Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the ''representation wars'' of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing's account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits a resemblance-based representational architecture with organism-relative contents that functions in the service of pragmatic success, not veridical representation. Finally, I argue that internal representation so understood is either impervious to the three anti-representationalist challenges I outline or can actively embrace them.

I clarify and defend the hypothesis that human belief formation is sensitive to social rewards and punishments, such that beliefs are sometimes formed based on unconscious expectations of their likely effects on other agents – agents who frequently reward us when we hold ungrounded beliefs and punish us when we hold reasonable ones. After clarifying this phenomenon and distinguishing it from other sources of bias in the psychological literature, I argue that the hypothesis is plausible on theoretical grounds and I show how it illuminates and unifies a range of psychological phenomena, including confabulation and rationalisation, positive illusions, and identity‐protective cognition.

Salt marsh sedimentary organic matter (SOM) is a mixture of organic carbon from several sources difficult to identify quantitatively. Geochemical analyses of sediment cores at 4 sites in salt marshes at North Inlet, South Carolina (USA), dominated by Spartina alterniflora, were used to investigate accumulation and diagenesis of organic matter in sediments. Stable carbon isotope ratios ( 6 C ) and concentrations of organic carbon in the fine fraction of SOM ranged from -22 to -17 %O and 2 to 9 %, respectively. 6° values were significantly more positive in sediments from a short-form Spartina zone than from intermediate or tall-form Spartina areas. Samples from the site dominated by short-form S. alterniflora also contained significantly higher amounts of organic carbon than sites closer to the tidal creek, and demonstrated a positive correlation between organic carbon content and isotopically more positive 6° values. Spartina litter buried for 1.25 yr and Spartina lignin had 613C values of -15.35 and -16.34 %o respectively and were significantly more depleted in "C than fresh S. alterniflora (-13.63 %o), but not as depleted as the fine fraction of SOM. However, litter harvested from the marsh surface after 1.25 yr of decomposition had a S1^C value of -13.75 %o. The S^C values of SOM appear to be influenced by a combination of processes, including the selective preservation of isotopically hght refractory carbon, aerobic and anaerobic decay processes, sedimentation of allochthonous carbon from plankton or terrestrial sources, and bioturbation.

When the costs of acquiring knowledge outweigh the benefits of possessing it, ignorance is rational. In this paper I clarify and explore a related but more neglected phenomenon: cases in which ignorance is motivated by the anticipated costs of possessing knowledge, not acquiring it. The paper has four aims. First, I describe the psychological and social factors underlying this phenomenon of motivated ignorance. Second, I describe those conditions in which it is instrumentally rational. Third, I draw on evidence from the social sciences to argue that this phenomenon of rational motivated ignorance plays an important but often unappreciated role in one of the most socially harmful forms of ignorance today: voter ignorance of societal risks such as climate change. Finally, I consider how to address the high social costs associated with rational motivated ignorance.

We argue that one important aspect of the "cognitive neuroscience revolution" identified by Boone and Piccinini (Synthese 193(5):1509-1534. doi:10.1007/ s11229-015-0783-4, 2015) is a dramatic shift away from thinking of cognitive representations as arbitrary symbols towards thinking of them as icons that replicate structural characteristics of their targets. We argue that this shift has been driven both "from below" and "from above"-that is, from a greater appreciation of what mechanistic explanation of information-processing systems involves ("from below"), and from a greater appreciation of the problems solved by bio-cognitive systems, chiefly regulation and prediction ("from above"). We illustrate these arguments by reference to examples from cognitive neuroscience, principally representational similarity analysis and the emergence of (predictive) dynamical models as a central postulate in neurocognitive research.

No abstract

No abstract

No abstract