Rule-dependent anticipatory activity in prefrontal neurons

Cracking and Packing Information about the Features of Expected Rewards in the Orbitofrontal Cortex

The orbitofrontal cortex (OFC) is crucial for tracking various aspects of expected outcomes, thereby helping to guide choices and support learning. Our previous study showed that the effects of reward timing and size on the activity of single units in OFC were dissociable when these attributes were manipulated independently ( Roesch et al., 2006). However, in real-life decision-making scenarios, outcome features often change simultaneously, so here we investigated how OFC neurons in male rats integrate information about the timing and identity (flavor) of reward and respond to changes in these features, according to whether they were changed simultaneously or separately. We found that a substantial number of OFC neurons fired differentially to immediate versus delayed reward and to the different reward flavors. However, contrary to the previous study, selectivity for timing was strongly correlated with selectivity for identity. Taken together with the previous research, these results suggest that when reward features are correlated, OFC tends to "pack" them into unitary constructs, whereas when they are independent, OFC tends to "crack" them into separate constructs. Furthermore, we found that when both reward timing and flavor were changed, reward-responsive OFC neurons showed unique activity patterns preceding and during the omission of an expected reward. Interestingly, this OFC activity is similar and slightly preceded the ventral tegmental area dopamine (VTA DA) activity observed in a previous study ( Takahashi et al., 2023), consistent with the role of OFC in providing predictive information to VTA DA neurons.

Rules warp feature encoding in decision-making circuits

We have the capacity to follow arbitrary stimulus-response rules, meaning simple policies that guide our behavior. Rule identity is broadly encoded across decision-making circuits, but there are less data on how rules shape the computations that lead to choices. One idea is that rules could simplify these computations. When we follow a rule, there is no need to encode or compute information that is irrelevant to the current rule, which could reduce the metabolic or energetic demands of decision-making. However, it is not clear if the brain can actually take advantage of this computational simplicity. To test this idea, we recorded from neurons in 3 regions linked to decision-making, the orbitofrontal cortex (OFC), ventral striatum (VS), and dorsal striatum (DS), while macaques performed a rule-based decision-making task. Rule-based decisions were identified via modeling rules as the latent causes of decisions. This left us with a set of physically identical choices that maximized reward and information, but could not be explained by simple stimulus-response rules. Contrasting rule-based choices with these residual choices revealed that following rules (1) decreased the energetic cost of decision-making; and (2) expanded rule-relevant coding dimensions and compressed rule-irrelevant ones. Together, these results suggest that we use rules, in part, because they reduce the costs of decision-making through a distributed representational warping in decision-making circuits.

Neural Correlates of Strategy Switching in the Macaque Orbital Prefrontal Cortex

We can adapt flexibly to environment changes and search for the most appropriate rule to a context. The orbital prefrontal cortex (PFo) has been associated with decision making, rule generation and maintenance, and more generally has been considered important for behavioral flexibility. To better understand the neural mechanisms underlying the flexible behavior, we studied the ability to generate a switching signal in monkey PFo when a strategy is changed. In the strategy task, we used a visual cue to instruct two male rhesus monkeys either to repeat their most recent choice (i.e., stay strategy) or to change it (i.e., shift strategy). To identify the strategy switching-related signal, we compared nonswitch and switch trials, which cued the same or a different strategy from the previous trial, respectively. We found that the switching-related signal emerged during the cue presentation and it was combined with the strategy signal in a subpopulation of cells. Moreover, the error analysis showed that the activity of the switch-related cells reflected whether the monkeys erroneously switched or not the strategy, rather than what was required for that trial. The function of the switching signal could be to prompt the use of different strategies when older strategies are no longer appropriate, conferring the ability to adapt flexibly to environmental changes. In our task, the switching signal might contribute to the implementation of the strategy cued, overcoming potential interference effects from the strategy previously cued. Our results support the idea that ascribes to PFo an important role for behavioral flexibility.SIGNIFICANCE STATEMENT We can flexibly adapt our behavior to a changing environment. One of the prefrontal areas traditionally associated with the ability to adapt to new contingencies is the orbital prefrontal cortex (PFo). We analyzed the switching related activity using a strategy task in which two rhesus monkeys were instructed by a visual cue either to repeat or change their most recent choice, respectively using a stay or a shift strategy. We found that PFo neurons were modulated by the strategy switching signal, pointing to the importance of PFo in behavioral flexibility by generating control over the switching of strategies.

