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1.1 Nim Chimpsky gestures "me," "hug," and "cat" to his trainer as an increasingly worried tabby (Felis catus) looks on
2.1 A schematic of Levelt and colleagues' speech production model
2.2 An example stimulus from a picture-word interference experiment
2.3 Representation of an interactive, spreading activation model for speech production
2.4 Sound spectrograms of the phrase to catch pink salmon created from real (top) and simplified, artificial speech (bottom)
2.5 The pattern playback machine
2.6 Artificial spectrogram for the syllables /di/ and /du/
2.7 Simplified acoustic stimuli that are perceived as /da/ or /ga/. Researchers edited the stimuli so that a formant transition would be played to one ear, while the "base" (the rest of the signal) was played to the other ear. People perceived the stimulus as consisting of a "whistle" or a "chirp" at one ear and the complete syllable (/da/ or /ga/, depending on which formant transition was played) at the other ear
2.8 Japanese quail (left) and Chinchilla (right). They perceive differences between different phonemes, they look good, and they taste good
3.1 A two-object universe
3.2 Another two-object universe
3.3 A piece of a semantic network
3.4 ERP results for a priming experiment involving associatively related and semantically related pairs of words. The ERP waveforms in the box show that associated pairs (the orange lines) decreased the magnitude of the N400 effect, but semantically related pairs (the black and gray lines) did not. The response to semantically related pairs (dashed black line) diverges from the response to the unrelated word pairs (solid gray line) at a later point in time
3.5 Connectivity for dinner and dog
3.6 A hypothetical "semantic" network
3.7 Another hypothetical "semantic" network
3.8 TMS and lexical decisions. The top picture shows where TMS was applied in the left and right hemispheres. Response times on the lexical decision task appear below the brain. Left-hemisphere stimulation affected lexical decision latencies, but right-hemisphere stimulation did not. In the left hemisphere, arm words were responded to more quickly following TMS over the part of the motor cortex that controls arm movements. A similar effect was observed for leg-related words after leg-area stimulation. Sham TMS had no effect
3.9 A hypothetical bottom-up model of lexical access (for simplicity, only some of the possible connections are illustrated). Information flows in the direction indicated by the arrows
3.10 A schematic of the information flow in John Morton's logogen model
3.11 The TRACE model of lexical access. The top part shows the basic architecture. Connections with arrows at the end indicate excitatory influences; connections with round ends indicate inhibitory influences
3.12 An example of degraded input that TRACE is good at processing
3.13 A schematic of Elman's simple recurrent network model of auditory word processing: (a) the architecture of the network; (b) the semantic space that emerged after the model was trained
3.14 Results from PET neuroimaging experiments. Triangles indicate greater neural activity when participants passively looked at words, compared to a fixation cross baseline condition (solid black shapes indicate left-hemisphere activity, open shapes indicate right-hemisphere activity). Squares indicate areas with greater activity in the action-generation task versus repeating nouns out loud. Circles indicate areas with greater activity during the dangerous animals task than passive viewing of nouns
3.15 PET data showing the neural response to a semantic judgment task (top) and a phonological judgment task (bottom)
3.16 The visual word form area. The left hemisphere appears on the right side of the figure
4.1 The garden path model of syntactic parsing
4.2 Sample visual displays and eye-movement patterns
4.3 A constraint-based outlook on syntactic parsing
5.1 Contextual prerequisites for understanding: some investigations of comprehension and recall
5.2 Three turtles, a fish, and a log
5.3 Estimated activation of inappropriate meanings based on a semantic judgment task. RT = reaction time; hphone = homophone; nonhphone = nonhomophone
5.4 ERP data
5.5 Goal failure and goal success
5.6 The effect of discourse cohesion on the brain's response to discourse
5.7 Brain regions that were analyzed by Mason and Just
5.8 The average number of activated voxels in left-hemisphere brain regions (left-most bars), right-hemisphere brain regions (middle bars), and the dorsolateral prefrontal cortex on both sides of the brain. The medium gray bars show data from the highly related condition. The dark gray bars show data from the moderately related condition. And the light gray bars show activity in the distantly related condition
6.1 Quick, what does space cowboy mean? And who does it refer to?
6.2 Visual-world eye-tracking results
8.1 Picture depicting "weird" ice pick instrument (left) or no instrument (right; the control condition)
8.2 Experimental set-up. The "occluder" prevents the listener ("addressee") from seeing one of the objects. The speaker's job is to get the listener to identify the target object, without being able to guess what is behind the occluder
8.3 Examples of grids. The listener's (addressee's) view is shown on the left. The director's view is on the right. Note that some of the objects were visible to both participants (e.g. the truck), but some were visible only to the listener (e.g. the apple, the block, and the smallest of the three candles)
9.1 Sonogram of the question Where are the silences between words? Note that there are no silent gaps between words in the signal
9.2 Patterns of dogs used to train and test 7-month-old infants
9.3 Stills from a video depicting a two-participant event (left) and a one-participant event (right) used to test young children's interpretation of the novel verb blicking
10.1 A representative pattern of fixations and saccades. The asterisks mark the positions of stable fixations, while the arrows mark saccade trajectories
10.2 How some speed-reading courses suggest you should move your eyes in order to increase your reading speed
10.3 Schematic of the E-Z reader model of eye-movement control in reading
10.4 Schematic diagram of the SWIFT eye-movement system
10.5 fMRI data from English (left) and Chinese (right) bilinguals reading English (top) and Chinese (bottom) script. Activations for both nationalities and both scripts include parts of the fusiform gyrus commonly known as the visual word form area
10.6 The dual-route cascaded (DRC) model of word reading. The model accesses word meanings from print either using a visual code (medium gray shading), via the orthographic input lexicon, or an auditory code (light gray shading), via a grapheme-to-phoneme conversion mechanism that maps letters or groups of letters onto speech sounds and uses the speech sounds to activate entries in the semantic (meaning) system
10.7 A general framework for lexical access (left) and a more specific mechanism for generating pronunciations from visual input (right)
10.8 Comparison of nonword reading by 11-12-year-old children and the FAN single-route neural network model
10.9 Accuracy at reading exception words (e.g. have, pint) and nonwords (e.g. bint, tade) for surface and phonological dyslexics compared to reading level (left) and age-matched controls (right). Notice that surface dyslexics are about equally impaired on exception and nonwords, while phonological (deep) dyslexics are far more impaired at reading nonwords than familiar exception words. Both groups of dyslexics are impaired on both kinds of targets compared to age-matched controls
11.1 The word association (WAM) and concept mediation (CM) models of L1-to-L2 links
11.2 The revised hierarchical model (RHM)
11.3 When this black bear (Ursus Americanus) walked through a parking lot at Yellowstone National Park in 2008, a German tourist told the author, in German, that the bear was limping. The German tourist switched to English right away because the author's German is nicht so gut
11.4 A schematic of Green's (1998, p. 69) inhibitory control model. G represents the system's current goal. The conceptualizer represents nonlinguistic semantic representations or concepts. SAS stands for Supervisory Attention System that monitors the current goal and the current language task schema. A language task schema is a set of mental processes that can satisfy the current goal (such as translate from L2 to L1; make a lexical decision, encode a concept in your second language). The lexico-semantic system contains the lemma and lexeme representations that the bilingual speaker needs to express or decipher meanings in the two languages. I stands for input. O stands for output
11.5 Difference between congruent and incongruent trials in the Simon task by...
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