolonging RT on average. These results reproduce recent investigations of the impact of value certainty ratings on DDM predictions Lee and Usher, Now, Appendix 1—figure 11 summarizes the accuracy of out-of-sample change of mind predictions.

It turns out that the MCD model exhibits the highest accuracy of out-of-sample change of mind predictions. This may not be surprising, given the longstanding success of the DDM on this issue Ratcliff et al. The result of this comparison, however, depends upon how the DDM is parameterized cf. More importantly, in our context, DDMs make poor out-of-sample predictions on decision outcomes, at least when compared to the MCD model.

For the purpose of predicting decision-related variables from effort-related variables, one would thus favor the MCD framework.

In other terms, the magnitude of the perturbation per unit of resources that one might expect when no resources have yet been allocated may be much higher than when most resources have already been allocated. In turn, Equation 6 would be replaced by:. Having said this, the modified MCD model is in principle more flexible than its simpler variant, and may thus exhibit additional explanatory power. In brief, we performed the same within-subject analysis as with the simpler MCD variant see main text.

We then measured the accuracy of model postdictions on each dependent variable and performed a random-effect group-level Bayesian model comparison Rigoux et al. The results of this comparison are summarized in Appendix 1—figure Left panel: The mean within-subject across-trial correlation between observed and postdicted data y-axis is plotted for dependent variable x-axis, from left to right: choice confidence, spreading of alternatives, change of mind, certainty gain, RT and subjective effort ratings and each model gray: MCD with linear efficacy, blue: MCD with saturating efficacy ; error bars depict s.

Right panel: Estimated model frequencies from the random-effect group-level Bayesian model comparison; error bars depict posterior standard deviations. We note that other variants of the MCD model may be proposed, with similar modifications e. Preliminary simulations seem to confirm that such modifications would not change the qualitative nature of MCD predictions.

In other terms, the MCD model may be quite robust to these kinds of assumptions. Note that these modifications would necessarily increase the statistical complexity of the model by inserting additional unknown parameters.

Therefore, the limited reliability of behavioral data such as we report here may not afford subtle deviations to the simple MCD model variant we evaluate here. The MCD model provides quantitative predictions for both effort-related and decision-related variables, from estimates of three native parameters effort unitary cost and two types of effort efficacy , which control all dependent variables.

However, the model prediction accuracy is not perfect, and one may wonder what is the added value of MCD compared to model-free analyses. To begin with, recall that one cannot make out-of-sample predictions in a model-free manner e. In contrast, a remarkable feature of model-based analyses is that training the model on some subset of variables is enough to make out-of-sample predictions on other yet unseen variables.

In this context, MCD-based analyses show that variations in response times, subjective effort ratings, changes of mind, spreading of alternatives, choice confidence, and precision gain can be predicted from each other under a small subset of modeling assumptions. Having said this, model-free analyses can be used to provide a reference for the accuracy of MCD postdictions. This provides a benchmark against which MCD postdiction accuracy can be evaluated.

To enable a fair statistical comparison, we re-performed MCD model fits, this time fitting each dependent variable one by one leaving the others out. The results of this analysis are summarized in Appendix 1—figure 13 :. The mean within-subject across-trial correlation between observed and postdicted data y-axis is plotted for each variable x-axis, from left to right: choice confidence, spreading of alternatives, change of mind, certainty gain, RT, and subjective effort ratings , and each fitting procedure gray: MCD full data fit, white: MCD 1-variable fit, and black: linear regression.

Error bars depict standard error of the mean. This is because the latter approach attempts to explain all dependent variables with the same parameter set, which requires finding a compromise between all dependent variables. A likely explanation here is that the MCD model includes constraints that prevent one-variable fits from matching the model-free postdiction accuracy level.

Having said this, these constraints necessarily derive from the modeling assumptions that enable the MCD model to make out-of-sample predictions. We comment on this and related issues in the Discussion section of the main text. Empirical data as well as model fitting code have been uploaded as part of this submission.

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses. This work addresses a timely and heavily debated subject: the role of mental effort in value-based decision-making. Plenty of models attempt to explain value-based choice behavior, and there is a growing number of computational accounts concerning the allocation of mental effort therein.

