Turning decisions into actions.

Are selection and control of action serial processes of separate neural modules? A new study in PLOS Biology argues against this and in favor of an integrated process distributed across multiple brain regions, each contributing in a distinct way.

Emergence and suppression of cooperation by action visibility in transparent games.

Real-world agents, humans as well as animals, observe each other during interactions and choose their own actions taking the partners' ongoing behaviour into account. Yet, classical game theory assumes that players act either strictly sequentially or strictly simultaneously without knowing each other's current choices. To account for action visibility and provide a more realistic model of interactions under time constraints, we introduce a new game-theoretic setting called transparent games, where each player has a certain probability of observing the partner's choice before deciding on its own action. By means of evolutionary simulations, we demonstrate that even a small probability of seeing the partner's choice before one's own decision substantially changes the evolutionary successful strategies. Action visibility enhances cooperation in an iterated coordination game, but reduces cooperation in a more competitive iterated Prisoner's Dilemma. In both games, "Win-stay, lose-shift" and "Tit-for-tat" strategies are predominant for moderate transparency, while a "Leader-Follower" strategy emerges for high transparency. Our results have implications for studies of human and animal social behaviour, especially for the analysis of dyadic and group interactions.

The cone method: Inferring decision times from single-trial 3D movement trajectories in choice behavior.

Ongoing goal-directed movements can be rapidly adjusted following new environmental information, e.g., when chasing pray or foraging. This makes movement trajectories in go-before-you-know decision-making a suitable behavioral readout of the ongoing decision process. Yet, existing methods of movement analysis are often based on statistically comparing two groups of trial-averaged trajectories and are not easily applied to three-dimensional data, preventing them from being applicable to natural free behavior. We developed and tested the cone method to estimate the point of overt commitment (POC) along a single two- or three-dimensional trajectory, i.e., the position where the movement is adjusted towards a newly selected spatial target. In Experiment 1, we established a "ground truth" data set in which the cone method successfully identified the experimentally constrained POCs across a wide range of all but the shallowest adjustment angles. In Experiment 2, we demonstrate the power of the method in a typical decision-making task with expected decision time differences known from previous findings. The POCs identified by cone method matched these expected effects. In both experiments, we compared the cone method's single trial performance with a trial-averaging method and obtained comparable results. We discuss the advantages of the single-trajectory cone method over trial-averaging methods and possible applications beyond the examples presented in this study. The cone method provides a distinct addition to existing tools used to study decisions during ongoing movement behavior, which we consider particularly promising towards studies of non-repetitive free behavior.

What makes a reach movement effortful? Physical effort discounting supports common minimization principles in decision making and motor control.

When deciding between alternative options, a rational agent chooses on the basis of the desirability of each outcome, including associated costs. As different options typically result in different actions, the effort associated with each action is an essential cost parameter. How do humans discount physical effort when deciding between movements? We used an action-selection task to characterize how subjective effort depends on the parameters of arm transport movements and controlled for potential confounding factors such as delay discounting and performance. First, by repeatedly asking subjects to choose between 2 arm movements of different amplitudes or durations, performed against different levels of force, we identified parameter combinations that subjects experienced as identical in effort (isoeffort curves). Movements with a long duration were judged more effortful than short-duration movements against the same force, while movement amplitudes did not influence effort. Biomechanics of the movements also affected effort, as movements towards the body midline were preferred to movements away from it. Second, by introducing movement repetitions, we further determined that the cost function for choosing between effortful movements had a quadratic relationship with force, while choices were made on the basis of the logarithm of these costs. Our results show that effort-based action selection during reaching cannot easily be explained by metabolic costs. Instead, force-loaded reaches, a widely occurring natural behavior, imposed an effort cost for decision making similar to cost functions in motor control. Our results thereby support the idea that motor control and economic choice are governed by partly overlapping optimization principles.

Directed Interaction Between Monkey Premotor and Posterior Parietal Cortex During Motor-Goal Retrieval from Working Memory.

