A number of studies in the apparent motion literature were examined using the cognitive penetrability criterion to determine the extent to which beliefs affect the perception of apparent motion. It was found that the interaction between the perceptual processes mediating apparent motion and higher order processes appears to be limited. In addition, perceptual and inferential beliefs appear to have different effects on perceived motion optimality and direction. Our findings suggest that the system underlying apparent motion perception has more than one (...) stage and is informationally encapsulated from cognitive factors. (shrink)

How do we know about other minds on the basis of perception? The two most common answers to this question are that we literally perceive others’ mental states, or that we infer their mental states on the basis of perceiving something else. In this paper, I argue for a different answer. On my view, we don’t perceive mental states, and yet perceptual experiences often immediately justify mental state attributions. In a slogan: other minds are neither seen nor inferred. I argue (...) that this view offers the best explanation of our deeply equivocal intuitions about perception-based mental state attributions, and also holds substantial interest for the epistemology of perception more generally. (shrink)

Abstract“Force dynamics” refers to a previously neglected semantic category—how entities interact with respect to force. This category includes such concepts as: the exertion of force, resistance to such exertion and the overcoming of such resistance, blockage of a force and the removal of such blockage, and so forth. Force dynamics is a generalization over the traditional linguistic notion of “causative”: it analyzes “causing” into finer primitives and sets it naturally within a framework that also includes “letting,”“hindering,”“helping,” and still further notions. (...) Force dynamics, moreover, appears to be the semantic category that uniquely characterizes the grammatical category of modals, in both their basic and epistemic usages. In addition, on the basis of force dynamic parameters, numerous lexical items fall into systematic semantic patterns, and there exhibit parallelisms between physical and psychosocial reference. Further, from research on the relation of semantic structure to general cognitive structure, it appears that the concepts of force interaction that are encoded within language closely parallel concepts that appear both in early science and in naive physics and psychology. Overall, force dynamics thus emerges as a fundamental notional system that structures conceptual material pertaining to force interaction in a common way across a linguistic range: the physical, psychological, social, inferential, discourse, and mental-model domains of reference and conception. (shrink)

The underlying structures that are common to the world's languages bear an intriguing connection with early emerging forms of “core knowledge”, which are frequently studied by infant researchers. In particular, grammatical systems often incorporate distinctions that reflect those made in core knowledge. Here, I argue that this connection occurs because non-verbal core knowledge systematically biases processes of language evolution. This account potentially explains a wide range of cross-linguistic grammatical phenomena that currently lack an adequate explanation. Second, I suggest that developmental (...) researchers and cognitive scientists interested in knowledge representation can exploit this connection to language by using observations about cross-linguistic grammatical tendencies to inspire hypotheses about core knowledge. (shrink)

The underlying structures that are common to the world's languages bear an intriguing connection with early emerging forms of “core knowledge” (Spelke & Kinzler, 2007), which are frequently studied by infant researchers. In particular, grammatical systems often incorporate distinctions (e.g., the mass/count distinction) that reflect those made in core knowledge (e.g., the non-verbal distinction between an object and a substance). Here, I argue that this connection occurs because non-verbal core knowledge systematically biases processes of language evolution. This account potentially explains (...) a wide range of cross-linguistic grammatical phenomena that currently lack an adequate explanation. Second, I suggest that developmental researchers and cognitive scientists interested in (non-verbal) knowledge representation can exploit this connection to language by using observations about cross-linguistic grammatical tendencies to inspire hypotheses about core knowledge. (shrink)

Experiments with young infants provide evidence for early-developing capacities to represent physical objects and to reason about object motion. Early physical reasoning accords with 2 constraints at the center of mature physical conceptions: continuity and solidity. It fails to accord with 2 constraints that may be peripheral to mature conceptions: gravity and inertia. These experiments suggest that cognition develops concurrently with perception and action and that development leads to the enrichment of conceptions around an unchanging core. The experiments challenge claims (...) that cognition develops on a foundation of perceptual or motor experience, that initial conceptions are inappropriate to the world, and that initial conceptions are abandoned or radically.. (shrink)

The notion that visual attention can operate over visual objects in addition to spatial locations has recently received much empirical support, but there has been relatively little empirical consideration of what can count as an `object' in the ®rst place. We have investi- gated this question in the context of the multiple object tracking paradigm, in which subjects must track a number of independently and unpredictably moving identical items in a ®eld of identical distractors. What types of feature clusters can (...) be tracked in this manner? In other words, what counts as an `object' in this task? We investigated this question with a technique we call target merging: we alter tracking displays so that distinct target and distractor loca- tions appear perceptually to be parts of the same object by merging pairs of items (one target with one distractor) in various ways ± for example, by connecting item locations with a simple line segment, by drawing the convex hull of the two items, and so forth. The data show that target merging makes the tracking task far more dif®cult to varying degrees depending on exactly how the items are merged. The effect is perceptually salient, involving in some conditions a total destruction of subjects' capacity to track multiple items. These studies provide strong evidence for the object-based nature of tracking, con®rming that in some contexts attention must be allocated to objects rather than arbitrary collections of features. In addition, the results begin to reveal the types of spatially organized scene components that can be independently attended as a function of properties such as connectedness, part struc- ture, and other types of perceptual grouping. q 2001 Elsevier Science B.V. All rights reserved. (shrink)

Philosophical Issues, Volume 30, Issue 1, Page 273-292, October 2020.

