Should I stay or should I go? Conceptual underpinnings of goal-directed actions

Giovanni Mirabella, Giovanni Mirabella

Abstract

All actions, even the simplest like moving an arm to grasp a pen, are associated with energy costs. Thus all mobile organisms possess the ability to evaluate resources and select those behaviors that are most likely to lead to the greatest accrual of valuable items (reward) in the near or, especially in the case of humans, distant future. The evaluation process is performed at all possible stages of the series of decisions that lead to the building of a goal-directed action or to its suppression. This is because all animals have a limited amount of energy and resources; to survive and be able to reproduce they have to minimize the costs and maximize the outcomes of their actions. These computations are at the root of behavioral flexibility. Two executive functions play a major role in generating flexible behaviors: (i) the ability to predict future outcomes of goal-directed actions; and (ii) the ability to cancel them when they are unlikely to accomplish valuable results. These two processes operate continuously during the entire course of a movement: during its genesis, its planning and even its execution, so that the motor output can be modulated or suppressed at any time before its execution. In this review, functional interactions of the extended neural network subserving generation and inhibition of goal-directed movements will be outlined, leading to the intriguing hypothesis that the performance of actions and their suppression are not specified by independent sets of brain regions. Rather, it will be proposed that acting and stopping are functions emerging from specific interactions between largely overlapping brain regions, whose activity is intimately linked (directly or indirectly) to the evaluations of pros and cons of an action. Such mechanism would allow the brain to perform as a highly efficient and flexible system, as different functions could be computed exploiting the same components operating in different configurations.

Keywords: behavioral flexibility; countermanding task; decision-making; reaching arm movements; reward; voluntary motor control.

