Rational inattention in mice

Behavior exhibited by humans and other organisms is generally inconsistent and biased, and thus is often labeled irrational. However, the origins of this seemingly suboptimal behavior remain elusive. We developed a behavioral task and normative framework to reveal how organisms should allocate their limited processing resources such that there is an advantage to being imprecise and biased for a given metabolic investment that guarantees maximal utility. We found that mice act as rational-inattentive agents by adaptively allocating their sensory resources in a way that maximizes reward consumption in novel stimulus-reward association environments. Surprisingly, perception to commonly occurring stimuli was relatively imprecise, however this apparent statistical fallacy implies "awareness" and efficient adaptation to their neurocognitive limitations. Interestingly, distributional reinforcement learning mechanisms efficiently regulate sensory precision via top-down normalization. These findings establish a neurobehavioral foundation for how organisms efficiently perceive and adapt to environmental states of the world within the constraints imposed by neurobiology.

Pre-training RNNs on ecologically relevant tasks explains sub-optimal behavioral reset

When faced with a new task, animals′ cognitive capabilities are determined both by individual experience and by structural priors evolved to leverage the statistics of natural environments. Rats can quickly learn to capitalize on the trial sequence correlations of two-alternative forced choice (2AFC) tasks after correct trials, but consistently deviate from optimal behavior after error trials, when they waive the accumulated evidence. To understand this outcome-dependent gating, we first show that Recurrent Neural Networks (RNNs) trained in the same 2AFC task outperform animals as they can readily learn to use previous trials′ information both after correct and error trials. We hypothesize that, while RNNs can optimize their behavior in the 2AFC task without a priori restrictions, rats′ strategy is constrained by a structural prior adapted to a natural environment in which rewarded and non-rewarded actions provide largely asymmetric information. When pre-training RNNs in a more ecological task with more than two possible choices, networks develop a strategy by which they gate off the across-trial evidence after errors, mimicking rats′ behavior. Our results suggest that the observed suboptimal behavior reflects the influence of a structural prior that, adaptive in a natural multi-choice environment, constrains performance in a 2AFC laboratory task.

Valuation of knowledge and ignorance in mesolimbic reward circuitry

The pursuit of knowledge is a basic feature of human nature. However, in domains ranging from health to finance people sometimes choose to remain ignorant. Here, we show that valence is central to the process by which the human brain evaluates the opportunity to gain information, explaining why knowledge may not always be preferred. We reveal that the mesolimbic reward circuitry selectively treats the opportunity to gain knowledge about future favorable outcomes, but not unfavorable outcomes, as if it has positive utility. This neural coding predicts participants’ tendency to choose knowledge about future desirable outcomes more often than undesirable ones, and to choose ignorance about future undesirable outcomes more often than desirable ones. Strikingly, participants are willing to pay both for knowledge and ignorance as a function of the expected valence of knowledge. The orbitofrontal cortex (OFC), however, responds to the opportunity to receive knowledge over ignorance regardless of the valence of the information. Connectivity between the OFC and mesolimbic circuitry could contribute to a general preference for knowledge that is also modulated by valence. Our findings characterize the importance of valence in information seeking and its underlying neural computation. This mechanism could lead to suboptimal behavior, such as when people reject medical screenings or monitor investments more during bull than bear markets.

The Value of Research in Comparative Cognition

Most research of comparative cognition has focused on the degree to which cognitive phenomena that have been reported in humans, especially children, can also be demonstrated in other animals. The value of such comparative research has not only been the finding that other animals show behavior that is qualitatively similar to that of humans but because the comparative approach calls for the careful control of variables often confounded with the mechanisms being tested, the comparative approach has identified procedures that could also improve the design of research with humans. The comparative approach has also been used to study the degree to which other animals demonstrate human biases and suboptimal behavior (e.g., commercial gambling). When applied to this field of research, the comparative approach has generally taken the position that human biases generally thought to be established by complex social and societal mechanisms (e.g., social reinforcement and entertainment) may be more parsimoniously accounted for by simpler mechanisms (i.e., conditioned reinforcement and positive contrast). When explained in terms of these mechanisms, the results have implications for explaining in simpler and more general terms the results of similar research with humans. Thus, comparative psychology tells us not only about the similarities and possible differences in behavior among species but it also may have implications for our understanding of similar behavior in humans.

The Power of Goal-Directed Processes in the Causation of Emotional and Other Actions

Standard dual-process models in the action domain postulate that stimulus-driven processes are responsible for suboptimal behavior because they take them to be rigid and automatic and therefore the default. We propose an alternative dual-process model in which goal-directed processes are the default instead. We then transfer the dual- process logic from the action domain to the emotion domain. This reveals that emotional behavior is often attributed to stimulus-driven processes. Our alternative model submits that goal-directed processes could be the primary determinant of emotional behavior instead. We evaluate the type of empirical evidence required for validating our model and we consider implications of our model for behavior change, encouraging strategies focused on the expectancies and values of action outcomes.

The Benefits of Forced Experimentation: Striking Evidence from the London Underground Network*

We present evidence that a significant fraction of commuters on the London Underground do not travel on their optimal route. We show that a strike on the Underground, which forced many commuters to experiment with new routes, brought lasting changes in behavior. This effect is stronger for commuters who live in areas where the Underground map is more distorted, which points to the importance of informational imperfections. Information resulting from the strike improved network efficiency. Search costs alone are unlikely to explain the suboptimal behavior.

Suboptimality in Perceptual Decision Making

Short AbstractHuman perceptual decisions are often described as optimal, but this view remains controversial. To elucidate the issue, we review the vast literature on suboptimalities in perceptual tasks and compile the proposed hypotheses about the origins of suboptimal behavior. Further, we argue that general claims about optimality are virtually meaningless and result in a false sense of progress. Instead, real progress can be achieved by building observer models that account for both optimal and suboptimal behavior. To achieve such progress, the field should focus on assessing the hypotheses about suboptimal behavior compiled here and stop chasing optimality.Long AbstractHuman perceptual decisions are often described as optimal. Critics of this view have argued that claims of optimality are overly flexible and lack explanatory power. Meanwhile, advocates for optimality have countered that such criticisms single out a few selected papers. To elucidate the issue of optimality in perceptual decision making, we review the extensive literature on suboptimal performance in perceptual tasks. We discuss eight different classes of suboptimal perceptual decisions, including improper placement, maintenance, and adjustment of perceptual criteria, inadequate tradeoff between speed and accuracy, inappropriate confidence ratings, misweightings in cue combination, and findings related to various perceptual illusions and biases. In addition, we discuss conceptual shortcomings of a focus on optimality, such as definitional difficulties and the limited value of optimality claims in and of themselves. We therefore advocate that the field drop its emphasis on whether observed behavior is optimal and instead concentrate on building and testing detailed observer models that explain behavior across a wide range of tasks. To facilitate this transition, we compile the proposed hypotheses regarding the origins of suboptimal perceptual decisions reviewed here. We argue that verifying, rejecting, and expanding these explanations for suboptimal behavior – rather than assessing optimality per se – should be among the major goals of the science of perceptual decision making.