Over- and underweighting of extreme values in decisions from sequential samples.

People routinely make decisions based on samples of numerical values. A common conclusion from the literature in psychophysics and behavioral economics is that observers subjectively compress magnitudes, such that extreme values have less sway over people's decisions than prescribed by a normative model (underweighting). However, recent studies have reported evidence for anti-compression, that is, the relative overweighting of extreme values. Here, we investigate potential reasons for this discrepancy in findings and propose that it might reflect adaptive responses to different task requirements. We performed a large-scale study (n = 586) of sequential numerical integration, manipulating (a) the task requirement (averaging a single stream or comparing two interleaved streams of numbers), (b) the distribution of sample values (uniform or Gaussian), and (c) their range (1-9 or 100-900). The data showed compression of subjective values in the averaging task, but anticompression in the comparison task. This pattern held for both distribution types and for both ranges. In model simulations, we show that either compression or anticompression can be beneficial for noisy observers, depending on the sample-level processing demands imposed by the task. This suggests that the empirically observed patterns of over- and underweighting might reflect adaptive responses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Geometry of visuospatial working memory information in miniature gaze patterns.

Stimulus-dependent eye movements have been recognized as a potential confound in decoding visual working memory information from neural signals. Here we combined eye-tracking with representational geometry analyses to uncover the information in miniature gaze patterns while participants (n = 41) were cued to maintain visual object orientations. Although participants were discouraged from breaking fixation by means of real-time feedback, small gaze shifts (<1°) robustly encoded the to-be-maintained stimulus orientation, with evidence for encoding two sequentially presented orientations at the same time. The orientation encoding on stimulus presentation was object-specific, but it changed to a more object-independent format during cued maintenance, particularly when attention had been temporarily withdrawn from the memorandum. Finally, categorical reporting biases increased after unattended storage, with indications of biased gaze geometries already emerging during the maintenance periods before behavioural reporting. These findings disclose a wealth of information in gaze patterns during visuospatial working memory and indicate systematic changes in representational format when memory contents have been unattended.

EEG-representational geometries and psychometric distortions in approximate numerical judgment.

When judging the average value of sample stimuli (e.g., numbers) people tend to either over- or underweight extreme sample values, depending on task context. In a context of overweighting, recent work has shown that extreme sample values were overly represented also in neural signals, in terms of an anti-compressed geometry of number samples in multivariate electroencephalography (EEG) patterns. Here, we asked whether neural representational geometries may also reflect a relative underweighting of extreme values (i.e., compression) which has been observed behaviorally in a great variety of tasks. We used a simple experimental manipulation (instructions to average a single-stream or to compare dual-streams of samples) to induce compression or anti-compression in behavior when participants judged rapid number sequences. Model-based representational similarity analysis (RSA) replicated the previous finding of neural anti-compression in the dual-stream task, but failed to provide evidence for neural compression in the single-stream task, despite the evidence for compression in behavior. Instead, the results indicated enhanced neural processing of extreme values in either task, regardless of whether extremes were over- or underweighted in subsequent behavioral choice. We further observed more general differences in the neural representation of the sample information between the two tasks. Together, our results indicate a mismatch between sample-level EEG geometries and behavior, which raises new questions about the origin of common psychometric distortions, such as diminishing sensitivity for larger values.

Control over sampling boosts numerical evidence processing in human decisions from experience.

When acquiring information about choice alternatives, decision makers may have varying levels of control over which and how much information they sample before making a choice. How does control over information acquisition affect the quality of sample-based decisions? Here, combining variants of a numerical sampling task with neural recordings, we show that control over when to stop sampling can enhance (i) behavioral choice accuracy, (ii) the build-up of parietal decision signals, and (iii) the encoding of numerical sample information in multivariate electroencephalogram patterns. None of these effects were observed when participants could only control which alternatives to sample, but not when to stop sampling. Furthermore, levels of control had no effect on early sensory signals or on the extent to which sample information leaked from memory. The results indicate that freedom to stop sampling can amplify decisional evidence processing from the outset of information acquisition and lead to more accurate choices.

Asymmetric reinforcement learning facilitates human inference of transitive relations.

