Theoretical models of decision making emphasise the importance of evidence accumulation across time until a categorical choice is reached (Bogacz et al., 2006; Gold and Shadlen, 2007). One widely studied class of evidence accumulation models are cortical attractor networks, originally derived from studies of working memory (Amit and Brunel, 1997; Wang, 1999, 2002). These rely upon strong recurrent connections between similarly tuned neurons to integrate evidence across time, and exhibit temporally extended persistent activity that stores the outcome of the decision process in memory (Wang, 2002; Wong and Wang, 2006). In value-guided decision making tasks, attractor network models predict the emergence of correlates of chosen value during choice (Hunt et al., 2012; Rustichini and Padoa-Schioppa, 2015). These value correlates result from varying speeds of decision formation across different trials, an issue we explored closely in our previous paper (Hunt et al., 2015). However, in contrast to the relative homogeneity of chosen value correlates within such models, it is known that decision correlates are highly heterogeneous across different cells within a given region (Kennerley et al., 2009; Wallis and Kennerley, 2010; Meister et al., 2013). The source and functional significance of this neuronal heterogeneity remains unclear.

Neurons also exhibit heterogeneity in their temporal receptive fields of integration (Chen et al., 2015). The temporal receptive field of a neuron can be established by examining its spike-count autocorrelation function (ACF) at rest (Ogawa and Komatsu, 2010). A slowly decaying ACF whilst at rest reflects temporal stability in firing, suggesting that the neuron integrates information across long periods of time; by contrast, a fast-decaying ACF reflects temporal variability in firing. Recently, this approach was used to demonstrate a hierarchy of temporal receptive fields across areas of cortex (Murray et al., 2014), with populations of neurons in lower and higher cortical areas exhibiting brief and extended temporal receptive fields, respectively. Those areas with temporally extended receptive fields thus appear intrinsically adapted to cognitive tasks involving extended integration of information across time, such as working memory and decision making (Mazurek et al., 2003; Gold and Shadlen, 2007; Wang, 2012; Chaudhuri et al., 2015; Chen et al., 2015). Yet in addition to the heterogeneity of temporal fields across regions, similar heterogeneity is also evident within cortical areas (Ogawa and Komatsu, 2010; Nishida et al., 2014). It remains unknown whether this intra-regional heterogeneity in temporal specialisation might predict the computations served by different neurons in decision-making tasks.

In our previous study of reward-guided decision making (Hunt et al., 2015), we provided evidence that correlates of chosen value may emerge as a consequence of varying rates of evidence accumulation. A corollary of this idea is that neurons functionally specialised to perform temporally extended computations (such as evidence accumulation) might exhibit stronger chosen value correlates during choice. We hypothesised that this would be indexed by measuring individual neurons’ temporal receptive fields whilst at rest. We also hypothesised that this functional specialisation might support other temporally extended computations during reward-guided choice, such as the maintenance of value coding until reward delivery. This could be one component of a mechanism for credit assignment in learning, which is known to rely upon PFC and in particular orbitofrontal cortex (Walton et al., 2010; Takahashi et al., 2011; Chau et al., 2015; Jocham et al., 2016), with the other component being a representation of the chosen stimulus identity, which is also encoded by OFC neurons (Raghuraman and Padoa-Schioppa, 2014; Lopatina et al., 2015). We therefore sought to link variability in spike-rate autocorrelation at rest with the variability of neuronal responses during reward-guided choices.

We re-examined the neural correlates of chosen value during choice within rhesus macaque prefrontal cortex (PFC) (Hosokawa et al., 2013; Hunt et al., 2015), and extended our analysis to the time of reward delivery (Figure 1, Figure 1—figure supplement 1). During choice, chosen value correlates were remarkably similar across all three PFC brain regions (dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC)) at the population level (Figure 1A). However, this was not the case at the time of outcome, where the chosen value correlates predominated in OFC (Figure 1B). This value signal at outcome contained information about both the chosen benefit and chosen cost (Figure 1—figure supplement 2). As well as variability in value correlates across time, there was a large degree of variability at the level of single neurons constituting the population averages, both at choice and outcome (Figure 1C–D). Within each region there were some neurons with strong chosen value correlates, but other neurons with weak or non-selective responses to chosen value.

Figure 1 with 2 supplements see all

Download asset Open asset

We hypothesised that this variability might be accounted for by intrinsic firing properties of the neurons at rest, reflecting different neurons’ temporal specialisation. We characterised resting properties of neuronal firing by examining their spike rate autocorrelation during pre-trial fixation. The decay of the autocorrelation function (ACF) provides a metric of each neuron’s temporal stability in firing rate. Careful inspection of ACFs at the level of single neurons demonstrated marked heterogeneity of ACFs across individual neurons (Figure 2), complementing previous descriptions that have examined average population responses (Murray et al., 2014; Chaudhuri et al., 2015). We fitted an exponential decay function (Murray et al., 2014) to all neurons that could be described by such an equation, yielding a single decay time constant, τ, for each neuron (446 of 857 neurons, see Figure 2—figure supplement 1 and Materials and methods). We found a large degree of heterogeneity in time constants across neurons, both within and between cortical areas (Figure 2C). Time constants were larger in the DLPFC and ACC population (Kruskal-Wallis test, p=0.0007), but most variable within OFC and ACC populations (Bartlett’s Statistic = 11.913, p=0.0026). Averaging across the ACFs of individual neurons prior to fitting the exponential equation yielded similar qualitative results to the population averages reported in Murray et al. (2014) (Figure 2—figure supplements 2 and 3).

