Value-Based Search Efficiency Is Encoded in the Substantia Nigra Reticulata Firing Rate, Spiking Irregularity and Local Field Potential

Recent results show that valuable objects can pop out in visual search, yet its neural mechanisms remain unexplored. Given the role of substantia nigra reticulata (SNr) in object value memory and control of gaze, we recorded its single-unit activity while male macaque monkeys engaged in efficient or inefficient search for a valuable target object among low-value objects. The results showed that efficient search was concurrent with stronger inhibition and higher spiking irregularity in the target-present (TP) compared with the target-absent (TA) trials in SNr. Importantly, the firing rate differentiation of TP and TA trials happened within ∼100 ms of display onset, and its magnitude was significantly correlated with the search times and slopes (search efficiency). Time-frequency analyses of local field potential (LFP) after display onset revealed significant modulations of the gamma band power with search efficiency. The greater reduction of SNr firing in TP trials in efficient search can create a stronger disinhibition of downstream superior colliculus, which in turn can facilitate saccade to obtain valuable targets in competitive environments.

Cortical and subcortical substrates of minutes and days-long object value memory in humans

Obtaining valuable objects motivates many of our daily decisions. However, the neural underpinnings of object processing based on human value memory are not yet fully understood. Here, we used whole-brain functional magnetic resonance imaging (fMRI) to examine activations due to value memory as participants passively viewed objects before, minutes after, and 1-70 days following value training. Significant value memory for objects was evident in the behavioral performance, which nevertheless faded over the days following training. Minutes after training, the occipital, ventral temporal, interparietal, and frontal areas showed strong value discrimination. Days after training, activation in the frontal, temporal, and occipital regions decreased, whereas the parietal areas showed sustained activation. In addition, days-long value responses emerged in certain subcortical regions, including the caudate, ventral striatum, and thalamus. Resting-state analysis revealed that these subcortical areas were functionally connected. Furthermore, the activation in the striatal cluster was positively correlated with participants' performance in days-long value memory. These findings shed light on the neural basis of value memory in humans with implications for object habit formation and cross-species comparisons.

Prefrontal cortex encodes value pop-out in visual search

Recent evidence demonstrates that long-term object value association can enhance visual search efficiency, a phenomenon known as value pop-out. However, the neural mechanism underlying this effect is not fully understood. Given the known role of the ventrolateral prefrontal cortex (vlPFC) in visual search and value memory, we recorded its single-unit activity (n = 526) in two macaque monkeys while they engaged in the value-driven search. Monkeys had to determine whether a high-value target was present within a variable number of low-value objects. Differential neural firing, as well as gamma-band power, indicated the presence of a target within ∼150ms of display onset. Notably, this differential activity was negatively correlated with search time and had reduced set-size dependence during efficient search. On the other hand, neural firing and its variability were higher in inefficient search. These findings implicate vlPFC in rapid detection of valuable targets which would be a crucial skill in competitive environments.

The Covid-19 pandemic had polarizing effects on trait scores depending on a person's resilience and predispositions: A longitudinal prospective study

While Covid-19 is, first and foremost, a pernicious physical illness, its highly contagious nature has led to significant disruption in social life and psychological stress, occasionally resulting in dire mental health consequences that are still not fully understood. To address this issue, a prospective longitudinal design study was conducted by administering standard self-reporting questionnaires covering the NEO-five factor inventory (NEO-FFI), shyness, alexithymia, autism quotient, anxiety, depression, and sensory processing sensitivity (SPS). A total of 114 participants (of which 71.93% were females) with an average age of 30.29 (standard deviation = 11.04) completed the survey before and a few months after the pandemic. Results revealed the distribution of population scores to become more extreme in either positive or negative trait directions despite the stability of average trait scores across the population. Higher resilience was found to be positively correlated with improved trait scores post-pandemic but corona anxiety score was not correlated with trait score changes. In addition, in the subjects with moderate negative trait scores, agreeableness and autism scores and in subjects with high negative trait scores, openness, SPS and shyness scores were significantly correlated with trait scores changes post-pandemic. These results reveal the nuanced effects of the pandemic on the people's psychological well-being and highlight vulnerabilities for certain groups despite the overall stability of population that needs to be taken into account for mental health policies going forward.

