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Idiosyncratic choice bias in decision tasks naturally emerges from neuronal network dynamics, Lebovich, Lior, Darshan Ran, Lavi Yoni, Hansel David, and Loewenstein Yonatan , (Submitted) Abstract
Idiosyncratic tendency to choose one alternative over others in the absence of an identified reason, is a common observation in two-alternative forced-choice experiments. It is tempting to account for it as resulting from the (unknown) participant-specific history and thus treat it as a measurement noise. Indeed, idiosyncratic choice biases are typically considered as nuisance. Care is taken to account for them by adding an ad-hoc bias parameter or by counterbalancing the choices to average them out. Here we quantify idiosyncratic choice biases in a perceptual discrimination task and a motor task. We report substantial and significant biases in both cases. Then, we present theoretical evidence that even in idealized experiments, in which the settings are symmetric, idiosyncratic choice bias is expected to emerge from the dynamics of competing neuronal networks. We thus argue that idiosyncratic choice bias reflects the microscopic dynamics of choice and therefore is virtually inevitable in any comparison or decision task.
Inhibitory connectivity defines the realm of excitatory plasticity, Mongillo, Gianluigi, Rumpel Simon, and Loewenstein Yonatan , Nature Neuroscience, 09/2018, Volume 21, p.1463–1470, (2018) AbstractPDF
Recent experiments demonstrate substantial volatility of excitatory connectivity in the absence of any learning. This challenges the hypothesis that stable synaptic connections are necessary for long-term maintenance of acquired information. Here we measure ongoing synaptic volatility and use theoretical modeling to study its consequences on cortical dynamics. We show that in the balanced cortex, patterns of neural activity are primarily determined by inhibitory connectivity, despite the fact that most synapses and neurons are excitatory. Similarly, we show that the inhibitory network is more effective in storing memory patterns than the excitatory one. As a result, network activity is robust to ongoing volatility of excitatory synapses, as long as this volatility does not disrupt the balance between excitation and inhibition. We thus hypothesize that inhibitory connectivity, rather than excitatory, controls the maintenance and loss of information over long periods of time in the volatile cortex.
Striatal action-value neurons reconsidered, Elber-Dorozko, Lotem, and Loewenstein Yonatan , eLife, 05/2018, Volume 7, p.e34248, (2018) Abstract PDF
It is generally believed that during economic decisions, striatal neurons represent the valuesassociated with different actions. This hypothesis is based on a large number of electrophysiological studies, in which the neural activity of striatal neurons was measured while the subject was learning to prefer the more rewarding action. Here we present an alternative interpretation of the electrophysiological findings. We show that the standard statistical methods that were used to identify action-value neurons in the striatum erroneously detect the same action-value representations in unrelated neuronal recordings. This is due to temporal correlations in the neuronal data. We propose an alternative statistical method for identifying action-value representations that is not subject to this caveat. We apply it to previously identified action-value neurons in the basal ganglia and fail to detect action-value representations. In conclusion, we argue that there is no conclusive evidence for the generally accepted hypothesis that striatal neurons encode action-values.
DORA The Explorer: Directed Outreaching Reinforcement Action-Selection, Fox, Lior, Choshen Leshem, and Loewenstein Yonatan , ICLR, 04/2018, Volume 2018, (2018) AbstractPDF
Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection. Exploration, however, can be more efficient if directed toward gaining new world knowledge. Visit-counters have been proven useful both in practice and in theory for directed exploration. However, a major limitation of counters is their locality. While there are a few model-based solutions to this shortcoming, a model-free approach is still missing. We propose $E$-values, a generalization of counters that can be used to evaluate the propagating exploratory value over state-action trajectories. We compare our approach to commonly used RL techniques, and show that using $E$-values improves learning and performance over traditional counters. We also show how our method can be implemented with function approximation to efficiently learn continuous MDPs. We demonstrate this by showing that our approach surpasses state of the art performance in the Freeway Atari 2600 game.
