Triple dissociation of attention and decision computations across prefrontal cortex

Abstract

Naturalistic decision-making typically involves sequential deployment of attention to choice alternatives to gather information before a decision is made. Attention filters how information enters decision circuits, thus implying that attentional control may shape how decision computations unfold. We recorded neuronal activity from three subregions of the prefrontal cortex (PFC) while monkeys performed an attention-guided decision-making task. From the first saccade to decision-relevant information, a triple dissociation of decision- and attention-related computations emerged in parallel across PFC subregions. During subsequent saccades, orbitofrontal cortex activity reflected the value comparison between currently and previously attended information. In contrast, the anterior cingulate cortex carried several signals reflecting belief updating in light of newly attended information, the integration of evidence to a decision bound and an emerging plan for what action to choose. Our findings show how anatomically dissociable PFC representations evolve during attention-guided information search, supporting computations critical for value-guided choice.

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Fig. 1: Experimental paradigm and basic subject behavior.
Fig. 2: Information-sampling behavior in attribute and option trials reveals preference for sampling the current best alternative.
Fig. 3: Recording locations.
Fig. 4: Triple dissociation of task-evoked neural codes across OFC, DLPFC and ACC at cue 1 presentation.
Fig. 5: Valuation subspaces for attended (‘online’) and stored cues, supporting attention-guided value comparison in the OFC.
Fig. 6: Multiple signals in the ACC reflect belief confirmation, commitment to a course of action and accumulation of evidence for left/right movement.
Fig. 7: Single-neuron analysis of attention-guided value comparison and belief confirmation recapitulates findings of population analysis.
Fig. 8: Single-neuron correlates of task variables at cue 1 presentation.

Data availability

The raw neuronal data that support the findings in this study have been made freely available for download at the CRCNS data repository (http://crcns.org/) under dataset pfc-7 (ref. 55).

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Acknowledgements

L.T.H. was supported by a Henry Wellcome Fellowship (098830/Z/12/Z) and Henry Dale Fellowship (208789/Z/17/Z) from the Wellcome Trust; a NARSAD Young Investigator Grant from the Brain and Behavior Research Foundation; and the NIHR Oxford Health Biomedical Research Centre. W.M.N.M. was supported by funding from the Astor Foundation, Rosetrees Trust and Middlesex Hospital Medical School General Charitable Trust. A.O.d.B. was supported by a PhD studentship from the MRC. B.M. was supported by the Fundação para a Ciência e Tecnologia (scholarship SFRH/BD/51711/2011). T.E.J.B. was supported by a Wellcome Trust Senior Research Fellowship (WT104765MA) and funding from the James S. McDonnell Foundation (JSMF220020372). S.W.K. was supported by a Wellcome Trust New Investigator Award (096689/Z/11/Z). The views expressed are those of the authors and are not necessarily those of the NHS, the NIHR or the Department of Health.

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L.T.H. designed experiments, collected and analyzed data and wrote the manuscript; W.M.N.M. designed experiments and collected and analyzed data; A.O.d.B. analyzed data; B.M. collected data; S.F.F. designed experiments; T.E.J.B. designed experiments and supervised analysis; S.W.K. supervised all aspects of the project and wrote the manuscript.

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Correspondence to Laurence T. Hunt or Steven W. Kennerley.

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Integrated supplementary information

Supplementary Figure 1 Additional analysis of subject behavior.

(a)/(b) Logistic regression shows weighting of all four presented cues and both probability and magnitude attributes on subject choices. In (a), bars show mean + /- s.e.m. of regression coefficient for the final viewed picture, penultimate viewed picture (n-1), antepenultimate viewed picture (n-2), and first viewed picture on trials where subject viewed all four cues (n-3), as well as bias towards choosing the left cue. Both subjects use all four cues to guide their choices, with a slight upweighting of the antepenultimate cue. In (b), Bars show mean + /- of the regression coefficient for left minus right probability, and left minus right magnitude, separately for option and attribute trials. Also included in the model are a bias towards choosing the left cue and a bias towards choosing the first side (note that both subjects show a small but significant bias towards choosing the second side on option trials, also visible in main Fig. 1b). Regression models were fit to all trials, collapsed across sessions (n = 14,251 trials (subject M), n = 9,863 trials (subject F)). (c) Subjects paradoxically were more likely to choose optimally, on average, on trials where 2 pictures were viewed rather than 3 or 4 pictures. (d) This effect can be straightforwardly explained by subjects terminating information sampling earlier, on average, on trials where the first two cues are more informative (and so the decision is easier). Bars show mean + /- standard error; n = 14,251 trials (subject M), n = 9,863 trials (subject F).

