Trajectory recognition as the basis for object individuation: A functional model of object file instantiation and object token encoding

The perception of persisting visual objects is mediated by transient intermediate representations, object files, that are instantiated in response to some, but not all, visual trajectories. The standard object file concept does not, however, provide a mechanism sufficient to account for all experimental data on visual object persistence, object tracking, and the ability to perceive spatially-disconnected stimuli as coherent objects. Based on relevant anatomical, functional, and developmental data, a functional model is developed that bases object individuation on the specific recognition of visual trajectories. This model is shown to account for a wide range of data, and to generate a variety of testable predictions. Individual variations of the model parameters are expected to generate distinct trajectory and object recognition abilities. Over-encoding of trajectory information in stored object tokens in early infancy, in particular, is expected to disrupt the ability to re-identify individuals across perceptual episodes, and lead to developmental outcomes with characteristics of autism spectrum disorders

High temporal discounters overvalue immediate rewards rather than undervalue future rewards : an event-related brain potential study

Impulsivity is characterized in part by heightened sensitivity to immediate relative to future rewards. Although previous research has suggested that "high discounters" in intertemporal choice tasks tend to prefer immediate over future rewards because they devalue the latter, it remains possible that they instead overvalue immediate rewards. To investigate this question, we recorded the reward positivity, a component of the event-related brain potential (ERP) associated with reward processing, with participants engaged in a task in which they received both immediate and future rewards and nonrewards. The participants also completed a temporal discounting task without ERP recording. We found that immediate but not future rewards elicited the reward positivity. High discounters also produced larger reward positivities to immediate rewards than did low discounters, indicating that high discounters relatively overvalued immediate rewards. These findings suggest that high discounters may be more motivated than low discounters to work for monetary rewards, irrespective of the time of arrival of the incentives

The Economic Effects of Unions in Latin America: Teachers' Unions and Education in Argentina

This paper considers the effects of trade unions on the education sector in Argentina. We have provided a substantial amount of new information and we have found useful preliminary results on some of the channels of union influence on the performance of this crucial sector. We find that those provinces where teacher unionism is fragmented, where union density is higher and where political relations with the governor are more conflictual, have more strikes (fewer class days). Based on estimates of education production functions both in this paper and elsewhere, we expect this to translate into lower student performance. We then find a number of weak conclusions related to the impact that unions have on several variables that affect students’ performance (i. e. , teachers’ tenure, job satisfaction, class size, education budget and teachers’ salaries). Reviewing these results, we conclude that the impact of unions on students’ performance depends on the channel and kind of political market where unions operate, but not on the existence of unions per se.

Active inference, evidence accumulation, and the urn task

Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one that is not completely understood. This issue is particularly important because a tendency to sample less information (often known as reflection impulsivity) is a feature in several psychopathologies, such as psychosis. A formal understanding of information sampling may therefore clarify the computational anatomy of psychopathology. In this theoretical letter, we consider evidence accumulation in terms of active (Bayesian) inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behavior--in this case, that they will make informed decisions. Normative decision making is then modeled using variational Bayes to minimize surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain (at least at a heuristic level). Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step toward understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference and may cast light on why this process is disordered in psychopathology

A Local Circuit Model of Learned Striatal and Dopamine Cell Responses under Probabilistic Schedules of Reward