A Computational Model of Dyslexics’ Perceptual Difficulties as Impaired Inference of Sound Statistics, Jaffe-Dax, S., Raviv O., Loewenstein Y., and Ahissar M. , Computational Models of Brain and Behavior, 08/2017, Volume 3, (2017) PDF
Intrinsic volatility of synaptic connections — a challenge to the synaptic trace theory of memory, Mongillo, Gianluigi, Rumpel Simon, and Loewenstein Yonatan , Current Opinion in Neurobiology, 07/2017, Volume 46, p.7-13, (2017) Abstractpdf
According to the synaptic trace theory of memory, activity-induced changes in the pattern of synaptic connections underlie the storage of information for long periods. In this framework, the stability of memory critically depends on the stability of the underlying synaptic connections. Surprisingly however, synaptic connections in the living brain are highly volatile, which poses a fundamental challenge to the synaptic trace theory. Here we review recent experimental evidence that link the initial formation of a memory with changes in the pattern of connectivity, but also evidence that synaptic connections are considerably volatile even in the absence of learning. Then we consider different theoretical models that have been put forward to explain how memory can be maintained with such volatile building blocks.
Neuroscience: Formation of a Percept in the Rat Cortex, Mongillo, Gianluigi, and Loewenstein Yonatan , Current Biology, 06/2017, Volume 27, Issue 11, p.1585-1596.e6, (2017) Abstractpdf
It has long been known that we subjectively experience longer stimuli as being more intense. A recent study sheds light on the neural mechanisms underlying this bias by tracking the formation of a percept of intensity in the rat brain.
Distinct Sources of Deterministic and Stochastic Components of Action Timing Decisions in Rodent Frontal Cortex, Murakami, Masayoshi, Shteingart Hanan, Loewenstein Yonatan, and Mainen Zachary F. , Neuron, 05/2017, Volume 94, Issue 4, p.908–919, (2017) Abstractmmc2.pdf
The selection and timing of actions are subject to determinate influences such as sensory cues and internal state as well as to effectively stochastic variability. Although stochastic choice mechanisms are assumed by many theoretical models, their origin and mechanisms remain poorly understood. Here we investigated this issue by studying how neural circuits in the frontal cortex determine action timing in rats performing a waiting task. Electrophysiological recordings from two regions necessary for this behavior, medial prefrontal cortex (mPFC) and secondary motor cortex (M2), revealed an unexpected functional dissociation. Both areas encoded deterministic biases in action timing, but only M2 neurons reflected stochastic trial-by-trial fluctuations. This differential coding was reflected in distinct timescales of neural dynamics in the two frontal cortical areas. These results suggest a two-stage model in which stochastic components of action timing decisions are injected by circuits downstream of those carrying deterministic bias signals.
Association of Catastrophic Neonatal Outcomes With Increased Rate of Subsequent Cesarean Deliveries, Dan, Ohad, Hochner-Celnikier Drorith, Solnica Amy RN, and Loewenstein Yonatan , Obstetrics & Gynecology, 04/2017, Volume 129, Issue 4, p.671-675, (2017) Abstractpdf Supplemental Digital Content
OBJECTIVE: To evaluate whether full-term deliveries resulting in neonates diagnosed with hypoxic–ischemic encephalopathy are associated with a significant increase in the rate of subsequent unscheduled cesarean deliveries. METHODS: We conducted a retrospective chart review study and examined all deliveries in the Department of Obstetrics and Gynecology at Hadassah University Hospital, Mt. Scopus campus, Jerusalem, Israel, during 2009–2014. We reviewed all cases of hypoxic–ischemic encephalopathy in singleton, term, liveborn neonates and identified seven such cases, three of which were attributed to obstetric mismanagement and four that were not. We measured the rate of unscheduled cesarean deliveries before and after the events and their respective hazard ratio. RESULTS: Before a mismanaged delivery resulting in hypoxic–ischemic encephalopathy, the baseline rate of unscheduled cesarean deliveries was approximately 80 unscheduled cesarean deliveries for every 1,000 deliveries. In the first 4 weeks immediately after each of the three identified cases, there was a significant increase in the rate of unscheduled cesarean deliveries by an additional 48 unscheduled cesarean deliveries per 1,000 deliveries (95% confidence interval [CI] 27–70/1,000). This increase was transient and lasted approximately 4 weeks. We estimated that each case was associated with approximately 17 additional unscheduled cesarean deliveries (95% CI 8–27). There was no increase in the rate of unscheduled cesarean deliveries in cases of hypoxic–ischemic encephalopathy that were not associated with mismanagement. CONCLUSION: The increase in the rate of unscheduled cesarean deliveries after a catastrophic neonatal outcome may result in short-term changes in obstetricians' risk evaluation.