Yet, little theoretical work has been done to relate the two literatures. The current paper contributes a novel and inspiring step in this direction. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Tobias Donner as the Reviewing Editor and Michael Frank as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Andrew Westbrook Reviewer 3. The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv if it is not already there along with this decision letter and a formal designation that the manuscript is "in revision at eLife ". Please let us know if you would like to pursue this option. This manuscript addresses a timely subject: the role of cognitive control or mental effort in value-based decision making.

While there are plenty of models explaining value-based choice, and there is a growing number of computational accounts concerning effort-allocation, little theoretical work has been done to relate the two literatures.

This manuscript contributes a novel and interesting step in this direction, by introducing a computational account of meta-control in value-based decision making. According to this account, meta-control can be described as a cost-benefit analysis that weighs the benefits of allocating mental effort against associated costs.

The benefits of mental effort pertain to the integration of value-relevant information to form posterior beliefs about option values. Given a small set of parameters, as well as pre-choice value ratings and pre-choice uncertainty ratings as inputs to the model, it can predict relevant decision variables as outputs, such as choice accuracy, choice confidence, choice induced preference changes, response time and subjective effort ratings.

The study fits the model to data from a behavioral experiment involving value-based decisions between food items. The resulting behavioral fits reproduce a number of predictions derived from the model.

Finally, the article describes how the model relates to established accumulator models of decision-making. The relatively simple model is impressive in its apparent ability to reproduce qualitative patterns across diverse data including choices, RTs, choice confidence ratings, subjective effort, and choice-induced changes in relative preferences successfully.

The model also appears well-motivated, well-reasoned, and well-formulated. While all reviewers agreed that the manuscript is of potential interest, they also all felt that a stronger case needs to be made for the explanatory power of the model, and that the model should be embedded more thoroughly in the existing literature on this topic. Parameter recoverability: Please include an analysis of parameter recoverability: How well can the fitting procedure recover model parameters from data generated by the model?

Fitting procedure: Rather than fitting the model to all dependent variables at once, it would be more compelling to fit the model to a subset of established decision-related variables e. The latter would be a more stringent test of the model, and may serve to highlight its value for linking variables related to value-based decision making to variables related to meta-control.

Model complexity: Assess through model comparison how many degrees of freedom are needed to account for the data e. Currently, the authors show that their model explains more variance in dependent variables when fit to real data than random data. Almost any model which systematically relates independent variables to dependent variables would explain more variance when fit to real data than to data. It would be more useful to know whether and if so, how much the model explains data better, than, e.

The model appears to do fairly well in predicting aggregate, group-level data, but does it predict subject-level data? Or, does it sometimes make unrealistic predictions when fitting to individual subjects? The Authors should provide evidence of whether it can or cannot describe subject level choices, confidence ratings, subjective effort, etc.

Is this realistic? If the two options have approx the same value, then R should be small it doesn't matter which one you choose ; if the options have different value, it is important to choose the correct one.

Of course, the probability P c continuously differentiates between the two options, but that is not the same as the reward. Can the predictions generalise toward a more general R that depends on value difference? Is it reasonable to assume that variance would increase as a linear function of resource allocation? It seems to me that variance might increase initially, but then each increment of resources would add diminishing variance to the mode since, e.

How sensitive are model predictions to this assumption? What about if each increment of resources added to variance in an exponentially decreasing fashion? What about anchoring biases? Because anchoring biases suggest that we estimate things with reference to other value cues, should we always expect that additional resources increase the expected value difference, or might additional effort actually yield smaller value differences over time?

If we relax this assumption, how does this impact model predictions? Does the current model predict the diverse dependent variables better than a standard accumulator models of decision-making? The model could also situate itself better in the broader existing literature on the topic. For instance, how does the model compares to existing computational work on this matter, e. We understand that the presented model can account for some phenomena that the other models cannot account for, at least without auxiliary assumptions e.

Finally, it would seem fair to relate the presented account to emerging, more mechanistically explicit accounts of meta-control in value-based decision making e. Ideally, some of the above would be addressed in the form of formal model comparisons, but we realise that this may be difficult to achieve in practice within a reasonable time frame.