Figures

FIGURE 1
FIGURE 1
Model of goal-directed actions. The model consists of a set of a multi-step decision processes leading either to the execution or to the inhibition of an action, according to the evaluation of its pros and cons. This model does not have a strictly serial or parallel architecture. Some processes must occur before others (e.g., the early “should-I-stay-or-should-I-go” decision aimed at evaluating whether acting is worthwhile must occur before goal or action selection), but other processes occur in parallel (e.g., the monitoring system, whose role is to compute predictions about future reward and to measure discrepancies between expected and actual outcomes, is active during all the steps). See text for further details.
FIGURE 2
FIGURE 2
Medial frontal cortex (details of the medial portion of Brodmann areas 6 and 8). Midsagittal view of the medial wall (left) and lateral prefrontal cortex (LPFC) surface (right), delineating the supplementary motor area (SMA), supplementary eye field (SEF) and pre-supplementary motor area (pre-SMA). Reproduced with permission from Ridderinkhof et al. (2011).
FIGURE 3
FIGURE 3
Areas composing the prefrontal cortex (PFC) according to the parcellation of Petrides and Pandya (1999, . (A) Areas of the lateral PFC. The inferior frontal gyrus (IFG) corresponds to Brodmann area (BA) 44, or pars opercolaris, to BA45, or pars triangularis, and to BA47/12, or pars orbitalis. The dorsolateral prefrontal cortex (DLPFC) corresponds to BA9/46 and BA46. (B) Areas of the medial wall of the PFC. The anterior cingulate cortex (ACC) corresponds to BA32, BA24, and BA25 (dorsocaudal portions are indicated with a hyphen). BA10 corresponds to the frontopolar cortex. The orbitofrontal cortex (OFC) corresponds to BA11 and BA14. (C) Areas of ventral orbital surface of the PFC. BA10 corresponds to the frontopolar cortex. Area BA47/12 corresponds to the pars orbitalis of the IFG. The OFC corresponds to BA11, BA13, and BA14. Reproduced with permission from Ridderinkhof et al. (2004).
FIGURE 4
FIGURE 4
Role of dorsolateral prefrontal cortex during a “strategy task.” (A) Temporal sequence of the visual displays during the task and behavioral responses required of the monkeys. Each trial began when monkeys fixated on a spot at the center of the display (gaze direction is indicated by the dashed lines). After a delay, a cue appeared. When it disappeared, the monkeys had to make a saccadic eye movement to one of three positions (unfilled squares). Monkeys were required to remember both the cue and the goal of the trial just performed because the response on the next trial (current trial) depended on the previous choice, i.e., if the cue was the same as in the previous successful trial, the monkeys repeated the response (repeat trial), while if the cue was different they had to change their response (change trial). Thus, monkeys were forced to change strategy according to the past trial history, adopting either a repeat-stay strategy or a change-shift strategy (B) Neural activity reflecting the previous goal (red), the future goal (blue), the correct strategy (solid green line), and the wrong strategy (dashed green line). Previous-goal signal decreased after cue onset as the signals for the correct strategy and future goal increased. In contrast, when monkeys chose the wrong strategy, a weak or absent strategy signal occurred during the time of goal selection. Reproduced with permission from Wise (2008).
FIGURE 5
FIGURE 5
Time course of population activity in the dorsal premotor cortex during a reach-selection task. The diagrams on the left replicate the temporal sequence of the visual displays during the task. Each trial began with the monkeys moving the cursor (+) into a central green circle. Next, a red and a blue cue circle appeared at two of eight possible target positions, in opposite directions from the center for about a second (first display, “Spatial cues”). Then the cues disappeared (second display, “Memory period”) and after a variable period the central circle changed color to red or blue (third display, “Color cue”). Finally, the go signal was delivered (fourth display). The central circle disappeared and green circles appeared at all eight positions. To perform correctly the monkeys had to move the cursor from the central circle to the target indicated by the color cue. The 3-D colored surface on the right depicts changes of neural activity along time with respect to baseline, with cells sorted by their preferred direction along the bottom edge. Note that during the entire period of ambiguity until the presentation of the color cue, the population encoded both potential directions. When the color cue provided the information for selecting the correct action, its neural representation was strengthened while the other was suppressed. Reproduced with permission from Cisek and Kalaska (2010).
FIGURE 6
FIGURE 6
Causal role of neurons of the dorsal premotor cortex (PMd) in reactive inhibition. (A) Temporal sequence of the visual displays for no-stop and stop trials in the reaching version of the countermanding task. All trials began with the presentation of a central stimulus. After a variable holding delay (500–800 ms) it disappeared and simultaneously a target appeared to the right, acting as a go-signal. In the no-stop trials subjects had to start a speeded reaching movement toward the peripheral target. Randomly, on a fraction of interleaved trials (33%), the central stimulus reappeared after variable delays (SSDs), instructing subjects to inhibit movement initiation. In these stop trials, if subjects countermanded the planned movement keeping the arm on the central stimulus the trial was scored as a stop-success trial. Otherwise, if subjects executed the reaching movement the trial was scored as a stop-failure trial (not shown). Reproduced with permission from Mattia et al. (2012). (B) Changes of