Heterogeneous Suppression of Sequential Effects in Random Sequence Generation, but Not in Operant Learning, Shteingart, Hanan, and Loewenstein Yonatan , PloS one, 08/2016, Volume 11, Issue 8, p.e0157643, (2016) AbstractPDF
There is a long history of experiments in which participants are instructed to generate a long sequence of binary random numbers. The scope of this line of research has shifted over the years from identifying the basic psychological principles and/or the heuristics that lead to deviations from randomness, to one of predicting future choices. In this paper, we used generalized linear regression and the framework of Reinforcement Learning in order to address both points. In particular, we used logistic regression analysis in order to characterize the temporal sequence of participants’ choices. Surprisingly, a population analysis indicated that the contribution of the most recent trial has only a weak effect on behavior, compared to more preceding trials, a result that seems irreconcilable with standard sequential effects that decay monotonously with the delay. However, when considering each participant separately, we found that the magnitudes of the sequential effect are a monotonous decreasing function of the delay, yet these individual sequential effects are largely averaged out in a population analysis because of heterogeneity. The substantial behavioral heterogeneity in this task is further demonstrated quantitatively by considering the predictive power of the model. We show that a heterogeneous model of sequential dependencies captures the structure available in random sequence generation. Finally, we show that the results of the logistic regression analysis can be interpreted in the framework of reinforcement learning, allowing us to compare the sequential effects in the random sequence generation task to those in an operant learning task. We show that in contrast to the random sequence generation task, sequential effects in operant learning are far more homogenous across the population. These results suggest that in the random sequence generation task, different participants adopt different cognitive strategies to suppress sequential dependencies when generating the “random” sequences.
A Computational Model of Implicit Memory Captures Dyslexics' Perceptual Deficits, Jaffe-Dax, Sagi, Raviv Ofri, Jacoby Nori, Loewenstein Yonatan, and Ahissar Merav , The Journal of Neuroscience, 09/2015, Volume 35, p.12116 –12126, (2015) AbstractPDF
Dyslexics are diagnosed for their poor reading skills, yet they characteristically also suffer from poor verbal memory and often from poor auditory skills. To date, this combined profile has been accounted for in broad cognitive terms. Here we hypothesize that the perceptual deficits associated with dyslexia can be understood computationally as a deficit in integrating prior information with noisy observations. To test this hypothesis we analyzed the performance of human participants in an auditory discrimination task using a two-parameter computational model. One parameter captures the internal noise in representing the current event, and the other captures the impact of recently acquired prior information. Our findings show that dyslexics' perceptual deficit can be accounted for by inadequate adjustment of these components; namely, low weighting of their implicit memory of past trials relative to their internal noise. Underweighting the stimulus statistics decreased dyslexics' ability to compensate for noisy observations. ERP measurements (P2 component) while participants watched a silent movie indicated that dyslexics' perceptual deficiency may stem from poor automatic integration of stimulus statistics. This study provides the first description of a specific computational deficit associated with dyslexia. SIGNIFICANCE STATEMENT This study presents the first attempt to specify the mechanisms underlying dyslexics' perceptual difficulties computationally by applying a specific model, inspired by the Bayesian framework. This model dissociates between the contribution of sensory noise and that of the prior statistics in an auditory perceptual decision task. We show that dyslexics cannot compensate for their perceptual noise by incorporating prior information. By contrast, adequately reading controls' usage of previous information is often close to optimal. We used ERP measurements to assess the neuronal stage of this deficit. We found that unlike their peers, dyslexics' ERP responses are not sensitive to the relations between the current observation and the prior observation, indicating that they cannot establish a reliable prior.
