You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction
============

The primate fronto-striatal system, which plays an important role in temporal coordination of goal-directed behavior, consists of a network of neuronal circuits that integrate spatial and timing information for behavioral purpose (Alexander and Crutcher, [@B1]; Hoshi and Tanji, [@B19]; Staddon [@B58]; Miller and Phelps, [@B36]). Previous studies have demonstrated that pre-movement firing in fronto-parietal cortex and basal ganglia mediates preparation and initiation of both sensory guided and self-initiated movements (Horak and Anderson, [@B17]; Gardiner and Nelson, [@B13]; Romo et al., [@B49]; Turner and Anderson, [@B62]; Lee and Assad, [@B30]; Churchland et al., [@B4]; Tsujimoto et al., [@B61]). In particular, it has been suggested that basal ganglia modulate motor performance ("dynamics" or "movement vigor") under the effect of motivational factors quantified as context-specific cost/reward functions (for review see Hayden et al., [@B15]; Turner and Desmurget, [@B63]). Motor planning involves programming of the direction of movement, the kinematics, and the goal of movement (Kalaska and Crammond, [@B20]; McCoy and Platt, [@B35]; Platt and Huettel, [@B46]; for review Opris and Bruce, [@B41]). Motor areas of the brain also specify movement vigor which is overtly represented by the reaction time (RT) and the speed with which a movement is performed. The choice of these behavioral parameters is mediated by the activation of midbrain\'s dopaminergic projections to fronto-parietal cortex and dorsal striatum that track successful and erroneous behaviors and the contingencies between the behaviors and rewards (Romo and Schultz, [@B50]; Gaspar et al., [@B14]; Kiyatkin and Rebec, [@B23]; Fiorillo et al., [@B9]).

Although reward probability is an important factor in shaping animal\'s behavior (Herrnstein, [@B12]; Sugrue et al., [@B60]), it is not well understood *how the cortico-striatal circuits translate reward probability into the vigor of movement*. We hypothesized that the dorsal striatum (Putamen and Caudate Nucleus) of primates is part of the system that modulates the movement vigor (i.e., RT and speed), depending on the probability of the expected reward. This hypothesis is supported by the finding that changes in dorsal striatal activity occur shortly after go-cues and clearly earlier than the movements (100--200 ms before movements). Therefore, it is possible that changes in reward expectation are processed by the neostriatum (NS), which biases both motor planning and preparation (Mirenowicz and Schultz, [@B37]; Shidara et al., [@B56]; Lauwereyns et al., [@B25]; Simmons and Richmond, [@B57]). Dorsal striatum may be responsible for enhancing movement vigor when rewards are certain and decreasing the vigor when rewards become uncertain (Seideman et al., [@B54]; Ditterich, [@B8]; Wittmann et al., [@B65]; Van der Meer and Redish, [@B64]; Machens et al., [@B33]).

To elucidate NS modulations that putatively mediate the translation of reward probability into the changes of movement vigor, we trained two rhesus monkeys in a RT task in which they produced wrist flexions or extensions in response to vibratory and visually cues (Lebedev and Nelson, [@B28]). Trial outcome was made uncertain by rewarding the monkeys for correct performance only in 75% of the trials. Monkeys were uncertain about an upcoming reward in all trials except for the trials that immediately followed withheld rewards. In these trials the monkeys were certain about the outcome because they were always rewarded. Given these trial to trial changes in reward probability, we determined if the activity of dorsal striatal neurons that was associated with motor preparation, varied as a function of reward expectation and whether it was correlated with changes in movement timing and wrist kinematics.

Materials and Methods
=====================

Experimental apparatus and behavioral paradigm
----------------------------------------------

Two adult male rhesus monkeys (*Macaca mulatta*: E, N) were trained to make wrist flexion and extension movements in response to vibratory or visual go-cues (Lebedev and Nelson, [@B26], [@B28]; Liu et al., [@B32]). The monkeys were cared for in accordance with the National Research Council Guide for the Care and Use of Laboratory Animals. Experimental protocols were approved by the Animal Care and Use Committee of The University of Tennessee Health Science Center, Memphis. Detailed descriptions of the experimental apparatus have been provided elsewhere (Lebedev and Nelson, [@B26], [@B28]; Liu et al., [@B31]). A brief description is provided below.

### Experimental apparatus

Each monkey sat in an acrylic monkey chair, with its right palm on a movable plate. One end of the plate was attached to the axle of a brushless D.C. torque motor (Colburn and Evarts, [@B6]). A load of 0.07 Nm was applied to the plate. The load assisted wrist extensions and opposed wrist flexions. Feedback of current wrist position was provided by a visual display consisting of 31 light-emitting diodes (LEDs), located 35 cm in front of the animal. The middle, red LED corresponded to a centered wrist position. Yellow LEDs above and below the middle LED indicate successive angular deviations of 1°. Two instructional LED were located in the upper left corner of the visual display. When the first, red LED was illuminated at the start of a trial, it indicated that extension movements should be made; otherwise flexions were required. When the second, green LED was illuminated, it informed the monkey that the go-cue for that trial would be palmar vibration; otherwise, the go-cue was the illumination of one of two LEDs which were each 5° from the center. Neuronal activity was triggered by vibratory cues at 57 Hz or by visual go-cues.

