Automated prediction of preferences using facial expressions.

We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers' preferences between images (e.g., of celebrities) based on covert videos of the observers' faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available.

Thought beyond language: neural dissociation of algebra and natural language.

A central question in cognitive science is whether natural language provides combinatorial operations that are essential to diverse domains of thought. In the study reported here, we addressed this issue by examining the role of linguistic mechanisms in forging the hierarchical structures of algebra. In a 3-T functional MRI experiment, we showed that processing of the syntax-like operations of algebra does not rely on the neural mechanisms of natural language. Our findings indicate that processing the syntax of language elicits the known substrate of linguistic competence, whereas algebraic operations recruit bilateral parietal brain regions previously implicated in the representation of magnitude. This double dissociation argues against the view that language provides the structure of thought across all cognitive domains.

Logic, language and the brain.

What is the role of language in human cognition? Within the domain of deductive reasoning, the issue has been the focus of numerous investigations without the emergence of a consensus view. Here we consider some of the reasons why neuroimaging studies of deductive reasoning have generated mixed results. We then review recent evidence suggesting that the role of language in deductive reasoning is confined to an initial stage in which verbally presented information is encoded as non-verbal representations. These representations are then manipulated by mental operations that are not based on the neural mechanisms of natural language. This article is part of a Special Issue entitled "The Cognitive Neuroscience".

The boundaries of language and thought in deductive inference.

Is human thought fully embedded in language, or do some forms of thought operate independently? To directly address this issue, we focus on inference-making, a central feature of human cognition. In a 3T fMRI study we compare logical inferences relying on sentential connectives (e.g., not, or, if ... then) to linguistic inferences based on syntactic transformation of sentences involving ditransitive verbs (e.g., give, say, take). When contrasted with matched grammaticality judgments, logic inference alone recruited "core" regions of deduction [Brodmann area (BA) 10p and 8m], whereas linguistic inference alone recruited perisylvian regions of linguistic competence, among others (BA 21, 22, 37, 39, 44, and 45 and caudate). In addition, the two inferences commonly recruited a set of general "support" areas in frontoparietal cortex (BA 6, 7, 8, 40, and 47). The results indicate that logical inference is not embedded in natural language and confirm the relative modularity of linguistic processes.

Induction as conditional probability judgment.

Existing research on category-based induction has primarily focused on reasoning about blank properties, or predicates that are designed to elicit little prior knowledge. Here, we address reasoning about nonblank properties. We introduce a model of conditional probability that assumes that the conclusion prior probability is revised to the extent warranted by the evidence in the premise. The degree of revision is a function of the relevance of the premise category to the conclusion and the informativeness of the premise statement. An algebraic formulation with no free parameters accurately predicted conditional probabilities for single- and two-premise conditionals (Experiments 1 and 3), as well as problems involving negative evidence (Experiment 2).

Functional neuroanatomy of deductive inference: a language-independent distributed network.

Studies of brain areas supporting deductive reasoning show inconsistent results, possibly because of the variety of tasks and baselines used. In two event-related functional magnetic imaging studies we employed a cognitive load paradigm to isolate the neural correlates of deductive reasoning and address the role (if any) of language in deduction. Healthy participants evaluated the logical status of arguments varying in deductive complexity but matched in linguistic complexity. Arguments also varied in lexical content, involving blocks and pseudo-words in Experiment I and faces and houses in Experiment II. For each experiment, subtraction of simple from complex arguments (collapsing across contents) revealed a network of activations disjoint from regions traditionally associated with linguistic processing and also disjoint from regions recruited by mere reading. We speculate that this network is divided into "core" and "support" regions. The latter include left frontal (BA 6, 47) and parietal (BA 7, 40) cortices, which maintain the formal structure of arguments. Core regions, in the left rostral (BA 10p) and bilateral medial (BA 8) prefrontal cortex, perform deductive operations. Finally, restricting the complex-simple subtraction to each lexical content uncovered additional activations which may reflect the binding of logical variables to lexical items.