Mario Pannunzi

Confidence-Related Decision Making

Neurons have been recorded that reflect in their firing rates the confidence in a decision. Here we show how this could arise as an emergent property in an integrate-and-fire attractor network model of decision making. The attractor network has populations of neurons that respond to each of the possible choices, each biased by the evidence for that choice, and there is competition between the attractor states until one population wins the competition and finishes with high firing that represents the decision. Noise resulting from the random spiking times of individual neurons makes the decision making probabilistic. We also show that a second attractor network can make decisions based on the confidence in the first decision. This system is supported by and accounts for neuronal responses recorded during decision making and makes predictions about the neuronal activity that will be found when a decision is made about whether to stay with a first decision or to abort the trial and start again. The research shows how monitoring can be performed in the brain and this has many implications for understanding cognitive functioning.

Classification of correlated patterns with a configurable analog vlsi neural network of spiking neurons and self-regulating plastic synapses

We describe the implementation and illustrate the learning performance of an analog VLSI network of 32 integrate-and-fire neurons with spike-frequency adaptation and 2016 Hebbian bistable spike-driven stochastic synapses, endowed with a self-regulating plasticity mechanism, which avoids unnecessary synaptic changes. The synaptic matrix can be flexibly configured and provides both recurrent and external connectivity with address-event representation compliant devices. We demonstrate a marked improvement in the efficiency of the network in classifying correlated patterns, owing to the self-regulating mechanism.

Resting-state fMRI correlations: From link-wise unreliability to whole brain stability

&NA; The functional architecture of spontaneous BOLD fluctuations has been characterized in detail by numerous studies, demonstrating its potential relevance as a biomarker. However, the systematic investigation of its consistency is still in its infancy. Here, we analyze within‐ and between‐subject variability and test‐retest reliability of resting‐state functional connectivity (FC) in a unique data set comprising multiple fMRI scans (42) from 5 subjects, and 50 single scans from 50 subjects. We adopt a statistical framework that enables us to identify different sources of variability in FC. We show that the low reliability of single links can be significantly improved by using multiple scans per subject. Moreover, in contrast to earlier studies, we show that spatial heterogeneity in FC reliability is not significant. Finally, we demonstrate that despite the low reliability of individual links, the information carried by the whole‐brain FC matrix is robust and can be used as a functional fingerprint to identify individual subjects from the population.

The influence of spatiotemporal structure of noisy stimuli in decision making.

Decision making is a process of utmost importance in our daily lives, the study of which has been receiving notable attention for decades. Nevertheless, the neural mechanisms underlying decision making are still not fully understood. Computational modeling has revealed itself as a valuable asset to address some of the fundamental questions. Biophysically plausible models, in particular, are useful in bridging the different levels of description that experimental studies provide, from the neural spiking activity recorded at the cellular level to the performance reported at the behavioral level. In this article, we have reviewed some of the recent progress made in the understanding of the neural mechanisms that underlie decision making. We have performed a critical evaluation of the available results and address, from a computational perspective, aspects of both experimentation and modeling that so far have eluded comprehension. To guide the discussion, we have selected a central theme which revolves around the following question: how does the spatiotemporal structure of sensory stimuli affect the perceptual decision-making process? This question is a timely one as several issues that still remain unresolved stem from this central theme. These include: (i) the role of spatiotemporal input fluctuations in perceptual decision making, (ii) how to extend the current results and models derived from two-alternative choice studies to scenarios with multiple competing evidences, and (iii) to establish whether different types of spatiotemporal input fluctuations affect decision-making outcomes in distinctive ways. And although we have restricted our discussion mostly to visual decisions, our main conclusions are arguably generalizable; hence, their possible extension to other sensory modalities is one of the points in our discussion.

“If you are good, I get better”: the role of social hierarchy in perceptual decision-making

So far, it was unclear if social hierarchy could influence sensory or perceptual cognitive processes. We evaluated the effects of social hierarchy on these processes using a basic visual perceptual decision task. We constructed a social hierarchy where participants performed the perceptual task separately with two covertly simulated players (superior, inferior). Participants were faster (better) when performing the discrimination task with the superior player. We studied the time course when social hierarchy was processed using event-related potentials and observed hierarchical effects even in early stages of sensory-perceptual processing, suggesting early top-down modulation by social hierarchy. Moreover, in a parallel analysis, we fitted a drift-diffusion model (DDM) to the results to evaluate the decision making process of this perceptual task in the context of a social hierarchy. Consistently, the DDM pointed to nondecision time (probably perceptual encoding) as the principal period influenced by social hierarchy.