### Behavioral task

The behavioral paradigm is illustrated schematically in Figure [1](#F1){ref-type="fig"}A. Monkeys made vibratory and visually cued wrist flexion and extension movements after holding a steady position during an instructed delay period lasting 0.5--2.0 s. Wrist movements were guided by either vibratory cues (VIB-trials) or visual cues (VIS-trials). For vibratory stimulus (VIB) trials, movements were triggered by vibration to the monkey\'s palm. For the visual stimulus (VIS) trials, movements were initiated by the appearance of a visual target that indicated the movement endpoint. Trials began when the monkey centered the plate. Each task trial had three basic phases: the instructed delay phase, reaction phase (partition of RT is shown in Figure [1](#F1){ref-type="fig"}B) and movement phase. Correct performance in the task was rewarded pseudo-randomly in only 75% of the trials, with the unrewarded trials never being imposed sequentially.

![**(A)** Schematic description of the behavioral paradigm. The direction cue was given by a red LED that was illuminated during extension trials, but not during flexion trials. The modality cue was a green LED that was illuminated during vibratory cued trials but not during visually cued trials. The onset of instructional cues was coincident with the onset of the hold period. They remained lit until the end of the trial, coincident with reward delivery. Go-cues that signaled the monkeys could initiate wrist movements were presented after a variable time delay of 0.5, 1.0, 1.5, or 2.0 s (pseudo-randomized). **(B)** Divisions of the reaction time (RT) interval. RT has been split into two intervals: R1, the latency from cue onset (COS) to pre-movement activity onset (AOS), and R2, the time from AOS until movement onset (MOS).](fnins-05-00061-g001){#F1}

Our pseudo-random reward schedule used the following types of trials: (i) unrewarded trials, (ii) trials immediately following the unrewarded ones called after trials ("A" trials), and (iii) rewarded trials, for which the current and the preceding trial were rewarded, called regular (R) trials). We grouped individual trials by the number of previously rewarded trials that preceded each trial in the group, as well as, by the direction of the movement made in that trial. In some instances there were trials in which the animal failed to perform properly (i.e., made a movement in the wrong direction). These error trials are conceptually different from the A trials since rewards were withheld because of incorrect performance rather than arbitrarily. These were marked separately in the data stream, not being under consideration here. For analyses of sequential effects, we required that each group from the records of a neuron have at least four valid trials. If any single group of records had fewer than four trials, the data from that group were not included in the analyses.

### Reward probability

The probability of reward was not indicated to the animal except via prior experience. The key manipulation in the task was to distinguish between "certain" rewards that occurred only in trials following withheld rewards (25% of trials), and the "uncertain" rewards occurring in the subsequent 50% of the trials. In Figure [2](#F2){ref-type="fig"}A we show two blocks of trials with unrewarded (U) and rewarded ("A" being the first rewarded trial and R the subsequent rewards) trials. The unrewarded U trial acts as a cue indicating a certain reward, coded as "A" trial. In order to properly address the temporal aspect of movement planning under certain vs. uncertain reward, trials were re-coded to reflect the number of previously rewarded trials that occurred, in sequence, prior to the trial in question. Trials belonging to these groups had been preceded by none, one, two, or three previously rewarded trials in sequence ("A," S-1, S-2, or S-3). The next trial groups in the sequence usually contain less than four trials, that are not enough to be considered for statistical analyses Thus, as it is shown in Figure [2](#F2){ref-type="fig"}B (depicting the probability of reward in each group), reward was certain in group "A," and uncertain in the groups S-1 to S-3 (with reward uncertainty increasing as trials advanced from group S-1 to S-3).

![**(A)** Sequential grouping of rewarded trials. Each block of 10 trials contained rewarded (R) and unrewarded (U) trials and rewarded trials (with A being the first rewarded trial following the no-reward trial). Trials are grouped based the number of previously rewarded. Trials belonging to these groups had been preceded by none, one, two, or three previously rewarded trials in sequence (A, S-1, S-2, or S-3). **(B)** Reward probability for trial groups. Trials are split in certain rewarded trials (A group) and uncertain rewarded trials (S-1, S-2, and S-3). The gray shadow suggests the progression from certain (white) to uncertain (gray) rewards.](fnins-05-00061-g002){#F2}

Electrophysiological recordings and histology
---------------------------------------------

Once an animal reached a stable daily performance level (∼2000 rewarded trials per experimental session), it was prepared for recording. A stainless steel recording chamber was surgically implanted over the skull to allow for extracellular recordings of the activity of basal ganglia neurons by using platinum--iridium microelectrodes with impedances of 1--2 MΩ (see Gardiner and Nelson, [@B13]; Liu et al., [@B32]). Transdural penetrations began no sooner than 1 week after the chamber implantation. In each recording session, a microelectrode was lowered into the striatum and the activity of single units was amplified, discriminated, and stored in a computer by conventional means (Lebedev and Nelson, [@B26]; Liu et al., [@B32]). Neuronal receptive fields (RFs) were examined by lightly touching punctuate skin surfaces, manipulating joints, and palpating muscles. On the last recording day, electrolytic lesions were made to mark some recording locations by passing 10 μA of current for 10--20 s. These lesions provided references for the histological reconstruction of the recording sites. The animal was then deeply anesthetized with sodium pentobarbital and transcardially perfused with 10% buffered formol-saline. The brain was removed from the skull, and cut on a freezing microtome into 50 μm thick coronal sections. Histological sections of the basal ganglia were stained for Nissl substance. Recording sites were reconstructed based on the depth of each electrode penetration and its location with respect to the marking lesions.

