Determining monkey free choice long before the choice is made: the principal role of prefrontal neurons involved in both decision and motor processes

When choices are made freely, they might emerge from pre-existing neural activity. However, whether neurons in the prefrontal cortex (PF) show this anticipatory effect and, if so, in which part of the process they are involved is still debated. To answer this question, we studied PF activity in monkeys while they performed a strategy task. In this task when the stimulus changed from the previous trial, the monkeys had to shift their response to one of two spatial goals, excluding the one that had been previously selected. Under this free-choice condition, the prestimulus activity of the same neurons that are involved in decision and motor processes predicted future choices. These neurons developed the same goal preferences during the prestimulus presentation as they did later in the decision phase. In contrast, the same effect was not observed in motor-only neurons and it was present but weaker in decision-only neurons. Overall, our results suggest that the PF neuronal activity predicts upcoming actions mainly through the decision-making network that integrate in time decision and motor task aspects

Interference between space and time estimations: from behavior to neurons

Influences between time and space can be found in our daily life in which we are surrounded by numerous spatial metaphors to refer to time. For instance, when we move files from one folder to another in our computer a horizontal line that grows from left to right informs us about the elapsed and remaining time to finish the procedure and, similarly, in our communication we use several spatial terms to refer to time. Although with some differences in the degree of interference, not only space has an influence on time but both magnitudes influence each other. Indeed, since our childhood our estimations of time are influenced by space even when space should be irrelevant and the same occurs when estimating space with time as distractor. Such interference between magnitudes has also been observed in monkeys even if they do not use language or computers, suggesting that the two magnitudes are tightly coupled beyond communication and technology. Imaging and lesion studies have indicated that same brain areas are involved during the processing of both magnitudes and have suggested that rather than coding the specific magnitude itself the brain represents them as abstract concepts. Recent neurophysiological studies in prefrontal cortex, however, have shown that the coding of absolute and relative space and time in this area is realized by independent groups of neurons. Interestingly, instead, a high overlap was observed in this same area in the coding of goal choices across tasks. These results suggest that rather than during perception or estimation of space and time the interference between the two magnitudes might occur, at least in the prefrontal cortex, in a subsequent phase in which the goal has to be chosen or the response provided

Independent coding of absolute duration and distance magnitudes in the prefrontal cortex

The estimation of space and time can interfere with each other, and neuroimaging studies have shown overlapping activation in the parietal and prefrontal cortical areas. We used duration and distance discrimination tasks to determine whether space and time share resources in prefrontal cortex (PF) neurons. Monkeys were required to report which of two stimuli, a red circle or blue square, presented sequentially, were longer and farther, respectively, in the duration and distance tasks. In a previous study, we showed that relative duration and distance are coded by different populations of neurons and that the only common representation is related to goal coding. Here, we examined the coding of absolute duration and distance. Our results support a model of independent coding of absolute duration and distance metrics by demonstrating that not only relative magnitude but also absolute magnitude are independently coded in the PF

Coding of self and other's future choices in dorsal premotor cortex during social interaction

Representing others’ intentions is central to primate social life. We explored the role of dorsal premotor cortex (PMd) in discriminating between self and others’ behavior while two male rhesus monkeys performed a non-match-to-goal task in a monkey-human paradigm. During each trial, two of four potential targets were randomly presented on the right and left parts of a screen, and the monkey or the human was required to choose the one that did not match the previously chosen target. Each agent had to monitor the other's action in order to select the correct target in that agent's own turn. We report neurons that selectively encoded the future choice of the monkey, the human agent, or both. Our findings suggest that PMd activity shows a high degree of self-other differentiation during face-to-face interactions, leading to an independent representation of what others will do instead of entailing self-centered mental rehearsal or mirror-like activities. Understanding others’ intentions is essential to successful primate social life. Cirillo et al. explore the role of dorsal premotor cortex (PMd) in discriminating between self and others’ behavior while macaques interacted with humans. They show that the majority of neurons encoding the future choice did so selectively for the monkey or the human agent. PMd thus differentiates self from others’ behavior, leading to independent representations of future actions

