In two eye-tracking studies we investigated the influence of Mandarin numeral classifiers – a grammatical category in the language – on online overt attention. Mandarin speakers were presented with simple sentences through headphones while their eye-movements to objects presented on a computer screen were monitored. The crucial question is what participants look at while listening to a pre-specified target noun. If classifier categories influence Mandarin speakers' general conceptual processing, then on hearing the target noun they should look at objects that (...) are members of the same classifier category – even when the classifier is not explicitly present. The data show that when participants heard a classifier they shifted overt attention significantly more to classifiermatch objects than to distractor objects, but when the classifier was not explicitly presented in speech, overt attention to classifier-match objects and distractor objects did not differ. This suggests that although classifier distinctions do influence eye-gaze behavior, they do so only during linguistic processing of that distinction and not in moment-to-moment general conceptual processing. (shrink)

Language has been shown to influence the ability to form categories. Nevertheless, in most prior work, the effects of language could have been bolstered by the fact that linguistic labels were introduced by the experimenter prior to the categorization task in ways that could have highlighted their relevance for the task. Here, we compared the potency of labels to that of other non‐linguistic cues on how people categorized novel, perceptually ambiguous natural kinds (e.g., flowers or birds). Importantly, we varied whether (...) these cues were explicitly presented as relevant to the categorization task. In Experiment 1, we compared labels, numbers, and symbols: One group of participants was told to pay attention to these cues because they would be helpful (Relevant condition), a second group was told that the cues were irrelevant and should be ignored (Irrelevant condition), and a third group was told nothing about the cues (Neutral condition). Even though task relevance affected overall reliance on cues during categorization, participants were more likely to use labels to determine category boundaries, compared to numbers or symbols. In Experiments 2 and 3, we replicated and fine‐tuned the advantage of labels in more stringent categorization tasks. These results offer novel evidence for the position that labels offer unique indications of category membership, compared to non‐linguistic cues. (shrink)

Models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli. In this paper, we focus on categories acquired from natural language stimuli, that is, words. We present a Bayesian model that, unlike previous work, learns both categories and their features in a single process. We model category induction as two interrelated subproblems: the acquisition of features that discriminate among categories, and the grouping of concepts into categories based on those features. (...) Our model learns categories incrementally using particle filters, a sequential Monte Carlo method commonly used for approximate probabilistic inference that sequentially integrates newly observed data and can be viewed as a plausible mechanism for human learning. Experimental results show that our incremental learner obtains meaningful categories which yield a closer fit to behavioral data compared to related models while at the same time acquiring features which characterize the learned categories. (shrink)

Two experiments were conducted to examine adult learners' ability to extract multiple statistics in simultaneously presented visual and auditory input. Experiment 1 used a cross‐situational learning paradigm to test whether English speakers were able to use co‐occurrences to learn word‐to‐object mappings and concurrently form object categories based on the commonalities across training stimuli. Experiment 2 replicated the first experiment and further examined whether speakers of Mandarin, a language in which final syllables of object names are more predictive of category membership (...) than English, were able to learn words and form object categories when trained with the same type of structures. The results indicate that both groups of learners successfully extracted multiple levels of co‐occurrence and used them to learn words and object categories simultaneously. However, marked individual differences in performance were also found, suggesting possible interference and competition in processing the two concurrent streams of regularities. (shrink)

Proponents of cognitive penetration often argue for the thesis on the basis of combined intuitions about categorical perception and perceptual learning. The claim is that beliefs penetrate perceptions in the course of learning to perceive categories. I argue that this "diachronic" penetration thesis is false. In order to substantiate a robust notion of penetration, the beliefs that enable learning must describe the particular ability that subjects learn. However, they cannot do so, since in order to help with learning they must (...) instruct learners to employ previously existing abilities. I argue that a better approach recognizes that we can have sophisticated causal precursors to perceptual learning, but that the learning process itself must operate outside of cognitive influence. (shrink)

The standard assumption for what perception must do in order to represent a “higher level” content—say, tiger—is that it must represent the kind as such. I argue that this “as such condition” is not constitutive of what it means for a content to be “higher‐level”, and that embracing it produces a range of unfortunate dialectical consequences. After offering this critique, I give an alternative construal, the “extended perceptual space” view of higher‐level contents. This view captures the phenomena targeted by the (...) “higher‐level content” thesis, is empirically supported, and avoids the negative dialectical consequences of the as such condition. (shrink)

Experiencing a stimulus in one sensory modality is often associated with an experience in another sensory modality. For instance, seeing a lemon might produce a sensation of sourness. This might indicate some kind of cross-modal correspondence between vision and gustation. The aim of the current study was to provide explore whether such cross-modal correspondences influence cross-modal integration during perceptual learning. To that end, we conducted 2 experiments. Using a speeded classification task, Experiment 1 established a cross-modal correspondence between visual lightness (...) and the frequency of an auditory tone. Using a short-term priming procedure, Experiment 2 showed that manipulation of such cross-modal correspondences led to the creation of a crossmodal unit regardless of the nature of the correspondence (i.e., congruent, Experiment 2a or incongruent, Experiment 2b). However, a comparison of priming-effects sizes suggested that cross-modal correspondences modulate cross-modal integration during learning and thus leading to new learned units that have different stability over time. We discuss the implications of our results for the relation between cross-modal correspondence and perceptual learning in the context of a Bayesian explanation of cross-modal correspondences. (shrink)