Predicting the Dynamics of Network Connectivity in the Neocortex, Loewenstein, Yonatan, Yanover Uri, and Rumpel Simon , The Journal of Neuroscience, 09/2015, Volume 35, Issue 36, p.12535–12544, (2015) AbstractPDF
Dynamic remodeling of connectivity is a fundamental feature of neocortical circuits. Unraveling the principles underlying these dynamics is essential for the understanding of how neuronal circuits give rise to computations. Moreover, as complete descriptions of the wiring diagram in cortical tissues are becoming available, deciphering the dynamic elements in these diagrams is crucial for relating them to cortical function. Here, we used chronic in vivo two-photon imaging to longitudinally follow a few thousand dendritic spines in the mouse auditory cortex to study the determinants of these spines' lifetimes. We applied nonlinear regression to quantify the independent contribution of spine age and several morphological parameters to the prediction of the future survival of a spine. We show that spine age, size, and geometry are parameters that can provide independent contributions to the prediction of the longevity of a synaptic connection. In addition, we use this framework to emulate a serial sectioning electron microscopy experiment and demonstrate how incorporation of morphological information of dendritic spines from a single time-point allows estimation of future connectivity states. The distinction between predictable and nonpredictable connectivity changes may be used in the future to identify the specific adaptations of neuronal circuits to environmental changes. The full dataset is publicly available for further analysis. SIGNIFICANCE STATEMENT The neural architecture in the neocortex exhibits constant remodeling. The functional consequences of these modifications are poorly understood, in particular because the determinants of these changes are largely unknown. Here, we aimed to identify those modifications that are predictable from current network state. To that goal, we repeatedly imaged thousands of dendritic spines in the auditory cortex of mice to assess the morphology and lifetimes of synaptic connections. We developed models based on morphological features of dendritic spines that allow predicting future turnover of synaptic connections. The dynamic models presented in this paper provide a quantitative framework for adding putative temporal dynamics to the static description of a neuronal circuit from single time-point connectomics experiments.
The processing of polar quantifiers, and numerosity perception, Deschamps, Isabelle, Agmon Galit, Loewenstein Yonatan, and Grodzinsky Yosef , Cognition, 06/2015, Volume 143, p.115-128, (2015) AbstractPDF
We investigated the course of language processing in the context of a verification task that required numerical estimation and comparison. Participants listened to sentences with complex quantifiers that contrasted in Polarity, a logical property (e.g., more-than-half, less-than-half), and then performed speeded verification on visual scenarios that displayed a proportion between 2 discrete quantities. We varied systematically not only the sentences, but also the visual materials, in order to study their effect on the verification process. Next, we used the same visual scenarios with analogous non-verbal probes that featured arithmetical inequality symbols (<, >). This manipulation enabled us to measure not only Polarity effects, but also, to compare the effect of different probe types (linguistic, non-linguistic) on processing. Like many previous studies, our results demonstrate that perceptual difficulty affects error rate and reaction time in keeping with Weber’s Law. Interestingly, these performance parameters are also affected by the Polarity of the quantifiers used, despite the fact that sentences had the exact same meaning, sentence structure, number of words, syllables, and temporal structure. Moreover, an analogous contrast between the non-linguistic probes (<, >) had no effect on performance. Finally, we observed no interaction between performance parameters governed by Weber’s Law and those affected by Polarity. We consider 4 possible accounts of the results (syntactic, semantic, pragmatic, frequency-based), and discuss their relative merit.
The Effect of Sample Size and Cognitive Strategy on Probability Estimation Bias, Shteingart, Hanan, and Loewenstein Yonatan , Decison, 04/2015, Volume 2, Issue 2, p.107-117, (2015) AbstractPDF
Probability estimation is an essential cognitive function in perception, motor control, and decision making. Many studies have shown that when making decisions in a stochastic operant conditioning task, people and animals behave as if they underesti- mate the probability of rare events. It is commonly assumed that this behavior is a natural consequence of estimating a probability from a small sample, also known as sampling bias. The objective of this article is to challenge this common lore. We show that, in fact, probabilities estimated from a small sample can lead to behaviors that will be interpreted as underestimating or as overestimating the probability of rare events, depending on the cognitive strategy used. Moreover, this sampling bias hypothesis makes an implausible prediction that minute differences in the values of the sample size or the underlying probability will determine whether rare events will be underweighted or overweighed. We discuss the implications of this sensitivity for the design and interpretation of experiments. Finally, we propose an alternative sequential learning model with a resetting of initial conditions for probability estimation and show that this model predicts the experimentally observed robust underweighting of rare events.