Data analysis
-------------

Neuronal activity data, recorded on-line (Lebedev and Nelson, [@B26], [@B27], [@B28]), were processed by off-line analysis programs and displayed as rasters, peri-event histograms (PEH), cumulative sum plots (CUSUM), and traces of position, aligned on the task events. The changes of neuronal activity associated with wrist movement were analyzed using PEHs and raster displays. In addition, the CUSUM plots (see, e.g., Lebedev and Nelson, [@B26]) in which mean firing rates are given by the plot\'s slopes, illustrate the onset of significant increase in discharge before movement onset (MOS). The baseline activity (Bkg) of each recorded neuron was calculated as its mean firing rate during the 250 ms prior to the presentation of cues, while the animal held his wrist in a centered position. The first change in the CUSUM of more than 3 SDs, lasting for at least 40 ms, was designated as the activity onset (Onset or AOS). The total number of spikes occurring from AOS until MOS divided by the interval divided by the number of trials was designated as the cell\'s pre-movement response (Resp). The period between AOS and MOS is the pre-movement time (R2) defined in Figure [1](#F1){ref-type="fig"}B. The time between the presentations of go-cue (Cue onsets, COS) and MOS represents the RT and the time between MOS and movement offset (MOF) is defined as the movement time (MT). Both MOS and MOF were determined from the position traces during movement as the times of significant changes in the wrist position, matching the wrist velocity onset or offset, respectively.

Results
=======

Database
--------

A total of 236 neurons were recorded, of these 149 (∼63%) were selected for further analysis, because each neuron: (i) had pre-movement activity (PMA) changes following the vibratory or visual go-cue onset and prior to MOS, (ii) had a PMA firing rate that was at least 3 SDs different from the baseline firing rate, and (iii) was held long enough to record at least 25 trials for each movement direction. Of these, 99/149 (∼66%) also had a complete set of recordings during visually cued trials. Of the selected NS neurons, 104/149 (∼70%) neurons were located in Putamen, 20/149 (∼13%) in the Caudate Nucleus, 18/149 (∼12%) in the cellular bridges in between these structures and 7/149 neurons were localized in the nearby regions. The total number of neostriatal cells categorized by the cue modality to which they responded (vibratory, VIB; visual, VIS), movement direction (flexions, Flex, extensions, Ext) and reward sequence (A, S-1, S-2, S-3) is shown in Table [1](#T1){ref-type="table"}.

###### 

**Neurons having sufficient trials for timing and activity analyses as a function of reward (un)certainty**.

  Sensory modality   Movement direction   Reward               
  ------------------ -------------------- -------- ----- ----- -----
  VIB                Flex                 147      149   140   117
                     Ext                  149      146   139   126
  VIS                Flex                 99       98    94    86
                     Ext                  99       97    95    85

Neostriatal cell firing for certain and uncertain rewards
---------------------------------------------------------

A significant proportion of neurons in dorsal striatum modulated their firing during this task. Figure [3](#F3){ref-type="fig"}A shows an example of a striatal neuron with increased modulations in trials with certain rewards ("A" trials). This neuron was recorded from the cellular bridge between caudate and putamen. Pre-movement firing is illustrated for vibratory cued trials. The PETHs and spike rasters are aligned on MOS. COS are indicated by blue dots, and reward delivery by red dots. Wrist flexion trials with certain rewards ("A" trials) and the subsequent trials with uncertain rewards (S-1 to S-3) are shown. The PEHs indicate that this neuron\'s activity was modulated during both the RT epoch (from COS to MOS) and during movements. Wrist trajectories are shown in Figure [3](#F3){ref-type="fig"}B. It can be seen that in "A" trials the monkey initiated flexions earlier and moved faster and that the activity of the illustrated neuron was higher during these trials.

![**Example of dorsal striatal cell recorded under unpredicted reward schedule**. **(A)** Each peri-event histogram illustrates neuronal activity expressed as mean firing rate (in spikes/s), together with raster displays aligned on MOS. The left panel display the NS activity during certain reward trials and the next panels represent the activity during uncertain reward trials. In the raster display, rows represent individual trials, dots represent single spikes, while the left and right bold dots represent vibratory cue onset and reward delivery, respectively. Bin width was equal to 5 ms. **(B)** Wrist position traces for each flexion trial are presented at the bottom of each panel.](fnins-05-00061-g003){#F3}

### Modulation of pre-movement activity by reward uncertainty

It has been suggested that reward probability biases neural activity by altering either the rate or the duration of cell firing (Lauwereyns et al., [@B25]). Figure [4](#F4){ref-type="fig"} illustrates these features for our experiment. Average RT was the shortest for "A" trials, and as the RT "rubber-band" (Renoult et al., [@B48]) got shorter, so did the timing of the illustrated striatal neuron. For the illustrated striatal neuron, the duration of PMA (i.e., the interval between activity onset, AOS, and MOS) decreased, from 147, 139, and 157 ms in S-1, S-2, and S-3 trials, respectively, to 102 ms in "A" trials. Thus, the change in movement vigor manifested itself as change in RT and wrist velocity and accompanying changes in the timing of a striatal neuron\'s activity. Note also that the slope of rate change in the striatal neuron increased in "A" trials (compare with similar findings in Lebedev et al., [@B29]). When the reward was certain, RT shortened, wrist velocity increased, the duration of striatal pre-movement firing contracted and the slope of pre-movement modulation increased in the striatum.