Humans and other animals are capable of inferring never-experienced relations (for example, A > C) from other relational observations (for example, A > B and B > C). The processes behind such transitive inference are subject to intense research. Here we demonstrate a new aspect of relational learning, building on previous evidence that transitive inference can be accomplished through simple reinforcement learning mechanisms. We show in simulations that inference of novel relations benefits from an asymmetric learning policy, where observers update only their belief about the winner (or loser) in a pair. Across four experiments (n = 145), we find substantial empirical support for such asymmetries in inferential learning. The learning policy favoured by our simulations and experiments gives rise to a compression of values that is routinely observed in psychophysics and behavioural economics. In other words, a seemingly biased learning strategy that yields well-known cognitive distortions can be beneficial for transitive inferential judgements.

Concurrent visual working memory bias in sequential integration of approximate number.

Previous work has shown bidirectional crosstalk between Working Memory (WM) and perception such that the contents of WM can alter concurrent percepts and vice versa. Here, we examine WM-perception interactions in a new task setting. Participants judged the proportion of colored dots in a stream of visual displays while concurrently holding location- and color information in memory. Spatiotemporally resolved psychometrics disclosed a modulation of perceptual sensitivity consistent with a bias of visual spatial attention towards the memorized location. However, this effect was short-lived, suggesting that the visuospatial WM information was rapidly deprioritized during processing of new perceptual information. Independently, we observed robust bidirectional biases of categorical color judgments, in that perceptual decisions and mnemonic reports were attracted to each other. These biases occurred without reductions in overall perceptual sensitivity compared to control conditions without a concurrent WM load. The results conceptually replicate and extend previous findings in visual search and suggest that crosstalk between WM and perception can arise at multiple levels, from sensory-perceptual to decisional processing.

Optimal utility and probability functions for agents with finite computational precision.

When making economic choices, such as those between goods or gambles, humans act as if their internal representation of the value and probability of a prospect is distorted away from its true value. These distortions give rise to decisions which apparently fail to maximize reward, and preferences that reverse without reason. Why would humans have evolved to encode value and probability in a distorted fashion, in the face of selective pressure for reward-maximizing choices? Here, we show that under the simple assumption that humans make decisions with finite computational precision--in other words, that decisions are irreducibly corrupted by noise--the distortions of value and probability displayed by humans are approximately optimal in that they maximize reward and minimize uncertainty. In two empirical studies, we manipulate factors that change the reward-maximizing form of distortion, and find that in each case, humans adapt optimally to the manipulation. This work suggests an answer to the longstanding question of why humans make "irrational" economic choices.

Selective Integration during Sequential Sampling in Posterior Neural Signals.

Decisions are typically made after integrating information about multiple attributes of alternatives in a choice set. Where observers are obliged to consider attributes in turn, a computational framework known as "selective integration" can capture salient biases in human choices. The model proposes that successive attributes compete for processing resources and integration is biased towards the alternative with the locally preferred attribute. Quantitative analysis shows that this model, although it discards choice-relevant information, is optimal when the observers' decisions are corrupted by noise that occurs beyond the sensory stage. Here, we used electroencephalography (EEG) to test a neural prediction of the model: that locally preferred attributes should be encoded with higher gain in neural signals over the posterior cortex. Over two sessions, human observers judged which of the two simultaneous streams of bars had the higher (or lower) average height. The selective integration model fits the data better than a rival model without bias. Single-trial analysis showed that neural signals contralateral to the preferred attribute covaried more steeply with the decision information conferred by locally preferred attributes. These findings provide neural evidence in support of selective integration, complementing existing behavioral work.

Centro-parietal EEG potentials index subjective evidence and confidence during perceptual decision making.