Outcome modulation across tasks in the primate dorsolateral prefrontal cortex

Animals need to learn and to adapt to new and changing environments so that appropriate actions that lead to desirable outcomes are acquired within each context. The prefrontal cortex (PF) is known to underlie such function that directly implies that the outcome of each response must be represented in the brain for behavioral policies update. However, whether such PF signal is context dependent or it is a general representation beyond the specificity of a context is still unclear. Here, we analyzed the activity of neurons in the dorsolateral PF (PFdl) recorded while two monkeys performed two perceptual magnitude discrimination tasks. Both tasks were well known by the monkeys and unexpected changes did not occur but the difficulty of the task varied from trial to trial and thus the monkeys made mistakes in a proportion of trials. We show a context-independent coding of the response outcome with neurons maintaining similar selectivity in both task contexts. Using a classification method of the neural activity, we also show that the trial outcome could be well predicted from the activity of the same neurons in the two contexts. Altogether, our results provide evidence of high degree of outcome generality in PFdl

Nootropic effects of LSD: Behavioral, molecular and computational evidence

The therapeutic use of classical psychedelic substances such as d-lysergic acid diethylamide (LSD) surged in recent years. Studies in rodents suggest that these effects are produced by increased neural plasticity, including stimulation of the mTOR pathway, a key regulator of metabolism, plasticity, and aging. Could psychedelic-induced neural plasticity be harnessed to enhance cognition? Here we show that LSD treatment enhanced performance in a novel object recognition task in rats, and in a visuo-spatial memory task in humans. A proteomic analysis of human brain organoids showed that LSD affected metabolic pathways associated with neural plasticity, including mTOR. To gain insight into the relation of neural plasticity, aging and LSD-induced cognitive gains, we emulated the experiments in rats and humans with a neural network model of a cortico-hippocampal circuit. Using the baseline strength of plasticity as a proxy for age and assuming an increase in plasticity strength related to LSD dose, the simulations provided a good fit for the experimental data. Altogether, the results suggest that LSD has nootropic effects.This project was supported by the Beckley Foundation; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) – Finance Code 001, Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (grants 308775/2015-5 and 408145/2016-1), São Paulo Research Foundation grants (2013/07699-0, 2014/10068-4, 2017/25588-1 and 2019/00098-7), intramural grants from D'Or Institute and Federal University of Rio Grande do Norte, and a Juan de la Cierva-Incorporación Scholarship (IJCI-2016-27864) from the Spanish Ministry of Science, Innovation and Universities, and a Newton International Fellowship from the Royal Society.Peer reviewe

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Embodied decision making and its neural substrate

Decisions are the result of a deliberative process that evaluates the suitability of specific options. Studies about decision making have been mainly conducted by using restricted tasks in which humans or animals are requested to discriminate between options. However, the influence that factors related to embodiment, such as motor cost, might have on this process has frequently been ignored. In this thesis, we adopt a combined experimental and theoretical approach to examine the effect that such factors have on decision making. Our results confirm an important bias of behavior and neural activity resulting from factors related to embodiment that are external to the goal of the task itself. We use computational models to account for this bias and to shed some light on the neural mechanisms producing it. Our results translate into significant progress in the understanding of embodied decision making, providing new insights into neural mechanisms and theoretical models.Las decisiones son el resultado de un proceso de deliberación que evalúa la idoneidad de opciones específicas. Los estudios acerca de la toma de decisiones han estado principalmente dirigidos usando tareas restringidas en las que a los humanos o animales se les pide escoger entre opciones. Sin embargo, la influencia que factores relacionados con la corporificación de la toma de decisiones podrían tener en este proceso se ha ignorado frecuentemente. En esta tesis, adoptamos un enfoque experimental y teórico combinado para examinar la influencia que estos factores tienen en la toma de decisiones. Nuestros resultados confirman un importante sesgado del comportamiento y de la actividad neuronal causados por factores que son externos al objetivo de la tarea en sí. Utilizamos modelos computacionales para interpretar este sesgado que, a su vez, nos da una intuición del mecanismo neuronal que los está produciendo. Nuestros resultados se traducen en un significante progreso en la comprensión de la toma de decisiones corporificada, aportando nuevos conocimientos sobre los mecanismos neuronales y modelos teóricos.Les decisions són el resultat d'un procés de deliberació que avalua la idoneïtat d'opcions específiques. Els estudis sobre la presa de decisions han estat principalment dirigits fent servir tasques restringides a les quals, als humans o animals, se'ls demana escollir entre opcions. No obstant, la influència que factors relacionats amb la corporificació de la presa de decisions podrien tenir en aquest procés s'ha ignorat freqüentment. En aquesta tesi, adoptem un enfocament experimental i teòric combinat per tal d'examinar la influència que aquests factors tenen en la presa de decisions. Els nostres resultats confirmen un important esbiaixat del comportament i de l'activitat neuronal degut a factors externs a l'objectiu de la tasca en sí. Fem servir models computacionals per tal d'interpretar aquest esbiaixat que, a la vegada, ens dóna una intuïció del mecanisme que l'està produint. La tesi conclou amb la presentació d'un únic model que integra tots els descobriments presentats i que podria utilitzar-se com a nou marc teòric per a recerques futures. En general, els resultats inclosos aquí es tradueixen en un significant progrés a la comprensió de la presa de decisions corporificada, aportant nous coneixements sobre els mecanismes neuronals i models teòrics