![**Movement plans as a function of the probability of expected reward**. Smoothed peri-event histograms (on the left), aligned on movement onset, represent the pre-movement activity epochs (from the yellow line-corresponding to activity onset to MOS) as a function of reward probability (with the certain reward A on top and the sequence of uncertain rewards S-1 to S-3 following bellow). On the right we show hand position trajectories and movement velocity profiles (averaged across trials) that depict the vigor of movements when reward is certain (top traces) and when reward becomes uncertain (bottom). Abbreviations: MOS, movement onset; AOS, activity onset; COS, cue onset; and MOF, movement offset.](fnins-05-00061-g004){#F4}

### Changes in activity onset time

We observed several types of neuronal modulations. To describe these types of neuronal patterns, neuronal responses of each cell were sorted by activity onset time (see Figure [1](#F1){ref-type="fig"}B) and grouped into three categories: short, normal and long latencies. In Figure [5](#F5){ref-type="fig"} we compared pre-movement and baseline firing of each latency group for certain ("A" trials) and uncertain rewards (S-1 trials). The short latency group responded with higher pre-movement firing rate (*Resp*) under VIB and VIS conditions (*p* \< 0.01 for "short" latency and *p* \< 0.05 for "normal" latencies; *post hoc* test), while the "long" latency group responds with a lower mean firing rate. The baseline firing (*Bkg*) increased slightly (*p* \< 0.05, *post hoc* test) in "normal" latency group, compared to the other two groups. For visual cues, the short latency group "A" trials had higher pre-movement firing rate (by ∼5 spk/s) during flexion trials than the S-1 trials (*p* \< 0.01; *post hoc* test) but not for vibratory cues. These results indicate that modulations in NS neurons reflected changes in movement vigor, as well as movement direction and cue type.

![**Pre-movement firing activity as a function of onset time**. Mean firing rate for pre-movement activity and baseline are compared between certain reward "A" trials and uncertain reward S-1 trials under vibratory cues \[**(A)** VIB--Flex and **(B)** VIB--Ext\] and visual cues \[**(C)** VIS--Flex and **(D)** VIS--Ext\]. Pre-movement activity (*Resp*) and baseline (*Bkg*) are in red and blue color lines, respectively. The abscissa category "latency R1" is split into three unequal time intervals: short, normal, and long containing equal neuron numbers. The ordinate is showing the mean firing rate in spikes per second (spk/s) for each category group. Asterisks (\**p* \< 0.05, \*\**p* \< 0.01) indicate significant differences in mean firing rates for A vs. S-1 trials.](fnins-05-00061-g005){#F5}

### Modulation of activity timing by reward uncertainty

To quantify pre-movement timing at the population level for trials with certain and uncertain rewards, we partitioned the RT period (see Figure [1](#F1){ref-type="fig"}B) into latency (R1) and pre-movement epochs (R2; see Table [2](#T2){ref-type="table"}).

###### 

**Activity onset timing**. The reaction time interval was divided into R1, which was the time from go-stimulus onset to significant activity change, and R2 (the pre-movement epoch) form activity change until MOS.

  Sensory direction                 Movement reward   Certain           Uncertain rewardmodality                     
  --------------------------------- ----------------- ----------------- -------------------------- ----------------- -----------------
  **PRE-MOVEMENT DURATIONS (MS)**                                                                                    
  VIB                               Flex              149.414 ± 4.340   172.872 ± 6.067            177.707 ± 6.666   187.830 ± 7.958
                                    Ext               143.166 ± 3.746   159.328 ± 4.750            156.423 ± 5.052   164.169 ± 5.512
  VIS                               Flex              188.081 ± 5.201   207.781 ± 6.278            203.073 ± 6.714   204.103 ± 7.509
                                    Ext               162.011 ± 3.511   163.862 ± 4.031            166.203 ± 3.970   172.255 ± 5.025
  **LATENCY (MS)**                                                                                                   
  VIB                               Flex              141.592 ± 3.031   150.954 ± 3.874            146.559 ± 4.012   150.438 ± 5.198
                                    Ext               142.128 ± 3.055   147.771 ± 3.804            144.676 ± 3.699   143.553 ± 4.332
  VIS                               Flex              178.168 ± 3.398   176.877 ± 3.540            184.252 ± 4.481   181.824 ± 4.990
                                    Ext               164.018 ± 3.224   170.922 ± 3.670            171.282 ± 3.651   167.389 ± 4.862

#### Pre-movement activity duration

Pre-movement durations, R2s, increased significantly with the increase in the number of consecutively rewarded trials in vibratory cued trials (Figures [6](#F6){ref-type="fig"}A,B), for both flexion and extension movements (for all conditions *p* \< 0.001; *post hoc* test, except for A vs. S-2 extensions the level of significance was *p* \< 0.01, *post hoc* test). R2s increased less with the increase in the number of consecutively rewarded trials for visually cued trials (Figures [6](#F6){ref-type="fig"}C,D), but significantly (*p* \< 0.05; *post hoc* test) for flexions and for "A" vs. S-3 extension movements (see Table [2](#T2){ref-type="table"}). The general trend in PMA following withheld rewards was that the duration of pre-movement time became shorter when reward was certain ("A" trials), and longer when reward was uncertain (in the subsequent trials S-1 to S-3). Moreover, we found increased slopes of rate changes in "A" trials (*p* \< 0.01, *post hoc* test). Thus, changes in reward probability caused both changes in characteristics of behavior (RT and movement speed) and in NS modulations.

