Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

The theory of affordances

People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms-for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world's alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several "visual Turing tests" probing the model's creative generalization abilities, which in many cases are indistinguishable from human behavior.

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Human-level concept learning through probabilistic program induction.

Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

A sensorimotor account of vision and visual consciousness-Authors' Response-Acting out our sensory experience

http://mriquestions.com/uploads/3/4/5/7/34572113/language_review1-s2.0-s1053811912004703-main.pdf

A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading

The Case for Letter Expertise

The concrete-abstract categorization scheme has guided several research programs. A popular way to classify words into one of these categories is to calculate a word's mean value in a Concreteness or Imageability rating scale. However, this procedure has several limitations. For instance, results can be highly distorted by outliers, ascribe differences among words when none may exist, and neglect rating trends in participants. We suggest using an alternative procedure to analyze rating scale data called median polish analysis (MPA). MPA is tolerant to outliers and accounts for information in multiple dimensions, including trends among participants. MPA performance can be readily evaluated using an effect size measure called analog R 2 and be integrated with bootstrap 95% confidence intervals, which can prevent assigning inexistent differences among words. To compare these analysis procedures, we asked 80 participants to rate a set of nouns and verbs using four different rating scales: Action, Concreteness, Imageability, and Multisensory. We analyzed the data using both two-way and three-way MPA models. We also calculated 95% CIs for the two-way models. Categorizing words with the Action scale revealed a continuum of word meaning for both nouns and verbs. The remaining scales produced dichotomous or stratified results for nouns, and continuous results for verbs. While the sample mean analysis generated continua irrespective of the rating scale, MPA differentiated among dichotomies and continua. We conclude that MPA allowed us to better classify words by discarding outliers, focusing on main trends, and considering the differences in rating criteria among participants.

A new statistical model for analyzing rating scale data pertaining to word meaning

The movements we produce or observe others produce can help us learn. Two forms of movement that are commonplace in our daily lives are actions, hand movements that directly manipulate our environment, and gestures, hand movements that accompany speech and represent ideas but do not lead to physical changes in the environment. Both action and gesture have been found to influence cognition, facilitating our ability to learn and remember new information (e.g., Calvo-Merino, Glaser, Grezes, Passingham, & Haggard, 2005; Casile & Giese, 2006; Chao & Martin, 2000; Cook, Mitchell, & GoldinMeadow, 2008; Goldin-Meadow, Cook, & Mitchell, 2009; Goldin-Meadow et al., 2012; James, 2010; James & Atwood, 2009; James & Gauthier, 2006; James & Maouene, 2009; James & Swain, 2011; Longcamp, Anton, Roth, & Velay, 2003; Longcamp, Tanskanen, & Hari, 2006; Pulvermüller, 2001; Wakefield & James, 2015). However, the two types of movement may affect learning in different ways. In previous work, the effects of action and gesture on learning have been considered separately (but see Novack, Congdon, Hemani-Lopez, & Goldin-Meadow, 2014). Our goal here is to directly compare children’s ability to learn from actions on objects versus gestures off objects. We consider this question in the realm of word learning, specifically, teaching children verbs for actions that are performed on objects. We also ask whether learning through these movements unfolds differently when movements are produced versus observed by a child. More broadly, our study is a first step in understanding how information is learned, generalized, and retained based on whether it is expressed through action or gesture.

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Representational Gesture as a Tool for Promoting Verb Learning in Young Children

In the age of technology, writing by hand has become less common than texting and keyboarding. Learning letters by hand, however, has been shown to have profound developmental importance. One aspect of writing by hand that has been understudied is the effect of learning symbols stroke-by-stroke, a dynamic action that does not occur with keyboarding. We trained children to draw novel symbols in either an instructed stroke order or in a self-directed stroke order and tested: (1) whether learning novel symbols in a self-directed stroke order benefits subsequent recognition more than learning in a specified stroke order, (2) whether seeing novel symbols unfold in the stroke order that was taught would aid in recognition, and (3) whether any effects are age-dependent. Our results demonstrate that producing a symbol with a self-directed stroke order provides more benefit to symbol recognition than instructed stroke orders in 4.0-4.5-year-old children but not in 4.5-5.0-year-old children. We found, further, that the observed recognition benefits were not affected by seeing the symbol unfold in the same stroke order it was learned during testing, suggesting that the learning was not reliant upon the exact stroke order experienced during learning. These results stress the importance of allowing children to produce symbols in a self-directed manner and, by extension, that constraining how a child learns to write can adversely affect subsequent recognition.

/pdf/constraining-stroke-order-during-manual-symbol-learning-1szsiicr6h.pdf

Constraining Stroke Order During Manual Symbol Learning Hinders Subsequent Recognition in Children Under 4 1/2 Years.

To set the stage for this multidisciplinary discussion among experts on the challenging topic of learning with multimodal technology, weasksomebasic questions:• What have neuroscience, cognitive and learning sciences, and humancomputer interaction findings taught us about how humans learn?• What are the implications for designing more effective educational technologies, in particular ones that leverage emerging multimodal-multisensor capabilities?Computer technologies are becoming an increasingly influential set of tools in a student's classroom experience. However, large-scale assessments conducted on laptop initiatives and other technology programs have not always yielded significantly improved student performance on standardized achievement tests or cognitive measures. The purpose of this chapter is to discuss what role multimodalmultisensor technologies potentially could play in improving support for learning and education. The expert neuroscience, cognitive and learning sciences, and computer science discussants involved in this exchange consider what we know about how multisensory-multimodal learning occurs, and the implications for how we could develop multimodal-multisensor interfaces that more effectively stimulate thinking and learning than past computer interfaces.

Karin H. James

Papers

The Case for Letter Expertise

A new statistical model for analyzing rating scale data pertaining to word meaning

Representational Gesture as a Tool for Promoting Verb Learning in Young Children

Constraining Stroke Order During Manual Symbol Learning Hinders Subsequent Recognition in Children Under 4 1/2 Years.

Perspectives on learning with multimodal technology