The importance of urgency in decision making based on dynamic information

A standard view in the literature is that decisions are the result of a process that accumulates evidence in favor of each alternative until such accumulation reaches a threshold and a decision is made. However, this view has been recently questioned by an alternative proposal that suggests that, instead of accumulated, evidence is combined with an urgency signal. Both theories have been mathematically formalized and supported by a variety of decision-making tasks with constant information. However, recently, tasks with changing information have shown to be more effective to study the dynamics of decision making. Recent research using one of such tasks, the tokens task, has shown that decisions are better described by an urgency mechanism than by an accumulation one. However, the results of that study could depend on a task where all fundamental information was noiseless and always present, favoring a mechanism of non-integration, such as the urgency one. Here, we wanted to address whether the same conclusions were also supported by an experimental paradigm in which sensory evidence was removed shortly after it was provided, making working memory necessary to properly perform the task. Here, we show that, under such condition, participants’ behavior could be explained by an urgency-gating mechanism that low-pass filters the mnemonic information and combines it with an urgency signal that grows with time but not by an accumulation process that integrates the same mnemonic information. Thus, our study supports the idea that, under certain situations with dynamic sensory information, decisions are better explained by an urgency-gating mechanism than by an accumulation one.EM, AG and LF were supported by Sapienza University of Rome (“Avvio alla Ricerca 2016” and Ateneo 2018). EM was also supported by Spanish Ministry of Science, Innovation and Universities (Juan de la Cierva-incorporación scholarship, IJCI-2016-27864) and by the Spanish State Research Agency through the Severo Ochoa Program for Centres of Excellence in R&D (SEV- 2017-0723).Peer reviewe

Hidden markov models predict the future choice better than a PSTH-based method

Beyond average firing rate, other measurable signals of neuronal activity are fundamental to an understanding of behavior. Recently, hidden Markov models (HMMs) have been applied to neural recordings and have described how neuronal ensembles process information by going through sequences of different states. Such collective dynamics are impossible to capture by just looking at the average firing rate. To estimate how well HMMs can decode information contained in single trials, we compared HMMs with a recently developed classification method based on the peristimulus time histogram (PSTH). The accuracy of the two methods was tested by using the activity of prefrontal neurons recorded while two monkeys were engaged in a strategy task. In this task, the monkeys had to select one of three spatial targets based on an instruction cue and on their previous choice. We show that by using the single trial's neural activity in a period preceding action execution, both models were able to classify the monkeys' choice with an accuracy higher than by chance. Moreover, the HMM was significantly more accurate than the PSTH-based method, even in cases in which the HMM performance was low, although always above chance. Furthermore, the accuracy of both methods was related to the number of neurons exhibiting spatial selectivity within an experimental session. Overall, our study shows that neural activity is better described when not only the mean activity of individual neurons is considered and that therefore, the study of other signals rather than only the average firing rate is fundamental to an understanding of the dynamics of neuronal ensembles.Peer reviewe