Contradictory Behavioral Biases Result from the Influence of Past Stimuli on Perception, Ofri, Raviv, Itay Lieder, Loewenstein Yonatan, and Ahissar Merav , PLoS Computational Biology , 12/2014, Volume 10, Issue 12, p.e1003948, (2014) Abstract
Biases such as the preference of a particular response for no obvious reason, are an integral part of psychophysics. Such biases have been reported in the common two-alternative forced choice (2AFC) experiments, where participants are instructed to compare two consecutively presented stimuli. However, the principles underlying these biases are largely unknown and previous studies have typically used ad-hoc explanations to account for them. Here we consider human performance in the 2AFC tone frequency discrimination task, utilizing two standard protocols. In both protocols, each trial contains a reference stimulus. In one (Reference-Lower protocol), the frequency of the reference stimulus is always lower than that of the comparison stimulus, whereas in the other (Reference protocol), the frequency of the reference stimulus is either lower or higher than that of the comparison stimulus. We find substantial interval biases. Namely, participants perform better when the reference is in a specific interval. Surprisingly, the biases in the two experiments are opposite: performance is better when the reference is in the first interval in the Reference protocol, but is better when the reference is second in the Reference-Lower protocol. This inconsistency refutes previous accounts of the interval bias, and is resolved when experiments statistics is considered. Viewing perception as incorporation of sensory input with prior knowledge accumulated during the experiment accounts for the seemingly contradictory biases both qualitatively and quantitatively. The success of this account implies that even simple discriminations reflect a combination of sensory limitations, memory limitations, and the ability to utilize stimuli statistics.
Spatial Generalization in Operant Learning: Lessons from Professional Basketball, Tal, Neiman, and Yonatan Loewenstein , PLoS Computational Biology , 05/2014, Volume 10, Issue 5, p.e1003623, (2014) Abstract
In operant learning, behaviors are reinforced or inhibited in response to the consequences of similar actions taken in the past. However, because in natural environments the “same” situation never recurs, it is essential for the learner to decide what “similar” is so that he can generalize from experience in one state of the world to future actions in different states of the world. The computational principles underlying this generalization are poorly understood, in particular because natural environments are typically too complex to study quantitatively. In this paper we study the principles underlying generalization in operant learning of professional basketball players. In particular, we utilize detailed information about the spatial organization of shot locations to study how players adapt their attacking strategy in real time according to recent events in the game. To quantify this learning, we study how a make \ miss from one location in the court affects the probabilities of shooting from different locations. We show that generalization is not a spatially-local process, nor is governed by the difficulty of the shot. Rather, to a first approximation, players use a simplified binary representation of the court into 2 pt and 3 pt zones. This result indicates that rather than using low-level features, generalization is determined by high-level cognitive processes that incorporate the abstract rules of the game.
The Misbehavior of Reinforcement Learning, Gianluigi, Mongillo, Hanan Shteingart, and Yonatan Loewenstein , Proceedings of the IEEE, 04/2014, Volume 102, Issue 4, p.528-541, (2014) Abstract pdf FURTHER THOUGHTS
Organisms modify their behavior in response to its consequences, a phenomenon referred to as operant learning. The computational principles and neural mechanisms underlying operant learning are a subject of extensive experimental and theoretical investigations. Theoretical approaches largely rely on concepts and algorithms from reinforcement learning. The dominant view is that organisms maintain a value function, that is, a set of estimates of the cumulative future rewards associated with the different behavioral options. These values are then used to select actions. Learning in this framework results from the update of these values depending on experience of the consequences of past actions. An alternative view questions the applicability of such a computational scheme to many real-life situations. Instead, it posits that organisms exploit the intrinsic variability in their action–selection mechanism(s) to modify their behavior, e.g., via stochastic gradient ascent, without the need of an explicit representation of values. In this review, we compare these two approaches in terms of their computational power and flexibility, their putative neural correlates, and, finally, in terms of their ability to account for behavior as observed in repeated-choice experiments. We discuss the successes and failures of these alternative approaches in explaining the observed patterns of choice behavior. We conclude by identifying some of the important challenges to a comprehensive theory of operant learning.