Learning selective top-down control enhances performance in a visual categorization task

We model the putative neuronal and synaptic mechanisms involved in learning a visual categorization task, taking inspiration from single-cell recordings in inferior temporal cortex (ITC). Our working hypothesis is that learning the categorization task involves both bottom-up, ITC to prefrontal cortex (PFC), and top-down (PFC to ITC) synaptic plasticity and that the latter enhances the selectivity of the ITC neurons encoding the task-relevant features of the stimuli, thereby improving the signal-to-noise ratio. We test this hypothesis by modeling both areas and their connections with spiking neurons and plastic synapses, ITC acting as a feature-selective layer and PFC as a category coding layer. This minimal model gives interesting clues as to properties and function of the selective feedback signal from PFC to ITC that help solving a categorization task. In particular, we show that, when the stimuli are very noisy because of a large number of nonrelevant features, the feedback structure helps getting better categorization performance and decreasing the reaction time. It also affects the speed and stability of the learning process and sharpens tuning curves of ITC neurons. Furthermore, the model predicts a modulation of neural activities during error trials, by which the differential selectivity of ITC neurons to task-relevant and task-irrelevant features diminishes or is even reversed, and modulations in the time course of neural activities that appear when, after learning, corrupted versions of the stimuli are input to the network.

Deconstructing multisensory enhancement in detection

The mechanisms responsible for the integration of sensory information from different modalities have become a topic of intense interest in psychophysics and neuroscience. Many authors now claim that early, sensory-based cross-modal convergence improves performance in detection tasks. An important strand of supporting evidence for this claim is based on statistical models such as the Pythagorean model or the probabilistic summation model. These models establish statistical benchmarks representing the best predicted performance under the assumption that there are no interactions between the two sensory paths. Following this logic, when observed detection performances surpass the predictions of these models, it is often inferred that such improvement indicates cross-modal convergence. We present a theoretical analyses scrutinizing some of these models and the statistical criteria most frequently used to infer early cross-modal interactions during detection tasks. Our current analysis shows how some common misinterpretations of these models lead to their inadequate use and, in turn, to contradictory results and misleading conclusions. To further illustrate the latter point, we introduce a model that accounts for detection performances in multimodal detection tasks but for which surpassing of the Pythagorean or probabilistic summation benchmark can be explained without resorting to early cross-modal interactions. Finally, we report three experiments that put our theoretical interpretation to the test and further propose how to adequately measure multimodal interactions in audiotactile detection tasks.

Neural correlates of metacognition: A critical perspective on current tasks

Humans have a remarkable ability to reflect upon their behavior and mental processes, a capacity known as metacognition. Recent neurophysiological experiments have attempted to elucidate the neural correlates of metacognition in other species. Despite this increased attention, there is still no operational definition of metacognition and the ability of behavioral tasks to reflect metacognition is the subject of debate. The most widely used task for studying metacognition in animals, the uncertain-option task, has been criticized because it can be solved by simple associative mechanisms. Here we propose a broad perspective that generalizes those critiques to another task, post-decision wagering. Moreover, we extend this critical view to account for recent neurophysiological evidence. We argue these tasks are simple enough that any animal could solve them using very simple mechanisms such as sensory-motor associations. In this case, it is impossible to know whether all animals are metacognitive, or if the tasks are simply not appropriate. Therefore, we suggest using better defined concepts until a suitable task for metacognition is available.

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.

Scrutinizing integrative effects in a multi-stimuli detection task

The level of processing at which different modalities interact to either facilitate or interfere with detection has been a matter of debate for more than half a century. This question has been mainly addressed by means of statistical models (Green, 1958), or by biologically plausible models (Schnupp et al., 2005). One of the most widely accepted statistical frameworks is the signal detection theory (SDT; Green and Swets, 1966) because it provides a straightforward way to assess whether two sensory stimuli are judged independently of one another, that is when the detectability (d′) of the compound stimulus exceeds the Pythagorean sum of the d′ of the components. Here, we question this logic, and propose a different baseline to evaluate integrative effects in multi-stimuli detection tasks based on the probabilistic summation. To this aim, we show how a simple theoretical hypothesis based on probabilistic summation can explain putative multisensory enhancement in an audio-tactile detection task. In addition, we illustrate how to measure integrative effects from multiple stimuli in two experiments, one using a multisensory audio-tactile detection task (Experiment 1) and another with a unimodal double-stimulus auditory detection task (Experiment 2). Results from Experiment 1 replicate extant multisensory detection data, and also refuse the hypothesis that auditory and tactile stimuli integrated into a single percept, leading to any enhancement. In Experiment 2, we further support the probabilistic summation model using a unimodal integration detection task.