Recent studies suggest that a centro-parietal positivity (CPP) in the EEG signal tracks the absolute (unsigned) strength of accumulated evidence for choices that require the integration of noisy sensory input. Here, we investigated whether the CPP might also reflect the evidence for decisions based on a quantitative comparison between two sequentially presented stimuli (a signed quantity). We recorded EEG while participants decided whether the latter of two vibrotactile frequencies was higher or lower than the former in six variants of this task (n = 116). To account for biases in sequential comparisons, we applied a behavioral model based on Bayesian inference that estimated subjectively perceived frequency differences. Immediately after the second stimulus, parietal ERPs reflected the signed value of subjectively perceived differences and afterwards their absolute value. Strikingly, the modulation by signed difference was evident in trials without any objective evidence for either choice and correlated with choice-selective premotor beta band amplitudes. Modulations by the absolute strength of subjectively perceived evidence - a direct indicator of task difficulty - exhibited all features of statistical decision confidence. Together, our data suggest that parietal EEG signals first index subjective evidence, and later include a measure of confidence in the context of perceptual decision making.

Neural structure mapping in human probabilistic reward learning.

Humans can learn abstract concepts that describe invariances over relational patterns in data. One such concept, known as magnitude, allows stimuli to be compactly represented on a single dimension (i.e. on a mental line). Here, we measured representations of magnitude in humans by recording neural signals whilst they viewed symbolic numbers. During a subsequent reward-guided learning task, the neural patterns elicited by novel complex visual images reflected their payout probability in a way that suggested they were encoded onto the same mental number line, with 'bad' bandits sharing neural representation with 'small' numbers and 'good' bandits with 'large' numbers. Using neural network simulations, we provide a mechanistic model that explains our findings and shows how structural alignment can promote transfer learning. Our findings suggest that in humans, learning about reward probability is accompanied by structural alignment of value representations with neural codes for the abstract concept of magnitude.

Memory deficits in Parkinson's disease are associated with reduced beta power modulation.

There is an increasing recognition of the significant non-motor symptoms that burden people with Parkinson's disease. As such, there is a pressing need to better understand and investigate the mechanisms underpinning these non-motor deficits. The electrical activity within the brains of people with Parkinson's disease is known to exhibit excessive power within the beta range (12-30 Hz), compared with healthy controls. The weight of evidence suggests that this abnormally high level of beta power is the cause of bradykinesia and rigidity in Parkinson's disease. However, less is known about how the abnormal beta rhythms seen in Parkinson's disease impact on non-motor symptoms. In healthy adults, beta power decreases are necessary for successful episodic memory formation, with greater power decreases during the encoding phase predicting which words will subsequently be remembered. Given the raised levels of beta activity in people with Parkinson's disease, we hypothesized that the necessary decrease in power during memory encoding would be diminished and that this would interfere with episodic memory formation. Accordingly, we conducted a cross-sectional, laboratory-based experimental study to investigate whether there was a direct relationship between decreased beta modulation and memory formation in Parkinson's disease. Electroencephalography recordings were made during an established memory-encoding paradigm to examine brain activity in a cohort of adults with Parkinson's disease (N = 28, 20 males) and age-matched controls (N = 31, 18 males). The participants with Parkinson's disease were aged 65 ± 6 years, with an average disease duration of 6 ± 4 years, and tested on their normal medications to avoid the confound of exacerbated motor symptoms. Parkinson's disease participants showed impaired memory strength (P = 0.023) and reduced beta power decreases (P = 0.014) relative to controls. Longer disease duration was correlated with a larger reduction in beta modulation during encoding, and a concomitant reduction in memory performance. The inability to sufficiently decrease beta activity during semantic processing makes it a likely candidate to be the central neural mechanism underlying this type of memory deficit in Parkinson's disease. These novel results extend the notion that pathological beta activity is causally implicated in the motor and (lesser appreciated) non-motor deficits inherent to Parkinson's disease. These findings provide important empirical evidence that should be considered in the development of intelligent next-generation therapies.

Intravenous thrombolysis in acute central retinal artery occlusion - A prospective interventional case series.