Goal-directed behavior requires cognitive control of action, putatively by means of frontal-lobe impact on posterior brain areas. We investigated frontoparietal directed interaction (DI) in monkeys during memory-guided rule-based reaches, to test if DI supports motor-goal selection or working memory (WM) processes. We computed DI between the parietal reach region (PRR) and dorsal premotor cortex (PMd) with a Granger-causality measure of intracortical local field potentials (LFP). LFP mostly in the beta (12-32 Hz) and low-frequency (f≤10Hz) ranges contributed to DI. During movement withholding, beta-band activity in PRR had a Granger-causal effect on PMd independent of WM content. Complementary, low-frequency PMd activity had a transient Granger-causing effect on PRR specifically during WM retrieval of spatial motor goals, while no DI was associated with preliminary motor-goal selection. Our results support the idea that premotor and posterior parietal cortices interact functionally to achieve cognitive control during goal-directed behavior, in particular, that frontal-to-parietal interaction occurs during retrieval of motor-goal information from spatial WM.

Planning Movements in Visual and Physical Space in Monkey Posterior Parietal Cortex.

Neurons in the posterior parietal cortex respond selectively for spatial parameters of planned goal-directed movements. Yet, it is still unclear which aspects of the movement the neurons encode: the spatial parameters of the upcoming physical movement (physical goal), or the upcoming visual limb movement (visual goal). To test this, we recorded neuronal activity from the parietal reach region while monkeys planned reaches under either normal or prism-reversed viewing conditions. We found predominant encoding of physical goals while fewer neurons were selective for visual goals during planning. In contrast, local field potentials recorded in the same brain region exhibited predominant visual goal encoding, similar to previous imaging data from humans. The visual goal encoding in individual neurons was neither related to immediate visual input nor to visual memory, but to the future visual movement. Our finding suggests that action planning in parietal cortex is not exclusively a precursor of impending physical movements, as reflected by the predominant physical goal encoding, but also contains spatial kinematic parameters of upcoming visual movement, as reflected by co-existing visual goal encoding in neuronal spiking. The co-existence of visual and physical goals adds a complementary perspective to the current understanding of parietal spatial computations in primates.

Asymmetric generalization in adaptation to target displacement errors in humans and in a neural network model.

Different error signals can induce sensorimotor adaptation during visually guided reaching, possibly evoking different neural adaptation mechanisms. Here we investigate reach adaptation induced by visual target errors without perturbing the actual or sensed hand position. We analyzed the spatial generalization of adaptation to target error to compare it with other known generalization patterns and simulated our results with a neural network model trained to minimize target error independent of prediction errors. Subjects reached to different peripheral visual targets and had to adapt to a sudden fixed-amplitude displacement ("jump") consistently occurring for only one of the reach targets. Subjects simultaneously had to perform contralateral unperturbed saccades, which rendered the reach target jump unnoticeable. As a result, subjects adapted by gradually decreasing reach errors and showed negative aftereffects for the perturbed reach target. Reach errors generalized to unperturbed targets according to a translational rather than rotational generalization pattern, but locally, not globally. More importantly, reach errors generalized asymmetrically with a skewed generalization function in the direction of the target jump. Our neural network model reproduced the skewed generalization after adaptation to target jump without having been explicitly trained to produce a specific generalization pattern. Our combined psychophysical and simulation results suggest that target jump adaptation in reaching can be explained by gradual updating of spatial motor goal representations in sensorimotor association networks, independent of learning induced by a prediction-error about the hand position. The simulations make testable predictions about the underlying changes in the tuning of sensorimotor neurons during target jump adaptation.

Synchronization patterns suggest different functional organization in parietal reach region and dorsal premotor cortex.

The parietal reach region (PRR) and dorsal premotor cortex (PMd) form part of the fronto-parietal reach network. While neural selectivity profiles of single-cell activity in these areas can be remarkably similar, other data suggest that both areas serve different computational functions in visually guided reaching. Here we test the hypothesis that different neural functional organizations characterized by different neural synchronization patterns might be underlying the putatively different functional roles. We use cross-correlation analysis on single-unit activity (SUA) and multiunit activity (MUA) to determine the prevalence of synchronized neural ensembles within each area. First, we reliably find synchronization in PRR but not in PMd. Second, we demonstrate that synchronization in PRR is present in different cognitive states, including "idle" states prior to task-relevant instructions and without neural tuning. Third, we show that local field potentials (LFPs) in PRR but not PMd are characterized by an increased power and spike field coherence in the beta frequency range (12-30 Hz), further indicating stronger synchrony in PRR compared with PMd. Finally, we show that neurons with similar tuning properties tend to be correlated in their random spike rate fluctuations in PRR but not in PMd. Our data support the idea that PRR and PMd, despite striking similarity in single-cell tuning properties, are characterized by unequal local functional organization, which likely reflects different network architectures to support different functional roles within the fronto-parietal reach network.

