Attention-driven imitation in consumer reviews

Product reviews on e-commerce platforms can have a pronounced effect on consumers' decisions. Less is known, however, whether the reviews written by others can shape a person's own written opinion of a product. We hypothesized that people who compose reviews on digital storefronts will try to imitate successful reviews, such that their content will show similarity with other reviews displayed at the time of writing. More specifically, we predicted that reviews will be more semantically similar to the most successful, salient, and readily accessible reviews written by others. To investigate this hypothesis, we extracted over 3 million reviews from a major online distribution platform and traced the reviews that were displayed at the time when each review was being composed. Using word embeddings from a pre-trained language model, we quantified the semantic similarity between a given review and other reviews that were visible (or not) to a user. We found that reviewers imitate the most helpful reviews written by others, especially those that are visually salient. Their reviews, in turn, gather more helpfulness ratings in the future, leading to a cascade of similar reviews. Our findings suggest that the default sorting and display format of reviews on online platforms will have a pronounced effect on the style and content of new reviews

The repulsion effect in preferential choice and its relation to perceptual choice

People rely on the choice context to guide their decisions, violating fundamental principles of rational choice theory and exhibiting phenomena called context effects. Recent research has uncovered that dominance relationships can both increase or decrease the choice share of the dominating option, marking the two ends of an attraction–repulsion continuum. However, empirical links between the two opposing effects are scarce and theoretical accounts are missing altogether. The present study (N = 55) used eye tracking alongside a within-subject design that contrasts a perceptual task and a preferential-choice analog in order to bridge this gap and uncover the underlying information-search processes. Although individuals differed in their perceptual and preferential choices, they generally engaged in alternative-wise comparisons and a repulsion effect was present in both conditions that became weaker the more predominant the attribute-wise comparisons were. Altogether, our study corroborates the notion that repulsion effects are a robust and general phenomenon that theoretical accounts need to take seriously

A reinforcement learning diffusion decision model for value-based decisions

Psychological models of value-based decision-making describe how subjective values are formed and mapped to single choices. Recently, additional efforts have been made to describe the temporal dynamics of these processes by adopting sequential sampling models from the perceptual decision-making tradition, such as the diffusion decision model (DDM). These models, when applied to value-based decision-making, allow mapping of subjective values not only to choices but also to response times. However, very few attempts have been made to adapt these models to situations in which decisions are followed by rewards, thereby producing learning effects. In this study, we propose a new combined reinforcement learning diffusion decision model (RLDDM) and test it on a learning task in which pairs of options differ with respect to both value difference and overall value. We found that participants became more accurate and faster with learning, responded faster and more accurately when options had more dissimilar values, and decided faster when confronted with more attractive (i.e., overall more valuable) pairs of options. We demonstrate that the suggested RLDDM can accommodate these effects and does so better than previously proposed models. To gain a better understanding of the model dynamics, we also compare it to standard DDMs and reinforcement learning models. Our work is a step forward towards bridging the gap between two traditions of decision-making research

Determinants of the context dependency of choices

One of the most fundamental assumptions of axiomatic economic decision-making theories is the notion of independence, according to which individuals evaluate choice options in isolation of each other. In other words, the presence of one option should not affect the value of other options. Decades of research accumulated a whole body of evidence that this assumption is systematically violated, resulting in so-called context effects. A triad comprising the similarity, attraction, and compromise effect has attracted the most interest so far, became a benchmark for multi-alternative decision-making models, and has been regarded as fundamental to decision making. Nevertheless, these context effects' universality has been challenged by identifying various boundary conditions, for example, desirability of the choice set. However, one important moderator variable that has not been systematically explored so far is presentation format. This is particularly important in light of recent observations that presentation format can have a substantial influence on decision making. For example, elicited risk attitudes change substantially when decisions are based on experiences and not on descriptions. In my dissertation, I aim to systematically explore how presentation format moderates context effects and which cognitive mechanisms underlie this a change of behavior. In Spektor, Gluth, Fontanesi, and Rieskamp (2017), we used an experience-based paradigm to assess the occurrence of context effects in this setting and thereby test a novel learning model. This model assumes that salient outcomes receive more attention and are thus perceived as more attractive. In line with our model's predictions, we observed the similarity effect and reversals of the compromise and attraction effects. Another recent promising advance is the use of perceptual decision-making tasks as proxies for preferential choice. In Spektor, Kellen, and Hotaling (2017), we adapted one such popular task, the rectangle-size task, to investigate the boundary conditions of the attraction effect and the existence of repulsion effects. We observed that the arrangement of stimuli on-screen had a substantially stronger influence on choices than stimulus design. Using a somewhat similar approach, in Gluth, Spektor, and Rieskamp (2017), we used a preferential task with perceptually coded features to investigate an apparent inconsistency regarding the influence of a third option's value on relative choice accuracy between the other two options. We found that the third option's value did not have this influence. However, without time pressure, we observed classical context effects that disappeared under time pressure. We found that value-based attentional capture provided a coherent account of the data in the experiments. I conclude that context effects of preferential choice are highly dependent on the presentation format and argue that attention plays a crucial role for this dependency. Future research should investigate such an attentional explanation of the presentation-format dependency of context effects

The relative merit of empirical priors in non-identifiable and sloppy models: Applications to models of learning and decision-making : Empirical priors

Formal modeling approaches to cognition provide a principled characterization of observed responses in terms of a set of postulated processes, specifically in terms of parameters that modulate the latter. These model-based characterizations are useful to the extent that there is a clear, one-to-one mapping between parameters and model expectations (identifiability) and that parameters can be recovered from reasonably sized data using a typical experimental design (recoverability). These properties are sometimes not met for certain combinations of model classes and data. One suggestion to improve parameter identifiability and recoverability involves the use of "empirical priors", which constrain parameters according to a previously observed distribution of values. We assessed the efficacy of this proposal using a combination of real and artificial data. Our results showed that a point-estimate variant of the empirical-prior method could not improve parameter recovery systematically. We identified the source of poor parameter recovery in the low information content of the data. As a follow-up step, we developed a fully Bayesian variant of the empirical-prior method and assessed its performance. We find that even such a method that takes the covariance structure of the parameter distributions into account cannot reliably improve parameter recovery. We conclude that researchers should invest additional efforts in improving the informativeness of their experimental designs, as many of the problems associated to impoverished designs cannot be alleviated by modern statistical methods alone