Salience driven value integration explains decision biases and preference reversal

Abstract

Human choice behavior exhibits many paradoxical and challenging patterns. Traditional explanations focus on how values are represented, but little is known about how values are integrated. Here we outline a psychophysical task for value integration that can be used as a window on high-level, multiattribute decisions. Participants choose between alternative rapidly presented streams of numerical values. By controlling the temporal distribution of the values, we demonstrate that this process underlies many puzzling choice paradoxes, such as temporal, risk, and framing biases, as well as preference reversals. These phenomena can be explained by a simple mechanism based on the integration of values, weighted by their salience. The salience of a sampled value depends on its temporal order and momentary rank in the decision context, whereas the direction of the weighting is determined by the task framing. We show that many known choice anomalies may arise from the microstructure of the value integration process. decision making | decoy effects | value psychophysics | expanded judgement R ecent research on the psychology and neuroscience of simple, evidence-based choices (e.g., integrating perceptual or reward information) has made impressive progress, leading to the conclusion that the brain is optimized to make the fastest decision for a specified accuracy (1-5). Accordingly, the observer is assumed to infer the most probable cause of a perceived experience by sequentially accumulating samples of noisy evidence until a response criterion is reached. The idea that simple, evidence-based decision making is optimal contrasts with findings in more complex, motivation-based decisions, focused on multiple goals with tradeoffs (e.g., choices among cars or flats). Here, a number of paradoxical and puzzling choice behaviors (6-8) have been revealed, posing a serious challenge to the development of a unified theory of choice.Can a common theoretical framework between evidence-based and motivation-based decisions be established? A natural starting point is to propose that, in the latter, the cognitive system integrates subjective values (rather than, say, pieces of perceptual evidence), that depend on how each alternative matches the decision maker's goals (9). In particular, when alternatives are characterized by different attributes (e.g., product price and quality), preference is shaped through shifting attention across these attributes (8, 10), assessing an item's subjective value on each attribute, integrating these values across time, and finally making a choice when some threshold is reached (11-13). A detailed understanding of these computations might explain the systematic anomalies observed in motivation-based decisions.This line of research has been difficult to pursue, however, because classical laboratory preference tasks provide little control of the moment-by-moment processes of value sampling and integration. This stands in contrast with psychophysical paradigms for studying evidence-based perceptual choice wher...

From anomalies to forecasts: Toward a descriptive model of decisions under risk, under ambiguity, and from experience.

“…increased risk taking. These results differ from the common observation of risk aversion in the gain domain (e.g., Kahneman & Tversky, 1979) and are consistent with recent studies that demonstrated that feedback can lead to risk seeking in the gain domain (Ludvig & Spetch, 2011;Tsetsos, Chater, & Usher, 2012 x2, p2; …; y) refers to a prospect that yields a payoff of x1 with probability p1, a payoff of x2 with probability p2, …, and y otherwise. Proportions of the riskier choice are shown in five blocks of five trials each (Block 1:…”

Economic irrationality is optimal during noisy decision making

“…Email: k.tsetsos62@gmail.com. Previously, we showed that, when a third (decoy) alternative (D) alters the ranking of two existing alternatives, A and B, the model successfully predicts a preference reversal, despite the fact that the attribute values of A and B remain intact (Table S1) (14). In the current report, we show that selective integration also explains intransitive choice behavior in humans.…”

“…Here, we describe an alternative theory, known as "selective integration" (14), that overcomes this challenge by offering a normative justification of choice irrationality. Building on psychophysical research into the neural and computational mechanisms by which decisions are formed via sequential sampling (15), we assume that choice attributes (e.g., the expense, weather, or culture encountered on holiday) are sampled in turn (2), and integrated toward a cumulative decision variable (16).…”

Rapid makes risky: Time pressure increases risk seeking in decisions from experience

“…Some recent studies of decisions from experience have observed risk seeking for gains (e.g., Ludvig, Madan, Pisklak, et al, 2014;Tsetsos et al, 2012;Zeigenfuse et al, 2014), even though the bulk of studies does not (e.g., Erev, Glozman, & Hertwig, 2008;Erev et al, 2010). Though the experimental procedures used in these studies differ on a variety of dimensions (e.g., number of outcomes and presence of extreme values), the two studies that involved some time pressure observed risk seeking for gains (Tsetsos et al, 2012;Zeigenfuse et al, 2014), though not for losses. The current results further suggest that time pressure may indeed be an important contributor to these differences in risky-choice behaviour in the gain domain.…”

“…One manipulation that can induce risk seeking involves priming memories for past winning outcomes (Ludvig, Madan, & Spetch, 2015). A second manipulation involves altering the decision context: risky options that lead to the best possible outcome in a context are more likely to be chosen, a pattern termed the extreme-outcome rule (Ludvig, Madan, Pisklak, & Spetch, 2014b;; see also Tsetsos, Chater, & Usher, 2012;Zeigenfuse, Pleskac, & Liu, 2014). In this paper, we evaluate whether time pressure, known to increase risk seeking in decisions from description, is a third manipulation that would increase risk seeking in decisions from experience.…”