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Learning the complexities of business acumen in a webinar, a book, or worse - in Powerpoint - is pointless, right? Most generic lessons are too abstract and theoretical. There are too many intricacies and interdependencies that need to be practiced, not just learned by rote. Human Resource managers need to take time and spend money on immersive business acumen training that is hands-on and not merely theory based. Generic business acumen training is like training a commercial airline pilot how to fly by teaching them to fly a 2 seat Cessna prop plane.

HR training should focus on obtaining real world outcomes and results. Giving HR managers the ability to drive a real business in real time is invaluable. Topics such as finance, innovation, leadership, marketing, product development, strategy development, and employee management should all become familiar to HR managers. Modern training tools such as business simulations are able to train Human Resource managers in a fast, focused, applicable, engaging, and cost effective way.

This type of training is most effective when HR managers go through this training together as a group. Yes, the hands-on training is great, but real learning and application happens when the HR managers are able to reflect, discuss, and apply the learning together. The results of hands-on, applicable, and focused business acumen training for HR managers is the ability to become fluent in the language of corporate strategy. Tap here to turn on desktop notifications to get the news sent straight to you. What is the role of Human Resources? What is one of the biggest challenges for Human Resources?

Why are these challenges so difficult to overcome? How can Human Resources overcome this problem? What type of training should they undertake? Business Entrepreneurship Human Resources. Can You Speak My Language? These studies, however, do not directly test whether language context actually affects how well faces are recognized. For example, participants are less accurate at recognizing those faces that have been previously associated with another university out-group member than those associated with their own university in-group member see also Shriver et al.

Thus, just believing that a target is a meaningful in-group member is enough to increase attention during facial encoding, which in turn leads to better recognition. In this context, it seems inevitable to think that language, as a powerful source of social categorization, will modulate memory processes underlying face recognition as belonging to the same University does. The second reason to expect an effect of language on face recognition relates to the processing burden faced by comprehenders when listening to a FL.

This is often revealed by slower response times and more errors when processing a FL than a NL e. For accounts such as the multiple resources model in multitasking Wickens, , the effect of language on face recognition would stem from the more resources needed to process sentences in a FL than in a NL.

In particular, the extra resources allocated for processing a FL would result in fewer resources available for encoding the face, thus hampering its encoding and later recognition. Bank-robbers could speak either with a native or a foreign accent. When participants were later asked they retrieved more details about a bank-robber when speaking with a native accent than with a foreign accent.

Nevertheless, when participants were asked to recognize the bank-robber in a photo line-up, no accent effect was observed. These results suggest that differences of cognitive processing in one modality e. This posits some limits to models assuming cross-modal interference e. In sum, while social and cognitive accounts would predict an impact of the language spoken on the subsequent recognition of a face, the experimental evidence is very scarce and rather elusive.

On the one hand, there is evidence of social categorization by language e. On the other hand, while processing of a FL requires more cognitive resources than processing of a NL Pickel and Staller, , this does not seem to affect the subsequent recognition of a face. Thus, the novelty of the present study lies in testing how the language context determines how accurate a face will be recognized.

Therefore, the present study is very relevant for two reasons: In our task, participants had to decide in a recognition phase whether a given face had been presented old or not during the previous encoding phase. In the recognition phase, the faces were presented without any acoustical information.

Additionally, the in-group memory advantage has been mainly associated with two ERP components, the frontal e.


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Differences between in-group and out-group faces appear to be especially evident at parietal sites, taken as an indication that more details are retrieved during in-group face recognition. Altogether, these results provide clear hypotheses for our study: If faces paired with the NL are categorized as in-group or more cognitive resources are devoted to their encoding as fewer resources are devoted to the processing of the NL , then faces paired with the NL should lead to a better recognition than faces paired with the FL. In sum, our aim was to explore the impact of language processing on memory for faces.

We did so by assessing whether face recognition is affected by the language native vs. They all declared not having any visual, hearing, nor neurological problems and English was their FL see Table 1. Speech comprehension in the FL was evaluated by asking participants to listen to a 6 min recording and then respond to different eight comprehension questions. On average, participants responded correctly to 6.


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From the initial pool of participants, one was discarded because of problems during the recording session. Moreover, as will be described in the ERP recording section, eight participants were excluded for different reasons. Eighty gray-scale photographs of Caucasian faces half male and half female were downloaded from free electronic datasets and other resources on the web.

All of them were emotionally neutral and had no extra visual details e. Twenty native Spanish speakers 10 males and 10 females and 20 English native speakers 10 males and 10 females recorded the sentences. Therefore, a given sentence could be produced by four speakers: Spanish female, Spanish male, English female, and English male. Recording durations for sentences in Spanish and English considering the female and male voice for each sentence did not differ significantly [1, s vs. Thus, the final design consisted of photographs of faces accompanied by a voice speaking either in Spanish NL or in English FL.

Across participants, faces were presented in all conditions Spanish old, English old, and new conditions and sentences were associated with Spanish or English faces, females and males.

That is, faces and sentences were cycled through the different conditions across participants. English proficiency of the new pool of participants was similar to that indicated by the participants in the experiment comprehension: After translation, the sentences were coded with a 1, 0. The experiment consisted of two phases: In the encoding phase, face photographs were displayed along with the auditory presentation of the sentences SOA 0.

