scholarly journals Linguistic Overhypotheses in Category Learning: Explaining the Label Advantage Effect

2020 ◽  
Author(s):  
Anna Aleksandrovna Ivanova ◽  
Matthias Hofer
Keyword(s):  
Computational Model ◽  
Experimental Work ◽  
Category Learning ◽  
Verbal Label ◽  
Category Structure ◽  
Prior Beliefs ◽  
The World

When learning to partition the world into categories, people rely on a set of assumptions (overhypotheses) about possible category structures. Here we propose that the nature of these overhypotheses depends on the presence of a verbal label associated with a given category. We describe a computational model that demonstrates how labels can either accelerate or hinder category learning, depending on whether or not the prior beliefs imposed by their presence align with the true category structure. This account provides an explanation for the phenomena described in prior experimental work (Lupyan, Rakison, & McClelland, 2007; Brojde, Porter, & Colunga, 2011) that have remained unexplained by other models. Based on these results, we argue that the overhypothesis theory of label effects provides a way to formalize and quantify the effect of language on category learning and to develop a more precise delineation between linguistic and non-linguistic thought.

scholarly journals A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders

2020 ◽  
Vol 16 (12) ◽  
pp. e1008484
Author(s):  
Ryan Smith ◽  
Rayus Kuplicki ◽  
Justin Feinstein ◽  
Katherine L. Forthman ◽  
Jennifer L. Stewart ◽  
...  
Keyword(s):  
Substance Use ◽  
Computational Model ◽  
Substance Use Disorders ◽  
The Body ◽  
Breath Holding ◽  
Healthy Individuals ◽  
Prior Beliefs ◽  
Future Studies ◽  
Psychiatric Conditions ◽  
Processing Mechanisms

Recent neurocomputational theories have hypothesized that abnormalities in prior beliefs and/or the precision-weighting of afferent interoceptive signals may facilitate the transdiagnostic emergence of psychopathology. Specifically, it has been suggested that, in certain psychiatric disorders, interoceptive processing mechanisms either over-weight prior beliefs or under-weight signals from the viscera (or both), leading to a failure to accurately update beliefs about the body. However, this has not been directly tested empirically. To evaluate the potential roles of prior beliefs and interoceptive precision in this context, we fit a Bayesian computational model to behavior in a transdiagnostic patient sample during an interoceptive awareness (heartbeat tapping) task. Modelling revealed that, during an interoceptive perturbation condition (inspiratory breath-holding during heartbeat tapping), healthy individuals (N = 52) assigned greater precision to ascending cardiac signals than individuals with symptoms of anxiety (N = 15), depression (N = 69), co-morbid depression/anxiety (N = 153), substance use disorders (N = 131), and eating disorders (N = 14)–who failed to increase their precision estimates from resting levels. In contrast, we did not find strong evidence for differences in prior beliefs. These results provide the first empirical computational modeling evidence of a selective dysfunction in adaptive interoceptive processing in psychiatric conditions, and lay the groundwork for future studies examining how reduced interoceptive precision influences visceral regulation and interoceptively-guided decision-making.

Animals as Means of Evaluating Pasture Production

1948 ◽  
Vol 1948 (01) ◽  
pp. 7-21 ◽  
Author(s):  
S. J. Watson
Keyword(s):  
Experimental Work ◽  
Live Weight ◽  
Experimental Period ◽  
Early Years ◽  
Sheep Grazing ◽  
Pasture Production ◽  
Experimental Station ◽  
The World ◽  
Before And After ◽  
Agricultural Literature

The use of the animal as a means of evaluating pasture is an obvious approach to the problem, since this is the form in which the practical man will ultimately measure the return from his grassland. If anything has to be sacrificed it is the sward and not the animal, a point too often overlooked in our grassland work generally. Though many grazing trials of an observational nature are to be found in the early agricultural literature, the earliest experimental work was most probably that of the late Sir William Somerville. When Professor of Agriculture at Newcastle in 1897, he commenced the world-famous experiment at Cockle Park Agricultural Experimental Station on pasture improvement. An effort was then made to measure the effect of various systems of manuring in terms of the live-weight increase of sheep grazing on the different plots. In this experiment on Tree Field the relative values of the different systems of manuring were measured by weighing the sheep before and after the experimental period. In the early years the plots were stocked with hoggs, but in order to get closer grazing lambs and ewes are now used and have shown greater increases per acre.

scholarly journals Word Senses

2018 ◽  
Author(s):  
Hugh Rabagliati ◽  
Mahesh Srinivasan
Keyword(s):  
Experimental Work ◽  
The World ◽  
Word Senses

