Elisa Tartaglia | University of Chicago (original) (raw)

Papers by Elisa Tartaglia

Learning & Perception, 2009

Perceptual learning is often considered one of the simplest and basic forms of learning in genera... more Perceptual learning is often considered one of the simplest and basic forms of learning in general. Ac-cordingly, it is usually modeled with simple and basic neural networks which show good results in grasping the empirical data. Simple meets simple. Complex forms of ...

Electrophysiological recordings in cortex in vivo have revealed a rich variety of dynamical regim... more 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. Electrophysiological recordings in anaesthetised, asleep and awake animals have revealed that cortical networks exhibit a diversity of dynamical states. In awake cats and monkeys, recordings seem to be compatible with an asynchronous network state in which the population firing rate is relatively constant in time, neurons receive synaptic inputs that are in average subthreshold but large fluctuations in these inputs lead to spiking 1–3. Other studies, most prominently in rodents, have offered a different picture. In those recordings, synaptic inputs to neu-rons seem to be highly synchronous 4–8. In some circumstances, recordings reveal an alternation between so-called UP states, in which neurons are depolarized compared to their resting potential, receive a large amount of excit-atory and inhibitory inputs and emit spikes at a rates of a few Hz to a few tens of Hz, depending on cell type; and DOWN states, essentially quiescent states in which most neurons have their membrane potential close to the resting potential and fire very few spikes, if any 1,9–11. Similar UP and DOWN state alternations have been observed in in vitro preparations 12–16. The same networks can alternate between synchronous and asynchronous states, depending on the state of the animal (anesthetized, awake or asleep 9), sensory stimulation 3 , and/or arousal 17. Most theoretical studies of cortical dynamics have focused either on asynchronous states or UP/DOWN state alternations, but have not explained how both types of dynamics could be observed in the same network and what could lead to transitions between both types of behaviors. The dominant model for asynchronous states in cortex has been the 'balanced network' model, in which strong excitatory and inhibitory inputs approximately cancel each other, leading to subthreshold average membrane potential, whose large fluctuations generate irregular firing at low rates 18–22. Such a state can be shown to be stable in a wide parameter range, provided inhibition is sufficiently strong to dominate the strong positive feedback induced by recurrent excitation, and external inputs are supra-threshold. A previous analytical study of a sparsely connected network of excitatory and inhibitory leaky integrate-and-fire neurons 21 revealed the potential presence of oscillatory instabilities of this asynchronous irregular state, both for strong external inputs (leading to fast network oscillations) and weaker external inputs Published: xx xx xxxx OPEN

Many of the decisions we make in our everyday lives are sequential and entail sparse rewards. Whi... more Many of the decisions we make in our everyday lives are sequential and entail sparse rewards. While sequential decision-making has been extensively investigated in theory (e.g., by reinforcement learning models) there is no systematic experimental paradigm to test it. Here, we developed such a paradigm and investigated key components of reinforcement learning models: the eligibility trace (i.e., the memory trace of previous decision steps), the external reward, and the ability to exploit the statistics of the environment's structure (model-free vs. model-based mechanisms). We show that the eligibility trace decays not with sheer time, but rather with the number of discrete decision steps made by the participants. We further show that, unexpectedly, neither monetary rewards nor the environment's spatial regularity significantly modulate behavioral performance. Finally, we found that model-free learning algorithms describe human performance better than model-based algorithms.

Journal of Vision, 2014

ABSTRACT Perceptual learning is learning to perceive. For example, in a bisection task three para... more ABSTRACT Perceptual learning is learning to perceive. For example, in a bisection task three parallel lines are presented. The central line is slightly offset towards the right or the left outer line. Observers indicate the offset direction. Training greatly improves performance. In models of perceptual learning, learning occurs by synaptic changes determined by the learning algorithm and the stimulus presentation. None of the models can learn when the very same stimulus is presented during training. Here we show that, surprisingly, humans can improve performance in such "impossible" conditions. We trained observers with a line bisection task where the central line was always exactly in the middle, i.e., the stimulus was the same in all 4160 trials. Participants were not told about the zero offset and were instructed to indicate the offset direction as in a normal bisection task. Surprisingly, performance improved with gains similar to "normal" bisection experiments where both the left and right offset are presented. These results cannot be explained by most of current models of perceptual learning and reproduce previous studies in the auditory domain (Amitay, Irwin & Moore 2006). We suggest that learning occurs by mental imagery in accordance with previous results (Tartaglia, Bamert, Mast & Herzog, 2009, 2012).