Tonic exploration governs both flexibility and lapses

In many cognitive tasks, lapses (spontaneous errors) are tacitly dismissed as the result of nuisance processes like sensorimotor noise, fatigue, or disengagement. However, some lapses could also be caused by exploratory noise: randomness in behavior that facilitates learning in changing environments. If so, then strategic processes would need only up-regulate (rather than generate) exploration to adapt to a changing environment. This view predicts that more frequent lapses should be associated with greater flexibility because these behaviors share a common cause. Here, we report that when rhesus macaques performed a set-shifting task, lapse rates were negatively correlated with perseverative error frequency across sessions, consistent with a common basis in exploration. The results could not be explained by local failures to learn. Furthermore, chronic exposure to cocaine, which is known to impair cognitive flexibility, did increase perseverative errors, but, surprisingly, also improved overall set-shifting task performance by reducing lapse rates. We reconcile these results with a state-switching model in which cocaine decreases exploration by deepening attractor basins corresponding to rule states. These results support the idea that exploratory noise contributes to lapses, affecting rule-based decision-making even when it has no strategic value, and suggest that one key mechanism for regulating exploration may be the depth of rule states.

Behavioral evidence for the use of functional categories during group reversal task performance in monkeys

A functional category is a set of stimuli that are regarded as equivalent independently of their physical properties and elicit the same behavioral responses. Major psychological theories suggest the ability to form and utilize functional categories as a basis of higher cognition that markedly increases behavioral flexibility. Vaughan claimed the category use in pigeons on the basis of partition, a mathematical criterion for equivalence, however, there have been some criticisms that the evidence he showed was insufficient. In this study, by using a group reversal task, a procedure originally used by Vaughan, we aimed to gather further evidence to prove the category use in animals. Macaque monkeys, which served as subjects in our study, could efficiently perform the task not only with familiar stimulus sets as Vaughan demonstrated but also with novel sets, and furthermore the task performance was stable even when the number of stimuli in a set was increased, which we consider as further evidence for the category use in animals. In addition, by varying the timing of the reversal, we found that a category formation takes place soon after encountering new stimuli, i.e. in a few blocks of trial after a novel stimulus set was introduced.

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.

Rule Encoding in Orbitofrontal Cortex and Striatum Guides Selection

Active maintenance of rules, like other executive functions, is often thought to be the domain of a discrete executive system. An alternative view is that rule maintenance is a broadly distributed function relying on widespread cortical and subcortical circuits. Tentative evidence supporting this view comes from research showing some rule selectivity in the orbitofrontal cortex and dorsal striatum. We recorded in these regions and in the ventral striatum, which has not been associated previously with rule representation, as macaques performed a Wisconsin Card Sorting Task. We found robust encoding of rule category (color vs shape) and rule identity (six possible rules) in all three regions. Rule identity modulated responses to potential choice targets, suggesting that rule information guides behavior by highlighting choice targets. The effects that we observed were not explained by differences in behavioral performance across rules and thus cannot be attributed to reward expectation. Our results suggest that rule maintenance and rule-guided selection of options are distributed processes and provide new insight into orbital and striatal contributions to executive control.

SIGNIFICANCE STATEMENT

Rule maintenance, an important executive function, is generally thought to rely on dorsolateral brain regions. In this study, we examined activity of single neurons in orbitofrontal cortex and in ventral and dorsal striatum of macaques in a Wisconsin Card Sorting Task. Neurons in all three areas encoded rules and rule categories robustly. Rule identity also affected neural responses to potential choice options, suggesting that stored information is used to influence decisions. These results endorse the hypothesis that rule maintenance is a broadly distributed mental operation.

