Normative Inference Research Articles

Normative Inference Tickets

Abstract We argue that stereotypes associated with concepts like he-said–she-said, conspiracy theory, sexual harassment, and those expressed by paradigmatic slurs provide “normative inference tickets”: conceptual permissions to automatic, largely unreflective normative conclusions. These “mental shortcuts” are underwritten by associated stereotypes. Because stereotypes admit of exceptions, normative inference tickets are highly flexible and productive, but also liable to create serious epistemic and moral harms. Epistemically, many are unreliable, yielding false beliefs which resist counterexample; morally, many perpetuate bigotry and oppression. Still, some normative inference tickets, like some activated by sexual harassment, constitute genuine moral and hermeneutical advances. For example, our framework helps explain Miranda Fricker's notion of “hermeneutical lacunae”: what early victims of “sexual harassment” – as well as their harassers – lacked before the term was coined was a communal normative inference ticket – one that could take us, collectively, from “this is happening” to “this is wrong.”

Continuous time causal structure induction with prevention and generation

Most research into causal learning has focused on atemporal contingency data settings while fewer studies have examined learning and reasoning about systems exhibiting events that unfold in continuous time. Of these, none have yet explored learning about preventative causal influences. How do people use temporal information to infer which components of a causal system are generating or preventing activity of other components? In what ways do generative and preventative causes interact in shaping the behavior of causal mechanisms and their learnability? We explore human causal structure learning within a space of hypotheses that combine generative and preventative causal relationships. Participants observe the behavior of causal devices as they are perturbed by fixed interventions and subject to either regular or irregular spontaneous activations. We find that participants are capable learners in this setting, successfully identifying the large majority of generative, preventative and non-causal relationships but making certain attribution errors. We lay out a computational-level framework for normative inference in this setting and propose a family of more cognitively plausible algorithmic approximations. We find that participants’ judgment patterns can be both qualitatively and quantitatively captured by a model that approximates normative inference via a simulation and summary statistics scheme based on structurally local computation using temporally local evidence.

Adaptive Learning through Temporal Dynamics of State Representation.

People adjust their learning rate rationally according to local environmental statistics and calibrate such adjustments based on the broader statistical context. To date, no theory has captured the observed range of adaptive learning behaviors or the complexity of its neural correlates. Here, we attempt to do so using a neural network model that learns to map an internal context representation onto a behavioral response via supervised learning. The network shifts its internal context on receiving supervised signals that are mismatched to its output, thereby changing the "state" to which feedback is associated. A key feature of the model is that such state transitions can either increase learning or decrease learning depending on the duration over which the new state is maintained. Sustained state transitions that occur after changepoints facilitate faster learning and mimic network reset phenomena observed in the brain during rapid learning. In contrast, state transitions after one-off outlier events are short lived, thereby limiting the impact of outlying observations on future behavior. State transitions in our model provide the first mechanistic interpretation for bidirectional learning signals, such as the P300, that relate to learning differentially according to the source of surprising events and may also shed light on discrepant observations regarding the relationship between transient pupil dilations and learning. Together, our results demonstrate that dynamic latent state representations can afford normative inference and provide a coherent framework for understanding neural signatures of adaptive learning across different statistical environments.SIGNIFICANCE STATEMENT How humans adjust their sensitivity to new information in a changing world has remained largely an open question. Bridging insights from normative accounts of adaptive learning and theories of latent state representation, here we propose a feedforward neural network model that adjusts its learning rate online by controlling the speed of transitioning its internal state representations. Our model proposes a mechanistic framework for explaining learning under different statistical contexts, explains previously observed behavior and brain signals, and makes testable predictions for future experimental studies.

Biased evaluations emerge from inferring hidden causes.

How do we evaluate a group of people after a few negative experiences with some members but mostly positive experiences otherwise? How do rare experiences influence our overall impression? We show that rare events may be overweighted due to normative inference of the hidden causes that are believed to generate the observed events. We propose a Bayesian inference model that organizes environmental statistics by combining similar events and separating outlying observations. Relying on the model’s inferred latent causes for group evaluation overweighs rare or variable events. We tested the model’s predictions in eight experiments where subjects observed a sequence of social or non-social behaviors and estimated their average. As predicted, estimates were biased toward sparse events when estimating after seeing all observations, but not when tracking a summary value as observations accrued. Our results suggest that biases in evaluation may arise from inferring the hidden causes of group members’ behaviors.