Figure 2 with 4 supplements see all

Download asset Open asset

Our main question pertained to whether the observed variability in single-cell resting activity within PFC may determine different functional computations during a cost-benefit decision making task (Hosokawa et al., 2013; Hunt et al., 2015). We first sought to visually identify a potential relationship with chosen value by sorting the matrices in Figure 1C by time constant. To maximise our sensitivity, and because of the similarity in chosen value correlates across PFC brain regions at choice (Figure 1A), we collapsed this analysis across all three PFC regions (n = 446 neurons). We found that more neurons with high chosen value coefficient of partial determination (CPD) were more apparent at the bottom of the sorted matrix than at the top (Figure 3A), implying a relationship between chosen value coding and resting τ. To test this relationship statistically, neurons were subdivided into high and low time constant populations using a median split (Figure 3B). The population with a higher τ (more stable activity at rest) had more variance explained by chosen value during choice (permutation test (see Materials and methods), p=0.0298). We further demonstrated this relationship by performing a rank correlation between each neuron’s coefficient of partial determination (CPD) at the time of the maximum population-average CPD with its time constant (Correlation Coefficient = 0.148, p=0.0018; 95% CI [0.0556, 0.2373], Figure 3—figure supplement 1). This relationship was also present when controlling for the baseline firing rate and brain area using multiple regression (see Materials and methods, β = 0.3315, p=0.0254; 95% CI [0.0878 0.5751]).

Figure 3 with 1 supplement see all

Download asset Open asset

We then repeated the analysis in Figure 3B across all three regions. We found that the relationship between high τ and chosen coding was particularly prominent in OFC and ACC, but observed no significant difference in the chosen value coding between populations with high/low τ in DLPFC (Figure 4). If the chosen value correlates were purely related to the dynamics of choice processes, we might expect them to return to baseline levels after the choice had been executed. Although this was largely the case, a degree of chosen value coding persisted until reward outcome, particularly within OFC (Figure 1B). Within OFC, we found that persistent coding of chosen value from choice to outcome was more evident within the high τ neuronal population than within the low τ population, particularly during the experience of reward delivery (Figure 4; permutation test at time of outcome (see Materials and methods), p=0.0082). Such sustained coding of chosen value from choice through outcome was not present in ACC and DLPFC. This implies a unique neuronal signature within OFC which could contribute to the linking of choices to outcomes, a process critical for learning.

Figure 4 with 1 supplement see all

Download asset Open asset

Given the above result, we sought to address whether the same OFC neurons were signalling chosen value at the time of both choice and outcome. We performed a cross-temporal pattern analysis on data from the OFC (Kennerley et al., 2011; Stokes et al., 2013). This involves cross-correlating the chosen value regression coefficients of the entire neuronal population at all of the different time bins. If the same neurons encode chosen value at timepoints t and t+δt, one would expect a high correlation between these two timepoints; conversely, coding of chosen value by different neural ensembles would yield a far smaller, or zero, correlation. By examining the matrix of correlation coefficients at all possible timelags, different types of population neural coding can be revealed (such as transient, reactivation, or sustained coding; see Figure 5A). To avoid this analysis being confounded by noise correlations, we performed a ‘split half’ cross correlation analysis, calculating the regression coefficients for chosen value separately for odd and even trials.

Figure 5 with 2 supplements see all

Download asset Open asset

During the choice epoch, there was unsurprisingly evidence for on-diagonal coding (top left quadrant of Figure 5B). The OFC neuronal population code was also persistent across time during this epoch (warm off-diagonal elements in top left quadrant of Figure 5B), and even more so during the outcome epoch (warm off-diagonal elements in bottom right quadrant of Figure 5B). This sustained activity reflects the notion that dynamical decision processes within the OFC population may take place over several hundreds of milliseconds. Crucially, however, there was also evidence for sustained coding: the same neuronal population in OFC at choice encoded chosen value from at least 1000 ms before outcome through to 1000 ms after outcome (warm colours in Figure 5B, grey and black dashed boxes, permutation tests (see Materials and methods), largest clusters p<0.0001); such sustained coding of value from choice through outcome was absent within DLPFC (Figure 5—figure supplement 1A) and ACC (Figure 5—figure supplement 2A) neuronal populations. Within OFC, this sustained population code appeared most prominent in the neurons with a high resting time constant τ (Figure 5C), but absent in those with a low τ (Figure 5D). Note, however, that this difference should be interpreted cautiously, as a formal comparison of cluster size within the high and low τ populations (using a non-parametric permutation test, see Materials and methods) was not significant (p=0.59). Nonetheless, the sustained population code from choice through outcome was much stronger in OFC (Figure 5B–C) than in both the ACC and DLPFC populations (Figure 5—figure supplement 1 and 2; permutation tests, OFC v DLPFC, p=0008; OFC v ACC, p<0.0001; see Materials and methods). This demonstrates that OFC neurons with persistent activity at rest encode a 'sustained' representation of chosen value until an expected outcome is experienced, and that this neural signature appears unique to OFC.

