The study compared four sequences of representation (abstract → abstract, contextualized → contextualized, contextualized → abstract, or abstract → contextualized) during computer-based learning to determine which of the four sequences best promotes student learning.
However, it must be considered that deep learning algorithms do not necessarily attempt to construct a pre-defined sequence of representations at each layer (such as edges, eyes, faces), but instead more generally perform non-linear transformations in different layers.
Whereas Stich (1983) accepts that mental processes are computational, but denies that computations are sequences of mental representations, others accept the notion of mental representation, but deny that CTM provides the correct account of mental states and processes.
Normal speech consists of sequences of spoken representations of words, conveyed from the speaker to the listener.
We opted here for an approach that allowed us to determine the neural correlates of the sequence-specific part of sequence representations comparing trained to untrained sequences under well-controlled behavioral conditions.
In the absence of whole-genome draft sequences, high-throughput sequencing of genome representations of multiple accessions in parallel using CRoPS will supply sufficient genetic (single nucleotide) polymorphisms to allow marker-assisted selection using existing genotyping platforms.
Functional interpretations are then supported by a step-by-step sequence of graphical representations.
With our method, investigation of microarray data is supported by a step-by-step sequence of graphical representations.
It is a combination of complexity reduction methods developed initially for array-based DArT and sequencing of resulting representations on next-generation sequencing platforms.
How the sequencing of these representation formats influences learning of conceptual knowledge has been examined in prior research.
