scholarly journals Linking normative models of natural tasks to descriptive models of neural response

2017 ◽  
Vol 17 (12) ◽  
pp. 16 ◽  
Author(s):  
Priyank Jaini ◽  
Johannes Burge
Keyword(s):  
Neural Response ◽  
Normative Models ◽  
Natural Tasks ◽  
Descriptive Models

scholarly journals Linking normative models for natural tasks and subunit models of neural response

2017 ◽  
Vol 17 (10) ◽  
pp. 411
Author(s):  
Johannes Burge ◽  
Priyank Jaini
Keyword(s):  
Neural Response ◽  
Normative Models ◽  
Natural Tasks

scholarly journals Linking Normative Models of Natural Tasks to Descriptive Models of Neural Response

2017 ◽  
Author(s):  
Priyank Jaini ◽  
Johannes Burge
Keyword(s):  
Latent Variables ◽  
Receptive Fields ◽  
Neural Response ◽  
Energy Model ◽  
Disparity Estimation ◽  
Quadratic Model ◽  
Sensory Stimuli ◽  
Visual Tasks ◽  
Natural Tasks ◽  
Descriptive Models

AbstractUnderstanding how nervous systems exploit task relevant properties of sensory stimuli to perform natural tasks is fundamental to the study of perceptual systems. However, there are few formal methods for determining which stimulus properties are most useful for a given task. As a consequence, it is difficult to develop principled models for how to compute task-relevant latent variables from natural signals, and it is difficult to evaluate descriptive models fit to neural response. Accuracy Maxmization Analysis (AMA) is a recently developed Bayesian method for finding the optimal task-specific filters (receptive fields). Here, we introduce AMA-Gauss, a new faster form of AMA that incorporates the assumption that the class-conditional filter responses are Gaussian distributed. Next, we use AMA-Gauss to show that its assumptions are justified for two fundamental visual tasks: retinal speed estimation and binocular disparity estimation. Then, we show that AMA-Gauss has striking formal similarities to popular quadratic models of neural response: the energy model and the Generalized Quadratic Model (GQM). Together, these developments deepen our understanding of why the energy model of neural response have proven useful, improve our ability to evaluate results from subunit model fits to neural data, and should help accelerate psychophysics and neuroscience research with natural stimuli.

scholarly journals Normative Models and Their Success

2020 ◽  
pp. 004839312097090
Author(s):  
Lukas Beck ◽  
Marcel Jahn
Keyword(s):  
Formal Models ◽  
Tentative Conclusion ◽  
Normative Models ◽  
Descriptive Models

In this paper, we explore an under-investigated question concerning the class of formal models that aim at providing normative guidance. We call such models normative models. In particular, we examine the question of how normative models can successfully exert normative guidance. First, we highlight the absence of a discussion of this question—which is surprising given the extensive debate about the success conditions of descriptive models—and motivate its importance. Second, we introduce and discuss two potential accounts of the success conditions of normative models. Our tentative conclusion is that the second account is more promising.

biting the utilitarian bullet

2005 ◽  
Vol 28 (4) ◽  
pp. 545-546
Author(s):  
jonathan baron
Keyword(s):  
Normative Theory ◽  
Heuristics And Biases ◽  
Clear Separation ◽  
Normative Models ◽  
Descriptive Models

the heuristics-and-biases approach requires a clear separation of normative and descriptive models. normative models cannot be justified by intuition, or by consensus. the lack of consensus on normative theory is a problem for prescriptive approaches. one solution to the prescriptive problem is to argue contingently: if you are concerned about consequences, here is a way to make them better.

Heuristics and Biases

2014 ◽  
pp. 2-27 ◽  
Author(s):  
Jonathan Baron
Keyword(s):  
Decision Making ◽  
Systems Theory ◽  
Judgment And Decision Making ◽  
Isolation Effect ◽  
Heuristics And Biases ◽  
Naive Theories ◽  
Two Systems ◽  
Alternative Approaches ◽  
Normative Models ◽  
Descriptive Models

The field of judgment and decision-making is characterized by three types of “models”: normative, prescriptive, and descriptive. Normative models provide standards for evaluation of judgments and decisions. Descriptive models are psychological accounts of how people either conform or depart from these models systematically (i.e., have biases). Prescriptive models suggest ways of helping people come closer to the normative models. This chapter reviews the main categories of descriptive models, including the major categories heuristics, such as the ideas of isolation effect, attribute substitution, and two-systems theory. It also discusses alternative approaches such as naive theories. It also describes a general approach to prescription, the idea of actively open-minded thinking, which can reduce some of the biases.

Sunk Costs and Resistance to Change

2000 ◽  
Vol 14 (4) ◽  
pp. 355-371 ◽  
Author(s):  
Robert L. Leahy
Keyword(s):  
Decision Making ◽  
Resistance To Change ◽  
Sunk Costs ◽  
Base Rates ◽  
Subjective Utility ◽  
Future Utility ◽  
Normative Models ◽  
Hedonic Calculus ◽  
Descriptive Models ◽  
Actual Decision

Normative models of decision making imply that individuals will utilize a hedonic calculus about future utility ratios (subjective utilities) in considering current alternatives. In contrast, descriptive models of actual decision making indicate that individuals utilize heuristics, ignore base rates, and consider previous decisions when considering future choices. Sunk costs are reflected in basing future decisions on previous commitments or investments, thereby ignoring subjective utility expectations. The effects of sunk costs on resistance to change are discussed and interventions to overcome these effects are examined.

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