We have shown that characterising the temporal receptive field of integration of individual PFC neurons based upon their resting activity has significant predictive power for describing their role in decision-making computations. These include the accumulation of evidence during choice, and the persistence of value encoding until the experience of outcome delivery.

Circuits within the prefrontal cortex are endowed with several features that may support persistent activity. These include complex pyramidal cell morphology, strong reciprocal connections, slow-decaying NMDA-Receptor transmission and augmenting synapses (Wang, 2001; Elston, 2003; Wang et al., 2006, 2008; Freeman, 1995; Wang et al., 2013). These factors may account for both the prolonged resting stability within PFC, and the ability of its neurons to support computations that subserve flexible cognition (Miller et al., 1996). However, there are different cell-classes within the PFC, with substantial heterogeneity in their morphology, synapses and expression of slow-decaying NMDA-Receptors (Wong and Wang, 2006; Zaitsev et al., 2009; Wang et al., 2013). When randomly sampling neurons within the macaque PFC, the morphology, cell-type, cortical layer and synaptic features are unknown. Recorded neurons are therefore likely sampled from separate subnetworks with differing resting stabilities and distinct roles in cognitive processing (Wang et al., 2013). This may explain the heterogeneity we observed in both resting activity and involvement in decision making computations observed across PFC neurons. Recent evidence has shown diversity in functional responses of PFC neurons dependent upon the cell-type and cortical layer in which they were located (Zhou et al., 2012; Pinto and Dan, 2015).

Most importantly in this study, we demonstrated that neurons with higher resting time constants had strong chosen value correlates at choice. Following on from our previous work (Hunt et al., 2015) – where we demonstrated that chosen value correlates can arise indirectly from the dynamics of decision processes – our result implies that neurons with more persistent resting activity are more involved in value-based choice. This provides new experimental evidence to support computational theories which attribute evidence integration to strongly recurrent attractor networks (Wang, 2002; Wong and Wang, 2006). Neurons located within these reverberant PFC subnetworks would be expected to have both higher time constants and stronger value correlates. It also indicates that such models need refinement if they are to encompass the heterogeneous correlates of decisions varaiables that we and others have observed (Kennerley et al., 2009; Meister et al., 2013). Our findings facilitate several testable predictions for research into single-neuron mechanisms of decision making. For perceptual decisions, such as the random dot-motion task, which involve the integration of evidence over time more explicitly than our cost-benefit decision paradigm, we would predict task-related neurons would also have high time constants (Gold and Shadlen, 2007).

Cross-temporal pattern analysis (Stokes et al., 2015) provides a powerful tool to allow for the interrogation of maintained activity within neuronal populations. In addition to decision-making, computational models of working memory also rely upon stable, persistent activity within richly reverberant networks for the retention of information across delays (Wang, 1999). Our data showing that evidence maintenance is indeed fulfilled by neurons with higher time constants concurs with this hypothesis. The ability to maintain a representation of chosen value across delays may explain why OFC is essential for delay-based decision making (Rudebeck et al., 2006) and why OFC damage causes decision-making and credit assignment deficits (Rudebeck et al., 2008; Noonan et al., 2010; Walton et al., 2010; Camille et al., 2011; Chau et al., 2015).

Our data on single neuron time constants have provided new insights into potential credit assignment mechanisms within the orbitofrontal cortex. Several imaging and lesion studies have argued that the OFC is involved in the assignment of credit during learning and decision-making (Walton et al., 2010; Takahashi et al., 2011; Chau et al., 2015; Akaishi et al., 2016). Single neuron studies have demonstrated OFC cells encode the reward identity across delays (Lara et al., 2009), encode specific outcome features during learning (Raghuraman and Padoa-Schioppa, 2014; Lopatina et al., 2015), and in some cases the same neurons are involved in both choice and outcome processes (Kennerley and Wallis, 2009). Indeed, there is a large body of evidence suggesting OFC signals outcome expectancies (Rangel and Hare, 2010; Schoenbaum et al., 2010). However, despite ideas that OFC is critical for credit assignment during learning, we are not aware of any study that has demonstrated what a neuronal signature of credit assignment might resemble. Here we show that OFC neurons with high temporal specializations not only encode an integrated chosen value signal during choice, but that the same OFC neurons maintain this representation through to the experience of an outcome. This neural signature - when combined with a representation of the chosen stimulus identity, which is also encoded in OFC (Raghuraman and Padoa-Schioppa, 2014; Lopatina et al., 2015) - could be a key computation for credit assignment processes.

