Elisa M. Tartaglia

Perceptual learning with Chevrons requires a minimal number of trials, transfers to untrained directions, but does not require sleep

In most models of perceptual learning, the amount of improvement of performance does not depend on the regime of stimulus presentations, but only on the sheer number of trials. Here, we kept the number of stimulus presentations constant while varying the number of trials per session. We show that a minimal number of stimulus presentations per session is necessary, transfer depends strongly on the presentation regime, but sleep has only weak, if at all, effects.

Human perceptual learning by mental imagery

Perceptual learning is learning to perceive. For example, a radiologist is able to easily identify anomalies in medical images only after extended training. Theoretical and psychophysical studies [1-12] suggest that such improvements of performance are accomplished by neural synaptic changes driven by the repetitive presentation of stimuli. Here, we demonstrate that an equally reliable improvement can also occur in the absence of physical stimulation. Imagining the crucial part of a bisection stimulus was sufficient for successful perceptual learning. Hence, the neural processes underlying perceptual learning, which are usually assumed to be primarily dependent on stimulus processing, can be equally based on mentally generated signals.

Perceptual learning and roving: Stimulus types and overlapping neural populations.

In perceptual learning, performance usually improves when observers train with one type of stimulus, for example, a bisection stimulus. Roving denotes the situation when, instead of one, two or more types of stimuli are presented randomly interleaved, for example, a bisection stimulus and a vernier. For some combinations of stimulus types, performance improves in roving situations whereas for others it does not. To investigate when roving impedes perceptual learning, we conducted four experiments. Performance improved, for example, when we roved a bisection stimulus and a vernier but not when we roved certain types of bisection stimuli. We propose that roving hinders perceptual learning when the stimulus types are clearly distinct from each other but still excite overlapping but not identical neural populations.

Perceptual learning of motion discrimination by mental imagery

Perceptual learning can occur when stimuli are only imagined, i.e., without proper stimulus presentation. For example, perceptual learning improved bisection discrimination when only the two outer lines of the bisection stimulus were presented and the central line had to be imagined. Performance improved also with other static stimuli. In non-learning imagery experiments, imagining static stimuli is different from imagining motion stimuli. We hypothesized that those differences also affect imagery perceptual learning. Here, we show that imagery training also improves motion direction discrimination. Learning occurs when no stimulus at all is presented during training, whereas no learning occurs when only noise is presented. The interference between noise and mental imagery possibly hinders learning. For static bisection stimuli, the pattern is just the opposite. Learning occurs when presented with the two outer lines of the bisection stimulus, i.e., with only a part of the visual stimulus, while no learning occurs when no stimulus at all is presented.

On the relationship between persistent delay activity, repetition enhancement and priming

Human efficiency in processing incoming stimuli (in terms of speed and/or accuracy) is typically enhanced by previous exposure to the same, or closely related stimuli—a phenomenon referred to as priming. In spite of the large body of knowledge accumulated in behavioral studies about the conditions conducive to priming, and its relationship with other forms of memory, the underlying neuronal correlates of priming are still under debate. The idea has repeatedly been advanced that a major neuronal mechanism supporting behaviorally-expressed priming is repetition suppression, a widespread reduction of spiking activity upon stimulus repetition which has been routinely exposed by single-unit recordings in non-human primates performing delayed-response, as well as passive fixation tasks. This proposal is mainly motivated by the observation that, in human fMRI studies, priming is associated to a significant reduction of the BOLD signal (widely interpreted as a proxy of the level of spiking activity) upon stimulus repetition. Here, we critically re-examine a large part of the electrophysiological literature on repetition suppression in non-human primates and find that repetition suppression is systematically accompanied by stimulus-selective delay period activity, together with repetition enhancement, an increase of spiking activity upon stimulus repetition in small neuronal populations. We argue that repetition enhancement constitutes a more viable candidate for a putative neuronal substrate of priming, and propose a minimal framework that links together, mechanistically and functionally, repetition suppression, stimulus-selective delay activity and repetition enhancement.