Supplementary Figure 2 Reproducibility of representational similarity matrices across subjects.

Data are as presented in main Fig. 4a-c, plotted separately for subjects M and F. Correlation is computed with Pearson’s correlation, as in main Fig. 4. n = 189 units (ACC), 135 units (DLPFC), 183 units (OFC).

Supplementary Figure 3 Example single neurons to provide intuition regarding how different task variables are represented at cue 1 presentation.

Each subplot shows a different single neuron example. Bar plots show average firing rate of the neuron to each of the 10 stimuli when presented on left and right of the screen, averaged from 300-500 ms following cue 1 presentation. Line plots show peri-stimulus histograms for each of these conditions, timelocked in ms to cue 1 presentation. (a) Example DLPFC unit reflecting spatial position (high firing for all stimuli on right side of screen. (b) Example OFC unit showing ‘stimulus identity’ coding (esoteric high firing for certain stimuli, replicated on both left/right sides of screen). (c) Example OFC unit showing ‘attended value’ coding (firing linearly scales with value, irrespective of attribute or spatial position). (d) Example ACC unit reflecting action value (high firing for high valued stimuli on left or low valued stimuli on right). (e) Example ACC unit showing ‘accept/reject’ coding (high firing for stimuli ranked 4 or 5; low firing for stimuli ranked 1 or 2).

Supplementary Figure 4 Representational similarity across all 40 conditions at cue 1 presentation.

Data are as presented in main a-c, but are now subdivided into trials where cue 1 was presented in the top versus bottom half of the screen. The key results from this analysis primarily replicate the findings when top and bottom cues are collapsed. Note, however, that in DLPFC, representational similarity is modulated by top/bottom stimulus location (compare, for example, the average brightness for top left→top left versus top left → bottom left). This indicates that DLPFC activity primarily represents spatial position rather than simply left/right action. This finding was also recapitulated in single unit analyses of DLPFC neurons (see Supplementary Note). Correlation is computed with Pearson’s correlation, as in main Fig. 4. n = 189 units (ACC), 135 units (DLPFC), 183 units (OFC).

Supplementary Figure 5 Comparison of latencies for different features in representational similarity matrices within each subregion.

(a) This plot shows the same information as in bottom panels of main Fig. 4d-h, but sorted by subregion rather than by regressor. This shows more clearly the relative latencies of different variables – i.e. that spatial attention affects representational similarity earliest in each subregion; stimulus identity, attended value and accept/reject emerge roughly simultaneously, and left/right value emerges last. Whilst these findings can be used to compare latencies of these different responses, we caution that the magnitude of CPD values for different variables are not directly comparable with each other, because they depend upon the correlation structure of the design matrix. Instead, CPD values for the same variables should be compared with each other across subregions (as in main Fig. 4). (b) This plot shows the same information as in part (a), but after each regressor has been normalized to its peak value. Regressors explaining little variance have been removed from this plot, for clarity. The dashed line denotes the value at which 75% of maximal CPD is reached, which we denote as t75. Note that in both figures, CPD is estimated in sliding 200 ms bins, and is timelocked to cue onset (after the subject has saccaded to the cue). (c) Box-and-whisker plot of t75 for key variables/brain regions, estimated using different simulated observations of the noise (see Methods for details). The distributions of t75 for spatial attention do not overlap with those of attended value, accept/reject, or stimulus identity, demonstrating that spatial attention is encoded significantly earlier than other variables. Similarly, left/right value (in ACC and DLPFC) is encoded significantly later than other variables. Red lines denote median values; notches denote 95% CIs of the median; edge of boxes denote 25/75 percentiles of data; whiskers denote maximum/minimum values (excluding outliers, as estimated using MATLAB boxplot algorithm). n = 189 units (ACC), 135 units (DLPFC), 183 units (OFC).

Supplementary Figure 6 Value subspaces for attended and stored code in the ACC.

Figure layout is as for main Fig. 5. Note that like OFC, ACC shows consistent single neuron coding of value (a), and that population subspaces for attended (b) and stored (f) subspaces are present in ACC. However, there is no evidence of inhibition between the attended and stored subspaces (parts (c)-(e)). Lines in part (a) denote mean +/- s.e.m. of coefficient of partial determination across neurons. Lines in parts (B)-(F) denote line of best fit +/- 95% CI. n = 189 units.