Position- and scale-invariant object-centered spatial localization in monkey frontoparietal cortex dynamically adapts to cognitive demand.

Egocentric encoding is a well-known property of brain areas along the dorsal pathway. Different to previous experiments, which typically only demanded egocentric spatial processing during movement preparation, we designed a task where two male rhesus monkeys memorized an on-the-object target position and then planned a reach to this position after the object re-occurred at variable location with potentially different size. We found allocentric (in addition to egocentric) encoding in the dorsal stream reach planning areas, parietal reach region and dorsal premotor cortex, which is invariant with respect to the position, and, remarkably, also the size of the object. The dynamic adjustment from predominantly allocentric encoding during visual memory to predominantly egocentric during reach planning in the same brain areas and often the same neurons, suggests that the prevailing frame of reference is less a question of brain area or processing stream, but more of the cognitive demands.

Choice-based severity scale (CSS): assessing the relative severity of procedures from a laboratory animal's perspective.

One primary goal of laboratory animal welfare science is to provide a comprehensive severity assessment of the experimental and husbandry procedures or conditions these animals experience. The severity, or degree of suffering, of these conditions experienced by animals are typically scored based on anthropocentric assumptions. We propose to (a) assess an animal's subjective experience of condition severity, and (b) not only rank but scale different conditions in relation to one another using choice-based preference testing. The Choice-based Severity Scale (CSS) utilizes animals' relative preferences for different conditions, which are compared by how much reward is needed to outweigh the perceived severity of a given condition. Thus, this animal-centric approach provides a common scale for condition severity based on the animal's perspective. To assess and test the CSS concept, we offered three opportunistically selected male rhesus macaques (Macaca mulatta) choices between two conditions: performing a cognitive task in a typical neuroscience laboratory setup (laboratory condition) versus the monkey's home environment (cage condition). Our data show a shift in one individual's preference for the cage condition to the laboratory condition when we changed the type of reward provided in the task. Two additional monkeys strongly preferred the cage condition over the laboratory condition, irrespective of reward amount and type. We tested the CSS concept further by showing that monkeys' choices between tasks varying in trial duration can be influenced by the amount of reward provided. Altogether, the CSS concept is built upon laboratory animals' subjective experiences and has the potential to de-anthropomorphize severity assessments, refine experimental protocols, and provide a common framework to assess animal welfare across different domains.

A brain machine interface framework for exploring proactive control of smart environments.

Brain machine interfaces (BMIs) can substantially improve the quality of life of elderly or disabled people. However, performing complex action sequences with a BMI system is onerous because it requires issuing commands sequentially. Fundamentally different from this, we have designed a BMI system that reads out mental planning activity and issues commands in a proactive manner. To demonstrate this, we recorded brain activity from freely-moving monkeys performing an instructed task and decoded it with an energy-efficient, small and mobile field-programmable gate array hardware decoder triggering real-time action execution on smart devices. Core of this is an adaptive decoding algorithm that can compensate for the day-by-day neuronal signal fluctuations with minimal re-calibration effort. We show that open-loop planning-ahead control is possible using signals from primary and pre-motor areas leading to significant time-gain in the execution of action sequences. This novel approach provides, thus, a stepping stone towards improved and more humane control of different smart environments with mobile brain machine interfaces.

Sensorimotor learning biases choice behavior: a learning neural field model for decision making.

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations.

Deciding While Acting-Mid-Movement Decisions Are More Strongly Affected by Action Probability than Reward Amount.

