Matthew H. Davis

Cognitive Neuroscience · 2002–2012

A researcher in the Science corpus: 13 papers; trusts 50 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

Whom Matthew H. Davis trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. K. Forster
  2. Matthew Brett
  3. Gregory Hickok
  4. Yoav Benjamini
  5. Athanassios Protopapas
  6. Catherine-Marie Longtin
  7. James L. McClelland
  8. James M. McQueen
  9. Jeffrey L. Elman
  10. Christopher R. Genovese

Who trusts Matthew H. Davis overall

  1. James M. McQueen
  2. Cathy J. Price
  3. Kathleen Rastle
  4. Davide Crepaldi
  5. Ellen F. Lau
  6. Kevin Diependaele
  7. Friedemann Pulvermüller
  8. Roel M. Willems
  9. Peter Indefrey
  10. Keith Johnson

Papers from Matthew H. Davis's perspective

Ordered by how strongly the researchers Matthew H. Davis trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Controlling the false discovery rate: a practical and powerful approach to multiple testing
  2. DMDX: A Windows display program with millisecond accuracy (Behavoir research methods, instruments & computers)
  3. A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge.
  4. Human temporal lobe activation by speech and nonspeech sounds. (Cerebral Cortex)
  5. Towards a functional neuroanatomy of speech perception. (Trends in Cognitive Sciences)
  6. Maps and streams in the auditory cortex: nonhuman primates illuminate human speech processing (Nature Neuroscience)
  7. A voxel‐based morphometry study of semantic dementia: Relationship between temporal lobe atrophy and semantic memory (Annals of Neurology)
  8. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory.
  9. Finding Structure in Time
  10. Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem

Their papers