As well as our findings at the single-neuron level, our results reiterate the value of assigning timescales at the level of a cortical area (Murray et al., 2014). We replicated the findings of Murray et al. (2014) showing that the anterior cingulate cortex (ACC) had the longest timescale within the PFC regions studied. It is possible the ACC may be supporting extended cognitive processes that our experimental paradigm was not designed to capture. These include the encoding or integrating of reward, planning and/or choice information across multiple trials (Matsumoto et al., 2007; Seo and Lee, 2007; Bernacchia et al., 2011; Hayden et al., 2011; Kennerley et al., 2011; Stoll et al., 2016). Future studies might explore the timescales of other prefrontal regions proposed to have unique roles in storing information across multiple trials, such as frontal polar cortex (Boorman et al., 2009; Donoso et al., 2014).

We demonstrate that calculating the decay in a neuron’s intrinsic resting-state autocorrelation can provide a powerful tool for predicting functional properties during cognitive tasks. Our findings therefore have important implications for how neurophysiological datasets are collected and analysed. One current method of avoiding variability in neuronal responses during cognitive tasks is pre-selection of neurons based upon their response properties; neurons with stable, persistent responses on memory guided saccade tasks are preferentially selected for analysis in decision-making tasks (Roitman and Shadlen, 2002; Huk and Shadlen, 2005; Yang and Shadlen, 2007; Mante et al., 2013; Kira et al., 2015). This method may lead investigators to record from neurons with longer temporal receptive fields, as evidenced (Murray et al., 2014) by the higher population-level time constants within the lateral intraparietal area (LIP) when neurons are screened prior to recording (Freedman and Assad, 2006) versus when they are not (Seo et al., 2009). A more unbiased characterisation of the heterogeneity of neuronal responses may be obtained by recording from all encountered neurons and categorising them post-hoc, as is more common practice in PFC studies (Freedman et al., 2001; Padoa-Schioppa and Assad, 2006; Kim et al., 2008; Kennerley et al., 2009; Hanks et al., 2015). In the context of decision-making, this has highlighted several ‘non-classical’ neuronal response profiles in regions such as LIP (Meister et al., 2013). Indeed, even in spite of pre-screening neurons prior to the task, substantial heterogeneity in task-related responses can nonetheless remain (Premereur et al., 2011). A more complete understanding of decision-making computations requires us to understand the roles of all of the neurons in these decision processes.

In summary, we have shown that functional specialisation for temporally extended computations predicts the involvement of PFC neurons in specific aspects of value-guided decision making. We anticipate that this approach may become significant in predicting the role of neurons in many other temporally extended computations dependent upon prefrontal cortex. These might include working memory (Wang et al., 2013), strategic (Seo et al., 2014) and rule-based reasoning (Buschman et al., 2012), and foraging behaviours (Hayden et al., 2011).

Neurophysiological procedures, task structure and regression analysis of single-neuron responses have been reported previously (Hosokawa et al., 2013; Hunt et al., 2015). In brief, four male rhesus macaques served as subjects. Recordings were taken from dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC) and the anterior cingulate cortex (ACC). The sample size of neurons recorded was therefore predetermined from this pre-existing dataset. The number of neurons and cortical areas recorded from each of the four subjects have been reported previously (Hosokawa et al., 2013). The regression model for analysing correlates of chosen value was the same as defined previously (Hunt et al., 2015).

1. 1. Cavanagh S
   2. Wallis J
   3. Kennerley S
   4. Hunt L

   (2016) Data from: Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

   Available at Dryad Digital Repository under a CC0 Public Domain Dedication.

   http://dx.doi.org/10.5061/dryad.5b331

1. 1. Akaishi R
   2. Kolling N
   3. Brown JW
   4. Rushworth M

   (2016) Neural mechanisms of credit assignment in a multicue environment

   Journal of Neuroscience 36:1096–1112.

   https://doi.org/10.1523/JNEUROSCI.3159-15.2016

   • Google Scholar
2. 1. Amit DJ
   2. Brunel N

   (1997) Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex

   Cerebral Cortex 7:237–252.

   https://doi.org/10.1093/cercor/7.3.237

   • Google Scholar
3. 1. Bernacchia A
   2. Seo H
   3. Lee D
   4. Wang XJ

   (2011) A reservoir of time constants for memory traces in cortical neurons

   Nature Neuroscience 14:366–372.

   https://doi.org/10.1038/nn.2752

   • Google Scholar
4. 1. Bogacz R
   2. Brown E
   3. Moehlis J
   4. Holmes P
   5. Cohen JD

   (2006) The physics of optimal decision making: a formal analysis of models of performance in two-alternative forced-choice tasks

   Psychological Review 113:700–765.

   https://doi.org/10.1037/0033-295X.113.4.700

   • Google Scholar
5. 1. Boorman ED
   2. Behrens TE
   3. Woolrich MW
   4. Rushworth MF

   (2009) How green is the grass on the other side? Frontopolar cortex and the evidence in favor of alternative courses of action

   Neuron 62:733–743.

   https://doi.org/10.1016/j.neuron.2009.05.014

   • Google Scholar
6. 1. Buschman TJ
   2. Denovellis EL
   3. Diogo C
   4. Bullock D
   5. Miller EK

   (2012) Synchronous oscillatory neural ensembles for rules in the prefrontal cortex

   Neuron 76:838–846.

   https://doi.org/10.1016/j.neuron.2012.09.029

   • Google Scholar
7. 1. Camille N
   2. Tsuchida A
   3. Fellows LK

   (2011) Double dissociation of stimulus-value and action-value learning in humans with orbitofrontal or anterior cingulate cortex damage

