In the last decade, reading research has seen a paradigmatic shift. A new wave of computational models of orthographic processing that offer various forms of noisy position or context-sensitive coding have revolutionized the field of visual word recognition. The influx of such models stems mainly from consistent findings, coming mostly from European languages, regarding an apparent insensitivity of skilled readers to letter order. Underlying the current revolution is the theoretical assumption that the insensitivity of readers to letter order reflects the (...) special way in which the human brain encodes the position of letters in printed words. The present article discusses the theoretical shortcomings and misconceptions of this approach to visual word recognition. A systematic review of data obtained from a variety of languages demonstrates that letter-order insensitivity is neither a general property of the cognitive system nor a property of the brain in encoding letters. Rather, it is a variant and idiosyncratic characteristic of some languages, mostly European, reflecting a strategy of optimizing encoding resources, given the specific structure of words. Since the main goal of reading research is to develop theories that describe the fundamental and invariant phenomena of reading across orthographies, an alternative approach to model visual word recognition is offered. The dimensions of a possible universal model of reading, which outlines the common cognitive operations involved in orthographic processing in all writing systems, are discussed. (shrink)

Sibley et al. (2008) report a recurrent neural network model designed to learn wordform representations suitable for written and spoken word identification. The authors claim that their sequence encoder network overcomes a key limitation associated with models that code letters by position (e.g., CAT might be coded as C‐in‐position‐1, A‐in‐position‐2, T‐in‐position‐3). The problem with coding letters by position (slot‐coding) is that it is difficult to generalize knowledge across positions; for example, the overlap between CAT and TOMCAT is lost. Although we (...) agree this is a critical problem with many slot‐coding schemes, we question whether the sequence encoder model addresses this limitation, and we highlight another deficiency of the model. We conclude that alternative theories are more promising. (shrink)

I argue, contra Frost, that when prime lexicality and target density are considered, it is not clear that there are fundamental differences between form priming effects in Semitic and European languages. Furthermore, identifying and naming printed words in these languages raises common theoretical problems. Solving these problems and developing a universal model of reading necessitates the orthographic input code.

People’s perception of their own efficacy is a critical precursor for adaptive behavioural responses to the threat posed by climate change. The present study investigated whether components of climate efficacy could be enhanced by short video messages. An online study (N = 161) compared groups of participants who received messages focusing on individual or collective behaviour. Relative to a control group, these groups showed increased levels of response efficacy but not self-efficacy. However, this did not translate to increased climate commitment; (...) mediation analysis suggested that the video messages, while increasing efficacy, may also have had a counterproductive effect on behavioural intentions, possibly by reducing the perceived urgency of action. This finding reinforces the challenge faced by climate communicators seeking to craft a message that boosts efficacy and simultaneously motivates adaptive responses to the climate crisis. (shrink)

We argue that Bayesian models are best categorized as methodological or theoretical. That is, models are used as tools to constrain theories, with no commitment to the processes that mediate cognition, or models are intended to approximate the underlying algorithmic solutions. We argue that both approaches are flawed, and that the Enlightened Bayesian approach is unlikely to help.

We introduce this special issue on Thinking about Climate Change by reflecting on the role of psychology in responding adaptively to catastrophic global threats. By way of illustration we compare the threat posed by climate change with the extinction-level threat considered in the recent film Don’t Look Up [McKay, A. (Director). (2021). Don’t Look Up [Film]. Hyperobject Industries]. Human psychology is a critical element in both scenarios. The papers in this special issue discuss the importance of clear communication of scientific (...) information, the dangers of misinformation and the possible role played by motivated reasoning, all themes that are taken up in the film. Ultimately, though, it is not enough to consider psychological factors in isolation: we must also acknowledge that cognitive flaws and psychological motivations are exploited by vested interests that profit from delaying climate action. A global response to a global crisis requires us to ‘look up’ to recognise the threat and to ‘look around’ to go beyond specialist disciplines and national boundaries. (shrink)