Reinforcement learning and human behavior, Shteingart, Hanan, and Loewenstein Yonatan , Current Opinion in Neurobiology, 04/2014, Volume 25, p.93-98, (2014) AbstractPDF
The dominant computational approach to model operant learning and its underlying neural activity is model-free reinforcement learning (RL). However, there is accumulating behavioral and neuronal-related evidence that human (and animal) operant learning is far more multifaceted. Theoretical advances in RL, such as hierarchical and model-based RL extend the explanatory power of RL to account for some of these findings. Nevertheless, some other aspects of human behavior remain inexplicable even in the simplest tasks. Here we review developments and remaining challenges in relating RL models to human operant learning. In particular, we emphasize that learning a model of the world is an essential step before or in parallel to learning the policy in RL and discuss alternative models that directly learn a policy without an explicit world model in terms of state-action pairs.
Complex Population Response of Dorsal Putamen Neurons Predicts the Ability to Learn, Laquitaine, Steeve, Piron Camille, Abellanas David, Loewenstein Yonatan, and Boraud Thomas , PloS one, 11/2013, Volume 8, Issue 11, p.e80683, (2013) Abstract
Day-to-day variability in performance is a common experience. We investigated its neural correlate by studying learning behavior of monkeys in a two-alternative forced choice task, the two-armed bandit task. We found substantial session-to-session variability in the monkeys’ learning behavior. Recording the activity of single dorsal putamen neurons we uncovered a dual function of this structure. It has been previously shown that a population of neurons in the DLP exhibits firing activity sensitive to the reward value of chosen actions. Here, we identify putative medium spiny neurons in the dorsal putamen that are cue-selective and whose activity builds up with learning. Remarkably we show that session-to-session changes in the size of this population and in the intensity with which this population encodes cue-selectivity is correlated with session-to-session changes in the ability to learn the task. Moreover, at the population level, dorsal putamen activity in the very beginning of the session is correlated with the performance at the end of the session, thus predicting whether the monkey will have a "good" or "bad" learning day. These results provide important insights on the neural basis of inter-temporal performance variability.
Stochasticity, bistability and the wisdom of crowds: a model for associative learning in genetic regulatory networks., Sorek, Matan, Balaban Nathalie Q., and Loewenstein Yonatan , PLoS computational biology, 08/2013, Volume 9, Issue 8, p.e1003179, (2013) AbstractPDF
It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population.
The Role of First Impression in Operant Learning., Shteingart, Hanan, Neiman Tal, and Loewenstein Yonatan , Journal of Experimental Psychology: General, 05/2013, Volume 142, Issue 2, US, p.476-488, (2013) Abstract PDF
We quantified the effect of first experience on behavior in operant learning and studied its underlying computational principles. To that goal, we analyzed more than 200,000 choices in a repeated-choice experiment. We found that the outcome of the first experience has a substantial and lasting effect on participants' subsequent behavior, which we term outcome primacy. We found that this outcome primacy can account for much of the underweighting of rare events, where participants apparently underestimate small probabilities. We modeled behavior in this task using a standard, model-free reinforcement learning algorithm. In this model, the values of the different actions are learned over time and are used to determine the next action according to a predefined action-selection rule. We used a novel nonparametric method to characterize this action-selection rule and showed that the substantial effect of first experience on behavior is consistent with the reinforcment learning model if we assume that the outcome of first experience resets the values of the experienced actions, but not if we assume arbitrary initial conditions. Moreover, the predictive power of our resetting model outperforms previouly published models regarding the aggregate choice behavior. These findings suggest that first experience has a disproportionately large effect on subsequent actions, similar to primacy effects in other fields of cognitive psychology. The mechanism of resetting of the initial conditions that underlies outcome primacy may thus also account for other forms of primacy. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Covariance-Based Synaptic Plasticity in an Attractor Network Model Accounts for Fast Adaptation in Free Operant Learning, Neiman, Tal, and Loewenstein Yonatan , The Journal of Neuroscience, 01/2013, Volume 33, Issue 4, p.1521-1534, (2013) Abstract PDF
In free operant experiments, subjects alternate at will between targets that yield rewards stochastically. Behavior in these experiments is typically characterized by (1) an exponential distribution of stay durations, (2) matching of the relative time spent at a target to its relative share of the total number of rewards, and (3) adaptation after a change in the reward rates that can be very fast. The neural mechanism underlying these regularities is largely unknown. Moreover, current decision-making neural network models typically aim at explaining behavior in discrete-time experiments in which a single decision is made once in every trial, making these models hard to extend to the more natural case of free operant decisions. Here we show that a model based on attractor dynamics, in which transitions are induced by noise and preference is formed via covariance-based synaptic plasticity, can account for the characteristics of behavior in free operant experiments. We compare a specific instance of such a model, in which two recurrently excited populations of neurons compete for higher activity, to the behavior of rats responding on two levers for rewarding brain stimulation on a concurrent variable interval reward schedule (Gallistel et al., 2001). We show that the model is consistent with the rats' behavior, and in particular, with the observed fast adaptation to matching behavior. Further, we show that the neural model can be reduced to a behavioral model, and we use this model to deduce a novel “conservation law,” which is consistent with the behavior of the rats.
