Validity of participant recorded pedometer step logs in free-living adults.

Purposes. The purposes of this study were to (1) examine the validity of participant recorded pedometer step logs, (2) examine the relationship between steps per day and percent bodyfat (% BF), and (3) examine differences in steps per day by BMI category (< 25 m/kg2 vs. ≥ 25 kg/m2). Methods . Participants (N = 89; Male: n = 29, age = 37.97 +/- 9.41 years, BMI = 25.87 +/- 4.42 kg/m2, % BF = 21.66 +/- 6.21%; Female: n = 60, age = 40.07 +/- 10.72, BMI = 24.83 +/- 4.72 kg/m 2, % BF = 33.73 +/- 8.11%) in this cross-sectional, descriptive study simultaneously wore a sealed pedometer, unsealed pedometer, and Actigraph accelerometer for nine consecutive days. Body composition was assessed via air-displacement plethysmography (BOD POD). Descriptive statistics, tests of equivalence, correlation coefficients, and independent t-tests were calculated. Three conditions were examined for validity: raw Actigraph steps per day (RAW) vs. participant recorded steps per day (PSD), Actigraph steps corrected for vehicular travel (CORRECTED) vs. PSD, and total accumulated steps from the sealed pedometer (SEALED) vs. total accumulated steps from the participant recorded pedometer (PTOT). Results. There was a strong correlation between RAW and PSD (r = 0.88, p < 0.0001). However, RAW and PSD were not equivalent. Similarly, CORRECTED and PSD resulted in a strong correlation (r = 0.88, p < 0.0001), but they were not equivalent. Comparing SEALED and PTOT indicated a strong correlation (r = 0.96, p < 0.0001) and equivalence. All correlations for steps per day and % BF were moderate (range: r = 0.40 to 0.45). There was a significant difference in steps per day by BMI category in PSD (p = 0.03), but not in RAW and CORRECTED. Conclusions. These results indicate (1) acceptable validity for participant recorded pedometer step logs, (2) moderate relationships between steps per day and % BF, and (3) a significant difference in steps per day by BMI category in PST, but not in RAW and CORRECTED. Future research should attempt to further explain the relationship between Actigraph and pedometer-derived steps

The human brain reactivates context-specific past information at event boundaries of naturalistic experiences

Although we perceive the world in a continuous manner, our experience is partitioned into discrete events. However, to make sense of these events, they must be stitched together into an overarching narrative – a model of unfolding events. It has been proposed that such a stitching process happens in offline neural reactivations when rodents build models of spatial environments. Here we show that, whilst understanding a natural narrative, humans reactivate neural representations of past events. Similar to offline replay, these reactivations occur in hippocampus and default mode network, where reactivations are selective to relevant past events. However, these reactivations occur, not during prolonged offline periods, but at the boundaries between ongoing narrative events. These results, replicated across two datasets, suggest reactivations as a candidate mechanism for binding temporally distant information into a coherent understanding of ongoing experience

Prediction and Generalisation over Directed Actions by Grid Cells

Knowing how the effects of directed actions generalise to new situations (e.g. moving North, South, East and West, or turning left, right, etc.) is key to rapid generalisation across new situations. Markovian tasks can be characterised by a state space and a transition matrix and recent work has proposed that neural grid codes provide an efficient representation of the state space, as eigenvectors of a transition matrix reflecting diffusion across states, that allows efficient prediction of future state distributions. Here we extend the eigenbasis prediction model, utilising tools from Fourier analysis, to prediction over arbitrary translation-invariant directed transition structures (i.e. displacement and diffusion), showing that a single set of eigenvectors can support predictions over arbitrary directed actions via action-specific eigenvalues. We show how to define a "sense of direction" to combine actions to reach a target state (ignoring task-specific deviations from translation-invariance), and demonstrate that adding the Fourier representations to a deep Q network aids policy learning in continuous control tasks. We show the equivalence between the generalised prediction framework and traditional models of grid cell firing driven by self-motion to perform path integration, either using oscillatory interference (via Fourier components as velocity-controlled oscillators) or continuous attractor networks (via analysis of the update dynamics). We thus provide a unifying framework for the role of the grid system in predictive planning, sense of direction and path integration: supporting generalisable inference over directed actions across different tasks.Comment: In Proceedings of ICLR 202

Counterfactual Choice and Learning in a Neural Network Centered on Human Lateral Frontopolar Cortex

Decision making and learning in a real-world context require organisms to track not only the choices they make and the outcomes that follow but also other untaken, or counterfactual, choices and their outcomes. Although the neural system responsible for tracking the value of choices actually taken is increasingly well understood, whether a neural system tracks counterfactual information is currently unclear. Using a three-alternative decision-making task, a Bayesian reinforcement-learning algorithm, and fMRI, we investigated the coding of counterfactual choices and prediction errors in the human brain. Rather than representing evidence favoring multiple counterfactual choices, lateral frontal polar cortex (lFPC), dorsomedial frontal cortex (DMFC), and posteromedial cortex (PMC) encode the reward-based evidence favoring the best counterfactual option at future decisions. In addition to encoding counterfactual reward expectations, the network carries a signal for learning about counterfactual options when feedback is available—a counterfactual prediction error. Unlike other brain regions that have been associated with the processing of counterfactual outcomes, counterfactual prediction errors within the identified network cannot be related to regret theory. Furthermore, individual variation in counterfactual choice-related activity and prediction error-related activity, respectively, predicts variation in the propensity to switch to profitable choices in the future and the ability to learn from hypothetical feedback. Taken together, these data provide both neural and behavioral evidence to support the existence of a previously unidentified neural system responsible for tracking both counterfactual choice options and their outcomes