Neuronal responses support a role for orbitofrontal cortex in cognitive set reconfiguration

We are often faced with the need to abandon no-longer beneficial rules and adopt new ones. This process, known as cognitive set reconfiguration, is a hallmark of executive control. Although cognitive functions like reconfiguration are most often associated with dorsal prefrontal structures, recent evidence suggests that the orbitofrontal cortex (OFC) may play an important role as well. We recorded the activity of OFC neurons while rhesus macaques performed an analogue of the Wisconsin Card Sorting Task that involved a trial and error stage. The OFC neurons demonstrated two types of switch-related activity, an early (switch-away) signal and a late (switch-to) signal, when the new task set was established. We also found a pattern of match modulation: a significant change in activity for the stimulus that matched the current perceptual rule (and would therefore be selected). These results extend our understanding of the executive functions of the OFC. They also allow us to directly compare the OFC with the complementary datasets we previously collected in the ventral (VS) and dorsal (DS) striatum. Although both effects are observed in all three areas, the timing of responses aligns the OFC more closely with DS than with VS.

Comparative Overview of Visuospatial Working Memory in Monkeys and Rats

Neural mechanisms of working memory, particularly its visuospatial aspect, have long been studied in non-human primates. On the other hand, rodents are becoming more important in systems neuroscience, as many of the innovative research methods have become available for them. There has been a question on whether primates and rodents have similar neural backgrounds for working memory. In this article, we carried out a comparative overview of the neural mechanisms of visuospatial working memory in monkeys and rats. In monkeys, a number of lesion studies indicate that the brain region most responsible for visuospatial working memory is the ventral dorsolateral prefrontal cortex (vDLPFC), as the performance in the standard tests for visuospatial working memory, such as delayed response and delayed alternation tasks, are impaired by lesions in this region. Single-unit studies revealed a characteristic firing pattern in neurons in this area, a sustained delay activity. Further studies indicated that the information maintained in the working memory, such as cue location and response direction in a delayed response, is coded in the sustained delay activity. In rats, an area comparable to the monkey vDLPFC was found to be the dorsal part of the medial prefrontal cortex (mPFC), as the delayed alternation in a T-maze is impaired by its lesion. Recently, the sustained delay activity similar to that found in monkeys has been found in the dorsal mPFC of rats performing the delayed response task. Furthermore, anatomical studies indicate that the vDLPFC in monkeys and the dorsal mPFC in rats have much in common, such as that they are both the major targets of parieto-frontal projections. Thus lines of evidence indicate that in both monkeys and rodents, the PFC plays a critical role in working memory.

Representation of Functional Category in the Monkey Prefrontal Cortex and Its Rule-Dependent Use for Behavioral Selection

Humans, monkeys, and other animals are considered to have the cognitive ability to use functional categories--that is, stimulus groups based on functional equivalence independent of physical properties. To investigate the underlying neural mechanisms of the use of functional categories, we recorded single-unit activity in the prefrontal cortex of monkeys performing a behavioral task in which the rule-dependent usage of functional category was needed to select an appropriate response. We found a neural correlate of functional categories on the single-neuron level and found that category information is coded independently of other task-relevant information such as rule and contingency information. Analysis of the time course of the information activation suggested that contingency information used for action selection is derived by integrating incoming category information with rule information maintained throughout a session. Such neural computation can be considered as the neural background of flexible behavioral control based on category and rule.

Neurophysiology of rule switching in the corticostriatal circuit

The ability to adjust behavioral responses to cues in a changing environment is crucial for survival. Activity in the medial Prefrontal Cortex (mPFC) is thought to both represent rules to guide behavior as well as detect and resolve conflicts between rules in changing contingencies. While lesion and pharmacological studies have supported a crucial role for mPFC in this type of set-shifting, an understanding of how mPFC represents current rules or detects and resolves conflict between different rules is still unclear. Meanwhile, medial dorsal striatum (mDS) receives major projections from mPFC and neural activity of mDS is closely linked to action selection, making the mDS a potential major player for enacting rule-guided action policies. However, exactly what is signaled by mPFC and how this impacts neural signals in mDS is not well known. In this review, we will summarize what is known about neural signals of rules and set shifting in both prefrontal cortex and dorsal striatum, as well as provide questions and directions for future experiments.

