Naive Learning Through Probability Overmatching

Naïve Learning in a Binary Action, Social Network Environment In “Naïve Learning Through Probability Overmatching,” I. Arieli, Y. Babichenko, and M. Mueller-Frank consider an environment where privately informed agents select a binary action repeatedly observing the past actions of their neighbors in a social network. Rational inference has been shown to be exceedingly complex in this environment. Instead, this paper focuses on boundedly rational agents that form beliefs according to discretized DeGroot updating and apply a decision rule that assigns a (mixed) action to each belief. It is shown that naïve learning, where the long run actions of all agents are optimal given their pooled private information, can be achieved in any strongly connected network if beliefs satisfy a high level of inertia and the decision rule coincides with probability overmatching. The main difference to existing naïve learning results is that here it is shown to hold (1) for binary rather than uncountable action spaces and (2) even for network and information structures where Bayesian agents fail to learn.

Explaining the Evidence

How do we make sense of complex evidence? What are the cognitive principles that allow detectives to solve crimes, and lay people to puzzle out everyday problems? To address these questions, David Lagnado presents a novel perspective on human reasoning. At heart, we are causal thinkers driven to explain the myriad ways in which people behave and interact. We build mental models of the world, enabling us to infer patterns of cause and effect, linking words to deeds, actions to effects, and crimes to evidence. But building models is not enough; we need to evaluate these models against evidence, and we often struggle with this task. We have a knack for explaining, but less skill at evaluating. Fortunately, we can improve our reasoning by reflecting on inferential practices and using formal tools. This book presents a system of rational inference that helps us evaluate our models and make sounder judgments.

Agreement errors are predicted by rational inference in sentence processing

People are able to understand language in challenging settings which often require them to correct for speaker errors, environmental noise, and perceptual unreliability. To account for these abilities, it has recently been proposed that people are adapted to correct for noise during language comprehension, via rational Bayesian inference. In the present research, we demonstrate that a rational noisy-channel framework for sentence comprehension can account for a well-known phenomenon—subject-verb agreement errors (e.g. The key to the cabinets are…). A series of experiments provides evidence that: a) agreement errors are associated with misrepresentations of the sentence consistent with noisy-channel inferences and b) agreement errors are rationally sensitive to environmental statistics and properties of the noise. These findings support the hypothesis that agreement errors in production result in part from a sentence comprehension mechanism that is adapted to understanding language in noisy environments.

A systematic approach to inserting split inteins for Boolean logic gate engineering and basal activity reduction

Split inteins are powerful tools for seamless ligation of synthetic split proteins. Yet, their use remains limited because the already intricate split site identification problem is often complicated by the requirement of extein junction sequences. To address this, we augment a mini-Mu transposon-based screening approach and devise the intein-assisted bisection mapping (IBM) method. IBM robustly reveals clusters of split sites on five proteins, converting them into AND or NAND logic gates. We further show that the use of inteins expands functional sequence space for splitting a protein. We also demonstrate the utility of our approach over rational inference of split sites from secondary structure alignment of homologous proteins, and that basal activities of highly active proteins can be mitigated by splitting them. Our work offers a generalizable and systematic route towards creating split protein-intein fusions for synthetic biology.

Rational Rules

Moral systems, like normative systems more broadly, involve complex mental representations. Rational Rules offers an account of the acquisition of key aspects of normative systems in terms of general-purpose rational learning procedures. In particular, it offers statistical learning accounts of: (1) how people come to think that a rule is act-based, that is, the rule prohibits producing certain consequences but not allowing such consequences to occur or persist; (2) how people come to expect that a new rule will also be act-based; (3) how people come to believe a principle of liberty, according to which whatever is not expressly prohibited is permitted; and (4) how people come to think that some normative claims hold universally while others hold only relative to some group. This provides an empiricist theory of a key part of moral acquisition, since the learning procedures are domain general. It also entails that crucial parts of our moral system enjoy rational credentials since the learning procedures are forms of rational inference. There is another sense in which rules can be rational—they can be effective for achieving our ends, given our ecological settings. Rational Rules argues that at least some central components of our moral systems are indeed ecologically rational: they are good at helping us attain common goals. In addition, the book argues that a basic form of rule representation brings motivation along automatically. Thus, part of the explanation for why we follow moral rules is that we are built to follow rules quite generally.

