The diversity effect in inductive reasoning depends on sampling assumptions

Hayes, Brett K.; Navarro, Daniel J.; Stephens, Rachel Gloria; Ransom, Keith; Dilevski, Natali

doi:10.31234/osf.io/ve9sa

Cited by 3 publications

(3 citation statements)

References 19 publications

(48 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That said, it is an open question how a learner might solve the “communicative intention” problem. Some models of this process (e.g., Shafto et al, 2014; Voorspoels et al, 2015) assume a “fully recursive” theory of mind where teacher and learner behavior mutually constrain one another; whereas other models rely on limited recursion (e.g., Goodman & Frank, 2016; Ransom, Voorspoels, Perfors, & Navarro, 2017) and others avoid recursive inference entirely (e.g., Hayes et al, 2018; Tenenbaum & Griffiths, 2001). These approaches would be difficult to distinguish within the experimental paradigm adopted here, but this would be a worthwhile line of future investigation.…”

Section: Discussionmentioning

confidence: 99%

“…Because of this, these models are sensitive to the sampling assumptions held by participants—beliefs a learner has about how the information was selected. Sensitivity to sampling has been implicated in a variety of reasoning phenomena, including premise nonmonotonicity effects (Ransom, Perfors, & Navarro, 2016), the role of negative evidence (Voorspoels et al, 2015), and the value of evidential diversity (Hayes, Navarro, Stephens, Ransom, & Dilevski, 2018), all of which have proved amenable to Bayesian modeling.…”

Section: A Bayesian Account Of Reasoning and Associative Learningmentioning

confidence: 99%

See 1 more Smart Citation

Negative evidence and inductive reasoning in generalization of associative learning.

Lee¹,

Lovibond²,

Hayes³

et al. 2019

Journal of Experimental Psychology: General

View full text Add to dashboard Cite

When generalizing properties from known to novel instances, both positive evidence (instances known to possess a property) and negative evidence (instances known not to possess a property) must be integrated. The current study compared generalization based on positive evidence alone against a mixture of positive evidence and perceptually dissimilar negative evidence in an interdimensional discrimination procedure. In two experiments, we compared generalization following training with a single positive stimulus (that predicted shock) against groups where an additional negative stimulus (that did not predict shock) was presented in a causal judgement (Experiment 1) and a fear conditioning (Experiment 2) procedure. In contrast to animal conditioning studies, we found that adding a "distant" negative stimulus resulted in an overall increase in generalization to stimuli varying on the dimension of the positive stimulus, consistent with the inductive reasoning literature. We show that this key qualitative result can be simulated by a Bayesian model that incorporates helpful sampling assumptions. Our results suggest that similar processes underlie generalization in inductive reasoning and associative learning tasks.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: A Bayesian Account Of Reasoning and Associative Learningmentioning

confidence: 99%

Negative evidence and inductive reasoning in generalization of associative learning.

Lee¹,

Lovibond²,

Hayes³

et al. 2019

Journal of Experimental Psychology: General

View full text Add to dashboard Cite

show abstract

“…In contrast, the Twelve Helpful condition is designed to induce a strong sampling assumption by encouraging the belief that items are chosen from a specific category by a helpful teacher. Experimentally manipulating sampling assumptions has been applied fruitfully in a number of inductive generalization tasks including word learning (Xu & Tenenbaum, 2007a), property induction tasks (Ransom, Perfors, & Navarro, 2016;Hayes, Navarro, Stephens, Ransom, & Dilevski, 2019) and single category generalization tasks (Ransom, Hendrickson, Perfors, & Navarro, 2018;Ransom & Perfors, submitted). The consistent finding in these studies is that experimentally manipulating the sampling assumption does have an effect on generalization.…”

Section: Experiments 3: Manipulating Sampling Assumptionsmentioning

confidence: 99%

Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization

Hendrickson¹,

Perfors²,

Navarro³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Categorization and generalization are fundamentally related inference problems. Yet leading computational models of categorization (as exemplified by, e.g., Nosofsky, 1986) and generalization (as exemplified by, e.g., Tenenbaum & Griffiths, 2001) make qualitatively different predictions about how inference should change as a function of the number of items. Assuming all else is equal, categorization models predict that increasing the number of items in a category increases the chance of assigning a new item to that category; generalization models predict a decrease, or category tightening with additional exemplars. This paper investigates this discrepancy, showing that people do indeed perform qualitatively differently in categorization and generalization tasks even when all superficial elements of the task are kept constant. Furthermore, the effect of category frequency on generalization is moderated by assumptions about how the items are sampled. We show that neither model naturally accounts for the pattern of behavior across both categorization and generalization tasks, and discuss theoretical extensions of these frameworks to account for the importance of category frequency and sampling assumptions.

show abstract

Rejuvenating Human Capital in Syariah Legal Institutions Through Fiqh Forensics Fundamental Module: A Preliminary Review

Ahmad

Baharuddin

Hashim

et al. 2020

MJSL

View full text Add to dashboard Cite

The development of science and technology today has contributed to the advancement of evidence law in the Civil Courts as well as Syariah Courts. The said application of science and technology refers to the scientific evidence produced from the results of analysis done by the expertise in forensic science. Numbers of reported Syariah cases has applied forensic science analysis and test to produce scientific evidence. These cases have portrayed the admission of scientific evidence in Syariah Courts and indicate the readiness of human capital in the Syariah Legal Institutions to apply the scientific evidence. Therefore, this study carry three purposes, to examine the main issues in handling the scientific evidence, to provide suggestion to rejuvenate human capital in Syariah Legal Institutions, and to suggest solution for the issues pointed out throughout the discussion. The result of this study elucidates two main issues that play integral part in handling the scientific evidence, which are the lack of knowledge and adequate experience. In order to overcome these particular issues, rejuvenation on the existing human capital in Syariah Legal Institutions must be done. This study focused on the rejuvenation in the aspect of knowledge and skills of human capital. The said module should contain two mains aspect, theoretical and practical aspect of forensic science. The target of this module is to serve the Syariah officers in the Syariah Legal Institutions as comprehensive guideline and instruction to lead the fundamentals of the forensic science in Syariah Courts into an appropriate platform. In conclusion, this study suggests the development of Fiqh Forensics Fundamental Module to curb the issues of lack of sufficient knowledge and adequate skills in handling scientific evidence.

show abstract

The diversity effect in inductive reasoning depends on sampling assumptions

Cited by 3 publications

References 19 publications

Negative evidence and inductive reasoning in generalization of associative learning.

Negative evidence and inductive reasoning in generalization of associative learning.

Sample size, number of categories and sampling assumptions: Exploring some differences between categorization and generalization

Rejuvenating Human Capital in Syariah Legal Institutions Through Fiqh Forensics Fundamental Module: A Preliminary Review

Contact Info

Product

Resources

About