Cognitive Science最新文献

We investigated the relations between self-reported math anxiety, task difficulty, and pupil dilation in adults and very young children during math tasks of varying difficulty levels. While task difficulty significantly influenced pupillary responses in both groups, the association between self-reported math anxiety and pupil dilation differed across age cohorts. The children exhibited resilience to the effects of math anxiety, hinting at additional influential factors such as formal math education experiences shaping their relations to mathematics and their impact on cognitive processes over time. Contrary to expectations, no significant association between self-reported math anxiety and pupil dilation during task anticipation was found in either group. In adults, math anxiety influenced pupil dilation exclusively during the initial phase of task processing indicating heightened cognitive load, but this influence diminished during sustained task processing. Theoretical implications emphasize the need for exploring individual differences, cognitive strategies, and the developmental trajectory of math anxiety in very young children.

While a large body of work in sentence comprehension has explored how different types of linguistic information are used to guide syntactic parsing, less is known about the effect of discourse structure. This study investigates this question, focusing on the main and subordinate discourse contrast manifested in the distinction between restrictive relative clauses (RRCs) and appositive relative clauses (ARCs) in American English. In three self-paced reading experiments, we examined whether both RRCs and ARCs interfere with the matrix clause content and give rise to the agreement attraction effect. While the standard attraction effect was consistently observed in the baseline RRC structures, the effect varied in the ARC structures. These results collectively suggest that discourse structure indeed constrains syntactic dependency resolution. Most importantly, we argue that what is at stake is not the static discourse structure properties at the global sentence level. Instead, attention should be given to the incremental update of the discourse structure in terms of which discourse questions are active at any given moment of a discourse. The current findings have implications for understanding the way discourse structure, specifically the active state of discourse questions, constrains memory retrieval.

Models of word meaning that exploit patterns of word usage across large text corpora to capture semantic relations, like the topic model and word2vec, condense word-by-context co-occurrence statistics to induce representations that organize words along semantically relevant dimensions (e.g., synonymy, antonymy, hyponymy, etc.). However, their reliance on latent representations leaves them vulnerable to interference, makes them slow learners, and commits to a dual-systems account of episodic and semantic memory. We show how it is possible to construct the meaning of words online during retrieval to avoid these limitations. We implement a spreading activation account of word meaning in an associative net, a one-layer highly recurrent network of associations, called a Dynamic-Eigen-Net, that we developed to address the limitations of earlier variants of associative nets when scaling up to deal with unstructured input domains like natural language text. We show that spreading activation using a one-hot coded Dynamic-Eigen-Net outperforms the topic model and reaches similar levels of performance as word2vec when predicting human free associations and word similarity ratings. Latent Semantic Analysis vectors reached similar levels of performance when constructed by applying dimensionality reduction to the Shifted Positive Pointwise Mutual Information but showed poorer predictability for free associations when using an entropy-based normalization. An analysis of the rate at which the Dynamic-Eigen-Net reaches asymptotic performance shows that it learns faster than word2vec. We argue in favor of the Dynamic-Eigen-Net as a fast learner, with a single-store, that is not subject to catastrophic interference. We present it as an alternative to instance models when delegating the induction of latent relationships to process assumptions instead of assumptions about representation.

Visual working memory (VWM) refers to the temporary storage and manipulation of visual information. Although visually different, objects we view and remember can share the same higher-level category information, such as an apple, orange, and banana all being classified as fruit. We study the influence of category information on VWM, focusing on the question of whether stimulus category coherence (i.e., whether all to-be-remembered items belong to the same semantic category) influences VWM performance. This question is addressed in two behavioral experiments using a change-detection paradigm and a rational analysis using an ideal observer based on a Bayesian model. Both experimental participants and the ideal observer often, but not always, performed numerically better on coherent trials (i.e., when all stimuli belonged to the same category). We hypothesize that the influence of category coherence information on VWM may be task-dependent and/or stimulus-dependent. In conditions when category coherence information is highly valuable for task performance, as indicated by the ideal observer, then participants tended to make use of it. However, when the ideal observer suggested this information was not crucial to performance, participants did not. In addition, both participants and the ideal observer showed a bias toward responding “same,” and often showed a stronger influence of category coherence on change trials. The consistencies between participant and ideal observer responses suggest participants often behaved as they did because these behaviors are optimal (or approximately so) for maximizing task performance. This may help explain conflicting results reported in the scientific literature.

When people talk about kinship systems, they often use co-speech gestures and other representations to elaborate. This paper investigates such polysemiotic (spoken, gestured, and drawn) descriptions of kinship relations, to see if they display recurring patterns of conventionalization that capture specific social structures. We present an exploratory hypothesis-generating study of descriptions produced by a lesser-known ethnolinguistic community to the cognitive sciences: the Paamese people of Vanuatu. Forty Paamese speakers were asked to talk about their family in semi-guided kinship interviews. Analyses of the speech, gesture, and drawings produced during these interviews revealed that lineality (i.e., mother's side vs. father's side) is lateralized in the speaker's gesture space. In other words, kinship members of the speaker's matriline are placed on the left side of the speaker's body and those of the patriline are placed on their right side, when they are mentioned in speech. Moreover, we find that the gesture produced by Paamese participants during verbal descriptions of marital relations are performed significantly more often on two diagonal directions of the sagittal axis. We show that these diagonals are also found in the few diagrams that participants drew on the ground to augment their verbo-gestural descriptions of marriage practices with drawing. We interpret this behavior as evidence of a spatial template, which Paamese speakers activate to think and communicate about family relations. We therefore argue that extending investigations of kinship structures beyond kinship terminologies alone can unveil additional key factors that shape kinship cognition and communication and hereby provide further insights into the diversity of social structures.