![**Latency and pre-movement activity durations as a function of reward sequence**. *Left*: Pre-movement durations (mean ± SEM) are plotted for vibratory cues \[**(A)** flexions sequence trials and **(B)** extensions). Similarly, R2s for visual cues \[**(C)** flexions and **(D)** extensions). *Right*: Neural latencies R1s (mean ± SEM) are plotted for vibratory cues \[**(E)** flexions and **(F)** extensions) and for visual cues \[**(G)** flexions and **(H)** extensions\]. The horizontal gray lines represent the temporal bias reference lines \[Bias Line in **(B)**\]. Temporal selection bias (Bias) is represented on the Time axis in panel b together with the key events: activity onset (AOS) and movement onset (MOS). Numerical values of R1s and R2s are shown in Table [2](#T2){ref-type="table"}. Asterisks (\**p* \< 0.05, \*\**p* \< 0.001) indicate significant differences in mean pre-movement times for A vs. S-1, S-2, and S-3 trials.](fnins-05-00061-g006){#F6}

#### Neural latency

The time epoch in the PETH from cue onset to the onset of firing modulation is called here "neural latency" and corresponds to R1 in Figure [1](#F1){ref-type="fig"}B. R1s increased slightly with the increase in the number of consecutively rewarded trials in vibratory cued trials (Figures [6](#F6){ref-type="fig"}E,F), for both flexion and extension movements (for all conditions, *p* \< 0.05, *post hoc* test, except for "A" vs. S-1 flexions in which the level of significance was *p* \< 0.001). Under visual cues (Figures [6](#F6){ref-type="fig"}G,H), R1s increased with the increase in the number of consecutively rewarded trials for several conditions (*p* \< 0.05; *post hoc* test for "A" vs. S-2 and S-3, flexions and also for "A" vs. S-1 and S-3 extensions; see Table [2](#T2){ref-type="table"}). This suggests that reward uncertainty mediated a "rubber-band" temporal effect (Renoult et al., [@B48]) for the RT period: when RTs increased, R1s and R2s increased, as well.

### Comparison of pre-movement modulations across modality

The parameters of movements and neuronal modulations in the striatum depended on the sensory modality of the go-cue (VIB vs. VIS go-cues). Comparisons of pre-movement times R2s between visual and vibratory cues showed significantly longer R2s (∼20--40 ms) for flexions cued by visual VIS stimuli ("A" trials: *p* \< 0.001, unpaired two-tails *t*-test; S-1, S-2, and S-3 trials: *p* \< 0.01, *post hoc* test) than those cued by VIB stimulation. Also, R2s for extension were slightly longer (∼5--20 ms) when cued by VIS stimuli ("A" trials, *p* \< 0.001, unpaired two-tails *t*-test and *p* \< 0.01, *post hoc* test; S-1, S-2, and S-3 trials: *p* \< 0.01, *post hoc* test) compared to vibratory VIB cues.

On the other hand, comparisons of R1s across sensory modality showed significantly longer latencies for flexions (VIB vs. VIS cues; "A" to S-3 conditions: *p* \< 0.001, unpaired two-tails *t*-test; "A" and S-1 trials: *p* \< 0.01, *post hoc* test) and extensions under vibratory cues ("A" to S-3 conditions: *p* \< 0.001, unpaired two-tails *t*-test) compared to visual cues. Thus, the temporal bias caused by the changes in reward probability varied differentially with the sensory modality, *occurring faster for visual cues than for vibratory stimuli*.

The modality effect was reflected also by the monkeys' behavior in the error trials. The animals made more than four error trials per session (required to be considered for analysis) in 46/198 (23%) visual sessions and in 139/296 (47%) sessions. Of these, flexion trials errors occurred in 94/247 (38%) sessions and extension trial errors in 91/247 (37%) sessions. Thus, animals were more than twice likely to make errors in VIB-trials in which the vibratory cue did not provide a directional instruction than in VIS-trials in which the visual cue clearly indicated such instruction.

### Wrist movement velocity varies with reward uncertainty

The changes in wrist velocity with reward probability are shown in Figures [7](#F7){ref-type="fig"}A--D that depicts the distribution of mean wrist movement velocities across reward conditions. Wrist movements were performed with higher velocities in trials with certain rewards ("A" trials) than in trials with uncertain rewards (S-1 to S-3 trials) for all modalities and directions (*p* \< 0.05; unpaired two-tails *t*-test, *post hoc* test). Across modalities (VIS vs. VIB) wrist Flex velocities (Figures [7](#F7){ref-type="fig"}A,C) were slower with ∼2--4°/s than for Ext velocities (Figures [7](#F7){ref-type="fig"}B,D; *p* \< 0.001; unpaired two-tails *t*-test, *post hoc* test).

