Iterated learning: Intergenerational knowledge transmission reveals inductive biases

Cultural transmission of information plays a central role in shaping human knowledge. Some of the most complex knowledge that people acquire, such as languages or cultural norms, can only be learned from other people, who themselves learned from previous generations. The prevalence of this process of “iterated learning” as a mode of cultural transmission raises the question of how it affects the information being transmitted. Analyses of iterated learning utilizing the assumption that the learners are Bayesian agents predict that this process should converge to an equilibrium that reflects the inductive biases of the learners. An experiment in iterated function learning with human participants confirmed this prediction, providing insight into the consequences of intergenerational knowledge transmission and a method for discovering the inductive biases that guide human inferences.     

Internet addiction among Norwegian adults: a stratified probability sample study

Most Norwegians are Internet users. We conducted a stratified probability sample study (Norway, 2007, age-group 16–74 years, N = 3,399, response rate 35.3%, 87.1% Internet users) to assess the prevalence of Internet addiction and at-risk Internet use by the Young Diagnostic Questionnaire (YDQ). The prevalence of Internet addiction (YDQ score 5–8) was 1.0% and an additional 5.2% were at-risk Internet users (YDQ score 3–4). Internet addiction and at-risk Internet use was strongly dependent on gender and age with highest prevalences among young males (16–29 years 4.1% and 19.0%, 30–39 years 3.3% and 10.7%). Logistic regression showed that male gender, young age, university level education, and an unsatisfactory financial situation were factors positively associated with "problematic Internet use" (at-risk and addicted use combined). Time spent on the Internet and prevalence of self-reported sleeping disorders, depression, and other psychological impairments increased linearly with YDQ score. Problematic Internet use clearly affects the lives of many people.     

Introducing Identity

The best-known syntactic account of the logical constants is inferentialism . Following Wittgenstein’s thought that meaning is use, inferentialists argue that meanings of expressions are given by introduction and elimination rules. This is especially plausible for the logical constants, where standard presentations divide inference rules in just this way. But not just any rules will do, as we’ve learnt from Prior’s famous example of tonk, and the usual extra constraint is harmony. Where does this leave identity? It’s usually taken as a logical constant but it doesn’t seem harmonious: standardly, the introduction rule (reflexivity) only concerns a subset of the formulas canvassed by the elimination rule (Leibniz’s law). In response, Read [5, 8] and Klev [3] amend the standard approach. We argue that both attempts fail, in part because of a misconception regarding inferentialism and identity that we aim to identify and clear up.

     

Learning How to Generalize

Generalization is a fundamental problem solved by every cognitive system in essentially every domain. Although it is known that how people generalize varies in complex ways depending on the context or domain, it is an open question how people learn the appropriate way to generalize for a new context. To understand this capability, we cast the problem of learning how to generalize as a problem of learning the appropriate hypothesis space for generalization. We propose a normative mathematical framework for learning how to generalize by learning inductive biases for which properties are relevant for generalization in a domain from the statistical structure of features and concepts observed in that domain. More formally, the framework predicts that an ideal learner should learn to generalize by either taking the weighted average of the results of generalizing according to each hypothesis space, with weights given by how well each hypothesis space fits the previously observed concepts, or by using the most likely hypothesis space. We compare the predictions of this framework to human generalization behavior with three experiments in one perceptual (rectangles) and two conceptual (animals and numbers) domains. Across all three studies we find support for the framework's predictions, including individual‐level support for averaging in the third study.     

Human coffee drinking: manipulation of concentration and caffeine dose.

In a residential research ward coffee drinking was studied in 9 volunteer human subjects with histories of heavy coffee drinking. A series of five experiments was undertaken to characterize adlibitum coffee consumption and to investigate the effects of manipulating coffee concentration, caffeine dose per cup, and caffeine preloads prior to coffee drinking. Manipulations were double-blind and scheduled in randomized sequences across days. When cups of coffee were freely available, coffee drinking tended to be rather regularly spaced during the day with intercup intervals becoming progressively longer throughout the day; experimental manipulations showed that this lengthening of intercup intervals was not due to accumulating caffeine levels. Number of cups of coffee consumed was an inverted U-shaped function of both coffee concentration and caffeine dose per cup; however, coffee-concentration and dose-per-cup manipulations did not produce similar effects on other measures of coffee drinking (intercup interval, time to drink a cup, within-day distribution of cups). Caffeine preload produced dose-related decreases in number of cups consumed. As a whole, these experiments provide some limited evidence for both the suppressive and the reinforcing effects of caffeine on coffee consumption. Examination of total daily coffee and caffeine intake across experiments, however, provides no evidence for precise regulation (i.e., titration) of coffee or caffeine intake.