The transitive inference task to study the neuronal correlates of memory-driven decision making: A monkey neurophysiology perspective

A vast amount of literature agrees that rank-ordered information as A>B>C>D>E>F is mentally represented in spatially organized schemas after learning. This organization significantly influences the process of decision-making, using the acquired premises, i.e. deciding if B is higher than D is equivalent to comparing their position in this space. The implementation of non-verbal versions of the transitive inference task has provided the basis for ascertaining that different animal species explore a mental space when deciding among hierarchically organized memories. In the present work, we reviewed several studies of transitive inference that highlighted this ability in animals and, consequently, the animal models developed to study the underlying cognitive processes and the main neural structures supporting this ability. Further, we present the literature investigating which are the underlying neuronal mechanisms. Then we discuss how non-human primates represent an excellent model for future studies, providing ideal resources for better understanding the neuronal correlates of decision-making through transitive inference tasks.

A Brief History of the Encoding of Hand Position by the Cerebral Cortex: Implications for Motor Control and Cognition

Encoding hand position by the cerebral cortex is essential not only for the neural representation of the body image but also for different actions based on eye-hand coordination. These include reaching for visual objects as well as complex movement sequences, such as tea-making, tool use, and object construction, among many others. All these functions depend on a continuous refreshing of the hand position representation, relying on both predictive signaling and afferent information. The hand position influence on neural activity in the parietofrontal system, together with eye position signals, are the basic elements of an eye-hand matrix from which all the above functions can emerge and could be regarded as key features of a network with several entry points, command nodes and outflow pathways, as confirmed by the discovery of a direct parietospinal projection for the control of hand action. The integrity of this system is crucial for daily life, as testified by the consequences of cortical lesions, spanning from severe paralysis to complex forms of apraxia. In this review, I will sketch my personal understanding of the scientific and conceptual trajectory of a line of investigation with many unexpected influences on cortical function and disease, from motor behavior to cognition.

Characteristics of Kinematic Parameters in Decoding Intended Reaching Movements Using Electroencephalography (EEG)

The utility of premovement electroencephalography (EEG) for decoding movement intention during a reaching task has been demonstrated. However, the kind of information the brain represents regarding the intended target during movement preparation remains unknown. In the present study, we investigated which movement parameters (i.e., direction, distance, and positions for reaching) can be decoded in premovement EEG decoding. Eight participants performed 30 types of reaching movements that consisted of 1 of 24 movement directions, 7 movement distances, 5 horizontal target positions, and 5 vertical target positions. Event-related spectral perturbations were extracted using independent components, some of which were selected via an analysis of variance for further binary classification analysis using a support vector machine. When each parameter was used for class labeling, all possible binary classifications were performed. Classification accuracies for direction and distance were significantly higher than chance level, although no significant differences were observed for position. For the classification in which each movement was considered as a different class, the parameters comprising two vectors representing each movement were analyzed. In this case, classification accuracies were high when differences in distance were high, the sum of distances was high, angular differences were large, and differences in the target positions were high. The findings further revealed that direction and distance may provide the largest contributions to movement. In addition, regardless of the parameter, useful features for classification are easily found over the parietal and occipital areas.

Two Brains in Action: Joint-Action Coding in the Primate Frontal Cortex

Daily life often requires the coordination of our actions with those of another partner. After 50 years (1968-2018) of behavioral neurophysiology of motor control, the neural mechanisms that allow such coordination in primates are unknown. We studied this issue by recording cell activity simultaneously from dorsal premotor cortex (PMd) of two male interacting monkeys trained to coordinate their hand forces to achieve a common goal. We found a population of "joint-action cells" that discharged preferentially when monkeys cooperated in the task. This modulation was predictive in nature, because in most cells neural activity led in time the changes of the "own" and of the "other" behavior. These neurons encoded the joint-performance more accurately than "canonical action-related cells", activated by the action per se, regardless of the individual versus interactive context. A decoding of joint-action was obtained by combining the two brains' activities, using cells with directional properties distinguished from those associated to the "solo" behaviors. Action observation-related activity studied when one monkey observed the consequences of the partner's behavior, i.e., the cursor's motion on the screen, did not sharpen the accuracy of joint-action cells' representation, suggesting that it plays no major role in encoding joint-action. When monkeys performed with a non-interactive partner, such as a computer, joint-action cells' representation of the other (non-cooperative) behavior was significantly degraded. These findings provide evidence of how premotor neurons integrate the time-varying representation of the self-action with that of a co-actor, thus offering a neural substrate for successful visuomotor coordination between individuals.SIGNIFICANCE STATEMENT The neural bases of intersubject motor coordination were studied by recording cell activity simultaneously from the frontal cortex of two interacting monkeys, trained to coordinate their hand forces to achieve a common goal. We found a new class of cells, preferentially active when the monkeys cooperated, rather than when the same action was performed individually. These "joint-action neurons" offered a neural representation of joint-behaviors by far more accurate than that provided by the "canonical action-related cells", modulated by the action per se regardless of the individual/interactive context. A neural representation of joint-performance was obtained by combining the activity recorded from the two brains. Our findings offer the first evidence concerning neural mechanisms subtending interactive visuomotor coordination between co-acting agents.

Neural correlates of visuomotor adjustments during scaling of human finger movements

Visually guided finger movements include online feedback of current effector position to guide target approach. This visual feedback may be scaled or otherwise distorted by unpredictable perturbations. Although adjustments to visual feedback scaling have been studied before, the underlying brain activation differences between upscaling (visual feedback larger than real movement) and downscaling (feedback smaller than real movement) are currently unknown. Brain activation differences between upscaling and downscaling might be expected because within-trial adjustments during upscaling require corrective backwards accelerations, whereas correcting for downscaling requires forward accelerations. In this behavioural and fMRI study we investigated adjustments during up- and downscaling in a target-directed finger flexion-extension task with real-time visual feedback. We found that subjects made longer and more complete within-trial corrections for downscaling perturbations than for upscaling perturbations. The finger task activated primary motor (M1) and somatosensory (S1) areas, premotor and parietal regions, basal ganglia, and cerebellum. General scaling effects were seen in the right pre-supplementary motor area, dorsal anterior cingulate cortex, inferior parietal lobule, and dorsolateral prefrontal cortex. Stronger activations for down- than for upscaling were observed in M1, supplementary motor area (SMA), S1 and anterior cingulate cortex. We argue that these activation differences may reflect differing online correction for upscaling vs. downscaling during finger flexion-extension.

Forward Prediction in the Posterior Parietal Cortex and Dynamic Brain-Machine Interface

While remarkable progress has been made in brain-machine interfaces (BMIs) over the past two decades, it is still difficult to utilize neural signals to drive artificial actuators to produce predictive movements in response to dynamic stimuli. In contrast to naturalistic limb movements largely based on forward planning, brain-controlled neuroprosthetics mainly rely on feedback without prior trajectory formation. As an important sensorimotor interface integrating multisensory inputs and efference copy, the posterior parietal cortex (PPC) might play a proactive role in predictive motor control. Here it is proposed that predictive neural activity in PPC could be decoded to provide prosthetic control signals for guiding BMI systems in dynamic environments.