Hierarchical Error Representation: A Computational Model of Anterior Cingulate and Dorsolateral Prefrontal Cortex

Anterior cingulate and dorsolateral prefrontal cortex (ACC and dlPFC, respectively) are core components of the cognitive control network. Activation of these regions is routinely observed in tasks that involve monitoring the external environment and maintaining information in order to generate appropriate responses. Despite the ubiquity of studies reporting coactivation of these two regions, a consensus on how they interact to support cognitive control has yet to emerge. In this letter, we present a new hypothesis and computational model of ACC and dlPFC. The error representation hypothesis states that multidimensional error signals generated by ACC in response to surprising outcomes are used to train representations of expected error in dlPFC, which are then associated with relevant task stimuli. Error representations maintained in dlPFC are in turn used to modulate predictive activity in ACC in order to generate better estimates of the likely outcomes of actions. We formalize the error representation hypothesis in a new computational model based on our previous model of ACC. The hierarchical error representation (HER) model of ACC/dlPFC suggests a mechanism by which hierarchically organized layers within ACC and dlPFC interact in order to solve sophisticated cognitive tasks. In a series of simulations, we demonstrate the ability of the HER model to autonomously learn to perform structured tasks in a manner comparable to human performance, and we show that the HER model outperforms current deep learning networks by an order of magnitude.

Dendritic nonlinearities reduce network size requirements and mediate ON and OFF states of persistent activity in a PFC microcircuit model

Technological advances have unraveled the existence of small clusters of co-active neurons in the neocortex. The functional implications of these microcircuits are in large part unexplored. Using a heavily constrained biophysical model of a L5 PFC microcircuit, we recently showed that these structures act as tunable modules of persistent activity, the cellular correlate of working memory. Here, we investigate the mechanisms that underlie persistent activity emergence (ON) and termination (OFF) and search for the minimum network size required for expressing these states within physiological regimes. We show that (a) NMDA-mediated dendritic spikes gate the induction of persistent firing in the microcircuit. (b) The minimum network size required for persistent activity induction is inversely proportional to the synaptic drive of each excitatory neuron. (c) Relaxation of connectivity and synaptic delay constraints eliminates the gating effect of NMDA spikes, albeit at a cost of much larger networks. (d) Persistent activity termination by increased inhibition depends on the strength of the synaptic input and is negatively modulated by dADP. (e) Slow synaptic mechanisms and network activity contain predictive information regarding the ability of a given stimulus to turn ON and/or OFF persistent firing in the microcircuit model. Overall, this study zooms out from dendrites to cell assemblies and suggests a tight interaction between dendritic non-linearities and network properties (size/connectivity) that may facilitate the short-memory function of the PFC.

Working memory, the ability to retain information for a short period of time, is a fundamental cognitive function that shapes behavior. The cellular correlate of working memory is the prolonged spiking (persistent) activity of neurons in the prefrontal cortex. Impairments of prefrontal cortex functionalities and working memory have been associated with a variety of cognitive disorders, such as schizophrenia, the attention deficit hyperactivity disorder, and drug addiction. Hence, understanding how neurons embedded in the local circuitry support and maintain persistent activity is of outmost importance. Our work uses a multi-level integrative approach spanning from the dendritic, to the neuronal and network levels to identify the key biophysical and anatomical mechanisms contributing to persistent activity, leading to a number of high impact findings: it predicts a tradeoff between dendritic regenerative events and the size of a network expressing persistent activity. It also proposes when and how the persistent state can be stabilized, opening new avenues for pharmacological interventions. Finally, it describes decoding mechanisms for upcoming ON/OFF state transitions, furthering our understanding of information processing in the PFC and shedding new light on the emergence of anticipatory behaviors.