Journal of vision, 2015

Perceptual learning is usually thought to be exclusively driven by the stimuli presented during t... more 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.,...

F1000 - Post-publication peer review of the biomedical literature, 2014

Background and aim of the study: Formation of implicit memory during general anaesthesia is still... more Background and aim of the study: Formation of implicit memory during general anaesthesia is still debated. Perceptual learning is the ability to learn to perceive. In this study, an auditory perceptual learning paradigm, using frequency discrimination, was performed to investigate the implicit memory. It was hypothesized that auditory stimulation would successfully induce perceptual learning. Thus, initial thresholds of the frequency discrimination postoperative task should be lower for the stimulated group (group S) compared to the control group (group C). Material and method: Eighty-seven patients ASA I-III undergoing visceral and orthopaedic surgery during general anaesthesia lasting more than 60 minutes were recruited. The anaesthesia procedure was standardized (BISR monitoring included). Group S received auditory stimulation (2000 pure tones applied for 45 minutes) during the surgery. Twenty-four hours after the operation, both groups performed ten blocks of the frequency discr...

Journal of Vision, 2010

Performance usually improves when observers train with one type of a visual stimulus. Roving deno... more Performance usually improves when observers train with one type of a visual stimulus. Roving denotes the situation when, instead of one, two or more types of stimuli are presented randomly interleaved (one per trial). For some stimulus types, performance improves also in roving ...

Journal of Vision, 2010

A common assumption in perceptual learning is that the improvement of performance basically depen... more A common assumption in perceptual learning is that the improvement of performance basically depends on the amount of training. However, other factors such as sleep and training intensity (trials per session) have also been shown to be important. We trained four groups with ...

Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where ... more 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 . 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.

Persistent activity and match effects are widely regarded as neuronal correlates of short-term st... more 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 remain 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.

Perceptual learning is learning to perceive. For example, a radiologist is able to easily identif... more 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 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.

We are able to extract detailed information from mental images that we were not explicitly aware ... more 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 . 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.

In perceptual learning, performance usually improves when observers train with one type of stimul... more 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.

Background: An auditory perceptual learning paradigm was used to investigate whether implicit mem... more Background: An auditory perceptual learning paradigm was used to investigate whether implicit memories are formed during general anesthesia.

Learning & Perception, 2009

Perceptual learning is often considered one of the simplest and basic forms of learning in genera... more Perceptual learning is often considered one of the simplest and basic forms of learning in general. Ac-cordingly, it is usually modeled with simple and basic neural networks which show good results in grasping the empirical data. Simple meets simple. Complex forms of ...

Electrophysiological recordings in cortex in vivo have revealed a rich variety of dynamical regim... more 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. Electrophysiological recordings in anaesthetised, asleep and awake animals have revealed that cortical networks exhibit a diversity of dynamical states. In awake cats and monkeys, recordings seem to be compatible with an asynchronous network state in which the population firing rate is relatively constant in time, neurons receive synaptic inputs that are in average subthreshold but large fluctuations in these inputs lead to spiking 1–3. Other studies, most prominently in rodents, have offered a different picture. In those recordings, synaptic inputs to neu-rons seem to be highly synchronous 4–8. In some circumstances, recordings reveal an alternation between so-called UP states, in which neurons are depolarized compared to their resting potential, receive a large amount of excit-atory and inhibitory inputs and emit spikes at a rates of a few Hz to a few tens of Hz, depending on cell type; and DOWN states, essentially quiescent states in which most neurons have their membrane potential close to the resting potential and fire very few spikes, if any 1,9–11. Similar UP and DOWN state alternations have been observed in in vitro preparations 12–16. The same networks can alternate between synchronous and asynchronous states, depending on the state of the animal (anesthetized, awake or asleep 9), sensory stimulation 3 , and/or arousal 17. Most theoretical studies of cortical dynamics have focused either on asynchronous states or UP/DOWN state alternations, but have not explained how both types of dynamics could be observed in the same network and what could lead to transitions between both types of behaviors. The dominant model for asynchronous states in cortex has been the 'balanced network' model, in which strong excitatory and inhibitory inputs approximately cancel each other, leading to subthreshold average membrane potential, whose large fluctuations generate irregular firing at low rates 18–22. Such a state can be shown to be stable in a wide parameter range, provided inhibition is sufficiently strong to dominate the strong positive feedback induced by recurrent excitation, and external inputs are supra-threshold. A previous analytical study of a sparsely connected network of excitatory and inhibitory leaky integrate-and-fire neurons 21 revealed the potential presence of oscillatory instabilities of this asynchronous irregular state, both for strong external inputs (leading to fast network oscillations) and weaker external inputs Published: xx xx xxxx OPEN