![**Distribution of mean wrist velocity across reward probabilities**. Mean wrist velocity are compared under vibratory cues \[**(A)** VIB--Flex and **(B)** VIB--Ext\] and visual cues \[**(C)** VIS--Flex and **(D)** VIS--Ext\]. Movements were performed with higher velocities under certain rewards (A trials) than under uncertain rewards (S-1 to S-3 trials).](fnins-05-00061-g007){#F7}

### Correlation between pre-movement time and wrist velocity

Pre-movement changes in duration were correlated with RTs, MTs, and hand wrist velocity (Figures [8](#F8){ref-type="fig"} and [9](#F9){ref-type="fig"}).

![**Correlation between movement and pre-movement times**. Comparison of Pearson correlation coefficients are shown for vibratory cues \[**(A)** VIB Flex\] and for visual cues \[**(B)** VIS Flex\]. Pearson correlation coefficients are statistically significant for S-1 to S-3 trials (*p* \< 0.05) and not significant for A trials.](fnins-05-00061-g008){#F8}

![**Correlation between mean wrist velocity and pre-movement time across sensory modality and movement directions**. Scatter plot of mean wrist velocity vs. pre-movement activity duration for vibratory cues (violet) VIB--Flex and (pink) VIB--Ext) and visual cues (blue) VIS--Flex and (red) VIS--Ext) shows a consistent trend across movement direction and sensory modalities under reward uncertainty.](fnins-05-00061-g009){#F9}

#### Correlation between reaction time and pre-movement activity duration

As shown in Figure [6](#F6){ref-type="fig"}, both components of the RT: latency R1 and pre-movement duration R2 show a clear dependency on reward probability which followed the previously reported "rubber-band" relationship (Renoult et al., [@B48]). These changes in neural timing were found for both VIB and VIS stimuli. Pearson correlations of R2s with RTs varied between *r* = 0.52 and 0.87 with significant *p*-values (*p* \< 0.001; two-tailed). The correlation coefficients for R1s vs. RTs were slightly lower (between *r* = 0.22 and 0.67) and the *p*-values also significant (*p* \< 0.01; two-tailed).

#### Correlation between movement time and pre-movement activity duration

To examine whether PMA duration was correlated with movement parameters, we calculated Pearson correlation coefficients between these two variables. Figure [8](#F8){ref-type="fig"}A shows a linear dependence of Flex MTs on R2s under VIB cues reflected in the correlation coefficients *r* = 0.259 for S-1, *r* = 0.336 for S-2, *r* = 0.385 for S-3. Similarly under VIS cues (Figure [8](#F8){ref-type="fig"}B), the coefficients values were *r* = 0.265 for S-1, *r* = 0.335 for S-2, *r* = 0.250 for S-3. Pearson\'s coefficients were statistically significant for S-1 to S-3 trials (*p* \< 0.05) in both modalities (VIB and VIS), but not significant for "A" trials in which R2s were more stable.

#### Correlation between mean wrist velocity and pre-movement time across sensory modality and movement direction

We found a consistent correlation between wrist velocity and pre-movement time in NS neurons across sensory modalities and movement directions (shown in Figure [9](#F9){ref-type="fig"}) and clustered within the same modality/direction category. This relationship was noticeable when Pearson correlation between pre-movement duration and average wrist movement velocity (Figure [9](#F9){ref-type="fig"}) were examined across reward groups (*n* = 4) under both VIB and VIS cued flexions (*r* = −0.999, *p* = 0.001; two-tailed). Extension movements performed under VIB cues (*r* = −0.989, *p* = 0.011), and VIS cues (*r* = −0.991, *p* = 0.009) revealed this effect. Thus, in a consistent manner, average wrist velocity decreased in cohort with the increase in the pre-movement duration across modalities.

### Variability in movement planning induced by reward unpredictability

The coefficient of variation (CV) represents the ratio between SD and the mean. We compared the degree of variability in neural/behavioral measures (latency, PMA time, RT, and MT) between certain and uncertain rewards. Figure [10](#F10){ref-type="fig"}A shows a sequence of spike rasters indicating the increase in variability of event timing as a function of reward uncertainty. The CVs in Figure [10](#F10){ref-type="fig"}B indicate that variability in pre-movement time (R2) tended to be higher than that in latency (R1) and MT. Such increase in CVs may be explained by variations in reward unpredictability.

![**Variability under reward uncertainty**. **(A)** Example of a sequence raster with event timing variability. Events relevant to the task are: cue onset (COS), significant activity onset (AOS), movement onset (MOS) and movement offset (MOF). **(B)** Coefficients of variation (CVs). CVs for latency (R1), pre-movement time (R2), reaction time (RT), and movement time are plotted as a function reward sequence for both flexions (F) and extensions (E) cued by vibratory (VIB) and visual (VIS) cues.](fnins-05-00061-g010){#F10}

Discussion
==========

In the present study we recorded the activity of neostriatal neurons in two rhesus monkeys performing wrist movements in a pseudo-random reward task. We analyzed the PMA and behavioral data under three conditions: (a) certain vs. uncertain rewards, (b) vibratory vs. visual go-cues, and (c) flexion vs. extension movements. Our results show that both PMA of most dorsal striatal neurons and wrist movement parameters changed as a function of reward contingency (results published in abstract form, Nelson et al. [@B39], [@B40]).