Rational Inference Objection Due to The Invalidity of Its Grammatical Reasoning

Grammatical controversy is one of the topics that still need serious study and pause from researchers and scholars who study Arabic grammar in order to complete its image, and it is based on a strong leg, and a pillar that runs alongside grammatical applications, especially if it is linked to mental evidence, because it enriches the grammatical lesson with discussion The analysis and evidence and evaluation of evidence, and then assessed, and the statement of strong evidence from the weak, and the student helps to understand the particles that may be overlooked, and what is most evident in the grammatical reason that is the most important part in the measurement, and what is going on around it, and the grammarians were aware of all that the grammatical cause could add to the grammatical lesson and the understanding of grammatical rulings, whether the reason is a party to the measurement, or that the grammatical rule is interpreted (the explanation), and it is not surprising that there is a lot of objection to it in the place of dispute Grammar, and it is known that its response means the issue hesitates between nullity and weakness or anomalies.

Intein-assisted bisection mapping systematically splits proteins for Boolean logic and inducibility engineering

Split inteins are powerful tools for seamless ligation of synthetic split proteins. Yet, their use remains limited because the already intricate split site identification problem is often complicated by the requirement of extein junction sequences. To address this, we augmented a mini-Mu transposon-based screening approach and devised the intein-assisted bisection mapping (IBM) method. IBM robustly revealed clusters of split sites on five proteins, converting them into AND or NAND logic gates. We further showed that the use of inteins expands functional sequence space for splitting a protein. We also demonstrated the utility of our approach over rational inference of split sites from secondary structure alignment of homologous proteins. Furthermore, the intein inserted at an identified site could be engineered by the transposon again to become partially chemically inducible, and to some extent enabled post-translational tuning on host protein function. Our work offers a generalizable and systematic route towards creating split protein-intein fusions and conditional inteins for protein activity control.

An ERP index of real-time error correction within a noisy-channel framework of human communication

Recent evidence suggests that language processing is well-adapted to noise in the input (e.g., speech errors, mishearing) and readily corrects the input via rational inference over possible intended sentences and probable noise corruptions. However, it remains unclear whether this inference takes the form of an offline re-analysis or a rapid, real-time correction to the representations of the input. We hypothesize that noise inferences happen online during processing and that well-studied ERP components may serve as a useful index of this process. In particular, a reduced N400 effect and increased P600 effect appear to accompany sentences where the probability that the message was corrupted by noise exceeds the probability that it was produced intentionally and perceived accurately. Indeed, semantic violations that are attributable to noise—for example, in “The storyteller could turn any incident into an amusing antidote”, where the implausible word “antidote” is orthographically and phonologically close to the intended “anecdote”—elicit a reduced N400 effect and larger P600 effect. Further, the magnitude of this P600 effect is shown to relate to the probability that the comprehender will retrieve a plausible alternative. This work thus adds to the growing body of literature that suggests that many aspects of language processing are well-adapted to noise in the input and opens the door to electrophysiologic investigations of these processes

The Bayesian Transition Theory

Chapter 4 discusses the Bayesian transition theory. The distinction is drawn between dynamics and kinematics, and it’s argued that the theory of rational inference belongs to the former rather than the latter. It’s shown that Jeffrey’s rule is thus not a rule of rational inference. Credence lent to a conditional is explained and compared to conditional credence. Two problems for Bayesian kinematics then come into focus: conditional credence is never changing in the model, nor is it ever the contact-point of rational shift-in-view. A natural conception of conditional commitment is then put forward and used to solve both these problems. Along the way it’s argued that modus-ponens-style arguments do not function in thought as logical syllogisms, since modus-ponens-style arguments specify obligatory paths forward in thought.

Inference and Rationality

A theory of rational state transition must answer four questions: are shifts within its domain brought about by agents or do they simply happen to them? Is the approach part a theory’s dynamics or kinematics? Does the approach make use of everyday or ideal rationality? Are the mental states involved coarse- or fine-grained? The questions are used to generate a sixteen-fold classification of rational shift-in-view. It is then argued that rational inference leads to the idea of a coordinated epistemic reason: roughly, a reason where causal-efficacy and evidential-relevance fuse together. This idea is illustrated with everyday examples and it is then argued that the theory of rational inference turns crucially on the non-ideal rationality of agential dynamics. The chapter closes by developing a theory of rational inference and a take on the human mind to go with it.