A novel methodology for exact targeting of human and non-human primate brain structures and skull implants using atlas-based 3D reconstruction

BACKGROUND

Accurate targeting of brain areas for stimulation and/or electrophysiological recording is key in many therapeutic applications and basic neuroscience research. Nevertheless, there are currently no end-to-end packages that accommodate all steps required for exact localization, visualization and targeting regions of interest (ROIs) using standard atlases and for designing skull implants.

NEW METHOD

We have implemented a new processing pipeline that addresses this issue in macaques and humans including various preprocessing, registration, warping procedures and 3D reconstructions and provide a noncommercial open-source graphical software which we refer to as the MATLAB-based reconstruction for recording and stimulation (MATres).

RESULTS

The results of skull stripping were shown to work seamlessly in humans and monkeys. Linear and nonlinear warping of the standard atlas to the native space outperformed state-of-the-art using AFNI with improvements being more prominent in humans which had a more complex gyration geometry. The skull surface extracted by MATres using MRI images had more than 90% match with CT ground truth and could be used to design skull implants that conformed well to the skull's local curvature.

COMPARISON WITH EXISTING METHOD(S)

The accuracy of the various steps including skull stripping, standard atlas registration and skull reconstruction in MATres was compared with and shown to outperform the AFNI. The localization accuracy of the recording chambers designed with MATres and implanted in two macaque monkeys was further confirmed using MRI imaging.

CONCLUSIONS

Precise localization of ROIs offered by MATres can be used for planning electrode penetrations for recording and for shallow or deep brain stimulation (DBS).

EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms

Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it.

Coordinated multivoxel coding beyond univariate effects is not likely to be observable in fMRI data

Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can be gleaned from a collection of univariate effects (UVE) using functional magnetic resonance imaging (fMRI). Here, we argue that regardless of patterns revealed, conventional MVPA is merely a decoding tool with increased sensitivity arising from considering a large number of 'weak classifiers' (i.e., single voxels) in higher dimensions. We propose instead that 'real' multivoxel coding should result in changes in higher-order statistics across voxels between conditions such as second-order multivariate effects (sMVE). Surprisingly, analysis of conditions with robust multivariate effects (MVE) revealed by MVPA failed to show significant sMVE in two species (humans and macaques). Further analysis showed that while both MVE and sMVE can be readily observed in the spiking activity of neuronal populations, the slow and nonlinear hemodynamic coupling and low spatial resolution of fMRI activations make the observation of higher-order statistics between voxels highly unlikely. These results reveal inherent limitations of fMRI signals for studying coordinated coding across voxels. Together, these findings suggest that care should be taken in interpreting significant MVPA results as representing anything beyond a collection of univariate effects.

Localizing sensory processing sensitivity and its subdomains within its relevant trait space: a data-driven approach

Sensitivity arising from enhanced processing of external and internal stimuli or sensory processing sensitivity (SPS) is known to be present in a sizable portion of the population. Yet a clear localization of SPS and its subdomains with respect to other relevant traits is currently lacking. Here, we used a data-driven approach including hierarchical clustering, t-distributed stochastic neighbor embedding (t-SNE) and graph learning to portrait SPS as measured by Highly Sensitive Person Scale (HSPS) in relation to the Big-Five Inventory (neuroticism, extraversion, openness, agreeableness, and conscientiousness) as well as to shyness, alexithymia, autism quotient, anxiety, and depression (11 total traits) using data from more than 800 participants. Analysis revealed SPS subdomains to be divided between two trait clusters with questions related to aesthetic sensitivity (AES) falling within a cluster of mainly positive traits and neighbored by openness while questions addressing ease of excitation (EOE) and low sensory threshold (LST) to be mostly contained within a cluster of negative traits and neighbored by neuroticism. A similar spread across clusters was seen for questions addressing autism consistent with it being a spectrum disorder, in contrast, alexithymia subdomains were closely fit within the negative cluster. Together, our results support the view of SPS as a distinct yet non-unitary trait and provide insights for further refinements of the current SPS concept and scales.

