Is the delay discounting paradigm useful in understanding social anxiety?

The delay discounting paradigm was used to examine its utility in understanding motivational factors among socially anxious individuals. Participants (n=88) who reported high and low levels of social anxiety were randomly assigned to either a social threat or non-threat condition and their subsequent rates of discounting were examined. A significant difference in rates of discounting was found between the high and low social anxiety groups within the non-threat condition, with high social anxiety participants showing increased discounting. This study suggests that a modified version of the delay discounting paradigm may be useful in understanding motivational factors in social anxiety.     

DO ADJUSTING‐AMOUNT AND ADJUSTING‐DELAY PROCEDURES PRODUCE EQUIVALENT ESTIMATES OF SUBJECTIVE VALUE IN PIGEONS?

The current experiment examined whether adjusting-amount and adjusting-delay procedures provide equivalent measures of discounting. Pigeons' discounting on the two procedures was compared using a within-subject yoking technique in which the indifference point (number of pellets or time until reinforcement) obtained with one procedure determined the value of the corresponding variable in the yoked condition with the other procedure. Behavior on each procedure was well described by a hyperbolic discounting function. Results revealed no systematic differences in the degree of discounting as measured by the discounting rate parameter of the hyperbola in Experiment 1, which used 20-mg pellets. These results were replicated in Experiment 2 using smaller, 14-mg pellets, which potentially yield more precise measurement of indifference points on the adjusting-amount procedure. The finding that estimates of the k parameter in the hyperbolic discounting function obtained with one procedure did not differ systematically from estimates obtained from the same subjects with the other procedure represents strong support for the hypothesis that the same process underlies the discounting of delayed rewards on both adjusting-amount and adjusting-delay procedures.     

Addendum to Killeen's (2019) Bidding for Delayed Rewards

Killeen (2019) portrayed an intimate relation between diverse economic indices, in particular compensation functions, discount functions, and demand functions. The article bemused some experts, however, by its counterintuitive prediction of an increase in the amount bid as the delay increased. Furthermore, the article failed to provide an explicit treatment of the small–soon versus large–late choice paradigm, to cite several papers that provided precedent for the current work, and to demonstrate the adequacy of his expenditure functions for data on purchasing decisions. These shortcomings are remedied in the current note, and some additional extensions offered.     

What is adaptive about adaptive decision making? A parallel constraint satisfaction account

There is broad consensus that human cognition is adaptive. However, the vital question of how exactly this adaptivity is achieved has remained largely open. Herein, we contrast two frameworks which account for adaptive decision making, namely broad and general single-mechanism accounts vs. multi-strategy accounts. We propose and fully specify a single-mechanism model for decision making based on parallel constraint satisfaction processes (PCS-DM) and contrast it theoretically and empirically against a multi-strategy account. To achieve sufficiently sensitive tests, we rely on a multiple-measure methodology including choice, reaction time, and confidence data as well as eye-tracking. Results show that manipulating the environmental structure produces clear adaptive shifts in choice patterns – as both frameworks would predict. However, results on the process level (reaction time, confidence), in information acquisition (eye-tracking), and from cross-predicting choice consistently corroborate single-mechanisms accounts in general, and the proposed parallel constraint satisfaction model for decision making in particular.     

The origins of probabilistic inference in human infants

Reasoning under uncertainty is the bread and butter of everyday life. Many areas of psychology, from cognitive, developmental, social, to clinical, are interested in how individuals make inferences and decisions with incomplete information. The ability to reason under uncertainty necessarily involves probability computations, be they exact calculations or estimations. What are the developmental origins of probabilistic reasoning? Recent work has begun to examine whether infants and toddlers can compute probabilities; however, previous experiments have confounded quantity and probability—in most cases young human learners could have relied on simple comparisons of absolute quantities, as opposed to proportions, to succeed in these tasks. We present four experiments providing evidence that infants younger than 12 months show sensitivity to probabilities based on proportions. Furthermore, infants use this sensitivity to make predictions and fulfill their own desires, providing the first demonstration that even preverbal learners use probabilistic information to navigate the world. These results provide strong evidence for a rich quantitative and statistical reasoning system in infants.     

SLAP: Specification logic of actions with probability

A logic for specifying probabilistic transition systems is presented. Our perspective is that of agents performing actions. A procedure for deciding whether sentences in this logic are valid is provided. One of the main contributions of the paper is the formulation of the decision procedure: a tableau system which appeals to solving systems of linear equations. The tableau rules eliminate propositional connectives, then, for all open branches of the tableau tree, systems of linear equations are generated and checked for feasibility. Proofs of soundness, completeness and termination of the decision procedure are provided.     

Stochastic λ-calculi: An extended abstract

It is shown how the operators in the “graph model” for λ-calculus (which can function as a programming language for Recursive Function Theory) can be expanded to allow for “random combinators”. The result then is a model for a new language for random algorithms.     

Is Probabilistic Evidence a Source of Knowledge?

We report a series of experiments examining whether people ascribe knowledge for true beliefs based on probabilistic evidence. Participants were less likely to ascribe knowledge for beliefs based on probabilistic evidence than for beliefs based on perceptual evidence (Experiments 1 and 2A) or testimony providing causal information (Experiment 2B). Denial of knowledge for beliefs based on probabilistic evidence did not arise because participants viewed such beliefs as unjustified, nor because such beliefs leave open the possibility of error. These findings rule out traditional philosophical accounts for why probabilistic evidence does not produce knowledge. The experiments instead suggest that people deny knowledge because they distrust drawing conclusions about an individual based on reasoning about the population to which it belongs, a tendency previously identified by “judgment and decision making” researchers. Consistent with this, participants were more willing to ascribe knowledge for beliefs based on probabilistic evidence that is specific to a particular case (Experiments 3A and 3B).     

Information Density and Syntactic Repetition

In noun phrase (NP) coordinate constructions (e.g., NP and NP), there is a strong tendency for the syntactic structure of the second conjunct to match that of the first; the second conjunct in such constructions is therefore low in syntactic information. The theory of uniform information density predicts that low‐information syntactic constructions will be counterbalanced by high information in other aspects of that part of the sentence, and high‐information constructions will be counterbalanced by other low‐information components. Three predictions follow: (a) lexical probabilities (measured by N‐gram probabilities and head‐dependent probabilities) will be lower in second conjuncts than first conjuncts; (b) lexical probabilities will be lower in matching second conjuncts (those whose syntactic expansions match the first conjunct) than nonmatching ones; and (c) syntactic repetition should be especially common for low‐frequency NP expansions. Corpus analysis provides support for all three of these predictions.