Penentuan Awal Bulan Kamariah Untuk Ibadah

This paper presents data regarding the formula for determining the start of the month for worship at the time of the Prophet. This is important because the determination of the beginning of the month among Muslims has always been a polemic, even Muslims until now still do not have a definite and unique calendar to serve as a common guideline. The method usedin this research is the method introducedby Louay Safi "Unified Aproach to Shari'ah Inference"; namely textual normative inference, empirical-historical-contextualist inference, and integrated inference. The results were as follows: d natural inference stualis te k-normative-legis, fast orders clearly to be given information about ordinances that rukyatul hilal; and if it is cloudy or covered in fog, then it is ordered to calculate, estimate, or fulfill the month into 30 days and the rule of law in matters of mahdah worship such as fasting cannot be changed, sentences that are already text and sharih do not have the character of ijtihadi; and anything that creates controversy in society can be eliminated by the government or the authorities; whereas in the contextualist historical-empirical inference it can be said that worship which includes the obligation of rukyat has encouraged Muslims to study and deepen the science of astronomy ; and in unified inference, the computation of the reckoning at the time of the prophet with the fulfillment becomes thirty days because at that time the Madinans did not have zij or astronomical tables showing the positions of the moon, earth and sun. From this perspective, rukyat often called taabbudiy be part of the entity taaquliy for a silver lining can be found in the annals of the Muslims, and Reckoning ser ing said taaqquliy also part of ta'abbudiy entity because it is part and result of activity namely rukyat taabbudiy .

"Second guessing yourself all the time about what they really mean…": Cognitive differences between autistic and non-autistic adults in understanding implied meaning.

This study investigated cognitive differences between autistic and non-autistic people in understanding implied meaning in conversation using a novel computerized test, the Implicature Comprehension Test. Controlling for core language ability, autistic participants (N = 66) were over twice as likely to endorse a non-normative interpretation of an implied meaning and over five times as likely to select "do not know" when asked about the presence of an implied meaning, compared to non-autistic participants (N = 118). A further experiment suggested that the selection of "do not know" reflected a cognitive preference for certainty and explicit communication, and that the normative inference could often be made when the test format was more constrained. Our research supports the hypothesis that autistic individuals can find it challenging to process language in its pragmatic context, and that cognitive preferences play a role in this. LAY SUMMARY: We investigated differences between autistic and non-autistic people in understanding implied meanings in conversation. We found that autistic people were more likely to select a different interpretation of implied meanings compared to other people, and also much more likely to avoid processing implied meanings when the task allowed this. Our research supports the view that autistic people can find it challenging to process indirect meanings, and that they tend to prefer explicit forms of communication.

Reasons in the Scientonomic Ontology

The question of how we come to accept new theories is a central area of inquiry in scientonomic discourse. However, there has yet to be a formal discussion of the subjective reasons an agent may have for accepting theories. This paper explores these epistemic reasons and constructs a historically sensitive definition of reason. This formulation takes an abstractionist stance towards the ontology of reasons and makes use of a composite basing relation. The descriptive and normative components of reasons are fully formulated in scientonomic terms through the application of the newly introduced notion of implication, and its separation from the notion of inference. In addition, the paper provides scientonomic definitions for sufficient reason, support, and normative inference. The fruitfulness of this formulation of reasons is illustrated by a few examples.
 Suggested Modifications
 [Sciento-2019-0009]: Accept the following definition of implication:
 
 Implication ≡ a logical transition from one theory to another.
 
 [Sciento-2019-0010]: Accept the following definitions of sufficient reason, reason, support, and normative inference:
 
 Sufficient Reason ≡ an agent takes theory A to be a sufficient reason for (accepting) theory B iff the following four conditions are met:
 
 (1) The agent accepts A.
 (2) The agent accepts that A→B.
 (3) The agent employs ε.
 (4) The agent accepts (ε, A, A→B) →ε (Should accept B).
 
 Support ≡ an agent takes theory A to be supporting theory B iff the agent accepts A and accepts that A→B.
 Reason ≡ an agent takes theory A to be a reason for theory B iff the agent accepts that A→B, employs ε, and accepts (ε, A, A→B) →ε (Should accept B).
 Normative Inference ≡ An agent takes theory A to normatively infer theory B iff the agent accepts A, accepts that A→B, and accepts (ε, A, A→B) →ε (Should accept B).
 