A tweaking principle for executive control: neuronal circuit mechanism for rule-based task switching and conflict resolution

A hallmark of executive control is the brain's agility to shift between different tasks depending on the behavioral rule currently in play. In this work, we propose a "tweaking hypothesis" for task switching: a weak rule signal provides a small bias that is dramatically amplified by reverberating attractor dynamics in neural circuits for stimulus categorization and action selection, leading to an all-or-none reconfiguration of sensory-motor mapping. Based on this principle, we developed a biologically realistic model with multiple modules for task switching. We found that the model quantitatively accounts for complex task switching behavior: switch cost, congruency effect, and task-response interaction; as well as monkey's single-neuron activity associated with task switching. The model yields several testable predictions, in particular, that category-selective neurons play a key role in resolving sensory-motor conflict. This work represents a neural circuit model for task switching and sheds insights in the brain mechanism of a fundamental cognitive capability.

Electrophysiological responses to feedback during the application of abstract rules

Much research focuses on how people acquire concrete stimulus-response associations from experience; however, few neuroscientific studies have examined how people learn about and select among abstract rules. To address this issue, we recorded ERPs as participants performed an abstract rule-learning task. In each trial, they viewed a sample number and two test numbers. Participants then chose a test number using one of three abstract mathematical rules they freely selected from: greater than the sample number, less than the sample number, or equal to the sample number. No one rule was always rewarded, but some rules were rewarded more frequently than others. To maximize their earnings, participants needed to learn which rules were rewarded most frequently. All participants learned to select the best rules for repeating and novel stimulus sets that obeyed the overall reward probabilities. Participants differed, however, in the extent to which they overgeneralized those rules to repeating stimulus sets that deviated from the overall reward probabilities. The feedback-related negativity (FRN), an ERP component thought to reflect reward prediction error, paralleled behavior. The FRN was sensitive to item-specific reward probabilities in participants who detected the deviant stimulus set, and the FRN was sensitive to overall reward probabilities in participants who did not. These results show that the FRN is sensitive to the utility of abstract rules and that the individual's representation of a task's states and actions shapes behavior as well as the FRN.

Neuronal activity during a cued strategy task: comparison of dorsolateral, orbital, and polar prefrontal cortex

We compared neuronal activity in the dorsolateral (PFdl), orbital (PFo), and polar (PFp) prefrontal cortex as monkeys performed three tasks. In two tasks, a cue instructed one of two strategies: stay with the previous response or shift to the alternative. Visual stimuli served as cues in one of these tasks; in the other, fluid rewards did so. In the third task, visuospatial cues instructed each response. A delay period followed each cue. As reported previously, PFdl encoded strategies (stay or shift) and responses (left or right) during the cue and delay periods, while PFo encoded strategies and PFp encoded neither strategies nor responses; during the feedback period, all three areas encoded responses, but not strategies. Four novel findings emerged from the present analysis. (1) The strategy encoded by PFdl and PFo cells during the cue and delay periods was modality specific. (2) The response encoded by PFdl cells was task and modality specific during the cue period, but during the delay and feedback periods it became task and modality general. (3) Although some PFdl and PFo cells responded to or anticipated rewards, we could rule out reward effects for most strategy- and response-related activity. (4) Immediately before feedback, only PFp signaled responses that were correct according to the cued strategy; after feedback, only PFo signaled the response that had been made, whether correct or incorrect. These signals support a role in generating responses by PFdl, assigning outcomes to choices by PFo, and assigning outcomes to cognitive processes by PFp.

Predictive features of persistent activity emergence in regular spiking and intrinsic bursting model neurons

Proper functioning of working memory involves the expression of stimulus-selective persistent activity in pyramidal neurons of the prefrontal cortex (PFC), which refers to neural activity that persists for seconds beyond the end of the stimulus. The mechanisms which PFC pyramidal neurons use to discriminate between preferred vs. neutral inputs at the cellular level are largely unknown. Moreover, the presence of pyramidal cell subtypes with different firing patterns, such as regular spiking and intrinsic bursting, raises the question as to what their distinct role might be in persistent firing in the PFC. Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS) and an intrinsic bursting (IB) model neuron. We identify synaptic location within the basal dendrites as a feature of stimulus selectivity. Specifically, persistent activity-inducing stimuli consist of activated synapses that are located more distally from the soma compared to non-inducing stimuli, in both model cells. In addition, the action potential (AP) latency and the first few inter-spike-intervals of the neuronal response can be used to reliably detect inducing vs. non-inducing inputs, suggesting a potential mechanism by which downstream neurons can rapidly decode the upcoming emergence of persistent activity. While the two model neurons did not differ in the coding features of persistent activity emergence, the properties of persistent activity, such as the firing pattern and the duration of temporally-restricted persistent activity were distinct. Collectively, our results pinpoint to specific features of the neuronal response to a given stimulus that code for its ability to induce persistent activity and predict differential roles of RS and IB neurons in persistent activity expression.

