Making decisions based on relative rather than absolute information processing is tied to choice optimality via the accumulation of evidence differences and to canonical neural processing via accumulation of evidence ratios. These theoretical frameworks predict invariance of decision latencies to absolute intensities that maintain differences and ratios, respectively. While information about the absolute values of the choice alternatives is not necessary for choosing the best alternative, it may nevertheless hold valuable information about the context of the decision. To test the sensitivity of human decision making to absolute values, we manipulated the intensities of brightness stimuli pairs while preserving either their differences or their ratios. Although asked to choose the brighter alternative relative to the other, participants responded faster to higher absolute values. Thus, our results provide empirical evidence for human sensitivity to task irrelevant absolute values indicating a hard-wired mechanism that precedes executive control. Computational investigations of several modelling architectures reveal two alternative accounts for this phenomenon, which combine absolute and relative processing. One account involves accumulation of differences with activation dependent processing noise and the other emerges from accumulation of absolute values subject to the temporal dynamics of lateral inhibition. The potential adaptive role of such choice mechanisms is discussed.

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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.decision making | irrationality | choice optimality | selective integration | evidence accumulation D aily decisions, such as choosing a holiday destination or accepting a job offer, involve comparing alternatives that are characterized by different attributes (1, 2). Understanding how the brain combines information from different attributes into unitary decision values is a key challenge in psychology and the neurosciences (3, 4). From a normative perspective, the value of an alternative should be independent of factors, such as the attractiveness of competing alternatives or the context in which preferences are elicited (5). Thus, the preference relationship between two alternatives ought to remain stable, regardless of changes to the choice set, incurred for example by the addition or removal of other choice alternatives (6).However, human preferences are often driven by irrelevant factors (7,8). For instance, an initial preference for one holiday destination (e.g., Bali) over another (e.g., Berlin) can reverse when an inferior alternative (e.g., Dresden) is added to the choice set, even if this "decoy" alternative is never chosen (9, 10). Similarly, an individual preferring a holiday in Bali to Berlin, and Berlin to Boston, will sometimes show a systematic "intransitive" (or inconsistent) preference for Boston over Bali (11). A canonical argument states that such violations of decision theory (hereafter "economic" or "choice irrationality") disclose fundamental limita...

A well-established notion in cognitive neuroscience proposes that multiple brain systems contribute to choice behaviour. These include: (1) a model-free system that uses values cached from the outcome history of alternative actions, and (2) a model-based system that considers action outcomes and the transition structure of the environment. The widespread use of this distinction, across a range of applications, renders it important to index their distinct influences with high reliability. Here we consider the two-stage task, widely considered as a gold standard measure for the contribution of model-based and model-free systems to human choice. We tested the internal/temporal stability of measures from this task, including those estimated via an established computational model, as well as an extended model using drift-diffusion. Drift-diffusion modeling suggested that both choice in the first stage, and RTs in the second stage, are directly affected by a model-based/free trade-off parameter. Both parameter recovery and the stability of model-based estimates were poor but improved substantially when both choice and RT were used (compared to choice only), and when more trials (than conventionally used in research practice) were included in our analysis. The findings have implications for interpretation of past and future studies based on the use of the two-stage task, as well as for characterising the contribution of model-based processes to choice behaviour.

A key computation underlying perceptual decisions is the temporal integration of "evidence" in favor of different states of the world. Studies from psychology and neuroscience have shown that observers integrate multiple samples of noisy perceptual evidence over time toward a decision. An influential model posits perfect evidence integration (i.e., without forgetting), enabling optimal decisions based on stationary evidence. However, in real-life environments, the perceptual evidence typically changes continuously. We used a computational model to show that, under such conditions, performance can be improved by means of leaky (forgetful) integration, if the integration timescale is adapted toward the predominant signal duration. We then tested whether human observers employ such an adaptive integration process. Observers had to detect visual luminance "signals" of variable strength, duration, and onset latency, embedded within longer streams of noise. Different sessions entailed predominantly short or long signals. The rate of performance improvement as a function of signal duration indicated that observers indeed changed their integration timescale with the predominant signal duration, in accordance with the adaptive integration account. Our findings establish that leaky integration of perceptual evidence is flexible and that cognitive control mechanisms can exploit this flexibility for optimizing the decision process.

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Recollection is currently modeled as a univariate retrieval process in which memory probes provoke conscious awareness of contextual details of earlier target presentations. However, that conception cannot explain why some manipulations that increase recollection in recognition experiments suppress false memory in false memory experiments, whereas others increase false memory. Such contrasting effects can be explained if recollection is bivariate-if memory probes can provoke conscious awareness of target items per se, separately from awareness of contextual details, with false memory being suppressed by the former but increased by the latter. Interestingly, these 2 conceptions of recollection have coexisted for some time in different segments of the memory literature. Independent support for the dual-recollection hypothesis is provided by some surprising effects that it predicts, such as release from recollection rejection, false persistence, negative relations between false alarm rates and target remember/know judgments, and recollection without remembering. We implemented the hypothesis in 3 bivariate recollection models, which differ in the degree to which recollection is treated as a discrete or a graded process: a pure multinomial model, a pure signal detection model, and a mixed multinomial/signal detection model. The models were applied to a large corpus of conjoint recognition data, with fits being satisfactory when both recollection processes were present and unsatisfactory when either was deleted. Factor analyses of the models' parameter spaces showed that target and context recollection never loaded on a common factor, and the 3 models converged on the same process loci for the effects of important experimental manipulations. Thus, a variety of results were consistent with bivariate recollection. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

A prominent source of polarised and entrenched beliefs is confirmation bias, where evidence against one's position is selectively disregarded. This effect is most starkly evident when opposing parties are highly confident in their decisions. Here we combine human magnetoencephalography (MEG) with behavioural and neural modelling to identify alterations in post-decisional processing that contribute to the phenomenon of confirmation bias. We show that holding high confidence in a decision leads to a striking modulation of post-decision neural processing, such that integration of confirmatory evidence is amplified while disconfirmatory evidence processing is abolished. We conclude that confidence shapes a selective neural gating for choice-consistent information, reducing the likelihood of changes of mind on the basis of new information. A central role for confidence in shaping the fidelity of evidence accumulation indicates that metacognitive interventions may help ameliorate this pervasive cognitive bias.