163 research outputs found
Strategically managing learning during perceptual decision making
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
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
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
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
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!
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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A Flexible Comparison Process as a Critical Mechanism for Context Effects
Context effects such as the attraction, compromise, and similarity effects demonstrate that a comparison process, i.e., a method of comparing dimension values, plays an important role in choice behavior. Recent research suggests that this same comparison process, made more flexible by allowing for a variety of comparisons, may provide an elegant account of observed correlations between context effects by differentially highlighting dimension-level and alternative-level stimulus characteristics. Thus, the present experiments test the comparison process as a critical mechanism underlying context-dependent choice behavior. Experiment 1 provides evidence that increasing a dimension-level property, spread, promotes the attraction and compromise effects and reduces the similarity effect, whereas increasing an alternative-level property, dispersion, introduces an alternative-level bias that influences choice in concert with the decoy. Experiment 2 utilizes eyetracking to test the influence of stimulus presentation format on information acquisition patterns and context-dependent choice behavior. Contrary to predictions, a By-Alternative presentation format appears to increase within-dimension transitions in eye fixations relative to a By-Dimension presentation format. Lastly, four computational models with theoretical accounts of the development of context effects over time were fit to joint choice and response time data. Though the MLBA provided the best fits to the subject-level mean choice proportions, it could not capture the crossover in preference between the target and competitor across RT quantiles; rather, MDFT and the AAM performed best in this regard. The present work therefore not only provides new insights into the relationship between choice and response times in preferential choice but sets important new constraints for theoretical models that seek to account for such behavior
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