Consumer Online Search with Partially Revealed Information

Modern-day search platforms generally have two layers of information presentation. The outer layer displays the collection of search results with attributes selected by platforms, and consumers click on a product to reveal all its attributes in the inner layer. The information revealed in the outer layer affects the search costs and the probability of finding a match. To address the managerial question of optimal information layout, we create an information complexity measure of the outer layer, namely orderedness entropy, and study the consumer search process for information at the expense of time and cognitive costs. We first conduct online random experiments to show that consumers respond to and actively reduce cognitive cost for which our information complexity measure provides a representation. Then, using a unique and rich panel tracking consumer search behaviors at a large online travel agency (OTA), we specify a novel sequential search model that jointly describes the refinement search and product clicking decisions. We find that cognitive cost is a major component of search cost, while loading time cost has a much smaller share. By varying the information revealed in the outer layer, we propose information layouts that Pareto-improve both revenue and consumer welfare for our OTA. This paper was accepted by Juanjuan Zhang, marketing.

(Over)Estimating Shared Cultural Knowledge: Evidence for early use of a knowledge prediction heuristic in children

<p>Both adults and children accurately and efficiently predict what other people know, despite interacting with a diverse range of individuals who each have different knowledge sets. To reduce the cognitive cost of predicting each individual’s knowledge, there is evidence that we use heuristics to make generalisable predictions about the way specific kinds of knowledge are shared with others. Yet, little research examines the function of a knowledge prediction heuristic, the input needed to produce accurate knowledge predictions, or changes across development. I propose that children use a heuristic to predict others’ knowledge, and that this heuristic functions by considering the type of knowledge being predicted, and characteristics of the individual whose knowledge is being predicted. Chapter 2 demonstrates that 3- to 6-year-old children accurately and selectively predict who shares different pieces of their knowledge. Children also predict knowledge accurately in a third-party task, providing evidence for the use of a generalisable heuristic rather than simple associations or personal experience. Chapter 3 and Chapter 4 demonstrate knowledge overestimation errors, predicted by the heuristic I propose. 4-year-olds, but not 6-year-olds, overattribute knowledge to others if the knowledge item being predicted is an example of a cultural knowledge item (typically shared with strangers from the same social groups). Yet, even 4-year-olds do not make this over-attribution error when predicting an example of a typically episodic knowledge item (not typically shared with any strangers). Chapter 4 provides initial evidence that feelings of closeness or shared episodic knowledge with a partner (but not simply shared group membership) decrease 4- and 6-year-olds consideration of this partner’s perspective. Taken together, these findings provide evidence for an early-emerging knowledge prediction heuristic which considers the type of knowledge being predicted and characteristics of the individual whose knowledge is being predicted to facilitate accurate yet efficient knowledge predictions.</p>

(Over)Estimating Shared Cultural Knowledge: Evidence for early use of a knowledge prediction heuristic in children

<p>Both adults and children accurately and efficiently predict what other people know, despite interacting with a diverse range of individuals who each have different knowledge sets. To reduce the cognitive cost of predicting each individual’s knowledge, there is evidence that we use heuristics to make generalisable predictions about the way specific kinds of knowledge are shared with others. Yet, little research examines the function of a knowledge prediction heuristic, the input needed to produce accurate knowledge predictions, or changes across development. I propose that children use a heuristic to predict others’ knowledge, and that this heuristic functions by considering the type of knowledge being predicted, and characteristics of the individual whose knowledge is being predicted. Chapter 2 demonstrates that 3- to 6-year-old children accurately and selectively predict who shares different pieces of their knowledge. Children also predict knowledge accurately in a third-party task, providing evidence for the use of a generalisable heuristic rather than simple associations or personal experience. Chapter 3 and Chapter 4 demonstrate knowledge overestimation errors, predicted by the heuristic I propose. 4-year-olds, but not 6-year-olds, overattribute knowledge to others if the knowledge item being predicted is an example of a cultural knowledge item (typically shared with strangers from the same social groups). Yet, even 4-year-olds do not make this over-attribution error when predicting an example of a typically episodic knowledge item (not typically shared with any strangers). Chapter 4 provides initial evidence that feelings of closeness or shared episodic knowledge with a partner (but not simply shared group membership) decrease 4- and 6-year-olds consideration of this partner’s perspective. Taken together, these findings provide evidence for an early-emerging knowledge prediction heuristic which considers the type of knowledge being predicted and characteristics of the individual whose knowledge is being predicted to facilitate accurate yet efficient knowledge predictions.</p>

