arXiv subjects

Bahador Bahrami

Publications and source records attributed to Bahador Bahrami.

6 recordsLinked to original sources

arXiv2026

Private Noise and Public Error in Collective Information Acquisition

Collective information acquisition requires groups to combine personal evidence with social information while remaining coupled to the external state. Communication noise can affect this process, but the role of noise remains unclear. In an online experiment, 600 participants worked in four-person human groups estimating a room temperature across 25 rounds while receiving either faithful social information, comprehension noise in which each receiver saw independently perturbed social information, or production noise in which perturbations were stored before display and could be seen by multiple receivers. The thermometer cue was objectively veridical, but its reliability was subjectively uncertain and the unitless 50--250 room-temperature range created a task-induced conflict between displayed evidence and everyday temperature expectations. Production-noise groups spent more rounds tightly clustered around a wrong value than comprehension-noise groups (\(p=0.016\), group-level permutation). Production noise more often created a wrong common signal (\(p=0.025\), Fisher's exact test) and made that signal persist across more rounds (\(p=0.004\), permutation). Dynamic update models showed that production noise was not more harmful because people followed peers more strongly, but because the same peer influence acted on more correlated production-noise perturbations. Exploratory human analyses linked the mechanism to psychological patterns while a GPT-agent experiment clarified a boundary condition: GPT agents registered uncertainty through reduced confidence without reproducing human-scale production-noise vulnerability. Overall, noise did not simply degrade collective information acquisition. Comprehension noise could sometimes improve correction relative to the faithful control, whereas production noise could turn perturbations into common evidence and stabilize consensus on error.

physics.soc-ph↗

arXiv2024

Choice confidence bridges credit assignment to levels of decision hierarchy

Everyday decisions often involve many different levels. What connects these higher and lower level decisions hierarchy to one another determines how the cause(s) of failures are interpreted. It is hypothesized that decision confidence guides the assignment of blame to the correct level of hierarchy but this hypothesis has only been tested by manipulation of sensory evidence itself. We examined the consequences of modulating subjective confidence in hierarchical decision making via extra-sensory, social influence. Participants who made hierarchical, motion-plus-bandit decisions also received social information from a partner that advised the participant in the motion task. The strength of social advice -- independently from sensory signals -- modulated the likelihood of strategy change after negative feedback. Our findings therefore provide strong empirical evidence that subjective confidence per se acts as the bridge in assignment of credit and blame to various levels of decision hierarchy.

q-bio.NC↗

arXiv2024

A recommender network perspective on the informational value of critics and crowds

How do the ratings of critics and amateurs compare and how should they be combined? Previous research has produced mixed results about the first question, while the second remains unanswered. We have created a new, unique dataset, with wine ratings from critics and amateurs, and simulated a recommender system using the k-nearest-neighbor algorithm. We then formalized the advice seeking network spanned by that algorithm and studied people's relative influence. We find that critics are more consistent than amateurs, and thus their advice is more predictive than advice from amateurs. Getting advice from both groups can further boost performance. Our network theoretic approach allows us to identify influential critics, talented amateurs, and the information flow between groups. Our results provide evidence about the informational function of critics, while our framework is broadly applicable and can be leveraged to devise good decision strategies and more transparent recommender systems.

cs.SI↗

arXiv2024

Sequential sampling without comparison to boundary through model-free reinforcement learning

Although evidence integration to the boundary model has successfully explained a wide range of behavioral and neural data in decision making under uncertainty, how animals learn and optimize the boundary remains unresolved. Here, we propose a model-free reinforcement learning algorithm for perceptual decisions under uncertainty that dispenses entirely with the concepts of decision boundary and evidence accumulation. Our model learns whether to commit to a decision given the available evidence or continue sampling information at a cost. We reproduced the canonical features of perceptual decision-making such as dependence of accuracy and reaction time on evidence strength, modulation of speed-accuracy trade-off by payoff regime, and many others. By unifying learning and decision making within the same framework, this model can account for unstable behavior during training as well as stabilized post-training behavior, opening the door to revisiting the extensive volumes of discarded training data in the decision science literature.

cs.NE↗

arXiv2024

Awareness in robotics: An early perspective from the viewpoint of the EIC Pathfinder Challenge "Awareness Inside''

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices connected with projects funded by the EIC Pathfinder Challenge called ``Awareness Inside'', a nonrecurring call for proposals within Horizon Europe designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems.

cs.RO↗

arXiv2017

Aggregated knowledge from a small number of debates outperforms the wisdom of large crowds

The aggregation of many independent estimates can outperform the most accurate individual judgment. This centenarian finding, popularly known as the wisdom of crowds, has been applied to problems ranging from the diagnosis of cancer to financial forecasting. It is widely believed that social influence undermines collective wisdom by reducing the diversity of opinions within the crowd. Here, we show that if a large crowd is structured in small independent groups, deliberation and social influence within groups improve the crowd's collective accuracy. We asked a live crowd (N=5180) to respond to general-knowledge questions (e.g., what is the height of the Eiffel Tower?). Participants first answered individually, then deliberated and made consensus decisions in groups of five, and finally provided revised individual estimates. We found that averaging consensus decisions was substantially more accurate than aggregating the initial independent opinions. Remarkably, combining as few as four consensus choices outperformed the wisdom of thousands of individuals.

cs.SI↗