How Recent History Affects Perception: The Normative Approach and Its Heuristic Approximation, Raviv, Ofri, Ahissar Merav, and Loewenstein Yonatan , PLoS Comput Biol, 10/2012, Volume 8, Issue 10, p.e1002731, (2012) AbstractPDF
Author Summary

In this paper we study how history affects perception using an auditory delayed comparison task, in which human participants repeatedly compare the frequencies of two, temporally-separated pure tones. We demonstrate that the history of the experiment has a substantial effect on participants' performance: when both tones are high relative to past stimuli, people tend to report that the 2nd tone was higher, and when they are relatively low, they tend to report that the 1st tone was higher. Interestingly, only the most recent trials bias performance, which can be interpreted as if the participants assume that the statistics of stimuli in the experiment is highly volatile. Moreover, this bias persists even in settings, in which it is detrimental to performance. These results demonstrate the abilities, as well as limitations, of the cognitive system when incorporating expectations in perception.

Reinforcement learning in professional basketball players., Neiman, Tal, and Loewenstein Yonatan , Nature communications, 12/2011, Volume 2, p.569, (2011) AbstractPDF
Reinforcement learning in complex natural environments is a challenging task because the agent should generalize from the outcomes of actions taken in one state of the world to future actions in different states of the world. The extent to which human experts find the proper level of generalization is unclear. Here we show, using the sequences of field goal attempts made by professional basketball players, that the outcome of even a single field goal attempt has a considerable effect on the rate of subsequent 3 point shot attempts, in line with standard models of reinforcement learning. However, this change in behaviour is associated with negative correlations between the outcomes of successive field goal attempts. These results indicate that despite years of experience and high motivation, professional players overgeneralize from the outcomes of their most recent actions, which leads to decreased performance.
Alternative Sites of Synaptic Plasticity in Two Homologous "Fan-out Fan-in" Learning and Memory Networks., Shomrat, Tal, Graindorge Nicolas, Bellanger Cécile, Fiorito Graziano, Loewenstein Yonatan, and Hochner Binyamin , Current biology : CB, 10/2011, Volume 21, p.1773-1782, (2011) Abstract PDF | Supplementary
BACKGROUND: To what extent are the properties of neuronal networks constrained by computational considerations? Comparative analysis of the vertical lobe (VL) system, a brain structure involved in learning and memory, in two phylogenetically close cephalopod mollusks, Octopus vulgaris and the cuttlefish Sepia officinalis, provides a surprising answer to this question. RESULTS: We show that in both the octopus and the cuttlefish the VL is characterized by the same simple fan-out fan-in connectivity architecture, composed of the same three neuron types. Yet, the sites of short- and long-term synaptic plasticity and neuromodulation are different. In the octopus, synaptic plasticity occurs at the fan-out glutamatergic synaptic layer, whereas in the cuttlefish plasticity is found at the fan-in cholinergic synaptic layer. CONCLUSIONS: Does this dramatic difference in physiology imply a difference in function? Not necessarily. We show that the physiological properties of the VL neurons, particularly the linear input-output relations of the intermediate layer neurons, allow the two different networks to perform the same computation. The convergence of different networks to the same computational capacity indicates that it is the computation, not the specific properties of the network, that is self-organized or selected for by evolutionary pressure.