Crick and Koch (2003)

Crick, F. and Koch, C A framework for consciousness. Nature Neuroscience (2003) 6, 119-126

Abstract

Here we summarize our present approach to the problem of consciousness. After an introduction outlining our general strategy, we describe what is meant by the term "framework" and set it out under ten headings. This framework proposes a coherent scheme for explaining the neural correlates of (visual) consciousness in terms of competing cellular assemblies. Most of the ideas we favor have been suggested before, but their combination is original. We also outline some general experimental approaches to the problem and, finally, acknowledge some relevant aspects of the brain that has been left out of the proposed framework.
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特に以下の３項が興味深い。
7. Snapshots
8. Attention and binding
9. Styles of firing

VanRullen & Koch (2003)

VanRullen, R. & Koch, C. Visual selective behavior can be triggered by a feed-forward process. Journal of Cognitive Neuroscience (2003) 15, 209-217

Abstract

The ventral visual pathway implements object recognition and categorization in a hierarchy of processing areas with neuronal selectivities of increasing complexity. The presence of massive feed-back connections within this hierarchy raises the possibility that normal visual processing relies on the use of computational loops. It is not known however whether object recognition can be performed at all without such loops, i.e. in a purely feed-forward mode. By analyzing the time course of reaction times in a masked natural scene categorization paradigm, we show that the human visual system can generate selective motor responses on the basis of a single feed-forward pass. We confirm these results using a more constrained letter discrimination task, in which the rapid succession of a target and mask is actually perceived as a distractor. We show that a masked stimulus presented for only 26 ms -and often not consciously perceived- can fully determine the earliest selective motor responses: the neural representations of the stimulus and mask are thus kept separated during a short period of time corresponding to the feed-forward "sweep". Therefore, feed-back loops do not appear to be "mandatory" for visual processing. Rather, we found that such loops allow the masked stimulus to reverberate in the visual system and affect behavior for nearly 150 ms after the feed-forward sweep.

References
Christof Koch's Home Page
http://www.klab.caltech.edu/~koch/

Transmitting and decoding facial expressions.

Psychol Sci. 2005 Mar;16(3):184-9.

Smith ML, Cottrell GW, Gosselin F, Schyns PG.
Department of Psychology, University of Glasgow, 58 Hillhead Street, Glasgow, Scotland G12 0DX, United Kingdom.

This article examines the human face as a transmitter of expression signals and the brain as a decoder of these expression signals. If the face has evolved to optimize transmission of such signals, the basic facial expressions should have minimal overlap in their information. If the brain has evolved to optimize categorization of expressions, it should be efficient with the information available from the transmitter for the task. In this article, we characterize the information underlying the recognition of the six basic facial expression signals and evaluate how efficiently each expression is decoded by the underlying brain structures.

Cognitive Forum : Week4

Week 4: Cog Forum - (3:30 PM - 5:00 PM) 10/28/2005

Title: Mechanisms of Face Categorization from Cortical Activity

Speaker: Phillipe Schyns

From: University of Glasgow

Abstract: To study face categorization mechanisms in an
information system such as the brain, three generic questions must
be addressed to relate brain activity to the states of an automaton
(i.e. to derive an algorithm). The first question is that of
form: What is the nature of the brain activity supporting face
processing (i.e. the states of the brain correlated with face
processing?) The second question is that of content: What is the
information content processed in these brain states? The third
question is that of transition: How does information flow from one
brain state to the next between stimulus onset and behavioral
response? I will present new methods and results that illustrate
how information states can be estimated and interpreted directly
from brain activity.
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Quite interesting !!

θ波（EEG: 4～8Hz）が表情の処理（happy vs. neutral) に関連し、その処理時間特性が temporal window に対応するとすれば興味深い。

検定かモデル選択か

生態学の分野でも研究手法としての統計的検定とモデル選択の問題が話題になっていることを見つけた。第52回日本生態学会大会での研究集会の記録（下記）を見ると、具体的な議論と提案がなされていて興味深い（PDF資料多数）。
心理学でも、特に大学院ならば、モデル選択についての概括的かつ実際的教育が必要な段階であると思う。

References
デ－タ解析で出会う統計的問題 -- 検定かモデル選択か. 2005年 3月 30 日, 第52回日本生態学会大会 (大阪) の自由集会
http://hosho.ees.hokudai.ac.jp/~kubo/ce/2005/index.html

主観的輪郭線補間の生成モデル

Ringach & Shapley (1996)は、主観的輪郭線補間の微小生成過程に以下の確率論的モデルを適用して、パラメータを最尤推定法で推定している。
p(α)=1-0.5exp{-(α/αth)^β}
ここで、
α：Indicing Figure(Packman)のスライスされた辺が水平垂直からずれた角度（傾き）（物理量、操作された変数）
αth：弁別閾>0：推定値が81.6%に達するthreshold
β>0 ：上記の点での精神測定関数の傾き
p(α)：Induced Figure が正しく同定される(Fat or Thin) 確率（推定値）
なお、この確率論的モデルについては下記の Watson (1979)を参照。

References
Ringach, D., & Shapley, R. (1996). Spatial and temporal properties of illusory contours and amodal boundary completion, Vision Research, Vol.36, No.19, pp.3037-3050.
Watson, A. B. (1979 ). Probability summation over time. Vision Research, 19, 515-522.

最尤推定法の計算

最尤推定法を用いた確率論的モデルのパラメータ推定に、フリーの解析ソフト「R」が利用できる。
検討する価値ありと思う。

References
久保拓弥 （北大　計算生態学）2005-09-07　尤度を使った推定法あれこれ
http://hosho.ees.hokudai.ac.jp/~kubo/ml/
The R Project for Statistical Computing
http://www.r-project.org/
RjpWiki　オープンソースの統計解析システム 《R》 に関する情報交換を目的とした Wiki
http://www.okada.jp.org/RWiki/index.php?RjpWiki