Rate limits in sensorimotor synchronization with auditory and visual sequences: the synchronization threshold and the benefits and costs of interval subdivision

Synchronization of finger taps with an isochronous event sequence becomes difficult when the event rate exceeds a certain limit. In Experiment 1, the synchronization threshold was reached at interonset intervals (IOIs) above 100 ms with auditory tone sequences (in a 1:4 tapping task) but at IOIs above 400 ms with visual flash sequences (1:1 tapping). Using IOIs above those limits, the author investigated in Experiment 2 the reduction in the variability of asynchronies that tends to occur when the intervals between target events are subdivided by additional identical events (1:1 vs. 1:n tapping). The subdivision benefit was found to decrease with IOI duration and to turn into a cost at IOIs of 200-250 ms in auditory sequences and at IOIs of 450-500 ms in visual sequences. The auditory results are relevant to the limits of metrical subdivision and beat rate in music. The visual results demonstrate the remarkably weak rhythmicity of (nonmoving) visual stimuli.     

Do separate processes identify objects as exemplars versus members of basic-level categories? Evidence from hemispheric specialization

When an object is identified as a specific exemplar, is it analyzed differently than when it is identified at the basic level? On the basis of a previous theory, we predicted that the left hemisphere (LH) is specialized for classifying objects at the basic level and the right hemisphere (RH) is specialized for classifying objects as specific exemplars. To test this prediction, participants were asked to view lateralized pictures of animals, artifacts, and faces of famous people; immediately after each picture was presented, a label was read aloud by the computer, and the participants decided whether the label was correct for that picture. A label could name the object at either the basic level (e.g., bird) or as an exemplar (e.g., robin). As predicted, we found that basic-level labels were matched faster when pictures were presented in the right visual field (and hence encoded initially in the LH), whereas exemplar labels were matched faster when pictures were presented in the left visual field (and hence encoded initially in the RH).     

Conversation and Behavior Games in the Pragmatics of Dialogue

In this article we present the bases for a computational theory of the cognitive processes underlying human communication. The core of the article is devoted to the analysis of the phases in which the process of comprehension of a communicative act can be logically divided: (1) literal meaning, where the reconstruction of the mental states literally expressed by the actor takes place: (2) speaker's meaning, where the partner reconstructs the communicative intentions of the actor; (3) communicative effect, where the partner possibly modifies his own beliefs and intentions; (4) reaction, where the intentions for the generation of the response are produced; and (5) response, where an overt response is constructed. The model appears to be compatible with relevant facts about human behavior. Our hypothesis is that, through communication, an actor tries to exploit the motivational structures of a partner so that the desired goal is generated. A second point is that social behavior requires that cooperation be maintained at some level. In the case of communication, cooperation is, in general, pursued even when the partner does not adhere to the actor's goals, and therefore no cooperation occurs at the behavioral level. This important distinction is reflected in the two kinds of game we introduce to account for communication. The main concept implied in communication is that two agents overtly reach a situation of shared mental states. Our model deals with sharedness through two primitives: shared beliefs and communicative intentions.     

Automated verbal credibility assessment of intentions: The model statement technique and predictive modeling

Recently, verbal credibility assessment has been extended to the detection of deceptive intentions, the use of a model statement, and predictive modeling. The current investigation combines these 3 elements to detect deceptive intentions on a large scale. Participants read a model statement and wrote a truthful or deceptive statement about their planned weekend activities (Experiment 1). With the use of linguistic features for machine learning, more than 80% of the participants were classified correctly. Exploratory analyses suggested that liars included more person and location references than truth‐tellers. Experiment 2 examined whether these findings replicated on independent‐sample data. The classification accuracies remained well above chance level but dropped to 63%. Experiment 2 corroborated the finding that liars' statements are richer in location and person references than truth‐tellers' statements. Together, these findings suggest that liars may over‐prepare their statements. Predictive modeling shows promise as an automated veracity assessment approach but needs validation on independent data.     

Using finite mixture of GLMs to explore variability in children's flexibility in a task-switching paradigm

The present study illustrates the usefulness of finite mixture of generalized linear models (GLMs) to examine variability in cognitive strategies during childhood. More precisely, it addresses this variability in set-shifting situations where task-goal updating is endogenously driven. In a task-switching paradigm 5–6-year-olds had to switch between color- and shape-matching rules as a function of a predetermined, predictable task sequence. A finite mixture of GLMs was fitted to explore individual differences in performance. The statistical model revealed five response profiles, defined by accuracy and response times. These response profiles likely correspond to different cognitive strategies with varying efficiency and differential relations to working memory capacity (assessed by backward digit span). These results illustrate the heuristic value of statistical modeling to reveal the behavioral and cognitive variability in the temporal dynamics of children's cognitive functioning.     

Rats build and update topological representations through exploration

Although rats are able to build complex spatial representations of their surroundings during exploration, the nature of the encoded information is still a matter for debate. In particular, it is not well established if rats can process the topological structure of the environment in such a way that they are aware of the connections existing between remote places. Here, rats were first exposed for four 5-min trials to a complex environment divided into several sectors that were separated by doors allowing either unrestricted or restricted access to other sectors. In the fifth test trial, we measured the behavior of the animals while they explored the same environment in which, however, they faced changes that either altered or did not alter the topological structure of the environment. In experiment 1, closing previously opened doors prevented the rat from having direct access between corresponding sectors. In experiment 2, opening previously closed doors allowed direct access between sectors that had not been directly accessible. In each experiment, control doors allowed us to discard the mere influence of door manipulation. We compared the rats’ exploratory behavior in response to door manipulations that either strongly altered or did not alter the ability to commute between sectors and found evidence that the animals displayed differential reactions to the two types of door manipulations. This implies that during exploration rats build a precise map of the connectivity of space that can be flexibly updated and used for efficient navigation.     

Defining face perception areas in the human brain: a large-scale factorial fMRI face localizer analysis

A number of human brain areas showing a larger response to faces than to objects from different categories, or to scrambled faces, have been identified in neuroimaging studies. Depending on the statistical criteria used, the set of areas can be overextended or minimized, both at the local (size of areas) and global (number of areas) levels. Here we analyzed a whole-brain factorial functional localizer obtained in a large sample of right-handed participants (40). Faces (F), objects (O; cars) and their phase-scrambled counterparts (SF, SO) were presented in a block design during a one-back task that was well matched for difficulty across conditions. A conjunction contrast at the group level {(F-SF) and (F-O)} identified six clusters: in the pulvinar, inferior occipital gyrus (so-called OFA), middle fusiform gyrus (so-called FFA), posterior superior temporal sulcus, amygdala, and anterior infero-temporal cortex, which were all strongly right lateralized. While the FFA showed the largest difference between faces and cars, it also showed the least face-selective response, responding more to cars than scrambled cars. Moreover, the FFA's larger response to scrambled faces than scrambled cars suggests that its face-sensitivity is partly due to low-level visual cues. In contrast, the pattern of activation in the OFA points to a higher degree of face-selectivity. A BOLD latency mapping analysis suggests that face-sensitivity emerges first in the right FFA, as compared to all other areas. Individual brain analyses support these observations, but also highlight the large amount of interindividual variability in terms of number, height, extent and localization of the areas responding preferentially to faces in the human ventral occipito-temporal cortex. This observation emphasizes the need to rely on different statistical thresholds across the whole brain and across individuals to define these areas, but also raises some concerns regarding any objective labeling of these areas to make them correspond across individual brains. This large-scale analysis helps understanding the set of face-sensitive areas in the human brain, and encourages in-depth single participant analyses in which the whole set of areas is considered in each individual brain.