Although the study of visual perception has made more progress in the past 40 years than any other area of cognitive science, there remain major disagreements as to how closely vision is tied to general cognition. This paper sets out some of the arguments for both sides and defends the position that an important part of visual perception, which may be called early vision or just vision, is prohibited from accessing relevant expectations, knowledge and utilities - in other words it (...) is cognitively impenetrable. That part of vision is complex and articulated and provides a representation of the 3-D surfaces of objects sufficient to serve as an index into memory, with somewhat different outputs being made available to other systems such as those dealing with motor control. The paper also addresses certain conceptual and methodological issues, including the use of signal detection theory and event-related potentials to assess cognitive penetration of vision. A distinction is made among several stages in visual processing. These include, in addition to the inflexible early-vision stage, a pre-perceptual attention allocation stage and a post-perceptual evaluation, memory-accessing, and inference stage which provide several different highly constrained ways in which cognition can affect the outcome of visual perception. The paper discusses arguments that have been presented in both computer vision and psychology showing that vision is "intelligent" and involves elements of problem solving". It is suggested that these cases do not show cognitive penetration, but rather they show that certain natural constraints on interpretation, concerned primarily with optical and geometrical properties of the world, have been compiled into the visual system. The paper also examines a number of examples where instructions and "hints" are alleged to affect. (shrink)

Fundamental to spatial knowledge in all species are the representations underlying object recognition, object search, and navigation through space. But what sets humans apart from other species is our ability to express spatial experience through language. This target article explores the language ofobjectsandplaces, asking what geometric properties are preserved in the representations underlying object nouns and spatial prepositions in English. Evidence from these two aspects of language suggests there are significant differences in the geometric richness with which objects and places (...) are encoded. When an object is named (i.e., with count nouns), detailed geometric properties – principally the object's shape (axes, solid and hollow volumes, surfaces, and parts) – are represented. In contrast, when an object plays the role of either “figure” (located object) or “ground” (reference object) in a locational expression, only very coarse geometric object properties are represented, primarily the main axes. In addition, the spatial functions encoded by spatial prepositions tend to be nonmetric and relatively coarse, for example, “containment,” “contact,” “relative distance,” and “relative direction.” These properties are representative of other languages as well. The striking differences in the way language encodes objects versus places lead us to suggest two explanations: First, there is a tendency for languages to level out geometric detail from both object and place representations. Second, a nonlinguistic disparity between the representations of “what” and “where” underlies how language represents objects and places. The language of objects and places converges with and enriches our understanding of corresponding spatial representations. (shrink)

In this article, I revisit Landau and Jackendoff's () paper, “What and where in spatial language and spatial cognition,” proposing a friendly amendment and reformulation. The original paper emphasized the distinct geometries that are engaged when objects are represented as members of object kinds, versus when they are represented as figure and ground in spatial expressions. We provided empirical and theoretical arguments for the link between these distinct representations in spatial language and their accompanying nonlinguistic neural representations, emphasizing the “what” (...) and “where” systems of the visual system. In the present paper, I propose a second division of labor between two classes of spatial prepositions in English that appear to be quite distinct. One class includes prepositions such as in and on, whose core meanings engage force-dynamic, functional relationships between objects, with geometry only a marginal player. The second class includes prepositions such as above/below and right/left, whose core meanings engage geometry, with force-dynamic relationships a passing or irrelevant variable. The insight that objects’ force-dynamic relationships matter to spatial terms’ uses is not new; but thinking of these terms as a distinct set within spatial language has theoretical and empirical consequences that are new. I propose three such consequences, rooted in the fact that geometric knowledge is highly constrained and early-emerging in life, while force-dynamic knowledge of objects and their interactions is relatively unconstrained and needs to be learned piecemeal over a lengthy timeline. First, the two classes will engage different learning problems, with different developmental trajectories for both first and second language learners; second, the classes will naturally lead to different degrees of cross-linguistic variation; and third, they may be rooted in different neural representations. (shrink)

Landau also pushes the role of syntax and its mapping to semantics in learning what some would consider “easy words”—the simplest spatial prepositions in English, in and on. Taking as a starting point that the syntactic distribution of a word is a reflex of its meaning, Landau shows that careful study of how children and adults linguistically encode a range of containment and support configurations reveals a special status for “core” configurations in each domain. She proposes that children come to (...) learning spatial terms with the expectation that configurations reflecting core concepts of containment and support will be expressed with the maximally simple, unmarked, linguistic expression. (shrink)

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking (...) machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)