Common coding of expected value and value uncertainty memories in the prefrontal cortex and basal ganglia output

Recent evidence implicates both basal ganglia and ventrolateral prefrontal cortex (vlPFC) in encoding value memories. However, comparative roles of cortical and basal nodes in value memory are not well understood. Here, single-unit recordings in vlPFC and substantia nigra reticulata (SNr), within macaque monkeys, revealed a larger value signal in SNr that was nevertheless correlated with and had a comparable onset to the vlPFC value signal. The value signal was maintained for many objects (>90) many weeks after reward learning and was resistant to extinction in both regions and to repetition suppression in vlPFC. Both regions showed comparable granularity in encoding expected value and value uncertainty, which was paralleled by enhanced gaze bias during free viewing. The value signal dynamics in SNr could be predicted by combining responses of vlPFC neurons according to their value preferences consistent with a scheme in which cortical neurons reached SNr via direct and indirect pathways.

Brain Networks Sensitive to Object Novelty, Value, and Their Combination

Novel and valuable objects are motivationally attractive for animals including primates. However, little is known about how novelty and value processing is organized across the brain. We used fMRI in macaques to map brain responses to visual fractal patterns varying in either novelty or value dimensions and compared the results with the structure of functionally connected brain networks determined at rest. The results show that different brain networks possess unique combinations of novelty and value coding. One network identified in the ventral temporal cortex preferentially encoded object novelty, whereas another in the parietal cortex encoded the learned value. A third network, broadly composed of temporal and prefrontal areas (TP network), along with functionally connected portions of the striatum, amygdala, and claustrum, encoded both dimensions with similar activation dynamics. Our results support the emergence of a common currency signal in the TP network that may underlie the common attitudes toward novel and valuable objects.

A New Nonlinear Sparse Component Analysis for a Biologically Plausible Model of Neurons

It is known that brain can create a sparse representation of the environment in both sensory and mnemonic forms (Olshausen & Field, 2004). Such sparse representation can be combined in downstream areas to create rich multisensory responses to support various cognitive and motor functions. Determining the components present in neuronal responses in a given region is key to deciphering its functional role and connection with upstream areas. One approach for parsing out various sources of information in a single neuron is by using linear blind source separation (BSS) techniques. However, applying linear techniques to neuronal spiking activity is likely to be suboptimal due to inherent and unknown nonlinearity of neuronal responses to inputs. This letter proposes a nonlinear sparse component analysis (SCA) method to separate jointly sparse inputs to neurons with post summation nonlinearity, or SCA for post-nonlinear neurons (SCAPL). Specifically, a linear clustering approach followed by principal curve regression (PCR) and a nonlinear curve fitting are used to separate sources. Analysis using simulated data shows that SCAPL accuracy outperforms ones obtained by linear SCA, as well as other separating methods, including linear independent and principal component analyses. In SCAPL, the number of derived sparse components is not limited by the number of neurons, unlike most BSS methods. Furthermore, this method allows for a broad range of post-summation nonlinearities that could differ among neurons. The sensitivity of our method to noise, joint sparseness, degree, and shape of nonlinearity and mixing ill conditions is discussed and compared to existing methods. Our results show that the proposed method can successfully separate input components in a population of neurons provided that they are temporally sparse to some degree. Application of SCAPL should facilitate comparison of functional roles across regions by parsing various elements present in a region.

Prefrontal Cortex Represents Long-Term Memory of Object Values for Months

As a central hub for cognitive control, prefrontal cortex (PFC) is thought to utilize memories. However, unlike working or short-term memory, the neuronal representation of long-term memory in PFC has not been systematically investigated. Using single-unit recordings in macaques, we show that PFC neurons rapidly update and maintain responses to objects based on short-term reward history. Interestingly, after repeated object-reward association, PFC neurons continue to show value-biased responses to objects even in the absence of reward. This value-biased response is retained for several months after training and is resistant to extinction and to interference from new object-reward learning for many complex objects (>90). Accordingly, the monkeys remember the values of the learned objects for several months in separate testing. These findings reveal that in addition to flexible short-term and low-capacity memories, primate PFC represents stable long-term and high-capacity memories, which could prioritize valuable objects far into the future.

