Selective Integration during Sequential Sampling in Posterior Neural Signals.

Decisions are typically made after integrating information about multiple attributes of alternatives in a choice set. Where observers are obliged to consider attributes in turn, a computational framework known as "selective integration" can capture salient biases in human choices. The model proposes that successive attributes compete for processing resources and integration is biased towards the alternative with the locally preferred attribute. Quantitative analysis shows that this model, although it discards choice-relevant information, is optimal when the observers' decisions are corrupted by noise that occurs beyond the sensory stage. Here, we used electroencephalography (EEG) to test a neural prediction of the model: that locally preferred attributes should be encoded with higher gain in neural signals over the posterior cortex. Over two sessions, human observers judged which of the two simultaneous streams of bars had the higher (or lower) average height. The selective integration model fits the data better than a rival model without bias. Single-trial analysis showed that neural signals contralateral to the preferred attribute covaried more steeply with the decision information conferred by locally preferred attributes. These findings provide neural evidence in support of selective integration, complementing existing behavioral work.

Economic irrationality is optimal during noisy decision making.

According to normative theories, reward-maximizing agents should have consistent preferences. Thus, when faced with alternatives A, B, and C, an individual preferring A to B and B to C should prefer A to C. However, it has been widely argued that humans can incur losses by violating this axiom of transitivity, despite strong evolutionary pressure for reward-maximizing choices. Here, adopting a biologically plausible computational framework, we show that intransitive (and thus economically irrational) choices paradoxically improve accuracy (and subsequent economic rewards) when decision formation is corrupted by internal neural noise. Over three experiments, we show that humans accumulate evidence over time using a "selective integration" policy that discards information about alternatives with momentarily lower value. This policy predicts violations of the axiom of transitivity when three equally valued alternatives differ circularly in their number of winning samples. We confirm this prediction in a fourth experiment reporting significant violations of weak stochastic transitivity in human observers. Crucially, we show that relying on selective integration protects choices against "late" noise that otherwise corrupts decision formation beyond the sensory stage. Indeed, we report that individuals with higher late noise relied more strongly on selective integration. These findings suggest that violations of rational choice theory reflect adaptive computations that have evolved in response to irreducible noise during neural information processing.

Clarifying the role of an unavailable distractor in human multiattribute choice.

Decisions between two economic goods can be swayed by a third unavailable 'decoy' alternative, which does not compete for choice, notoriously violating the principles of rational choice theory. Although decoy effects typically depend on the decoy's position in a multiattribute choice space, recent studies using risky prospects (i.e., varying in reward and probability) reported a novel 'positive' decoy effect operating on a single value dimension: the higher the 'expected value' (EV) of an unavailable (distractor) prospect was, the easier the discrimination between two available target prospects became, especially when their expected-value difference was small. Here, we show that this unidimensional distractor effect affords alternative interpretations: it occurred because the distractor's EV covaried positively with the subjective utility difference between the two targets. Looking beyond this covariation, we report a modest 'negative' distractor effect operating on subjective utility, as well as classic multiattribute decoy effects. A normatively meaningful model (selective integration), in which subjective utilities are shaped by intra-attribute information distortion, reproduces the multiattribute decoy effects, and as an epiphenomenon, the negative unidimensional distractor effect. These findings clarify the modulatory role of an unavailable distracting option, shedding fresh light on the mechanisms that govern multiattribute decisions.

Recasting a biologically motivated computational model within a Fechnerian and random utility framework.

The selective integration model of Tsetsos et al. (2016a) is a biologically motivated computational framework that aims to model intransitive preference and choice. Tsetsos et al. (2016a) concluded that a noisy system can lead to violations of transitivity in otherwise rational agents optimizing a task. We show how their model can be interpreted from a Fechnerian perspective and within a random utility framework. Specifically, we spell out the connection between the selective integration model and two probabilistic models of transitive preference, weak stochastic transitivity and the triangle inequalities, tested by Tsetsos et al. (2016a).