58 research outputs found
How 'paternalistic' is spatial perception? Why wearing a heavy backpack doesn't -- and couldn't -- make hills look steeper
Performance vs. competence in humanâmachine comparisons
Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach âhuman-levelâ accuracy in an astounding variety of domains, and even predict human brain activityâraising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines fail in strange and âunhumanlikeâ ways, threatening their status as models of our minds. How can we know when humanâmachine behavioral differences reflect deep disparities in their underlying capacities, vs. when such failures are only superficial or peripheral? This article draws on a foundational insight from cognitive scienceâthe distinction between performance and competenceâto encourage âspecies-fairâ comparisons between humans and machines. The performance/competence distinction urges us to consider whether the failure of a system to behave as ideally hypothesized, or the failure of one creature to behave like another, arises not because the system lacks the relevant knowledge or internal capacities (âcompetenceâ), but instead because of superficial constraints on demonstrating that knowledge (âperformanceâ). I argue that this distinction has been neglected by research comparing human and machine behavior, and that it should be essential to any such comparison. Focusing on the domain of image classification, I identify three factors contributing to the species-fairness of humanâmachine comparisons, extracted from recent work that equates such constraints. Species-fair comparisons level the playing field between natural and artificial intelligence, so that we can separate more superficial differences from those that may be deep and enduring
Can resources save rationality? âAnti-Bayesianâ updating in cognition and perception
Resource rationality may explain suboptimal patterns of reasoning; but what of âanti-Bayesianâ
effects where the mind updates in a direction opposite the one it should? We present two
phenomena â belief polarization and the size-weight illusion â that are not obviously
explained by performance- or resource-based constraints, nor by the authorsâ brief discussion
of reference repulsion. Can resource rationality accommodate them
Sustained Representation of Perspectival Shape
Arguably the most foundational principle in perception research is that our experience of the world goes beyond the retinal image; we perceive the distal environment itself, not the proximal stimulation it causes. Shape may be the paradigm case of such âunconscious inferenceâ: When a coin is rotated in depth, we infer the circular object it truly is, discarding the perspectival ellipse projected on our eyes. But is this really the fate of such perspectival shapes? Or does a tilted coin retain an elliptical appearance even when we know itâs circular? This question has generated heated debate from Locke and Hume to the present; but whereas extant arguments rely primarily on introspection, this problem is also open to empirical test. If tilted coins bear a representational similarity to elliptical objects, then a circular coin should, when rotated, impair search for a distal ellipse. Here, nine experiments demonstrate that this is so, suggesting that perspectival shapes persist in the mind far longer than traditionally assumed. Subjects saw search arrays of three-dimensional âcoins,â and simply had to locate a distally elliptical coin. Surprisingly, rotated circular coins slowed search for elliptical targets, even when subjects clearly knew the rotated coins were circular. This pattern arose with static and dynamic cues, couldnât be explained by strategic responding or unfamiliarity, generalized across shape classes, and occurred even with sustained viewing. Finally, these effects extended beyond artificial displays to real-world objects viewed in naturalistic, full-cue conditions. We conclude that objects have a remarkably persistent dual character: their objective shape âout there,â and their perspectival shape âfrom here.
Curious objects: How visual complexity guides attention and engagement
Some things look more complex than others. For example, a crenulate and richly organized leaf may seem more complex than a plain stone. What is the nature of this experienceâand why do we have it in the first place? Here, we explore how object complexity serves as an efficiently extracted visual signal that the object merits further exploration. We algorithmically generated a library of geometric shapes and determined their complexity by computing the cumulative surprisal of their internal skeletonsâessentially quantifying the âamount of informationâ within each shapeâand then used this approach to ask new questions about the perception of complexity. Experiments 1â3 asked what kind of mental process extracts visual complexity: a slow, deliberate, reflective process (as when we decide that an object is expensive or popular) or a fast, effortless, and automatic process (as when we see that an object is big or blue)? We placed simple and complex objects in visual search arrays and discovered that complex objects were easier to find among simple distractors than simple objects are among complex distractorsâa classic search asymmetry indicating that complexity is prioritized in visual processing. Next, we explored the function of complexity: Why do we represent object complexity in the first place? Experiments 4â5 asked subjects to study serially presented objects in a selfâpaced manner (for a later memory test); subjects dwelled longer on complex objects than simple objectsâeven when object shape was completely taskâirrelevantâsuggesting a connection between visual complexity and exploratory engagement. Finally, Experiment 6 connected these implicit measures of complexity to explicit judgments. Collectively, these findings suggest that visual complexity is extracted efficiently and automatically, and even arouses a kind of âperceptual curiosityâ about objects that encourages subsequent attentional engagement
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