   Journal of Neuroscience 31:15048–15052.

   https://doi.org/10.1523/JNEUROSCI.3164-11.2011

   • Google Scholar
8. Data

   1. Cavanagh SE
   2. Wallis JD
   3. Kennerley SW
   4. Hunt LT

   (authors) (2016) Data from: Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

   Dryad Digital Repository.

   https://doi.org/10.5061/dryad.5b331
9. 1. Chau BK
   2. Sallet J
   3. Papageorgiou GK
   4. Noonan MP
   5. Bell AH
   6. Walton ME
   7. Rushworth MF

   (2015) Contrasting roles for orbitofrontal cortex and amygdala in credit assignment and learning in macaques

   Neuron 87:1106–1118.

   https://doi.org/10.1016/j.neuron.2015.08.018

   • Google Scholar
10. 1. Chaudhuri R
    2. Knoblauch K
    3. Gariel MA
    4. Kennedy H
    5. Wang XJ

    (2015) A large-scale circuit mechanism for hierarchical dynamical processing in the primate cortex

    Neuron 88:419–431.

    https://doi.org/10.1016/j.neuron.2015.09.008

    • Google Scholar
11. 1. Chen J
    2. Hasson U
    3. Honey CJ
    4. Christopher J

    (2015) Processing timescales as an organizing principle for primate cortex

    Neuron 88:244–246.

    https://doi.org/10.1016/j.neuron.2015.10.010

    • Google Scholar
12. 1. Donoso M
    2. Collins AG
    3. Koechlin E

    (2014) Human cognition. Foundations of human reasoning in the prefrontal cortex

    Science 344:1481–1486.

    https://doi.org/10.1126/science.1252254

    • Google Scholar
13. 1. Elston GN

    (2003) Cortex, cognition and the cell: new insights into the pyramidal neuron and prefrontal function

    Cerebral Cortex 13:1124–1138.

    https://doi.org/10.1093/cercor/bhg093

    • Google Scholar
14. 1. Freedman DJ
    2. Assad JA

    (2006) Experience-dependent representation of visual categories in parietal cortex

    Nature 443:85–88.

    https://doi.org/10.1038/nature05078

    • Google Scholar
15. 1. Freedman DJ
    2. Riesenhuber M
    3. Poggio T
    4. Miller EK

    (2001) Categorical representation of visual stimuli in the primate prefrontal cortex

    Science 291:312–316.

    https://doi.org/10.1126/science.291.5502.312

    • Google Scholar
16. 1. Freeman WJ

    (1995) The Hebbian paradigm reintegrated: Local reverberations as internal representations

    Behavioral and Brain Sciences 18:631.

    https://doi.org/10.1017/S0140525X0004022X

    • Google Scholar
17. 1. Gold JI
    2. Shadlen MN

    (2007) The neural basis of decision making

    Annual Review of Neuroscience 30:535–574.

    https://doi.org/10.1146/annurev.neuro.29.051605.113038

    • Google Scholar
18. 1. Hanks TD
    2. Kopec CD
    3. Brunton BW
    4. Duan CA
    5. Erlich JC
    6. Brody CD

    (2015) Distinct relationships of parietal and prefrontal cortices to evidence accumulation

    Nature 520:220–223.

    https://doi.org/10.1038/nature14066

    • Google Scholar
19. 1. Hayden BY
    2. Pearson JM
    3. Platt ML

    (2011) Neuronal basis of sequential foraging decisions in a patchy environment

    Nature Neuroscience 14:933–939.

    https://doi.org/10.1038/nn.2856

    • Google Scholar
20. 1. Hosokawa T
    2. Kennerley SW
    3. Sloan J
    4. Wallis JD

    (2013) Single-neuron mechanisms underlying cost-benefit analysis in frontal cortex

    Journal of Neuroscience 33:17385–17397.

    https://doi.org/10.1523/JNEUROSCI.2221-13.2013

    • Google Scholar
21. 1. Huk AC
    2. Shadlen MN

    (2005) Neural activity in macaque parietal cortex reflects temporal integration of visual motion signals during perceptual decision making

    Journal of Neuroscience 25:10420–10436.

    https://doi.org/10.1523/JNEUROSCI.4684-04.2005

    • Google Scholar
22. 1. Hunt LT
    2. Behrens TE
    3. Hosokawa T
    4. Wallis JD
    5. Kennerley SW

    (2015) Capturing the temporal evolution of choice across prefrontal cortex

    eLife 4:e11945.

    https://doi.org/10.7554/eLife.11945

    • Google Scholar
23. 1. Hunt LT
    2. Kolling N
    3. Soltani A
    4. Woolrich MW
    5. Rushworth MF
    6. Behrens TE

    (2012) Mechanisms underlying cortical activity during value-guided choice

    Nature Neuroscience 15:470–476.

    https://doi.org/10.1038/nn.3017

    • Google Scholar
24. 1. Jocham G
    2. Brodersen KH
    3. Constantinescu AO
    4. Kahn MC
    5. Ianni AM
    6. Walton ME
    7. Rushworth MF
    8. Behrens TE

