Natural logic and baby LoTH.

Language-of-thought hypothesis (LoTH) is having a profound impact on cognition studies. However, much remains unknown about its basic primitives and generative operations. Infant studies are fundamental, but methodologically very challenging. By distilling potential primitives from work in natural-language semantics, an approach beyond the corset of standard formal logic may be undertaken. Still, the road ahead is challenging and long.

Seeing inferences: brain dynamics and oculomotor signatures of non-verbal deduction.

We often express our thoughts through words, but thinking goes well beyond language. Here we focus on an elementary but basic thinking process, disjunction elimination, elicited by elementary visual scenes deprived of linguistic content, describing its neural and oculomotor correlates. We track two main components of a nonverbal deductive process: the construction of a logical representation (A or B), and its simplification by deduction (not A, therefore B). We identify the network active in the two phases and show that in the latter, but not in the former, it overlaps with areas known to respond to verbal logical reasoning. Oculomotor markers consistently differentiate logical processing induced by the construction of a representation, its simplification by deductive inference, and its maintenance when inferences cannot be drawn. Our results reveal how integrative logical processes incorporate novel experience in the flow of thoughts induced by visual scenes.

Precursors of logical reasoning in preverbal human infants.

Infants are able to entertain hypotheses about complex events and to modify them rationally when faced with inconsistent evidence. These capacities suggest that infants can use elementary logical representations to frame and prune hypotheses. By presenting scenes containing ambiguities about the identity of an object, here we show that 12- and 19-month-old infants look longer at outcomes that are inconsistent with a logical inference necessary to resolve such ambiguities. At the moment of a potential deduction, infants' pupils dilated, and their eyes moved toward the ambiguous object when inferences could be computed, in contrast to transparent scenes not requiring inferences to identify the object. These oculomotor markers resembled those of adults inspecting similar scenes, suggesting that intuitive and stable logical structures involved in the interpretation of dynamic scenes may be part of the fabric of the human mind.

Infants anticipate probabilistic but not deterministic outcomes.

Infants look at physically impossible events longer than at physically possible events, and at improbable events longer than at probable events. Such behaviors are generally interpreted as showing that infants have expectations about future events and are surprised to see them violated. It is unknown, however, whether and under what conditions infants form proactive expectations about the future, as opposed to realizing post hoc that outcomes do not comply with their previous knowledge or experience. Here we investigate the relation between expectation and surprise at probabilistic or deterministic events in preverbal infants. When a situation is uncertain, 12-month-olds anticipate probable outcomes and are surprised at improbable continuations of the scene. However, they do not anticipate the only possible outcome of a physically deterministic situation, although they are surprised when it does not occur. The results suggest that infants are sensitive to the tradeoff between information gain and programming efforts, showing higher propensity to anticipate those future events that carry novel knowledge.

Words, rules, and mechanisms of language acquisition.

We review recent artificial language learning studies, especially those following Endress and Bonatti (Endress AD, Bonatti LL. Rapid learning of syllable classes from a perceptually continuous speech stream. Cognition 2007, 105:247-299), suggesting that humans can deploy a variety of learning mechanisms to acquire artificial languages. Several experiments provide evidence for multiple learning mechanisms that can be deployed in fluent speech: one mechanism encodes the positions of syllables within words and can be used to extract generalization, while the other registers co-occurrence statistics of syllables and can be used to break a continuum into its components. We review dissociations between these mechanisms and their potential role in language acquisition. We then turn to recent criticisms of the multiple mechanisms hypothesis and show that they are inconsistent with the available data. Our results suggest that artificial and natural language learning is best understood by dissecting the underlying specialized learning abilities, and that these data provide a rare opportunity to link important language phenomena to basic psychological mechanisms. For further resources related to this article, please visit the WIREs website.

Numerical representations and intuitions of probabilities at 12 months.

Recent research shows that preverbal infants can reason about single-case probabilities without relying on observed frequencies, adapting their predictions to relevant dynamic parameters of the situation (Téglás, Vul, Girotto, Gonzalez, Tenenbaum & Bonatti, ; Téglás, Girotto, Gonzalez & Bonatti, ). Here we show that intuitions of probabilities may derive from the ability to represent a limited number of possibilities. After watching a scene containing moving objects of two ensembles, 12-month-olds looked longer at an unlikely than at a likely single-case outcome when the objects were within the parallel individuation range. However, they did not do so when the scene contained the same ratio between ensembles but a larger number of objects. At the same time, they could form rational expectations about single-case outcomes in scenes containing the same large number of objects when they could exploit subtle physical parameters induced by the objects' movements and their spatial configuration. Our findings demonstrate that at early stages of development the mental representations involved in probability estimations of future individual situations are powerful and sophisticated, but at the same time they depend on infants' overall cognitive architecture, being constrained by the numerical representations spontaneously induced by the situations.

Finding words and word structure in artificial speech: the development of infants' sensitivity to morphosyntactic regularities.

