62,070 research outputs found
The nature of the animacy organization in human ventral temporal cortex
The principles underlying the animacy organization of the ventral temporal
cortex (VTC) remain hotly debated, with recent evidence pointing to an animacy
continuum rather than a dichotomy. What drives this continuum? According to the
visual categorization hypothesis, the continuum reflects the degree to which
animals contain animal-diagnostic features. By contrast, the agency hypothesis
posits that the continuum reflects the degree to which animals are perceived as
(social) agents. Here, we tested both hypotheses with a stimulus set in which
visual categorizability and agency were dissociated based on representations in
convolutional neural networks and behavioral experiments. Using fMRI, we found
that visual categorizability and agency explained independent components of the
animacy continuum in VTC. Modeled together, they fully explained the animacy
continuum. Finally, clusters explained by visual categorizability were
localized posterior to clusters explained by agency. These results show that
multiple organizing principles, including agency, underlie the animacy
continuum in VTC.Comment: 16 pages, 5 figures, code+data at -
https://doi.org/10.17605/OSF.IO/VXWG9 Update - added supplementary results
and edited abstrac
Structure and Problem Hardness: Goal Asymmetry and DPLL Proofs in<br> SAT-Based Planning
In Verification and in (optimal) AI Planning, a successful method is to
formulate the application as boolean satisfiability (SAT), and solve it with
state-of-the-art DPLL-based procedures. There is a lack of understanding of why
this works so well. Focussing on the Planning context, we identify a form of
problem structure concerned with the symmetrical or asymmetrical nature of the
cost of achieving the individual planning goals. We quantify this sort of
structure with a simple numeric parameter called AsymRatio, ranging between 0
and 1. We run experiments in 10 benchmark domains from the International
Planning Competitions since 2000; we show that AsymRatio is a good indicator of
SAT solver performance in 8 of these domains. We then examine carefully crafted
synthetic planning domains that allow control of the amount of structure, and
that are clean enough for a rigorous analysis of the combinatorial search
space. The domains are parameterized by size, and by the amount of structure.
The CNFs we examine are unsatisfiable, encoding one planning step less than the
length of the optimal plan. We prove upper and lower bounds on the size of the
best possible DPLL refutations, under different settings of the amount of
structure, as a function of size. We also identify the best possible sets of
branching variables (backdoors). With minimum AsymRatio, we prove exponential
lower bounds, and identify minimal backdoors of size linear in the number of
variables. With maximum AsymRatio, we identify logarithmic DPLL refutations
(and backdoors), showing a doubly exponential gap between the two structural
extreme cases. The reasons for this behavior -- the proof arguments --
illuminate the prototypical patterns of structure causing the empirical
behavior observed in the competition benchmarks
- …