    (2016) Reward-guided learning with and without causal attribution

    Neuron 90:177–190.

    https://doi.org/10.1016/j.neuron.2016.02.018

    • Google Scholar
25. 1. Kennerley SW
    2. Behrens TE
    3. Wallis JD

    (2011) Double dissociation of value computations in orbitofrontal and anterior cingulate neurons

    Nature Neuroscience 14:1581–1589.

    https://doi.org/10.1038/nn.2961

    • Google Scholar
26. 1. Kennerley SW
    2. Dahmubed AF
    3. Lara AH
    4. Wallis JD

    (2009) Neurons in the frontal lobe encode the value of multiple decision variables

    Journal of Cognitive Neuroscience 21:1162–1178.

    https://doi.org/10.1162/jocn.2009.21100

    • Google Scholar
27. 1. Kennerley SW
    2. Wallis JD

    (2009) Evaluating choices by single neurons in the frontal lobe: outcome value encoded across multiple decision variables

    European Journal of Neuroscience 29:2061–2073.

    https://doi.org/10.1111/j.1460-9568.2009.06743.x

    • Google Scholar
28. 1. Kim S
    2. Hwang J
    3. Lee D

    (2008) Prefrontal coding of temporally discounted values during intertemporal choice

    Neuron 59:161–172.

    https://doi.org/10.1016/j.neuron.2008.05.010

    • Google Scholar
29. 1. Kira S
    2. Yang T
    3. Shadlen MN

    (2015) A neural implementation of Wald's sequential probability ratio test

    Neuron 85:861–873.

    https://doi.org/10.1016/j.neuron.2015.01.007

    • Google Scholar
30. 1. Lara AH
    2. Kennerley SW
    3. Wallis JD

    (2009) Encoding of gustatory working memory by orbitofrontal neurons

    Journal of Neuroscience 29:765–774.

    https://doi.org/10.1523/JNEUROSCI.4637-08.2009

    • Google Scholar
31. 1. Lopatina N
    2. McDannald MA
    3. Styer CV
    4. Sadacca BF
    5. Cheer JF
    6. Schoenbaum G

    (2015) Lateral orbitofrontal neurons acquire responses to upshifted, downshifted, or blocked cues during unblocking

    eLife 4:e11299.

    https://doi.org/10.7554/eLife.11299

    • Google Scholar
32. 1. Mante V
    2. Sussillo D
    3. Shenoy KV
    4. Newsome WT

    (2013) Context-dependent computation by recurrent dynamics in prefrontal cortex

    Nature 503:78–84.

    https://doi.org/10.1038/nature12742

    • Google Scholar
33. 1. Matsumoto M
    2. Matsumoto K
    3. Abe H
    4. Tanaka K

    (2007) Medial prefrontal cell activity signaling prediction errors of action values

    Nature Neuroscience 10:647–656.

    https://doi.org/10.1038/nn1890

    • Google Scholar
34. 1. Mazurek ME
    2. Roitman JD
    3. Ditterich J
    4. Shadlen MN

    (2003) A role for neural integrators in perceptual decision making

    Cerebral Cortex 13:1257–1269.

    https://doi.org/10.1093/cercor/bhg097

    • Google Scholar
35. 1. Meister ML
    2. Hennig JA
    3. Huk AC

    (2013) Signal multiplexing and single-neuron computations in lateral intraparietal area during decision-making

    Journal of Neuroscience 33:2254–2267.

    https://doi.org/10.1523/JNEUROSCI.2984-12.2013

    • Google Scholar
36. 1. Miller EK
    2. Erickson CA
    3. Desimone R

    (1996)

    Neural mechanisms of visual working memory in prefrontal cortex of the macaque

    Journal of Neuroscience 16:5154–5167.

    • Google Scholar
37. 1. Murray JD
    2. Bernacchia A
    3. Freedman DJ
    4. Romo R
    5. Wallis JD
    6. Cai X
    7. Padoa-Schioppa C
    8. Pasternak T
    9. Seo H
    10. Lee D
    11. Wang XJ

    (2014) A hierarchy of intrinsic timescales across primate cortex

    Nature Neuroscience 17:1661–1663.

    https://doi.org/10.1038/nn.3862

    • Google Scholar
38. 1. Nichols TE
    2. Holmes AP

    (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples

    Human Brain Mapping 15:1–25.

    https://doi.org/10.1002/hbm.1058

    • Google Scholar
39. 1. Nishida S
    2. Tanaka T
    3. Shibata T
    4. Ikeda K
    5. Aso T
    6. Ogawa T

    (2014) Discharge-rate persistence of baseline activity during fixation reflects maintenance of memory-period activity in the macaque posterior parietal cortex

    Cerebral Cortex 24:1671–1685.

    https://doi.org/10.1093/cercor/bht031

    • Google Scholar
40. 1. Noonan MP
    2. Walton ME
    3. Behrens TE
    4. Sallet J
    5. Buckley MJ
    6. Rushworth MF

    (2010) Separate value comparison and learning mechanisms in macaque medial and lateral orbitofrontal cortex