BACKGROUND: No evidence-based therapy exists for non-arteritic central retinal artery occlusion (NA-CRAO). Retinal ischemic tolerance is low; irreversible damage occurs within four hours of experimental NA-CRAO. In previous randomized trials evaluating intra-arterial or intravenous thrombolysis (IVT) in NA-CRAO, only one patient was treated this early. In December 2013, the Departments of Neurology & Stroke and Ophthalmology at University Hospital Tuebingen, Germany, decided to treat patients using IVT within 4.5 hours of NA-CRAO, the therapeutic window established for ischemic stroke. MATERIALS AND METHODS: Consecutive NA-CRAO patients with severe visual loss received IVT after exclusion of intracranial hemorrhage. Follow-up was conducted at day 5 (d5) and day 30 (d30). Visual outcomes were compared to the conservative standard treatment (CST) arm of the EAGLE-trial. RESULTS: Until August 2016, 20 patients received IVT within 4.5 hours after NA-CRAO with a median onset-to-treatment time of 210 minutes (IQR 120-240). Visual acuity improved from baseline mean logarithm of the minimum angle of resolution 2.46±0.33 (SD) (light perception) to 1.52±1.09 (Snellen equivalent: 6/200) at d5 (p = 0.002) and 1.60±1.08 (Snellen equivalent: 6/240) at d30. Compared to the EAGLE CST-arm, functional recovery to reading ability occurred more frequently after IVT: 6/20 (30%) versus 1/39 (3%) at d5 (p = 0.005) and at d30 5/20 (25%) versus 2/37 (5%) (p = 0.045). Two patients experienced serious adverse events (one angioedema and one bleeding from an abdominal aortic aneurysm) but recovered without sequelae. CONCLUSIONS: IVT within 4.5 hours after symptom onset may represent an effective treatment of NA-CRAO. Randomized trials are warranted to evaluate efficacy and safety of early IVT in acute NA-CRAO.

Beyond the Status Quo: A Role for Beta Oscillations in Endogenous Content (Re)Activation.

Among the rhythms of the brain, oscillations in the beta frequency range (∼13-30 Hz) have been considered the most enigmatic. Traditionally associated with sensorimotor functions, beta oscillations have recently become more broadly implicated in top-down processing, long-range communication, and preservation of the current brain state. Here, we extend and refine these views based on accumulating new findings of content-specific beta-synchronization during endogenous information processing in working memory (WM) and decision making. We characterize such content-specific beta activity as short-lived, flexible network dynamics supporting the endogenous (re)activation of cortical representations. Specifically, we suggest that beta-mediated ensemble formation within and between cortical areas may awake, rather than merely preserve, an endogenous cognitive set in the service of current task demands. This proposal accommodates key aspects of content-specific beta modulations in monkeys and humans, integrates with timely computational models, and outlines a functional role for beta that fits its transient temporal characteristics.

The Distributed Nature of Working Memory.

Studies in humans and non-human primates have provided evidence for storage of working memory contents in multiple regions ranging from sensory to parietal and prefrontal cortex. We discuss potential explanations for these distributed representations: (i) features in sensory regions versus prefrontal cortex differ in the level of abstractness and generalizability; and (ii) features in prefrontal cortex reflect representations that are transformed for guidance of upcoming behavioral actions. We propose that the propensity to produce persistent activity is a general feature of cortical networks. Future studies may have to shift focus from asking where working memory can be observed in the brain to how a range of specialized brain areas together transform sensory information into a delayed behavioral response.

Predictive factors for functional improvement following intravitreal bevacizumab injections after central retinal vein occlusion.

PURPOSE: Vision loss in central retinal vein occlusion (CRVO) is mostly caused by macular edema (ME) and can be treated with intravitreal bevacizumab injections. The goal of this study was to identify predictive factors for improvement in visual acuity. METHODS: Three hundred and sixteen eyes of six centres having received intravitreal bevacizumab for ME due to CRVO were enrolled in this multicentre, retrospective, interventional case series. The follow-up time was 24 to 48 weeks. Investigated patient characteristics were pretreatment, duration of CRVO prior to the first injection, initial best-corrected visual acuity (BCVA), baseline central retinal thickness as measured by optical coherence tomography, gender, eye, age, comorbidity with glaucoma, systemic hypertension, or diabetes mellitus. RESULTS: Multiple regression analysis confirmed the following baseline predictive factors for an increase in visual acuity: low BCVA (p < 0.001), high CRT (p < 0.02), and treatment naïve patients (p = 0.03). None of the other investigated patient characteristics could be identified as prognostic factors for increase in visual acuity (p > 0.1). CONCLUSIONS: Intravitreal injections of bevacizumab in a routine clinical setting effectively improved and stabilized BCVA in CRVO. Our large multicenter study identified initial BCVA, baseline CRT, and pre-treatment as prognostic factors for visual improvement.