Flexible and Stable Value Coding Areas in Caudate Head and Tail Receive Anatomically Distinct Cortical and Subcortical Inputs

Anatomically distinct areas within the basal ganglia encode flexible- and stable-value memories for visual objects (Hikosaka et al., 2014), but an important question remains: do they receive inputs from the same or different brain areas or neurons? To answer this question, we first located flexible and stable value-coding areas in the caudate head (CDh) and caudate tail (CDt) of two rhesus macaque monkeys, and then injected different retrograde tracers into these areas of each monkey. We found that CDh and CDt received different inputs from several cortical and subcortical areas including temporal cortex, prefrontal cortex, cingulate cortex, amygdala, claustrum and thalamus. Superior temporal cortex and inferior temporal cortex projected to both CDh and CDt, with more CDt-projecting than CDh-projecting neurons. In superior temporal cortex and dorsal inferior temporal cortex, layers 3 and 5 projected to CDh while layers 3 and 6 projected to CDt. Prefrontal and cingulate cortex projected mostly to CDh bilaterally, less to CDt unilaterally. A cluster of neurons in the basolateral amygdala projected to CDt. Rostral-dorsal claustrum projected to CDh while caudal-ventral claustrum projected to CDt. Within the thalamus, different nuclei projected to either CDh or CDt. The medial centromedian nucleus and lateral parafascicular nucleus projected to CDt while the medial parafascicular nucleus projected to CDh. The inferior pulvinar and lateral dorsal nuclei projected to CDt. The ventral anterior and medial dorsal nuclei projected to CDh. We found little evidence of neurons projecting to both CDh and CDt across the brain. These data suggest that CDh and CDt can control separate functions using anatomically separate circuits. Understanding the roles of these striatal projections will be important for understanding how value memories are created and stored.

Parallel basal ganglia circuits for decision making

The basal ganglia control body movements, mainly, based on their values. Critical for this mechanism is dopamine neurons, which sends unpredicted value signals, mainly, to the striatum. This mechanism enables animals to change their behaviors flexibly, eventually choosing a valuable behavior. However, this may not be the best behavior, because the flexible choice is focused on recent, and, therefore, limited, experiences (i.e., short-term memories). Our old and recent studies suggest that the basal ganglia contain separate circuits that process value signals in a completely different manner. They are insensitive to recent changes in value, yet gradually accumulate the value of each behavior (i.e., movement or object choice). These stable circuits eventually encode values of many behaviors and then retain the value signals for a long time (i.e., long-term memories). They are innervated by a separate group of dopamine neurons that retain value signals, even when no reward is predicted. Importantly, the stable circuits can control motor behaviors (e.g., hand or eye) quickly and precisely, which allows animals to automatically acquire valuable outcomes based on historical life experiences. These behaviors would be called 'skills', which are crucial for survival. The stable circuits are localized in the posterior part of the basal ganglia, separately from the flexible circuits located in the anterior part. To summarize, the flexible and stable circuits in the basal ganglia, working together but independently, enable animals (and humans) to reach valuable goals in various contexts.

Ecological Origins of Object Salience: Reward, Uncertainty, Aversiveness, and Novelty