Pre-movement modulations in dorsal striatal neurons have been hypothesized to be related to movement planning (Alexander and Crutcher, [@B1]; Hoshi and Tanji, [@B19]; Hori et al., [@B18]). In our experiments, the magnitude of pre-movement firing did not change substantially across reward conditions, likely because monkeys produced movement of similar amplitude. What changed instead were the RTs, the onsets of the modulation in the firing rates of dorsal striatal neurons and the slopes of their rate changes. These changes in neural timing also manifested themselves as alterations of neural latency and pre-movement time, in agreement with Mirenowicz and Schultz ([@B37]) and Blazquez et al. ([@B3]). Thus, reward probability affected both bottom up sensory processing reflected by neural latency and the top down flow of information through the basal ganglia-thalamo-cortical loops expressed as pre-movement time, rate, and rate slope.

Unpredictable reward and the action/movement plan
-------------------------------------------------

*Does reward expectation modulate the temporal and kinematic parameters represented by a movement plan*? It is well documented that reward schedule is a key factor in the shaping of animal\'s behavior (Herrnstein, [@B12]; Staddon [@B58]; Sugrue et al., [@B60]). According to Herrnstein\'s matching rule, an animal\'s choice optimizes reinforcement probability, so that the choice matches the probability of reinforcement (Herrnstein, [@B12]; Sugrue et al., [@B60]; Lau and Glimcher, [@B24]; Platt and Huettel, [@B46]). To modulate the timing of a movement plan the brain evaluates the utility of each option and selects the most valuable action (Seideman et al., [@B54]; Schall, [@B53]; Samejima et al., [@B52]; Maimon and Assad, [@B34]; O\'Shea et al., [@B43]; Pasquereau et al., [@B45]; Hori et al., [@B18]), by activating neuronal circuits in fronto-parietal cortex, striatum, and the subcortical regions in the brain (Simmons and Richmond, [@B57]; Opris et al., [@B42]; Hikosaka and Isoda, [@B16]; Tsujimoto et al., [@B61]; Turner and Desmurget, [@B63]).

Our results show changes in pre-movement firing (Figure [5](#F5){ref-type="fig"}) and timing (Figure [6](#F6){ref-type="fig"}) in dorsal striatum with reward expectation, suggesting that NS is involved in the modulation of movement vigor (Mirenowicz and Schultz, [@B37]; Blazquez et al., [@B3]; Turner and Desmurget, [@B63]). Manipulations of reward probability produced both types of changes (in motor parameters and in neuronal modulations) in our experiments. When reward was uncertain MOS shifted in time (away from COS) and movement was initiated after a delay of ∼30--60 ms (depending on sensory modality and movement direction; Figure [6](#F6){ref-type="fig"}). Conversely, when reward becomes certain wrist movements were initiated sooner. The velocity of wrist movements increased when reward was certain and decreased when reward became uncertain, showing evidence for a role of reward contingency in movement "vigor" modulation (Figures [4](#F4){ref-type="fig"} and [7](#F7){ref-type="fig"}). These changes in movement parameters were linked to changes in dorsal striatal activity as a function of reward probability. Our results show that changes in PMA and movement velocity were correlated (Figure [9](#F9){ref-type="fig"}). Thus, such correlation provides evidence for a linkage between movement vigor and the optimization (discounting) of action-based reward value in time (Shadmehr et al., [@B55]).

Role of basal ganglia activity in movement vigor and temporal bias
------------------------------------------------------------------

Horak and Anderson ([@B17]) and more recently Turner and Desmurget ([@B63]) have suggested that basal ganglia influence the "vigor" of movements.

### Relationship to movement vigor

It is reasonable to suggest that when a monkey is expecting a reward it becomes more "excited" and moves more quickly toward the goal than when the reward becomes uncertain. An uncertain reward, on the other hand, will only reduce animal\'s vigor. Dorsal striatum likely has a role in the modulation of movement vigor, as suggested by the study showing that it mediates cortical signals necessary for behavioral switching (Hikosaka and Isoda, [@B16]). Thus, dorsal striatal circuits may modulate movement vigor through a switch that is related to the reward mechanism and differentially biases movements (Ding and Hikosaka, [@B7]). Therefore, based on the pre-movement timing, dorsal striatum cells may modulate movement vigor before the pallidal cells do (Horak and Anderson, [@B17]; Turner and Desmurget, [@B63]).

### Temporal bias

We define temporal bias as a temporal shift in movement initiation with respect to the cue onset. Such bias may have a role in the "proactive timing of action" (Maimon and Assad, [@B34]). Our data (Figures [5](#F5){ref-type="fig"}A--D) show a temporal bias in the pre-movement timing as a function of reward expectation. When the reward was certain, a temporal shift in MOS caused the movement to occur *sooner*, and when it was uncertain the MOS came *later*. Consequently, the velocity of movement became *faster* in trials with certain rewards or *slower* when the reward was uncertain. Changes in temporal bias and accompanying changes in striatal activity that we observed here are somewhat analogous to the well-known modulations of behavioral choices and selections by caudate-putamen and other components of the basal ganglia-thalamo-cortical loops that act as switches between the representations of many behavioral degrees of freedom (Redgrave et al., [@B47]; Salinas et al., [@B51]; Kimura et al., [@B22]; Schall, [@B53]; O\'Shea et al., [@B43]). The difference between the selection and temporal bias is that selection involves choosing between discrete options whereas the temporal bias represents a continuous modulation of motor preparation.