Barrow cemeteries in the Neolithic of north-western Europe. The case of Western Mecklenburg (Germany)

Neolithic funerary monuments across north-west Europe are considered as cemeteries and here divided into two types: single-mound cemeteries, and multi-mound cemeteries. Their general characteristics are discussed in relation to models of access to the internal chambers, and the distribution of chambers within their cover-mounds. The 238 megalithic tombs recorded in Western Mecklenburg are classified according to whether they are single-mound cemeteries or components of multi-mound cemeteries, and the distributions compared. Examples and case studies are described, and possible understandings of the emergence of multi-mound cemeteries are considered in relation to social organization and connections with the landscape. Abstrakt: Neolityczne grobowce w Europie Północno-Zachodniej występują w postaci pojedynczych nasypów i skupisk kurhanów. W pracy przedyskutowano ich podstawowe cechy z uwzględnieniem dostępu do głównej komory i jej lokalizacji wewnątrz nasypów. W zachodniej Meklemburgii zarejestrowano 238 grobowców megalitycznych, które sklasyfikowano pod względem liczby konstrukcji na stanowisku oraz porównano pod względem rozprzestrzenienia. Opisano przykłady oraz możliwe przyczyny pojawienia się cmentarzysk z wieloma konstrukcjami, rozważane na tle organizacji społecznej i relacji do elementów krajobrazu

Generalisation of structural knowledge in the hippocampal-entorhinal system

A central problem to understanding intelligence is the concept of generalisation. This allows previously learnt structure to be exploited to solve tasks in novel situations differing in their particularities. We take inspiration from neuroscience, specifically the hippocampal-entorhinal system known to be important for generalisation. We propose that to generalise structural knowledge, the representations of the structure of the world, i.e. how entities in the world relate to each other, need to be separated from representations of the entities themselves. We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories and generalise structural knowledge. Spatial neuronal representations mirroring those found in the brain emerge, suggesting spatial cognition is an instance of more general organising principles. We further unify many entorhinal cell types as basis functions for constructing transition graphs, and show these representations effectively utilise memories. We experimentally support model assumptions, showing a preserved relationship between entorhinal grid and hippocampal place cells across environments

Disentangling with Biological Constraints: A Theory of Functional Cell Types

Neurons in the brain are often finely tuned for specific task variables. Moreover, such disentangled representations are highly sought after in machine learning. Here we mathematically prove that simple biological constraints on neurons, namely nonnegativity and energy efficiency in both activity and weights, promote such sought after disentangled representations by enforcing neurons to become selective for single factors of task variation. We demonstrate these constraints lead to disentangling in a variety of tasks and architectures, including variational autoencoders. We also use this theory to explain why the brain partitions its cells into distinct cell types such as grid and object-vector cells, and also explain when the brain instead entangles representations in response to entangled task factors. Overall, this work provides a mathematical understanding of why, when, and how neurons represent factors in both brains and machines, and is a first step towards understanding of how task demands structure neural representations

Generative replay underlies compositional inference in the hippocampal-prefrontal circuit

Human reasoning depends on reusing pieces of information by putting them together in new ways. However, very little is known about how compositional computation is implemented in the brain. Here, we ask participants to solve a series of problems that each require constructing a whole from a set of elements. With fMRI, we find that representations of novel constructed objects in the frontal cortex and hippocampus are relational and compositional. With MEG, we find that replay assembles elements into compounds, with each replay sequence constituting a hypothesis about a possible configuration of elements. The content of sequences evolves as participants solve each puzzle, progressing from predictable to uncertain elements and gradually converging on the correct configuration. Together, these results suggest a computational bridge between apparently distinct functions of hippocampal-prefrontal circuitry and a role for generative replay in compositional inference and hypothesis testing

Actionable Neural Representations: Grid Cells from Minimal Constraints

To afford flexible behaviour, the brain must build internal representations that mirror the structure of variables in the external world. For example, 2D space obeys rules: the same set of actions combine in the same way everywhere (step north, then south, and you won't have moved, wherever you start). We suggest the brain must represent this consistent meaning of actions across space, as it allows you to find new short-cuts and navigate in unfamiliar settings. We term this representation an `actionable representation'. We formulate actionable representations using group and representation theory, and show that, when combined with biological and functional constraints - non-negative firing, bounded neural activity, and precise coding - multiple modules of hexagonal grid cells are the optimal representation of 2D space. We support this claim with intuition, analytic justification, and simulations. Our analytic results normatively explain a set of surprising grid cell phenomena, and make testable predictions for future experiments. Lastly, we highlight the generality of our approach beyond just understanding 2D space. Our work characterises a new principle for understanding and designing flexible internal representations: they should be actionable, allowing animals and machines to predict the consequences of their actions, rather than just encode