Participants were instructed to pay attention to the faces and the sentences, because later they would have to do a task related to the encoding phase, but without explicitly mentioning that it would be a recognition task. The trial structure was as follows: Upon completion of the encoding phase, participants engaged in a distractor filler task for 5 min Tetris game to avoid having the recognition phase immediately after the encoding phase. After that, participants started the recognition phase, in which photographs were presented in silence. Participants were instructed to identify by means of two keys on a keyboard whether a given face was old or new.

The assigned key for new and old were counterbalanced across participants. A given trial included: Eighty faces were presented in this phase, 40 that were already presented in the encoding phase 20 in Spanish and 20 in English and 40 new faces. Half of the sentences were in Spanish and half in English. A trial was comprised of the presentation of an asterisk ms followed by the presentation of a sentence in the center of the screen that remained until participants judged whether the sentence was old or new.

The results from this task allowed us to ensure that participants were paying attention to the sentences during the encoding phase.

Correct and incorrect responses were coded during the recognition phase. Depending on the response of the participant and the type of trial, four types of responses were coded: Moreover, three external electrodes EOG were placed above, below and on the outer canthus of the right eye to register vertical and horizontal eye movements, respectively.

All active electrodes were on-line referenced to the left mastoid.

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EEG data was sampled at Hz with a bandpass of the hardware filter of 0. Data was filtered offline 0. Only those components clearly indexing vertical and horizontal eye-movements were selected and corrected. EEG was then epoched from ms before to ms after stimulus onset.

Thus, the final analysis included 33 participants.

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Averages were calculated for the encoding and the recognition phases separately. In the recognition phase, averages were calculated for three types of trials: Selection of the P time-range was made by selecting the maximal peak across electrodes around ms and including 40 ms before and after it — ms. A similar procedure was followed for the LPC time-window, with the exception that the time range comprised ms before and after the maximal peak around ms. We considered the factors: For the ERP analyses in the recognition phase, to identify time-windows of interest, we combined a priori knowledge based on previous literature with an assumption free procedure, the cluster-based permutation test.

Mean amplitudes at each time-window of interest were computed by averaging the activity of electrodes within each region, condition and participant.

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Secondly, although time-windows and ROIs were selected on the basis of previous studies and therefore independently of differences between our conditions, to further estimate reliable amplitude differences Type I errors; Kilner, we conducted a paired two-tailed cluster-based permutation test i. The test does not make any a priori assumption on when and where an effect might occur, thus limiting the possible confounding issues due to multiple comparisons. Significance levels of the F ratios were adjusted with the Greenhouse—Geisser correction.

Crej were further evaluated for each language separately with paired-samples t -tests. Recognition accuracy was different from chance for Hits in the NL [ Recognition accuracy for faces was further explored in a one-way ANOVA comparing the three types of trials: Pairwise comparisons revealed that participants were more accurate recognizing new than old faces [Crej vs.

Importantly, language had an impact on face recognition: Mean accuracy in the recognition memory for faces for hit-NL blue bar , hit-FL red bar and correct rejections black bar Error Bars depict mean standard errors. Sentence recognition served mainly to assess whether participants were attending to the sentences during the encoding phase. Three participants were not included in these analyses because of a technical problem during this phase.

Acknowledgements

Thus, analyses included 30 participants. The results showed that accuracy for hits [NL: As mentioned previously, analyses in the encoding phase included the factors: Two time-windows were explored: ERPs during the encoding phase. Lines represent encoding trials in the NL condition blue line and encoding trials in the FL condition red line. Negative is plotted up. Right figure represents a zoomed figure of the electrode F1. No other interaction with type of trial showed significance, revealing that differences between conditions were present across ROIs.

The results in the encoding phase revealed that both for the P — ms and the LPC — ms components, encoding of faces presented with the NL elicited larger amplitudes than encoding of faces presented with the FL. The main analyses included the factors: ERPs during the face recognition task.

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Plotted electrodes represent the four regions of interest ROI: The results in the time-window between and ms revealed a main effect of type of trial [ F 1. A main effect of type of trial [ F 1. The main analysis revealed early differences in the recognition of faces depending on the language to which they were previously paired. As mentioned previously, a paired two-tailed cluster mass permutation test was conducted Bullmore et al. The threshold for cluster inclusion had an alpha-level of 0. We computed permutations to estimate the distribution of the null hypothesis.

This test revealed a negative cluster, showing that Crej were more negative than Hits in the range, maximal between and ms at 20 electrode sites reaching 30 electrodes for some datapoints; see Figure 4 for the raster graph with the significant t -test scores. As can be appreciated in the raster plot, in this latency range, the difference was more pronounced over frontal-central electrodes. Raster diagram illustrating significant differences between ERPs to Crej and Hits regardless of language in the face recognition phase according to a cluster permutation test.

Note that electrodes are organized according to laterality and region. Midline electrodes are shown in the middle. Moreover, each group of electrodes is ordered from frontal to posterior electrodes. In the time window of interest — ms , the results revealed a significant difference between the trials [ F 1. Taken together, the ERP results revealed that the language to which a given face is paired has an impact during its recognition. This is important in showing that despite the number of trials per condition was low, the FN component is stable see similar consistency after few trials for the FRN component: The aim of this experiment was to investigate how language context, native or foreign, influences subsequent face recognition.