Recurrent processing improves occluded object recognition and gives rise to perceptual hysteresis

Distinct serial dependence between small and large numerosity processing

The serial dependence effect (SDE) is a perceptual bias where current stimuli are perceived as more similar to recently seen stimuli, possibly enhancing the stability and continuity of visual perception. Although SDE has been observed across many visual features, it remains unclear whether humans rely on a single mechanism of SDE to support numerosity processing across two distinct numerical ranges: subitizing (i.e., small numerosity processing, likely related to early object recognition) and estimation (i.e., large numerosity processing, likely related to ensemble numerosity extraction). Here, we show that subitizing and estimation exhibit distinct SDE patterns. Subitizing is characterized by an asymmetric SDE, whereas estimation demonstrates a symmetric SDE. Specifically, in subitizing, the SDE occurs only when the current magnitude is smaller than the previous magnitude but not when it is larger. In contrast, the SDE in estimation is present in both scenarios. We propose that these differences arise from distinct underlying mechanisms. A perceptual mechanism-namely, a 'temporal hysteresis' account, can explain the asymmetrical SDE in subitizing since object individuation resources are easily activated but resistant to deactivation. Conversely, a combination of perceptual and post-perceptual mechanisms can account for the SDEs in estimation, as both perceptual and post-perceptual interference can reduce the SDEs. Critically, a novel type of SDE characterized by reduced processing precision is found in subitizing only, implying that the continuity and stability of numerical processing can be dissociable in dynamic situations where numerical information is integrated over time. Our findings reveal the multifaceted nature of SDE mechanisms and suggest their engagement with cognitive modules likely subserving different functionalities.

Bounded Rational Decision Networks With Belief Propagation

Complex information processing systems that are capable of a wide variety of tasks, such as the human brain, are composed of specialized units that collaborate and communicate with each other. An important property of such information processing networks is locality: there is no single global unit controlling the modules, but information is exchanged locally. Here, we consider a decision-theoretic approach to study networks of bounded rational decision makers that are allowed to specialize and communicate with each other. In contrast to previous work that has focused on feedforward communication between decision-making agents, we consider cyclical information processing paths allowing for back-and-forth communication. We adapt message-passing algorithms to suit this purpose, essentially allowing for local information flow between units and thus enabling circular dependency structures. We provide examples that show how repeated communication can increase performance given that each unit's information processing capability is limited and that decision-making systems with too few or too many connections and feedback loops achieve suboptimal utility.

Violation of Leggett-Garg Inequality in Perceiving Cup-like Objects and Cognitive Contextuality

This paper is devoted to an experimental investigation of cognitive contextuality inspired by quantum contextuality research. This contextuality is related to, but not identical to context-sensitivity which is well-studied in cognitive psychology and decision making. This paper is a part of quantum-like modeling, i.e., exploring the methodology of quantum theory outside of physics. We examined the bistable perception of cup-like objects, which strongly depends on experimental contexts. Our experimental data confirmed the existence of cognitive hysteresis, the important role of memory, and the non-commutative structure of cognitive observables. In physics, quantum contextuality is assessed using Bell-CHSH inequalities, and their violation is incorrectly believed to imply the nonlocality of Nature. The violation of Bell-type inequalities in cognitive and social science strongly indicates that the metaphysical implications of these inequalities are quite limited. In our experiments, modified Leggett-Garg inequalities were also significantly violated, but this only means that experimental data from experiments performed in different contexts cannot be modeled by a unique set of noncontextual, jointly distributed random variables. In our experiments, we know the empirical probability distributions measured in different contexts; thus, we can obtain much more detailed and reliable information about contextuality in human cognition by performing nonparametric compatibility tests.

Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, and time. For an embodied agent such as a human, decisions are also shaped by physical aspects of actions. Beyond the effects of reward outcomes on learning processes, to what extent can modeling of behavior in a reinforcement-learning task be complicated by other sources of variance in sequential action choices? What of the effects of action bias (for actions per se) and action hysteresis determined by the history of actions chosen previously? The present study addressed these questions with incremental assembly of models for the sequential choice data from a task with hierarchical structure for additional complexity in learning. With systematic comparison and falsification of computational models, human choices were tested for signatures of parallel modules representing not only an enhanced form of generalized reinforcement learning but also action bias and hysteresis. We found evidence for substantial differences in bias and hysteresis across participants-even comparable in magnitude to the individual differences in learning. Individuals who did not learn well revealed the greatest biases, but those who did learn accurately were also significantly biased. The direction of hysteresis varied among individuals as repetition or, more commonly, alternation biases persisting from multiple previous actions. Considering that these actions were button presses with trivial motor demands, the idiosyncratic forces biasing sequences of action choices were robust enough to suggest ubiquity across individuals and across tasks requiring various actions. In light of how bias and hysteresis function as a heuristic for efficient control that adapts to uncertainty or low motivation by minimizing the cost of effort, these phenomena broaden the consilient theory of a mixture of experts to encompass a mixture of expert and nonexpert controllers of behavior.