Memory, referred to as the ability to retain, store and recall information, represents one of the most fundamental cognitive functions in daily life. A significant feature of memory processes is selectivity to particular events or items that are important to our survival and relevant to specific situations. For long-term memory, the selectivity to a specific stimulus is seen both at the behavioral as well as the cellular level. For working memory, a type of short-term memory involved in decision making and attention processes, stimulus selectivity has been observed in vivo using spatial working memory tasks. In addition, persistent activity, which is the cellular correlate of working memory, is also selective to specific stimuli for each neuron, suggesting that each neuron has a ‘memory field’. Our study proposes that both the location of incoming inputs onto the neuronal dendritic tree and specific temporal features of the neuronal response can be used to predict the emergence of persistent activity in two neuron models with different firing patterns, revealing possible mechanisms for generating and propagating stimulus-selectivity in working memory processes. The study also reveals that neurons with different firing patterns may have different roles in persistent activity expression.

Neural correlates of changing intention in the human FEF and IPS

Previous research demonstrates that our apparent mental flexibility depends largely on the strength of our prior intention; changing our intention in advance enables a smooth transition from one task to another (e.g., Astle DE, Jackson GM, Swainson R. J Cogn Neurosci 20: 255–267, 2008; Duncan J, Emslie H, Williams P, Johnson R, Freer C. Cogn Psychol 30: 257–303, 1996; Husain M, Parton A, Hodgson TL, Mort D, Rees G. Nat Neurosci 6: 117–118, 2003). However, these necessarily rapid anticipatory mechanisms have been difficult to study in the human brain. We used EEG and magnetoencephalography, specifically event-related potentials and fields (ERPs and ERFs), respectively, to explore the neural correlates of this important aspect of mental flexibility. Subjects performed a manual version of a pro/antisaccade task using preparatory cues to switch between the pro- and antirules. When subjects switched their intention, we observed a positivity over central electrodes, which correlated significantly with our behavioral data; the greater the ERP effect, the stronger the subject's change of intention. ERFs, alongside subject-specific structural MRIs, were used to project into source space. When subjects switched their intention, they showed significantly elevated activity in the right frontal eye field and left intraparietal sulcus (IPS); the greater the left IPS activity on switch trials, the stronger the subject's change of intention. This network has previously been implicated in the top-down control of eye movements, but here we demonstrate its role in the top-down control of a task set, in particular, that it is recruited when we change the task that we intend to perform.

Comparison of strategy signals in the dorsolateral and orbital prefrontal cortex

Abstract behavior-guiding rules and strategies allow monkeys to avoid errors in rarely encountered situations. In the present study, we contrasted strategy-related neuronal activity in the dorsolateral prefrontal cortex (PFdl) and the orbital prefrontal cortex (PFo) of rhesus monkeys. On each trial of their behavioral task, the monkeys responded to a foveal visual cue by making a saccade to one of two spatial targets. One response required a leftward saccade, the other required a saccade of equal magnitude to the right. The cues instructed the monkeys to follow one of two response strategies: to stay with their most recent successful response or to shift to the alternative response. Neurons in both areas encoded the stay and shift strategies after the cue appeared, but there were three major differences between the PFo and the PFdl: (1) many strategy-encoding cells in PFdl also encoded the response (left or right), but few, if any, PFo cells did so; (2) strategy selectivity appeared earlier in PFo than in PFdl; and (3) on error trials, PFo neurons encoded the correct strategy-the one that had been cued but not implemented-whereas in PFdl the strategy signals were weak or absent on error trials. These findings indicate that PFo and PFdl both contribute to behaviors guided by abstract response strategies, but do so differently, with PFo encoding a strategy and PFdl encoding a response based on a strategy.