Among many objects around us, some are more salient than others (i.e., attract our attention automatically). Some objects may be inherently salient (e.g., brighter), while others may become salient by virtue of their ecological relevance through experience. However, the role of ecological experience in automatic attention has not been studied systematically. To address this question, we let subjects (macaque monkeys) view a large number of complex objects (>300), each experienced repeatedly (>5 days) with rewarding, aversive or no outcome association (mere-perceptual exposure). Test of salience was done on separate days using free viewing with no outcome. We found that gaze was biased among the objects from the outset, affecting saccades to objects or fixations within objects. When the outcome was rewarding, gaze preference was stronger (i.e., positive) for objects with larger or equal but uncertain rewards. The effects of aversive outcomes were variable. Gaze preference was positive for some outcome associations (e.g., airpuff), but negative for others (e.g., time-out), possibly due to differences in threat levels. Finally, novel objects attracted gaze, but mere perceptual exposure of objects reduced their salience (learned negative salience). Our results show that, in primates, object salience is strongly influenced by previous ecological experience and is supported by a large memory capacity. Owing to such high capacity for learned salience, the ability to rapidly choose important objects can grow during the entire life to promote biological fitness.

Object-finding skill created by repeated reward experience

For most animals, survival depends on rapid detection of rewarding objects, but search for an object surrounded by many others is known to be difficult and time consuming. However, there is neuronal evidence for robust and rapid differentiation of objects based on their reward history in primates (Hikosaka, Kim, Yasuda, & Yamamoto, 2014). We hypothesized that such robust coding should support efficient search for high-value objects, similar to a pop-out mechanism. To test this hypothesis, we let subjects (n = 4, macaque monkeys) view a large number of complex objects with consistently biased rewards with variable training durations (1, 5, or 30 + days). Following training, subjects searched for a high-value object (Good) among a variable number of low-value objects (Bad). Consistent with our hypothesis, we found that Good objects were accurately and quickly targeted, often by a single and direct saccade with a very short latency (<200 ms). The dependence of search times on display size reduced significantly with longer reward training, giving rise to a more efficient search (40 ms/item to 16 ms/item). This object-finding skill showed a large capacity for value-biased objects and was maintained in the long-term memory with no interference from reward learning with other objects. Such object-finding skill, and in particular its large capacity and long term retention, would be crucial for maximizing rewards and biological fitness throughout life where many objects are experienced continuously and/or intermittently.

Dopamine Neurons Encoding Long-Term Memory of Object Value for Habitual Behavior

Dopamine neurons promote learning by processing recent changes in reward values, such that reward may be maximized. However, such a flexible signal is not suitable for habitual behaviors that are sustained regardless of recent changes in reward outcome. We discovered a type of dopamine neuron in the monkey substantia nigra pars compacta (SNc) that retains past learned reward values stably. After reward values of visual objects are learned, these neurons continue to respond differentially to the objects, even when reward is not expected. Responses are strengthened by repeated learning and are evoked upon presentation of the objects long after learning is completed. These "sustain-type" dopamine neurons are confined to the caudal-lateral SNc and project to the caudate tail, which encodes long-term value memories of visual objects and guides gaze automatically to stably valued objects. This population of dopamine neurons thus selectively promotes learning and retention of habitual behavior.

Separate groups of dopamine neurons innervate caudate head and tail encoding flexible and stable value memories

Dopamine (DA) neurons are thought to be critical for reward value-based learning by modifying synaptic transmissions in the striatum. Yet, different regions of the striatum seem to guide different kinds of learning. Do DA neurons contribute to the regional differences of the striatum in learning? As a first step to answer this question, we examined whether the head and tail of the caudate nucleus of the monkey (Macaca mulatta) receive inputs from the same or different DA neurons. We chose these caudate regions because we previously showed that caudate head neurons learn values of visual objects quickly and flexibly, whereas caudate tail neurons learn object values slowly but retain them stably. Here we confirmed the functional difference by recording single neuronal activity while the monkey performed the flexible and stable value tasks, and then injected retrograde tracers in the functional domains of caudate head and tail. The projecting dopaminergic neurons were identified using tyrosine hydroxylase immunohistochemistry. We found that two groups of DA neurons in the substantia nigra pars compacta project largely separately to the caudate head and tail. These groups of DA neurons were mostly separated topographically: head-projecting neurons were located in the rostral-ventral-medial region, while tail-projecting neurons were located in the caudal-dorsal-lateral regions of the substantia nigra. Furthermore, they showed different morphological features: tail-projecting neurons were larger and less circular than head-projecting neurons. Our data raise the possibility that different groups of DA neurons selectively guide learning of flexible (short-term) and stable (long-term) memories of object values.