We can perceive not only low-level features of events such as color and motion, but also seemingly higher-level properties such as causality. A prototypical example of causal perception is the ”launching effect’: one object moves toward a stationary second object until they are adjacent, at which point A stops and B starts moving in the same direction. Beyond these motions themselves --- and regardless of any higher-level beliefs --- this display induces a vivid visual impression of causality, wherein A is (...) seen to cause B’s motion. Do such percepts reflect a unitary category of visual processing, or might there be multiple distinct forms of causal perception? While launching is often simply equated with causal perception, researchers have sometimes described other phenomena such as ”triggering’ and ”entraining’. We used psychophysical methods to determine whether these labels really carve visual processing at its joints, and how putatively different forms of causal perception relate to each other. Previous research demonstrated retinotopically specific adaptation to causality: exposure to causal launching makes subsequent ambiguous events in that same location more likely to be seen as non-causal ”passing’. Here, after replicating this effect, we show that exposure to triggering also yields retinotopically specific adaptation for subsequent ambiguous launching displays, but that exposure to entraining does not. Collectively, these results reveal that visual processing distinguishes some types of causal interactions. (shrink)

Recently, several theorists have proposed that we can perceive a range of high-level features, including natural kind features (e.g., being a lemur), artifactual features (e.g., being a mandolin), and the emotional features of others (e.g., being surprised). I clarify the claim that we perceive high-level features and suggest one overlooked reason this claim matters: it would dramatically expand the range of actions perception-based theories of action might explain. I then describe the influential phenomenal contrast method of arguing for high-level perception (...) and discuss some of the objections that have been raised against this strategy. Finally, I describe two emerging defenses of high-level perception, one of which appeals to a certain class of perceptual deficits and one of which appeals to adaptation effects. I sketch a challenge for the latter approach. (shrink)

The phenomenal character of perceptual experience involves the representation of colour, shape and motion. Does it also involve the representation of high-level categories? Is the recognition of a tomato as a tomato contained within perceptual phenomenality? Proponents of a conservative view of the reach of phenomenal content say ’No’, whereas those who take a liberal view of perceptual phenomenality say ’Yes’. I clarify the debate between conservatives and liberals, and argue in favour of the liberal view that high-level content can (...) directly inform the phenomenal character of perception. (shrink)

Tye's book develops a persuasive and, in many respects, original argument for the view that the qualitative side of our mental life is representational in..

Siegel and Byrne debate whether perceptual experiences present rich properties or exclusively thin properties.

Because human languages vary in sound and meaning, children must learn which distinctions their language uses. For speech perception, this learning is selective: initially infants are sensitive to most acoustic distinctions used in any language1–3, and this sensitivity reflects basic properties of the auditory system rather than mechanisms specific to language4–7; however, infants’ sensitivity to non-native sound distinctions declines over the course of the first year8. Here we ask whether a similar process governs learning of word meanings. We investigated the (...) sensitivity of 5-month-old infants in an English-speaking environment to a conceptual distinction that is marked in Korean but not English; that is, the distinction between ‘tight’ and ‘loose’ fit of one object to another9,10. Like adult Korean speakers but unlike adult English speakers, these infants detected this distinction and divided a continuum of motion-into-contact actions into tightand loose-fit categories. Infants’ sensitivity to this distinction is linked to representations of object mechanics11that are shared by non-human animals12–14. Language learning therefore seems to develop by linking linguistic forms to universal, pre-existing representations of sound and meaning. Our research focuses on the crosscutting conceptual distinctions between actions producing loose- and tight-fitting contact relationships (compare left and right columns in Fig. 1a) and actions producing containment versus support relationships (compare first and second rows in Fig. 1a). As early as Korean and English children begin to talk about such actions, they categorize them differently from one another and similarly to Korean- and Englishspeaking adults9,15. Moreover, English and Korean adults differ in their performance on non-linguistic categorization tasks involving heterogeneous examples of these actions, in accord with the differing semantics of their languages16,17, whereas the performance of young children on such tasks has been mixed9,10,18,19.. (shrink)

Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach “human-level” accuracy in an astounding variety of domains, and even predict human brain activity—raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines fail in strange and “unhumanlike” ways, threatening their status as models of our minds. How can we know when human–machine 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 science—the distinction between performance and competence—to encourage “species-fair” 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 (“competence”), but instead because of superficial constraints on demonstrating that knowledge (“performance”). 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 human–machine 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. (shrink)

Illusions that produce perceptual suppression despite constant retinal input are used to manipulate visual consciousness. Here we report on a powerful variant of existing techniques, Continuous Flash Suppression. Distinct images flashed successively around 10 Hz into one eye reliably suppress an image presented to the other eye. Compared to binocular rivalry, the duration of perceptual suppression increased more than 10-fold. Using this tool we show that the strength of the negative afterimage of an adaptor was reduced by half when it (...) was perceptually suppressed by input from the other eye. The more likely the adaptor was completely suppressed, the larger the reduction of the afterimage intensity. Paradoxically, trial-to-trial visibility of the adaptor did not correlate with the degree of suppression. Our results imply that formation of afterimages involves neuronal structures that access input from both eyes, but that do not correspond directly to the neuronal correlates of perceptual awareness. (shrink)