This study examines the impact of board gender diversity on financial performance of listed Indian firms in a dynamic modelling framework. Using a firm-year unit of analysis, a sample of 148 publicly listed firms across multiple industries have been studied over a period of five financial years namely FY 2008–2009 to FY 2012–2013. Employing panel data analysis, percentage of women directors is taken as the independent variable and firm performance measured by return on assets and Tobin’s Q as the dependent (...) variables. The primary results of the study using ordinary least squares and fixed effects estimation models point towards a positive and significant relationship between percentage of independent women directors and firm performance. However, results are reversed when the theoretically superior Arellano Bond estimation is used. Findings of the study indicate that the number of companies with no IWDs is reducing across the 5 years of study. This may be due to external pressure created by stakeholders. Also, the number of companies with one IWD is increasing over the period of the study, while the number of companies with two or three or more directors is more or less the same. The number of two or three IWD companies remaining the same could arguably be due to lack of availability of independent directors who are women. (shrink)

It is widely held that children’s linguistic input underdetermines the correct grammar, and that language learning must therefore be guided by innate linguistic constraints. Here, we show that a Bayesian model can learn a standard poverty‐of‐stimulus example, anaphoric one, from realistic input by relying on indirect evidence, without a linguistic constraint assumed to be necessary. Our demonstration does, however, assume other linguistic knowledge; thus, we reduce the problem of learning anaphoric one to that of learning this other knowledge. We discuss (...) whether this other knowledge may itself be acquired without linguistic constraints. (shrink)

Regier, Kay, and Khetarpal report the results of computer simulations that cluster color stimuli on the basis of their coordinates in CIELAB space, one of two commonly used perceptual color spaces. Regier and coauthors find partitions of those stimuli that are strikingly similar to the way actual color lexicons partition color space. They do not argue for the custom-made clustering method used in their simulations, nor for the assumption of CIELAB space. The present paper aims to answer the question (...) to what extent their computational results depend on these assumptions. It does this by applying a great variety of known clustering methods to Regier et al.’s stimuli, and by assuming not only CIELAB space but also CIELUV space, the other main color space. (shrink)

Things appear in perception. My article will ask whether we can recognize the perceived object, without having a concept of that object, or even a concept "object". For example, can I experience a specific shade of red, without having a concept of that specific shade? Some philosophers, like McDowell (1994) and Brewer (1999), claim for the necessity of concepts for perception. Using simple examples the article will challenge the idea that recognizing the object is based on the use of concepts. (...) Object recognition is rather based on the appearing features of the object. Finally, my idea for a new research direction is to explicate object recognition by a term of memory. When the previously experienced object is reexperienced, this environmental content is matched to stored memory representations in order to recognize the object. Perception, therefore, is not dependent on thinking. (shrink)