Supplementary Figure 7 Value subspaces for attended and stored code in the DLPFC.

Figure layout is as for main Fig. 5. Value coding is weaker in DLPFC than in either of the other two regions (a), but population subspaces for attended (b) and stored (f) subspaces are nonetheless present in DLPFC. However, there is little evidence of inhibition between the attended and stored subspaces (parts (c)-(e)). Lines in part (a) denote mean +/- s.e.m. of coefficient of partial determination across neurons. Lines in parts (b)-(f) denote line of best fit +/- 95% CI. n = 135 units.

Supplementary Figure 8 Design of ‘belief-confirmation regressors’ (i.e., EV13–EV16 in general linear model).

Each of the four EVs is depicted by a different panel, and refers to a different trial type/timepoint through the trial. Crucially, however, the interpretation of the four EVs is very similar. Whenever the evidence presented to the subject thus far suggests that the currently attended side should be chosen (green dots), then ‘belief confirmation’ scales positively with value. Whenever the evidence suggests that the unattended side should be chosen (red dots), then ‘belief confirmation’ scales negatively with value. Note that all four regressors were thus orthogonal to currently attended value (see Figure S9). (a) EV13, reflecting belief confirmation at second saccade of option trials. (b) EV14, reflecting belief confirmation at second saccade of attribute trials. (c) EV15, reflecting belief confirmation at third saccade of option trials. (d) EV16, reflecting belief confirmation at third saccade of attribute trials (depending upon whether subjects’ third saccade was to (i) side 1, or (ii) side 2).

Supplementary Figure 9 Mean correlation between EVs in the general linear model.

Note that most EVs of interest (1-6, 13-18) are decorrelated from one another by design, with the exception of EV16 (whose value depends upon where the subject looked at Cue 3, and this saccade depends systematically upon the relative value of cue 1 and cue 2 (see main Fig. 2)). EVs 11/12 are indicator variables for trial type.

Supplementary Figure 10 ACC has a robust belief-confirmation signal across different cues and trial types.

Parameter estimates for all four ‘belief confirmation’ regressors (at Cues 2/3 on both option and attribute trials) are positively correlated with each other across the ACC neural population. They are also positively correlated with value coding at cue 1. The Pearson correlation coefficients from each of these plots are also shown in main Fig. 6a, right-hand plot. Lines denote line of best fit + /- 95% CI. n = 189 units.

Supplementary Figure 11 Analysis of RSA by using alternative ‘attended value’ and ‘left/right value’ templates (with equal similarity for midvalue and extreme-value stimuli).

This analysis replaces the ‘attended value’ and ‘left/right value’ templates with an alternative formulation (equal on-diagonal similarity), and produces qualitatively similar functional dissociations to those observed in main Fig. 4. See Supplementary Note for further details. See Methods for full description of other regressors in regression model, and statistical inference via non-parametric permutation test. * denotes p < 0.05, ** denotes p < 0.005, *** denotes p < 0.0005. n = 189 units (ACC), 135 units (DLPFC), 183 units (OFC).

Supplementary Figure 12 Results of simulated RSA matrix for a population of neurons with linear encoding of value.

This simulation reproduces the result found in OFC that extreme-valued stimuli have strong representational similarity, whereas mid-valued stimuli do not. See Supplementary Note for details of simulation. Correlations shown are Pearson’s correlation, as in other RSA figures.

Supplementary Information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Table 1 and Supplementary Note

Reporting Summary

Supplementary Video 1

Temporal evolution of representational similarity at first saccade to fixate value-related information. This video depicts the temporal evolution of representational similarity around the time of Cue 1 fixation (see main Figure 4). Each movie frame represents average firing rates of +/− 100ms around the timebin of interest. The bottom panels show the evolution of the template-based regression presented in main Figure 4

Supplementary Video 2

Orthogonal subspaces for belief confirmation and action selection in ACC. This video shows the relationship between ‘belief confirmation’ and ‘left/right action selection’ population subspaces, both of which show ramping prior to action selection in anterior cingulate cortex

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Hunt, L.T., Malalasekera, W.M.N., de Berker, A.O. et al. Triple dissociation of attention and decision computations across prefrontal cortex. Nat Neurosci 21, 1471–1481 (2018). https://doi.org/10.1038/s41593-018-0239-5

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