Quantifying error in effect size estimates in attention, executive function, and implicit learning.

Accurate quantification of effect sizes has the power to motivate theory and reduce misinvestment of scientific resources by informing power calculations during study planning. However, a combination of publication bias and small sample sizes (∼N = 25) hampers certainty in current effect size estimates. We sought to determine the extent to which sample sizes may produce errors in effect size estimates for four commonly used paradigms assessing attention, executive function, and implicit learning (attentional blink, multitasking, contextual cueing, and serial response task). We combined a large data set with a bootstrapping approach to simulate 1,000 experiments across a range of N (13-313). Beyond quantifying the effect size and statistical power that can be anticipated for each study design, we demonstrate that experiments with lower N may double or triple information loss. We also show that basing power calculations on effect sizes from similar studies yields a problematically imprecise estimate between 40% and 67% of the time, given commonly used sample sizes. Last, we show that skewness of intersubject behavioral effects may serve as a predictor of an erroneous estimate. We conclude with practical recommendations for researchers and demonstrate how our simulation approach can yield theoretical insights that are not readily achieved by other methods such as identifying the information gained from rejecting the null hypothesis and quantifying the contribution of individual variation to error in effect size estimates. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Stimulus-outcome associations are required for the expression of specific Pavlovian-instrumental transfer.

A series of experiments employed a specific Pavlovian-instrumental transfer (PIT) task in rats to determine the capacity of various treatments to undermine two outcome-specific stimulus-outcome (S-O) associations. Experiment 1 tested a random treatment, which involved uncorrelated presentations of the two stimuli and their predicted outcomes. This treatment disrupted the capacity of the outcome-specific S-O associations to drive specific PIT. Experiment 2 used a negative-contingency treatment during which the predicted outcomes were exclusively delivered in the absence of their associated stimulus. This treatment spared specific PIT, suggesting that it left the outcome-specific S-O associations relatively intact. The same outcome was obtained in Experiment 3, which implemented a zero-contingency treatment consisting of delivering the predicted outcomes in the presence and absence of their associated stimulus. Experiment 4 tested a mixed treatment, which distributed the predicted outcomes at an equal rate during each stimulus. This treatment disrupted the capacity of the outcome-specific S-O associations to drive specific PIT. We suggest that the mixed treatment disrupted specific PIT by generating new and competing outcome-specific S-O associations. By contrast, we propose that the random treatment disrupted specific PIT by undermining the original outcome-specific S-O associations, indicating that these associations must be retrieved to express specific PIT. We discuss how these findings inform our theoretical understanding of the mechanisms underlying this phenomenon. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Brainhack: Developing a culture of open, inclusive, community-driven neuroscience.

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress.

Evidence against the Detectability of a Hippocampal Place Code Using Functional Magnetic Resonance Imaging.

Individual hippocampal neurons selectively increase their firing rates in specific spatial locations. As a population, these neurons provide a decodable representation of space that is robust against changes to sensory- and path-related cues. This neural code is sparse and distributed, theoretically rendering it undetectable with population recording methods such as functional magnetic resonance imaging (fMRI). Existing studies nonetheless report decoding spatial codes in the human hippocampus using such techniques. Here we present results from a virtual navigation experiment in humans in which we eliminated visual- and path-related confounds and statistical limitations present in existing studies, ensuring that any positive decoding results would represent a voxel-place code. Consistent with theoretical arguments derived from electrophysiological data and contrary to existing fMRI studies, our results show that although participants were fully oriented during the navigation task, there was no statistical evidence for a place code.

The race to learn: spike timing and STDP can coordinate learning and recall in CA3.

The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1.