When deciding while acting, such as sequentially selecting targets during naturalistic foraging, movement trajectories reveal the dynamics of the unfolding decision process. Ongoing and planned actions may impact decisions in these situations in addition to expected reward outcomes. Here, we test how strongly humans weigh and how fast they integrate individual constituents of expected value, namely the prior probability (PROB) of an action and the prior expected reward amount (AMNT) associated with an action, when deciding based on the combination of both together during an ongoing movement. Unlike other decision-making studies, we focus on PROB and AMNT priors, and not final evidence, in that correct actions were either instructed or could be chosen freely. This means, there was no decision-making under risk. We show that both priors gradually influence movement trajectories already before mid-movement instructions of the correct target and bias free-choice behavior. These effects were consistently stronger for PROB compared with AMNT priors. Participants biased their movements toward a high-PROB target, committed to it faster when instructed or freely chosen, and chose it more frequently even when it was associated with a lower AMNT prior than the alternative option. Despite these differences in effect magnitude, the time course of the effect of both priors on movement direction was highly similar. We conclude that prior action probability, and hence the associated possibility to plan actions accordingly, has higher behavioral relevance than prior action value for decisions that are expressed by adjusting already ongoing movements.

Human and macaque pairs employ different coordination strategies in a transparent decision game.

Many real-world decisions in social contexts are made while observing a partner's actions. To study dynamic interactions during such decisions, we developed a setup where two agents seated face-to-face to engage in game-theoretical tasks on a shared transparent touchscreen display ('transparent games'). We compared human and macaque pairs in a transparent version of the coordination game 'Bach-or-Stravinsky', which entails a conflict about which of two individually-preferred opposing options to choose to achieve coordination. Most human pairs developed coordinated behavior and adopted dynamic turn-taking to equalize the payoffs. All macaque pairs converged on simpler, static coordination. Remarkably, two animals learned to coordinate dynamically after training with a human confederate. This pair selected the faster agent's preferred option, exhibiting turn-taking behavior that was captured by modeling the visibility of the partner's action before one's own movement. Such competitive turn-taking was unlike the prosocial turn-taking in humans, who equally often initiated switches to and from their preferred option. Thus, the dynamic coordination is not restricted to humans but can occur on the background of different social attitudes and cognitive capacities in rhesus monkeys. Overall, our results illustrate how action visibility promotes the emergence and maintenance of coordination when agents can observe and time their mutual actions.

To live with others is to make concessions. You may want to go to the movies tonight, but your partner may prefer the theatre: reaching a mutually desirable goal ­ that is, spending time together ­ requires adjusting your preferences to theirs. Many other social species also make such decisions, in particular monkeys that live in large groups. Conceptually, these interactions are known as coordination games. In such scenarios, two players must coordinate their actions to attain a coveted reward, but they must also resolve a conflict about who gets the larger share. This makes the joint strategy non-trivial, and different pairs of players might resort to different strategies. In the laboratory, coordination games are often tested in settings which do not allow participants to monitor each other's behaviors as they make these complex choices. In real life, however, individuals making a joint decision can often observe each other and receive immediate feedback. In response, Moeller et al. developed a new way to test coordination games that allows more realistic social interactions. In their setup, two participants face each other and use a shared see-through touchscreen to perform a task. This new design was used to test how humans and macaque monkeys solved a simplified version of the 'Bach or Stravinsky' coordination game, which involves choosing between a red and blue target on the screen. Players in a pair had been trained to 'prefer' opposite colors. In this game, collaboration is beneficial (both individuals get a better prize if they choose the same color) but also creates unfairness (the reward is higher for the participant whose 'favorite' color is selected). When paired up, both humans and monkeys learned to collaborate and to go for the same color (or, in some monkey pairs, the same side of the screen). However, only humans took turns selecting red or blue so that players could alternate getting the highest reward. Monkeys usually settled on one color throughout the game, unless they had learned the 'turn-taking' strategy from a human partner; in that case, the color chosen in each trial was typically determined by the monkey who was the faster to move. These experiments show how monkeys and humans use visual information about their partner's actions to coordinate their choices, paving the way for further decision-making studies that accurately reflect how interactions unfold in real life. Moeller et al. expect that this will help to understand how cooperation and competition emerge in these two species, including how direct face-to-face contact, or lack thereof in some aspects of our modern world, shapes our social behavior.