Before choosing, it helps to know both the expected value signaled by a predictive cue and the associated uncertainty that the reward will be forthcoming. Recently, Fiorillo et al. (2003) found the dopamine (DA) neurons of the SNc exhibit sustained responses related to the uncertainty that a cure will be followed by reward, in addition to phasic responses related to reward prediction errors (RPEs). This suggests that cue-dependent anticipations of the timing, magnitude, and uncertainty of rewards are learned and reflected in components of the DA signals broadcast by SNc neurons. What is the minimal local circuit model that can explain such multifaceted reward-related learning? A new computational model shows how learned uncertainty responses emerge robustly on single trial along with phasic RPE responses, such that both types of DA responses exhibit the empirically observed dependence on conditional probability, expected value of reward, and time since onset of the reward-predicting cue. The model includes three major pathways for computing: immediate expected values of cures, timed predictions of reward magnitudes (and RPEs), and the uncertainty associated with these predictions. The first two model pathways refine those previously modeled by Brown et al. (1999). A third, newly modeled, pathway is formed by medium spiny projection neurons (MSPNs) of the matrix compartment of the striatum, whose axons co-release GABA and a neuropeptide, substance P, both at synapses with GABAergic neurons in the SNr and with the dendrites (in SNr) of DA neurons whose somas are in ventral SNc. Co-release enables efficient computation of sustained DA uncertainty responses that are a non-monotonic function of the conditonal probability that a reward will follow the cue. The new model's incorporation of a striatal microcircuit allowed it to reveals that variability in striatal cholinergic transmission can explain observed difference, between monkeys, in the amplitutude of the non-monotonic uncertainty function. Involvement of matriceal MSPNs and striatal cholinergic transmission implpies a relation between uncertainty in the cue-reward contigency and action-selection functions of the basal ganglia. The model synthesizes anatomical, electrophysiological and behavioral data regarding the midbrain DA system in a novel way, by relating the ability to compute uncertainty, in parallel with other aspects of reward contingencies, to the unique distribution of SP inputs in ventral SN.National Science Foundation (SBE-354378); Higher Educational Council of Turkey; Canakkale Onsekiz Mart University of Turke

The Impact of Information Technology on Emergency Health Care Outcomes

This paper analyzes the productivity of technology and job design in emergency response systems, or 911 systems.' During the 1990s, many 911 systems adopted Enhanced 911' (E911), where information technology is used to link automatic caller identification to a database of address and location information. A potential benefit to E911 is improved timeliness of the emergency response. We evaluate the returns to E911 in the context of a panel dataset of Pennsylvania counties during 1994-1996, when almost half of the 67 counties experienced a change in technology. We measure productivity using an index of health status of cardiac patients at the time of ambulance arrival, where the index should be improved by timely response. We also consider the direct effect of E911 on several patient outcomes, including mortality within the first hours following the incident and the total hospital charges incurred by the patient. Our main finding is that E911 increases the short-term survival rates for patients with cardiac diagnoses by about 1%, from a level of 96.2%. We also provide evidence that E911 reduces hospital charges. Finally, we analyze the effect of job design, in particular the use of Emergency Medical Dispatching' (EMD), where call-takers gather medical information, provide medical instructions over the telephone, and prioritize the allocation of ambulance and paramedic services. Controlling for EMD adoption does not affect our results about E911, and we find that EMD and E911 do not have significant interactions in determining outcomes (that is, they are neither substitutes nor complements).

Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay

Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are explicitly tasked with increasing users' long-term satisfaction. In this context, we study a content exploration task, which we formalize as a multi-armed bandit problem with delayed rewards. We observe that there is an apparent trade-off in choosing the learning signal: Waiting for the full reward to become available might take several weeks, hurting the rate at which learning happens, whereas measuring short-term proxy rewards reflects the actual long-term goal only imperfectly. We address this challenge in two steps. First, we develop a predictive model of delayed rewards that incorporates all information obtained to date. Full observations as well as partial (short or medium-term) outcomes are combined through a Bayesian filter to obtain a probabilistic belief. Second, we devise a bandit algorithm that takes advantage of this new predictive model. The algorithm quickly learns to identify content aligned with long-term success by carefully balancing exploration and exploitation. We apply our approach to a podcast recommendation problem, where we seek to identify shows that users engage with repeatedly over two months. We empirically validate that our approach results in substantially better performance compared to approaches that either optimize for short-term proxies, or wait for the long-term outcome to be fully realized.Comment: Presented at the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23