Analogies between temporal bias and covert choice
-------------------------------------------------

Consistent with the free choice hypothesis, a *decision mechanism* may select an option based on (i) reward value, (ii) knowledge from previous experience, and (iii) the accumulation of sensory evidence (for review see Schall, [@B53]; Opris and Bruce, [@B41]; Padoa-Schioppa and Assad, [@B44]; Beck et al., [@B2]; Platt and Huettel, [@B46]). In our experiment there are faster or slower movements and certain vs. uncertain rewards, with the certain reward having a higher value than the uncertain one. Also, the previous "no-reward" trial acts as a cue indicating a certain reward in the current trial, thus providing the prior information. Since reward availability/probability is not indicated by an instruction cue, no sensory accumulation occurs and the choice is covert.

Comparison with other studies
-----------------------------

Previously, Lauwereyns et al. ([@B25]) identified neurons in the primate Caudate Nucleus that create a spatially selective "response bias." Their response bias was associated to the spatial location of the visual target. In our case the bias signal (triggered by the uncertainty of reward) is associated to the temporal dimension because it affects the onset of movement initiation and the velocity of wrist movements (Seideman et al., [@B54]; Frederick et al., [@B10]; Ditterich, [@B8]; Machens et al., [@B33]). Looking from the value of the action perspective (Samejima et al., [@B52]; Lau and Glimcher, [@B24]; Hori et al., [@B18]) a certain reward has a higher value and it is likely to activate the selection circuitry of faster movements, while an uncertain reward carries a lower value and will activate the circuitry for slower movements (Samejima et al., [@B52]; O\'Shea et al., [@B43]; Shadmehr et al., [@B55]). The difference in timing between fast and slow pre-movement times is 30--60 ms, but long enough for a decision to take place (Schall, [@B53]; Stanford et al., [@B59]).

Another aspect of movement planning under uncertain reward deals with pre-movement variability (Figure [10](#F10){ref-type="fig"}). Movements are planned such that their variability gets minimized (Harris and Wolpert, [@B11]; Mohr and Nagel, [@B38]). Churchland et al. ([@B5]) argues that variability in arm movements originates mostly in central movement planning. In our experiments, the sources of variability for RTs, especially of pre-movement times (as shown in Figure [10](#F10){ref-type="fig"}B) are coming from changes in reward probability. These results support the idea that reward contingency contributes to the variability in movement planning and in wrist movement trajectories.

Relevance to neuroeconomics
---------------------------

Our study is relevant to the neuroeconomics field for the dissociation of movement planning ("vigor" and temporal bias) in dorsal striatum under certain vs. uncertain rewards. A vigorous (forceful) movement can be viewed as a "valuable investment," being engaged only when the monkey is sure about a trial\'s outcome. Indeed, fast movements are somewhat more expensive since they involve more muscle contraction, probably more brain and energy resources. In other cases the monkey moves slowly, because the animal has "invested" less in that action (Kim et al., [@B21]; Shadmehr et al., [@B55]).

Action planning and choice under uncertain conditions are risky and difficult since no plan of action is available to the decision maker for a specific outcome (Kim et al., [@B21]; Platt and Huettel, [@B46]). From our results emerges a clear clue that an increase in reward uncertainty delays a plan for action, while a switch from uncertain to certain rewards speeds up the plan to act. In fact, this is another pointer to neuroeconomics, based on the fact that velocity of wrist movements increased when reward was certain and decreased when reward became uncertain. This provides evidence for optimization vs. discounting of action in time based on reward value (Shadmehr et al., [@B55]).

In conclusion, our results show several important features of a motor planning mechanism depending on reward contingency, providing evidence for a neural substrate of movement "vigor" planning in dorsal striatum. First, in the trials with certain rewards, striatal neurons modulated their firing rates earlier. Second, pre-movement changes in firing rate timing are correlated with the MTs and wrist movement velocity. Third, these modulations depended on the sensory modality of the cue (visual vs. vibratory) and/or movement direction (flexions vs. extensions). Finally, the motor planning mechanism in dorsal striatum may be responsible for the modulation of movement "vigor" according to reward contingency.

Conflict of Interest Statement
==============================

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The authors wish to thank John M. Denton who assisted during the gathering of data that was presented in this manuscript, to John E. R. Staddon, D. James Surmeier, and Charles J. Wilson for detailed comments on versions of this manuscript and to Michael Platt and Scott Huettel for insightful discussions. We would also like to thank the two reviewers for their insightful suggestions. This work was supported by NIH Program Project Grant NS26473, and conducted under the auspices of a project thereof under the direction of Randall J. Nelson.

[^1]: Edited by: Daeyeol Lee, Yale University School of Medicine, USA

[^2]: Reviewed by: Paul Cisek, University of Montreal, Canada; Soyoun Kim, Yale University School of Medicine, USA

[^3]: This article was submitted to Frontiers in Decision Neuroscience, a specialty of Frontiers in Neuroscience.