Home-Enclosure-Based Behavioral and Wireless Neural Recording Setup for Unrestrained Rhesus Macaques.

Electrophysiological studies with behaving nonhuman primates often require the separation of animals from their social group as well as partial movement restraint to perform well-controlled experiments. When the research goal per se does not mandate constraining the animals' movements, there are often still experimental needs imposed by tethered data acquisition. Recent technological advances meanwhile allow wireless neurophysiological recordings at high band-width in limited-size enclosures. Here, we demonstrate wireless neural recordings at single unit resolution from unrestrained rhesus macaques while they performed self-paced, structured visuomotor tasks on our custom-built, stand-alone touchscreen system [eXperimental Behavioral Instrument (XBI)] in their home environment. We were able to successfully characterize neural tuning to task parameters, such as visuo-spatial selectivity during movement planning and execution, as expected from existing findings obtained via setup-based neurophysiology recordings. We conclude that when movement restraint and/or a highly controlled, insulated environment are not necessary for scientific reasons, cage-based wireless neural recordings are a viable option. We propose an approach that allows the animals to engage in a self-paced manner with our XBI device, both for fully automatized training and cognitive testing, as well as neural data acquisition in their familiar environment, maintaining auditory and sometimes visual contact with their conspecifics.

Universal Guide for Skull Extraction and Custom-Fitting of Implants to Continuous and Discontinuous Skulls.

Intracranial neurophysiological recordings require chronic implants to provide transcranial access to the brain. Especially in larger animals, which participate in experiments over extended periods of time, implants should match the skull curvature to promote osseointegration and avoid tissue and bacterial ingress over time. Proposed CAD methods for designing implants to date have focused on naive animals with continuous and even skull surfaces and calculate Boolean differences between implant and skull surface to fit the implant to the skull curvature. However, custom-fitting by calculating the difference fails, if a discontinuous skull surface needs to be matched. Also, the difference method does not allow designs with constant material thickness along the skull curvature, e.g., to allow fixed screw lengths. We present a universal step-by-step guide for custom-fitting implants which overcomes these limitations. It is suited for unusual skull conditions, like surface discontinuities or irregularities and includes virtual bending as a process to match skull surfaces while maintaining implant thickness. We demonstrate its applicability for a wide spectrum of scenarios, ranging from complex-shaped single-pieced implants to detailed multicomponent implant systems built on even or discontinuous skull. The guide uses only a few software tools and the final virtual product can be manufactured using CNC milling or 3D printing. A detailed description of this process is available on GitHub including step-by-step video instructions suitable for users without any prior knowledge in CAD programming. We report the experience with these implants over several years in 11 rhesus monkeys.

The cortical timeline for deciding on reach motor goals.

Flexible sensorimotor planning is the basis for goal-directed behavior. We investigated the integration of visuospatial information with context-specific transformation rules during reach planning. We were especially interested in the relative timing of motor-goal decisions in monkey dorsal premotor cortex (PMd) and parietal reach region (PRR). We used a rule-based mapping task with different cueing conditions to compare task-dependent motor-goal latencies. The task allowed us a separation of cue-related from motor-related activity, and a separation of activity related to motor planning from activity related to motor initiation or execution. The results show that selectivity for the visuospatial goal of a pending movement occurred earlier in PMd than PRR whenever the task required spatial remapping. Such remapping was needed if the spatial representation of a cue or of a default motor plan had to be transformed into a spatially incongruent representation of the motor goal. In contrast, we did not find frontoparietal latency differences if the spatial representation of the cue or the default plan was spatially congruent with the motor goal. The fact that frontoparietal latency differences occurred only in conditions with spatial remapping was independent of the subjects' partial a priori knowledge about the pending goal. Importantly, frontoparietal latency differences existed for motor-goal representations during movement planning, without immediate motor execution. We interpret our findings as being in support of the hypothesis that latency differences reflect a dynamic reorganization of network activity in PRR, and suggest that the reorganization is contingent on frontoparietal projections from PMd.

What is 'anti' about anti-reaches? Reference frames selectively affect reaction times and endpoint variability.