    PNAS 107:20547–20552.

    https://doi.org/10.1073/pnas.1012246107

    • Google Scholar
41. 1. Ogawa T
    2. Komatsu H

    (2010) Differential temporal storage capacity in the baseline activity of neurons in macaque frontal eye field and area V4

    Journal of Neurophysiology 103:2433–2445.

    https://doi.org/10.1152/jn.01066.2009

    • Google Scholar
42. 1. Padoa-Schioppa C
    2. Assad JA

    (2006) Neurons in the orbitofrontal cortex encode economic value

    Nature 441:223–226.

    https://doi.org/10.1038/nature04676

    • Google Scholar
43. 1. Pinto L
    2. Dan Y

    (2015) Cell-type-specific activity in prefrontal cortex during goal-directed behavior

    Neuron 87:437–450.

    https://doi.org/10.1016/j.neuron.2015.06.021

    • Google Scholar
44. 1. Premereur E
    2. Vanduffel W
    3. Janssen P

    (2011) Functional heterogeneity of macaque lateral intraparietal neurons

    Journal of Neuroscience 31:12307–12317.

    https://doi.org/10.1523/JNEUROSCI.2241-11.2011

    • Google Scholar
45. 1. Raghuraman AP
    2. Padoa-Schioppa C

    (2014) Integration of multiple determinants in the neuronal computation of economic values

    Journal of Neuroscience 34:11583–11603.

    https://doi.org/10.1523/JNEUROSCI.1235-14.2014

    • Google Scholar
46. 1. Rangel A
    2. Hare T

    (2010) Neural computations associated with goal-directed choice

    Current Opinion in Neurobiology 20:262–270.

    https://doi.org/10.1016/j.conb.2010.03.001

    • Google Scholar
47. 1. Roitman JD
    2. Shadlen MN

    (2002)

    Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task

    Journal of Neuroscience 22:9475–9489.

    • Google Scholar
48. 1. Rudebeck PH
    2. Behrens TE
    3. Kennerley SW
    4. Baxter MG
    5. Buckley MJ
    6. Walton ME
    7. Rushworth MF

    (2008) Frontal cortex subregions play distinct roles in choices between actions and stimuli

    Journal of Neuroscience 28:13775–13785.

    https://doi.org/10.1523/JNEUROSCI.3541-08.2008

    • Google Scholar
49. 1. Rudebeck PH
    2. Walton ME
    3. Smyth AN
    4. Bannerman DM
    5. Rushworth MF

    (2006) Separate neural pathways process different decision costs

    Nature Neuroscience 9:1161–1168.

    https://doi.org/10.1038/nn1756

    • Google Scholar
50. 1. Rustichini A
    2. Padoa-Schioppa C

    (2015) A neuro-computational model of economic decisions

    Journal of Neurophysiology 114:1382–1398.

    https://doi.org/10.1152/jn.00184.2015

    • Google Scholar
51. 1. Schoenbaum G
    2. Roesch MR
    3. Stalnaker TA
    4. Takahashi YK

    (2010) A new perspective on the role of the orbitofrontal cortex in adaptive behaviour

    Nature Reviews Neuroscience 10:885–892.

    https://doi.org/10.1038/nrn2753

    • Google Scholar
52. 1. Seo H
    2. Barraclough DJ
    3. Lee D

    (2009) Lateral intraparietal cortex and reinforcement learning during a mixed-strategy game

    Journal of Neuroscience 29:7278–7289.

    https://doi.org/10.1523/JNEUROSCI.1479-09.2009

    • Google Scholar
53. 1. Seo H
    2. Cai X
    3. Donahue CH
    4. Lee D

    (2014) Neural correlates of strategic reasoning during competitive games

    Science 346:340–343.

    https://doi.org/10.1126/science.1256254

    • Google Scholar
54. 1. Seo H
    2. Lee D

    (2007) Temporal filtering of reward signals in the dorsal anterior cingulate cortex during a mixed-strategy game

    Journal of Neuroscience 27:8366–8377.

    https://doi.org/10.1523/JNEUROSCI.2369-07.2007

    • Google Scholar
55. 1. Stokes MG
    2. Kusunoki M
    3. Sigala N
    4. Nili H
    5. Gaffan D
    6. Duncan J

    (2013) Dynamic coding for cognitive control in prefrontal cortex

    Neuron 78:364–375.

    https://doi.org/10.1016/j.neuron.2013.01.039

    • Google Scholar
56. 1. Stokes MG
    2. Wolff MJ
    3. Spaak E

    (2015) Decoding rich spatial information with high temporal resolution

    Trends in Cognitive Sciences 19:636–638.

    https://doi.org/10.1016/j.tics.2015.08.016

    • Google Scholar
57. 1. Stoll FM
    2. Fontanier V
    3. Procyk E

    (2016) Specific frontal neural dynamics contribute to decisions to check

    Nature Communications 7:11990.

    https://doi.org/10.1038/ncomms11990

    • Google Scholar
58. 1. Takahashi YK
    2. Roesch MR
    3. Wilson RC
    4. Toreson K
    5. O'Donnell P
    6. Niv Y
    7. Schoenbaum G

