Ansgar D. Endress

The Role of Salience in the Extraction of Algebraic Rules

Recent research suggests that humans and other animals have sophisticated abilities to extract both statistical dependencies and rule-based regularities from sequences. Most of this research stresses the flexibility and generality of such processes. Here the authors take up an equally important project, namely, to explore the limits of such processes. As a case study for rule-based generalizations, the authors demonstrate that only repetition-based structures with repetitions at the edges of sequences (e.g., ABCDEFF but not ABCDDEF) can be reliably generalized, although token repetitions can easily be discriminated at both sequence edges and middles. This finding suggests limits on rule-based sequence learning and new interpretations of earlier work alleging rule learning in infants. Rather than implementing a computerlike, formal process that operates over all patterns equally well, rule-based learning may be a highly constrained and piecemeal process driven by perceptual primitives--specialized type operations that are highly sensitive to perceptual factors.

Perceptual and memory constraints on language acquisition

A wide variety of organisms employ specialized mechanisms to cope with the demands of their environment. We suggest that the same is true for humans when acquiring artificial grammars, and at least some basic properties of natural grammars. We show that two basic mechanisms can explain many results in artificial grammar learning experiments, and different linguistic regularities ranging from stress assignment to interfaces between different components of grammar. One mechanism is sensitive to identity relations, whereas the other uses sequence edges as anchor points for extracting positional regularities. This piecemeal approach to mental computations helps to explain otherwise perplexing data, and offers a working hypothesis on how statistical and symbolic accounts of cognitive processes could be bridged.

Word segmentation with universal prosodic cues.

When listening to speech from ones native language, words seem to be well separated from one another, like beads on a string. When listening to a foreign language, in contrast, words seem almost impossible to extract, as if there was only one bead on the same string. This contrast reveals that there are language-specific cues to segmentation. The puzzle, however, is that infants must be endowed with a language-independent mechanism for segmentation, as they ultimately solve the segmentation problem for any native language. Here, we approach the acquisition problem by asking whether there are language-independent cues to segmentation that might be available to even adult learners who have already acquired a native language. We show that adult learners recognize words in connected speech when only prosodic cues to word-boundaries are given from languages unfamiliar to the participants. In both artificial and natural speech, adult English speakers, with no prior exposure to the test languages, readily recognized words in natural languages with critically different prosodic patterns, including French, Turkish and Hungarian. We suggest that, even though languages differ in their sound structures, they carry universal prosodic characteristics. Further, these language-invariant prosodic cues provide a universally accessible mechanism for finding words in connected speech. These cues may enable infants to start acquiring words in any language even before they are fine-tuned to the sound structure of their native language.

Large capacity temporary visual memory

Visual working memory (WM) capacity is thought to be limited to 3 or 4 items. However, many cognitive activities seem to require larger temporary memory stores. Here, we provide evidence for a temporary memory store with much larger capacity than past WM capacity estimates. Further, based on previous WM research, we show that a single factor--proactive interference--is sufficient to bring capacity estimates down to the range of previous WM capacity estimates. Participants saw a rapid serial visual presentation of 5-21 pictures of familiar objects or words presented at rates of 4/s or 8/s, respectively, and thus too fast for strategies such as rehearsal. Recognition memory was tested with a single probe item. When new items were used on all trials, no fixed memory capacities were observed, with estimates of up to 9.1 retained pictures for 21-item lists, and up to 30.0 retained pictures for 100-item lists, and no clear upper bound to how many items could be retained. Further, memory items were not stored in a temporally stable form of memory but decayed almost completely after a few minutes. In contrast, when, as in most WM experiments, a small set of items was reused across all trials, thus creating proactive interference among items, capacity remained in the range reported in previous WM experiments. These results show that humans have a large-capacity temporary memory store in the absence of proactive interference, and raise the question of whether temporary memory in everyday cognitive processing is severely limited, as in WM experiments, or has the much larger capacity found in the present experiments.

The apes’ edge: positional learning in chimpanzees and humans

A wide variety of organisms produce actions and signals in particular temporal sequences, including the motor actions recruited during tool-mediated foraging, the arrangement of notes in the songs of birds, whales and gibbons, and the patterning of words in human speech. To accurately reproduce such events, the elements that comprise such sequences must be memorized. Both memory and artificial language learning studies have revealed at least two mechanisms for memorizing sequences, one tracking co-occurrence statistics among items in sequences (i.e., transitional probabilities) and the other one tracking the positions of items in sequences, in particular those of items in sequence-edges. The latter mechanism seems to dominate the encoding of sequences after limited exposure, and to be recruited by a wide array of grammatical phenomena. To assess whether humans differ from other species in their reliance on one mechanism over the other after limited exposure, we presented chimpanzees (Pan troglodytes) and human adults with brief exposure to six items, auditory sequences. Each sequence consisted of three distinct sound types (X, A, B), arranged according to two simple temporal rules: the A item always preceded the B item, and the sequence-edges were always occupied by the X item. In line with previous results with human adults, both species primarily encoded positional information from the sequences; that is, they kept track of the items that occurred in the sequence-edges. In contrast, the sensitivity to co-occurrence statistics was much weaker. Our results suggest that a mechanism to spontaneously encode positional information from sequences is present in both chimpanzees and humans and may represent the default in the absence of training and with brief exposure. As many grammatical regularities exhibit properties of this mechanism, it may be recruited by language and constrain the form that certain grammatical regularities take.