To achieve language proficiency, infants must find the building blocks of speech and master the rules governing their legal combinations. However, these problems are linked: words are also built according to rules. Here, we explored early morphosyntactic sensitivity by testing when and how infants could find either words or within-word structure in artificial speech snippets embodying properties of morphological constructions. We show that 12-month-olds use statistical relationships between syllables to extract words from continuous streams, but find word-internal regularities only if the streams are segmented. Seven-month-olds fail both tasks. Thus, 12-month-olds infants possess the resources to analyze the internal composition of words if the speech contains segmentation information. However, 7-month-old infants may not possess them, although they can track several statistical relations. This developmental difference suggests that morphosyntactic sensitivity may require computational resources extending beyond the detection of simple statistics.

Words and possible words in early language acquisition.

In order to acquire language, infants must extract its building blocks-words-and master the rules governing their legal combinations from speech. These two problems are not independent, however: words also have internal structure. Thus, infants must extract two kinds of information from the same speech input. They must find the actual words of their language. Furthermore, they must identify its possible words, that is, the sequences of sounds that, being morphologically well formed, could be words. Here, we show that infants' sensitivity to possible words appears to be more primitive and fundamental than their ability to find actual words. We expose 12- and 18-month-old infants to an artificial language containing a conflict between statistically coherent and structurally coherent items. We show that 18-month-olds can extract possible words when the familiarization stream contains marks of segmentation, but cannot do so when the stream is continuous. Yet, they can find actual words from a continuous stream by computing statistical relationships among syllables. By contrast, 12-month-olds can find possible words when familiarized with a segmented stream, but seem unable to extract statistically coherent items from a continuous stream that contains minimal conflicts between statistical and structural information. These results suggest that sensitivity to word structure is in place earlier than the ability to analyze distributional information. The ability to compute nontrivial statistical relationships becomes fully effective relatively late in development, when infants have already acquired a considerable amount of linguistic knowledge. Thus, mechanisms for structure extraction that do not rely on extensive sampling of the input are likely to have a much larger role in language acquisition than general-purpose statistical abilities.

The probable and the possible at 12 months: intuitive reasoning about the uncertain future.

How do infants predict the next future event, when such a prediction requires estimating the event's probability? The literature suggests that adult humans often fail this task because their probability estimates are affected by heuristics and biases or because they can reason about the frequency of classes of events but not about the probability of single events. Recent evidence suggests instead that already at 12 months infants have an intuitive notion of probability that applies to single, never experienced events and that they may use it to predict what will happen next. We present a theory according to which infants' intuitive grasp of the probability of future events derives from their representation of logically consistent future possibilities. We compare it and other theories against the currently available data. Although the evidence does not speak uniquely in favor of one theory, the results presented and the theories currently being developed to account for them suggest that infants have surprisingly sophisticated reasoning abilities. These conclusions are incompatible with most current theories of adult logical and probabilistic reasoning.

Large scale brain activations predict reasoning profiles.

Deduction is the ability to draw necessary conclusions from previous knowledge. Here we propose a novel approach to understanding the neural basis of deduction, which exploits fine-grained inter-participant variability in such tasks. Participants solved deductive problems and were grouped by the behavioral strategies employed, i.e., whether they were sensitive to the logical form of syllogistic premises, whether the problems were solved correctly, and whether heuristic strategies were employed. Differential profiles of neural activity can predict membership of the first two of these groups. The predictive power of activity profiles is distributed non-uniformly across the brain areas activated by deduction. Activation in left ventro-lateral frontal (BA47) and lateral occipital (BA19) cortices predicts whether logically valid solutions are sought. Activation of left inferior lateral frontal (BA44/45) and superior medial frontal (BA6/8) cortices predicts sensitivity to the logical structure of problems. No specific pattern of activation was associated with the use of a non-logical heuristic strategy. Not only do these findings corroborate the hypothesis that left BA47, BA44/45 and BA6/8 are critical for making syllogistic deductions, but they also imply that they have different functional roles as components of a dedicated network. We propose that BA44/45 and BA6/8 are involved in the extraction and representation of the formal structure of a problem, while BA47 is involved in the selection and application of relevant inferential rules. Finally, our findings suggest that deductive reasoning can be best described as a cascade of cognitive processes requiring the concerted operation of several, functionally distinct, brain areas.

Pure reasoning in 12-month-old infants as probabilistic inference.

Many organisms can predict future events from the statistics of past experience, but humans also excel at making predictions by pure reasoning: integrating multiple sources of information, guided by abstract knowledge, to form rational expectations about novel situations, never directly experienced. Here, we show that this reasoning is surprisingly rich, powerful, and coherent even in preverbal infants. When 12-month-old infants view complex displays of multiple moving objects, they form time-varying expectations about future events that are a systematic and rational function of several stimulus variables. Infants' looking times are consistent with a Bayesian ideal observer embodying abstract principles of object motion. The model explains infants' statistical expectations and classic qualitative findings about object cognition in younger babies, not originally viewed as probabilistic inferences.