Across languages, most frequent words are also highly ambiguous, and carry multiple distinct but related senses of meaning. This chapter aims to explain why words have the particular senses that they do, discussing to what degree word senses are arbitrary cultural conventions or reflections of how speakers conceptualize the world around them. The chapter reviews theoretical proposals about the nature of word senses drawn from linguistics and psychology, and evaluates these proposals against a large recent body of experimental work on the topic. Finally, the chapter suggests that word senses reflect a balance between two communicative pressures: A pressure toward facilitating fast and efficient conversational exchanges, and one toward making languages easier for children to learn.

scholarly journals Prior Beliefs Modulate Projection

Open Mind ◽  
2021 ◽  
pp. 1-12
Author(s):  
Judith Degen ◽  
Judith Tonhauser
Keyword(s):  
Language Processing ◽  
Prior Belief ◽  
Prior Beliefs ◽  
Language Understanding ◽  
The World ◽  
Cognitive Theories

Abstract Beliefs about the world affect language processing and interpretation in several empirical domains. In two experiments, we tested whether subjective prior beliefs about the probability of utterance content modulate projection, that is, listeners’ inferences about speaker commitment to that content. We find that prior beliefs predict projection at both the group and the by-participant level: the higher the prior belief in a content, the more speakers are taken to be committed to it. This result motivates the integration of formal analyses of projection with cognitive theories of language understanding.

scholarly journals Who went fishing? Inferences from social evaluations

2021 ◽  
Author(s):  
Zachary J Davis ◽  
Kelsey Rebecca Allen ◽  
Tobias Gerstenberg
Keyword(s):  
Computational Model ◽  
Physical State ◽  
Generative Model ◽  
Causal Role ◽  
Prior Work ◽  
Causal History ◽  
Current State ◽  
The World ◽  
History Of

Humans have a remarkable ability to go beyond the observable. From seeing the current state of our shared kitchen, we can infer what happened and who did it. Prior work has shown how the physical state of the world licenses inferences about the causal history of events, and the agents that participated in these events. Here, we investigate a previously unstudied source of evidence about what happened: social evaluations. In our experiment, we present situations in which a group failed to optimally coordinate their actions. Participants learn how much each agent was blamed for the outcome, and their task is to make inferences about the situation, the agents' actions, as well as the agents' capabilities. We develop a computational model that accurately captures participants' inferences. The model assumes that people blame others by considering what they should have done, and what causal role their action played. By inverting this generative model of blame, people can figure out what happened.

Factors influencing the update of beliefs regarding controversial political issues

2022 ◽  
Author(s):  
Tobias Kube
Keyword(s):  
Prediction Error ◽  
Public Discourse ◽  
Prior Beliefs ◽  
Belief Updating ◽  
Main Hypothesis ◽  
Political Issues ◽  
New Information ◽  
The World ◽  
Disconfirming Information ◽  
Selective Integration

When updating beliefs in light of new information, people preferentially integrate information that is consistent with their prior beliefs and helps them construe a coherent view of the world. Such a selective integration of new information likely contributes to belief polarisation and compromises public discourse. Therefore, it is crucial to understand the factors that underlie biased belief updating. To this end, I conducted three pre-registered experiments covering different controversial political issues (i.e., Experiment 1: climate change, Experiment 2: speed limit on highways, Experiment 3: immigration in relation to violent crime). The main hypothesis was that negative reappraisal of new information (referred to as “cognitive immunisation”) hinders belief updating. Support for this hypothesis was found only in Experiment 2. In all experiments, the magnitude of the prediction error (i.e., the discrepancy between prior beliefs and new information) was strongly related to belief updating. Across experiments, participants’ general attitudes regarding the respective issue influenced the strength of beliefs, but not their update. The present findings provide some indication that the engagement in cognitive immunisation can lead to the maintenance of beliefs despite disconfirming information. However, by far the largest association with belief updating was with the magnitude of the prediction error.

scholarly journals The distributional properties of exemplars affect category learning and generalization

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Paulo F. Carvalho ◽  
Chi-hsin Chen ◽  
Chen Yu
Keyword(s):  
Real World ◽  
Category Learning ◽  
Experimental Studies ◽  
Learning Theories ◽  
Common Assumption ◽  
Uniform Distributions ◽  
Corpus Studies ◽  
Distributional Properties ◽  
The World ◽  
Normal Input

AbstractWhat we learn about the world is affected by the input we receive. Many extant category learning studies use uniform distributions as input in which each exemplar in a category is presented the same number of times. Another common assumption on input used in previous studies is that exemplars from the same category form a roughly normal distribution. However, recent corpus studies suggest that real-world category input tends to be organized around skewed distributions. We conducted three experiments to examine the distributional properties of the input on category learning and generalization. Across all studies, skewed input distributions resulted in broader generalization than normal input distributions. Uniform distributions also resulted in broader generalization than normal input distributions. Our results not only suggest that current category learning theories may underestimate category generalization but also challenge current theories to explain category learning in the real world with skewed, instead of the normal or uniform distributions often used in experimental studies.

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