At the least, the manuscript should discuss in detail how the above-mentioned models differ from the presented model here. We have now included a parameter recovery analysis of the MCD model.

It is now included as part of the new section 3 of the revised Appendix. Importantly, our parameter recovery was performed under simulated data with similar SNR as our empirical data.

In brief, the reliability of MCD parameter recovery does not suffer from any strong non-identifiability issue. However, its reliability is much weaker than in the ideal case, where data is not polluted with simulation noise. This is an excellent suggestion. We did this for each subject, each time estimating a single within-subject set of model parameters.

Note: the latter are formally derived from parameter estimates obtained when leaving the corresponding data out.

The accuracy of postdictions and out-of-sample predictions is summarized on Figure 4 of the revised Results section. In our opinion, this analysis also addresses the point 1. Note that we also report group-level summaries of out-of-sample predictions for each dependent variable, when plotted against pre-choice value ratings and value certainty ratings along with experimental data and model postdictions, see Figures 5 to 11 of the revised Results section.

We have now revised our Results section to provide evidence for this. More precisely, under the MCD model, non-zero type 1 efficacy trivially implies that the precision of post-choice value representations should be higher than the precision of pre-choice value representations. Similarly, under the MCD model, non-zero type 2 efficacy implies the existence of spreading of alternatives. In our modified manuscript, we highlight and assess these predictions using simple significance testing on our data see Figures 10 and 11 in the revised Results section.

We note that we find this procedure more robust than model comparison in this case, given the limited reliability of parameter recovery. We entirely agree with you. In the revised manuscript, we now report the accuracy of within-subject postdictions and out-of-sample predictions. These results are reported in the section 4. If you mean that people do not care about the decision when the pre-choice values are similar, then we disagree with you.

In brief, we have shown that both response time and subjective effort ratings decrease when the difference in pre-choice value increases NB: this result has been reproduced many times for RT. In other words, effort is maximal when pre-choice values are similar.

This is direct evidence against the idea that decision importance i. Of course, decision importance is a critical component of the MCD model. In our revised manuscript, we highlight this qualitative prediction and its corresponding empirical test cf. Figure 7 in section 4. Having said this, we acknowledge that decision importance falls short of a complete and concise computational definition.

In the previous version of our manuscript, we had discussed possible cognitive determinants of decision importance that would be independent of option values.

Now, whether and how decision importance depends upon the prior assessment of choice options is virtually unknown.

We have now modified the paragraph of the related Discussion as follows new lines :. This is an intriguing suggestion. We recognize that, under some simple Bayesian algorithm for value estimation, one would expect some form of saturating type 2 efficacy.

We thus implemented and tested such a model. We report the results of this analysis in the section 10 of our revised Appendix. This is a fair point, to which we wholeheartedly concur. We have then compared these models with MCD, w. When someone is this many tools in one, he is a toolbox. He said his Honda had a V8 and ran 5's When someone is so dense and pathetic that they are not just a tool , they are an entire toolbox.

Toolboxes have a tendency not to think for themselves. Go to first, last, parent, child, next, previous, current, focus, real, foreground, top-level, or other windows directly. Get a window's class, name, control ID, display style, dimensions and location, contents, etc.

Move to first, last, next, previous, current, or focused SDM control and test all functions you might use to present the control to the user. Navigate among MSAA objects by all standard MSAA methods spatial, logical, hierarchical, or to focus or selection and query all properties of each object name, role, state, value, etc. Explore UIA trees, filter their content, collect UIA elements matching specific criteria, examine all available properties, list available UIA patterns for an element and test related properties and methods, and move among related UIA elements, MSAA elements, window handles, and screen rectangles.

Test navigation and properties in an AccessibleObject tree obtained from a window handle. In Internet Explorer, navigate an HTML document's tree of elements, query for attributes, fire events by name on specific elements, retrieve HTML source code blocks by element for display or copying, and edit live DOM elements or trees by hand in place and test the results.

Explore events captured from UIA. Navigate and examine the JAWS internal object information hierarchy by level.