Reach movement planning involves the representation of spatial target information in different reference frames. Neurons at parietal and premotor stages of the cortical sensorimotor system represent target information in eye- or hand-centered reference frames, respectively. How the different neuronal representations affect behavioral parameters of motor planning and control, i.e. which stage of neural representation is relevant for which aspect of behavior, is not obvious from the physiology. Here, we test with a behavioral experiment if different kinematic movement parameters are affected to a different degree by either an eye- or hand-reference frame. We used a generalized anti-reach task to test the influence of stimulus-response compatibility (SRC) in eye- and hand-reference frames on reach reaction times, movement times, and endpoint variability. While in a standard anti-reach task, the SRC is identical in the eye- and hand-reference frames, we could separate SRC for the two reference frames. We found that reaction times were influenced by the SRC in eye- and hand-reference frame. In contrast, movement times were only influenced by the SRC in hand-reference frame, and endpoint variability was only influenced by the SRC in eye-reference frame. Since movement time and endpoint variability are the result of planning and control processes, while reaction times are consequences of only the planning process, we suggest that SRC effects on reaction times are highly suited to investigate reference frames of movement planning, and that eye- and hand-reference frames have distinct effects on different phases of motor action and different kinematic movement parameters.

The future of General Movement Assessment: The role of computer vision and machine learning - A scoping review.

BACKGROUND: The clinical and scientific value of Prechtl general movement assessment (GMA) has been increasingly recognised, which has extended beyond the detection of cerebral palsy throughout the years. With advancing computer science, a surging interest in developing automated GMA emerges. AIMS: In this scoping review, we focused on video-based approaches, since it remains authentic to the non-intrusive principle of the classic GMA. Specifically, we aimed to provide an overview of recent video-based approaches targeting GMs; identify their techniques for movement detection and classification; examine if the technological solutions conform to the fundamental concepts of GMA; and discuss the challenges of developing automated GMA. METHODS AND PROCEDURES: We performed a systematic search for computer vision-based studies on GMs. OUTCOMES AND RESULTS: We identified 40 peer-reviewed articles, most (n = 30) were published between 2017 and 2020. A wide variety of sensing, tracking, detection, and classification tools for computer vision-based GMA were found. Only a small portion of these studies applied deep learning approaches. A comprehensive comparison between data acquisition and sensing setups across the reviewed studies, highlighting limitations and advantages of each modality in performing automated GMA is provided. CONCLUSIONS AND IMPLICATIONS: A "method-of-choice" for automated GMA does not exist. Besides creating large datasets, understanding the fundamental concepts and prerequisites of GMA is necessary for developing automated solutions. Future research shall look beyond the narrow field of detecting cerebral palsy and open up to the full potential of applying GMA to enable an even broader application.

Implementation of spatial transformation rules for goal-directed reaching via gain modulation in monkey parietal and premotor cortex.

Planning goal-directed movements requires the combination of visuospatial with abstract contextual information. Our sensory environment constrains possible movements to a certain extent. However, contextual information guides proper choice of action in a given situation and allows flexible mapping of sensory instruction cues onto different motor actions. We used anti-reach tasks to test the hypothesis that spatial motor-goal representations in cortical sensorimotor areas are gain modulated by the behavioral context to achieve flexible remapping of spatial cue information onto arbitrary motor goals. We found that gain modulation of neuronal reach goal representations is commonly induced by the behavioral context in individual neurons of both, the parietal reach region (PRR) and the dorsal premotor cortex (PMd). In addition, PRR showed stronger directional selectivity during the planning of a reach toward a directly cued goal (pro-reach) compared with an inferred target (anti-reach). PMd, however, showed stronger overall activity during reaches toward inferred targets compared with directly cued targets. Based on our experimental evidence, we suggest that gain modulation is the computational mechanism underlying the integration of spatial and contextual information for flexible, rule-driven stimulus-response mapping, and thereby forms an important basis of goal-directed behavior. Complementary contextual effects in PRR versus PMd are consistent with the idea that posterior parietal cortex preferentially represents sensory-driven, "automatic" motor goals, whereas frontal sensorimotor areas are stronger engaged in the representation of rule-based, "inferred" motor goals.