By Marinella Cappelletti, Wim Fias
The Mathematical mind around the Lifespan is the newest quantity within the Progress in mind Research sequence that specializes in new traits and advancements. This proven foreign sequence examines significant parts of simple and scientific learn in the neurosciences, in addition to well known and rising subfields.
- Provides a complete evaluation of the newest growth within the mathematical mind around the lifespan
- Explores new tendencies and advancements within the field
- Enhances the literature of neuroscience by way of additional increasing the confirmed, ongoing overseas sequence Progress in mind Research
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Extra info for The Mathematical Brain Across the Lifespan
2005), that respond similarly to a particular attribute of an event—ranging from more generic to very specific. , 2006). For example, different cliques can, respectively, encode (A) unusual events in general, (B) disturbing motion (C) shaking, and (D) dropping. The earthquake would be encoded as an increased activity of cliques A, B, and C but not D (binary code: 1110), whereas elevator drop as an activity of cliques A, B, and D but not C (binary code: 1101). These findings can be extrapolated to the symbolic numerals, which hierarchical relations in counting sequences are explicated by the use of positional notation.
393) but not implemented in the model. , 2004). Moreover, the emerging Gaussians in the study by Verguts and Fias (2004) showed a positive skew in the activity distribution, which is consistent with an idea that a “readout” of the magnitude is represented on a compressed number line. In the remainder of the chapter, we will refer to a model that assumes a logarithmic spacing between Gaussian magnitude representations on a number line as the analogue magnitude system (AMS). 33 34 CHAPTER 2 Nature of the preverbal system There is a clear theoretical rationale for proposing log compression for preverbal magnitude representations.
Trends Cogn. Sci. 12(6), p. 214. 004. fed to a layer, here called “summation,” where activation and noise are linearly proportional to the number of objects. The additional feature of this model is that the read-out for a given level of activation in the summation layer is represented in another layer as a Gaussian on a number line. Notably, in the study by Verguts and Fias (2004) both summation coding and Gaussian coding emerged as a result of training, whereas in Dehaene and Changeux (1993) these network properties had been handwired.