Drawing on the philosophy of psychological explanation, we suggest that psychological science, by focusing on effects, may lose sight of its primary explananda: psychological capacities. We revisit Marr’s levels-of-analysis framework, which has been remarkably productive and useful for cognitive psychological explanation. We discuss ways in which Marr’s framework may be extended to other areas of psychology, such as social, developmental, and evolutionary psychology, bringing new benefits to these fields. We then show how theoretical analyses can endow a theory with minimal plausibility even before contact with empirical data: We call this the theoretical cycle. Finally, we explain how our proposal may contribute to addressing critical issues in psychological science, including how to leverage effects to understand capacities better.

The recognition that human minds/brains are finite systems with limited resources for computation has led some researchers to advance the Tractable Cognition thesis: Human cognitive capacities are constrained by computational tractability. This thesis, if true, serves cognitive psychology by constraining the space of computational-level theories of cognition. To utilize this constraint, a precise and workable definition of "computational tractability" is needed. Following computer science tradition, many cognitive scientists and psychologists define computational tractability as polynomial-time computability, leading to the P-Cognition thesis. This article explains how and why the P-Cognition thesis may be overly restrictive, risking the exclusion of veridical computational-level theories from scientific investigation. An argument is made to replace the P-Cognition thesis by the FPT-Cognition thesis as an alternative formalization of the Tractable Cognition thesis (here, FPT stands for fixed-parameter tractable). Possible objections to the Tractable Cognition thesis, and its proposed formalization, are discussed, and existing misconceptions are clarified. One of the primary aims of cognitive psychology is to explain human cognitive capacities (Cummins, 2000). Cognitive capacities are often believed to be the result of the human mind/brain's ability to transform certain input states (e.g., sensations, perceptions, and concepts) into certain output states (e.g., inferences, decisions, plans, and overt responses). Cognitive scientists attempt to model such capacities by constructing precise characterizations of the hypothesized inputs and outputs of cognitive capacities as well as the functional mappings between them. This is what David Marr (1982) called the computational-level theory of a cognitive process.A problem faced by cognitive scientists is that computational-level theories are grossly underconstrained by the available empirical data; not only because any finite number of Correspondence should be sent to Iris van Rooij,

Acknowledgements: A. Szollosi and C. Donkin are supported by Australian Research Council grants (DP130100124 and DP190101675). Authors thank Jared Hotaling and Ben R. Newell for useful discussion, and various others for their constructive comments on a preprint of the current paper (entitled "Preregistration is redundant, at best"). Contribution statement: A. Szollosi and C. Donkin prepared the original outline, and A. Szollosi converted the outline into a draft. All authors contributed to improving the draft into its final version. All authors reviewed the text for final revisions.

Human referential communication is often thought as codingdecoding a set of symbols, neglecting that establishing shared meanings requires a computational mechanism powerful enough to mutually negotiate them. Sharing the meaning of a novel symbol might rely on similar conceptual inferences across communicators or on statistical similarities in their sensorimotor behaviors. Using magnetoencephalography, we assess spectral, temporal, and spatial characteristics of neural activity evoked when people generate and understand novel shared symbols during live communicative interactions. Solving those communicative problems induced comparable changes in the spectral profile of neural activity of both communicators and addressees. This shared neuronal up-regulation was spatially localized to the right temporal lobe and the ventromedial prefrontal cortex and emerged already before the occurrence of a specific communicative problem. Communicative innovation relies on neuronal computations that are shared across generating and understanding novel shared symbols, operating over temporal scales independent from transient sensorimotor behavior.social interaction | theory of mind | experimental semiotics | MEG | broadband spectral change

This study investigates the contribution of frequency learning and teleological reasoning to action prediction in 9-month-old infants and adults. Participants observed how an agent repeatedly walked to a goal while taking the longer of 2 possible paths, as the shorter and more efficient path was impassable. In the subsequent test phase, both paths were passable. In the 1st test trial, infants and adults anticipated the agent to take the longer path. Unlike adults, infants kept anticipating movements to the longer path even after observing that the agent now took the more efficient path, indicating that the frequency of previous observations dominates action prediction. These results provide evidence, contrary to existing claims in the developmental literature, that frequency learning underlies action prediction in infancy, whereas teleological reasoning might gain importance later on in life.

The discovery of mirror neurons in monkeys, and the finding of motor activity during action observation in humans are generally regarded to support motor theories of action understanding. These theories take motor resonance to be essential in the understanding of observed actions and the inference of action goals. However, the notions of "resonance," "action understanding," and "action goal" appear to be used ambiguously in the literature. A survey of the literature on mirror neurons and motor resonance yields two different interpretations of the term "resonance," three different interpretations of action understanding, and again three different interpretations of what the goal of an action is. This entails that, unless it is specified what interpretation is used, the meaning of any statement about the relation between these concepts can differ to a great extent. By discussing an experiment we will show that more precise definitions and use of the concepts will allow for better assessments of motor theories of action understanding and hence a more fruitful scientific debate. Lastly, we will provide an example of how the discussed experimental setup could be adapted to test other interpretations of the concepts.

Recently there has been growing interest among psychologists in human performance on the Euclidean traveling salesperson problem (E-TSP). A debate has been initiated on what strategy people use in solving visually presented E-TSP instances. The most prominent hypothesis is the convex-hull hypothesis, originally proposed by MacGregor and Ormerod (1996). We argue that, in the literature so far, there is no evidence for this hypothesis. Alternatively we propose and motivate the hypothesis that people aim at avoiding crossings.

In daily life, complex events are perceived in a causal manner, suggesting that the brain relies on predictive processes to model them. Within predictive coding theory, oscillatory beta-band activity has been linked to top-down predictive signals and gamma-band activity to bottom-up prediction errors. However, neurocognitive evidence for predictive coding outside lower-level sensory areas is scarce. We used magnetoencephalography to investigate neural activity during probability-dependent action perception in three areas pivotal for causal inference, superior temporal sulcus, temporoparietal junction and medial prefrontal cortex, using bowling action animations. Within this network, Granger-causal connectivity in the beta-band was found to be strongest for backward top-down connections and gamma for feed-forward bottom-up connections. Moreover, beta-band power in TPJ increased parametrically with the predictability of the action kinematics-outcome sequences. Conversely, gamma-band power in TPJ and MPFC increased with prediction error. These findings suggest that the brain utilizes predictive-coding-like computations for higher-order cognition such as perception of causal events.