Isolating event-related neuronal responses by deconvolution

Although many studies in neuroscience are based on comparing neuronal responses to single, isolated sensory or motor events, multiple events frequently occur in close temporal proximity in freely moving animals. This often obscures the precise temporal correlation between each event and the relevant brain activity. By simulating neuronal responses in multi-event tasks, we show that perievent time histograms (PETHs) greatly distort the underlying true responses. We propose a multi-event deconvolution method that can separate the contribution of each event to the overall neuronal activity. The improvements over PETH in analyzing real data are demonstrated using simulated data and a sample electrophysiological recording obtained from rats in a task involving responses to a reward predictive cue.

Generalization of motor learning depends on the history of prior action

Generalization of motor learning refers to our ability to apply what has been learned in one context to other contexts. When generalization is beneficial, it is termed transfer, and when it is detrimental, it is termed interference. Insight into the mechanism of generalization may be acquired from understanding why training transfers in some contexts but not others. However, identifying relevant contextual cues has proven surprisingly difficult, perhaps because the search has mainly been for cues that are explicit. We hypothesized instead that a relevant contextual cue is an implicit memory of action with a particular body part. To test this hypothesis we considered a task in which participants learned to control motion of a cursor under visuomotor rotation in two contexts: by moving their hand through motion of their shoulder and elbow, or through motion of their wrist. Use of these contextual cues led to three observations: First, in naive participants, learning in the wrist context was much faster than in the arm context. Second, generalization was asymmetric so that arm training benefited subsequent wrist training, but not vice versa. Third, in people who had prior wrist training, generalization from the arm to the wrist was blocked. That is, prior wrist training appeared to prevent both the interference and transfer that subsequent arm training should have caused. To explain the data, we posited that the learner collected statistics of contextual history: all upper arm movements also move the hand, but occasionally we move our hands without moving the upper arm. In a Bayesian framework, history of limb segment use strongly affects parameter uncertainty, which is a measure of the covariance of the contextual cues. This simple Bayesian prior dictated a generalization pattern that largely reproduced all three findings. For motor learning, generalization depends on context, which is determined by the statistics of how we have previously used the various parts of our limbs.

Interacting adaptive processes with different timescales underlie short-term motor learning

Multiple processes may contribute to motor skill acquisition, but it is thought that many of these processes require sleep or the passage of long periods of time ranging from several hours to many days or weeks. Here we demonstrate that within a timescale of minutes, two distinct fast-acting processes drive motor adaptation. One process responds weakly to error but retains information well, whereas the other responds strongly but has poor retention. This two-state learning system makes the surprising prediction of spontaneous recovery (or adaptation rebound) if error feedback is clamped at zero following an adaptation-extinction training episode. We used a novel paradigm to experimentally confirm this prediction in human motor learning of reaching, and we show that the interaction between the learning processes in this simple two-state system provides a unifying explanation for several different, apparently unrelated, phenomena in motor adaptation including savings, anterograde interference, spontaneous recovery, and rapid unlearning. Our results suggest that motor adaptation depends on at least two distinct neural systems that have different sensitivity to error and retain information at different rates.

This study presents evidence for two learning processes with distinct time courses that contribute to motor skill acquisition, and a computational model of the interactions between these processes that unifies much of the literature in motor adaptation.

Domestic violence: a cross-sectional study in an Iranian city

To determine the prevalence of domestic physical violence against women and its associated factors in Sanandaj city, Islamic Republic of Iran, a random sample of 1000 married women completed a questionnaire. Of the respondents, 15% had been assaulted by their husbands at least once in the previous year and 38% at some time during the marriage. Economic problems were the most frequent cause of domestic quarrels. There was a significant association between husbands' educational level and violence against wives. Physical violence against housewives was significantly more frequent than against employed women. Husband's job was also significantly associated with violence. The existence of a child or daughter in the family was associated with less domestic physical violence against women.