The computer analogy of the mind has been as widely adopted in contemporary cognitive neuroscience as was the analogy of the brain as a collection of organs in phrenology. Just as the phrenologist ...would insist that each organ must have its particular function, so contemporary cognitive neuroscience is committed to the notion that each brain region must have its fundamental computation. InAfter Phrenology, Michael Anderson argues that to achieve a fully post-phrenological science of the brain, we need to reassess this commitment and devise an alternate, neuroscientifically grounded taxonomy of mental function. Anderson contends that the cognitive roles played by each region of the brain are highly various, reflecting different neural partnerships established under different circumstances. He proposes quantifying the functional properties of neural assemblies in terms of their dispositional tendencies rather than their computational or information-processing operations. Exploring larger-scale issues, and drawing on evidence from embodied cognition, Anderson develops a picture of thinking rooted in the exploitation and extension of our early-evolving capacity for iterated interaction with the world. He argues that the multidimensional approach to the brain he describes offers a much better fit for these findings, and a more promising road toward a unified science of minded organisms.
This book offers a provocative account of interdisciplinary research across the neurosciences, social sciences and humanities. Setting itself against standard accounts of interdisciplinary ...'integration,' and rooting itself in the authors' own experiences, the book establishes a radical agenda for collaboration across these disciplines. Rethinking Interdisciplinarity does not merely advocate interdisciplinary research, but attends to the hitherto tacit pragmatics, affects, power dynamics, and spatial logics in which that research is enfolded. Understanding the complex relationships between brains, minds, and environments requires a delicate, playful and genuinely experimental interdisciplinarity, and this book shows us how it can be done.
Despite the increasing popularity of Bayesian inference in empirical research, few practical guidelines provide detailed recommendations for how to apply Bayesian procedures and interpret the ...results. Here we offer specific guidelines for four different stages of Bayesian statistical reasoning in a research setting:
planning
the analysis,
executing
the analysis,
interpreting
the results, and
reporting
the results. The guidelines for each stage are illustrated with a running example. Although the guidelines are geared towards analyses performed with the open-source statistical software JASP, most guidelines extend to Bayesian inference in general.
Word associations have been used widely in psychology, but the validity of their application strongly depends on the number of cues included in the study and the extent to which they probe all ...associations known by an individual. In this work, we address both issues by introducing a new English word association dataset. We describe the collection of word associations for over 12,000 cue words, currently the largest such English-language resource in the world. Our procedure allowed subjects to provide multiple responses for each cue, which permits us to measure weak associations. We evaluate the utility of the dataset in several different contexts, including lexical decision and semantic categorization. We also show that measures based on a mechanism of spreading activation derived from this new resource are highly predictive of direct judgments of similarity. Finally, a comparison with existing English word association sets further highlights systematic improvements provided through these new norms.
This paper introduces the R package WRS2 that implements various robust statistical methods. It elaborates on the basics of robust statistics by introducing robust location, dispersion, and ...correlation measures. The location and dispersion measures are then used in robust variants of independent and dependent samples
t
tests and ANOVA, including between-within subject designs and quantile ANOVA. Further, robust ANCOVA as well as robust mediation models are introduced. The paper targets applied researchers; it is therefore kept rather non-technical and written in a tutorial style. Special emphasis is placed on applications in the social and behavioral sciences and illustrations of how to perform corresponding robust analyses in R. The R code for reproducing the results in the paper is given in the
Supplementary Materials
.
NeuroKit2 is an open-source, community-driven, and user-centered Python package for neurophysiological signal processing. It provides a comprehensive suite of processing routines for a variety of ...bodily signals (e.g., ECG, PPG, EDA, EMG, RSP). These processing routines include high-level functions that enable data processing in a few lines of code using validated pipelines, which we illustrate in two examples covering the most typical scenarios, such as an event-related paradigm and an interval-related analysis. The package also includes tools for specific processing steps such as rate extraction and filtering methods, offering a trade-off between high-level convenience and fine-tuned control. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation. Its design philosophy is centred on user-experience and accessibility to both novice and advanced users.
Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and
p
values. In part I of this series we outline ten ...prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al.
this issue
).
In recent years, Mechanical Turk (MTurk) has revolutionized social science by providing a way to collect behavioral data with unprecedented speed and efficiency. However, MTurk was not intended to be ...a research tool, and many common research tasks are difficult and time-consuming to implement as a result. TurkPrime was designed as a research platform that integrates with MTurk and supports tasks that are common to the social and behavioral sciences. Like MTurk, TurkPrime is an Internet-based platform that runs on any browser and does not require any downloads or installation. Tasks that can be implemented with TurkPrime include: excluding participants on the basis of previous participation, longitudinal studies, making changes to a study while it is running, automating the approval process, increasing the speed of data collection, sending bulk e-mails and bonuses, enhancing communication with participants, monitoring dropout and engagement rates, providing enhanced sampling options, and many others. This article describes how TurkPrime saves time and resources, improves data quality, and allows researchers to design and implement studies that were previously very difficult or impossible to carry out on MTurk. TurkPrime is designed as a research tool whose aim is to improve the quality of the crowdsourcing data collection process. Various features have been and continue to be implemented on the basis of feedback from the research community. TurkPrime is a free research platform.
Participant attentiveness is a concern for many researchers using Amazon’s Mechanical Turk (MTurk). Although studies comparing the attentiveness of participants on MTurk versus traditional subject ...pool samples have provided mixed support for this concern, attention check questions and other methods of ensuring participant attention have become prolific in MTurk studies. Because MTurk is a population that
learns
, we hypothesized that MTurkers would be more attentive to instructions than are traditional subject pool samples. In three online studies, participants from MTurk and collegiate populations participated in a task that included a measure of attentiveness to instructions (an instructional manipulation check: IMC). In all studies, MTurkers were more attentive to the instructions than were college students, even on novel IMCs (Studies 2 and 3), and MTurkers showed larger effects in response to a minute text manipulation. These results have implications for the sustainable use of MTurk samples for social science research and for the conclusions drawn from research with MTurk and college subject pool samples.
It is a crucial cognitive skill to select the relevant ones and exclude the irrelevant ones from all the stimuli we are exposed to in our constantly changing visual environment. While the ...long-standing early-late selection debates on the efficient attentional selection are still relevant, a hybrid theory was proposed by Lavie and Tsal (1994). Perceptual load theory is similar to the early selection approach in that it emphasizes limited capacity, while it is similar to late selection approaches in that it emphasizes automatic processing. In line with the theory, distractor processing depends on the task-relevant perceptual load. As perceptual load increases, unrelated stimuli can easily be excluded from the attention filter; because the capacity is full. The distractor interference effect is inevitable if the perceptual load is not high enough to fill the restricted capacity. According to the theory, the perceptual load is a key factor of the locus of selection. Although many support studies have been carried out after the theory was supposed, the number of studies inconsistent with the theory's assumptions, especially in recent years, cannot be ignored. Diverse studies have shown the importance of other factors in selective attention, such as salience, proximity, similarity, and dilution effect. In conclusion, despite being an important factor, the perceptual load is not the primary determinant in efficient attentional selection.
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