Correlating cognitive effort and noun role in spoken Japanese

Models of language processing assume that the cognitive cost to integrate a noun with a verb depends on the distance between the noun and the verb. Such models predict that subjects require more cognitive effort than objects in SOV languages, such as Japanese. This study tests that prediction by investigating apparent cognitive effort differences in topic, nominative, accusative, dative, genitive, and predicative noun usage, using two corpora of spoken Japanese. A cognitive effort index score was determined for each text in the two corpora. The correlations between index scores and usage rates for each grammatical role were determined. Accusative, dative, and genitive noun usage significantly correlated with cognitive effort index scores, but topic, nominative, and predicate noun usage rates did not. These results suggest that the cognitive cost of noun integration depends not only on distance but also on the grammatical role of the noun.

Pronominal Feature Re-assembly: L1 and L2 Pronoun Resolution of Spanish Epicene and Common Gender Antecedents

In this paper, we propose the implementation of a full-fledged feature-based lexicalist syntactic theory as a way to represent the possible configurations of features in the learner’s interlanguage and formalize a theory of acquisition based in feature reassembly. We describe gender agreement pronominal coindexation in Spanish using Head-driven Phrase Structure Grammar (HPSG) and use it to analyze the results of a self-paced reading test with L1 and L2 speakers. We find that the specification of the gender feature value at the syntactic level in epicene antecedents facilitates pronominal resolution in L1 Spanish speakers. Conversely, there is a cognitive cost when the gender feature is underspecified at the syntactic level in common gender antecedents; this cost is not found among L2 speakers. The detailed descriptions in terms of feature specification in the HPSG framework allow us to observe differences between the L1 and L2 grammars in fine-grained detail and represent optionality at the lexical level.

In poetry, if meter has to help memory, it takes its time

To test the idea that poetic meter emerged as a cognitive schema to aid verbal memory, we focused on classical Italian poetry and on three components of meter: rhyme, accent, and verse length. Meaningless poems were generated by introducing prosody-invariant non-words into passages from Dante’s Divina Commedia and Ariosto’s Orlando Furioso. We then ablated rhymes, modified accent patterns, or altered the number of syllables. The resulting versions of each non-poem were presented to Italian native speakers, who were then asked to retrieve three target non-words. Surprisingly, we found that the integrity of Dante’s meter has no significant effect on memory performance. With Ariosto, instead, removing each component downgrades memory proportionally to its contribution to perceived metric plausibility. Counterintuitively, the fully metric versions required longer reaction times, implying that activating metric schemata involves a cognitive cost. Within schema theories, this finding provides evidence for high-level interactions between procedural and episodic memory.

Contextual Information Helps Understand Messages Written with Textisms

The present study investigated the influence of the use of textisms, a form of written language used in phone-mediated conversations, on the cognitive cost of French participants in an online conversation. Basing our thinking on the relevance theory of Sperber and Wilson, we tried to assess whether knowing the context and topic of a conversation can produce a significant decrease in the cognitive cost required to read messages written in textism by giving additional clues to help infer the meaning of these messages. In order to do so, participants played the judges in a Turing test between a normal conversation (written with the traditional writing style) and a conversation in which the experimenter was conversing with textisms, in a random order. The results indicated that participants answered messages written in textism faster when they were in the second conversation. We concluded that prior knowledge about the conversation can help interpret the messages written in textisms by decreasing the cognitive cost required to infer their meaning.

Biases and Variability from Costly Bayesian Inference

When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations of probability. This is especially salient when beliefs are updated as a function of sequential observations. We introduce a theoretical framework in which biases and variability emerge from a trade-off between Bayesian inference and the cognitive cost of carrying out probabilistic computations. We consider two forms of the cost: a precision cost and an unpredictability cost; these penalize beliefs that are less entropic and less deterministic, respectively. We apply our framework to the case of a Bernoulli variable: the bias of a coin is inferred from a sequence of coin flips. Theoretical predictions are qualitatively different depending on the form of the cost. A precision cost induces overestimation of small probabilities, on average, and a limited memory of past observations, and, consequently, a fluctuating bias. An unpredictability cost induces underestimation of small probabilities and a fixed bias that remains appreciable even for nearly unbiased observations. The case of a fair (equiprobable) coin, however, is singular, with non-trivial and slow fluctuations in the inferred bias. The proposed framework of costly Bayesian inference illustrates the richness of a `resource-rational’ (or `bounded-rational’) picture of seemingly irrational human cognition.