Many of the decisions we make in our everyday lives are sequential and entail sparse rewards. Whi... more Many of the decisions we make in our everyday lives are sequential and entail sparse rewards. While sequential decision-making has been extensively investigated in theory (e.g., by reinforcement learning models) there is no systematic experimental paradigm to test it. Here, we developed such a paradigm and investigated key components of reinforcement learning models: the eligibility trace (i.e., the memory trace of previous decision steps), the external reward, and the ability to exploit the statistics of the environment's structure (model-free vs. model-based mechanisms). We show that the eligibility trace decays not with sheer time, but rather with the number of discrete decision steps made by the participants. We further show that, unexpectedly, neither monetary rewards nor the environment's spatial regularity significantly modulate behavioral performance. Finally, we found that model-free learning algorithms describe human performance better than model-based algorithms.

Journal of Vision, 2014

ABSTRACT Perceptual learning is learning to perceive. For example, in a bisection task three para... more ABSTRACT Perceptual learning is learning to perceive. For example, in a bisection task three parallel lines are presented. The central line is slightly offset towards the right or the left outer line. Observers indicate the offset direction. Training greatly improves performance. In models of perceptual learning, learning occurs by synaptic changes determined by the learning algorithm and the stimulus presentation. None of the models can learn when the very same stimulus is presented during training. Here we show that, surprisingly, humans can improve performance in such "impossible" conditions. We trained observers with a line bisection task where the central line was always exactly in the middle, i.e., the stimulus was the same in all 4160 trials. Participants were not told about the zero offset and were instructed to indicate the offset direction as in a normal bisection task. Surprisingly, performance improved with gains similar to "normal" bisection experiments where both the left and right offset are presented. These results cannot be explained by most of current models of perceptual learning and reproduce previous studies in the auditory domain (Amitay, Irwin & Moore 2006). We suggest that learning occurs by mental imagery in accordance with previous results (Tartaglia, Bamert, Mast & Herzog, 2009, 2012).

Journal of vision, 2015

Perceptual learning is usually thought to be exclusively driven by the stimuli presented during t... more 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.,...

F1000 - Post-publication peer review of the biomedical literature, 2014

Background and aim of the study: Formation of implicit memory during general anaesthesia is still... more Background and aim of the study: Formation of implicit memory during general anaesthesia is still debated. Perceptual learning is the ability to learn to perceive. In this study, an auditory perceptual learning paradigm, using frequency discrimination, was performed to investigate the implicit memory. It was hypothesized that auditory stimulation would successfully induce perceptual learning. Thus, initial thresholds of the frequency discrimination postoperative task should be lower for the stimulated group (group S) compared to the control group (group C). Material and method: Eighty-seven patients ASA I-III undergoing visceral and orthopaedic surgery during general anaesthesia lasting more than 60 minutes were recruited. The anaesthesia procedure was standardized (BISR monitoring included). Group S received auditory stimulation (2000 pure tones applied for 45 minutes) during the surgery. Twenty-four hours after the operation, both groups performed ten blocks of the frequency discr...

Journal of Vision, 2010

Performance usually improves when observers train with one type of a visual stimulus. Roving deno... more Performance usually improves when observers train with one type of a visual stimulus. Roving denotes the situation when, instead of one, two or more types of stimuli are presented randomly interleaved (one per trial). For some stimulus types, performance improves also in roving ...

Journal of Vision, 2010

A common assumption in perceptual learning is that the improvement of performance basically depen... more A common assumption in perceptual learning is that the improvement of performance basically depends on the amount of training. However, other factors such as sleep and training intensity (trials per session) have also been shown to be important. We trained four groups with ...

Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where ... more 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 . 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.

Persistent activity and match effects are widely regarded as neuronal correlates of short-term st... more 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 remain 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.

Perceptual learning is learning to perceive. For example, a radiologist is able to easily identif... more 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 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.

We are able to extract detailed information from mental images that we were not explicitly aware ... more 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 . 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.

In perceptual learning, performance usually improves when observers train with one type of stimul... more 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.

Background: An auditory perceptual learning paradigm was used to investigate whether implicit mem... more Background: An auditory perceptual learning paradigm was used to investigate whether implicit memories are formed during general anesthesia.