163 research outputs found

    Strategically managing learning during perceptual decision making

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    Making optimal decisions in the face of noise requires balancing short-term speed and accuracy. But a theory of optimality should account for the fact that short-term speed can influence long-term accuracy through learning. Here, we demonstrate that long-term learning is an important dynamical dimension of the speed-accuracy trade-off. We study learning trajectories in rats and formally characterize these dynamics in a theory expressed as both a recurrent neural network and an analytical extension of the drift-diffusion model that learns over time. The model reveals that choosing suboptimal response times to learn faster sacrifices immediate reward, but can lead to greater total reward. We empirically verify predictions of the theory, including a relationship between stimulus exposure and learning speed, and a modulation of reaction time by future learning prospects. We find that rats' strategies approximately maximize total reward over the full learning epoch, suggesting cognitive control over the learning process

    A Theory of Reaction Time Distributions

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    We develop a general theory of reaction time (RT) distributions in psychological experiments, deriving from the distribution of the quotient of two normal random variables, that of the task difficulty (top-down information), and that of the external evidence that becomes available to solve it (bottom-up information). The theory provides a unied account of known changes in the shape of the distributions depending on properties of the task and of the participants, and it predicts additional changes that should be observed. A number of known properties of RT distributions are homogeneously accounted\ud for by variations in the value of two easily interpretable parameters: the coefficients of variation of the two normal variables. The predictions of the theory are compared with those of multiple families of distributions that have been proposed to account for RTs, indicating our theory provides a significantly better account of experimental data. For this purpose, we provide comparisons with four large datasets across tasks and modalitities. Finally,\ud we show how the theory links to neurobiological models of response latencies

    On Cognitive Modeling and Other Minds

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    Scientists and philosophers alike debate whether various systems such as plants and bacteria exercise cognition. One strategy for resolving such debates is to ground claims about nonhuman cognition in evidence from mathematical models of cognitive capacities. In this paper, I show that proponents of this strategy face two major challenges: demarcating phenomenological models from process models and overcoming underdetermination by model fit. I argue that even if the demarcation problem is resolved, fitting a process model to behavioral data is, on its own, not strong evidence for any cognitive process, let alone processes shared with humans

    Social Interaction-Aware Dynamical Models and Decision Making for Autonomous Vehicles

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    Interaction-aware Autonomous Driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the autonomous vehicle to be able to understand and predict the behaviour of human road users. In this literature review, the current state of IAAD research is surveyed in this work. Commencing with an examination of terminology, attention is drawn to challenges and existing models employed for modelling the behaviour of drivers and pedestrians. Next, a comprehensive review is conducted on various techniques proposed for interaction modelling, encompassing cognitive methods, machine learning approaches, and game-theoretic methods. The conclusion is reached through a discussion of potential advantages and risks associated with IAAD, along with the illumination of pivotal research inquiries necessitating future exploration

    Generative Models of Cortical Oscillations: Neurobiological Implications of the Kuramoto Model

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    Understanding the fundamental mechanisms governing fluctuating oscillations in large-scale cortical circuits is a crucial prelude to a proper knowledge of their role in both adaptive and pathological cortical processes. Neuroscience research in this area has much to gain from understanding the Kuramoto model, a mathematical model that speaks to the very nature of coupled oscillating processes, and which has elucidated the core mechanisms of a range of biological and physical phenomena. In this paper, we provide a brief introduction to the Kuramoto model in its original, rather abstract, form and then focus on modifications that increase its neurobiological plausibility by incorporating topological properties of local cortical connectivity. The extended model elicits elaborate spatial patterns of synchronous oscillations that exhibit persistent dynamical instabilities reminiscent of cortical activity. We review how the Kuramoto model may be recast from an ordinary differential equation to a population level description using the nonlinear Fokker–Planck equation. We argue that such formulations are able to provide a mechanistic and unifying explanation of oscillatory phenomena in the human cortex, such as fluctuating beta oscillations, and their relationship to basic computational processes including multistability, criticality, and information capacity

    Attention please!

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    We study the impact of manipulating the attention of a decision-maker who learns sequentially about a number of items before making a choice. Under natural assumptions on the decision-maker’s strategy, directing attention toward one item increases its likelihood of being chosen regardless of its value. This result applies when the decisionmaker can reject all items in favor of an outside option with known value; if no outside option is available, the direction of the effect of manipulation depends on the value of the item. A similar result applies to manipulation of choices in bandit problems
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