Profile Url: jan-drugowitsch
Researcher at Department of Neurobiology, Harvard Medical School
In uncertain environments, seeking information about the accuracy of alternative strategies is essential for adapting behavior to changes in task contingencies. However, information seeking often co-occurs with changes-of-mind about the perceived accuracy of the current strategy, making it difficult to isolate its specific mechanisms. Here we leveraged the fact that genuine information seeking requires instrumental control to study its cognitive signatures in an adaptive decision-making task tested with and without control. We found that changes-of-mind occurring in controllable environments require more evidence against the current strategy, are associated with reduced confidence, but are nevertheless more likely to be confirmed on the next decision. Computational modelling explained these effects of information seeking through a decrease in the perceived volatility of controllable environments, resulting in stronger and more prolonged effects of changes-of-mind on cognition and behavior. Together, these findings explain the high degree of subjective uncertainty associated with information seeking.
Nature Neuroscience, 2019-08-05
Every-day decisions frequently require choosing among multiple alternatives. Yet, the optimal policy for such decisions is unknown. Here we derive the normative policy for general multi-alternative decisions. This strategy requires evidence accumulation to nonlinear, time-dependent bounds, that trigger choices. A geometric symmetry in those boundaries allows the optimal strategy to be implemented by a simple neural circuit involving a normalization with fixed decision bounds and an urgency signal. The model captures several key features of the response of decision-making neurons as well as the increase in reaction time as a function of the number of alternatives, known as Hick's law. In addition, we show that, in the presence of divisive normalization and internal variability, our model can account for several so called 'irrational' behaviors such as the similarity effect as well as the violation of both the independent irrelevant alternative principle and the regularity principle.
Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Multivariate patterns of magnetoencephalo-graphic (MEG) activity reflected this behavioral difference in the neural interaction between inferred beliefs and incoming evidence, an effect originating from associative regions in the temporal lobe. Together, these findings indicate that the degree of control over the sampling of volatile environments shapes human learning and decision-making under uncertainty. ### Competing Interest Statement The authors have declared no competing interest.
Traditional accumulation-to-bound decision-making models assume that all choice options are processed simultaneously with equal attention. In real life decisions, however, humans tend to alternate their visual fixation between individual items in order to efficiently gather relevant information (Yang et al., 2016; Hoppe & Rothkopf, 2016; Chukoskie et al., 2013). These fixations also causally affect one's choices, biasing them toward the longer-fixated item (Shimojo et al., 2003; Armel et al., 2008). We derive a normative decision-making model in which fixating a choice item boosts information about that item. In contrast to previous models (Krajbich et al., 2010; Song et al., 2019), we assume that attention enhances the reliability of information rather than its magnitude, consistent with neurophysiological findings (Averbeck et al., 2006; Cohen & Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation patterns and fixation-related choice biases seen in human decision-makers, and provides a Bayesian computational rationale for the fixation bias. This insight led to additional behavioral predictions that we confirmed in human behavioral data. Finally, we explore the consequences of changing the relative allocation of cognitive resources to the attended versus the unattended item, and show that decision performance is benefited by a more balanced spread of cognitive resources. ### Competing Interest Statement The authors have declared no competing interest.
Motion relations in visual scenes carry an abundance of behaviorally relevant information, but little is known about the computations underlying the identification of visual motion structure by humans. We addressed this gap in two psychophysics experiments and found that participants identified hierarchically organized motion relations in close correspondence with Bayesian structural inference. We demonstrate that, for our tasks, a choice model based on the Bayesian ideal observer can accurately match many facets of human structural inference, including task performance, perceptual error patterns, single-trial responses, participant-specific differences, and subjective decision confidence, particularly when motion scenes are ambiguous. Our work can guide future neuroscience experiments to reveal the neural mechanisms underlying higher-level visual motion perception. ### Competing Interest Statement The authors have declared no competing interest.
Decision-makers are often faced with limited information about the outcomes of their choices. Current formalizations of uncertain choice, such as the explore-exploit dilemma, do not apply well to decisions in which search capacity can be allocated to each option in variable amounts. Such choices confront decision-makers with the need to tradeoff between breadth (allocating a small amount of capacity to each of many options) and depth (focusing capacity on a few options). We formalize the breadth-depth dilemma through a finite sample capacity model. We find that, if capacity is smaller than 4-7 samples, it is optimal to draw one sample per alternative, favoring breadth. However, for larger capacities, a sharp transition is observed, and it becomes best to deeply sample a very small fraction of alternatives, that decreases with the square root of capacity. Thus, ignoring most options, even when capacity is large enough to shallowly sample all of them, reflects a signature of optimal behavior. Our results also provide a rich casuistic for metareasoning in multi-alternative decisions with bounded capacity.