A dissociation between judged causality and imagined locations in simple dynamic scenes.

To mentally extrapolate the trajectory of a moving object that disappears from sight, different sources of information can be exploited: memory of its last visible position, its inferred movement through time, and general understanding of the causal structure of the scene. It is often assumed that these cues are integrated into unified analog mental representations. In our experiment, participants predicted the position of an object that disappeared behind an occluder and estimated the degree to which the movement was caused by another object. They made considerable errors in predicting imagined displacements. Moreover, their predictions were misaligned with their judgments of causality. They predicted the positions of the invisible moving objects better in events that they judged less causally correct than in events that they judged more causally correct. These results suggest that physical and cognitive parameters of imagined dynamic events do not merge into unitary mental models simulating actual states of the world.

Cortical bases of elementary deductive reasoning: inference, memory, and metadeduction.

Elementary deduction is the ability of unreflectively drawing conclusions from explicit or implicit premises, on the basis of their logical forms. This ability is involved in many aspects of human cognition and interactions. To date, limited evidence exists on its cortical bases. We propose a model of elementary deduction in which logical inferences, memory, and meta-logical control are separable subcomponents. We explore deficits in patients with left, medial and right frontal lesions, by both studying patients' deductive abilities and providing measures of their meta-logical sensitivity for proof difficulty. We show that lesions to left lateral and medial frontal cortex impair abilities at solving elementary deductive problems, but not so lesions to right frontal cortex. Furthermore, we show that memory deficits differentially affect patients according to the locus of the lesion. Left lateral patients with working memory deficits had defective deductive abilities, but not so left lateral patients with spared working memory. In contrast, in medial patients both deductive and meta-deductive abilities were affected regardless of the presence of memory deficits. Overall, the results are compatible with a componential view of elementary deduction, and call for the elaboration of more fine-grained models of deductive abilities.

Finding words and rules in a speech stream: functional differences between vowels and consonants.

We have proposed that consonants give cues primarily about the lexicon, whereas vowels carry cues about syntax. In a study supporting this hypothesis, we showed that when segmenting words from an artificial continuous stream, participants compute statistical relations over consonants, but not over vowels. In the study reported here, we tested the symmetrical hypothesis that when participants listen to words in a speech stream, they tend to exploit relations among vowels to extract generalizations, but tend to disregard the same relations among consonants. In our streams, participants could segment words on the basis of transitional probabilities in one tier and could extract a structural regularity in the other tier. Participants used consonants to extract words, but vowels to extract a structural generalization. They were unable to extract the same generalization using consonants, even when word segmentation was facilitated and the generalization made simpler. Our results suggest that different signal-driven computations prime lexical and grammatical processing.

Intuitions of probabilities shape expectations about the future at 12 months and beyond.

Rational agents should integrate probabilities in their predictions about uncertain future events. However, whether humans can do this, and if so, how this ability originates, are controversial issues. Here, we show that 12-month-olds have rational expectations about the future based on estimations of event possibilities, without the need of sampling past experiences. We also show that such natural expectations influence preschoolers' reaction times, while frequencies modify motor responses, but not overt judgments, only after 4 years of age. Our results suggest that at the onset of human decision processes the mind contains an intuition of elementary probability that cannot be reduced to the encountered frequency of events or elementary heuristics.

Rapid learning of syllable classes from a perceptually continuous speech stream.

To learn a language, speakers must learn its words and rules from fluent speech; in particular, they must learn dependencies among linguistic classes. We show that when familiarized with a short artificial, subliminally bracketed stream, participants can learn relations about the structure of its words, which specify the classes of syllables occurring in first and last word positions. By studying the effect of familiarization length, we compared the general predictions of associative theories of learning and those of models postulating separate mechanisms for quickly extracting the word structure and for tracking the syllable distribution in the stream. As predicted by the dual-mechanism model, the preference for structurally correct items was negatively correlated with the familiarization length. This result is difficult to explain by purely associative schemes; an extensive set of neural network simulations confirmed this difficulty. Still, we show that powerful statistical computations operating on the stream are available to our participants, as they are sensitive to co-occurrence statistics among non-adjacent syllables. We suggest that different learning mechanisms analyze speech on-line: A rapid mechanism extracting structural information about the stream, and a slower mechanism detecting statistical regularities among the items occurring in it.

How to hit Scylla without avoiding Charybdis: comment on Perruchet, Tyler, Galland, and Peereman (2004).

M. Peña, L. L. Bonatti, M. Nespor, and J. Mehler argued that humans compute nonadjacent statistical relations among syllables in a continuous artificial speech stream to extract words, but they use other computations to determine the structural properties of words. Instead, when participants are familiarized with a segmented stream, structural generalizations about words are quickly established. P. Perruchet, M. D. Tyler, N. Galland, and R. Peereman criticized M. Peña et al.'s work and dismissed their results. In this article, the authors show that P. Perruchet et al.'s criticisms are groundless.