    (2011) Expectancy-related changes in firing of dopamine neurons depend on orbitofrontal cortex

    Nature Neuroscience 14:1590–1597.

    https://doi.org/10.1038/nn.2957

    • Google Scholar
59. 1. Wallis JD
    2. Kennerley SW

    (2010) Heterogeneous reward signals in prefrontal cortex

    Current Opinion in Neurobiology 20:191–198.

    https://doi.org/10.1016/j.conb.2010.02.009

    • Google Scholar
60. 1. Walton ME
    2. Behrens TE
    3. Buckley MJ
    4. Rudebeck PH
    5. Rushworth MF

    (2010) Separable learning systems in the macaque brain and the role of orbitofrontal cortex in contingent learning

    Neuron 65:927–939.

    https://doi.org/10.1016/j.neuron.2010.02.027

    • Google Scholar
61. 1. Wang H
    2. Stradtman GG
    3. Wang XJ
    4. Gao WJ

    (2008) A specialized NMDA receptor function in layer 5 recurrent microcircuitry of the adult rat prefrontal cortex

    PNAS 105:16791–16796.

    https://doi.org/10.1073/pnas.0804318105

    • Google Scholar
62. 1. Wang M
    2. Yang Y
    3. Wang CJ
    4. Gamo NJ
    5. Jin LE
    6. Mazer JA
    7. Morrison JH
    8. Wang XJ
    9. Arnsten AF

    (2013) NMDA receptors subserve persistent neuronal firing during working memory in dorsolateral prefrontal cortex

    Neuron 77:736–749.

    https://doi.org/10.1016/j.neuron.2012.12.032

    • Google Scholar
63. 1. Wang XJ

    (1999)

    Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory

    Journal of Neuroscience 19:9587–9603.

    • Google Scholar
64. 1. Wang XJ

    (2001) Synaptic reverberation underlying mnemonic persistent activity

    Trends in Neurosciences 24:455–463.

    https://doi.org/10.1016/S0166-2236(00)01868-3

    • Google Scholar
65. 1. Wang XJ

    (2002) Probabilistic decision making by slow reverberation in cortical circuits

    Neuron 36:955–968.

    https://doi.org/10.1016/S0896-6273(02)01092-9

    • Google Scholar
66. 1. Wang XJ

    (2012) Neural dynamics and circuit mechanisms of decision-making

    Current Opinion in Neurobiology 22:1039–1046.

    https://doi.org/10.1016/j.conb.2012.08.006

    • Google Scholar
67. 1. Wang Y
    2. Markram H
    3. Goodman PH
    4. Berger TK
    5. Ma J
    6. Goldman-Rakic PS

    (2006) Heterogeneity in the pyramidal network of the medial prefrontal cortex

    Nature Neuroscience 9:534–542.

    https://doi.org/10.1038/nn1670

    • Google Scholar
68. 1. Wong KF
    2. Wang XJ

    (2006) A recurrent network mechanism of time integration in perceptual decisions

    Journal of Neuroscience 26:1314–1328.

    https://doi.org/10.1523/JNEUROSCI.3733-05.2006

    • Google Scholar
69. 1. Yang T
    2. Shadlen MN

    (2007) Probabilistic reasoning by neurons

    Nature 447:1075–1080.

    https://doi.org/10.1038/nature05852

    • Google Scholar
70. 1. Zaitsev AV
    2. Povysheva NV
    3. Gonzalez-Burgos G
    4. Rotaru D
    5. Fish KN
    6. Krimer LS
    7. Lewis DA

    (2009) Interneuron diversity in layers 2-3 of monkey prefrontal cortex

    Cerebral Cortex 19:1597–1615.

    https://doi.org/10.1093/cercor/bhn198

    • Google Scholar
71. 1. Zhou X
    2. Katsuki F
    3. Qi XL
    4. Constantinidis C

    (2012) Neurons with inverted tuning during the delay periods of working memory tasks in the dorsal prefrontal and posterior parietal cortex

    Journal of Neurophysiology 108:31–38.

    https://doi.org/10.1152/jn.01151.2011

    • Google Scholar

Author details

1. Sean E Cavanagh

   Sobell Department of Motor Neuroscience, University College London, London, United Kingdom

   Contribution

   SEC, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-9275-2725

2. Joni D Wallis

   1. Department of Psychology, University of California, Berkeley, Berkeley, United States
   2. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States

   Contribution

   JDW, Conception and design, Acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

3. Steven W Kennerley

   1. Sobell Department of Motor Neuroscience, University College London, London, United Kingdom
   2. Department of Psychology, University of California, Berkeley, Berkeley, United States
   3. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, United States

   Contribution

   SWK, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Laurence T Hunt

   For correspondence

   s.kennerley@ucl.ac.uk

   Competing interests

   The authors declare that no competing interests exist.

4. Laurence T Hunt

   1. Sobell Department of Motor Neuroscience, University College London, London, United Kingdom
   2. Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom

   Contribution

   LTH, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Contributed equally with

   Steven W Kennerley

   For correspondence

   laurence.hunt@ucl.ac.uk

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-8393-8533

• 5,511

  views

• 669

  downloads

• 94

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.