Primitive computations in speech processing

Previous research suggests that artificial-language learners exposed to quasi-continuous speech can learn that the first and the last syllables of words have to belong to distinct classes (e.g., Endress & Bonatti, 2007; Peña, Bonatti, Nespor, & Mehler, 2002). The mechanisms of these generalizations, however, are debated. Here we show that participants learn such generalizations only when the crucial syllables are in edge positions (i.e., the first and the last), but not when they are in medial positions (i.e., the second and the fourth in pentasyllabic items). In contrast to the generalizations, participants readily perform statistical analyses also in word middles. In analogy to sequential memory, we suggest that participants extract the generalizations using a simple but specific mechanism that encodes the positions of syllables that occur in edges. Simultaneously, they use another mechanism to track the syllable distribution in the speech streams. In contrast to previous accounts, this model explains why the generalizations are faster than the statistical computations, require additional cues, and break down under different conditions, and why they can be performed at all. We also show that that similar edge-based mechanisms may explain many results in artificial-grammar learning and also various linguistic observations.

Perceptual Constraints in Phonotactic Learning.

Structural regularities in language have often been attributed to symbolic or statistical general purpose computations, whereas perceptual factors influencing such generalizations have received less interest. Here, we use phonotactic-like constraints as a case study to ask whether the structural properties of specific perceptual and memory mechanisms may facilitate the acquisition of grammatical-like regularities. Participants learned that the consonants C and C had to come from distinct sets in words of the form CVccVC (where the critical consonants were in word edges) but not in words of the form cVCCVc (where the critical consonants were in word middles). Control conditions ruled out attentional or psychophysical difficulties in word middles. Participants did, however, learn such regularities in word middles when natural consonant classes were used instead of arbitrary consonant sets. We conclude that positional generalizations may be learned preferentially using edge-based positional codes, but that participants can also use other mechanisms when other linguistic cues are given.

The quest for generalizations over consonants: Asymmetries between consonants and vowels are not the by-product of acoustic differences

Consonants and vowels may play different roles during language processing, consonants being preferentially involved in lexical processing, and vowels tending to mark syntactic constituency through prosodic cues. In support of this view, artificial language learning studies have demonstrated that consonants (C) support statistical computations, whereas vowels (V) allow certain structural generalizations. Nevertheless, these asymmetries could be mere by-products of lower level acoustic differences between Cs and Vs, in particular the energy they carry, and thus their relative salience. Here we address this issue and show that vowels remain the preferred targets for generalizations, even when consonants are made highly salient or vowels barely audible. Participants listened to speech streams of nonsense CVCVCV words, in which consonants followed a simple ABA structure. Participants failed to generalize this structure over sonorant consonants (Experiment 1), even when vowel duration was reduced to one third of that of consonants (Experiment 2). When vowels were eliminated from the stream, participants showed only a marginal evidence of generalizations (Experiment 4). In contrast, participants readily generalized the structure over barely audible vowels (Experiment 3). These results show that different roles of consonants and vowels cannot be readily reduced to acoustical and perceptual differences between these phonetic categories.

Evidence of an evolutionary precursor to human language affixation in a non-human primate

Human language, and grammatical competence in particular, relies on a set of computational operations that, in its entirety, is not observed in other animals. Such uniqueness leaves open the possibility that components of our linguistic competence are shared with other animals, having evolved for non-linguistic functions. Here, we explore this problem from a comparative perspective, asking whether cotton-top tamarin monkeys (Saguinus oedipus) can spontaneously (no training) acquire an affixation rule that shares important properties with our inflectional morphology (e.g. the rule that adds –ed to create the past tense, as in the transformation of walk into walk-ed). Using playback experiments, we show that tamarins discriminate between bisyllabic items that start with a specific ‘prefix’ syllable and those that end with the same syllable as a ‘suffix’. These results suggest that some of the computational mechanisms subserving affixation in a diversity of languages are shared with other animals, relying on basic perceptual or memory primitives that evolved for non-linguistic functions.

Bayesian learning and the psychology of rule induction.

In recent years, Bayesian learning models have been applied to an increasing variety of domains. While such models have been criticized on theoretical grounds, the underlying assumptions and predictions are rarely made concrete and tested experimentally. Here, I use Frank and Tenenbaums (2011) Bayesian model of rule-learning as a case study to spell out the underlying assumptions, and to confront them with the empirical results Frank and Tenenbaum (2011) propose to simulate, as well as with novel experiments. While rule-learning is arguably well suited to rational Bayesian approaches, I show that their models are neither psychologically plausible nor ideal observer models. Further, I show that their central assumption is unfounded: humans do not always preferentially learn more specific rules, but, at least in some situations, those rules that happen to be more salient. Even when granting the unsupported assumptions, I show that all of the experiments modeled by Frank and Tenenbaum (2011) either contradict their models, or have a large number of more plausible interpretations. I provide an alternative account of the experimental data based on simple psychological mechanisms, and show that this account both describes the data better, and is easier to falsify. I conclude that, despite the recent surge in Bayesian models of cognitive phenomena, psychological phenomena are best understood by developing and testing psychological theories rather than models that can be fit to virtually any data.