activity driven by the stop-signal onset in PMd neurons modulated during the preparation of the movement. In each panel the upper graph represents the average spike density function while the lower graph shows the raster plots of neural activity in no-stop trials (green tick-marks) and stop-success trials (red tick-marks). Neural activity was always aligned to the go-signal onset (first vertical line). The gray band represents the estimated duration of the stop-signal reaction time (SSRT) in that session. The gray line represents the differential spike density function, while the dashed gray line represents the threshold value for significant divergence. The green and the orange vertical dotted lines in the top panels indicate the average RT and the average end of MT, respectively. The green dots in the rasters represent the end of the RTs. On the right, the activity of a representative “type A” countermanding neuron is shown. In this cell, neural activity during stop-success trials (red line) initially resembles that of no-stop trials (green line) but, with a delay after the stop-signal presentation, it suddenly starts to decrease and the differential spike density function crosses the threshold 34.4 ms before the end of the SSRT. On the left, the activity of a representative “type B” countermanding neuron is shown. In this instance, the activity in stop-success trials increases after stop-signal presentation with respect to that recorded during no-stop trials 39.9 ms before the end of the SSRT. Therefore both these two types of neurons exhibit a modulation of activity sufficient to control the suppression of an ongoing arm movement.
FIGURE 7
FIGURE 7
Spatiotemporal distribution of stop-event related potentials (ERPs) in successful-stop (SS) trials. (A) Average stop-ERPs (solid red curves) of SS trials centered on stop-signal appearance corresponding to the selected channels for one pharmacoresistant epileptic patient. Gray areas, time intervals at which the stop-ERP was significantly different from 0 (Wilcoxon signed-rank test, P < 0.01). Subplot labels: Brodmann areas (BAs) over which electrodes were positioned. Colored areas: electrodes placed over the primary motor cortex (red, BA4), the premotor cortex (yellow, BA6) and the DLPFC (green, BA9). (B) Histogram of the stop-ERP sizes of panel (A). Stop-ERP sizes were computed as the integral of absolute values of stop-ERP voltage deflections in the interval periods marked by gray areas within SSRT. Dashed line: threshold value for selecting the subset of channels with large enough stop-ERPs used for population analyses (see Mattia et al., 2012 for details). (C) Number of channels showing large enough average stop-ERPs across five patients (n = 39) grouped by BA. Blue bar (others) represents those areas where channels were not selected more than twice across all patients. (D) Box plot of stop-ERP onsets measured with respect to the end of SSRT across all selected channels in all patients. Stop-ERP onset was defined as the first time that an electrode voltage was significantly different from 0. Diamonds indicate average onset times. Tick bars indicate the first and the third quartile. Vertical lines indicate the extreme time lags in the channel group. Freely adapted from Mattia et al. (2012), with permission.
FIGURE 8
FIGURE 8
Deep brain stimulation (DBS) of subthalamic nucleus (STN) partially restores the appropriate motor strategy according to the contexts. (A) Cumulative distribution of RTs (solid lines) and MTs (dotted lines) of healthy subjects (n = 13) for go-only (red lines) and no-stop (black lines) trials. (B) Cumulative distributions of RTs (solid lines) and MTs (dotted lines) of DBS patients (n= 12) in DBS-ON and DBF-OFF conditions for both go-only (red lines) and no-stop (black lines) trials. For each condition the P-value of Kolmogorov–Smirnov test is given, both for RTs and for MTs. (C) Histograms of average RTs of no-stop and go-only trials in DBS-ON and DBF-OFF conditions. Bars represent the standard error of the mean. (D) Histograms of average MTs of no-stop and go-only trials in each DBS-ON and DBF-OFF condition. Bars represent the standard error of the mean. Reproduced from Mirabella et al. (2013), with permission from PLOS.
FIGURE 9
FIGURE 9
Role of ACC in predicting error likelihood. (A) Temporal sequence of the visual displays for the change-signal task. Initially a probability cue (plain line) was displayed. The cue could be either white or blue, predicting low or high error likelihood respectively. After a delay, a go signal was presented (left or right pointing arrow) indicating the required button-press response (left or right index press for left or right pointing arrow, respectively). Randomly on 33% of the trials a change signal was displayed (a larger arrow pointing in the opposite direction with respect to that presented as go signal). To this signal, subjects had to reverse the response from that indicated by the go signal. Error rates were controlled by a staircase procedure so that in the low error condition the delay between the go signal and the change signal was kept shorter and subjects made around 90% of correct responses. In the high error condition the delay was kept longer and subjects made around 50% correct responses. (B) Brain regions highlighted indicate the activation of ACC during the stop-change before and after learning (the greater the activation, the deeper the red color) for correct, wrong change-direction trials and for no-change trials (for the top to the bottom row) in the low and high error conditions. Interestingly, during no-change trials ACC activity increased with practice, especially in response to blue color cues, reflecting an improved ability to predict the likelihood of making an error. Freely adapted from Ridderinkhof and van den Wildenberg (2005), with permission.

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