In isolated English word reading, readers have the optimal performance when their initial eye fixation is directed to the area between the beginning and word center, that is, the optimal viewing position (OVP). Thus, how well readers voluntarily direct eye gaze to this OVP during isolated word reading may be associated with reading performance. Using Eye Movement analysis with Hidden Markov Models, we discovered two representative eye movement patterns during lexical decisions through clustering, which focused at the OVP and the word center, respectively. Higher eye movement similarity to the OVP-focusing pattern predicted faster lexical decision time in addition to cognitive abilities and lexical knowledge. However, the OVP-focusing pattern was associated with longer isolated single letter naming time, suggesting conflicting visual abilities required for identifying isolated letters and multi-letter words. In contrast, in both word and pseudoword naming, although clustering did not reveal an OVP-focused pattern, higher consistency of the first fixation as measured in entropy predicted faster naming time in addition to cognitive abilities and lexical knowledge. Thus, developing a consistent eye movement pattern focusing on the OVP is essential for word orthographic processing and reading fluency. This finding has important implications for interventions for reading difficulties.

Early number skills represent critical milestones in children's cognitive development and are shaped over years of interacting with quantities and numerals in various contexts. Several connectionist computational models have attempted to emulate how certain number concepts may be learned, represented, and processed in the brain. However, these models mainly used highly simplified inputs and focused on limited tasks. We expand on previous work in two directions: First, we train a model end-to-end on video demonstrations in a synthetic environment with multimodal visual and language inputs. Second, we use a more holistic dataset of 35 tasks, covering enumeration, set comparisons, symbolic digits, and seriation. The order in which the model acquires tasks reflects input length and variability, and the resulting trajectories mostly fit with findings from educational psychology. The trained model also displays symbolic and non-symbolic size and distance effects. Using techniques from interpretability research, we investigate how our attention-based model integrates cross-modal representations and binds them into context-specific associative networks to solve different tasks. We compare models trained with and without symbolic inputs and find that the purely non-symbolic model employs more processing-intensive strategies to determine set size.

How situated embodied agents may achieve goals using knowledge is the classical question of natural and artificial intelligence. How organisms achieve this with their nervous systems is a central challenge for a neural theory of embodied cognition. To structure this challenge, we borrow terms from Searle's analysis of intentionality in its two directions of fit and six psychological modes (perception, memory, belief, intention-in-action, prior intention, desire). We postulate that intentional states are instantiated by neural activation patterns that are stabilized by neural interaction. Dynamic instabilities provide the neural mechanism for initiating and terminating intentional states and are critical to organizing sequences of intentional states. Beliefs represented by networks of concept nodes are autonomously learned and activated in response to desired outcomes. The neural dynamic principles of an intentional agent are demonstrated in a toy scenario in which a robotic agent explores an environment and paints objects in desired colors based on learned color transformation rules.

Quantified modal inferences interest logicians, linguists, and computer scientists, but no previous psychological study of them appears to be in the literature. Here is an example of one:

People tend to conclude: Paulo is possibly a businessman (Experiment 1). It seems plausible, and it follows from an intuitive mental model in which Paulo is one of a set of artists who are businessmen. Further deliberation can yield a model of an alternative possibility in which Paulo is not one of the artists, which confirms that the conclusion is only a possibility. The snag is that standard modal logics, which deal with possibilities, cannot yield a particular conclusion to any premises: Infinitely many follow validly (from any premises) but they do not include the present conclusion. Yet, further experiments corroborated a new mental model theory's predictions for various inferences (Experiment 2), for the occurrence of factual conclusions drawn from premises about possibilities (Experiment 3) and for inferences from premises of modal syllogisms (Experiment 4). The theory is therefore plausible, but we explore the feasibility of a cognitive theory based on modifications to modal logic.

Speakers tend to produce disfluencies when naming unexpected or complex items; in turn, when perceiving disfluency, listeners tend to expect upcoming reference to items that are unexpected or complex to name. In two experiments, we examined if these disfluency-based expectations are routine, or instead, if they adapt to the way the speaker uses disfluency in the current context in a talker-specific manner. Participants listened to instructions to look at objects in contexts with several images, some of which lacked conventional names. We manipulated the co-occurrence of disfluency and reference to novel versus familiar objects in a single talker situation (Experiment 1) and in a multi-talker situation (Experiment 2). In the predictive condition, disfluent expressions referred to novel objects, and fluent expressions referred to familiar objects. In the nonpredictive condition, fluent and disfluent trials referred to either familiar or novel objects. Participants’ gaze revealed that listeners more readily predicted familiar images for fluent trials and novel images for disfluent trials in the predictive condition than in the nonpredictive condition. In sum, listeners adapted their expectations about upcoming words based on recent experience with disfluency. Disfluency is not invariably processed, but instead a cue that is flexibly interpreted depending on the local context even in a multi-talker setting.