 [Sciento-2019-0011]: Provided that modification [Sciento-2019-0010] is accepted, accept the sufficient reason theorem and its deduction from the definition of sufficient reason and the second law:
 
 Sufficient Reason theorem: a theory becomes accepted by an agent, when an agent has a sufficient reason for accepting it.
 
 Accept the following question as a legitimate topic of scientonomic inquiry:
 
 Theory Acceptance without Sufficient Reason: how do theories become accepted without a sufficient reason, i.e. in the cases of circularity or theories without a reason?

Normative inference in efficient markets

This paper develops a nonparametric method to infer social preferences over policies from prices of securities when agents have non-stationary heterogeneous preferences. We allow for arbitrary efficient risk-sharing mechanisms, formal and informal, and consider a large class of policies. We present a condition on the distribution of aggregate wealth that is necessary and sufficient for the revelation of social preferences over a universal set of policies. We also provide a weaker condition that is sufficient for revelation of social preferences for an arbitrary finite collection of policies.

A normative inference approach for optimal sample sizes in decisions from experience.

“Decisions from experience” (DFE) refers to a body of work that emerged in research on behavioral decision making over the last decade. One of the major experimental paradigms employed to study experience-based choice is the “sampling paradigm,” which serves as a model of decision making under limited knowledge about the statistical structure of the world. In this paradigm respondents are presented with two payoff distributions, which, in contrast to standard approaches in behavioral economics, are specified not in terms of explicit outcome-probability information, but by the opportunity to sample outcomes from each distribution without economic consequences. Participants are encouraged to explore the distributions until they feel confident enough to decide from which they would prefer to draw from in a final trial involving real monetary payoffs. One commonly employed measure to characterize the behavior of participants in the sampling paradigm is the sample size, that is, the number of outcome draws which participants choose to obtain from each distribution prior to terminating sampling. A natural question that arises in this context concerns the “optimal” sample size, which could be used as a normative benchmark to evaluate human sampling behavior in DFE. In this theoretical study, we relate the DFE sampling paradigm to the classical statistical decision theoretic literature and, under a probabilistic inference assumption, evaluate optimal sample sizes for DFE. In our treatment we go beyond analytically established results by showing how the classical statistical decision theoretic framework can be used to derive optimal sample sizes under arbitrary, but numerically evaluable, constraints. Finally, we critically evaluate the value of deriving optimal sample sizes under this framework as testable predictions for the experimental study of sampling behavior in DFE.

Truth in Advertising? Visuals, Sound, and the Factual Accuracy of Political Advertising

This paper uses a unique data set from the 2008 election to examine the extent to which the information contained in positive and negative political advertising factually correct. The current conventional wisdom about negative advertising that it engages the public, contains more issue information than positive advertising, and more specific and evidence-based. The normative inference that follows that negative advertising good for democracy because an engaged public with more information more desirable than a disengaged public with less information.While it may well be the case that negative ads contain more information and are more likely to engage voters, what has been missing from most of this analysis up to now whether the content of negative advertising more or less likely to be accurate. In 2008 the Video Monitoring Service provided us with video and frequency and spending data on all the presidential advertising on television and radio. In a unique exercise we checked all the claims made in positive and negative ads (most have supporting sources; 68% in 2000 according to Franz et al. 2008), using fact checking websites like adwatches, or our own validation of a candidate’s record. This included not only sourced claims but statements such as is a leader in Congress, where we checked how many bills the candidate initiated or simply signed on to, etc. or is inexperienced, where we looked at types of jobs held, ages and resume's of other candidates running for office We also coded all the ads as positive, negative or contrast using the Wisconsin Advertising Project's criteria, Jamieson's attack score and Geer's coding of appeals.

When underadditivity of factor effects in the Psychological Refractory Period paradigm implies a bottleneck: Evidence from psycholinguistics

The Psychological Refractory Period (PRP) paradigm is a dual-task procedure that can be used to examine the resource demands of specific cognitive processes. Inferences about the underlying processes are typically based on performance in the second of two speeded tasks. If the effect of a factor manipulated in Task 2 decreases as the stimulus onset asynchrony (SOA) between tasks decreases (underadditivity), the normative inference is that the effect of this factor occurs prior to a limited-capacity central processing mechanism. In contrast, if the effect of a factor is additive with SOA then the inference is that this indexes a process that either uses a limited-capacity central processing mechanism or occurs after some process that uses this mechanism. A heretofore unidentified exception to this logic arises when Task 2 involves two separate processes that operate in parallel, but compete. Interference with one process in Task 2 because of work on Task 1 will eliminate or reduce competition within Task 2 and is hence manifest as an underadditive interaction with decreasing SOA. This is illustrated here by reference to a PRP experiment in which the ubiquitous effect of spelling-to-sound regularity on reading aloud time is eliminated at a short SOA and by consideration of three converging lines of investigation in the PRP paradigm when Task 2 involves reading aloud.