Human and Machine Learning in Non-Markovian Decision Making

Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Markovian decision processes, where the next state depends on only the current state and action. Little is known about non-Markovian decision making, where the next state depends on more than the current state and action. Learning is non-Markovian, for example, when there is no unique mapping between actions and feedback. We have produced a model based on spiking neurons that can handle these non-Markovian conditions by performing policy gradient descent [1]. Here, we examine the model’s performance and compare it with human learning and a Bayes optimal reference, which provides an upper-bound on performance. We find that in all cases, our population of spiking neurons model well-describes human performance.

New Percepts via Mental Imagery

We are able to extract detailed information from mental images that we were not explicitly aware of during encoding. For example, we can discover a new figure when we rotate a previously seen image in our mind. However, such discoveries are not “really” new but just new “interpretations.” In two recent publications, we have shown that mental imagery can lead to perceptual learning (Tartaglia et al., 2009, 2012). Observers imagined the central line of a bisection stimulus for thousands of trials. This training enabled observers to perceive bisection offsets that were invisible before training. Hence, it seems that perceptual learning via mental imagery leads to new percepts. We will argue, however, that these new percepts can occur only within “known” models. In this sense, perceptual learning via mental imagery exceeds new discoveries in mental images. Still, the effects of mental imagery on perceptual learning are limited. Only perception can lead to really new perceptual experience.

Linking perceptual learning with identical stimuli to imagery perceptual learning

Perceptual learning is usually thought to be exclusively driven by the stimuli presented during training (and the underlying synaptic learning rules). In some way, we are slaves of our visual experiences. However, learning can occur even when no stimuli are presented at all. For example, Gabor contrast detection improves when only a blank screen is presented and observers are asked to imagine Gabor patches. Likewise, performance improves when observers are asked to imagine the nonexisting central line of a bisection stimulus to be offset either to the right or left. Hence, performance can improve without stimulus presentation. As shown in the auditory domain, performance can also improve when the very same stimulus is presented in all learning trials and observers were asked to discriminate differences which do not exist (observers were not told about the set up). Classic models of perceptual learning cannot handle these situations since they need proper stimulus presentation, i.e., variance in the stimuli, such as a left versus right offset in the bisection stimulus. Here, we show that perceptual learning with identical stimuli occurs in the visual domain, too. Second, we linked the two paradigms by telling observers that only the very same bisection stimulus was presented in all trials and asked them to imagine the central line to be offset either to the left or right. As in imagery learning, performance improved.

Bistability and up/down state alternations in inhibition-dominated randomly connected networks of LIF neurons

Electrophysiological recordings in cortex in vivo have revealed a rich variety of dynamical regimes ranging from irregular asynchronous states to a diversity of synchronized states, depending on species, anesthesia, and external stimulation. The average population firing rate in these states is typically low. We study analytically and numerically a network of sparsely connected excitatory and inhibitory integrate-and-fire neurons in the inhibition-dominated, low firing rate regime. For sufficiently high values of the external input, the network exhibits an asynchronous low firing frequency state (L). Depending on synaptic time constants, we show that two scenarios may occur when external inputs are decreased: (1) the L state can destabilize through a Hopf bifucation as the external input is decreased, leading to synchronized oscillations spanning d δ to β frequencies; (2) the network can reach a bistable region, between the low firing frequency network state (L) and a quiescent one (Q). Adding an adaptation current to excitatory neurons leads to spontaneous alternations between L and Q states, similar to experimental observations on UP and DOWN states alternations.

Modulation of network excitability by persistent activity: how working memory affects the response to incoming stimuli.

Persistent activity and match effects are widely regarded as neuronal correlates of short-term storage and manipulation of information, with the first serving active maintenance and the latter supporting the comparison between memory contents and incoming sensory information. The mechanistic and functional relationship between these two basic neurophysiological signatures of working memory remains elusive. We propose that match signals are generated as a result of transient changes in local network excitability brought about by persistent activity. Neurons more active will be more excitable, and thus more responsive to external inputs. Accordingly, network responses are jointly determined by the incoming stimulus and the ongoing pattern of persistent activity. Using a spiking model network, we show that this mechanism is able to reproduce most of the experimental phenomenology of match effects as exposed by single-cell recordings during delayed-response tasks. The model provides a unified, parsimonious mechanistic account of the main neuronal correlates of working memory, makes several experimentally testable predictions, and demonstrates a new functional role for persistent activity.