Ecology, Cognition, and the Hippocampus: A Tale of Two Frogs

Abstract

The underlying hypothesis that motivates research into the relationship between ecology, cognition, and the hippocampus is that selection to solve problems in nature shapes cognition through changes in the hippocampus. This hypothesis has been explored almost exclusively in mammals and birds. However, if one is interested in the principles that shape the evolution of vertebrate cognition, work in amphibians is essential. To address this gap, we have developed a research program contrasting cognitive abilities and hippocampal neurobiology in two species of frog with distinct social and spatial ecologies. We have found that the poison frog Dendrobates auratus, a diurnal species whose interactions with the physical and social environment are complex, is more adept and flexible at spatial learning and learned inhibition than the túngara frog, a nocturnal species that lacks complex interactions with the spatial and social environment. Because spatial learning and learned inhibition are closely associated with hippocampal function in other vertebrates, we used RNA sequencing to characterize molecular differences in the hippocampus of the two species. We have found that D. auratus has greater levels of expression of genes associated with neurogenesis, synaptic plasticity, and cellular activity, and lower levels of expression of genes associated with apoptosis, compared to the túngara frog. Our studies are consistent with the idea that D. auratus, with their more complex social and spatial ecology, have enhanced hippocampally-dependent cognitive abilities compared to túngara frogs. Further characterization of the features of hippocampal neurobiology that confer distinctive cognitive abilities will help elucidate the neural features that are necessary for the evolution of enhanced hippocampally-dependent cognition.

Risk-sensitive learning is a winning strategy for leading an urban invasion

“…But as eloquently argued elsewhere ( Audet and Lefebvre, 2017 ), this term is conceptually uninformative, given the many ways in which it is applied and assessed. Of these approaches, reversal learning and serial—multiple back-to-back—reversal learning tasks are the most common experimental assays of behavioural flexibility (non-exhaustive examples of each assay in: bees; Strang and Sherry, 2014 ; Raine and Chittka, 2012 ; birds; Bond et al, 2007 ; Morand-Ferron et al, 2022 ; fish; Lucon-Xiccato and Bisazza, 2014 ; Bensky and Bell, 2020 ; frogs; Liu et al, 2016 ; Burmeister, 2022 ; reptiles; Batabyal and Thaker, 2019 ; Gaalema, 2011 ; primates; Cantwell et al, 2022 ; Lacreuse et al, 2018 ; rodents; Rochais et al, 2021 ; Boulougouris et al, 2007 ). We have shown, however, at least for our grackles, faster reversal learning is governed primarily by risk-sensitive learning, so: firstly, these go-to reversal learning assays do not necessarily measure the unit they claim to measure (a point similarly highlighted in: Aljadeff and Lotem, 2021 ; Federspiel et al, 2017 ); and secondly, formal models based on the false premise that variation in learning speed relates to variation in behavioural flexibility require reassessment ( Lea et al, 2020 ; Blaisdell et al, 2021 ; Logan et al, 2022 ; Lukas et al, 2024 ; Logan et al, 2023a ; Logan et al, 2023b ).…”

Risk-sensitive learning is a winning strategy for leading an urban invasion

“…But as eloquently argued elsewhere [37], this term is conceptually uninformative, given the many ways in which it is applied and assessed. Of these approaches, reversal learning and serial—multiple back-to-back—reversal learning tasks are the most common experimental assays of behavioural flexibility (non-exhaustive examples of each assays in: bees [38, 39]; birds [40, 41]; fish [42, 43]; frogs [44, 45]; reptiles [10, 46]; primates [47, 48]; and rodents [49, 50]). We have shown, however, at least for our grackles, faster reversal learning is governed primarily by pronounced reward-payoff sensitivity, so: firstly, these go-to experimental assays do not necessarily measure the unit they claim to measure (a point similarly highlighted in [51]); and secondly, formal models based on the false premise that variation in learning speed relates to variation in behavioural flexibility require reassessment [35, 52].…”

“…But as eloquently argued elsewhere (Audet and Lefebvre, 2017), this term is conceptually uninformative, given the many ways in which it is applied and assessed. Of these approaches, reversal learning and serial—multiple back-to-back—reversal learning tasks are the most common experimental assays of behavioural flexibility (non-exhaustive examples of each assay in bees; Strang and Sherry 2014; Raine and Chittka 2012; birds; Bond et al 2007; Morand-Ferron et al 2022; fish; Lucon-Xiccato and Bisazza 2014; Bensky and Bell 2020; frogs; Liu et al 2016; Burmeister 2022; reptiles; Batabyal and Thaker 2019; Gaalema 2011; primates; Cantwell et al 2022; Lacreuse et al 2018; and rodents; Rochais et al 2021; Boulougouris et al 2007). We have shown, however, at least for our grackles, faster reversal learning is governed primarily by risk-sensitive learning, so: firstly, these go-to reversal learning assays do not necessarily measure the unit they claim to measure (a point similarly highlighted in: Aljadeff and Lotem, 2021; Fed-erspiel et al, 2017); and secondly, formal models based on the false premise that variation in learning speed relates to variation in behavioural flexibility require reassessment (Lea et al, 2020; Blaisdell et al, 2021; Logan et al, 2022b; Lukas et al, 2023; Logan et al, 2023b,c).…”

The emergence of enhanced intelligence in a brain-inspired cognitive architecture

“…The Causal Cognitive Architecture (Schneider, 2023 , 2024 ) is a brain-inspired cognitive architecture (BICA). It is hypothesized that the navigation circuits in the amniotic ancestors of mammals duplicated many times to eventually form the neocortex (Rakic, 2009 ; Butler et al, 2011 ; Chakraborty and Jarvis, 2015 ; Fournier et al, 2015 ; Kaas, 2019 ; Güntürkün et al, 2021 ; Burmeister, 2022 ). Thus, the millions of neocortical minicolumns are modeled in the Causal Cognitive Architecture as millions of spatial maps, which are termed “navigation maps.”…”