A Bayesian formulation of behavioral control

Helplessness, a belief that the world is not subject to behavioral control, has long been central to our understanding of depression, and has influenced cognitive theories, animal models and behavioral treatments. However, despite its importance, there is no fully accepted definition of helplessness or behavioral control in psychology or psychiatry, and the formal treatments in engineering appear to capture only limited aspects of the intuitive concepts. Here, we formalize controllability in terms of characteristics of prior distributions over affectively charged environments. We explore the relevance of this notion of control to reinforcement learning methods of optimising behavior in such environments and consider how apparently maladaptive beliefs can result from normative inference processes. These results are discussed with reference to depression and animal models thereof.

Positivism and Normative Inference: Two Key Legal Problems of Late Modernity

Two false dichotomies, no is from ought and either natural law or positivism impair current legal thought. This article exposes those dichotomies and explains why they are not in fact accurate using Professor Duncan Kennedy's work as a foil for the exposition.

Kelsen: A Purely Empty Theory of Law

Kelsen's position is founded upon an epistemology which is both objectivist, regarding the existence of truths, and subjectivist, regarding normative positions. This epistemological bifurcation leads to a variety of contradictions in Kelsen's positions, and explains his reversals regarding the possibility of normative inference, the real or metaphorical existence of a fundamental norm, and constructivism. Aside from these contradictions, this 'split' explains certain terminological ambiguities: Kelsen confounds conditionals (statements in the form of 'if then') with imperatives under one term, 'norm'. This ambiguity is not resolved through the distinction between and statement, for the distinction between legal norm and legal statement is a distinction between the domain of legal science and of law. The confusion of an order (an imperative statement) and a conditional (a statement in the form of if then) remains, for the distinction between legal statement and statement of legal science refers to another thing entirely: the epistemological distinction between two different domains of study, and not to the epistemological distinction between two different intentional entities. Reversals on constructivism, normative inference, and the real or metaphorical character of the fundamental norm reveal that Kelsen's theory suffers from a fundamental conceptual flaw in the definition of his basic unit of analysis. The result is a theory that is not merely devoid of prescriptive utility (no normative content) but also is internally incoherent. Table of Contents Kelsen : A Purely Empty Theory of Law 1 A. Normative Inference 3 1. Refusal of Normative Inference 4 2. The Normative Syllogism 13 3. Demonstration of Legal Inferencing 15 B. Critique: 18 1. Terminology 18 a. Polysemy 18 b. Ambiguity: 20 c. Confusion 21 d. What neutrality? 22 2. Useless Complexity 23 a. Terminological Multiplication 23 b. Multiplication of syllogisms 23 c. Imputation: A useless Distinction 24 d. A Special Juridical Logic? 24 e. The Posthumous Character of the ATN 24 3. Methodological Apory 25 4. Problem of Postulats 25 a. Separation of law and morality 25 b. The Basic Norm 25 5. Tautology of the Basic Norm 26 6. Rationalisation 26 7. From Subjective to Objective Signification 28 C. Conclusion 28

Is and Ought According to Hume (Etre Et Devoir Être Chez Hume)

Supposedly one cannot infer norms (infer ought from is or is from ought) because of the so-called law of Hume. Hume however never says normative inference be impossible. Rather he says that whoever wishes to infer norms must bear the burden of expliciting their normative steps and their proof. The article explains this misunderstanding hopefully clearing up a field of needless confusion in the law.

Can Electoral Mobility Diminish Economic Performance? Evidence from the US Telecommunications Sector

Electoral mobility (i.e., individuals' propensity to vote in elections) is popularly cited as reflecting a polity's health. Increased mobility can, however, diminish economic performance. For example, while mobile electorates can check producer monopolies, they can also facilitate consumer monopsonies and weaken regulatory commitments. Looking at a relatively controlled setting (i.e., the US local exchange sector), I find evidence that electoral mobility may have diminished economic performance through such channels. Moreover, I encounter considerable difficulty when attempting to dismiss this evidence as either an artifact of endogeneity bias or as support for an alternative normative inference.