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)

Neurodynamical theory of decision confidence

Decision confidence offers a window on introspection and onto the evaluation mechanisms associated with decision-making. Nonetheless we do not have yet a thorough understanding of its neurophysiological and computational substrate. There are mainly two experimental paradigms to measure decision confidence in animals: post-decision wagering and uncertain option. In this thesis we explore and try to shed light on the computational mechanisms underlying confidence based decision-making in both experimental paradigms. We propose that a double-layer attractor neural network can account for neural recordings and behavior of rats in a post-decision wagering experiment. In this model a decision-making layer takes the perceptual decision and a separate confidence layer monitors the activity of the decision-making layer and makes a judgment about the confidence in the decision. Moreover we test the prediction of the model by analyizing neuronal data from monkeys performing a decision-making task. We show the existence of neurons in ventral Premotor cortex that encode decision confidence. We also found that both a continuous and discrete encoding of decision confidence are present in the primate brain. In particular we show that different neurons encode confidence through three different mechanisms: 1. Switch time coding, 2. rate coding and 3. binary coding. Furthermore we propose a multiple-choice attractor network model in order to account for uncertain option tasks. In this model the confidence emerges from the stochastic dynamics of decision neurons, thus making a separate monitoring network (like in the model of the post-decision wagering task) unnecessary. The model explains the behavioral and neural data recorded in monkeys lateral intraparietal area as a result of the multistable dynamics of the attractor network, whereby it is possible to make several testable predictions. The rich neurophysiological representation and computational mechanisms of decision confidence evidence the basis of different functional aspects of confidence in the making of a decision.El estudio de la confianza en la decisión ofrece una perspectiva ventajosa sobre los procesos de introspección y sobre los procesos de evaluación de la toma de decisiones. No obstante todav'ia no tenemos un conocimiento exhaustivo del sustrato neurofisiológico y computacional de la confianza en la decisión. Existen principalmente dos paradigmas experimentales para medir la confianza en la decisión en los sujetos no humanos: apuesta post-decisional (post-decision wagering) y opción insegura (uncertain option). En esta tesis tratamos de aclarar los mecanísmos computacionales que subyacen a los procesos de toma de decisiones y juicios de confianza en ambos paradigmas experimentales. El modelo que proponemos para explicar los experimentos de apuesta post-decisional es una red neuronal de atractores de dos capas. En este modelo la primera capa se encarga de la toma de decisiones, mientras la segunda capa vigila la actividad de la primera capa y toma un juicio sobre la confianza en la decisión. Sucesivamente testeamos la predicción de este modelo analizando la actividad de neuronas registrada en el cerebro de dos monos, mientras estos desempeñaban una tarea de toma de decisiones. Con este análisis mostramos la existencia de neuronas en la corteza premotora ventral que codifican la confianza en la decisión. Nuestros resultados muestran también que en el cerebro de los primates existen tanto neuronas que codifican confianza como neuronas que la codifican de forma continua. Más en específico mostramos que existen tres mecanismos de codificación: 1. codificación por tiempo de cambio, 2. codificación por tasa de disparo, 3. codificación binaria. En relación a las tareas de opción insegura proponemos un modelo de red de atractores para opciones multiplas. En este modelo la confianza emerge de la dinámica estocástica de las neuronas de decisión, volviéndose así innecesaria la supervisión del proceso de toma de decisiones por parte de otra red (como en el modelo de la tarea de apuesta post-decisional). El modelo explica los datos de comportamiento de los monos y los registros de la actividad de neuronas del área lateral intraparietal como efectos de la dinámica multiestable de la red de atractores. Además el modelo produce interesantes y novedosas predicciones que se podrán testear en experimentos futuros. La compleja representación neurofisiológica y los distintos mecanísmos computacionales que emergen de este trabajo sugieren distintos aspectos funcionales de la confianza en la toma de decisiones

Multiple choice neurodynamical model of the uncertain option task

The uncertain option task has been recently adopted to investigate the neural systems underlying the decision confidence. Latterly single neurons activity has been recorded in lateral intraparietal cortex of monkeys performing an uncertain option task, where the subject is allowed to opt for a small but sure reward instead of making a risky perceptual decision. We propose a multiple choice model implemented in a discrete attractors network. This model is able to reproduce both behavioral and neurophysiological experimental data and therefore provides support to the numerous perspectives that interpret the uncertain option task as a sensory-motor association. The model explains the behavioral and neural data recorded in monkeys as the result of the multistable attractor landscape and produces several testable predictions. One of these predictions may help distinguish our model from a recently proposed continuous attractor model.This work was supported by MINECO (PSI2013-42091-P), Agència de Gestio d'Ajuts Universitaris i de Recerca (AGAUR-2014SGR856), European Research Council (ERC) Advanced Grant DYSTRUCTURE (n. 295129), FlagERA ChampMouse PCIN-2015- 127 and FET-Flagship HPB-SGA1 (720270). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Task description, psychophysics and model structure.

<p>(A) Experimental task procedure [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005250#pcbi.1005250.ref003" target="_blank">3</a>]. (B) Time course of input. Input to pool R, L and S is represented respectively by orange, dashed blue and green line (colors are consistent with B). A first spontaneous phase is followed by the onset of the target that elicit a strong input with temporal adaptation. At <i>t</i> = 1 s the motion input is turned on: pool L receives <i>λ</i>_<i>L</i> = <i>λ</i> − Δ<i>λ</i> and pools R receives <i>λ</i>_<i>R</i> = <i>λ</i> + Δ<i>λ</i>. After motion input, during the delay phase, the network only receives background noise. In free choice trials, pool S receives input due to the onset of the sure target 500 ms after the offset of the motion input. (C) Schematic representation of the attractor network model. Arrowhead connections represent excitatory projections, dot-head connections represent inhibitory projections. Arrows coming from outside represent external inputs. (D) Psychophysics data measured experimentally (top row) and produced by the model (bottom row). The model reproduces qualitatively the effect of both stimulus duration and coherence on the probability of correct responses in forced choice trials (open circles, right panel), as well as on the probability of choosing the “sure” option (left panel). Moreover the model correctly predicts the increased P(<i>correct</i>) in free choise trials (filled circles, right panel).</p

Probability of choosing the “sure target” as a function of Δ<i>λ</i> for early correct and error trials.

<p>Continuous line is the prediction from the spiking network. Dashed lines are predictions of the reduced probabilistic model (points are slightly shifted on the horizontal axis for better visibility).</p

Parameters used in the simulations.

<p>Parameters used in the simulations.</p

Bifurcation diagram and psychophysics measures in different regions.

<p>A-D: P(C) for trials when the sure target was (not) shown are represented with filled (open) circles. The color of the box and the arrows indicate the value of <i>λ</i> used. E: Bifurcation diagram over parameter <i>λ</i>. The curves represent the firing rates of pools L and R in the attractor (according to the mean-field reduction of the spiking network). Four regions appear (in order of increasing <i>λ</i>): A first tiny multistable region when both decision and spontaneous attractors appear; a bistable region where only the two decision attractor are present; a multistable region, where decision attractors coexist with a “mixed” attractor (both pool have intermediate firing rate); a monostable region, where only the “mixed” attractor exists. F-I: P(S) for different values of <i>λ</i> (color convention as in A-D). Error bars indicating the standard deviation have been omitted since they are always smaller than symbols.</p