How a Brain Sees: Constructing Reality

The distinction between seeing and knowing, and why our brains even bother to see, are discussed using vivid perceptual examples, including image features without visible qualia that can nonetheless be consciously recognized, The work of Helmholtz and Kanizsa exemplify these issues, including examples of the paradoxical facts that “all boundaries are invisible”, and that brighter objects look closer. Why we do not see the big holes in, and occluders of, our retinas that block light from reaching our photoreceptors is explained, leading to the realization that essentially all percepts are visual illusions. Why they often look real is also explained. The computationally complementary properties of boundary completion and surface filling-in are introduced and their unifying explanatory power is illustrated, including that “all conscious qualia are surface percepts”. Neon color spreading provides a vivid example, as do self-luminous, glary, and glossy percepts. How brains embody general-purpose self-organizing architectures for solving modal problems, more general than AI algorithms, but less general than digital computers, is described. New concepts and mechanisms of such architectures are explained, including hierarchical resolution of uncertainty. Examples from the visual arts and technology are described to illustrate them, including paintings of Baer, Banksy, Bleckner, da Vinci, Gene Davis, Hawthorne, Hensche, Matisse, Monet, Olitski, Seurat, and Stella. Paintings by different artists and artistic schools instinctively emphasize some brain processes over others. These choices exemplify their artistic styles. The role of perspective, T-junctions, and end gaps are used to explain how 2D pictures can induce percepts of 3D scenes.

How a Brain Sees: Neural Mechanisms

Multiple paradoxical visual percepts are explained using boundary completion and surface filling-in properties, including discounting the illuminant; brightness constancy, contrast, and assimilation; the Craik-O’Brien-Cornsweet Effect; and Glass patterns. Boundaries act as both generators and barriers to filling-in using specific cooperative and competitive interactions. Oriented local contrast detectors, like cortical simple cells, create uncertainties that are resolved using networks of simple, complex, and hypercomplex cells, leading to unexpected insights such as why Roman typeface letter fonts use serifs. Further uncertainties are resolved by interactions with bipole grouping cells. These simple-complex-hypercomplex-bipole networks form a double filter and grouping network that provides unified explanations of texture segregation, hyperacuity, and illusory contour strength. Discounting the illuminant suppresses illumination contaminants so that feature contours can hierarchically induce surface filling-in. These three hierarchical resolutions of uncertainty explain neon color spreading. Why groupings do not penetrate occluding objects is explained, as are percepts of DaVinci stereopsis, the Koffka-Benussi and Kanizsa-Minguzzi rings, and pictures of graffiti artists and Mooney faces. The property of analog coherence is achieved by laminar neocortical circuits. Variations of a shared canonical laminar circuit have explained data about vision, speech, and cognition. The FACADE theory of 3D vision and figure-ground separation explains much more data than a Bayesian model can. The same cortical process that assures consistency of boundary and surface percepts, despite their complementary laws, also explains how figure-ground separation is triggered. It is also explained how cortical areas V2 and V4 regulate seeing and recognition without forcing all occluders to look transparent.

Functional Parcellation of the Speech Production Cortex

Neuroimaging has revealed a core network of cortical regions that contribute to speech production, but the functional organization of this network remains poorly understood. Purpose We describe efforts to identify reliable boundaries around functionally homogenous regions within the cortical speech motor control network in order to improve the sensitivity of functional magnetic resonance imaging (fMRI) analyses of speech production and thus improve our understanding of the functional organization of speech production in the brain. Method We used a bottom-up, data-driven approach by pooling data from 12 previously conducted fMRI studies of speech production involving the production of monosyllabic and bisyllabic words and pseudowords that ranged from single vowels and consonant–vowel pairs to short sentences (163 scanning sessions, 136 unique participants, 39 different speech conditions). After preprocessing all data through the same pipeline and registering individual contrast maps to a common surface space, hierarchical clustering was applied to contrast maps randomly sampled from the pooled data set in order to identify consistent functional boundaries across subjects and tasks. Boundary completion was achieved by applying adaptive smoothing and watershed segmentation to the thresholded population-level boundary map. Hierarchical clustering was applied to the mean within–functional region of interest (fROI) response to identify networks of fROIs that respond similarly during speech. Results We identified highly reliable functional boundaries across the cortical areas involved in speech production. Boundary completion resulted in 117 fROIs in the left hemisphere and 109 in the right hemisphere. Clustering of the mean within-fROI response revealed a core sensorimotor network flanked by a speech motor planning network. The majority of the left inferior frontal gyrus clustered with the visual word form area and brain regions (e.g., anterior insula, dorsal anterior cingulate) associated with detecting salient sensory inputs and choosing the appropriate action. Conclusion The fROIs provide insight into the organization of the speech production network and a valuable tool for studying speech production in the brain by improving within-group and between-groups comparisons of speech-related brain activity. Supplemental Material https://doi.org/10.23641/asha.9402674

Boundary Completion in Illusory Contours: Interpolation or Extrapolation?

Most computational and neural-style models of contour completion (ie illusory and occluded contours) are based on interpolation: the filling in of an edge between two visible edges. The results of three experiments suggest an alternative conception, that units are formed as a result of extrapolation from visible edges. In three experiments, subjects reported illusory contours between standard illusory-contour inducing elements and forms that do not, by themselves, induce illusory contours. We suggest that these forms are not a special case of inducing elements but that they represent a different class— receiving elements. Receiving elements are forms that can receive an illusory contour but cannot generate one, and they can alter contour formation. In experiment 1, receiving elements increased the judged clarity of illusory contours. In experiment 2, illusory edges were seen to connect to corners, line ends, and even the edges of circles. Boundary formation in motion displays also appears to be based on extrapolation. In experiment 3, subjects reported that small moving dots altered the formation of spatiotemporally defined boundaries. Implications for higher-order operator and network models of boundary formation are discussed.