Selective overweighting of larger magnitudes during noisy numerical comparison.

Humans are often required to compare average magnitudes in numerical data; for example, when comparing product prices on two rival consumer websites. However, the neural and computational mechanisms by which numbers are weighted, integrated and compared during categorical decisions are largely unknown1,2,3,4,5. Here, we show a systematic deviation from 'optimality' in both visual and auditory tasks requiring averaging of symbolic numbers. Participants comparing numbers drawn from two categories selectively overweighted larger numbers when making a decision, and larger numbers evoked disproportionately stronger decision-related neural signals over the parietal cortex. A representational similarity analysis6 showed that neural (dis)similarity in patterns of electroencephalogram activity reflected numerical distance, but that encoding of number in neural data was systematically distorted in a way predicted by the behavioural weighting profiles, with greater neural distance between adjacent larger numbers. Finally, using a simple computational model, we show that although it is suboptimal for a lossless observer, this selective overweighting policy paradoxically maximizes expected accuracy by making decisions more robust to noise arising during approximate numerical integration2. In other words, although selective overweighting discards decision information, it can be beneficial for limited-capacity agents engaging in rapid numerical averaging.

Spectral EEG abnormalities during vibrotactile encoding and quantitative working memory processing in schizophrenia.

Schizophrenia is associated with a number of cognitive impairments such as deficient sensory encoding or working memory processing. However, it is largely unclear how dysfunctions on these various levels of cortical processing contribute to alterations of stimulus-specific information representation. To test this, we used a well-established sequential frequency comparison paradigm, in which sensory encoding of vibrotactile stimuli can be assessed via frequency-specific steady-state evoked potentials (SSEPs) over primary somatosensory cortex (S1). Further, we investigated the maintenance of frequency information in working memory (WM) in terms of parametric power modulations of induced beta-band EEG oscillations. In the present study schizophrenic patients showed significantly less pronounced SSEPs during vibrotactile stimulation than healthy controls. In particular, inter-trial phase coherence was reduced. While maintaining vibrotactile frequencies in WM, patients showed a significantly weaker prefrontal beta-power modulation compared to healthy controls. Crucially, patients exhibited no general disturbances in attention, as inferred from a behavioral test and from alpha-band event-related synchronization. Together, our results provide novel evidence that patients with schizophrenia show altered neural correlates of stimulus-specific sensory encoding and WM maintenance, suggesting an early somatosensory impairment as well as alterations in the formation of abstract representations of task-relevant stimulus information.

Upper Beta Band Oscillations in Human Premotor Cortex Encode Subjective Choices in a Vibrotactile Comparison Task.

Comparisons of sequentially presented vibrotactile frequencies have been extensively studied using electrophysiological recordings in nonhuman primates. Although neural signatures for working memory aspects of such tasks were recently also identified in human oscillatory EEG activity, homologue correlates of the comparison process are yet unknown. Here, we recorded EEG activity while participants decided which of two sequentially presented vibrotactile stimuli had a higher frequency. Because choices in this type of task are known to be systematically biased by the time-order effect, we applied Bayesian modeling to account for individual choice behavior. Using model-based EEG analysis, we found that upper beta band amplitude (∼20-30 Hz) was modulated by participants' choices. The modulation emerged ∼750 msec before a behavioral response was given and was source-localized to premotor areas.Importantly, the choice-dependent modulation of beta band amplitude was invariant to different motor response mappings and reflected the categorical outcome of the subjective comparison between the two frequencies. Consistently, this pattern was evident for both correct and incorrect trials, indicating that the beta band amplitude mirrors the internal representation of the comparison outcome. Our data complement previous findings in nonhuman primates and corroborate that the beta band activity in premotor areas reflects the categorical outcome of a sensory comparison prior to translation into an effector-specific motor command.